From 8a582a1181c4f0b7d297b00177b1bd67a18370c9 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sun, 31 Mar 2024 17:26:03 +0200 Subject: [PATCH 001/267] Added placeholders. --- mlcolvar/graph/__init__.py | 8 ++++++++ mlcolvar/graph/core/__init__.py | 0 mlcolvar/graph/core/nn/__init__.py | 0 mlcolvar/graph/cvs/__init__.py | 0 mlcolvar/graph/cvs/unsupervised/__init__.py | 0 mlcolvar/graph/data/__init__.py | 0 6 files changed, 8 insertions(+) create mode 100644 mlcolvar/graph/__init__.py create mode 100644 mlcolvar/graph/core/__init__.py create mode 100644 mlcolvar/graph/core/nn/__init__.py create mode 100644 mlcolvar/graph/cvs/__init__.py create mode 100644 mlcolvar/graph/cvs/unsupervised/__init__.py create mode 100644 mlcolvar/graph/data/__init__.py diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py new file mode 100644 index 00000000..e180d1d0 --- /dev/null +++ b/mlcolvar/graph/__init__.py @@ -0,0 +1,8 @@ +"""The graph neural network module.""" + +__all__ = [] + +# Add imports here +from .core import * +from .data import * +from .cvs import * diff --git a/mlcolvar/graph/core/__init__.py b/mlcolvar/graph/core/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/mlcolvar/graph/cvs/__init__.py b/mlcolvar/graph/cvs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/mlcolvar/graph/cvs/unsupervised/__init__.py b/mlcolvar/graph/cvs/unsupervised/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py new file mode 100644 index 00000000..e69de29b From e3d3e7896e8c51db1077f52c58818836ee83a315 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 01:12:26 +0200 Subject: [PATCH 002/267] Changed importing styles. --- mlcolvar/graph/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index e180d1d0..ee632b3f 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -3,6 +3,6 @@ __all__ = [] # Add imports here -from .core import * -from .data import * -from .cvs import * +from . import core +from . import data +from . import cvs From 7d2b499058b09ab6bc394f5286b5ff964cb7dbca Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 01:12:48 +0200 Subject: [PATCH 003/267] Added the neighbor list module. --- mlcolvar/graph/data/neighborhood.py | 157 ++++++++++++++++++++++++++++ 1 file changed, 157 insertions(+) create mode 100644 mlcolvar/graph/data/neighborhood.py diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py new file mode 100644 index 00000000..20bbe76f --- /dev/null +++ b/mlcolvar/graph/data/neighborhood.py @@ -0,0 +1,157 @@ +import numpy as np +from matscipy.neighbours import neighbour_list +from typing import Optional, Tuple, List + +""" +The neighbour list function. This module is taken from MACE directly: +https://github.com/ACEsuit/mace/blob/main/mace/data/neighborhood.py +""" + +__all__ = ['get_neighborhood'] + + +def get_neighborhood( + positions: np.ndarray, # [num_positions, 3] + cutoff: float, + pbc: Optional[Tuple[bool, bool, bool]] = None, + cell: Optional[np.ndarray] = None, # [3, 3] + true_self_interaction: Optional[bool] = False, + sender_indices: Optional[List[int]] = None, + receiver_indices: Optional[List[int]] = None, +) -> Tuple[np.ndarray, np.ndarray]: + if pbc is None: + pbc = (False, False, False) + + if cell is None or cell.any() == np.zeros((3, 3)).any(): + cell = np.identity(3, dtype=float) + + assert len(pbc) == 3 and all(isinstance(i, (bool, np.bool_)) for i in pbc) + assert cell.shape == (3, 3) + + pbc_x = pbc[0] + pbc_y = pbc[1] + pbc_z = pbc[2] + identity = np.identity(3, dtype=float) + max_positions = np.max(np.absolute(positions)) + 1 + # Extend cell in non-periodic directions + # For models with more than 5 layers, the multiplicative constant needs to + # be increased. + if not pbc_x: + cell[:, 0] = max_positions * 5 * cutoff * identity[:, 0] + if not pbc_y: + cell[:, 1] = max_positions * 5 * cutoff * identity[:, 1] + if not pbc_z: + cell[:, 2] = max_positions * 5 * cutoff * identity[:, 2] + + sender, receiver, unit_shifts = neighbour_list( + quantities="ijS", + pbc=pbc, + cell=cell, + positions=positions, + cutoff=cutoff, + # self_interaction=True, # we want edges from atom to itself in different periodic images + # use_scaled_positions=False, # positions are not scaled positions + ) + + if not true_self_interaction: + # Eliminate self-edges that don't cross periodic boundaries + true_self_edge = sender == receiver + true_self_edge &= np.all(unit_shifts == 0, axis=1) + keep_edge = ~true_self_edge + + # NOTE: after eliminating self-edges, it can be that no edges remain + # in this system + sender = sender[keep_edge] + receiver = receiver[keep_edge] + unit_shifts = unit_shifts[keep_edge] + + if sender_indices is not None: + sender_indices = np.array(sender_indices, dtype=int) + keep_edge = np.where(np.in1d(sender, sender_indices))[0] + sender = sender[keep_edge] + receiver = receiver[keep_edge] + unit_shifts = unit_shifts[keep_edge] + + if receiver_indices is not None: + receiver_indices = np.array(receiver_indices, dtype=int) + keep_edge = np.where(np.in1d(receiver, receiver_indices))[0] + sender = sender[keep_edge] + receiver = receiver[keep_edge] + unit_shifts = unit_shifts[keep_edge] + + # Build output + edge_index = np.stack((sender, receiver)) # [2, n_edges] + + # From the docs: With the shift vector S, the distances D between atoms + # can be computed from: D = positions[j]-positions[i]+S.dot(cell) + shifts = np.dot(unit_shifts, cell) # [n_edges, 3] + + return edge_index, shifts, unit_shifts + + +def test_get_neighborhood(): + + positions = np.array( + [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3]], dtype=float + ) + cell = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]], dtype=float) + + n, s, u = get_neighborhood(positions, cutoff=5.0) + assert ( + n == np.array( + [[0, 0, 1, 1, 1, 2, 2, 2, 3, 3], [1, 2, 0, 2, 3, 0, 1, 3, 1, 2]], + dtype=int + ) + ).all() + + n, s, u = get_neighborhood(positions, cutoff=5.0, receiver_indices=[0, 1]) + assert ( + n == np.array([[0, 1, 2, 2, 3], [1, 0, 0, 1, 1]], dtype=int) + ).all() + + n, s, u = get_neighborhood(positions, cutoff=5.0, sender_indices=[0, 1]) + assert ( + n == np.array([[0, 0, 1, 1, 1], [1, 2, 0, 2, 3]], dtype=int) + ).all() + + n, s, u = get_neighborhood(positions, cutoff=2.0) + assert ( + n == np.array([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]], dtype=int) + ).all() + + n, s, u = get_neighborhood( + positions, cutoff=1.0, pbc=[True] * 3, cell=cell + ) + assert ( + n == np.array([[0, 1, 2, 3], [2, 3, 0, 1]], dtype=int) + ).all() + assert ( + s == np.array( + [[-2, -2, -2], [-2, -2, -2], [2, 2, 2], [2, 2, 2]], + dtype=float + ) + ).all() + assert ( + u == np.array( + [[-1, -1, -1], [-1, -1, -1], [1, 1, 1], [1, 1, 1]], + dtype=int + ) + ).all() + + n, s, u = get_neighborhood( + positions, cutoff=1.0, pbc=[True] * 3, cell=cell, receiver_indices=[0] + ) + assert (n == np.array([[2], [0]], dtype=int)).all() + assert (s == np.array([[2, 2, 2]], dtype=float)).all() + assert (u == np.array([[1, 1, 1]], dtype=int)).all() + + n, s, u = get_neighborhood( + positions, cutoff=1.0, pbc=[True] * 3, cell=cell, sender_indices=[0] + ) + assert (n == np.array([[0], [2]], dtype=int)).all() + assert (s == np.array([[-2, -2, -2]], dtype=float)).all() + assert (u == np.array([[-1, -1, -1]], dtype=int)).all() + + +if __name__ == "__main__": + test_get_neighborhood() From a6fc30dcd04b05d9f4b00e1fc04aff5327f1ce3c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 17:21:34 +0200 Subject: [PATCH 004/267] Added more placeholders. --- mlcolvar/graph/__init__.py | 1 + mlcolvar/graph/utils/__init__.py | 0 2 files changed, 1 insertion(+) create mode 100644 mlcolvar/graph/utils/__init__.py diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index ee632b3f..ac963915 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -6,3 +6,4 @@ from . import core from . import data from . import cvs +from . import io diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py new file mode 100644 index 00000000..e69de29b From 12a944bed4cdf0d791997cd2550869266c9a6cf5 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 18:50:46 +0200 Subject: [PATCH 005/267] Added doc. --- mlcolvar/graph/data/neighborhood.py | 33 ++++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py index 20bbe76f..bbdebb54 100644 --- a/mlcolvar/graph/data/neighborhood.py +++ b/mlcolvar/graph/data/neighborhood.py @@ -18,7 +18,38 @@ def get_neighborhood( true_self_interaction: Optional[bool] = False, sender_indices: Optional[List[int]] = None, receiver_indices: Optional[List[int]] = None, -) -> Tuple[np.ndarray, np.ndarray]: +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Get the neighbour list of a given set atoms. + + Parameters + ---------- + positions: numpy.ndarray (shape: [N, 3]) + The positions array. + cutoff: float + The cutoff radius. + pbc: Tuple[bool, bool, bool] (shape: [3]) + If enable PBC in the directions of the three lattice vectors. + cell: numpy.ndarray (shape: [3, 3]) + The lattice vectors. + true_self_interaction: bool + If keep self-edges that don't cross periodic boundaries. + sender_indices: List[int] + Indices of senders. If given, only edges sent by these atoms will be + kept in the graph. + receiver_indices: List[int] + Indices of receiver. If given, only edges received by these atoms will + be kept in the graph. + + Returns + ------- + edge_index: numpy.ndarray (shape: [2, n_edges]) + The edge indices in the graph. + shifts: numpy.ndarray (shape: [n_edges, 3]) + The shift vectors (unit_shifts * cell_lengths). + unit_shifts: numpy.ndarray (shape: [n_edges, 3]) + The unit shift vectors (number of PBC croessed by the edges). + """ if pbc is None: pbc = (False, False, False) From f07d4b5f40eb2f1576a533fef82e47a87c89f555 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 19:21:09 +0200 Subject: [PATCH 006/267] Fixed some styles. --- mlcolvar/graph/data/neighborhood.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py index bbdebb54..79871492 100644 --- a/mlcolvar/graph/data/neighborhood.py +++ b/mlcolvar/graph/data/neighborhood.py @@ -3,7 +3,7 @@ from typing import Optional, Tuple, List """ -The neighbour list function. This module is taken from MACE directly: +The neighbor list function. This module is taken from MACE directly: https://github.com/ACEsuit/mace/blob/main/mace/data/neighborhood.py """ @@ -20,7 +20,7 @@ def get_neighborhood( receiver_indices: Optional[List[int]] = None, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ - Get the neighbour list of a given set atoms. + Get the neighbor list of a given set atoms. Parameters ---------- @@ -75,7 +75,7 @@ def get_neighborhood( cell[:, 2] = max_positions * 5 * cutoff * identity[:, 2] sender, receiver, unit_shifts = neighbour_list( - quantities="ijS", + quantities='ijS', pbc=pbc, cell=cell, positions=positions, From 17ad4ae3a9fb525f3d5543d5b915002535e8e086 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 21:35:31 +0200 Subject: [PATCH 007/267] Added more placeholders. --- mlcolvar/graph/io/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 mlcolvar/graph/io/__init__.py diff --git a/mlcolvar/graph/io/__init__.py b/mlcolvar/graph/io/__init__.py new file mode 100644 index 00000000..e69de29b From 11d55bded6f9caaa6343445539fa7ee6ec9934f0 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 21:36:22 +0200 Subject: [PATCH 008/267] Added the graph data module. --- mlcolvar/graph/__init__.py | 1 + mlcolvar/graph/data/data.py | 218 ++++++++++++++++++++++++++++ mlcolvar/graph/utils/__init__.py | 2 + mlcolvar/graph/utils/atomic.py | 124 ++++++++++++++++ mlcolvar/graph/utils/torch_tools.py | 47 ++++++ 5 files changed, 392 insertions(+) create mode 100644 mlcolvar/graph/data/data.py create mode 100644 mlcolvar/graph/utils/atomic.py create mode 100644 mlcolvar/graph/utils/torch_tools.py diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index ac963915..7dc48325 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -3,6 +3,7 @@ __all__ = [] # Add imports here +from . import utils from . import core from . import data from . import cvs diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py new file mode 100644 index 00000000..89ff372d --- /dev/null +++ b/mlcolvar/graph/data/data.py @@ -0,0 +1,218 @@ +import torch +import torch_geometric as tg +import numpy as np + +import mlcolvar.graph.utils as gutils + +from neighborhood import get_neighborhood + +""" +The graph data class. This module is taken from MACE: +https://github.com/ACEsuit/mace/blob/main/mace/data/atomic_data.py +""" + +__all__ = ['GraphData'] + + +class GraphData(tg.data.Data): + batch: torch.Tensor + edge_index: torch.Tensor + node_attrs: torch.Tensor + edge_vectors: torch.Tensor + edge_lengths: torch.Tensor + positions: torch.Tensor + shifts: torch.Tensor + unit_shifts: torch.Tensor + cell: torch.Tensor + node_labels: torch.Tensor + graph_labels: torch.Tensor + weight: torch.Tensor + + def __init__( + self, + edge_index: torch.Tensor, # shape: [2, n_edges] + node_attrs: torch.Tensor, # shape: [n_nodes, n_node_attrs] + positions: torch.Tensor, # shape: [n_nodes, 3] + shifts: torch.Tensor, # shape: [n_edges, 3], + unit_shifts: torch.Tensor, # shape: [n_edges, 3] + cell: torch.Tensor, # shape: [3, 3] + node_labels: torch.Tensor, # shape: [n_nodes, n_node_labels] + graph_labels: torch.Tensor, # shape: [n_graph_labels] + weight: torch.Tensor, # shape: [] + ): + # Check shapes + num_nodes = node_attrs.shape[0] + assert edge_index.shape[0] == 2 and len(edge_index.shape) == 2 + assert positions.shape == (num_nodes, 3) + assert shifts.shape[1] == 3 + assert unit_shifts.shape[1] == 3 + assert cell is None or cell.shape == (3, 3) + + # Aggregate data + data = { + 'num_nodes': num_nodes, + 'edge_index': edge_index, + 'shifts': shifts, + 'unit_shifts': unit_shifts, + 'positions': positions, + 'cell': cell, + 'node_attrs': node_attrs, + 'node_labels': node_labels, + 'graph_labels': graph_labels, + 'weight': weight, + } + super().__init__(**data) + + @classmethod + def from_config( + cls, + config: gutils.atomic.Configuration, + z_table: gutils.atomic.AtomicNumberTable, + cutoff: float, + ) -> 'GraphData': + + # fmt: off + assert ( + (config.node_labels is not None) or + (config.graph_labels is not None) + ), "Either node labels or graph labels should be given!" + # fmt: on + + edge_index, shifts, unit_shifts = get_neighborhood( + positions=config.positions, + cutoff=cutoff, + cell=config.cell, + pbc=config.pbc, + sender_indices=config.edge_senders, + receiver_indices=config.edge_receivers, + ) + edge_index = torch.tensor(edge_index, dtype=torch.long) + shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) + unit_shifts = torch.tensor( + unit_shifts, dtype=torch.get_default_dtype() + ) + + positions = torch.tensor( + config.positions, dtype=torch.get_default_dtype() + ) + cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) + + indices = z_table.zs_to_indices(config.atomic_numbers) + one_hot = gutils.torch_tools.to_one_hot( + torch.tensor(indices, dtype=torch.long).unsqueeze(-1), + n_classes=len(z_table), + ) + + node_labels = ( + torch.tensor(config.node_labels, dtype=torch.get_default_dtype()) + if config.node_labels is not None + else None + ) + + graph_labels = ( + torch.tensor(config.graph_labels, dtype=torch.get_default_dtype()) + if config.graph_labels is not None + else None + ) + + weight = ( + torch.tensor(config.weight, dtype=torch.get_default_dtype()) + if config.weight is not None + else 1 + ) + + return cls( + edge_index=edge_index, + shifts=shifts, + unit_shifts=unit_shifts, + positions=positions, + cell=cell, + node_attrs=one_hot, + node_labels=node_labels, + graph_labels=graph_labels, + weight=weight, + ) + + +def test_graph_data(): + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([1]) + node_labels = np.array([[0], [1], [1]]) + z_table = gutils.atomic.AtomicNumberTable.from_zs(numbers) + + config = gutils.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + data = GraphData.from_config(config, z_table, 0.1) + + assert data['num_nodes'] == 3 + + assert ( + data['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + + assert ( + data['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + + assert ( + data['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + + assert ( + data['positions'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0], + ]) + ).all() + + assert ( + data['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + + assert ( + data['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + + assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() + + assert (data['graph_labels'] == torch.tensor([1.0])).all() + + assert data['weight'] == 1.0 + + +if __name__ == '__main__': + test_graph_data() diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index e69de29b..f12565e0 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -0,0 +1,2 @@ +from . import atomic +from . import torch_tools diff --git a/mlcolvar/graph/utils/atomic.py b/mlcolvar/graph/utils/atomic.py new file mode 100644 index 00000000..9a873da9 --- /dev/null +++ b/mlcolvar/graph/utils/atomic.py @@ -0,0 +1,124 @@ +import numpy as np +from dataclasses import dataclass +from typing import List, Iterable, Optional + +""" +The helper functions for atomic data. This module is taken from MACE directly: +https://github.com/ACEsuit/mace/blob/main/mace/tools/utils.py +https://github.com/ACEsuit/mace/blob/main/mace/data/utils.py +""" + +__all__ = ['AtomicNumberTable', 'Configuration', 'Configurations'] + + +class AtomicNumberTable: + """ + The atomic number table. Used to map between one hot encodings and a given + set of actual atomic numbers. + + Parameters + ---------- + zs: List[int] + The atomic numbers in this table. + """ + + def __init__(self, zs: List[int]): + self.zs = zs + + def __len__(self) -> int: + """ + Number of elements in this table. + """ + return len(self.zs) + + def __str__(self): + return f'AtomicNumberTable: {tuple(s for s in self.zs)}' + + def index_to_z(self, index: int) -> int: + """ + Map the encoding to the actual atomic number. + + Parameters + ---------- + index: int + The encoding. + """ + return self.zs[index] + + def z_to_index(self, atomic_number: int) -> int: + """ + Map an atomic number to the encoding. + + Parameters + ---------- + atomic_number: int + The atomic number. + """ + return self.zs.index(atomic_number) + + def zs_to_indices(self, atomic_numbers: np.ndarray) -> np.ndarray: + """ + Map an array of atomic number to the encodings. + + Parameters + ---------- + atomic_numbers: numpy.ndarray + The atomic numbers. + """ + to_index_fn = np.vectorize(self.z_to_index) + return to_index_fn(atomic_numbers) + + @classmethod + def from_zs(cls, atomic_numbers: Iterable[int]) -> 'AtomicNumberTable': + """ + Build the table from an array atomic numbers. + + Parameters + ---------- + atomic_numbers: Iterable[int] + The atomic numbers. + """ + z_set = set() + for z in atomic_numbers: + z_set.add(z) + return cls(sorted(list(z_set))) + + +@dataclass +class Configuration: + """ + Internal helper class that describe a given configuration of the system. + """ + atomic_numbers: np.ndarray # shape: [n_atoms] + positions: np.ndarray # shape: [n_atoms, 3], units: nm + cell: np.ndarray # shape: [n_atoms, 3], units: nm + pbc: Optional[tuple] # shape: [3] + node_labels: Optional[np.ndarray] # shape: [n_atoms, n_node_labels] + graph_labels: Optional[np.ndarray] # shape: [n_graph_labels] + edge_senders: Optional[np.ndarray] = None # shape: [n_senders] + edge_receivers: Optional[np.ndarray] = None # shape: [n_receivers] + weight: Optional[float] = 1.0 # shape: [] + + +Configurations = List[Configuration] + + +def test_atomic_number_table(): + table = AtomicNumberTable([1, 6, 7, 8]) + + numbers = np.array([1, 7, 6, 8]) + assert ( + table.zs_to_indices(numbers) == np.array([0, 2, 1, 3], dtype=int) + ).all() + + numbers = np.array([1, 1, 1, 6, 8, 1]) + assert ( + table.zs_to_indices(numbers) == np.array([0, 0, 0, 1, 3, 0], dtype=int) + ).all() + + table_1 = AtomicNumberTable.from_zs([6] * 3 + [1] * 10 + [7] * 3 + [8] * 2) + assert table_1.zs == table.zs + + +if __name__ == '__main__': + test_atomic_number_table() diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py new file mode 100644 index 00000000..3763e9e8 --- /dev/null +++ b/mlcolvar/graph/utils/torch_tools.py @@ -0,0 +1,47 @@ +import torch + +""" +The helper functions for torch. This module is taken from MACE directly: +https://github.com/ACEsuit/mace/blob/main/mace/tools/torch_tools.py +""" + +__all__ = ['to_one_hot'] + + +def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: + """ + Generates one-hot encoding with `n_classes` classes from `indices` + + Parameters + ---------- + indices: torch.tensor (shape: [N, 1]) + Node incices. + n_classes: int + Number of classes. + + Returns + ------- + encoding: torch.tensor (shape: [N, n_classes]) + The one-hot encoding. + """ + shape = indices.shape[:-1] + (n_classes,) + oh = torch.zeros(shape, device=indices.device).view(shape) + + # scatter_ is the in-place version of scatter + oh.scatter_(dim=-1, index=indices, value=1) + + return oh.view(*shape) + + +def test_to_one_hot(): + i = torch.tensor([[0], [2], [1]], dtype=torch.int64) + e = to_one_hot(i, 4) + assert ( + e == torch.tensor( + [[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0]], dtype=torch.int64 + ) + ).all() + + +if __name__ == '__main__': + test_to_one_hot() From 417bb8527697cec8ee6a2801581136cc572bf7c3 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 21:54:04 +0200 Subject: [PATCH 009/267] Added doc and more tests. --- mlcolvar/graph/data/data.py | 99 +++++++++++++++++++++++++++++++++---- 1 file changed, 89 insertions(+), 10 deletions(-) diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index 89ff372d..12875cb5 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -15,6 +15,30 @@ class GraphData(tg.data.Data): + """ + The graph data class. + + Parameters + ---------- + edge_index: torch.Tensor (shape: [2, n_edges]) + The edge index array. + node_attrs: torch.Tensor (shape: [n_nodes, n_node_attrs]) + The node attribute array. + positions: torch.Tensor (shape: [n_nodes, 3], units: nm) + The node positions. + shifts: torch.Tensor (shape: [n_edges, 3], units: nm) + The shift array. + unit_shifts: torch.Tensor (shape: [n_edges, 3]) + The unit shift array. + cell: torch.Tensor (shape: [3, 3], units: nm) + The lattice vectors. + node_labels: torch.Tensor (shape: [n_nodes, n_node_labels]) + The node labels. + graph_labels: torch.Tensor (shape: [n_graph_labels]) + The graph labels. + weight: torch.Tensor (shape: []) + The configuration weight in the loss funciton. + """ batch: torch.Tensor edge_index: torch.Tensor node_attrs: torch.Tensor @@ -30,15 +54,15 @@ class GraphData(tg.data.Data): def __init__( self, - edge_index: torch.Tensor, # shape: [2, n_edges] - node_attrs: torch.Tensor, # shape: [n_nodes, n_node_attrs] - positions: torch.Tensor, # shape: [n_nodes, 3] - shifts: torch.Tensor, # shape: [n_edges, 3], - unit_shifts: torch.Tensor, # shape: [n_edges, 3] - cell: torch.Tensor, # shape: [3, 3] - node_labels: torch.Tensor, # shape: [n_nodes, n_node_labels] - graph_labels: torch.Tensor, # shape: [n_graph_labels] - weight: torch.Tensor, # shape: [] + edge_index: torch.Tensor, + node_attrs: torch.Tensor, + positions: torch.Tensor, + shifts: torch.Tensor, + unit_shifts: torch.Tensor, + cell: torch.Tensor, + node_labels: torch.Tensor, + graph_labels: torch.Tensor, + weight: torch.Tensor, ): # Check shapes num_nodes = node_attrs.shape[0] @@ -70,7 +94,18 @@ def from_config( z_table: gutils.atomic.AtomicNumberTable, cutoff: float, ) -> 'GraphData': - + """ + Build the graph data object from a configuration. + + Parameters + ---------- + config: mlcolvar.graph.utils.atomic.Configuration + The configuration. + z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. + cutoff: float + The graph cutoff radius. + """ # fmt: off assert ( (config.node_labels is not None) or @@ -213,6 +248,50 @@ def test_graph_data(): assert data['weight'] == 1.0 + config = gutils.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + edge_senders=[0], + ) + data = GraphData.from_config(config, z_table, 0.1) + + assert ( + data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) + ).all() + + config = gutils.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + edge_receivers=[1, 2], + ) + data = GraphData.from_config(config, z_table, 0.1) + + assert ( + data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) + ).all() + + config = gutils.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + edge_senders=[1, 2], + edge_receivers=[1, 2], + ) + data = GraphData.from_config(config, z_table, 0.1) + + assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() + if __name__ == '__main__': test_graph_data() From 727561c167819ee68967cbe7ac79ceb785bdde38 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Apr 2024 23:57:01 +0200 Subject: [PATCH 010/267] Minor fix. --- mlcolvar/graph/data/__init__.py | 1 + mlcolvar/graph/data/data.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index e69de29b..0a1fd99f 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -0,0 +1 @@ +from .data import GraphData diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index 12875cb5..e363e4d0 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -4,7 +4,7 @@ import mlcolvar.graph.utils as gutils -from neighborhood import get_neighborhood +from .neighborhood import get_neighborhood """ The graph data class. This module is taken from MACE: From 8c6c3b29b69d11af71b367a2015a3c7db03a5141 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 2 Apr 2024 01:13:01 +0200 Subject: [PATCH 011/267] Switched to new pyg grammar. --- mlcolvar/graph/data/__init__.py | 3 +- mlcolvar/graph/data/data.py | 230 +++++++++++--------------------- 2 files changed, 79 insertions(+), 154 deletions(-) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index 0a1fd99f..8f1e9d1e 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1 +1,2 @@ -from .data import GraphData +from . import neighborhood +from .data import from_configuration diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index e363e4d0..5326b0fe 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -4,172 +4,98 @@ import mlcolvar.graph.utils as gutils -from .neighborhood import get_neighborhood +from mlcolvar.graph.data.neighborhood import get_neighborhood """ -The graph data class. This module is taken from MACE: +Build the graph data from a configuration. This module is taken from MACE: https://github.com/ACEsuit/mace/blob/main/mace/data/atomic_data.py """ -__all__ = ['GraphData'] +__all__ = ['from_configuration'] -class GraphData(tg.data.Data): +def from_configuration( + config: gutils.atomic.Configuration, + z_table: gutils.atomic.AtomicNumberTable, + cutoff: float, +) -> tg.data.Data: """ - The graph data class. + Build the graph data object from a configuration. Parameters ---------- - edge_index: torch.Tensor (shape: [2, n_edges]) - The edge index array. - node_attrs: torch.Tensor (shape: [n_nodes, n_node_attrs]) - The node attribute array. - positions: torch.Tensor (shape: [n_nodes, 3], units: nm) - The node positions. - shifts: torch.Tensor (shape: [n_edges, 3], units: nm) - The shift array. - unit_shifts: torch.Tensor (shape: [n_edges, 3]) - The unit shift array. - cell: torch.Tensor (shape: [3, 3], units: nm) - The lattice vectors. - node_labels: torch.Tensor (shape: [n_nodes, n_node_labels]) - The node labels. - graph_labels: torch.Tensor (shape: [n_graph_labels]) - The graph labels. - weight: torch.Tensor (shape: []) - The configuration weight in the loss funciton. + config: mlcolvar.graph.utils.atomic.Configuration + The configuration. + z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. + cutoff: float + The graph cutoff radius. """ - batch: torch.Tensor - edge_index: torch.Tensor - node_attrs: torch.Tensor - edge_vectors: torch.Tensor - edge_lengths: torch.Tensor - positions: torch.Tensor - shifts: torch.Tensor - unit_shifts: torch.Tensor - cell: torch.Tensor - node_labels: torch.Tensor - graph_labels: torch.Tensor - weight: torch.Tensor - - def __init__( - self, - edge_index: torch.Tensor, - node_attrs: torch.Tensor, - positions: torch.Tensor, - shifts: torch.Tensor, - unit_shifts: torch.Tensor, - cell: torch.Tensor, - node_labels: torch.Tensor, - graph_labels: torch.Tensor, - weight: torch.Tensor, - ): - # Check shapes - num_nodes = node_attrs.shape[0] - assert edge_index.shape[0] == 2 and len(edge_index.shape) == 2 - assert positions.shape == (num_nodes, 3) - assert shifts.shape[1] == 3 - assert unit_shifts.shape[1] == 3 - assert cell is None or cell.shape == (3, 3) - - # Aggregate data - data = { - 'num_nodes': num_nodes, - 'edge_index': edge_index, - 'shifts': shifts, - 'unit_shifts': unit_shifts, - 'positions': positions, - 'cell': cell, - 'node_attrs': node_attrs, - 'node_labels': node_labels, - 'graph_labels': graph_labels, - 'weight': weight, - } - super().__init__(**data) - - @classmethod - def from_config( - cls, - config: gutils.atomic.Configuration, - z_table: gutils.atomic.AtomicNumberTable, - cutoff: float, - ) -> 'GraphData': - """ - Build the graph data object from a configuration. - - Parameters - ---------- - config: mlcolvar.graph.utils.atomic.Configuration - The configuration. - z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable - The atomic number table used to build the node attributes. - cutoff: float - The graph cutoff radius. - """ - # fmt: off - assert ( - (config.node_labels is not None) or - (config.graph_labels is not None) - ), "Either node labels or graph labels should be given!" - # fmt: on - - edge_index, shifts, unit_shifts = get_neighborhood( - positions=config.positions, - cutoff=cutoff, - cell=config.cell, - pbc=config.pbc, - sender_indices=config.edge_senders, - receiver_indices=config.edge_receivers, - ) - edge_index = torch.tensor(edge_index, dtype=torch.long) - shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) - unit_shifts = torch.tensor( - unit_shifts, dtype=torch.get_default_dtype() - ) - positions = torch.tensor( - config.positions, dtype=torch.get_default_dtype() - ) - cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) + # fmt: off + assert ( + (config.node_labels is not None) or + (config.graph_labels is not None) + ), "Either node labels or graph labels should be given!" + # fmt: on + + edge_index, shifts, unit_shifts = get_neighborhood( + positions=config.positions, + cutoff=cutoff, + cell=config.cell, + pbc=config.pbc, + sender_indices=config.edge_senders, + receiver_indices=config.edge_receivers, + ) + edge_index = torch.tensor(edge_index, dtype=torch.long) + shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) + unit_shifts = torch.tensor( + unit_shifts, dtype=torch.get_default_dtype() + ) - indices = z_table.zs_to_indices(config.atomic_numbers) - one_hot = gutils.torch_tools.to_one_hot( - torch.tensor(indices, dtype=torch.long).unsqueeze(-1), - n_classes=len(z_table), - ) + positions = torch.tensor( + config.positions, dtype=torch.get_default_dtype() + ) + cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) - node_labels = ( - torch.tensor(config.node_labels, dtype=torch.get_default_dtype()) - if config.node_labels is not None - else None - ) + indices = z_table.zs_to_indices(config.atomic_numbers) + one_hot = gutils.torch_tools.to_one_hot( + torch.tensor(indices, dtype=torch.long).unsqueeze(-1), + n_classes=len(z_table), + ) - graph_labels = ( - torch.tensor(config.graph_labels, dtype=torch.get_default_dtype()) - if config.graph_labels is not None - else None - ) + node_labels = ( + torch.tensor(config.node_labels, dtype=torch.get_default_dtype()) + if config.node_labels is not None + else None + ) - weight = ( - torch.tensor(config.weight, dtype=torch.get_default_dtype()) - if config.weight is not None - else 1 - ) + graph_labels = ( + torch.tensor(config.graph_labels, dtype=torch.get_default_dtype()) + if config.graph_labels is not None + else None + ) - return cls( - edge_index=edge_index, - shifts=shifts, - unit_shifts=unit_shifts, - positions=positions, - cell=cell, - node_attrs=one_hot, - node_labels=node_labels, - graph_labels=graph_labels, - weight=weight, - ) + weight = ( + torch.tensor(config.weight, dtype=torch.get_default_dtype()) + if config.weight is not None + else 1 + ) + return tg.data.Data( + edge_index=edge_index, + shifts=shifts, + unit_shifts=unit_shifts, + positions=positions, + cell=cell, + node_attrs=one_hot, + node_labels=node_labels, + graph_labels=graph_labels, + weight=weight, + ) -def test_graph_data(): + +def test_from_configuration(): numbers = [8, 1, 1] positions = np.array( [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], @@ -188,9 +114,7 @@ def test_graph_data(): node_labels=node_labels, graph_labels=graph_labels, ) - data = GraphData.from_config(config, z_table, 0.1) - - assert data['num_nodes'] == 3 + data = from_configuration(config, z_table, 0.1) assert ( data['edge_index'] == torch.tensor( @@ -257,7 +181,7 @@ def test_graph_data(): graph_labels=graph_labels, edge_senders=[0], ) - data = GraphData.from_config(config, z_table, 0.1) + data = from_configuration(config, z_table, 0.1) assert ( data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) @@ -272,7 +196,7 @@ def test_graph_data(): graph_labels=graph_labels, edge_receivers=[1, 2], ) - data = GraphData.from_config(config, z_table, 0.1) + data = from_configuration(config, z_table, 0.1) assert ( data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) @@ -288,10 +212,10 @@ def test_graph_data(): edge_senders=[1, 2], edge_receivers=[1, 2], ) - data = GraphData.from_config(config, z_table, 0.1) + data = from_configuration(config, z_table, 0.1) assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() if __name__ == '__main__': - test_graph_data() + test_from_configuration() From 0c095c4789fd800473a2d3f23ecb41c05433005f Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 2 Apr 2024 03:32:18 +0200 Subject: [PATCH 012/267] Modified file layouts. --- mlcolvar/graph/__init__.py | 4 ---- mlcolvar/graph/utils/__init__.py | 1 + mlcolvar/graph/{io/__init__.py => utils/io.py} | 0 3 files changed, 1 insertion(+), 4 deletions(-) rename mlcolvar/graph/{io/__init__.py => utils/io.py} (100%) diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index 7dc48325..95c5ea56 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -1,10 +1,6 @@ """The graph neural network module.""" -__all__ = [] - -# Add imports here from . import utils from . import core from . import data from . import cvs -from . import io diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index f12565e0..57ce92bd 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -1,2 +1,3 @@ +from . import io from . import atomic from . import torch_tools diff --git a/mlcolvar/graph/io/__init__.py b/mlcolvar/graph/utils/io.py similarity index 100% rename from mlcolvar/graph/io/__init__.py rename to mlcolvar/graph/utils/io.py From 6591511685f22dd511f890b6145240e9016a3a8e Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 2 Apr 2024 03:44:19 +0200 Subject: [PATCH 013/267] Added more helper functions. --- mlcolvar/graph/utils/torch_tools.py | 41 +++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index 3763e9e8..1ca91ebf 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -33,6 +33,28 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: return oh.view(*shape) +def set_default_dtype(dtype: str) -> None: + """ + Wrapper function of `torch.set_default_dtype`. + + Parameters + ---------- + dtype: str + The date type. + """ + if not isinstance(dtype, str): + raise TypeError('A string is required to set TORCH default dtype!') + dtype = dtype.lower() + if dtype in ['float', 'float32']: + torch.set_default_dtype(torch.float32) + elif dtype in ['double', 'float64']: + torch.set_default_dtype(torch.float64) + else: + raise RuntimeError( + 'Unknown/Unsupported data type: "{:s}"!'.format(dtype) + ) + + def test_to_one_hot(): i = torch.tensor([[0], [2], [1]], dtype=torch.int64) e = to_one_hot(i, 4) @@ -43,5 +65,24 @@ def test_to_one_hot(): ).all() +def test_set_default_dtype(): + set_default_dtype('float64') + t = torch.Tensor([1.0]) + assert t.dtype == torch.float64 + + set_default_dtype('float32') + t = torch.Tensor([1.0]) + assert t.dtype == torch.float32 + + set_default_dtype('double') + t = torch.Tensor([1.0]) + assert t.dtype == torch.float64 + + set_default_dtype('float') + t = torch.Tensor([1.0]) + assert t.dtype == torch.float32 + + if __name__ == '__main__': test_to_one_hot() + test_set_default_dtype() From 96edcd0c3223b55b5827b504e738b40541907cf1 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 2 Apr 2024 17:13:59 +0200 Subject: [PATCH 014/267] Added datamodule. --- mlcolvar/graph/data/__init__.py | 1 + mlcolvar/graph/data/datamodule.py | 367 ++++++++++++++++++++++++++++++ 2 files changed, 368 insertions(+) create mode 100644 mlcolvar/graph/data/datamodule.py diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index 8f1e9d1e..bddbff48 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1,2 +1,3 @@ from . import neighborhood from .data import from_configuration +from .datamodule import GraphDataModule diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py new file mode 100644 index 00000000..6fc1c399 --- /dev/null +++ b/mlcolvar/graph/data/datamodule.py @@ -0,0 +1,367 @@ +import torch +import lightning +import torch_geometric as tg +import numpy as np +from typing import Sequence, Union, Optional + +import mlcolvar.graph.utils as gutils +from mlcolvar.graph.data import from_configuration + +""" +The data module for lightning. +""" + +__all__ = ['GraphDataModule'] + + +class GraphDataModule(lightning.LightningDataModule): + """ + Lightning DataModule constructed for `torch_geometric.data.Data`. This data + module automatically splits the input graphs into training, validation, + and (optionally) test sets. + + Parameters + ---------- + dataset: List[torch_geometric.data.Data] + List of graph data. + lengths: List[int] + Lengths of the training, validation, and (optionally) test datasets. + This must be a list of (float) fractions summing to 1. + batch_size : Union[int, List[int]] + The batch size. + random_split: bool + Whether to randomly split train/valid/test or sequentially. + shuffle: Union[bool, List[bool]] + Whether to shuffle the batches in the ``DataLoader``. + seed: int + The random seed used to split the dataset. + """ + + def __init__( + self, + dataset: Sequence[tg.data.Data], + lengths: Sequence = (0.8, 0.2), + batch_size: Union[int, Sequence] = 1, + random_split: bool = True, + shuffle: Union[bool, Sequence] = True, + seed: Optional[int] = None, + ) -> None: + self._dataset = dataset + self._lengths = lengths + self._n_total = len(dataset) + + assert self._n_total > 0 + assert len(lengths) in [1, 2, 3] + assert np.abs(1 - sum(lengths)) < 1e-12 + + if self._n_total == 1: + self._n_train = 1 + else: + self._n_train = int(lengths[0] * self._n_total) + if len(lengths) == 3: + self._n_validation = int(lengths[1] * self._n_total) + self._n_test = self._n_total - self._n_train - self._n_validation + elif len(lengths) == 2: + self._n_validation = self._n_total - self._n_train + self._n_test = 0 + + indices = list(range(self._n_total)) + if random_split: + rng = np.random.default_rng(seed) + rng.shuffle(indices) + indices_train = indices[0:self._n_train] + if len(lengths) == 3: + indices_validation = indices[ + self._n_train:(self._n_train + self._n_validation) + ] + indices_test = indices[ + (self._n_train + self._n_validation):self._n_total + ] + elif len(lengths) == 2: + indices_validation = indices[ + self._n_train:(self._n_train + self._n_validation) + ] + indices_test = [] + else: + indices_validation = [] + indices_test = [] + self._dataset_indices = [ + indices_train, + indices_validation, + indices_test, + ] + + # Make sure batch_size and shuffle are lists. + if isinstance(batch_size, int): + self.batch_size = [batch_size for _ in lengths] + else: + self.batch_size = batch_size + if isinstance(shuffle, bool): + self.shuffle = [shuffle for _ in lengths] + else: + self.shuffle = shuffle + + # This is initialized in setup(). + self._dataset_split = None + + # dataloaders + self.train_loader = None + self.valid_loader = None + self.test_loader = None + + def setup(self, stage: Optional[str] = None) -> None: + """ + Set up the datasets. + """ + if self._dataset_split is None: + dataset_split = [ + [self._dataset[j] for j in i] for i in self._dataset_indices + ] + self._dataset_split = dataset_split + + def train_dataloader(self) -> tg.loader.DataLoader: + """ + Return training dataloader. + """ + self._check_setup() + if self.train_loader is None: + self.train_loader = tg.loader.DataLoader( + self._dataset_split[0], + batch_size=self.batch_size[0], + shuffle=self.shuffle[0], + ) + return self.train_loader + + def val_dataloader(self) -> tg.loader.DataLoader: + """ + Return validation dataloader. + """ + self._check_setup() + if len(self._dataset_indices[1]) == 0: + raise NotImplementedError( + 'Validation dataset not available, you need to pass two ' + + 'lengths to datamodule.' + ) + if self.valid_loader is None: + self.valid_loader = tg.loader.DataLoader( + self._dataset_split[1], + batch_size=self.batch_size[1], + shuffle=self.shuffle[1], + ) + return self.valid_loader + + def test_dataloader(self) -> tg.loader.DataLoader: + """ + Return test dataloader. + """ + self._check_setup() + if len(self._dataset_indices[2]) == 0: + raise NotImplementedError( + 'Test dataset not available, you need to pass three ' + + 'lengths to datamodule.' + ) + if self.test_loader is None: + self.test_loader = tg.loader.DataLoader( + self._dataset_split[2], + batch_size=self.batch_size[2], + shuffle=self.shuffle[2], + ) + return self.test_loader + + def predict_dataloader(self) -> tg.loader.DataLoader: + """ + Return predict dataloader. + """ + raise NotImplementedError() + + def teardown(self, stage: str) -> None: + pass + + def __repr__(self) -> str: + result = '' + n_digits = len(str(self._n_total)) + data_string_1 = '\033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' + data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' + shuffle_string_1 = '|\033[36m  \033[0m' + shuffle_string_2 = '|\033[36m  \033[0m' + + prefix = '[\033[1m\033[34m TOTAL DATA \033[0m]: ' + string = prefix + data_string_1.format(n_digits) + result += string.format(self._n_total) + '\n' + prefix = '[\033[1m\033[34m TRAINING \033[0m]: ' + string = prefix + data_string_1.format(n_digits) + result += string.format( + self._n_train, self._n_train / self._n_total * 100 + ) + string = data_string_2.format(n_digits) + result += string.format(self.batch_size[0]) + if self.shuffle[0]: + result += shuffle_string_1 + else: + result += shuffle_string_2 + + if self._n_validation > 0: + result += '\n' + prefix = '[\033[1m\033[34m VALIDATION \033[0m]: ' + string = prefix + data_string_1.format(n_digits) + result += string.format( + self._n_validation, self._n_validation / self._n_total * 100 + ) + string = data_string_2.format(n_digits) + result += string.format(self.batch_size[1]) + if self.shuffle[1]: + result += shuffle_string_1 + else: + result += shuffle_string_2 + + if self._n_test > 0: + result += '\n' + prefix = '[\033[1m\033[34m TEST \033[0m]: ' + string = prefix + data_string_1.format(n_digits) + result += string.format( + self._n_test, self._n_test / self._n_total * 100 + ) + string = data_string_2.format(n_digits) + result += string.format(self.batch_size[2]) + if self.shuffle[2]: + result += shuffle_string_1 + else: + result += shuffle_string_2 + return result + + def _check_setup(self) -> None: + """ + Raise an error if setup() has not been called. + """ + if self._dataset_split is None: + raise AttributeError( + 'The datamodule has not been set up yet. To get the ' + + 'dataloaders outside a Lightning trainer please call ' + + '.setup() first.' + ) + + +def test_datamodule(): + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([1]) + node_labels = np.array([[0], [1], [1]]) + z_table = gutils.atomic.AtomicNumberTable.from_zs(numbers) + + config = gutils.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + dataset = [from_configuration(config, z_table, 0.1) for i in range(10)] + for i, d in enumerate(dataset): + d['graph_labels'][0] = i + + loader = GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=True, + seed=1 + ) + loader.setup() + assert len(loader._dataset_indices[0]) == 10 + assert len(loader._dataset_indices[1]) == 0 + assert len(loader._dataset_indices[2]) == 0 + + loader = GraphDataModule( + dataset, + batch_size=10, + shuffle=True, + seed=1 + ) + loader.setup() + assert len(loader._dataset_indices[0]) == 8 + assert len(loader._dataset_indices[1]) == 2 + assert len(loader._dataset_indices[2]) == 0 + + loader = GraphDataModule( + dataset, + lengths=(0.6, 0.3, 0.1), + batch_size=10, + shuffle=False, + seed=1 + ) + loader.setup() + assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] + + data_dict = next(iter(loader.train_dataloader())).to_dict() + assert data_dict['edge_index'].shape == (2, 36) + assert ( + data_dict['graph_labels'] == torch.tensor([8, 4, 7, 0, 1, 2]) + ).all() + + data_dict = next(iter(loader.val_dataloader())).to_dict() + assert ( + data_dict['edge_index'] == torch.tensor([ + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], + [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] + ]) + ).all() + assert ( + data_dict['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) + ).all() + assert (data_dict['graph_labels'] == torch.tensor([5, 9, 6])).all() + assert (data_dict['ptr'] == torch.tensor([0, 3, 6, 9])).all() + + data_dict = next(iter(loader.test_dataloader())).to_dict() + assert (data_dict['graph_labels'] == torch.tensor([3])).all() + assert ( + data_dict['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + assert ( + data_dict['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data_dict['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data_dict['positions'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0], + ]) + ).all() + assert ( + data_dict['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + assert ( + data_dict['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + + +if __name__ == '__main__': + test_datamodule() From 7f4b830468a43a0c7223201150b0056c05535421 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 2 Apr 2024 17:33:26 +0200 Subject: [PATCH 015/267] Added more functions. --- mlcolvar/graph/data/__init__.py | 2 +- mlcolvar/graph/data/data.py | 22 ++++++++++++++++++++++ 2 files changed, 23 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index bddbff48..d95b97bf 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1,3 +1,3 @@ from . import neighborhood -from .data import from_configuration +from .data import from_configuration, from_configurations from .datamodule import GraphDataModule diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index 5326b0fe..511f6f6b 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -1,6 +1,7 @@ import torch import torch_geometric as tg import numpy as np +from typing import List import mlcolvar.graph.utils as gutils @@ -95,6 +96,27 @@ def from_configuration( ) +def from_configurations( + config: gutils.atomic.Configurations, + z_table: gutils.atomic.AtomicNumberTable, + cutoff: float, +) -> List[tg.data.Data]: + """ + Build graph data objects from configurations. + + Parameters + ---------- + config: mlcolvar.graph.utils.atomic.Configurations + The configurations. + z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. + cutoff: float + The graph cutoff radius. + """ + + return [from_configuration(c) for c in config] + + def test_from_configuration(): numbers = [8, 1, 1] positions = np.array( From 8cb72183ad1b76645e88a0f46845b6c5163d70a9 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 2 Apr 2024 17:57:01 +0200 Subject: [PATCH 016/267] Fixed some styles. --- mlcolvar/graph/data/data.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index 511f6f6b..b9146c8f 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -137,13 +137,11 @@ def test_from_configuration(): graph_labels=graph_labels, ) data = from_configuration(config, z_table, 0.1) - assert ( data['edge_index'] == torch.tensor( [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] ) ).all() - assert ( data['shifts'] == torch.tensor([ [0.0, 0.0, 0.0], @@ -154,7 +152,6 @@ def test_from_configuration(): [0.0, 0.0, 0.0], ]) ).all() - assert ( data['unit_shifts'] == torch.tensor([ [0.0, 0.0, 0.0], @@ -165,7 +162,6 @@ def test_from_configuration(): [0.0, 0.0, 0.0], ]) ).all() - assert ( data['positions'] == torch.tensor([ [0.0, 0.0, 0.0], @@ -173,7 +169,6 @@ def test_from_configuration(): [0.07, -0.07, 0.0], ]) ).all() - assert ( data['cell'] == torch.tensor([ [0.2, 0.0, 0.0], @@ -181,17 +176,13 @@ def test_from_configuration(): [0.0, 0.0, 0.2], ]) ).all() - assert ( data['node_attrs'] == torch.tensor([ [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() - assert (data['graph_labels'] == torch.tensor([1.0])).all() - assert data['weight'] == 1.0 config = gutils.atomic.Configuration( @@ -204,7 +195,6 @@ def test_from_configuration(): edge_senders=[0], ) data = from_configuration(config, z_table, 0.1) - assert ( data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) ).all() @@ -219,7 +209,6 @@ def test_from_configuration(): edge_receivers=[1, 2], ) data = from_configuration(config, z_table, 0.1) - assert ( data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) ).all() @@ -235,7 +224,6 @@ def test_from_configuration(): edge_receivers=[1, 2], ) data = from_configuration(config, z_table, 0.1) - assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() From 6c5e85c27a8eb6bbd9740de6621e557af96dcd79 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 3 Apr 2024 11:26:38 +0200 Subject: [PATCH 017/267] Added an alias to the dataset object. --- mlcolvar/graph/data/__init__.py | 2 +- mlcolvar/graph/data/data.py | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index d95b97bf..b5f0e827 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1,3 +1,3 @@ from . import neighborhood -from .data import from_configuration, from_configurations +from .data import GraphDataSet, from_configuration, from_configurations from .datamodule import GraphDataModule diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index b9146c8f..e1c56bf9 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -12,7 +12,10 @@ https://github.com/ACEsuit/mace/blob/main/mace/data/atomic_data.py """ -__all__ = ['from_configuration'] +__all__ = ['GraphDataSet', 'from_configuration', 'from_configurations'] + + +GraphDataSet = List[tg.data.Data] def from_configuration( @@ -100,7 +103,7 @@ def from_configurations( config: gutils.atomic.Configurations, z_table: gutils.atomic.AtomicNumberTable, cutoff: float, -) -> List[tg.data.Data]: +) -> GraphDataSet: """ Build graph data objects from configurations. From 3930be3065597c76ba6e6ddf592da3b41c12b204 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 3 Apr 2024 11:35:08 +0200 Subject: [PATCH 018/267] Resolved the circular dependency problem. --- mlcolvar/graph/data/data.py | 24 ++++++++++++------------ mlcolvar/graph/data/datamodule.py | 6 +++--- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index e1c56bf9..9770d134 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -3,8 +3,8 @@ import numpy as np from typing import List -import mlcolvar.graph.utils as gutils - +from mlcolvar.graph.utils import atomic +from mlcolvar.graph.utils import torch_tools from mlcolvar.graph.data.neighborhood import get_neighborhood """ @@ -19,8 +19,8 @@ def from_configuration( - config: gutils.atomic.Configuration, - z_table: gutils.atomic.AtomicNumberTable, + config: atomic.Configuration, + z_table: atomic.AtomicNumberTable, cutoff: float, ) -> tg.data.Data: """ @@ -63,7 +63,7 @@ def from_configuration( cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) indices = z_table.zs_to_indices(config.atomic_numbers) - one_hot = gutils.torch_tools.to_one_hot( + one_hot = torch_tools.to_one_hot( torch.tensor(indices, dtype=torch.long).unsqueeze(-1), n_classes=len(z_table), ) @@ -100,8 +100,8 @@ def from_configuration( def from_configurations( - config: gutils.atomic.Configurations, - z_table: gutils.atomic.AtomicNumberTable, + config: atomic.Configurations, + z_table: atomic.AtomicNumberTable, cutoff: float, ) -> GraphDataSet: """ @@ -129,9 +129,9 @@ def test_from_configuration(): cell = np.identity(3, dtype=float) * 0.2 graph_labels = np.array([1]) node_labels = np.array([[0], [1], [1]]) - z_table = gutils.atomic.AtomicNumberTable.from_zs(numbers) + z_table = atomic.AtomicNumberTable.from_zs(numbers) - config = gutils.atomic.Configuration( + config = atomic.Configuration( atomic_numbers=numbers, positions=positions, cell=cell, @@ -188,7 +188,7 @@ def test_from_configuration(): assert (data['graph_labels'] == torch.tensor([1.0])).all() assert data['weight'] == 1.0 - config = gutils.atomic.Configuration( + config = atomic.Configuration( atomic_numbers=numbers, positions=positions, cell=cell, @@ -202,7 +202,7 @@ def test_from_configuration(): data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) ).all() - config = gutils.atomic.Configuration( + config = atomic.Configuration( atomic_numbers=numbers, positions=positions, cell=cell, @@ -216,7 +216,7 @@ def test_from_configuration(): data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) ).all() - config = gutils.atomic.Configuration( + config = atomic.Configuration( atomic_numbers=numbers, positions=positions, cell=cell, diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 6fc1c399..2967945e 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -4,7 +4,7 @@ import numpy as np from typing import Sequence, Union, Optional -import mlcolvar.graph.utils as gutils +from mlcolvar.graph.utils import atomic from mlcolvar.graph.data import from_configuration """ @@ -249,9 +249,9 @@ def test_datamodule(): cell = np.identity(3, dtype=float) * 0.2 graph_labels = np.array([1]) node_labels = np.array([[0], [1], [1]]) - z_table = gutils.atomic.AtomicNumberTable.from_zs(numbers) + z_table = atomic.AtomicNumberTable.from_zs(numbers) - config = gutils.atomic.Configuration( + config = atomic.Configuration( atomic_numbers=numbers, positions=positions, cell=cell, From 0dc2e1dae338541128e4c64fa71c0e406392aedc Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 3 Apr 2024 11:41:37 +0200 Subject: [PATCH 019/267] Added missing type hints. --- mlcolvar/graph/data/data.py | 2 +- mlcolvar/graph/data/datamodule.py | 2 +- mlcolvar/graph/data/neighborhood.py | 2 +- mlcolvar/graph/utils/atomic.py | 6 +++--- mlcolvar/graph/utils/torch_tools.py | 4 ++-- 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index 9770d134..fd832ccf 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -120,7 +120,7 @@ def from_configurations( return [from_configuration(c) for c in config] -def test_from_configuration(): +def test_from_configuration() -> None: numbers = [8, 1, 1] positions = np.array( [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 2967945e..2fae1326 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -241,7 +241,7 @@ def _check_setup(self) -> None: ) -def test_datamodule(): +def test_datamodule() -> None: numbers = [8, 1, 1] positions = np.array( [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py index 79871492..244cffce 100644 --- a/mlcolvar/graph/data/neighborhood.py +++ b/mlcolvar/graph/data/neighborhood.py @@ -120,7 +120,7 @@ def get_neighborhood( return edge_index, shifts, unit_shifts -def test_get_neighborhood(): +def test_get_neighborhood() -> None: positions = np.array( [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3]], dtype=float diff --git a/mlcolvar/graph/utils/atomic.py b/mlcolvar/graph/utils/atomic.py index 9a873da9..fee9522d 100644 --- a/mlcolvar/graph/utils/atomic.py +++ b/mlcolvar/graph/utils/atomic.py @@ -22,7 +22,7 @@ class AtomicNumberTable: The atomic numbers in this table. """ - def __init__(self, zs: List[int]): + def __init__(self, zs: List[int]) -> None: self.zs = zs def __len__(self) -> int: @@ -31,7 +31,7 @@ def __len__(self) -> int: """ return len(self.zs) - def __str__(self): + def __str__(self) -> str: return f'AtomicNumberTable: {tuple(s for s in self.zs)}' def index_to_z(self, index: int) -> int: @@ -103,7 +103,7 @@ class Configuration: Configurations = List[Configuration] -def test_atomic_number_table(): +def test_atomic_number_table() -> None: table = AtomicNumberTable([1, 6, 7, 8]) numbers = np.array([1, 7, 6, 8]) diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index 1ca91ebf..c211f391 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -55,7 +55,7 @@ def set_default_dtype(dtype: str) -> None: ) -def test_to_one_hot(): +def test_to_one_hot() -> None: i = torch.tensor([[0], [2], [1]], dtype=torch.int64) e = to_one_hot(i, 4) assert ( @@ -65,7 +65,7 @@ def test_to_one_hot(): ).all() -def test_set_default_dtype(): +def test_set_default_dtype() -> None: set_default_dtype('float64') t = torch.Tensor([1.0]) assert t.dtype == torch.float64 From 8ff7203e925f1b7a431a41482c2027c61fb9c587 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 3 Apr 2024 13:06:19 +0200 Subject: [PATCH 020/267] Modified file layouts. --- mlcolvar/graph/data/__init__.py | 1 + mlcolvar/graph/{utils => data}/atomic.py | 0 mlcolvar/graph/data/data.py | 4 ++-- mlcolvar/graph/data/datamodule.py | 3 +-- mlcolvar/graph/utils/__init__.py | 1 - 5 files changed, 4 insertions(+), 5 deletions(-) rename mlcolvar/graph/{utils => data}/atomic.py (100%) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index b5f0e827..64892575 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1,3 +1,4 @@ +from . import atomic from . import neighborhood from .data import GraphDataSet, from_configuration, from_configurations from .datamodule import GraphDataModule diff --git a/mlcolvar/graph/utils/atomic.py b/mlcolvar/graph/data/atomic.py similarity index 100% rename from mlcolvar/graph/utils/atomic.py rename to mlcolvar/graph/data/atomic.py diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/data.py index fd832ccf..809e2605 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/data.py @@ -3,9 +3,9 @@ import numpy as np from typing import List -from mlcolvar.graph.utils import atomic -from mlcolvar.graph.utils import torch_tools +from mlcolvar.graph.data import atomic from mlcolvar.graph.data.neighborhood import get_neighborhood +from mlcolvar.graph.utils import torch_tools """ Build the graph data from a configuration. This module is taken from MACE: diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 2fae1326..1bc6f7c1 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -4,8 +4,7 @@ import numpy as np from typing import Sequence, Union, Optional -from mlcolvar.graph.utils import atomic -from mlcolvar.graph.data import from_configuration +from mlcolvar.graph.data import atomic, from_configuration """ The data module for lightning. diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index 57ce92bd..91bd1788 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -1,3 +1,2 @@ from . import io -from . import atomic from . import torch_tools From bb2edeca6bdce9d18d59cc7902d13e89a5191cb3 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 3 Apr 2024 17:06:26 +0200 Subject: [PATCH 021/267] Changed filenames. --- mlcolvar/graph/data/__init__.py | 2 +- mlcolvar/graph/data/{data.py => dataset.py} | 11 +---------- 2 files changed, 2 insertions(+), 11 deletions(-) rename mlcolvar/graph/data/{data.py => dataset.py} (96%) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index 64892575..ecea01af 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1,4 +1,4 @@ from . import atomic from . import neighborhood -from .data import GraphDataSet, from_configuration, from_configurations +from .dataset import GraphDataSet, from_configuration, from_configurations from .datamodule import GraphDataModule diff --git a/mlcolvar/graph/data/data.py b/mlcolvar/graph/data/dataset.py similarity index 96% rename from mlcolvar/graph/data/data.py rename to mlcolvar/graph/data/dataset.py index 809e2605..33abbff4 100644 --- a/mlcolvar/graph/data/data.py +++ b/mlcolvar/graph/data/dataset.py @@ -35,14 +35,6 @@ def from_configuration( cutoff: float The graph cutoff radius. """ - - # fmt: off - assert ( - (config.node_labels is not None) or - (config.graph_labels is not None) - ), "Either node labels or graph labels should be given!" - # fmt: on - edge_index, shifts, unit_shifts = get_neighborhood( positions=config.positions, cutoff=cutoff, @@ -116,8 +108,7 @@ def from_configurations( cutoff: float The graph cutoff radius. """ - - return [from_configuration(c) for c in config] + return [from_configuration(c, z_table, cutoff) for c in config] def test_from_configuration() -> None: From a6623467827b000739fe9eee2d5fb82cbdb8fc65 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 3 Apr 2024 18:02:55 +0200 Subject: [PATCH 022/267] Introduced the dataset class. --- mlcolvar/graph/data/__init__.py | 7 +- mlcolvar/graph/data/datamodule.py | 4 +- mlcolvar/graph/data/dataset.py | 114 +++++++++++++++++++++++++--- mlcolvar/graph/utils/torch_tools.py | 2 +- 4 files changed, 114 insertions(+), 13 deletions(-) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index ecea01af..5d00a726 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -1,4 +1,9 @@ from . import atomic from . import neighborhood -from .dataset import GraphDataSet, from_configuration, from_configurations +from .dataset import ( + GraphDataSet, + create_dataset_from_configurations, + save_dataset, + load_dataset +) from .datamodule import GraphDataModule diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 1bc6f7c1..b98b9e55 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -4,7 +4,7 @@ import numpy as np from typing import Sequence, Union, Optional -from mlcolvar.graph.data import atomic, from_configuration +from mlcolvar.graph.data import atomic, create_dataset_from_configurations """ The data module for lightning. @@ -258,7 +258,7 @@ def test_datamodule() -> None: node_labels=node_labels, graph_labels=graph_labels, ) - dataset = [from_configuration(config, z_table, 0.1) for i in range(10)] + dataset = create_dataset_from_configurations([config] * 10, z_table, 0.1) for i, d in enumerate(dataset): d['graph_labels'][0] = i diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 33abbff4..f8d0b5eb 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -12,13 +12,72 @@ https://github.com/ACEsuit/mace/blob/main/mace/data/atomic_data.py """ -__all__ = ['GraphDataSet', 'from_configuration', 'from_configurations'] +__all__ = [ + 'GraphDataSet', + 'create_dataset_from_configurations', + 'save_dataset', + 'load_dataset' +] -GraphDataSet = List[tg.data.Data] +class GraphDataSet(list): + """ + A very simple graph dataset class. + + Parameters + ---------- + data: List[torch_geometric.data.Data] + The data. + atomic_numbers: List[int] + The atomic numbers used to build the node attributes. + cutoff: float + The graph cutoff radius. + """ + + def __init__( + self, + data: List[tg.data.Data], + atomic_numbers: List[int], + cutoff: float + ) -> None: + super().__init__() + self.extend(data) + self.__atomic_numbers = list(atomic_numbers) + self.__cutoff = cutoff + def __repr__(self) -> str: + result = 'GRAPHDATASET [ ' -def from_configuration( + data_string = '\033[32m{:d}\033[0m\033[36m 󰡷 \033[0m' + result = result + data_string.format(len(self)) + result = result + '| ' + data_string = '[\033[32m{}\033[0m]\033[36m 󰝨 \033[0m' + result = result + data_string.format( + ('{:d} ' * len(self.atomic_numbers)).strip() + ).format(*self.atomic_numbers) + result = result + '| ' + data_string = '\033[32m{:f}\033[0m\033[36m 󰳁 \033[0m' + result = result + data_string.format(self.cutoff) + result = result + ']' + + return result + + @property + def cutoff(self) -> float: + """ + The graph cutoff radius. + """ + return self.__cutoff + + @property + def atomic_numbers(self) -> List[int]: + """ + The atomic numbers used to build the node attributes. + """ + return self.__atomic_numbers.copy() + + +def _create_dataset_from_configuration( config: atomic.Configuration, z_table: atomic.AtomicNumberTable, cutoff: float, @@ -91,7 +150,7 @@ def from_configuration( ) -def from_configurations( +def create_dataset_from_configurations( config: atomic.Configurations, z_table: atomic.AtomicNumberTable, cutoff: float, @@ -108,7 +167,44 @@ def from_configurations( cutoff: float The graph cutoff radius. """ - return [from_configuration(c, z_table, cutoff) for c in config] + data_list = [ + _create_dataset_from_configuration(c, z_table, cutoff) for c in config + ] + dataset = GraphDataSet(data_list, z_table.zs, cutoff) + + return dataset + + +def save_dataset(dataset: GraphDataSet, file_name: str) -> None: + """ + Save a dataset to disk. + + Parameters + ---------- + dataset: GraphDataSet + The dataset. + file_name: str + The filename. + """ + assert isinstance(dataset, GraphDataSet) + + torch.save(dataset, file_name) # super torch magic go brrrrrrrrr + + +def load_dataset(file_name: str) -> GraphDataSet: + """ + Load a dataset from disk. + + Parameters + ---------- + file_name: str + The filename. + """ + dataset = torch.load(file_name) + + assert isinstance(dataset, GraphDataSet) + + return dataset def test_from_configuration() -> None: @@ -130,7 +226,7 @@ def test_from_configuration() -> None: node_labels=node_labels, graph_labels=graph_labels, ) - data = from_configuration(config, z_table, 0.1) + data = _create_dataset_from_configuration(config, z_table, 0.1) assert ( data['edge_index'] == torch.tensor( [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] @@ -188,7 +284,7 @@ def test_from_configuration() -> None: graph_labels=graph_labels, edge_senders=[0], ) - data = from_configuration(config, z_table, 0.1) + data = _create_dataset_from_configuration(config, z_table, 0.1) assert ( data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) ).all() @@ -202,7 +298,7 @@ def test_from_configuration() -> None: graph_labels=graph_labels, edge_receivers=[1, 2], ) - data = from_configuration(config, z_table, 0.1) + data = _create_dataset_from_configuration(config, z_table, 0.1) assert ( data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) ).all() @@ -217,7 +313,7 @@ def test_from_configuration() -> None: edge_senders=[1, 2], edge_receivers=[1, 2], ) - data = from_configuration(config, z_table, 0.1) + data = _create_dataset_from_configuration(config, z_table, 0.1) assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index c211f391..1cd211f1 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -40,7 +40,7 @@ def set_default_dtype(dtype: str) -> None: Parameters ---------- dtype: str - The date type. + The data type. """ if not isinstance(dtype, str): raise TypeError('A string is required to set TORCH default dtype!') From 9eaf32329aaa6a1107fca0d3cca438f16f6e5b4e Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 01:34:29 +0200 Subject: [PATCH 023/267] Added the IO function. --- mlcolvar/graph/utils/io.py | 458 +++++++++++++++++++++++++++++++++++++ 1 file changed, 458 insertions(+) diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index e69de29b..604ce497 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -0,0 +1,458 @@ +import torch +import numpy as np +import mdtraj as md + +from typing import Union, List, Tuple + +from mlcolvar.graph.data import ( + atomic, GraphDataSet, create_dataset_from_configurations +) + +__all__ = ['create_dataset_from_trajectories'] + + +def create_dataset_from_trajectories( + trajectories: Union[List[List[str]], List[str], str], + top: Union[List[List[str]], List[str], str], + cutoff: float, + z_table: atomic.AtomicNumberTable = None, + folder: str = None, + create_labels: bool = None, + system_selection: str = None, + edge_sender_selection: str = None, + edge_receiver_selection: str = None, + return_trajectories: bool = False, +) -> Union[ + GraphDataSet, + Tuple[GraphDataSet, Union[List[List[md.Trajectory]], List[md.Trajectory]]] +]: + """ + Create a dataset from a set of trajectory files. + + Parameters + ---------- + trajectories: Union[List[List[str]], List[str], str] + Names of trajectories files. + top: Union[List[List[str]], List[str], str] + Names of topology files. + cutoff: float (units: nm) + The graph cutoff radius. + z_table: mlcolvar.graph.data.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. If not + given, it will be created from the given trajectories. + folder: str + Common path for the files to be imported. If set, filenames become + `folder/file_name`. + create_labels: bool + Assign a label to each file, default True if more than one set of files + is given, otherwise False. + system_selection: str + MDTraj style atom selections [1] of the edge senders. If given, only + selected atoms will be loaded from the trajectories. This option may + increase the speed of building graphs. + edge_sender_selection: str + MDTraj style atom selections [1] of the edge senders. If given, only + edges sent by these atoms will be kept in the graph. + edge_receiver_selection: str + MDTraj style atom selections [1] of the edge receivers. If given, only + edges received by these atoms will be kept in the graph. + return_trajectories: bool + If also return the loaded trajectory objects. + + Returns + ------- + dataset: mlcolvar.graph.data.GraphDataSet + The graph dataset. + trajectories: Union[List[List[mdtraj.Trajectory]], List[mdtraj.Trajectory]] + The loaded trajectory objects. + + Notes + ----- + The selections `edge_sender_selection` and `edge_receiver_selection` will + be made after the `system_selection` was made. Thus, absolute index based + edge selections may lead to unwanted results when `system_selection` is + given. + + References + ---------- + .. [1] https://www.mdtraj.org/1.9.8.dev0/atom_selection.html + """ + # fmt: off + assert type(trajectories) is type(top), ( + 'The `trajectories` and `top` parameters should have the same type!' + ) + if isinstance(trajectories, str): + trajectories = [trajectories] + top = [top] + assert len(trajectories) == len(top), ( + 'Numbers of trajectories and topology files should be the same!' + ) + # fmt: on + + for i in range(len(trajectories)): + assert type(trajectories[i]) is type(top[i]), ( + 'Each element of `trajectories` and `top` parameters ' + + 'should have the same type!' + ) + if isinstance(trajectories[i], list): + assert len(trajectories[i]) == len(top[i]), ( + 'Numbers of trajectories and topology files should be ' + + 'the same!' + ) + for j in range(len(trajectories[i])): + if folder is not None: + trajectories[i][j] = folder + '/' + trajectories[i][j] + top[i][j] = folder + '/' + top[i][j] + assert isinstance(trajectories[i][j], str) + assert isinstance(top[i][j], str) + else: + if folder is not None: + trajectories[i] = folder + '/' + trajectories[i] + top[i] = folder + '/' + top[i] + assert isinstance(trajectories[i], str) + assert isinstance(top[i], str) + + topologies = [] + trajectories_in_memory = [] + + for i in range(len(trajectories)): + if isinstance(trajectories[i], list): + traj = [ + md.load(trajectories[i][j], top=top[i][j]) + for j in range(len(trajectories[i])) + ] + for t in traj: + t.top = md.core.trajectory.load_topology(top[i][j]) + if system_selection is not None: + for j in range(len(traj)): + subset = traj[j].top.select(system_selection) + assert len(subset) > 0, ( + 'No atoms will be selected with system selection ' + + '"{:s}"!'.format(system_selection) + ) + traj[j] = traj[j].atom_slice(subset) + trajectories_in_memory.append(traj) + topologies.extend([t.top for t in traj]) + else: + traj = md.load(trajectories[i], top=top[i]) + traj.top = md.core.trajectory.load_topology(top[i]) + if system_selection is not None: + subset = traj.top.select(system_selection) + assert len(subset) > 0, ( + 'No atoms will be selected with system selection ' + + '"{:s}"!'.format(system_selection) + ) + traj = traj.atom_slice(subset) + trajectories_in_memory.append(traj) + topologies.append(traj.top) + + if z_table is None: + z_table = _z_table_from_top(topologies) + + configurations = [] + for i in range(len(trajectories_in_memory)): + if isinstance(trajectories_in_memory[i], list): + for j in range(len(trajectories_in_memory[i])): + configuration = _configures_from_trajectory( + trajectories_in_memory[i][j], + i, # NOTE: all these configurations have a label `i` + edge_sender_selection, + edge_receiver_selection, + ) + configurations.extend(configuration) + else: + configuration = _configures_from_trajectory( + trajectories_in_memory[i], + i, + edge_sender_selection, + edge_receiver_selection, + ) + configurations.extend(configuration) + + dataset = create_dataset_from_configurations( + configurations, z_table, cutoff + ) + + if return_trajectories: + return dataset, trajectories_in_memory + else: + return dataset + + +def _z_table_from_top(top: List[md.Topology]) -> atomic.AtomicNumberTable: + """ + Create an atomic number table from the topologies. + + Parameters + ---------- + top: List[mdtraj.Topology] + The topology objects. + """ + atomic_numbers = [] + for t in top: + atomic_numbers.extend([a.element.number for a in t.atoms]) + atomic_numbers = np.array(atomic_numbers, dtype=int) + z_table = atomic.AtomicNumberTable.from_zs(atomic_numbers) + return z_table + + +def _configures_from_trajectory( + trajectory: md.Trajectory, + label: int = None, + edge_sender_selection: str = None, + edge_receiver_selection: str = None, +) -> atomic.Configurations: + """ + Create configurations from one trajectory. + + Parameters + ---------- + trajectory: mdtraj.Trajectory + The MDTraj Trajectory object. + label: int + The graph label. + edge_sender_selection: str + MDTraj style atom selections [1] of the edge senders. If given, only + edges sent by these atoms will be kept in the graph. + edge_receiver_selection: str + MDTraj style atom selections [1] of the edge receivers. If given, only + edges received by these atoms will be kept in the graph. + """ + if label is not None: + label = np.array([label]) + + if edge_sender_selection is not None: + edge_senders = trajectory.top.select(edge_sender_selection) + assert len(edge_senders) > 0, ( + 'No atoms will be selected with edge_sender_selection selection ' + + '"{:s}"!'.format(edge_sender_selection) + ) + else: + edge_senders = None + if edge_receiver_selection is not None: + edge_receivers = trajectory.top.select(edge_receiver_selection) + assert len(edge_receivers) > 0, ( + 'No atoms will be selected with edge_receiver_selection selection ' + + '"{:s}"!'.format(edge_receiver_selection) + ) + else: + edge_receivers = None + + atomic_numbers = [a.element.number for a in trajectory.top.atoms] + if trajectory.unitcell_vectors is not None: + pbc = [True] * 3 + cell = trajectory.unitcell_vectors + else: + pbc = [False] * 3 + cell = [None] * len(trajectory) + + configurations = [] + for i in range(len(trajectory)): + configuration = atomic.Configuration( + atomic_numbers=atomic_numbers, + positions=trajectory.xyz[i], + cell=cell[i], + pbc=pbc, + graph_labels=label, + node_labels=None, # TODO: Add supports for per-node labels. + edge_senders=edge_senders, + edge_receivers=edge_receivers, + ) + configurations.append(configuration) + + return configurations + + +def test_create_dataset_from_trajectories( + text: str, system_selection: str +) -> None: + with open('test_dataset.pdb', 'w') as fp: + print(text, file=fp) + + dataset, trajectories = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 0.1, + system_selection=system_selection, + return_trajectories=True + ) + + assert len(dataset) == 6 + assert dataset.cutoff == 0.1 + assert dataset.atomic_numbers == [1, 8] + assert len(trajectories) == 2 + assert len(trajectories[0]) == 2 + assert len(trajectories[1]) == 2 + assert len(trajectories[1][0]) == 2 + assert len(trajectories[1][1]) == 2 + + assert dataset[0]['graph_labels'] == torch.tensor([0.0]) + assert dataset[1]['graph_labels'] == torch.tensor([0.0]) + assert dataset[2]['graph_labels'] == torch.tensor([1.0]) + assert dataset[3]['graph_labels'] == torch.tensor([1.0]) + assert dataset[4]['graph_labels'] == torch.tensor([1.0]) + assert dataset[5]['graph_labels'] == torch.tensor([1.0]) + + def check_data_1(data) -> None: + assert ( + data['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + assert ( + data['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data['positions'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0], + ]) + ).all() + assert ( + data['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + assert ( + data['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + + for i in range(6): + check_data_1(dataset[i]) + + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 0.1, + system_selection=system_selection, + edge_sender_selection='element O', + ) + + def check_data_2(data) -> None: + assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() + assert ( + data['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + + for i in range(6): + check_data_2(dataset[i]) + + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 0.1, + system_selection=system_selection, + edge_receiver_selection='element H', + ) + + def check_data_3(data) -> None: + assert ( + data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) + ).all() + assert ( + data['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + + for i in range(6): + check_data_3(dataset[i]) + + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 0.1, + system_selection=system_selection, + edge_sender_selection='element H', + edge_receiver_selection='element H', + ) + + def check_data_4(data) -> None: + assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() + assert ( + data['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + + for i in range(6): + check_data_4(dataset[i]) + + __import__('os').remove('test_dataset.pdb') + + +if __name__ == '__main__': + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, None) + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, "not resname XXXX") + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, "not resname XXXX") From 2beb7f3eca1c8003a13c23ed0a8a3a060363068d Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 02:33:43 +0200 Subject: [PATCH 024/267] Added selection checks and changed tests. --- mlcolvar/graph/data/dataset.py | 4 ++-- mlcolvar/graph/utils/io.py | 30 ++++++++++++++++++++++++------ 2 files changed, 26 insertions(+), 8 deletions(-) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index f8d0b5eb..e1fec42c 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -310,11 +310,11 @@ def test_from_configuration() -> None: pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels, - edge_senders=[1, 2], + edge_senders=[0], edge_receivers=[1, 2], ) data = _create_dataset_from_configuration(config, z_table, 0.1) - assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() + assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() if __name__ == '__main__': diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 604ce497..68881663 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -68,10 +68,16 @@ def create_dataset_from_trajectories( Notes ----- - The selections `edge_sender_selection` and `edge_receiver_selection` will - be made after the `system_selection` was made. Thus, absolute index based - edge selections may lead to unwanted results when `system_selection` is - given. + 1. Atoms selected by `edge_sender_selection` and `edge_receiver_selection` + should cover all atoms in the system. If only part of the system should be + used to build the graphs, the `system_selection` option should be used, and + all atoms selected by this selection should be again cover by the edge + selections. Thus, using the two types of edge selections requires extra + cautious. + 2. The selections `edge_sender_selection` and `edge_receiver_selection` + will be made after the `system_selection` was made. Thus, absolute index + based edge selections may lead to unwanted results when `system_selection` + is given. References ---------- @@ -238,6 +244,18 @@ def _configures_from_trajectory( else: edge_receivers = None + # here we check for dead nodes (all unselected atoms). + if edge_senders is not None and edge_receivers is not None: + alive_atoms = [*edge_senders.tolist(), *edge_receivers.tolist()] + alive_atoms = sorted(list(set(alive_atoms))) + if alive_atoms != list(range(trajectory.n_atoms)): + raise RuntimeError( + 'Your edge sender selection and edge receiver selection ' + + 'could not select all atoms in your trajectory! If you ' + + 'want to build graphs with only part of your system, ' + + 'consider using the `system_selection` parameter.' + ) + atomic_numbers = [a.element.number for a in trajectory.top.atoms] if trajectory.unitcell_vectors is not None: pbc = [True] * 3 @@ -387,12 +405,12 @@ def check_data_3(data) -> None: ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], 0.1, system_selection=system_selection, - edge_sender_selection='element H', + edge_sender_selection='element O', edge_receiver_selection='element H', ) def check_data_4(data) -> None: - assert (data['edge_index'] == torch.tensor([[1, 2], [2, 1]])).all() + assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() assert ( data['node_attrs'] == torch.tensor([ [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] From 2c876d1787f45d12697c239e830958f25f5ab99d Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 02:49:53 +0200 Subject: [PATCH 025/267] Updated `__repr__`. --- mlcolvar/graph/data/datamodule.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index b98b9e55..e8e3d9c5 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -179,15 +179,16 @@ def teardown(self, stage: str) -> None: def __repr__(self) -> str: result = '' n_digits = len(str(self._n_total)) - data_string_1 = '\033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' + data_string_1 = '[ \033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' - shuffle_string_1 = '|\033[36m  \033[0m' - shuffle_string_2 = '|\033[36m  \033[0m' + shuffle_string_1 = '|\033[36m  \033[0m ]' + shuffle_string_2 = '|\033[36m  \033[0m ]' - prefix = '[\033[1m\033[34m TOTAL DATA \033[0m]: ' - string = prefix + data_string_1.format(n_digits) - result += string.format(self._n_total) + '\n' - prefix = '[\033[1m\033[34m TRAINING \033[0m]: ' + prefix = '\033[1m\033[34m BASEDATA \033[0m: ' + result += ( + prefix + self._dataset.__repr__().split('GRAPHDATASET ')[1] + '\n' + ) + prefix = '\033[1m\033[34m TRAINING \033[0m: ' string = prefix + data_string_1.format(n_digits) result += string.format( self._n_train, self._n_train / self._n_total * 100 @@ -201,7 +202,7 @@ def __repr__(self) -> str: if self._n_validation > 0: result += '\n' - prefix = '[\033[1m\033[34m VALIDATION \033[0m]: ' + prefix = '\033[1m\033[34m VALIDATION \033[0m: ' string = prefix + data_string_1.format(n_digits) result += string.format( self._n_validation, self._n_validation / self._n_total * 100 @@ -215,7 +216,7 @@ def __repr__(self) -> str: if self._n_test > 0: result += '\n' - prefix = '[\033[1m\033[34m TEST \033[0m]: ' + prefix = '\033[1m\033[34m TEST \033[0m: ' string = prefix + data_string_1.format(n_digits) result += string.format( self._n_test, self._n_test / self._n_total * 100 From 31975aa0085f52e491264a9b8d3d4c3e19a8276b Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 16:35:08 +0200 Subject: [PATCH 026/267] Isolated nodes are not that bad. --- mlcolvar/graph/utils/io.py | 20 +------------------- 1 file changed, 1 insertion(+), 19 deletions(-) diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 68881663..8aa7b3a1 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -68,13 +68,7 @@ def create_dataset_from_trajectories( Notes ----- - 1. Atoms selected by `edge_sender_selection` and `edge_receiver_selection` - should cover all atoms in the system. If only part of the system should be - used to build the graphs, the `system_selection` option should be used, and - all atoms selected by this selection should be again cover by the edge - selections. Thus, using the two types of edge selections requires extra - cautious. - 2. The selections `edge_sender_selection` and `edge_receiver_selection` + The selections `edge_sender_selection` and `edge_receiver_selection` will be made after the `system_selection` was made. Thus, absolute index based edge selections may lead to unwanted results when `system_selection` is given. @@ -244,18 +238,6 @@ def _configures_from_trajectory( else: edge_receivers = None - # here we check for dead nodes (all unselected atoms). - if edge_senders is not None and edge_receivers is not None: - alive_atoms = [*edge_senders.tolist(), *edge_receivers.tolist()] - alive_atoms = sorted(list(set(alive_atoms))) - if alive_atoms != list(range(trajectory.n_atoms)): - raise RuntimeError( - 'Your edge sender selection and edge receiver selection ' - + 'could not select all atoms in your trajectory! If you ' - + 'want to build graphs with only part of your system, ' - + 'consider using the `system_selection` parameter.' - ) - atomic_numbers = [a.element.number for a in trajectory.top.atoms] if trajectory.unitcell_vectors is not None: pbc = [True] * 3 From b3b54206cf6c1cf56cdfbb36080051d0005c3ddb Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 19:22:41 +0200 Subject: [PATCH 027/267] Added radial functions. --- mlcolvar/graph/core/nn/__init__.py | 1 + mlcolvar/graph/core/nn/block.py | 92 ++++++++++++++ mlcolvar/graph/core/nn/radial.py | 193 +++++++++++++++++++++++++++++ 3 files changed, 286 insertions(+) create mode 100644 mlcolvar/graph/core/nn/block.py create mode 100644 mlcolvar/graph/core/nn/radial.py diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py index e69de29b..b70a8410 100644 --- a/mlcolvar/graph/core/nn/__init__.py +++ b/mlcolvar/graph/core/nn/__init__.py @@ -0,0 +1 @@ +from . import radial diff --git a/mlcolvar/graph/core/nn/block.py b/mlcolvar/graph/core/nn/block.py new file mode 100644 index 00000000..9abc9681 --- /dev/null +++ b/mlcolvar/graph/core/nn/block.py @@ -0,0 +1,92 @@ +import torch + +from mlcolvar.graph.core.nn import radial + +""" +The GNN building blocks. Some of the blocks are taken from MACE directly: +https://github.com/ACEsuit/mace/blob/main/mace/modules/blocks.py +""" + +__all__ = ['RadialEmbeddingBlock'] + + +class RadialEmbeddingBlock(torch.nn.Module): + """ + The radial embedding block using a Bessel basis set and a smooth cutoff + function [1]. + + Parameters + ---------- + cutoff: float + The cutoff radius. + n_basis: int + Size of the basis set. + n_polynomial: bool + Order of the polynomial. + + References + ---------- + .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + for Molecular Graphs; ICLR 2020. + """ + + def __init__( + self, + cutoff: float, + n_basis: int = 8, + n_polynomial: int = 6, + ) -> None: + super().__init__() + self.n_out = n_basis + self.bessel_fn = radial.BesselBasis(cutoff=cutoff, n_basis=n_basis) + self.cutoff_fn = radial.PolynomialCutoff(cutoff=cutoff, p=n_polynomial) + + def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + edge_lengths: torch.Tensor (shape: [n_edges, 1]) + Lengths of edges. + + Returns + ------- + edge_embedding: torch.Tensor (shape: [n_edges, n_basis]) + The radial edge embedding. + """ + r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_basis] + c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] + return r * c + + +def test_radial_embedding_block(): + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = torch.tensor([ + [0.302161784075405670, 0.603495363703668900], + [0.297344780473306900, 0.565583382110980900], + [0.285645292705329600, 0.479124599728231300], + [0.266549578182040000, 0.356712961747292670], + [0.238761404317085600, 0.216790818528859370], + [0.201179558989195350, 0.083655164534829570], + [0.154832684273361420, -0.016206633178216297], + [0.104419964978618930, -0.064535087460860160], + [0.057909938358517744, -0.063080025890725560], + [0.023554408472511446, -0.035008673547055544] + ]) + + embedding = RadialEmbeddingBlock(6, 2, 6) + + data_new = torch.stack( + [embedding(torch.ones(1) * i * 0.5 + 0.1) for i in range(0, 10)] + ) + + assert (torch.abs(data - data_new) < 1E-12).all() + + torch.set_default_dtype(dtype) + + +if __name__ == '__main__': + test_radial_embedding_block() diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py new file mode 100644 index 00000000..c0e2e5ca --- /dev/null +++ b/mlcolvar/graph/core/nn/radial.py @@ -0,0 +1,193 @@ +import torch +import numpy as np + +""" +The radial functions. This module is taken from MACE directly: +https://github.com/ACEsuit/mace/blob/main/mace/modules/radial.py +""" + +__all__ = ['BesselBasis', 'PolynomialCutoff'] + + +class BesselBasis(torch.nn.Module): + """ + The Bessel radial basis functions (equation (7) in [1]). + + Parameters + ---------- + cutoff: float + The cutoff radius. + n_basis: int + Size of the basis set. + trainable: bool + If use trainable basis set parameters. + + References + ---------- + .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + for Molecular Graphs; ICLR 2020. + """ + + def __init__(self, cutoff: float, n_basis=8, trainable=False) -> None: + super().__init__() + + bessel_weights = ( + np.pi + / cutoff + * torch.linspace( + start=1.0, + end=n_basis, + steps=n_basis, + dtype=torch.get_default_dtype(), + ) + ) + if trainable: + self.bessel_weights = torch.nn.Parameter(bessel_weights) + else: + self.register_buffer("bessel_weights", bessel_weights) + + self.register_buffer( + "cutoff", torch.tensor(cutoff, dtype=torch.get_default_dtype()) + ) + self.register_buffer( + "prefactor", + torch.tensor( + np.sqrt(2.0 / cutoff), dtype=torch.get_default_dtype() + ) + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + numerator = torch.sin(self.bessel_weights * x) + return self.prefactor * (numerator / x) + + def __repr__(self) -> str: + result = 'BESSELBASIS [ ' + + data_string = '\033[32m{:d}\033[0m\033[36m 󰯰 \033[0m' + result = result + data_string.format(len(self.bessel_weights)) + result = result + '| ' + data_string = '\033[32m{:f}\033[0m\033[36m 󰳁 \033[0m' + result = result + data_string.format(self.cutoff) + if self.bessel_weights.requires_grad: + result = result + '|\033[36m TRAINABLE \033[0m' + result = result + ']' + + return result + + +class PolynomialCutoff(torch.nn.Module): + """ + The Continuous cutoff function (equation (8) in [1]). + + Parameters + ---------- + cutoff: float + The cutoff radius. + p: int + Order of the polynomial. + + References + ---------- + .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + for Molecular Graphs; ICLR 2020. + """ + p: torch.Tensor + cutoff: torch.Tensor + + def __init__(self, cutoff: float, p: int = 6) -> None: + super().__init__() + self.register_buffer( + "p", torch.tensor(p, dtype=torch.get_default_dtype()) + ) + self.register_buffer( + "cutoff", torch.tensor(cutoff, dtype=torch.get_default_dtype()) + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # fmt: off + envelope = ( + 1.0 + - (self.p + 1.0) * (self.p + 2.0) / 2.0 + * torch.pow(x / self.cutoff, self.p) + + self.p * (self.p + 2.0) + * torch.pow(x / self.cutoff, self.p + 1) + - self.p * (self.p + 1.0) / 2 + * torch.pow(x / self.cutoff, self.p + 2) + ) + # fmt: on + + # noinspection PyUnresolvedReferences + return envelope * (x < self.cutoff) + + def __repr__(self) -> str: + result = 'POLYNOMIALCUTOFF [ ' + + data_string = '\033[32m{:d}\033[0m\033[36m 󰰚 \033[0m' + result = result + data_string.format(int(self.p)) + result = result + '| ' + data_string = '\033[32m{:f}\033[0m\033[36m 󰳁 \033[0m' + result = result + data_string.format(self.cutoff) + result = result + ']' + + return result + + +def test_bessel_basis() -> None: + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = torch.tensor([ + [0.30216178425160090, 0.603495364055576400], + [0.29735174147757487, 0.565596622727919000], + [0.28586135770645804, 0.479487014442650350], + [0.26815929064765680, 0.358867177503655900], + [0.24496326504279375, 0.222421990229218020], + [0.21720530022724968, 0.090319042449653110], + [0.18598678410040770, -0.019467592388889482], + [0.15252575991598738, -0.094266103787986490], + [0.11809918979627002, -0.128642857533393970], + [0.08398320341397922, -0.124823366088228150] + ]) + + rbf = BesselBasis(6.0, 2) + + data_new = torch.stack( + [rbf(torch.ones(1) * i * 0.5 + 0.1) for i in range(0, 10)] + ) + + assert (torch.abs(data - data_new) < 1E-12).all() + + torch.set_default_dtype(dtype) + + +def test_polynomial_cutoff() -> None: + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = torch.tensor([ + [1.0000000000000000], + [0.9999919136092714], + [0.9995588277320531], + [0.9957733154296875], + [0.9803383630544124], + [0.9390599059360889], + [0.8554687500000000], + [0.7184512221655127], + [0.5317786922725198], + [0.3214569091796875] + ]) + + cutoff_function = PolynomialCutoff(6.0) + + data_new = torch.stack( + [cutoff_function(torch.ones(1) * i * 0.5) for i in range(0, 10)] + ) + + assert (torch.abs(data - data_new) < 1E-12).all() + + torch.set_default_dtype(dtype) + + +if __name__ == '__main__': + test_bessel_basis() + test_polynomial_cutoff() From cab127dcff3b8b48a64d5cbcab25d687000f3b44 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 19:54:31 +0200 Subject: [PATCH 028/267] Modified file layouts. --- mlcolvar/graph/core/__init__.py | 1 + mlcolvar/graph/core/nn/__init__.py | 1 + mlcolvar/graph/core/nn/{block.py => blocks.py} | 0 3 files changed, 2 insertions(+) rename mlcolvar/graph/core/nn/{block.py => blocks.py} (100%) diff --git a/mlcolvar/graph/core/__init__.py b/mlcolvar/graph/core/__init__.py index e69de29b..416239f3 100644 --- a/mlcolvar/graph/core/__init__.py +++ b/mlcolvar/graph/core/__init__.py @@ -0,0 +1 @@ +from . import nn diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py index b70a8410..e8ce7e89 100644 --- a/mlcolvar/graph/core/nn/__init__.py +++ b/mlcolvar/graph/core/nn/__init__.py @@ -1 +1,2 @@ from . import radial +from . import blocks diff --git a/mlcolvar/graph/core/nn/block.py b/mlcolvar/graph/core/nn/blocks.py similarity index 100% rename from mlcolvar/graph/core/nn/block.py rename to mlcolvar/graph/core/nn/blocks.py From 39846f32eb0f5f7e54782fc60e8da832aa7251e7 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 4 Apr 2024 21:56:26 +0200 Subject: [PATCH 029/267] Added a dataset field to distinguish receivers. --- mlcolvar/graph/data/datamodule.py | 38 +++++++++++++++++++++++++++++++ mlcolvar/graph/data/dataset.py | 20 ++++++++++++++++ mlcolvar/graph/utils/io.py | 9 ++++++++ 3 files changed, 67 insertions(+) diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index e8e3d9c5..e0c25a49 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -301,6 +301,7 @@ def test_datamodule() -> None: assert ( data_dict['graph_labels'] == torch.tensor([8, 4, 7, 0, 1, 2]) ).all() + assert (data_dict['n_receivers'] == torch.tensor([3] * 6)).all() data_dict = next(iter(loader.val_dataloader())).to_dict() assert ( @@ -314,6 +315,7 @@ def test_datamodule() -> None: ).all() assert (data_dict['graph_labels'] == torch.tensor([5, 9, 6])).all() assert (data_dict['ptr'] == torch.tensor([0, 3, 6, 9])).all() + assert (data_dict['n_receivers'] == torch.tensor([3] * 3)).all() data_dict = next(iter(loader.test_dataloader())).to_dict() assert (data_dict['graph_labels'] == torch.tensor([3])).all() @@ -361,6 +363,42 @@ def test_datamodule() -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() + assert (data_dict['n_receivers'] == torch.tensor([3])).all() + + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + edge_receivers=[0], + node_labels=node_labels, + graph_labels=graph_labels, + ) + dataset = create_dataset_from_configurations([config] * 10, z_table, 0.1) + + loader = GraphDataModule( + dataset, + lengths=(0.6, 0.3, 0.1), + batch_size=10, + shuffle=False, + seed=1 + ) + + loader.setup() + assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] + data_dict = next(iter(loader.train_dataloader())).to_dict() + + assert (data_dict['n_receivers'] == torch.tensor([1] * 6)).all() + assert ( + data_dict['receiver_masks'] == torch.tensor([[1], [0], [0]] * 6) + ).all() + + data_dict = next(iter(loader.val_dataloader())).to_dict() + + assert (data_dict['n_receivers'] == torch.tensor([1] * 3)).all() + assert ( + data_dict['receiver_masks'] == torch.tensor([[1], [0], [0]] * 3) + ).all() if __name__ == '__main__': diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index e1fec42c..4f2c24f2 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -137,6 +137,18 @@ def _create_dataset_from_configuration( else 1 ) + n_receivers = ( + torch.tensor([len(config.edge_receivers)], dtype=torch.long) + if config.edge_receivers is not None + else torch.tensor([one_hot.shape[0]], dtype=torch.long) + ) + + if config.edge_receivers is not None: + receiver_masks = torch.zeros((one_hot.shape[0], 1), dtype=torch.bool) + receiver_masks[config.edge_receivers, 0] = 1 + else: + receiver_masks = None + return tg.data.Data( edge_index=edge_index, shifts=shifts, @@ -146,6 +158,8 @@ def _create_dataset_from_configuration( node_attrs=one_hot, node_labels=node_labels, graph_labels=graph_labels, + n_receivers=n_receivers, + receiver_masks=receiver_masks, weight=weight, ) @@ -273,6 +287,7 @@ def test_from_configuration() -> None: ).all() assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() assert (data['graph_labels'] == torch.tensor([1.0])).all() + assert (data['n_receivers'] == torch.tensor([3])).all() assert data['weight'] == 1.0 config = atomic.Configuration( @@ -288,6 +303,7 @@ def test_from_configuration() -> None: assert ( data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) ).all() + assert (data['n_receivers'] == torch.tensor([3])).all() config = atomic.Configuration( atomic_numbers=numbers, @@ -302,6 +318,8 @@ def test_from_configuration() -> None: assert ( data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) ).all() + assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() config = atomic.Configuration( atomic_numbers=numbers, @@ -315,6 +333,8 @@ def test_from_configuration() -> None: ) data = _create_dataset_from_configuration(config, z_table, 0.1) assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() + assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() if __name__ == '__main__': diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 8aa7b3a1..b2beb71a 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -338,6 +338,7 @@ def check_data_1(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() + assert (data['n_receivers'] == torch.tensor([3])).all() for i in range(6): check_data_1(dataset[i]) @@ -357,6 +358,10 @@ def check_data_2(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() + # NOTE: This is a very bad testing case: as you can see, the oxygen + # is one of the receivers, however no edge is pointing to it. + # This is the risk when you have too few edge senders. + assert (data['n_receivers'] == torch.tensor([3])).all() for i in range(6): check_data_2(dataset[i]) @@ -378,6 +383,8 @@ def check_data_3(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() + assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() for i in range(6): check_data_3(dataset[i]) @@ -398,6 +405,8 @@ def check_data_4(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() + assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() for i in range(6): check_data_4(dataset[i]) From 4d85d665ef6711e8e2212cbace6104745ae4ea61 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 15:26:18 +0200 Subject: [PATCH 030/267] Added edge encoding function. --- mlcolvar/graph/utils/torch_tools.py | 131 +++++++++++++++++++++++++++- 1 file changed, 129 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index 1cd211f1..dcdc4e37 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -1,11 +1,16 @@ import torch +from typing import Tuple """ The helper functions for torch. This module is taken from MACE directly: https://github.com/ACEsuit/mace/blob/main/mace/tools/torch_tools.py """ -__all__ = ['to_one_hot'] +__all__ = [ + 'to_one_hot', + 'set_default_dtype', + 'get_edge_vectors_and_lengths' +] def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: @@ -14,7 +19,7 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: Parameters ---------- - indices: torch.tensor (shape: [N, 1]) + indices: torch.Tensor (shape: [N, 1]) Node incices. n_classes: int Number of classes. @@ -33,6 +38,47 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: return oh.view(*shape) +def get_edge_vectors_and_lengths( + positions: torch.Tensor, # [n_nodes, 3] + edge_index: torch.Tensor, # [2, n_edges] + shifts: torch.Tensor, # [n_edges, 3] + normalize: bool = True, + eps: float = 1e-9, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate edge vectors and lengths by indices and shift vectors. + + Parameters + ---------- + position: torch.Tensor (shape: [n_atoms, 3]) + The position vector. + edge_index: torch.Tensor (shape: [2, n_edges]) + The edge indices. + shifts: torch.Tensor (shape: [n_edges, 3]) + The shift vector. + normalize: bool + If return the normalized distance vectors. + eps: float + The tolerance of zero-length vectors. + + Returns + ------- + vectors: torch.Tensor (shape: [n_edges, 3]) + The distance vectors. + lengths: torch.Tensor (shape: [n_edges, 1]) + The edge lengths. + """ + sender = edge_index[0] + receiver = edge_index[1] + vectors = positions[receiver] - positions[sender] + shifts # [n_edges, 3] + lengths = torch.linalg.norm(vectors, dim=-1, keepdim=True) # [n_edges, 1] + if normalize: + vectors_normed = vectors / (lengths + eps) + return vectors_normed, lengths + + return vectors, lengths + + def set_default_dtype(dtype: str) -> None: """ Wrapper function of `torch.set_default_dtype`. @@ -65,6 +111,86 @@ def test_to_one_hot() -> None: ).all() +def test_get_edge_vectors_and_lengths() -> None: + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = dict() + data['positions'] = torch.tensor( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=torch.float64 + ) + data['edge_index'] = torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + data['shifts'] = torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + + vectors, distances = get_edge_vectors_and_lengths(**data, normalize=False) + assert ( + torch.abs( + vectors + - torch.tensor([ + [0.0700, -0.0700, 0.0000], + [0.0700, 0.0700, 0.0000], + [-0.070, -0.0700, 0.0000], + [0.0000, 0.0600, 0.0000], + [0.0000, -0.0600, 0.0000], + [-0.070, 0.0700, 0.0000] + ]) + ) < 1E-12 + ).all() + assert ( + torch.abs( + distances + - torch.tensor([ + [0.09899494936611666], + [0.09899494936611666], + [0.09899494936611666], + [0.06000000000000000], + [0.06000000000000000], + [0.09899494936611666], + ]) + ) < 1E-12 + ).all() + + vectors, distances = get_edge_vectors_and_lengths(**data, normalize=True) + assert ( + torch.abs( + vectors + - torch.tensor([ + [0.70710677404369050, -0.7071067740436905, 0.0], + [0.70710677404369050, 0.7071067740436905, 0.0], + [-0.7071067740436905, -0.7071067740436905, 0.0], + [0.00000000000000000, 0.9999999833333336, 0.0], + [0.00000000000000000, -0.9999999833333336, 0.0], + [-0.7071067740436905, 0.7071067740436905, 0.0], + ]) + ) < 1E-12 + ).all() + assert ( + torch.abs( + distances + - torch.tensor([ + [0.09899494936611666], + [0.09899494936611666], + [0.09899494936611666], + [0.06000000000000000], + [0.06000000000000000], + [0.09899494936611666], + ]) + ) < 1E-12 + ).all() + + torch.set_default_dtype(dtype) + + def test_set_default_dtype() -> None: set_default_dtype('float64') t = torch.Tensor([1.0]) @@ -85,4 +211,5 @@ def test_set_default_dtype() -> None: if __name__ == '__main__': test_to_one_hot() + test_get_edge_vectors_and_lengths() test_set_default_dtype() From f8bfab659a9457f4d37b8f83fd8836ce24a3247b Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 15:44:39 +0200 Subject: [PATCH 031/267] Modified file layouts. --- mlcolvar/graph/core/nn/__init__.py | 1 - mlcolvar/graph/core/nn/blocks.py | 92 ------------------------------ mlcolvar/graph/core/nn/radial.py | 79 +++++++++++++++++++++++++ 3 files changed, 79 insertions(+), 93 deletions(-) delete mode 100644 mlcolvar/graph/core/nn/blocks.py diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py index e8ce7e89..b70a8410 100644 --- a/mlcolvar/graph/core/nn/__init__.py +++ b/mlcolvar/graph/core/nn/__init__.py @@ -1,2 +1 @@ from . import radial -from . import blocks diff --git a/mlcolvar/graph/core/nn/blocks.py b/mlcolvar/graph/core/nn/blocks.py deleted file mode 100644 index 9abc9681..00000000 --- a/mlcolvar/graph/core/nn/blocks.py +++ /dev/null @@ -1,92 +0,0 @@ -import torch - -from mlcolvar.graph.core.nn import radial - -""" -The GNN building blocks. Some of the blocks are taken from MACE directly: -https://github.com/ACEsuit/mace/blob/main/mace/modules/blocks.py -""" - -__all__ = ['RadialEmbeddingBlock'] - - -class RadialEmbeddingBlock(torch.nn.Module): - """ - The radial embedding block using a Bessel basis set and a smooth cutoff - function [1]. - - Parameters - ---------- - cutoff: float - The cutoff radius. - n_basis: int - Size of the basis set. - n_polynomial: bool - Order of the polynomial. - - References - ---------- - .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing - for Molecular Graphs; ICLR 2020. - """ - - def __init__( - self, - cutoff: float, - n_basis: int = 8, - n_polynomial: int = 6, - ) -> None: - super().__init__() - self.n_out = n_basis - self.bessel_fn = radial.BesselBasis(cutoff=cutoff, n_basis=n_basis) - self.cutoff_fn = radial.PolynomialCutoff(cutoff=cutoff, p=n_polynomial) - - def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: - """ - The forward pass. - - Parameters - ---------- - edge_lengths: torch.Tensor (shape: [n_edges, 1]) - Lengths of edges. - - Returns - ------- - edge_embedding: torch.Tensor (shape: [n_edges, n_basis]) - The radial edge embedding. - """ - r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_basis] - c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] - return r * c - - -def test_radial_embedding_block(): - dtype = torch.get_default_dtype() - torch.set_default_dtype(torch.float64) - - data = torch.tensor([ - [0.302161784075405670, 0.603495363703668900], - [0.297344780473306900, 0.565583382110980900], - [0.285645292705329600, 0.479124599728231300], - [0.266549578182040000, 0.356712961747292670], - [0.238761404317085600, 0.216790818528859370], - [0.201179558989195350, 0.083655164534829570], - [0.154832684273361420, -0.016206633178216297], - [0.104419964978618930, -0.064535087460860160], - [0.057909938358517744, -0.063080025890725560], - [0.023554408472511446, -0.035008673547055544] - ]) - - embedding = RadialEmbeddingBlock(6, 2, 6) - - data_new = torch.stack( - [embedding(torch.ones(1) * i * 0.5 + 0.1) for i in range(0, 10)] - ) - - assert (torch.abs(data - data_new) < 1E-12).all() - - torch.set_default_dtype(dtype) - - -if __name__ == '__main__': - test_radial_embedding_block() diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py index c0e2e5ca..85dacb38 100644 --- a/mlcolvar/graph/core/nn/radial.py +++ b/mlcolvar/graph/core/nn/radial.py @@ -132,6 +132,56 @@ def __repr__(self) -> str: return result +class RadialEmbeddingBlock(torch.nn.Module): + """ + The radial embedding block using a Bessel basis set and a smooth cutoff + function [1]. + + Parameters + ---------- + cutoff: float + The cutoff radius. + n_basis: int + Size of the basis set. + n_polynomial: bool + Order of the polynomial. + + References + ---------- + .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + for Molecular Graphs; ICLR 2020. + """ + + def __init__( + self, + cutoff: float, + n_basis: int = 8, + n_polynomial: int = 6, + ) -> None: + super().__init__() + self.n_out = n_basis + self.bessel_fn = BesselBasis(cutoff=cutoff, n_basis=n_basis) + self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomial) + + def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + edge_lengths: torch.Tensor (shape: [n_edges, 1]) + Lengths of edges. + + Returns + ------- + edge_embedding: torch.Tensor (shape: [n_edges, n_basis]) + The radial edge embedding. + """ + r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_basis] + c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] + return r * c + + def test_bessel_basis() -> None: dtype = torch.get_default_dtype() torch.set_default_dtype(torch.float64) @@ -188,6 +238,35 @@ def test_polynomial_cutoff() -> None: torch.set_default_dtype(dtype) +def test_radial_embedding_block(): + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = torch.tensor([ + [0.302161784075405670, 0.603495363703668900], + [0.297344780473306900, 0.565583382110980900], + [0.285645292705329600, 0.479124599728231300], + [0.266549578182040000, 0.356712961747292670], + [0.238761404317085600, 0.216790818528859370], + [0.201179558989195350, 0.083655164534829570], + [0.154832684273361420, -0.016206633178216297], + [0.104419964978618930, -0.064535087460860160], + [0.057909938358517744, -0.063080025890725560], + [0.023554408472511446, -0.035008673547055544] + ]) + + embedding = RadialEmbeddingBlock(6, 2, 6) + + data_new = torch.stack( + [embedding(torch.ones(1) * i * 0.5 + 0.1) for i in range(0, 10)] + ) + + assert (torch.abs(data - data_new) < 1E-12).all() + + torch.set_default_dtype(dtype) + + if __name__ == '__main__': test_bessel_basis() test_polynomial_cutoff() + test_radial_embedding_block() From 27a79bc3762373bc4a072da6f45a76dcde51064a Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 16:14:43 +0200 Subject: [PATCH 032/267] Fixed typos. --- mlcolvar/graph/core/nn/radial.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py index 85dacb38..144ab578 100644 --- a/mlcolvar/graph/core/nn/radial.py +++ b/mlcolvar/graph/core/nn/radial.py @@ -6,7 +6,7 @@ https://github.com/ACEsuit/mace/blob/main/mace/modules/radial.py """ -__all__ = ['BesselBasis', 'PolynomialCutoff'] +__all__ = ['RadialEmbeddingBlock'] class BesselBasis(torch.nn.Module): @@ -17,7 +17,7 @@ class BesselBasis(torch.nn.Module): ---------- cutoff: float The cutoff radius. - n_basis: int + n_bases: int Size of the basis set. trainable: bool If use trainable basis set parameters. @@ -28,7 +28,7 @@ class BesselBasis(torch.nn.Module): for Molecular Graphs; ICLR 2020. """ - def __init__(self, cutoff: float, n_basis=8, trainable=False) -> None: + def __init__(self, cutoff: float, n_bases=8, trainable=False) -> None: super().__init__() bessel_weights = ( @@ -36,8 +36,8 @@ def __init__(self, cutoff: float, n_basis=8, trainable=False) -> None: / cutoff * torch.linspace( start=1.0, - end=n_basis, - steps=n_basis, + end=n_bases, + steps=n_bases, dtype=torch.get_default_dtype(), ) ) @@ -141,7 +141,7 @@ class RadialEmbeddingBlock(torch.nn.Module): ---------- cutoff: float The cutoff radius. - n_basis: int + n_bases: int Size of the basis set. n_polynomial: bool Order of the polynomial. @@ -155,12 +155,12 @@ class RadialEmbeddingBlock(torch.nn.Module): def __init__( self, cutoff: float, - n_basis: int = 8, + n_bases: int = 8, n_polynomial: int = 6, ) -> None: super().__init__() - self.n_out = n_basis - self.bessel_fn = BesselBasis(cutoff=cutoff, n_basis=n_basis) + self.n_out = n_bases + self.bessel_fn = BesselBasis(cutoff=cutoff, n_bases=n_bases) self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomial) def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: @@ -174,10 +174,10 @@ def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: Returns ------- - edge_embedding: torch.Tensor (shape: [n_edges, n_basis]) + edge_embedding: torch.Tensor (shape: [n_edges, n_bases]) The radial edge embedding. """ - r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_basis] + r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_bases] c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] return r * c From c163ba20d6d9e1c30cb3bad91113cedb0f7ebaf6 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 16:16:15 +0200 Subject: [PATCH 033/267] Fixed typos. --- mlcolvar/graph/core/nn/radial.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py index 144ab578..f2f2fe9f 100644 --- a/mlcolvar/graph/core/nn/radial.py +++ b/mlcolvar/graph/core/nn/radial.py @@ -143,7 +143,7 @@ class RadialEmbeddingBlock(torch.nn.Module): The cutoff radius. n_bases: int Size of the basis set. - n_polynomial: bool + n_polynomials: bool Order of the polynomial. References @@ -156,12 +156,12 @@ def __init__( self, cutoff: float, n_bases: int = 8, - n_polynomial: int = 6, + n_polynomials: int = 6, ) -> None: super().__init__() self.n_out = n_bases self.bessel_fn = BesselBasis(cutoff=cutoff, n_bases=n_bases) - self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomial) + self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomials) def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: """ From 6b9deb50c1612cabcd92314413d985783a755fbe Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 17:39:20 +0200 Subject: [PATCH 034/267] Added scatter functions. --- mlcolvar/graph/utils/torch_tools.py | 135 ++++++++++++++++++++++++++-- 1 file changed, 130 insertions(+), 5 deletions(-) diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index dcdc4e37..d06c546f 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -1,9 +1,10 @@ import torch -from typing import Tuple +from typing import Tuple, Optional """ -The helper functions for torch. This module is taken from MACE directly: +Helper functions for torch. These modules are taken from MACE directly: https://github.com/ACEsuit/mace/blob/main/mace/tools/torch_tools.py +https://github.com/ACEsuit/mace/blob/main/mace/tools/scatter.py """ __all__ = [ @@ -39,9 +40,9 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: def get_edge_vectors_and_lengths( - positions: torch.Tensor, # [n_nodes, 3] - edge_index: torch.Tensor, # [2, n_edges] - shifts: torch.Tensor, # [n_edges, 3] + positions: torch.Tensor, + edge_index: torch.Tensor, + shifts: torch.Tensor, normalize: bool = True, eps: float = 1e-9, ) -> Tuple[torch.Tensor, torch.Tensor]: @@ -101,6 +102,108 @@ def set_default_dtype(dtype: str) -> None: ) +def _broadcast(src: torch.Tensor, other: torch.Tensor, dim: int): + """ + Helper function of scatter functions. + """ + if dim < 0: + dim = other.dim() + dim + if src.dim() == 1: + for _ in range(0, dim): + src = src.unsqueeze(0) + for _ in range(src.dim(), other.dim()): + src = src.unsqueeze(-1) + src = src.expand_as(other) + return src + + +@torch.jit.script +def scatter_sum( + src: torch.Tensor, + index: torch.Tensor, + dim: int = -1, + out: Optional[torch.Tensor] = None, + dim_size: Optional[int] = None, +) -> torch.Tensor: + """ + Basic `scatter_sum` operations from `torch_scatter`. + + Parameters + ---------- + src: torch.Tensor + The source tensor. + index: torch.Tensor + The indices of elements to scatter. + dim: int + The axis along which to index. + out: Optional[torch.Tensor] + The destination tensor. + dim_size: int + If out is not given, automatically create output with size dim_size at + dimension dim. If dim_size is not given, a minimal sized output tensor + according to index.max() + 1 is returned. + """ + index = _broadcast(index, src, dim) + if out is None: + size = list(src.size()) + if dim_size is not None: + size[dim] = dim_size + elif index.numel() == 0: + size[dim] = 0 + else: + size[dim] = int(index.max()) + 1 + out = torch.zeros(size, dtype=src.dtype, device=src.device) + return out.scatter_add_(dim, index, src) + else: + return out.scatter_add_(dim, index, src) + + +@torch.jit.script +def scatter_mean( + src: torch.Tensor, + index: torch.Tensor, + dim: int = -1, + out: Optional[torch.Tensor] = None, + dim_size: Optional[int] = None, +) -> torch.Tensor: + """ + Basic `scatter_mean` operations from `torch_scatter`. + + Parameters + ---------- + src: torch.Tensor + The source tensor. + index: torch.Tensor + The indices of elements to scatter. + dim: int + The axis along which to index. + out: Optional[torch.Tensor] + The destination tensor. + dim_size: int + If out is not given, automatically create output with size dim_size at + dimension dim. If dim_size is not given, a minimal sized output tensor + according to index.max() + 1 is returned. + """ + out = scatter_sum(src, index, dim, out, dim_size) + dim_size = out.size(dim) + + index_dim = dim + if index_dim < 0: + index_dim = index_dim + src.dim() + if index.dim() <= index_dim: + index_dim = index.dim() - 1 + + ones = torch.ones(index.size(), dtype=src.dtype, device=src.device) + count = scatter_sum(ones, index, index_dim, None, dim_size) + count[count < 1] = 1 + count = _broadcast(count, out, dim) + if out.is_floating_point(): + out.true_divide_(count) + else: + out.div_(count, rounding_mode="floor") + return out + + def test_to_one_hot() -> None: i = torch.tensor([[0], [2], [1]], dtype=torch.int64) e = to_one_hot(i, 4) @@ -209,7 +312,29 @@ def test_set_default_dtype() -> None: assert t.dtype == torch.float32 +def test_scatter() -> None: + src = torch.ones((2, 6, 2), dtype=torch.long) + index = torch.tensor([0, 1, 0, 1, 2, 1], dtype=torch.long) + + out = scatter_sum(src, index, dim=1) + assert ( + out == torch.tensor( + [[[2, 2], [3, 3], [1, 1]], [[2, 2], [3, 3], [1, 1]]], + dtype=torch.long + ) + ).all() + + out = scatter_mean(src, index, dim=1) + assert ( + out == torch.tensor( + [[[1, 1], [1, 1], [1, 1]], [[1, 1], [1, 1], [1, 1]]], + dtype=torch.long + ) + ).all() + + if __name__ == '__main__': test_to_one_hot() test_get_edge_vectors_and_lengths() test_set_default_dtype() + test_scatter() From 3cc1e36033321c98e5c393a6acc12f067c561e2c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 17:41:43 +0200 Subject: [PATCH 035/267] Minor fix. --- mlcolvar/graph/utils/torch_tools.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index d06c546f..8eaff4a8 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -10,7 +10,9 @@ __all__ = [ 'to_one_hot', 'set_default_dtype', - 'get_edge_vectors_and_lengths' + 'get_edge_vectors_and_lengths', + 'scatter_sum', + 'scatter_mean' ] From 12f37dd4af6e92713d70ac9f991fc12777706e1c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 20:05:25 +0200 Subject: [PATCH 036/267] Updated torch tools. --- mlcolvar/graph/utils/torch_tools.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py index 8eaff4a8..397d4365 100644 --- a/mlcolvar/graph/utils/torch_tools.py +++ b/mlcolvar/graph/utils/torch_tools.py @@ -46,7 +46,6 @@ def get_edge_vectors_and_lengths( edge_index: torch.Tensor, shifts: torch.Tensor, normalize: bool = True, - eps: float = 1e-9, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Calculate edge vectors and lengths by indices and shift vectors. @@ -61,8 +60,6 @@ def get_edge_vectors_and_lengths( The shift vector. normalize: bool If return the normalized distance vectors. - eps: float - The tolerance of zero-length vectors. Returns ------- @@ -75,9 +72,9 @@ def get_edge_vectors_and_lengths( receiver = edge_index[1] vectors = positions[receiver] - positions[sender] + shifts # [n_edges, 3] lengths = torch.linalg.norm(vectors, dim=-1, keepdim=True) # [n_edges, 1] + if normalize: - vectors_normed = vectors / (lengths + eps) - return vectors_normed, lengths + vectors = torch.nan_to_num(torch.div(vectors, lengths)) return vectors, lengths @@ -270,12 +267,12 @@ def test_get_edge_vectors_and_lengths() -> None: torch.abs( vectors - torch.tensor([ - [0.70710677404369050, -0.7071067740436905, 0.0], - [0.70710677404369050, 0.7071067740436905, 0.0], - [-0.7071067740436905, -0.7071067740436905, 0.0], - [0.00000000000000000, 0.9999999833333336, 0.0], - [0.00000000000000000, -0.9999999833333336, 0.0], - [-0.7071067740436905, 0.7071067740436905, 0.0], + [0.70710678118654757, -0.70710678118654757, 0.0], + [0.70710678118654757, 0.70710678118654757, 0.0], + [-0.7071067811865476, -0.70710678118654757, 0.0], + [0.00000000000000000, 1.00000000000000000, 0.0], + [0.00000000000000000, -1.00000000000000000, 0.0], + [-0.7071067811865476, 0.70710678118654757, 0.0], ]) ) < 1E-12 ).all() From 167e60859b29bcb36d7ef701fa309e757475a05b Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Apr 2024 22:44:33 +0200 Subject: [PATCH 037/267] Modified shapes of some dataset fields. --- mlcolvar/graph/data/datamodule.py | 10 +++++----- mlcolvar/graph/data/dataset.py | 17 ++++++++++------- mlcolvar/graph/utils/io.py | 8 ++++---- 3 files changed, 19 insertions(+), 16 deletions(-) diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index e0c25a49..92ef429a 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -301,7 +301,7 @@ def test_datamodule() -> None: assert ( data_dict['graph_labels'] == torch.tensor([8, 4, 7, 0, 1, 2]) ).all() - assert (data_dict['n_receivers'] == torch.tensor([3] * 6)).all() + assert (data_dict['n_receivers'] == torch.tensor([[3]] * 6)).all() data_dict = next(iter(loader.val_dataloader())).to_dict() assert ( @@ -315,7 +315,7 @@ def test_datamodule() -> None: ).all() assert (data_dict['graph_labels'] == torch.tensor([5, 9, 6])).all() assert (data_dict['ptr'] == torch.tensor([0, 3, 6, 9])).all() - assert (data_dict['n_receivers'] == torch.tensor([3] * 3)).all() + assert (data_dict['n_receivers'] == torch.tensor([[3]] * 3)).all() data_dict = next(iter(loader.test_dataloader())).to_dict() assert (data_dict['graph_labels'] == torch.tensor([3])).all() @@ -363,7 +363,7 @@ def test_datamodule() -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data_dict['n_receivers'] == torch.tensor([3])).all() + assert (data_dict['n_receivers'] == torch.tensor([[3]])).all() config = atomic.Configuration( atomic_numbers=numbers, @@ -388,14 +388,14 @@ def test_datamodule() -> None: assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] data_dict = next(iter(loader.train_dataloader())).to_dict() - assert (data_dict['n_receivers'] == torch.tensor([1] * 6)).all() + assert (data_dict['n_receivers'] == torch.tensor([[1]] * 6)).all() assert ( data_dict['receiver_masks'] == torch.tensor([[1], [0], [0]] * 6) ).all() data_dict = next(iter(loader.val_dataloader())).to_dict() - assert (data_dict['n_receivers'] == torch.tensor([1] * 3)).all() + assert (data_dict['n_receivers'] == torch.tensor([[1]] * 3)).all() assert ( data_dict['receiver_masks'] == torch.tensor([[1], [0], [0]] * 3) ).all() diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 4f2c24f2..e124e6a1 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -138,9 +138,12 @@ def _create_dataset_from_configuration( ) n_receivers = ( - torch.tensor([len(config.edge_receivers)], dtype=torch.long) - if config.edge_receivers is not None - else torch.tensor([one_hot.shape[0]], dtype=torch.long) + torch.tensor( + [[len(config.edge_receivers)]], dtype=torch.get_default_dtype() + ) if config.edge_receivers is not None + else torch.tensor( + [[one_hot.shape[0]]], dtype=torch.get_default_dtype() + ) ) if config.edge_receivers is not None: @@ -287,7 +290,7 @@ def test_from_configuration() -> None: ).all() assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() assert (data['graph_labels'] == torch.tensor([1.0])).all() - assert (data['n_receivers'] == torch.tensor([3])).all() + assert (data['n_receivers'] == torch.tensor([[3]])).all() assert data['weight'] == 1.0 config = atomic.Configuration( @@ -303,7 +306,7 @@ def test_from_configuration() -> None: assert ( data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) ).all() - assert (data['n_receivers'] == torch.tensor([3])).all() + assert (data['n_receivers'] == torch.tensor([[3]])).all() config = atomic.Configuration( atomic_numbers=numbers, @@ -318,7 +321,7 @@ def test_from_configuration() -> None: assert ( data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) ).all() - assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['n_receivers'] == torch.tensor([[2]])).all() assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() config = atomic.Configuration( @@ -333,7 +336,7 @@ def test_from_configuration() -> None: ) data = _create_dataset_from_configuration(config, z_table, 0.1) assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() - assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['n_receivers'] == torch.tensor([[2]])).all() assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index b2beb71a..3b1a6523 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -338,7 +338,7 @@ def check_data_1(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data['n_receivers'] == torch.tensor([3])).all() + assert (data['n_receivers'] == torch.tensor([[3]])).all() for i in range(6): check_data_1(dataset[i]) @@ -361,7 +361,7 @@ def check_data_2(data) -> None: # NOTE: This is a very bad testing case: as you can see, the oxygen # is one of the receivers, however no edge is pointing to it. # This is the risk when you have too few edge senders. - assert (data['n_receivers'] == torch.tensor([3])).all() + assert (data['n_receivers'] == torch.tensor([[3]])).all() for i in range(6): check_data_2(dataset[i]) @@ -383,7 +383,7 @@ def check_data_3(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['n_receivers'] == torch.tensor([[2]])).all() assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() for i in range(6): @@ -405,7 +405,7 @@ def check_data_4(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data['n_receivers'] == torch.tensor([2])).all() + assert (data['n_receivers'] == torch.tensor([[2]])).all() assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() for i in range(6): From 9a8867aa01925ece5faa2adba9100398f8ccea60 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 15:20:38 +0200 Subject: [PATCH 038/267] Added models. --- mlcolvar/graph/core/nn/__init__.py | 2 +- mlcolvar/graph/core/nn/gvp_layer.py | 460 ++++++++++++++++++++++++++++ mlcolvar/graph/core/nn/models.py | 322 +++++++++++++++++++ 3 files changed, 783 insertions(+), 1 deletion(-) create mode 100644 mlcolvar/graph/core/nn/gvp_layer.py create mode 100644 mlcolvar/graph/core/nn/models.py diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py index b70a8410..0650744f 100644 --- a/mlcolvar/graph/core/nn/__init__.py +++ b/mlcolvar/graph/core/nn/__init__.py @@ -1 +1 @@ -from . import radial +from . import models diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py new file mode 100644 index 00000000..d986ec1e --- /dev/null +++ b/mlcolvar/graph/core/nn/gvp_layer.py @@ -0,0 +1,460 @@ +import functools +import torch +from torch import nn +from torch_geometric.nn import MessagePassing +from typing import Tuple, Union + +from mlcolvar.graph.utils import torch_tools + +""" +The Geometric Vector Perceptron (GVP) layer. This module is taken from the +orginal repo directly: https://github.com/drorlab/gvp-pytorch +""" + +__all__ = ['GVPConvLayer', 'LayerNorm', 'Dropout'] + + +class GVP(nn.Module): + """ + Geometric Vector Perceptron. See manuscript and README.md + for more details. + + :param in_dims: tuple (n_scalar, n_vector) + :param out_dims: tuple (n_scalar, n_vector) + :param h_dim: intermediate number of vector channels, optional + :param activations: tuple of functions (scalar_act, vector_act) + :param vector_gate: whether to use vector gating. + (vector_act will be used as sigma^+ in vector gating if `True`) + """ + + def __init__( + self, + in_dims, + out_dims, + h_dim=None, + activations=(nn.functional.relu, torch.sigmoid), + vector_gate=True, + ): + super(GVP, self).__init__() + self.si, self.vi = in_dims + self.so, self.vo = out_dims + self.vector_gate = vector_gate + if self.vi: + self.h_dim = h_dim or max(self.vi, self.vo) + self.wh = nn.Linear(self.vi, self.h_dim, bias=False) + self.ws = nn.Linear(self.h_dim + self.si, self.so) + if self.vo: + self.wv = nn.Linear(self.h_dim, self.vo, bias=False) + if self.vector_gate: + self.wsv = nn.Linear(self.so, self.vo) + else: + self.ws = nn.Linear(self.si, self.so) + + self.scalar_act, self.vector_act = activations + self.dummy_param = nn.Parameter(torch.empty(0)) + + def forward(self, x): + """ + :param x: tuple (s, V) of `torch.Tensor`, + or (if vectors_in is 0), a single `torch.Tensor` + :return: tuple (s, V) of `torch.Tensor`, + or (if vectors_out is 0), a single `torch.Tensor` + """ + if self.vi: + s, v = x + v = torch.transpose(v, -1, -2) + vh = self.wh(v) + vn = _norm_no_nan(vh, axis=-2) + s = self.ws(torch.cat([s, vn], -1)) + if self.vo: + v = self.wv(vh) + v = torch.transpose(v, -1, -2) + if self.vector_gate: + gate = ( + self.wsv(self.vector_act(s)) + if self.vector_act + else self.wsv(s) + ) + v = v * torch.sigmoid(gate).unsqueeze(-1) + elif self.vector_act: + v = v * self.vector_act( + _norm_no_nan(v, axis=-1, keepdims=True) + ) + else: + s = self.ws(x) + if self.vo: + v = torch.zeros( + s.shape[0], self.vo, 3, device=self.dummy_param.device + ) + if self.scalar_act: + s = self.scalar_act(s) + + return (s, v) if self.vo else s + + +class GVPConv(MessagePassing): + """ + Graph convolution / message passing with Geometric Vector Perceptrons. + Takes in a graph with node and edge embeddings, + and returns new node embeddings. + + This does NOT do residual updates and pointwise feedforward layers + --- see `GVPConvLayer`. + + :param in_dims: input node embedding dimensions (n_scalar, n_vector) + :param out_dims: output node embedding dimensions (n_scalar, n_vector) + :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) + :param n_layers: number of GVPs in the message function + :param module_list: preconstructed message function, overrides n_layers + :param aggr: should be 'add' if some incoming edges are masked, as in + a masked autoregressive decoder architecture, otherwise 'mean' + :param activations: + tuple of functions (scalar_act, vector_act) to use in GVPs + :param vector_gate: whether to use vector gating. + (vector_act will be used as sigma^+ in vector gating if `True`) + """ + + def __init__( + self, + in_dims, + out_dims, + edge_dims, + n_layers=3, + module_list=None, + aggr='mean', + activations=(nn.functional.relu, torch.sigmoid), + vector_gate=True, + ): + super(GVPConv, self).__init__(aggr=aggr) + self.si, self.vi = in_dims + self.so, self.vo = out_dims + self.se, self.ve = edge_dims + + GVP_ = functools.partial( + GVP, activations=activations, vector_gate=vector_gate + ) + + module_list = module_list or [] + if not module_list: + if n_layers == 1: + module_list.append( + GVP_( + (2 * self.si + self.se, 2 * self.vi + self.ve), + (self.so, self.vo), + activations=(None, None), + ) + ) + else: + module_list.append( + GVP_( + (2 * self.si + self.se, 2 * self.vi + self.ve), + out_dims, + ) + ) + for i in range(n_layers - 2): + module_list.append(GVP_(out_dims, out_dims)) + module_list.append( + GVP_(out_dims, out_dims, activations=(None, None)) + ) + self.message_func = nn.Sequential(*module_list) + + def forward(self, x, edge_index, edge_attr): + """ + :param x: tuple (s, V) of `torch.Tensor` + :param edge_index: array of shape [2, n_edges] + :param edge_attr: tuple (s, V) of `torch.Tensor` + """ + x_s, x_v = x + message = self.propagate( + edge_index, + s=x_s, + v=x_v.contiguous().view(x_v.shape[0], x_v.shape[1] * 3), + edge_attr=edge_attr, + ) + return _split(message, self.vo) + + def message(self, s_i, v_i, s_j, v_j, edge_attr): + v_j = v_j.view(v_j.shape[0], v_j.shape[1] // 3, 3) + v_i = v_i.view(v_i.shape[0], v_i.shape[1] // 3, 3) + message = _tuple_cat((s_j, v_j), edge_attr, (s_i, v_i)) + message = self.message_func(message) + return _merge(*message) + + +class GVPConvLayer(nn.Module): + """ + Full graph convolution / message passing layer with + Geometric Vector Perceptrons. Residually updates node embeddings with + aggregated incoming messages, applies a pointwise feedforward + network to node embeddings, and returns updated node embeddings. + + To only compute the aggregated messages, see `GVPConv`. + + :param node_dims: node embedding dimensions (n_scalar, n_vector) + :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) + :param n_message: number of GVPs to use in message function + :param n_feedforward: number of GVPs to use in feedforward function + :param drop_rate: drop probability in all dropout layers + :param autoregressive: if `True`, this `GVPConvLayer` will be used + with a different set of input node embeddings for messages + where src >= dst + :param activations: + tuple of functions (scalar_act, vector_act) to use in GVPs + :param vector_gate: whether to use vector gating. + (vector_act will be used as sigma^+ in vector gating if `True`) + """ + + def __init__( + self, + node_dims, + edge_dims, + n_message=3, + n_feedforward=2, + drop_rate=0.1, + autoregressive=False, + activations=(nn.functional.relu, torch.sigmoid), + vector_gate=True, + residual=True, + ): + super(GVPConvLayer, self).__init__() + self.conv = GVPConv( + node_dims, + node_dims, + edge_dims, + n_message, + aggr='add' if autoregressive else 'mean', + activations=activations, + vector_gate=vector_gate, + ) + GVP_ = functools.partial( + GVP, activations=activations, vector_gate=vector_gate + ) + self.norm = nn.ModuleList([LayerNorm(node_dims) for _ in range(2)]) + self.dropout = nn.ModuleList([Dropout(drop_rate) for _ in range(2)]) + + ff_func = [] + if n_feedforward == 1: + ff_func.append( + GVP_(node_dims, node_dims, activations=(None, None)) + ) + else: + hid_dims = 4 * node_dims[0], 2 * node_dims[1] + ff_func.append(GVP_(node_dims, hid_dims)) + ff_func.extend( + GVP_(hid_dims, hid_dims) for _ in range(n_feedforward - 2) + ) + ff_func.append(GVP_(hid_dims, node_dims, activations=(None, None))) + self.ff_func = nn.Sequential(*ff_func) + self.residual = residual + + def forward( + self, x, edge_index, edge_attr, autoregressive_x=None, node_mask=None + ): + """ + :param x: tuple (s, V) of `torch.Tensor` + :param edge_index: array of shape [2, n_edges] + :param edge_attr: tuple (s, V) of `torch.Tensor` + :param autoregressive_x: tuple (s, V) of `torch.Tensor`. + If not `None`, will be used as src node embeddings + for forming messages where src >= dst. The corrent node + embeddings `x` will still be the base of the update and the + pointwise feedforward. + :param node_mask: array of type `bool` to index into the first + dim of node embeddings (s, V). If not `None`, only + these nodes will be updated. + """ + + if autoregressive_x is not None: + src, dst = edge_index + mask = src < dst + edge_index_forward = edge_index[:, mask] + edge_index_backward = edge_index[:, ~mask] + edge_attr_forward = _tuple_index(edge_attr, mask) + edge_attr_backward = _tuple_index(edge_attr, ~mask) + + dh = _tuple_sum( + self.conv(x, edge_index_forward, edge_attr_forward), + self.conv( + autoregressive_x, edge_index_backward, edge_attr_backward + ), + ) + + count = ( + torch_tools.scatter_sum( + torch.ones_like(dst), dst, dim_size=dh[0].size(0) + ) + .clamp(min=1) + .unsqueeze(-1) + ) + + dh = dh[0] / count, dh[1] / count.unsqueeze(-1) + + else: + dh = self.conv(x, edge_index, edge_attr) + + if node_mask is not None: + x_ = x + x, dh = _tuple_index(x, node_mask), _tuple_index(dh, node_mask) + + x = ( + self.norm[0](_tuple_sum(x, self.dropout[0](dh))) + if self.residual + else dh + ) + + dh = self.ff_func(x) + x = ( + self.norm[1](_tuple_sum(x, self.dropout[1](dh))) + if self.residual + else dh + ) + + if node_mask is not None: + x_[0][node_mask], x_[1][node_mask] = x[0], x[1] + x = x_ + return x + + +class LayerNorm(nn.Module): + """ + Combined LayerNorm for tuples (s, V). + Takes tuples (s, V) as input and as output. + """ + + def __init__(self, dims): + super(LayerNorm, self).__init__() + self.s, self.v = dims + self.scalar_norm = nn.LayerNorm(self.s) + + def forward( + self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] + ): + """ + :param x: tuple (s, V) of `torch.Tensor`, + or single `torch.Tensor` + (will be assumed to be scalar channels) + """ + if not self.v: + # assert isinstance(x, torch.Tensor) + return self.scalar_norm(x) + else: + # assert isinstance(x, Tuple[torch.Tensor, torch.Tensor]) + s, v = x + vn = _norm_no_nan(v, axis=-1, keepdims=True, sqrt=False) + vn = torch.sqrt(torch.mean(vn, dim=-2, keepdim=True)) + return self.scalar_norm(s), v / vn + + +class Dropout(nn.Module): + """ + Combined dropout for tuples (s, V). + Takes tuples (s, V) as input and as output. + """ + + def __init__(self, drop_rate): + super(Dropout, self).__init__() + self.sdropout = nn.Dropout(drop_rate) + self.vdropout = _VDropout(drop_rate) + + def forward(self, x): + """ + :param x: tuple (s, V) of `torch.Tensor`, + or single `torch.Tensor` + (will be assumed to be scalar channels) + """ + if not self.training: + return x + if type(x) is torch.Tensor: + return self.sdropout(x) + s, v = x + return self.sdropout(s), self.vdropout(v) + + +class _VDropout(nn.Module): + """ + Vector channel dropout where the elements of each + vector channel are dropped together. + """ + + def __init__(self, drop_rate): + super(_VDropout, self).__init__() + self.drop_rate = drop_rate + self.dummy_param = nn.Parameter(torch.empty(0)) + + def forward(self, x): + """ + :param x: `torch.Tensor` corresponding to vector channels + """ + device = self.dummy_param.device + if not self.training: + return x + mask = torch.bernoulli( + (1 - self.drop_rate) * torch.ones(x.shape[:-1], device=device) + ).unsqueeze(-1) + x = mask * x / (1 - self.drop_rate) + return x + + +def _tuple_sum(*args): + """ + Sums any number of tuples (s, V) elementwise. + """ + return tuple(map(sum, zip(*args))) + + +def _tuple_cat(*args, dim=-1): + """ + Concatenates any number of tuples (s, V) elementwise. + + :param dim: dimension along which to concatenate when viewed + as the `dim` index for the scalar-channel tensors. + This means that `dim=-1` will be applied as + `dim=-2` for the vector-channel tensors. + """ + dim %= len(args[0][0].shape) + s_args, v_args = list(zip(*args)) + return torch.cat(s_args, dim=dim), torch.cat(v_args, dim=dim) + + +def _tuple_index(x, idx): + """ + Indexes into a tuple (s, V) along the first dimension. + + :param idx: any object which can be used to index into a `torch.Tensor` + """ + return x[0][idx], x[1][idx] + + +def _norm_no_nan(x, axis=-1, keepdims=False, eps=1e-8, sqrt=True): + """ + L2 norm of tensor clamped above a minimum value `eps`. + + :param sqrt: if `False`, returns the square of the L2 norm + """ + out = torch.clamp(torch.sum(torch.square(x), axis, keepdims), min=eps) + return torch.sqrt(out) if sqrt else out + + +def _split(x, nv): + """ + Splits a merged representation of (s, V) back into a tuple. + Should be used only with `_merge(s, V)` and only if the tuple + representation cannot be used. + + :param x: the `torch.Tensor` returned from `_merge` + :param nv: the number of vector channels in the input to `_merge` + """ + s = x[..., :-3 * nv] + v = x[..., -3 * nv:].contiguous().view(x.shape[0], nv, 3) + return s, v + + +def _merge(s, v): + """ + Merges a tuple (s, V) into a single `torch.Tensor`, where the + vector channels are flattened and appended to the scalar channels. + Should be used only if the tuple representation cannot be used. + Use `_split(x, nv)` to reverse. + """ + v = v.contiguous().view(v.shape[0], v.shape[1] * 3) + return torch.cat([s, v], -1) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py new file mode 100644 index 00000000..15feaf2b --- /dev/null +++ b/mlcolvar/graph/core/nn/models.py @@ -0,0 +1,322 @@ +import torch +from torch import nn +import numpy as np +from typing import List, Dict, Tuple + +from mlcolvar.graph.utils import torch_tools +from mlcolvar.graph.core.nn import radial +from mlcolvar.graph.core.nn import gvp_layer +from mlcolvar.graph.data import ( + atomic, + GraphDataModule, + create_dataset_from_configurations +) + +""" +GNN models. +""" + +__all__ = ['BaseModel', 'GVPModel'] + + +class BaseModel(torch.nn.Module): + """ + The commen GNN interface for mlcolvar. + + Parameters + ---------- + n_out: int + Size of the output node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_bases: int + Size of the basis set. + n_polynomials: bool + Order of the polynomials in the basis functions. + """ + + def __init__( + self, + n_out: int, + cutoff: float, + atomic_numbers: List[int], + n_bases: int = 6, + n_polynomials: int = 6, + ) -> None: + super().__init__() + self._n_out = n_out + self._radial_embedding = radial.RadialEmbeddingBlock( + cutoff, n_bases, n_polynomials + ) + self.register_buffer( + 'n_out', torch.tensor(n_out, dtype=torch.int64) + ) + self.register_buffer( + 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) + ) + self.register_buffer( + 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) + ) + + def embed_edge( + self, data: Dict[str, torch.Tensor], normalize: bool = True + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Perform the edge embedding. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + normalize: bool + If return the normalized distance vectors. + + Returns + ------- + edge_length_embeddings: torch.Tensor (shape: [n_edges, n_bases]) + The edge length embeddings. + edge_unit_vectors: torch.Tensor (shape: [n_edges, 3]) + The normalized edge vectors. + """ + vectors, lengths = torch_tools.get_edge_vectors_and_lengths( + positions=data["positions"], + edge_index=data["edge_index"], + shifts=data["shifts"], + normalize=normalize, + ) + return self._radial_embedding(lengths), vectors + + +class GVPModel(BaseModel): + """ + The Geometric Vector Perceptron (GVP) model [1, 2] with vector gate [2]. + + Parameters + ---------- + n_out: int + Size of the output node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_bases: int + Size of the basis set. + n_polynomials: bool + Order of the polynomials in the basis functions. + n_layers: int + Number of the graph convolution layers. + n_messages: int + Number of GVPs to use in the message functions. + n_feedforwards: int + Number of GVPs to use in the feedforward functions. + n_scalars_node: int + Size of the scalar channel of the node embedding in hidden layers. + n_vectors_node: int + Size of the vector channel of the node embedding in hidden layers. + n_scalars_edge: int + Size of the scalar channel of the edge embedding in hidden layers. + drop_rate: int + Drop probability in all dropout layers. + activation: str + Name of the activation function to use in the GVPs (case sensitive). + + References + ---------- + .. [1] Jing, Bowen, et al. + "Learning from protein structure with geometric vector perceptrons." + International Conference on Learning Representations. 2020. + .. [2] Jing, Bowen, et al. + "Equivariant graph neural networks for 3d macromolecular structure." + arXiv preprint arXiv:2106.03843 (2021). + """ + def __init__( + self, + n_out: int, + cutoff: float, + atomic_numbers: List[int], + n_bases: int = 6, + n_polynomials: int = 6, + n_layers: int = 2, + n_messages: int = 2, + n_feedforwards: int = 1, + n_scalars_node: int = 16, + n_vectors_node: int = 8, + n_scalars_edge: int = 16, + drop_rate: int = 0.1, + activation: str = 'SiLU', + ) -> None: + super().__init__(n_out, cutoff, atomic_numbers, n_bases, n_polynomials) + + self.W_e = nn.Sequential( + gvp_layer.LayerNorm((n_bases, 1)), + gvp_layer.GVP( + (n_bases, 1), + (n_scalars_edge, 1), + activations=(None, None), + vector_gate=True, + ), + ) + + self.W_v = nn.Sequential( + gvp_layer.LayerNorm((len(atomic_numbers), 0)), + gvp_layer.GVP( + (len(atomic_numbers), 0), + (n_scalars_node, n_vectors_node), + activations=(None, None), + vector_gate=True, + ), + ) + + self.layers = nn.ModuleList( + gvp_layer.GVPConvLayer( + (n_scalars_node, n_vectors_node), + (n_scalars_edge, 1), + n_message=n_messages, + n_feedforward=n_feedforwards, + drop_rate=drop_rate, + activations=(eval(f'torch.nn.{activation}')(), None), + vector_gate=True, + ) + for _ in range(n_layers) + ) + + self.W_out = nn.Sequential( + gvp_layer.LayerNorm((n_scalars_node, n_vectors_node)), + gvp_layer.GVP( + (n_scalars_node, n_vectors_node), + (n_out, 0), + activations=(eval(f'torch.nn.{activation}')(), None), + vector_gate=True, + ), + ) + + def forward( + self, + data: Dict[str, torch.Tensor], + scatter_mean: bool = True, + ) -> torch.Tensor: + h_V = data['node_attrs'] + h_E = self.embed_edge(data) + h_E = (h_E[0], h_E[1].unsqueeze(-2)) + h_V = self.W_v(h_V) + h_E = self.W_e(h_E) + + batch_id = data['batch'] + if data.get('receiver_masks') is not None: + receiver_masks = data['receiver_masks'].squeeze(-1) + else: + receiver_masks = None + + for layer in self.layers: + h_V = layer( + h_V, + data['edge_index'], + h_E, + node_mask=receiver_masks + ) + + out = self.W_out(h_V) + + if scatter_mean: + if receiver_masks is None: + out = torch_tools.scatter_mean(out, batch_id, dim=0) + else: + out = out * data['receiver_masks'] + out = torch_tools.scatter_sum(out, batch_id, dim=0) + out = out / data['n_receivers'] + + return out + + +def test_get_data(receivers: List[int] = [0, 1, 2]) -> Dict[str, torch.Tensor]: + # TODO: This is not a real test, but a helper function for other tests. + # Maybe should change its name. + torch.manual_seed(0) + + numbers = [8, 1, 1] + positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], + [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], + ], + dtype=np.float64 + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([1]) + node_labels = np.array([[0], [1], [1]]) + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + config = [ + atomic.Configuration( + atomic_numbers=numbers, + positions=p, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + edge_receivers=receivers, + ) for p in positions + ] + dataset = create_dataset_from_configurations(config, z_table, 0.1) + + loader = GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + loader.setup() + + return next(iter(loader.train_dataloader())).to_dict() + + +def test_gvp() -> None: + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + model = GVPModel( + n_out=2, + cutoff=0.1, + atomic_numbers=[1, 8], + n_bases=6, + n_polynomials=6, + n_layers=2, + n_messages=2, + n_feedforwards=1, + n_scalars_node=16, + n_vectors_node=8, + n_scalars_edge=16, + drop_rate=0, + activation='SiLU', + ) + + data = test_get_data() + assert ( + torch.abs( + model(data) - + torch.tensor([[0.3952499007512221, -0.1116923232430907]] * 6) + ) < 1E-12 + ).all() + + data = test_get_data([0]) + assert ( + torch.abs( + model(data) - + torch.tensor([[0.3912415198336253, -0.1113128442154236]] * 6) + ) < 1E-12 + ).all() + + +if __name__ == '__main__': + test_gvp() From debec0e2c85d8f49e25c3ec753df2548d43672e1 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 15:25:52 +0200 Subject: [PATCH 039/267] Modified import schemes. --- mlcolvar/graph/core/__init__.py | 2 +- mlcolvar/graph/core/nn/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/core/__init__.py b/mlcolvar/graph/core/__init__.py index 416239f3..a8893db1 100644 --- a/mlcolvar/graph/core/__init__.py +++ b/mlcolvar/graph/core/__init__.py @@ -1 +1 @@ -from . import nn +from .nn import BaseModel, GVPModel diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py index 0650744f..32523f36 100644 --- a/mlcolvar/graph/core/nn/__init__.py +++ b/mlcolvar/graph/core/nn/__init__.py @@ -1 +1 @@ -from . import models +from .models import BaseModel, GVPModel From 6ed1c8789aa4bcb80fe1b9eb9a33135254e6c244 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 15:27:11 +0200 Subject: [PATCH 040/267] Renamed a directory. --- mlcolvar/graph/cvs/{unsupervised => supervised}/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename mlcolvar/graph/cvs/{unsupervised => supervised}/__init__.py (100%) diff --git a/mlcolvar/graph/cvs/unsupervised/__init__.py b/mlcolvar/graph/cvs/supervised/__init__.py similarity index 100% rename from mlcolvar/graph/cvs/unsupervised/__init__.py rename to mlcolvar/graph/cvs/supervised/__init__.py From 0998a661d8072aad920cd9876c933a60e3903c53 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 17:03:54 +0200 Subject: [PATCH 041/267] Modified import schemes. --- mlcolvar/graph/core/nn/models.py | 14 +++++--------- mlcolvar/graph/utils/io.py | 26 ++++++++++++++------------ 2 files changed, 19 insertions(+), 21 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 15feaf2b..284ec783 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -3,14 +3,10 @@ import numpy as np from typing import List, Dict, Tuple +from mlcolvar.graph import data as gdata from mlcolvar.graph.utils import torch_tools from mlcolvar.graph.core.nn import radial from mlcolvar.graph.core.nn import gvp_layer -from mlcolvar.graph.data import ( - atomic, - GraphDataModule, - create_dataset_from_configurations -) """ GNN models. @@ -255,10 +251,10 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> Dict[str, torch.Tensor]: cell = np.identity(3, dtype=float) * 0.2 graph_labels = np.array([1]) node_labels = np.array([[0], [1], [1]]) - z_table = atomic.AtomicNumberTable.from_zs(numbers) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) config = [ - atomic.Configuration( + gdata.atomic.Configuration( atomic_numbers=numbers, positions=p, cell=cell, @@ -268,9 +264,9 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> Dict[str, torch.Tensor]: edge_receivers=receivers, ) for p in positions ] - dataset = create_dataset_from_configurations(config, z_table, 0.1) + dataset = gdata.create_dataset_from_configurations(config, z_table, 0.1) - loader = GraphDataModule( + loader = gdata.GraphDataModule( dataset, lengths=(1.0,), batch_size=10, diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 3b1a6523..13eb74cd 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -1,12 +1,9 @@ import torch import numpy as np import mdtraj as md - from typing import Union, List, Tuple -from mlcolvar.graph.data import ( - atomic, GraphDataSet, create_dataset_from_configurations -) +from mlcolvar.graph import data as gdata __all__ = ['create_dataset_from_trajectories'] @@ -15,7 +12,7 @@ def create_dataset_from_trajectories( trajectories: Union[List[List[str]], List[str], str], top: Union[List[List[str]], List[str], str], cutoff: float, - z_table: atomic.AtomicNumberTable = None, + z_table: gdata.atomic.AtomicNumberTable = None, folder: str = None, create_labels: bool = None, system_selection: str = None, @@ -23,8 +20,11 @@ def create_dataset_from_trajectories( edge_receiver_selection: str = None, return_trajectories: bool = False, ) -> Union[ - GraphDataSet, - Tuple[GraphDataSet, Union[List[List[md.Trajectory]], List[md.Trajectory]]] + gdata.GraphDataSet, + Tuple[ + gdata.GraphDataSet, + Union[List[List[md.Trajectory]], List[md.Trajectory]] + ] ]: """ Create a dataset from a set of trajectory files. @@ -169,7 +169,7 @@ def create_dataset_from_trajectories( ) configurations.extend(configuration) - dataset = create_dataset_from_configurations( + dataset = gdata.create_dataset_from_configurations( configurations, z_table, cutoff ) @@ -179,7 +179,9 @@ def create_dataset_from_trajectories( return dataset -def _z_table_from_top(top: List[md.Topology]) -> atomic.AtomicNumberTable: +def _z_table_from_top( + top: List[md.Topology] +) -> gdata.atomic.AtomicNumberTable: """ Create an atomic number table from the topologies. @@ -192,7 +194,7 @@ def _z_table_from_top(top: List[md.Topology]) -> atomic.AtomicNumberTable: for t in top: atomic_numbers.extend([a.element.number for a in t.atoms]) atomic_numbers = np.array(atomic_numbers, dtype=int) - z_table = atomic.AtomicNumberTable.from_zs(atomic_numbers) + z_table = gdata.atomic.AtomicNumberTable.from_zs(atomic_numbers) return z_table @@ -201,7 +203,7 @@ def _configures_from_trajectory( label: int = None, edge_sender_selection: str = None, edge_receiver_selection: str = None, -) -> atomic.Configurations: +) -> gdata.atomic.Configurations: """ Create configurations from one trajectory. @@ -248,7 +250,7 @@ def _configures_from_trajectory( configurations = [] for i in range(len(trajectory)): - configuration = atomic.Configuration( + configuration = gdata.atomic.Configuration( atomic_numbers=atomic_numbers, positions=trajectory.xyz[i], cell=cell[i], From 016c1e4f035b2ece0f2cbda5e67cb11223b73199 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 17:27:39 +0200 Subject: [PATCH 042/267] Added a doc string. --- mlcolvar/graph/core/nn/models.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 284ec783..fca0650a 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -194,10 +194,19 @@ def __init__( ) def forward( - self, - data: Dict[str, torch.Tensor], - scatter_mean: bool = True, + self, data: Dict[str, torch.Tensor], scatter_mean: bool = True ) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + scatter_mean: bool + If perform the scatter mean to the model output. + """ h_V = data['node_attrs'] h_E = self.embed_edge(data) h_E = (h_E[0], h_E[1].unsqueeze(-2)) From ca194f215f8fa7219fbb32df0984436a55ff9abd Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 20:02:50 +0200 Subject: [PATCH 043/267] Changed test function outputs. --- mlcolvar/graph/core/nn/models.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index fca0650a..183b492a 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -1,6 +1,7 @@ import torch from torch import nn import numpy as np +import torch_geometric as tg from typing import List, Dict, Tuple from mlcolvar.graph import data as gdata @@ -240,7 +241,7 @@ def forward( return out -def test_get_data(receivers: List[int] = [0, 1, 2]) -> Dict[str, torch.Tensor]: +def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. # Maybe should change its name. torch.manual_seed(0) @@ -283,7 +284,7 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> Dict[str, torch.Tensor]: ) loader.setup() - return next(iter(loader.train_dataloader())).to_dict() + return next(iter(loader.train_dataloader())) def test_gvp() -> None: @@ -306,7 +307,7 @@ def test_gvp() -> None: activation='SiLU', ) - data = test_get_data() + data = test_get_data().to_dict() assert ( torch.abs( model(data) - @@ -314,7 +315,7 @@ def test_gvp() -> None: ) < 1E-12 ).all() - data = test_get_data([0]) + data = test_get_data([0]).to_dict() assert ( torch.abs( model(data) - From 31e0064afc1caf72a0383e2d2ba6f39e55276e14 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 20:20:16 +0200 Subject: [PATCH 044/267] Modified shapes of some dataset fields. --- mlcolvar/graph/core/nn/models.py | 2 +- mlcolvar/graph/data/atomic.py | 2 +- mlcolvar/graph/data/datamodule.py | 12 +++++++----- mlcolvar/graph/data/dataset.py | 7 +++++-- mlcolvar/graph/utils/io.py | 14 +++++++------- 5 files changed, 21 insertions(+), 16 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 183b492a..d8ef69a5 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -259,7 +259,7 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: dtype=np.float64 ) cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([1]) + graph_labels = np.array([[1]]) node_labels = np.array([[0], [1], [1]]) z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) diff --git a/mlcolvar/graph/data/atomic.py b/mlcolvar/graph/data/atomic.py index fee9522d..1e03fd15 100644 --- a/mlcolvar/graph/data/atomic.py +++ b/mlcolvar/graph/data/atomic.py @@ -94,7 +94,7 @@ class Configuration: cell: np.ndarray # shape: [n_atoms, 3], units: nm pbc: Optional[tuple] # shape: [3] node_labels: Optional[np.ndarray] # shape: [n_atoms, n_node_labels] - graph_labels: Optional[np.ndarray] # shape: [n_graph_labels] + graph_labels: Optional[np.ndarray] # shape: [n_graph_labels, 1] edge_senders: Optional[np.ndarray] = None # shape: [n_senders] edge_receivers: Optional[np.ndarray] = None # shape: [n_receivers] weight: Optional[float] = 1.0 # shape: [] diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 92ef429a..bd1cc7d9 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -247,7 +247,7 @@ def test_datamodule() -> None: [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float ) cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([1]) + graph_labels = np.array([[1]]) node_labels = np.array([[0], [1], [1]]) z_table = atomic.AtomicNumberTable.from_zs(numbers) @@ -261,7 +261,7 @@ def test_datamodule() -> None: ) dataset = create_dataset_from_configurations([config] * 10, z_table, 0.1) for i, d in enumerate(dataset): - d['graph_labels'][0] = i + d['graph_labels'][0][0] = i loader = GraphDataModule( dataset, @@ -299,7 +299,9 @@ def test_datamodule() -> None: data_dict = next(iter(loader.train_dataloader())).to_dict() assert data_dict['edge_index'].shape == (2, 36) assert ( - data_dict['graph_labels'] == torch.tensor([8, 4, 7, 0, 1, 2]) + data_dict['graph_labels'] == torch.tensor( + [[8], [4], [7], [0], [1], [2]] + ) ).all() assert (data_dict['n_receivers'] == torch.tensor([[3]] * 6)).all() @@ -313,12 +315,12 @@ def test_datamodule() -> None: assert ( data_dict['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) ).all() - assert (data_dict['graph_labels'] == torch.tensor([5, 9, 6])).all() + assert (data_dict['graph_labels'] == torch.tensor([[5], [9], [6]])).all() assert (data_dict['ptr'] == torch.tensor([0, 3, 6, 9])).all() assert (data_dict['n_receivers'] == torch.tensor([[3]] * 3)).all() data_dict = next(iter(loader.test_dataloader())).to_dict() - assert (data_dict['graph_labels'] == torch.tensor([3])).all() + assert (data_dict['graph_labels'] == torch.tensor([[3]])).all() assert ( data_dict['edge_index'] == torch.tensor( [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index e124e6a1..d1deb0f5 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -94,6 +94,9 @@ def _create_dataset_from_configuration( cutoff: float The graph cutoff radius. """ + + assert config.graph_labels is None or len(config.graph_labels.shape) == 2 + edge_index, shifts, unit_shifts = get_neighborhood( positions=config.positions, cutoff=cutoff, @@ -231,7 +234,7 @@ def test_from_configuration() -> None: dtype=float ) cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([1]) + graph_labels = np.array([[1]]) node_labels = np.array([[0], [1], [1]]) z_table = atomic.AtomicNumberTable.from_zs(numbers) @@ -289,7 +292,7 @@ def test_from_configuration() -> None: ]) ).all() assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() - assert (data['graph_labels'] == torch.tensor([1.0])).all() + assert (data['graph_labels'] == torch.tensor([[1.0]])).all() assert (data['n_receivers'] == torch.tensor([[3]])).all() assert data['weight'] == 1.0 diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 13eb74cd..73a9f359 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -221,7 +221,7 @@ def _configures_from_trajectory( edges received by these atoms will be kept in the graph. """ if label is not None: - label = np.array([label]) + label = np.array([[label]]) if edge_sender_selection is not None: edge_senders = trajectory.top.select(edge_sender_selection) @@ -288,12 +288,12 @@ def test_create_dataset_from_trajectories( assert len(trajectories[1][0]) == 2 assert len(trajectories[1][1]) == 2 - assert dataset[0]['graph_labels'] == torch.tensor([0.0]) - assert dataset[1]['graph_labels'] == torch.tensor([0.0]) - assert dataset[2]['graph_labels'] == torch.tensor([1.0]) - assert dataset[3]['graph_labels'] == torch.tensor([1.0]) - assert dataset[4]['graph_labels'] == torch.tensor([1.0]) - assert dataset[5]['graph_labels'] == torch.tensor([1.0]) + assert dataset[0]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[1]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[2]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[3]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[4]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[5]['graph_labels'] == torch.tensor([[1.0]]) def check_data_1(data) -> None: assert ( From 4b04f18d4d3bd799c4c5f6691e8a4aeb6f61c253 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 23:51:14 +0200 Subject: [PATCH 045/267] Minor fix. --- mlcolvar/graph/core/nn/models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index d8ef69a5..24c0ed5e 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -245,6 +245,7 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. # Maybe should change its name. torch.manual_seed(0) + torch_tools.set_default_dtype('float64') numbers = [8, 1, 1] positions = np.array( From 00b37344d8238d28a8af72311f175fa5c0f1af84 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 9 Apr 2024 23:51:46 +0200 Subject: [PATCH 046/267] Added DeepTDA. --- mlcolvar/graph/cvs/__init__.py | 2 + mlcolvar/graph/cvs/cv.py | 333 ++++++++++++++++++++++ mlcolvar/graph/cvs/supervised/__init__.py | 1 + mlcolvar/graph/cvs/supervised/deeptda.py | 162 +++++++++++ 4 files changed, 498 insertions(+) create mode 100644 mlcolvar/graph/cvs/cv.py create mode 100644 mlcolvar/graph/cvs/supervised/deeptda.py diff --git a/mlcolvar/graph/cvs/__init__.py b/mlcolvar/graph/cvs/__init__.py index e69de29b..c9eb3530 100644 --- a/mlcolvar/graph/cvs/__init__.py +++ b/mlcolvar/graph/cvs/__init__.py @@ -0,0 +1,2 @@ +from .cv import GraphBaseCV +from .supervised import GraphDeepTDA diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py new file mode 100644 index 00000000..feb9317a --- /dev/null +++ b/mlcolvar/graph/cvs/cv.py @@ -0,0 +1,333 @@ +import torch +import lightning +import numpy as np +import torch_geometric as tg +from typing import Dict, Any, List, Union, Tuple + +from mlcolvar.graph import data as gdata +from mlcolvar.graph.core.nn import models +from mlcolvar.graph.utils import torch_tools + +""" +Base collective variable class for Graph Neural Networks. +""" + +__all__ = ['GraphBaseCV'] + + +class GraphBaseCV(lightning.LightningModule): + """ + Base collective variable class for Graph Neural Networks. + + Parameters + ---------- + n_cvs: int + Number of components of the CV. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + model_name: str + Name of the GNN model. + model_options: Dict[Any, Any] + Model options. + optimizer_options: Dict[Any, Any] + Optimizer options. E.g.: + optimizer_options = { + 'optimizer': {'lr': 2E-3, 'weight_decay': 1E-4}, + 'lr_scheduler': { + 'scheduler': torch.optim.lr_scheduler.ExponentialLR, + 'gamma': 0.9999 + } + } + """ + + def __init__( + self, + n_cvs: int, + cutoff: float, + atomic_numbers: List[int], + model_name: str = 'GVPModel', + model_options: Dict[Any, Any] = {}, + optimizer_options: Dict[Any, Any] = {}, + *args, + **kwargs, + ) -> None: + """ + Base CV class options. + """ + super().__init__(*args, **kwargs) + + for key in ['n_out', 'cutoff', 'atomic_numbers']: + model_options.pop(key, None) + + if not hasattr(models, model_name): + raise RuntimeError(f'Unknown model: {model_name}') + self._model = eval(f'models.{model_name}')( + n_out=n_cvs, + cutoff=cutoff, + atomic_numbers=atomic_numbers, + **model_options + ) + + self._optimizer_name = 'Adam' + self.optimizer_kwargs = {} + self.lr_scheduler_kwargs = {} + self._parse_optimizer(optimizer_options) + + self.save_hyperparameters(ignore=['n_cvs', 'cutoff', 'atomic_numbers']) + + def __setattr__(self, key, value) -> None: + # PyTorch overrides __setattr__ to raise a TypeError when you try to + # assignan attribute that is a Module to avoid substituting the model's + # component by mistake. This means we can't simply assign to loss_fn a + # lambda function after it's been assigned a Module, but we need to + # delete the Module first. + # https://github.com/pytorch/pytorch/issues/51896 + # https://stackoverflow.com/questions/61116433 + try: + super().__setattr__(key, value) + except TypeError as e: + # We make an exception only for loss_fn. + if (key == 'loss_fn') and ('cannot assign' in str(e)): + del self.loss_fn + super().__setattr__(key, value) + + def _parse_optimizer(self, options: dict) -> None: + """ + Parse optimizer options. + + Parameters + ---------- + options: Dict[Any, Any] + The options + """ + optimizer_kwargs = options.get('optimizer') + if optimizer_kwargs is not None: + self.optimizer_kwargs.update(optimizer_kwargs) + + lr_scheduler_kwargs = options.get('lr_scheduler') + if lr_scheduler_kwargs is not None: + self.lr_scheduler_kwargs.update(lr_scheduler_kwargs) + + def forward( + self, data: Dict[str, torch.Tensor], *args, **kwargs + ) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + """ + return self._model(data, *args, **kwargs) + + def validation_step(self, *args, **kwargs) -> torch.Tensor: + """ + Equal to training step if not overridden. Different behaviors for + train/valid step can be enforced in `training_step` based on the + `self.training` variable. + """ + return self.training_step(*args, **kwargs) + + def test_step(self, *args, **kwargs) -> torch.Tensor: + """ + Equal to training step if not overridden. Different behaviors for + train/valid step can be enforced in `training_step` based on the + `self.training` variable. + """ + return self.training_step(*args, **kwargs) + + @property + def optimizer_name(self) -> str: + """ + Optimizer name. Options can be set using optimizer_kwargs. Actual + optimizer will be return during training from configure_optimizer + function. + """ + return self._optimizer_name + + @optimizer_name.setter + def optimizer_name(self, optimizer_name: str) -> None: + if not hasattr(torch.optim, optimizer_name): + raise AttributeError( + f'torch.optim does not have a {optimizer_name} optimizer.' + ) + self._optimizer_name = optimizer_name + + def configure_optimizers(self) -> Union[ + torch.optim.Optimizer, + Tuple[ + List[torch.optim.Optimizer], + List[torch.optim.lr_scheduler.LRScheduler] + ] + ]: + """ + Initialize the optimizer based on `self._optimizer_name` and + `self.optimizer_kwargs`. + """ + + optimizer = getattr(torch.optim, self._optimizer_name)( + self.parameters(), **self.optimizer_kwargs + ) + + if self.lr_scheduler_kwargs: + scheduler_cls = self.lr_scheduler_kwargs['scheduler'] + scheduler_kwargs = { + k: v for k, v in self.lr_scheduler_kwargs.items() + if k != 'scheduler' + } + lr_scheduler = scheduler_cls(optimizer, **scheduler_kwargs) + return [optimizer], [lr_scheduler] + else: + return optimizer + + @property + def n_cvs(self) -> int: + """ + Number of components of the CV. + """ + return self._model.n_out.item() + + @property + def cutoff(self) -> float: + """ + Number of components of the CV. + """ + return self._model.cutoff.item() + + @property + def example_input_array(self) -> None: + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=np.float64 + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + + config = gdata.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + edge_receivers=[0] + ) + dataset = gdata.create_dataset_from_configurations( + [config], z_table, 0.1 + ) + + return dataset.to_dict() + + +def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: + # TODO: This is not a real test, but a helper function for other tests. + # Maybe should change its name. + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + numbers = [8, 1, 1] + positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], + [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], + ], + dtype=np.float64 + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[[0]], [[1]]] * 3) + node_labels = np.array([[0], [1], [1]]) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + + config = [ + gdata.atomic.Configuration( + atomic_numbers=numbers, + positions=positions[i], + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels[i], + edge_receivers=receivers, + ) for i in range(0, 6) + ] + dataset = gdata.create_dataset_from_configurations(config, z_table, 0.1) + + loader = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + loader.setup() + + return next(iter(loader.train_dataloader())) + + +def test_base_cv() -> None: + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + cv = GraphBaseCV(2, 0.1, [1, 2, 3]) + + assert cv.n_cvs == 2 + assert (cv.cutoff - 0.1) < 1E-12 + assert (cv._model.atomic_numbers == torch.tensor([1, 2, 3])).all() + + assert cv.optimizer_name == 'Adam' + objects = cv.configure_optimizers() + assert isinstance(objects, torch.optim.Adam) + + cv.optimizer_name = 'SGD' + cv.optimizer_kwargs = {'lr': 2E-3, 'weight_decay': 1E-4} + objects = cv.configure_optimizers() + assert isinstance(objects, torch.optim.SGD) + assert objects.param_groups[0]['weight_decay'] == 1E-4 + assert objects.param_groups[0]['lr'] == 2E-3 + + cv.lr_scheduler_kwargs = { + 'scheduler': torch.optim.lr_scheduler.ExponentialLR, 'gamma': 0.9999 + } + objects = cv.configure_optimizers() + assert isinstance(objects[0][0], torch.optim.SGD) + assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) + assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 + assert objects[0][0].param_groups[0]['lr'] == 2E-3 + assert objects[1][0].gamma == 0.9999 + + cv = GraphBaseCV( + 2, + 0.1, + [1, 2, 3], + optimizer_options={ + 'optimizer': {'lr': 2E-3, 'weight_decay': 1E-4}, + 'lr_scheduler': { + 'scheduler': torch.optim.lr_scheduler.ExponentialLR, + 'gamma': 0.9999 + } + } + ) + + objects = cv.configure_optimizers() + assert isinstance(objects[0][0], torch.optim.Adam) + assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) + assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 + assert objects[0][0].param_groups[0]['lr'] == 2E-3 + assert objects[1][0].gamma == 0.9999 + + torch.set_default_dtype(dtype) + + +if __name__ == '__main__': + test_base_cv() diff --git a/mlcolvar/graph/cvs/supervised/__init__.py b/mlcolvar/graph/cvs/supervised/__init__.py index e69de29b..d1bbea46 100644 --- a/mlcolvar/graph/cvs/supervised/__init__.py +++ b/mlcolvar/graph/cvs/supervised/__init__.py @@ -0,0 +1 @@ +from .deeptda import GraphDeepTDA diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py new file mode 100644 index 00000000..0ca21836 --- /dev/null +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -0,0 +1,162 @@ +import torch +import torch_geometric as tg +from typing import Dict, Any, List, Union + +from mlcolvar.core.loss import TDALoss +from mlcolvar.graph.cvs import GraphBaseCV +from mlcolvar.graph.cvs.cv import test_get_data +from mlcolvar.graph.utils import torch_tools + +""" +The Deep Targeted Discriminant Analysis (Deep-TDA) CV based on Graph Neural +Networks (GNN). +""" + +__all__ = ['GraphDeepTDA'] + + +class GraphDeepTDA(GraphBaseCV): + """ + The Deep Targeted Discriminant Analysis (Deep-TDA) CV [1] based on Graph + Neural Networks (GNN). + + Parameters + ---------- + n_cvs: int + Number of components of the CV. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_cvs : int + Number of collective variables to be trained + target_centers : list + Centers of the Gaussian targets + target_sigmas : list + Standard deviations of the Gaussian targets + model_name: str + Name of the GNN model. + model_options: Dict[Any, Any] + Model options. + + References + ---------- + .. [1] E. Trizio and M. Parrinello, + 'From enhanced sampling to reaction profiles', + The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). + + See also + -------- + mlcolvar.core.loss.TDALoss + Distance from a simple Gaussian target distribution. + """ + + def __init__( + self, + n_cvs: int, + cutoff: float, + atomic_numbers: List[int], + target_centers: Union[List[float], List[List[float]]], + target_sigmas: Union[List[float], List[List[float]]], + model_name: str = 'GVPModel', + model_options: Dict[Any, Any] = {}, + **kwargs, + ) -> None: + + super().__init__( + n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs + ) + + # check size and type of targets + if not isinstance(target_centers, torch.Tensor): + target_centers = torch.Tensor(target_centers) + if not isinstance(target_sigmas, torch.Tensor): + target_sigmas = torch.Tensor(target_sigmas) + + if target_centers.shape != target_sigmas.shape: + raise ValueError( + 'Size of target_centers and target_sigmas should be the same!' + ) + if len(target_centers.shape) == 2 and n_cvs != target_centers.shape[1]: + raise ValueError( + 'Size of target_centers at dimension 1 should match the number' + + f' of cvs! Expected {n_cvs} found {target_centers.shape[1]}' + ) + elif not len(target_centers.shape) in [1, 2]: + raise ValueError('Too much target_centers dimensions!') + + self.loss_fn = TDALoss( + n_states=target_centers.shape[0], + target_centers=target_centers, + target_sigmas=target_sigmas, + ) + + def training_step( + self, train_batch: tg.data.Batch, *args, **kwargs + ) -> torch.Tensor: + """ + Compute and return the training loss and record metrics. + + Parameters + ---------- + train_batch: torch_geometric.data.Batch + The data batch. + """ + data = train_batch.to_dict() + labels = data['graph_labels'] + + output = self.forward(data) + + loss, loss_centers, loss_sigmas = self.loss_fn( + output, labels, return_loss_terms=True + ) + + name = 'train' if self.training else 'valid' + self.log(f'{name}_loss', loss, on_epoch=True) + self.log(f'{name}_loss_centers', loss_centers, on_epoch=True) + self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) + return loss + + +def test_deep_tda(): + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + cv = GraphDeepTDA( + 2, + 0.1, + [1, 8], + [-1, -1], + [1, 1], + model_options={ + 'n_bases': 6, + 'n_polynomials': 6, + 'n_layers': 2, + 'n_messages': 2, + 'n_feedforwards': 1, + 'n_scalars_node': 16, + 'n_vectors_node': 8, + 'n_scalars_edge': 16, + 'drop_rate': 0, + 'activation': 'SiLU', + } + ) + + data = test_get_data() + + assert ( + torch.abs( + cv(data) - + torch.tensor([[0.3952499007512221, -0.1116923232430907]] * 6) + ) < 1E-12 + ).all() + + assert torch.abs( + cv.training_step(data) - torch.tensor(203.8934445710926) + ) < 1E-12 + + +if __name__ == '__main__': + test_deep_tda() From 5b4550c333af22c50defded3a0aa77e9f2aa5338 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 10 Apr 2024 04:42:35 +0200 Subject: [PATCH 047/267] Fixed lightning training. --- mlcolvar/graph/cvs/cv.py | 36 +-------- mlcolvar/graph/cvs/supervised/deeptda.py | 98 +++++++++++++++++++----- mlcolvar/graph/data/datamodule.py | 2 + 3 files changed, 84 insertions(+), 52 deletions(-) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index feb9317a..2afd8ba4 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -6,7 +6,6 @@ from mlcolvar.graph import data as gdata from mlcolvar.graph.core.nn import models -from mlcolvar.graph.utils import torch_tools """ Base collective variable class for Graph Neural Networks. @@ -112,9 +111,7 @@ def _parse_optimizer(self, options: dict) -> None: if lr_scheduler_kwargs is not None: self.lr_scheduler_kwargs.update(lr_scheduler_kwargs) - def forward( - self, data: Dict[str, torch.Tensor], *args, **kwargs - ) -> torch.Tensor: + def forward(self, data: tg.data.Batch) -> torch.Tensor: """ The forward pass. @@ -124,7 +121,7 @@ def forward( The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. """ - return self._model(data, *args, **kwargs) + return self._model(data.to_dict()) def validation_step(self, *args, **kwargs) -> torch.Tensor: """ @@ -200,39 +197,10 @@ def cutoff(self) -> float: """ return self._model.cutoff.item() - @property - def example_input_array(self) -> None: - numbers = [8, 1, 1] - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - dtype=np.float64 - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - edge_receivers=[0] - ) - dataset = gdata.create_dataset_from_configurations( - [config], z_table, 0.1 - ) - - return dataset.to_dict() - def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. # Maybe should change its name. - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') numbers = [8, 1, 1] positions = np.array( diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 0ca21836..ac6e0ad7 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -1,10 +1,12 @@ import torch import torch_geometric as tg +import numpy as np from typing import Dict, Any, List, Union from mlcolvar.core.loss import TDALoss from mlcolvar.graph.cvs import GraphBaseCV from mlcolvar.graph.cvs.cv import test_get_data +from mlcolvar.graph import data as gdata from mlcolvar.graph.utils import torch_tools """ @@ -71,20 +73,33 @@ def __init__( # check size and type of targets if not isinstance(target_centers, torch.Tensor): - target_centers = torch.Tensor(target_centers) + target_centers = torch.tensor( + target_centers, dtype=torch.get_default_dtype() + ) if not isinstance(target_sigmas, torch.Tensor): - target_sigmas = torch.Tensor(target_sigmas) + target_sigmas = torch.tensor( + target_sigmas, dtype=torch.get_default_dtype() + ) + self._n_states = target_centers.shape[0] if target_centers.shape != target_sigmas.shape: raise ValueError( 'Size of target_centers and target_sigmas should be the same!' ) - if len(target_centers.shape) == 2 and n_cvs != target_centers.shape[1]: - raise ValueError( - 'Size of target_centers at dimension 1 should match the number' - + f' of cvs! Expected {n_cvs} found {target_centers.shape[1]}' - ) - elif not len(target_centers.shape) in [1, 2]: + if len(target_centers.shape) == 1: + if n_cvs != 1: + raise ValueError( + 'Size of target_centers at dimension 1 should match the ' + + f'number of cvs! Expected 1 found {n_cvs}' + ) + elif len(target_centers.shape) == 2: + if n_cvs != target_centers.shape[1]: + raise ValueError( + 'Size of target_centers at dimension 1 should match the ' + + f'number of cvs! Expected {n_cvs} found ' + + f'{target_centers.shape[1]}' + ) + elif len(target_centers.shape) > 2: raise ValueError('Too much target_centers dimensions!') self.loss_fn = TDALoss( @@ -104,13 +119,10 @@ def training_step( train_batch: torch_geometric.data.Batch The data batch. """ - data = train_batch.to_dict() - labels = data['graph_labels'] - - output = self.forward(data) + output = self.forward(train_batch) loss, loss_centers, loss_sigmas = self.loss_fn( - output, labels, return_loss_terms=True + output, train_batch.graph_labels, return_loss_terms=True ) name = 'train' if self.training else 'valid' @@ -119,6 +131,42 @@ def training_step( self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) return loss + @property + def example_input_array(self) -> tg.data.Batch: + """ + Example data. + """ + numbers = self._model.atomic_numbers.cpu().numpy().tolist() + positions = np.random.randn(2, len(numbers), 3) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[[0]], [[1]]]) + node_labels = np.array([[0]] * len(numbers)) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + + config = [ + gdata.atomic.Configuration( + atomic_numbers=numbers, + positions=positions[i], + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels[i], + ) for i in range(2) + ] + dataset = gdata.create_dataset_from_configurations( + config, z_table, 0.1 + ) + + loader = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + loader.setup() + + return next(iter(loader.train_dataloader())) + def test_deep_tda(): torch.manual_seed(0) @@ -128,8 +176,8 @@ def test_deep_tda(): 2, 0.1, [1, 8], - [-1, -1], - [1, 1], + [[-1, -1], [1, 1]], + [[1, 1], [1, 1]], model_options={ 'n_bases': 6, 'n_polynomials': 6, @@ -148,15 +196,29 @@ def test_deep_tda(): assert ( torch.abs( - cv(data) - - torch.tensor([[0.3952499007512221, -0.1116923232430907]] * 6) + cv(data) + - torch.tensor([[0.3952499007512221, -0.1116923232430907]] * 6) ) < 1E-12 ).all() assert torch.abs( - cv.training_step(data) - torch.tensor(203.8934445710926) + cv.training_step(data) - torch.tensor(404.6248899361754) ) < 1E-12 + try: + cv = GraphDeepTDA(2, 0.1, [1, 8], [-1, 1], [1, 1]) + except ValueError: + pass + else: + raise RuntimeError + + try: + cv = GraphDeepTDA(2, 0.1, [1, 8], [[-1, -1], [1, 1]], [1, 1]) + except ValueError: + pass + else: + raise RuntimeError + if __name__ == '__main__': test_deep_tda() diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index bd1cc7d9..7a8fcdc3 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -45,6 +45,8 @@ def __init__( shuffle: Union[bool, Sequence] = True, seed: Optional[int] = None, ) -> None: + super().__init__() + self._dataset = dataset self._lengths = lengths self._n_total = len(dataset) From ef44fc6a715a5acdd2558a6197899b8f132546b1 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 10 Apr 2024 04:46:32 +0200 Subject: [PATCH 048/267] Changed the default batch size. --- mlcolvar/graph/data/datamodule.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 7a8fcdc3..5a47f27b 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -40,7 +40,7 @@ def __init__( self, dataset: Sequence[tg.data.Data], lengths: Sequence = (0.8, 0.2), - batch_size: Union[int, Sequence] = 1, + batch_size: Union[int, Sequence] = None, random_split: bool = True, shuffle: Union[bool, Sequence] = True, seed: Optional[int] = None, @@ -93,6 +93,8 @@ def __init__( ] # Make sure batch_size and shuffle are lists. + if batch_size is None: + batch_size = len(dataset) if isinstance(batch_size, int): self.batch_size = [batch_size for _ in lengths] else: From 07db17d9022d8404329a32c60cd46e6a66464d62 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 10 Apr 2024 19:20:49 +0200 Subject: [PATCH 049/267] Removed nonlinearity in the readout function. --- mlcolvar/graph/core/nn/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 24c0ed5e..5f8c16fa 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -189,7 +189,7 @@ def __init__( gvp_layer.GVP( (n_scalars_node, n_vectors_node), (n_out, 0), - activations=(eval(f'torch.nn.{activation}')(), None), + activations=(None, None), vector_gate=True, ), ) From 9a5ad397b4146f246825d9c6de654bf5e993279d Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 11 Apr 2024 23:11:16 +0200 Subject: [PATCH 050/267] Updated default parameters. --- mlcolvar/graph/core/nn/models.py | 12 +++--- mlcolvar/graph/cvs/cv.py | 50 ++++++++++++++++-------- mlcolvar/graph/cvs/supervised/deeptda.py | 4 +- 3 files changed, 41 insertions(+), 25 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 5f8c16fa..ff06fab7 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -138,15 +138,15 @@ def __init__( n_out: int, cutoff: float, atomic_numbers: List[int], - n_bases: int = 6, + n_bases: int = 8, n_polynomials: int = 6, n_layers: int = 2, - n_messages: int = 2, + n_messages: int = 1, n_feedforwards: int = 1, n_scalars_node: int = 16, - n_vectors_node: int = 8, + n_vectors_node: int = 16, n_scalars_edge: int = 16, - drop_rate: int = 0.1, + drop_rate: int = 0.2, activation: str = 'SiLU', ) -> None: super().__init__(n_out, cutoff, atomic_numbers, n_bases, n_polynomials) @@ -312,7 +312,7 @@ def test_gvp() -> None: assert ( torch.abs( model(data) - - torch.tensor([[0.3952499007512221, -0.1116923232430907]] * 6) + torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) ) < 1E-12 ).all() @@ -320,7 +320,7 @@ def test_gvp() -> None: assert ( torch.abs( model(data) - - torch.tensor([[0.3912415198336253, -0.1113128442154236]] * 6) + torch.tensor([[0.6049081358540733, -0.2549507187584082]] * 6) ) < 1E-12 ).all() diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index 2afd8ba4..34e0a60d 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -33,14 +33,7 @@ class GraphBaseCV(lightning.LightningModule): model_options: Dict[Any, Any] Model options. optimizer_options: Dict[Any, Any] - Optimizer options. E.g.: - optimizer_options = { - 'optimizer': {'lr': 2E-3, 'weight_decay': 1E-4}, - 'lr_scheduler': { - 'scheduler': torch.optim.lr_scheduler.ExponentialLR, - 'gamma': 0.9999 - } - } + Optimizer options. """ def __init__( @@ -49,8 +42,25 @@ def __init__( cutoff: float, atomic_numbers: List[int], model_name: str = 'GVPModel', - model_options: Dict[Any, Any] = {}, - optimizer_options: Dict[Any, Any] = {}, + model_options: Dict[Any, Any] = { + 'n_bases': 8, + 'n_polynomials': 6, + 'n_layers': 2, + 'n_messages': 1, + 'n_feedforwards': 1, + 'n_scalars_node': 16, + 'n_vectors_node': 16, + 'n_scalars_edge': 16, + 'drop_rate': 0.2, + 'activation': 'SiLU', + }, + optimizer_options: Dict[Any, Any] = { + 'optimizer': {'lr': 1E-3, 'weight_decay': 1E-4}, + 'lr_scheduler': { + 'scheduler': torch.optim.lr_scheduler.ExponentialLR, + 'gamma': 0.9999 + } + }, *args, **kwargs, ) -> None: @@ -255,24 +265,30 @@ def test_base_cv() -> None: assert cv.optimizer_name == 'Adam' objects = cv.configure_optimizers() - assert isinstance(objects, torch.optim.Adam) + assert isinstance(objects[0][0], torch.optim.Adam) + assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) + assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 + assert objects[0][0].param_groups[0]['lr'] == 1E-3 + assert objects[1][0].gamma == 0.9999 cv.optimizer_name = 'SGD' cv.optimizer_kwargs = {'lr': 2E-3, 'weight_decay': 1E-4} objects = cv.configure_optimizers() - assert isinstance(objects, torch.optim.SGD) - assert objects.param_groups[0]['weight_decay'] == 1E-4 - assert objects.param_groups[0]['lr'] == 2E-3 + assert isinstance(objects[0][0], torch.optim.SGD) + assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 + assert objects[0][0].param_groups[0]['lr'] == 2E-3 cv.lr_scheduler_kwargs = { - 'scheduler': torch.optim.lr_scheduler.ExponentialLR, 'gamma': 0.9999 + 'scheduler': torch.optim.lr_scheduler.StepLR, + 'gamma': 0.999, + 'step_size': 1 } objects = cv.configure_optimizers() assert isinstance(objects[0][0], torch.optim.SGD) - assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) + assert isinstance(objects[1][0], torch.optim.lr_scheduler.StepLR) assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 assert objects[0][0].param_groups[0]['lr'] == 2E-3 - assert objects[1][0].gamma == 0.9999 + assert objects[1][0].gamma == 0.999 cv = GraphBaseCV( 2, diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index ac6e0ad7..8b10eb79 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -197,12 +197,12 @@ def test_deep_tda(): assert ( torch.abs( cv(data) - - torch.tensor([[0.3952499007512221, -0.1116923232430907]] * 6) + - torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) ) < 1E-12 ).all() assert torch.abs( - cv.training_step(data) - torch.tensor(404.6248899361754) + cv.training_step(data) - torch.tensor(405.4884342793402) ) < 1E-12 try: From 18c4c2721955a61125ce634e8c5008bb4ad74435 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 12 Apr 2024 23:22:01 +0200 Subject: [PATCH 051/267] Made everything compilable. Deleted `utils.py` to resolve the rebase problem. --- mlcolvar/core/loss/tda_loss.py | 26 +- mlcolvar/core/transform/utils.py | 180 ------------- mlcolvar/graph/core/nn/gvp_layer.py | 305 ++++++++++++++--------- mlcolvar/graph/core/nn/models.py | 35 ++- mlcolvar/graph/cvs/cv.py | 10 +- mlcolvar/graph/cvs/supervised/deeptda.py | 10 +- 6 files changed, 232 insertions(+), 334 deletions(-) delete mode 100644 mlcolvar/core/transform/utils.py diff --git a/mlcolvar/core/loss/tda_loss.py b/mlcolvar/core/loss/tda_loss.py index 6ddc57ec..4542cc1a 100644 --- a/mlcolvar/core/loss/tda_loss.py +++ b/mlcolvar/core/loss/tda_loss.py @@ -15,7 +15,7 @@ # GLOBAL IMPORTS # ============================================================================= -from typing import Union +from typing import Union, List, Tuple from warnings import warn import torch @@ -32,10 +32,10 @@ class TDALoss(torch.nn.Module): def __init__( self, n_states: int, - target_centers: Union[list, torch.Tensor], - target_sigmas: Union[list, torch.Tensor], - alpha: float = 1, - beta: float = 100, + target_centers: Union[List[float], torch.Tensor], + target_sigmas: Union[List[float], torch.Tensor], + alpha: float = 1.0, + beta: float = 100.0, ): """Constructor. @@ -62,7 +62,7 @@ def __init__( def forward( self, H: torch.Tensor, labels: torch.Tensor, return_loss_terms: bool = False - ) -> torch.Tensor: + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]: """Compute the value of the loss function. Parameters @@ -103,12 +103,12 @@ def tda_loss( H: torch.Tensor, labels: torch.Tensor, n_states: int, - target_centers: Union[list, torch.Tensor], - target_sigmas: Union[list, torch.Tensor], + target_centers: Union[List[float], torch.Tensor], + target_sigmas: Union[List[float], torch.Tensor], alpha: float = 1, beta: float = 100, return_loss_terms: bool = False, -) -> torch.Tensor: +) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]: """ Compute a loss function as the distance from a simple Gaussian target distribution. @@ -144,9 +144,9 @@ def tda_loss( term associated to the standard deviations of the target Gaussians. """ if not isinstance(target_centers, torch.Tensor): - target_centers = torch.Tensor(target_centers) + target_centers = torch.tensor(target_centers) if not isinstance(target_sigmas, torch.Tensor): - target_sigmas = torch.Tensor(target_sigmas) + target_sigmas = torch.tensor(target_sigmas) device = H.device target_centers = target_centers.to(device) @@ -161,7 +161,7 @@ def tda_loss( f"State {i} was not represented in this batch! Either use bigger batch_size or a more equilibrated dataset composition!" ) else: - H_red = H[torch.nonzero(labels == i, as_tuple=True)] + H_red = H[labels == i] # compute mean and standard deviation over the class i mu = torch.mean(H_red, 0) @@ -169,7 +169,7 @@ def tda_loss( warn( f"There is only one sample for state {i} in this batch! Std is set to 0, this may affect the training! Either use bigger batch_size or a more equilibrated dataset composition!" ) - sigma = 0 + sigma = torch.tensor(0) else: sigma = torch.std(H_red, 0) diff --git a/mlcolvar/core/transform/utils.py b/mlcolvar/core/transform/utils.py deleted file mode 100644 index cdbca532..00000000 --- a/mlcolvar/core/transform/utils.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch -from typing import Union -from warnings import warn - -__all__ = ["Inverse", "Statistics"] - - -class Inverse(torch.nn.Module): - "Wrapper to return the inverse method of a module as a torch.nn.Module" - - def __init__(self, module: torch.nn.Module): - """Return the inverse method of a module as a torch.nn.Module - - Parameters - ---------- - module : torch.nn.Module - Module to be inverted - """ - super().__init__() - if not hasattr(module, "inverse"): - raise AttributeError("The given module does not have a 'inverse' method!") - self.module = module - - def inverse(self, *args, **kwargs): - return self.module(*args, **kwargs) - - def forward(self, *args, **kwargs): - return self.module.inverse(*args, **kwargs) - -class Statistics(object): - """ - Calculate statistics (running mean and std.dev based on Welford's algorithm, as well as min and max). - If used with an iterable (such as a dataloader) provides the running estimates. - To get the dictionary with the results use the .to_dict() method. - """ - - def __init__(self, X: torch.Tensor = None): - self.count = 0 - - self.properties = ["mean", "std", "min", "max"] - - # initialize properties and temp var M2 - for prop in self.properties: - setattr(self, prop, None) - setattr(self, "M2", None) - - self.__call__(X) - - def __call__(self, x): - self.update(x) - - def update(self, x): - if x is None: - return - - # get batch size - ndim = x.ndim - if ndim == 0: - x = x.reshape(1, 1) - elif ndim == 1: - x = x.unsqueeze(1) - - batch_size = x.shape[0] - nfeatures = x.shape[1] - - new_count = self.count + batch_size - - # Initialize - if self.mean is None: - for prop in ["mean", "M2", "std"]: - setattr(self, prop, torch.zeros(nfeatures)) - - # compute sample mean - sample_mean = torch.mean(x, dim=0) - sample_m2 = torch.sum((x - sample_mean) ** 2, dim=0) - - # update stats - delta = sample_mean - self.mean - self.mean += delta * batch_size / new_count - corr = batch_size * self.count / new_count - self.M2 += sample_m2 + delta**2 * corr - self.count = new_count - self.std = torch.sqrt(self.M2 / self.count) - - # compute min/max - sample_min = torch.min(x, dim=0).values - sample_max = torch.max(x, dim=0).values - - if self.min is None: - self.min = sample_min - self.max = sample_max - else: - self.min = torch.min(torch.stack((sample_min, self.min)), dim=0).values - self.max = torch.max(torch.stack((sample_max, self.max)), dim=0).values - - def to_dict(self) -> dict: - return {prop: getattr(self, prop) for prop in self.properties} - - def __repr__(self): - repr = " " - for prop in self.properties: - repr += f"{prop}: {getattr(self,prop).numpy()} " - return repr - - -def test_inverse(): - from mlcolvar.core.transform import Transform - # create dummy model to scale the average to 0 - class ForwardModel(Transform): - def __init__(self, in_features=5, out_features=5): - super().__init__(in_features=5, out_features=5) - self.mean = 0 - - def update_mean(self, x): - self.mean = torch.mean(x) - - def forward(self, x): - x = x - self.mean - return x - - def inverse(self, x): - x = x + self.mean - return x - - forward_model = ForwardModel() - inverse_model = Inverse(forward_model) - - input = torch.rand(5) - forward_model.update_mean(input) - out = forward_model(input) - - assert(input.mean() == inverse_model(out).mean()) - -def test_statistics(): - # create fake data - X = torch.arange(0, 100) - X = torch.stack([X + 0.0, X + 100.0, X - 1000.0], dim=1) - y = X.square().sum(1) - print("X", X.shape) - print("y", y.shape) - - # compute stats - - stats = Statistics() - stats(X) - print(stats) - stats.to_dict() - - # create dataloader - from mlcolvar.data import DictLoader - - loader = DictLoader({"data": X, "target": y}, batch_size=20) - - # compute statistics of a single key of loader - key = "data" - stats = Statistics() - for batch in loader: - stats.update(batch[key]) - print(stats) - - # compute stats of all keys in dataloader - - # init a statistics object for each key - stats = {} - for batch in loader: - for key in loader.keys: - # initialize - if key not in stats: - stats[key] = Statistics(batch[key]) - # or accumulate - else: - stats[key].update(batch[key]) - - for key in loader.keys: - print(key,stats[key]) - - -if __name__ == "__main__": - test_inverse() - test_statistics() diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py index d986ec1e..3bb4d585 100644 --- a/mlcolvar/graph/core/nn/gvp_layer.py +++ b/mlcolvar/graph/core/nn/gvp_layer.py @@ -2,13 +2,11 @@ import torch from torch import nn from torch_geometric.nn import MessagePassing -from typing import Tuple, Union - -from mlcolvar.graph.utils import torch_tools +from typing import Tuple, Callable, Optional """ The Geometric Vector Perceptron (GVP) layer. This module is taken from the -orginal repo directly: https://github.com/drorlab/gvp-pytorch +orginal repo: https://github.com/drorlab/gvp-pytorch, and made compilable. """ __all__ = ['GVPConvLayer', 'LayerNorm', 'Dropout'] @@ -29,12 +27,14 @@ class GVP(nn.Module): def __init__( self, - in_dims, - out_dims, - h_dim=None, - activations=(nn.functional.relu, torch.sigmoid), - vector_gate=True, - ): + in_dims: Tuple[int, Optional[int]], + out_dims: Tuple[int, Optional[int]], + h_dim: Tuple[int, Optional[int]] = None, + activations: Tuple[ + Optional[Callable], Optional[Callable] + ] = (nn.functional.relu, torch.sigmoid), + vector_gate: bool = True, + ) -> None: super(GVP, self).__init__() self.si, self.vi = in_dims self.so, self.vo = out_dims @@ -47,49 +47,64 @@ def __init__( self.wv = nn.Linear(self.h_dim, self.vo, bias=False) if self.vector_gate: self.wsv = nn.Linear(self.so, self.vo) + else: + self.wv = None + self.wsv = None else: + self.wh = None + self.wv = None + self.wsv = None self.ws = nn.Linear(self.si, self.so) self.scalar_act, self.vector_act = activations self.dummy_param = nn.Parameter(torch.empty(0)) - def forward(self, x): + def forward( + self, + x: Tuple[torch.Tensor, Optional[torch.Tensor]] + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """ :param x: tuple (s, V) of `torch.Tensor`, or (if vectors_in is 0), a single `torch.Tensor` :return: tuple (s, V) of `torch.Tensor`, or (if vectors_out is 0), a single `torch.Tensor` """ - if self.vi: - s, v = x + s, v = x + if v is not None: + assert self.wh is not None v = torch.transpose(v, -1, -2) vh = self.wh(v) vn = _norm_no_nan(vh, axis=-2) s = self.ws(torch.cat([s, vn], -1)) if self.vo: + assert self.wv is not None v = self.wv(vh) v = torch.transpose(v, -1, -2) if self.vector_gate: + assert self.wsv is not None gate = ( self.wsv(self.vector_act(s)) - if self.vector_act + if self.vector_act is not None else self.wsv(s) ) v = v * torch.sigmoid(gate).unsqueeze(-1) - elif self.vector_act: + elif self.vector_act is not None: v = v * self.vector_act( _norm_no_nan(v, axis=-1, keepdims=True) ) else: - s = self.ws(x) + s = self.ws(s) if self.vo: v = torch.zeros( s.shape[0], self.vo, 3, device=self.dummy_param.device ) - if self.scalar_act: + else: + v = None + + if self.scalar_act is not None: s = self.scalar_act(s) - return (s, v) if self.vo else s + return s, v class GVPConv(MessagePassing): @@ -105,7 +120,6 @@ class GVPConv(MessagePassing): :param out_dims: output node embedding dimensions (n_scalar, n_vector) :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) :param n_layers: number of GVPs in the message function - :param module_list: preconstructed message function, overrides n_layers :param aggr: should be 'add' if some incoming edges are masked, as in a masked autoregressive decoder architecture, otherwise 'mean' :param activations: @@ -113,6 +127,12 @@ class GVPConv(MessagePassing): :param vector_gate: whether to use vector gating. (vector_act will be used as sigma^+ in vector gating if `True`) """ + propagate_type = { + 's': torch.Tensor, + 'v': torch.Tensor, + 'edge_attr_s': torch.Tensor, + 'edge_attr_v': torch.Tensor + } def __init__( self, @@ -120,11 +140,10 @@ def __init__( out_dims, edge_dims, n_layers=3, - module_list=None, aggr='mean', activations=(nn.functional.relu, torch.sigmoid), vector_gate=True, - ): + ) -> None: super(GVPConv, self).__init__(aggr=aggr) self.si, self.vi = in_dims self.so, self.vo = out_dims @@ -134,52 +153,78 @@ def __init__( GVP, activations=activations, vector_gate=vector_gate ) - module_list = module_list or [] - if not module_list: - if n_layers == 1: - module_list.append( - GVP_( - (2 * self.si + self.se, 2 * self.vi + self.ve), - (self.so, self.vo), - activations=(None, None), - ) - ) - else: - module_list.append( - GVP_( - (2 * self.si + self.se, 2 * self.vi + self.ve), - out_dims, - ) + self._module_list = torch.nn.ModuleList() + if n_layers == 1: + self._module_list.append( + GVP_( + (2 * self.si + self.se, 2 * self.vi + self.ve), + (self.so, self.vo), + activations=(None, None), ) - for i in range(n_layers - 2): - module_list.append(GVP_(out_dims, out_dims)) - module_list.append( - GVP_(out_dims, out_dims, activations=(None, None)) + ) + else: + self._module_list.append( + GVP_( + (2 * self.si + self.se, 2 * self.vi + self.ve), + out_dims, ) - self.message_func = nn.Sequential(*module_list) + ) + for i in range(n_layers - 2): + self._module_list.append(GVP_(out_dims, out_dims)) + self._module_list.append( + GVP_(out_dims, out_dims, activations=(None, None)) + ) - def forward(self, x, edge_index, edge_attr): + def forward( + self, + x: Tuple[torch.Tensor, torch.Tensor], + edge_index: torch.Tensor, + edge_attr: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: """ :param x: tuple (s, V) of `torch.Tensor` :param edge_index: array of shape [2, n_edges] :param edge_attr: tuple (s, V) of `torch.Tensor` """ x_s, x_v = x + assert x_v is not None message = self.propagate( edge_index, s=x_s, v=x_v.contiguous().view(x_v.shape[0], x_v.shape[1] * 3), - edge_attr=edge_attr, + edge_attr_s=edge_attr[0], + edge_attr_v=edge_attr[1] ) return _split(message, self.vo) - def message(self, s_i, v_i, s_j, v_j, edge_attr): + def message( + self, + s_i: torch.Tensor, + v_i: torch.Tensor, + s_j: torch.Tensor, + v_j: torch.Tensor, + edge_attr_s: torch.Tensor, + edge_attr_v: torch.Tensor + ) -> torch.Tensor: + assert edge_attr_s is not None + assert edge_attr_v is not None v_j = v_j.view(v_j.shape[0], v_j.shape[1] // 3, 3) v_i = v_i.view(v_i.shape[0], v_i.shape[1] // 3, 3) - message = _tuple_cat((s_j, v_j), edge_attr, (s_i, v_i)) + message = _tuple_cat( + (s_j, v_j), (edge_attr_s, edge_attr_v), (s_i, v_i) + ) message = self.message_func(message) return _merge(*message) + def message_func( + self, x: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + for m in self._module_list: + x = m(x) + output_1, output_2 = x + assert output_2 is not None + return output_1, output_2 + class GVPConvLayer(nn.Module): """ @@ -215,7 +260,7 @@ def __init__( activations=(nn.functional.relu, torch.sigmoid), vector_gate=True, residual=True, - ): + ) -> None: super(GVPConvLayer, self).__init__() self.conv = GVPConv( node_dims, @@ -232,24 +277,29 @@ def __init__( self.norm = nn.ModuleList([LayerNorm(node_dims) for _ in range(2)]) self.dropout = nn.ModuleList([Dropout(drop_rate) for _ in range(2)]) - ff_func = [] + self._module_list = nn.ModuleList() if n_feedforward == 1: - ff_func.append( + self._module_list.append( GVP_(node_dims, node_dims, activations=(None, None)) ) else: hid_dims = 4 * node_dims[0], 2 * node_dims[1] - ff_func.append(GVP_(node_dims, hid_dims)) - ff_func.extend( + self._module_list.append(GVP_(node_dims, hid_dims)) + self._module_list.extend( GVP_(hid_dims, hid_dims) for _ in range(n_feedforward - 2) ) - ff_func.append(GVP_(hid_dims, node_dims, activations=(None, None))) - self.ff_func = nn.Sequential(*ff_func) + self._module_list.append( + GVP_(hid_dims, node_dims, activations=(None, None)) + ) self.residual = residual def forward( - self, x, edge_index, edge_attr, autoregressive_x=None, node_mask=None - ): + self, + x: Tuple[torch.Tensor, torch.Tensor], + edge_index: torch.Tensor, + edge_attr: Tuple[torch.Tensor, torch.Tensor], + node_mask: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: """ :param x: tuple (s, V) of `torch.Tensor` :param edge_index: array of shape [2, n_edges] @@ -264,56 +314,50 @@ def forward( these nodes will be updated. """ - if autoregressive_x is not None: - src, dst = edge_index - mask = src < dst - edge_index_forward = edge_index[:, mask] - edge_index_backward = edge_index[:, ~mask] - edge_attr_forward = _tuple_index(edge_attr, mask) - edge_attr_backward = _tuple_index(edge_attr, ~mask) - - dh = _tuple_sum( - self.conv(x, edge_index_forward, edge_attr_forward), - self.conv( - autoregressive_x, edge_index_backward, edge_attr_backward - ), - ) - - count = ( - torch_tools.scatter_sum( - torch.ones_like(dst), dst, dim_size=dh[0].size(0) - ) - .clamp(min=1) - .unsqueeze(-1) - ) - - dh = dh[0] / count, dh[1] / count.unsqueeze(-1) - - else: - dh = self.conv(x, edge_index, edge_attr) + dh = self.conv(x, edge_index, edge_attr) + x_ = x if node_mask is not None: - x_ = x x, dh = _tuple_index(x, node_mask), _tuple_index(dh, node_mask) - x = ( - self.norm[0](_tuple_sum(x, self.dropout[0](dh))) - if self.residual - else dh - ) + if self.residual: + input_1, input_2 = self.dropout[0](dh) + assert input_2 is not None + output_1, output_2 = self.norm[0]( + _tuple_sum(x, (input_1, input_2)) + ) + assert output_2 is not None + x = (output_1, output_2) + else: + x = dh dh = self.ff_func(x) - x = ( - self.norm[1](_tuple_sum(x, self.dropout[1](dh))) - if self.residual - else dh - ) + if self.residual: + input_1, input_2 = self.dropout[1](dh) + assert input_2 is not None + output_1, output_2 = self.norm[1]( + _tuple_sum(x, (input_1, input_2)) + ) + assert output_2 is not None + x = (output_1, output_2) + else: + x = dh if node_mask is not None: x_[0][node_mask], x_[1][node_mask] = x[0], x[1] x = x_ return x + def ff_func( + self, x: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + for m in self._module_list: + x = m(x) + output_1 = x[0] + output_2 = x[1] + assert output_2 is not None + return output_1, output_2 + class LayerNorm(nn.Module): """ @@ -321,25 +365,25 @@ class LayerNorm(nn.Module): Takes tuples (s, V) as input and as output. """ - def __init__(self, dims): + def __init__(self, dims) -> None: super(LayerNorm, self).__init__() self.s, self.v = dims self.scalar_norm = nn.LayerNorm(self.s) def forward( - self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] - ): + self, + x: Tuple[torch.Tensor, Optional[torch.Tensor]] + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """ :param x: tuple (s, V) of `torch.Tensor`, or single `torch.Tensor` (will be assumed to be scalar channels) """ + s, v = x if not self.v: - # assert isinstance(x, torch.Tensor) - return self.scalar_norm(x) + return self.scalar_norm(s), None else: - # assert isinstance(x, Tuple[torch.Tensor, torch.Tensor]) - s, v = x + assert v is not None vn = _norm_no_nan(v, axis=-1, keepdims=True, sqrt=False) vn = torch.sqrt(torch.mean(vn, dim=-2, keepdim=True)) return self.scalar_norm(s), v / vn @@ -351,23 +395,26 @@ class Dropout(nn.Module): Takes tuples (s, V) as input and as output. """ - def __init__(self, drop_rate): + def __init__(self, drop_rate) -> None: super(Dropout, self).__init__() self.sdropout = nn.Dropout(drop_rate) self.vdropout = _VDropout(drop_rate) - def forward(self, x): + def forward( + self, + x: Tuple[torch.Tensor, Optional[torch.Tensor]] + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """ :param x: tuple (s, V) of `torch.Tensor`, or single `torch.Tensor` (will be assumed to be scalar channels) """ - if not self.training: - return x - if type(x) is torch.Tensor: - return self.sdropout(x) s, v = x - return self.sdropout(s), self.vdropout(v) + if v is None: + return self.sdropout(s), None + else: + assert v is not None + return self.sdropout(s), self.vdropout(v) class _VDropout(nn.Module): @@ -376,12 +423,12 @@ class _VDropout(nn.Module): vector channel are dropped together. """ - def __init__(self, drop_rate): + def __init__(self, drop_rate) -> None: super(_VDropout, self).__init__() self.drop_rate = drop_rate self.dummy_param = nn.Parameter(torch.empty(0)) - def forward(self, x): + def forward(self, x) -> torch.Tensor: """ :param x: `torch.Tensor` corresponding to vector channels """ @@ -395,14 +442,24 @@ def forward(self, x): return x -def _tuple_sum(*args): +def _tuple_sum( + input_1: Tuple[torch.Tensor, torch.Tensor], + input_2: Tuple[torch.Tensor, torch.Tensor] +) -> Tuple[torch.Tensor, torch.Tensor]: """ Sums any number of tuples (s, V) elementwise. """ - return tuple(map(sum, zip(*args))) + out = [i + j for i, j in zip(input_1, input_2)] + return out[0], out[1] -def _tuple_cat(*args, dim=-1): +@torch.jit.script +def _tuple_cat( + input_1: Tuple[torch.Tensor, torch.Tensor], + input_2: Tuple[torch.Tensor, torch.Tensor], + input_3: Tuple[torch.Tensor, torch.Tensor], + dim: int = -1 +) -> Tuple[torch.Tensor, torch.Tensor]: """ Concatenates any number of tuples (s, V) elementwise. @@ -411,12 +468,15 @@ def _tuple_cat(*args, dim=-1): This means that `dim=-1` will be applied as `dim=-2` for the vector-channel tensors. """ - dim %= len(args[0][0].shape) - s_args, v_args = list(zip(*args)) + dim = int(dim % len(input_1[0].shape)) + s_args, v_args = list(zip(input_1, input_2, input_3)) return torch.cat(s_args, dim=dim), torch.cat(v_args, dim=dim) -def _tuple_index(x, idx): +@torch.jit.script +def _tuple_index( + x: Tuple[torch.Tensor, torch.Tensor], idx: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: """ Indexes into a tuple (s, V) along the first dimension. @@ -425,7 +485,14 @@ def _tuple_index(x, idx): return x[0][idx], x[1][idx] -def _norm_no_nan(x, axis=-1, keepdims=False, eps=1e-8, sqrt=True): +@torch.jit.script +def _norm_no_nan( + x: torch.Tensor, + axis: int = -1, + keepdims: bool = False, + eps: float = 1e-8, + sqrt: bool = True +) -> torch.Tensor: """ L2 norm of tensor clamped above a minimum value `eps`. @@ -435,7 +502,8 @@ def _norm_no_nan(x, axis=-1, keepdims=False, eps=1e-8, sqrt=True): return torch.sqrt(out) if sqrt else out -def _split(x, nv): +@torch.jit.script +def _split(x: torch.Tensor, nv: int) -> Tuple[torch.Tensor, torch.Tensor]: """ Splits a merged representation of (s, V) back into a tuple. Should be used only with `_merge(s, V)` and only if the tuple @@ -449,7 +517,8 @@ def _split(x, nv): return s, v -def _merge(s, v): +@torch.jit.script +def _merge(s: torch.Tensor, v: torch.Tensor) -> torch.Tensor: """ Merges a tuple (s, V) into a single `torch.Tensor`, where the vector channels are flattened and appended to the scalar channels. diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index ff06fab7..d94c8fa3 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -151,25 +151,25 @@ def __init__( ) -> None: super().__init__(n_out, cutoff, atomic_numbers, n_bases, n_polynomials) - self.W_e = nn.Sequential( + self.W_e = nn.ModuleList([ gvp_layer.LayerNorm((n_bases, 1)), gvp_layer.GVP( (n_bases, 1), (n_scalars_edge, 1), activations=(None, None), vector_gate=True, - ), - ) + ) + ]) - self.W_v = nn.Sequential( + self.W_v = nn.ModuleList([ gvp_layer.LayerNorm((len(atomic_numbers), 0)), gvp_layer.GVP( (len(atomic_numbers), 0), (n_scalars_node, n_vectors_node), activations=(None, None), vector_gate=True, - ), - ) + ) + ]) self.layers = nn.ModuleList( gvp_layer.GVPConvLayer( @@ -184,7 +184,7 @@ def __init__( for _ in range(n_layers) ) - self.W_out = nn.Sequential( + self.W_out = nn.ModuleList([ gvp_layer.LayerNorm((n_scalars_node, n_vectors_node)), gvp_layer.GVP( (n_scalars_node, n_vectors_node), @@ -192,7 +192,7 @@ def __init__( activations=(None, None), vector_gate=True, ), - ) + ]) def forward( self, data: Dict[str, torch.Tensor], scatter_mean: bool = True @@ -208,11 +208,20 @@ def forward( scatter_mean: bool If perform the scatter mean to the model output. """ - h_V = data['node_attrs'] + h_V = (data['node_attrs'], None) + for w in self.W_v: + h_V = w(h_V) + h_V_1, h_V_2 = h_V + assert h_V_2 is not None + h_V = (h_V_1, h_V_2) + h_E = self.embed_edge(data) h_E = (h_E[0], h_E[1].unsqueeze(-2)) - h_V = self.W_v(h_V) - h_E = self.W_e(h_E) + for w in self.W_e: + h_E = w(h_E) + h_E_1, h_E_2 = h_E + assert h_E_2 is not None + h_E = (h_E_1, h_E_2) batch_id = data['batch'] if data.get('receiver_masks') is not None: @@ -228,7 +237,9 @@ def forward( node_mask=receiver_masks ) - out = self.W_out(h_V) + for w in self.W_out: + h_V = w(h_V) + out = h_V[0] if scatter_mean: if receiver_masks is None: diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index 34e0a60d..1957955b 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -58,7 +58,7 @@ def __init__( 'optimizer': {'lr': 1E-3, 'weight_decay': 1E-4}, 'lr_scheduler': { 'scheduler': torch.optim.lr_scheduler.ExponentialLR, - 'gamma': 0.9999 + 'gamma': 0.9997 } }, *args, @@ -121,7 +121,7 @@ def _parse_optimizer(self, options: dict) -> None: if lr_scheduler_kwargs is not None: self.lr_scheduler_kwargs.update(lr_scheduler_kwargs) - def forward(self, data: tg.data.Batch) -> torch.Tensor: + def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: """ The forward pass. @@ -131,7 +131,7 @@ def forward(self, data: tg.data.Batch) -> torch.Tensor: The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. """ - return self._model(data.to_dict()) + return self._model(data) def validation_step(self, *args, **kwargs) -> torch.Tensor: """ @@ -160,10 +160,6 @@ def optimizer_name(self) -> str: @optimizer_name.setter def optimizer_name(self, optimizer_name: str) -> None: - if not hasattr(torch.optim, optimizer_name): - raise AttributeError( - f'torch.optim does not have a {optimizer_name} optimizer.' - ) self._optimizer_name = optimizer_name def configure_optimizers(self) -> Union[ diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 8b10eb79..d25bd992 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -119,10 +119,12 @@ def training_step( train_batch: torch_geometric.data.Batch The data batch. """ - output = self.forward(train_batch) + output = self.forward(train_batch.to_dict()) loss, loss_centers, loss_sigmas = self.loss_fn( - output, train_batch.graph_labels, return_loss_terms=True + output, + train_batch.graph_labels.squeeze(), + return_loss_terms=True ) name = 'train' if self.training else 'valid' @@ -132,7 +134,7 @@ def training_step( return loss @property - def example_input_array(self) -> tg.data.Batch: + def example_input_array(self) -> Dict[str, torch.Tensor]: """ Example data. """ @@ -202,7 +204,7 @@ def test_deep_tda(): ).all() assert torch.abs( - cv.training_step(data) - torch.tensor(405.4884342793402) + cv.training_step(data) - torch.tensor(404.8752553674548) ) < 1E-12 try: From 99afa6a0e8cda890804cbf3264cb70f0feb7db8c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 12 Apr 2024 23:24:36 +0200 Subject: [PATCH 052/267] Change units to Angstrom. --- mlcolvar/graph/data/atomic.py | 4 ++-- mlcolvar/graph/utils/io.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/mlcolvar/graph/data/atomic.py b/mlcolvar/graph/data/atomic.py index 1e03fd15..1a1d4cd7 100644 --- a/mlcolvar/graph/data/atomic.py +++ b/mlcolvar/graph/data/atomic.py @@ -90,8 +90,8 @@ class Configuration: Internal helper class that describe a given configuration of the system. """ atomic_numbers: np.ndarray # shape: [n_atoms] - positions: np.ndarray # shape: [n_atoms, 3], units: nm - cell: np.ndarray # shape: [n_atoms, 3], units: nm + positions: np.ndarray # shape: [n_atoms, 3], units: Ang + cell: np.ndarray # shape: [n_atoms, 3], units: Ang pbc: Optional[tuple] # shape: [3] node_labels: Optional[np.ndarray] # shape: [n_atoms, n_node_labels] graph_labels: Optional[np.ndarray] # shape: [n_graph_labels, 1] diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 73a9f359..df4cd804 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -35,7 +35,7 @@ def create_dataset_from_trajectories( Names of trajectories files. top: Union[List[List[str]], List[str], str] Names of topology files. - cutoff: float (units: nm) + cutoff: float (units: Ang) The graph cutoff radius. z_table: mlcolvar.graph.data.atomic.AtomicNumberTable The atomic number table used to build the node attributes. If not @@ -252,7 +252,7 @@ def _configures_from_trajectory( for i in range(len(trajectory)): configuration = gdata.atomic.Configuration( atomic_numbers=atomic_numbers, - positions=trajectory.xyz[i], + positions=trajectory.xyz[i] * 10, cell=cell[i], pbc=pbc, graph_labels=label, From 98ac5b16d14b639f89894c3159a18c488fbb5711 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 12 Apr 2024 23:34:03 +0200 Subject: [PATCH 053/267] Fixed cell size. --- mlcolvar/graph/data/neighborhood.py | 2 +- mlcolvar/graph/utils/io.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py index 244cffce..e534b2cd 100644 --- a/mlcolvar/graph/data/neighborhood.py +++ b/mlcolvar/graph/data/neighborhood.py @@ -79,7 +79,7 @@ def get_neighborhood( pbc=pbc, cell=cell, positions=positions, - cutoff=cutoff, + cutoff=float(cutoff), # self_interaction=True, # we want edges from atom to itself in different periodic images # use_scaled_positions=False, # positions are not scaled positions ) diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index df4cd804..baffa0db 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -253,7 +253,7 @@ def _configures_from_trajectory( configuration = gdata.atomic.Configuration( atomic_numbers=atomic_numbers, positions=trajectory.xyz[i] * 10, - cell=cell[i], + cell=cell[i] * 10, pbc=pbc, graph_labels=label, node_labels=None, # TODO: Add supports for per-node labels. From 927c55d0cc416f7b342bd29b86e22fe7889fd788 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sat, 13 Apr 2024 00:37:20 +0200 Subject: [PATCH 054/267] Added more model attributes. --- mlcolvar/graph/cvs/cv.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index 1957955b..e95e6af9 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -203,6 +203,13 @@ def cutoff(self) -> float: """ return self._model.cutoff.item() + @property + def atomic_numbers(self) -> torch.Tensor: + """ + The atomic numbers mapping. + """ + return self._model.atomic_numbers + def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. From cda2b317a29d0ad70764dfde90c52ead351b753f Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sat, 13 Apr 2024 01:10:57 +0200 Subject: [PATCH 055/267] Fixed for plumed interface. --- mlcolvar/graph/cvs/cv.py | 42 +++++++++++++++++++--------------------- 1 file changed, 20 insertions(+), 22 deletions(-) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index e95e6af9..ed50ea23 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -69,6 +69,16 @@ def __init__( """ super().__init__(*args, **kwargs) + self.register_buffer( + 'n_cvs', torch.tensor(n_cvs, dtype=torch.int64) + ) + self.register_buffer( + 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) + ) + self.register_buffer( + 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) + ) + for key in ['n_out', 'cutoff', 'atomic_numbers']: model_options.pop(key, None) @@ -121,7 +131,11 @@ def _parse_optimizer(self, options: dict) -> None: if lr_scheduler_kwargs is not None: self.lr_scheduler_kwargs.update(lr_scheduler_kwargs) - def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: + def forward( + self, + data: Dict[str, torch.Tensor], + token: bool = False + ) -> torch.Tensor: """ The forward pass. @@ -130,7 +144,12 @@ def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: data: Dict[str, torch.Tensor] The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. + token: bool + To be used. """ + data['positions'].requires_grad_(True) + data['node_attrs'].requires_grad_(True) + return self._model(data) def validation_step(self, *args, **kwargs) -> torch.Tensor: @@ -189,27 +208,6 @@ def configure_optimizers(self) -> Union[ else: return optimizer - @property - def n_cvs(self) -> int: - """ - Number of components of the CV. - """ - return self._model.n_out.item() - - @property - def cutoff(self) -> float: - """ - Number of components of the CV. - """ - return self._model.cutoff.item() - - @property - def atomic_numbers(self) -> torch.Tensor: - """ - The atomic numbers mapping. - """ - return self._model.atomic_numbers - def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. From 6e50651076521813fc419001a15e042711fe0a52 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sat, 13 Apr 2024 01:24:40 +0200 Subject: [PATCH 056/267] Added tests --- mlcolvar/graph/cvs/cv.py | 2 +- mlcolvar/graph/utils/io.py | 24 ++++++++++++------------ 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index ed50ea23..3830ccd3 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -270,7 +270,7 @@ def test_base_cv() -> None: assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 assert objects[0][0].param_groups[0]['lr'] == 1E-3 - assert objects[1][0].gamma == 0.9999 + assert objects[1][0].gamma == 0.9997 cv.optimizer_name = 'SGD' cv.optimizer_kwargs = {'lr': 2E-3, 'weight_decay': 1E-4} diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index baffa0db..8c240874 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -274,13 +274,13 @@ def test_create_dataset_from_trajectories( dataset, trajectories = create_dataset_from_trajectories( ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 0.1, + 1.0, system_selection=system_selection, return_trajectories=True ) assert len(dataset) == 6 - assert dataset.cutoff == 0.1 + assert dataset.cutoff == 1.0 assert dataset.atomic_numbers == [1, 8] assert len(trajectories) == 2 assert len(trajectories[0]) == 2 @@ -306,8 +306,8 @@ def check_data_1(data) -> None: [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], + [0.0, 2.0, 0.0], + [0.0, -2.0, 0.0], [0.0, 0.0, 0.0], ]) ).all() @@ -324,15 +324,15 @@ def check_data_1(data) -> None: assert ( data['positions'] == torch.tensor([ [0.0, 0.0, 0.0], - [0.07, 0.07, 0.0], - [0.07, -0.07, 0.0], + [0.7, 0.7, 0.0], + [0.7, -0.7, 0.0], ]) ).all() assert ( data['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], + [2.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, 0.0, 2.0], ]) ).all() assert ( @@ -348,7 +348,7 @@ def check_data_1(data) -> None: dataset = create_dataset_from_trajectories( ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 0.1, + 1.0, system_selection=system_selection, edge_sender_selection='element O', ) @@ -371,7 +371,7 @@ def check_data_2(data) -> None: dataset = create_dataset_from_trajectories( ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 0.1, + 1.0, system_selection=system_selection, edge_receiver_selection='element H', ) @@ -394,7 +394,7 @@ def check_data_3(data) -> None: dataset = create_dataset_from_trajectories( ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 0.1, + 1.0, system_selection=system_selection, edge_sender_selection='element O', edge_receiver_selection='element H', From c2b821a9923b5a13a27e758b95305c171dba84da Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 16 Apr 2024 20:58:52 +0200 Subject: [PATCH 057/267] Fixed dtype. --- mlcolvar/graph/core/nn/gvp_layer.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py index 3bb4d585..9bbe5b82 100644 --- a/mlcolvar/graph/core/nn/gvp_layer.py +++ b/mlcolvar/graph/core/nn/gvp_layer.py @@ -96,7 +96,11 @@ def forward( s = self.ws(s) if self.vo: v = torch.zeros( - s.shape[0], self.vo, 3, device=self.dummy_param.device + s.shape[0], + self.vo, + 3, + device=self.dummy_param.device, + dtype=s.dtype ) else: v = None From c81dd4602ddbe5c55b3cf2ca7935b0c0f48ec331 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 19 Apr 2024 17:37:06 +0200 Subject: [PATCH 058/267] Modified default parameters. --- mlcolvar/graph/core/nn/models.py | 14 +++++++------- mlcolvar/graph/cvs/cv.py | 18 ++++-------------- mlcolvar/graph/cvs/supervised/deeptda.py | 5 +++++ 3 files changed, 16 insertions(+), 21 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index d94c8fa3..04a0a5f6 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -140,13 +140,13 @@ def __init__( atomic_numbers: List[int], n_bases: int = 8, n_polynomials: int = 6, - n_layers: int = 2, - n_messages: int = 1, - n_feedforwards: int = 1, - n_scalars_node: int = 16, - n_vectors_node: int = 16, - n_scalars_edge: int = 16, - drop_rate: int = 0.2, + n_layers: int = 1, + n_messages: int = 2, + n_feedforwards: int = 2, + n_scalars_node: int = 8, + n_vectors_node: int = 8, + n_scalars_edge: int = 8, + drop_rate: int = 0.1, activation: str = 'SiLU', ) -> None: super().__init__(n_out, cutoff, atomic_numbers, n_bases, n_polynomials) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index 3830ccd3..ffed5ef4 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -41,19 +41,8 @@ def __init__( n_cvs: int, cutoff: float, atomic_numbers: List[int], - model_name: str = 'GVPModel', - model_options: Dict[Any, Any] = { - 'n_bases': 8, - 'n_polynomials': 6, - 'n_layers': 2, - 'n_messages': 1, - 'n_feedforwards': 1, - 'n_scalars_node': 16, - 'n_vectors_node': 16, - 'n_scalars_edge': 16, - 'drop_rate': 0.2, - 'activation': 'SiLU', - }, + model_name: str, + model_options: Dict[Any, Any] = {}, optimizer_options: Dict[Any, Any] = { 'optimizer': {'lr': 1E-3, 'weight_decay': 1E-4}, 'lr_scheduler': { @@ -258,7 +247,7 @@ def test_base_cv() -> None: dtype = torch.get_default_dtype() torch.set_default_dtype(torch.float64) - cv = GraphBaseCV(2, 0.1, [1, 2, 3]) + cv = GraphBaseCV(2, 0.1, [1, 2, 3], 'GVPModel') assert cv.n_cvs == 2 assert (cv.cutoff - 0.1) < 1E-12 @@ -295,6 +284,7 @@ def test_base_cv() -> None: 2, 0.1, [1, 2, 3], + 'GVPModel', optimizer_options={ 'optimizer': {'lr': 2E-3, 'weight_decay': 1E-4}, 'lr_scheduler': { diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index d25bd992..6a49769b 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -42,6 +42,8 @@ class GraphDeepTDA(GraphBaseCV): Name of the GNN model. model_options: Dict[Any, Any] Model options. + optimizer_options: Dict[Any, Any] + Optimizer options. References ---------- @@ -64,8 +66,11 @@ def __init__( target_sigmas: Union[List[float], List[List[float]]], model_name: str = 'GVPModel', model_options: Dict[Any, Any] = {}, + optimizer_options: Dict[Any, Any] = {}, **kwargs, ) -> None: + if optimizer_options != {}: + kwargs['optimizer_options'] = optimizer_options super().__init__( n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs From eeec208538ffa470205817e48793ecb9b5404058 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 22 Apr 2024 21:13:26 +0200 Subject: [PATCH 059/267] Removed unused code. --- mlcolvar/graph/core/nn/gvp_layer.py | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py index 9bbe5b82..2098b58a 100644 --- a/mlcolvar/graph/core/nn/gvp_layer.py +++ b/mlcolvar/graph/core/nn/gvp_layer.py @@ -124,8 +124,6 @@ class GVPConv(MessagePassing): :param out_dims: output node embedding dimensions (n_scalar, n_vector) :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) :param n_layers: number of GVPs in the message function - :param aggr: should be 'add' if some incoming edges are masked, as in - a masked autoregressive decoder architecture, otherwise 'mean' :param activations: tuple of functions (scalar_act, vector_act) to use in GVPs :param vector_gate: whether to use vector gating. @@ -244,9 +242,6 @@ class GVPConvLayer(nn.Module): :param n_message: number of GVPs to use in message function :param n_feedforward: number of GVPs to use in feedforward function :param drop_rate: drop probability in all dropout layers - :param autoregressive: if `True`, this `GVPConvLayer` will be used - with a different set of input node embeddings for messages - where src >= dst :param activations: tuple of functions (scalar_act, vector_act) to use in GVPs :param vector_gate: whether to use vector gating. @@ -260,7 +255,6 @@ def __init__( n_message=3, n_feedforward=2, drop_rate=0.1, - autoregressive=False, activations=(nn.functional.relu, torch.sigmoid), vector_gate=True, residual=True, @@ -271,7 +265,7 @@ def __init__( node_dims, edge_dims, n_message, - aggr='add' if autoregressive else 'mean', + aggr='mean', activations=activations, vector_gate=vector_gate, ) @@ -308,11 +302,6 @@ def forward( :param x: tuple (s, V) of `torch.Tensor` :param edge_index: array of shape [2, n_edges] :param edge_attr: tuple (s, V) of `torch.Tensor` - :param autoregressive_x: tuple (s, V) of `torch.Tensor`. - If not `None`, will be used as src node embeddings - for forming messages where src >= dst. The corrent node - embeddings `x` will still be the base of the update and the - pointwise feedforward. :param node_mask: array of type `bool` to index into the first dim of node embeddings (s, V). If not `None`, only these nodes will be updated. From 58845a8a14c9c1be5df4adea2e488a346f0f60ca Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 22 Apr 2024 22:11:16 +0200 Subject: [PATCH 060/267] Added a dataset option. --- mlcolvar/graph/data/dataset.py | 8 ++++++++ mlcolvar/graph/utils/io.py | 5 ++++- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index d1deb0f5..54ee45db 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -174,6 +174,7 @@ def create_dataset_from_configurations( config: atomic.Configurations, z_table: atomic.AtomicNumberTable, cutoff: float, + remove_isolated_nodes: bool = False ) -> GraphDataSet: """ Build graph data objects from configurations. @@ -186,10 +187,17 @@ def create_dataset_from_configurations( The atomic number table used to build the node attributes. cutoff: float The graph cutoff radius. + remove_isolated_nodes: bool + If remove isolated nodes from the dataset. """ data_list = [ _create_dataset_from_configuration(c, z_table, cutoff) for c in config ] + + if remove_isolated_nodes: + transform = tg.transforms.remove_isolated_nodes.RemoveIsolatedNodes() + data_list = [transform(d) for d in data_list] + dataset = GraphDataSet(data_list, z_table.zs, cutoff) return dataset diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 8c240874..66b44920 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -19,6 +19,7 @@ def create_dataset_from_trajectories( edge_sender_selection: str = None, edge_receiver_selection: str = None, return_trajectories: bool = False, + remove_isolated_nodes: bool = True ) -> Union[ gdata.GraphDataSet, Tuple[ @@ -58,6 +59,8 @@ def create_dataset_from_trajectories( edges received by these atoms will be kept in the graph. return_trajectories: bool If also return the loaded trajectory objects. + remove_isolated_nodes: bool + If remove isolated nodes from the dataset. Returns ------- @@ -170,7 +173,7 @@ def create_dataset_from_trajectories( configurations.extend(configuration) dataset = gdata.create_dataset_from_configurations( - configurations, z_table, cutoff + configurations, z_table, cutoff, remove_isolated_nodes ) if return_trajectories: From c1bf864a1c7910f121d2cc1d67d7d7917fdebc7b Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 24 Apr 2024 18:05:17 +0200 Subject: [PATCH 061/267] Added a progress bar. --- mlcolvar/graph/data/dataset.py | 13 ++++++- mlcolvar/graph/utils/__init__.py | 1 + mlcolvar/graph/utils/io.py | 7 +++- mlcolvar/graph/utils/progress.py | 66 ++++++++++++++++++++++++++++++++ 4 files changed, 83 insertions(+), 4 deletions(-) create mode 100644 mlcolvar/graph/utils/progress.py diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 54ee45db..09814e3f 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -6,6 +6,7 @@ from mlcolvar.graph.data import atomic from mlcolvar.graph.data.neighborhood import get_neighborhood from mlcolvar.graph.utils import torch_tools +from mlcolvar.graph.utils import progress """ Build the graph data from a configuration. This module is taken from MACE: @@ -174,7 +175,8 @@ def create_dataset_from_configurations( config: atomic.Configurations, z_table: atomic.AtomicNumberTable, cutoff: float, - remove_isolated_nodes: bool = False + remove_isolated_nodes: bool = False, + show_progress: bool = True ) -> GraphDataSet: """ Build graph data objects from configurations. @@ -189,9 +191,16 @@ def create_dataset_from_configurations( The graph cutoff radius. remove_isolated_nodes: bool If remove isolated nodes from the dataset. + show_progress: bool + If show the progress bar. """ + if show_progress: + items = progress.pbar(config, frequency=0.0001, prefix='Making graphs') + else: + items = config + data_list = [ - _create_dataset_from_configuration(c, z_table, cutoff) for c in config + _create_dataset_from_configuration(c, z_table, cutoff) for c in items ] if remove_isolated_nodes: diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index 91bd1788..0d665b88 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -1,2 +1,3 @@ from . import io +from . import progress from . import torch_tools diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 66b44920..eaf49831 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -19,7 +19,8 @@ def create_dataset_from_trajectories( edge_sender_selection: str = None, edge_receiver_selection: str = None, return_trajectories: bool = False, - remove_isolated_nodes: bool = True + remove_isolated_nodes: bool = True, + show_progress: bool = True ) -> Union[ gdata.GraphDataSet, Tuple[ @@ -61,6 +62,8 @@ def create_dataset_from_trajectories( If also return the loaded trajectory objects. remove_isolated_nodes: bool If remove isolated nodes from the dataset. + show_progress: bool + If show the progress bar. Returns ------- @@ -173,7 +176,7 @@ def create_dataset_from_trajectories( configurations.extend(configuration) dataset = gdata.create_dataset_from_configurations( - configurations, z_table, cutoff, remove_isolated_nodes + configurations, z_table, cutoff, remove_isolated_nodes, show_progress ) if return_trajectories: diff --git a/mlcolvar/graph/utils/progress.py b/mlcolvar/graph/utils/progress.py new file mode 100644 index 00000000..f71dd249 --- /dev/null +++ b/mlcolvar/graph/utils/progress.py @@ -0,0 +1,66 @@ +import sys +import time +import typing + +""" +A simple progress bar. +""" + +__all__ = ['pbar'] + + +def pbar( + item: typing.List[int], + prefix: str = '', + size: int = 25, + frequency: int = 0.05, + use_unicode: bool = True, + file: typing.TextIO = sys.stdout +): + """ + A simple progress bar. Taken from stackoverflow: + https://stackoverflow.com/questions/3160699 + + Parameters + ---------- + it : List[int] + The looped item. + prefix : str + Prefix of the bar. + size : int + Size of the bar. + frequency : float + Flush frequency of the bar. + use_unicode : bool + If use unicode char to draw the bar. + file : TextIO + The output file. + """ + if (use_unicode): + c_1 = '' + c_2 = '█' + c_3 = '━' + c_4 = '' + else: + c_1 = '|' + c_2 = '|' + c_3 = '-' + c_4 = '|' + count = len(item) + start = time.time() + interval = max(int(count * frequency), 1) + + def show(j) -> None: + x = int(size * j / count) + remaining = ((time.time() - start) / j) * (count - j) + mins, sec = divmod(remaining, 60) + time_string = f'{int(mins):02}:{sec:02.1f}' + output = f' {prefix} {c_1}{c_2 * (x - 1) + c_4}{c_3 * (size - x)} ' + \ + f'{j}/{count} Est. {time_string}' + print('\x1b[1A\x1b[2K' + output, file=file, flush=True) + + for i, it in enumerate(item): + yield it + if ((i % interval) == 0 or i in [0, (count - 1)]): + show(i + 1) + print(flush=True, file=file) From b5d986c5ff1835c34c7dc2cd40e45c045f5bbcce Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 3 May 2024 16:03:04 +0200 Subject: [PATCH 062/267] Be quiet during testing. --- mlcolvar/graph/core/nn/models.py | 4 +++- mlcolvar/graph/cvs/cv.py | 4 +++- mlcolvar/graph/cvs/supervised/deeptda.py | 2 +- mlcolvar/graph/data/datamodule.py | 8 ++++++-- mlcolvar/graph/utils/io.py | 6 +++++- 5 files changed, 18 insertions(+), 6 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 04a0a5f6..d75bfe42 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -286,7 +286,9 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: edge_receivers=receivers, ) for p in positions ] - dataset = gdata.create_dataset_from_configurations(config, z_table, 0.1) + dataset = gdata.create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) loader = gdata.GraphDataModule( dataset, diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index ffed5ef4..1c49c127 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -230,7 +230,9 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: edge_receivers=receivers, ) for i in range(0, 6) ] - dataset = gdata.create_dataset_from_configurations(config, z_table, 0.1) + dataset = gdata.create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) loader = gdata.GraphDataModule( dataset, diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 6a49769b..9829b6cd 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -161,7 +161,7 @@ def example_input_array(self) -> Dict[str, torch.Tensor]: ) for i in range(2) ] dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1 + config, z_table, 0.1, show_progress=False ) loader = gdata.GraphDataModule( diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 5a47f27b..7ab9f9ba 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -263,7 +263,9 @@ def test_datamodule() -> None: node_labels=node_labels, graph_labels=graph_labels, ) - dataset = create_dataset_from_configurations([config] * 10, z_table, 0.1) + dataset = create_dataset_from_configurations( + [config] * 10, z_table, 0.1, show_progress=False + ) for i, d in enumerate(dataset): d['graph_labels'][0][0] = i @@ -380,7 +382,9 @@ def test_datamodule() -> None: node_labels=node_labels, graph_labels=graph_labels, ) - dataset = create_dataset_from_configurations([config] * 10, z_table, 0.1) + dataset = create_dataset_from_configurations( + [config] * 10, z_table, 0.1, show_progress=False + ) loader = GraphDataModule( dataset, diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index eaf49831..52eeffa1 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -282,7 +282,8 @@ def test_create_dataset_from_trajectories( ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], 1.0, system_selection=system_selection, - return_trajectories=True + return_trajectories=True, + show_progress=False ) assert len(dataset) == 6 @@ -357,6 +358,7 @@ def check_data_1(data) -> None: 1.0, system_selection=system_selection, edge_sender_selection='element O', + show_progress=False ) def check_data_2(data) -> None: @@ -380,6 +382,7 @@ def check_data_2(data) -> None: 1.0, system_selection=system_selection, edge_receiver_selection='element H', + show_progress=False ) def check_data_3(data) -> None: @@ -404,6 +407,7 @@ def check_data_3(data) -> None: system_selection=system_selection, edge_sender_selection='element O', edge_receiver_selection='element H', + show_progress=False ) def check_data_4(data) -> None: From 3d27e1abedf550636817f4b1c95b7e1171589b0e Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 3 May 2024 16:03:46 +0200 Subject: [PATCH 063/267] Fixed spaces. --- mlcolvar/graph/cvs/supervised/deeptda.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 9829b6cd..2f655efd 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -161,7 +161,7 @@ def example_input_array(self) -> Dict[str, torch.Tensor]: ) for i in range(2) ] dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False + config, z_table, 0.1, show_progress=False ) loader = gdata.GraphDataModule( From 39afb95537f8f5ff2865499c55f198f2680cc250 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 3 May 2024 16:13:31 +0200 Subject: [PATCH 064/267] Added a very basic readme. --- mlcolvar/graph/README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 mlcolvar/graph/README.md diff --git a/mlcolvar/graph/README.md b/mlcolvar/graph/README.md new file mode 100644 index 00000000..f74bc9ff --- /dev/null +++ b/mlcolvar/graph/README.md @@ -0,0 +1,11 @@ +# The Graph Neural Network (GNN) Module in MLCOLVAR. + +## INTRODUCTION + +WIP + +## DEPENDENCIES + +- `pytorch_geometric` >= 2.5 +- `matscipy` +- `mdtraj` From ec0b715cb67562a3f46b617812ab778144131d6f Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 8 May 2024 14:41:44 +0200 Subject: [PATCH 065/267] Fixed normal DeepTDA training. --- mlcolvar/core/loss/tda_loss.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mlcolvar/core/loss/tda_loss.py b/mlcolvar/core/loss/tda_loss.py index 4542cc1a..cc1e6f0a 100644 --- a/mlcolvar/core/loss/tda_loss.py +++ b/mlcolvar/core/loss/tda_loss.py @@ -144,9 +144,11 @@ def tda_loss( term associated to the standard deviations of the target Gaussians. """ if not isinstance(target_centers, torch.Tensor): - target_centers = torch.tensor(target_centers) + target_centers = torch.tensor(target_centers, dtype=H.dtype) + target_centers.requires_grad_(True) if not isinstance(target_sigmas, torch.Tensor): - target_sigmas = torch.tensor(target_sigmas) + target_sigmas = torch.tensor(target_sigmas, dtype=H.dtype) + target_sigmas.requires_grad_(True) device = H.device target_centers = target_centers.to(device) From c531e4ee8df83ee3e48ca3eb237ebfe97c301b92 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 9 May 2024 13:33:47 +0200 Subject: [PATCH 066/267] Added `utils.py` back. --- mlcolvar/core/transform/utils.py | 180 +++++++++++++++++++++++++++++++ 1 file changed, 180 insertions(+) create mode 100644 mlcolvar/core/transform/utils.py diff --git a/mlcolvar/core/transform/utils.py b/mlcolvar/core/transform/utils.py new file mode 100644 index 00000000..cdbca532 --- /dev/null +++ b/mlcolvar/core/transform/utils.py @@ -0,0 +1,180 @@ +import torch +from typing import Union +from warnings import warn + +__all__ = ["Inverse", "Statistics"] + + +class Inverse(torch.nn.Module): + "Wrapper to return the inverse method of a module as a torch.nn.Module" + + def __init__(self, module: torch.nn.Module): + """Return the inverse method of a module as a torch.nn.Module + + Parameters + ---------- + module : torch.nn.Module + Module to be inverted + """ + super().__init__() + if not hasattr(module, "inverse"): + raise AttributeError("The given module does not have a 'inverse' method!") + self.module = module + + def inverse(self, *args, **kwargs): + return self.module(*args, **kwargs) + + def forward(self, *args, **kwargs): + return self.module.inverse(*args, **kwargs) + +class Statistics(object): + """ + Calculate statistics (running mean and std.dev based on Welford's algorithm, as well as min and max). + If used with an iterable (such as a dataloader) provides the running estimates. + To get the dictionary with the results use the .to_dict() method. + """ + + def __init__(self, X: torch.Tensor = None): + self.count = 0 + + self.properties = ["mean", "std", "min", "max"] + + # initialize properties and temp var M2 + for prop in self.properties: + setattr(self, prop, None) + setattr(self, "M2", None) + + self.__call__(X) + + def __call__(self, x): + self.update(x) + + def update(self, x): + if x is None: + return + + # get batch size + ndim = x.ndim + if ndim == 0: + x = x.reshape(1, 1) + elif ndim == 1: + x = x.unsqueeze(1) + + batch_size = x.shape[0] + nfeatures = x.shape[1] + + new_count = self.count + batch_size + + # Initialize + if self.mean is None: + for prop in ["mean", "M2", "std"]: + setattr(self, prop, torch.zeros(nfeatures)) + + # compute sample mean + sample_mean = torch.mean(x, dim=0) + sample_m2 = torch.sum((x - sample_mean) ** 2, dim=0) + + # update stats + delta = sample_mean - self.mean + self.mean += delta * batch_size / new_count + corr = batch_size * self.count / new_count + self.M2 += sample_m2 + delta**2 * corr + self.count = new_count + self.std = torch.sqrt(self.M2 / self.count) + + # compute min/max + sample_min = torch.min(x, dim=0).values + sample_max = torch.max(x, dim=0).values + + if self.min is None: + self.min = sample_min + self.max = sample_max + else: + self.min = torch.min(torch.stack((sample_min, self.min)), dim=0).values + self.max = torch.max(torch.stack((sample_max, self.max)), dim=0).values + + def to_dict(self) -> dict: + return {prop: getattr(self, prop) for prop in self.properties} + + def __repr__(self): + repr = " " + for prop in self.properties: + repr += f"{prop}: {getattr(self,prop).numpy()} " + return repr + + +def test_inverse(): + from mlcolvar.core.transform import Transform + # create dummy model to scale the average to 0 + class ForwardModel(Transform): + def __init__(self, in_features=5, out_features=5): + super().__init__(in_features=5, out_features=5) + self.mean = 0 + + def update_mean(self, x): + self.mean = torch.mean(x) + + def forward(self, x): + x = x - self.mean + return x + + def inverse(self, x): + x = x + self.mean + return x + + forward_model = ForwardModel() + inverse_model = Inverse(forward_model) + + input = torch.rand(5) + forward_model.update_mean(input) + out = forward_model(input) + + assert(input.mean() == inverse_model(out).mean()) + +def test_statistics(): + # create fake data + X = torch.arange(0, 100) + X = torch.stack([X + 0.0, X + 100.0, X - 1000.0], dim=1) + y = X.square().sum(1) + print("X", X.shape) + print("y", y.shape) + + # compute stats + + stats = Statistics() + stats(X) + print(stats) + stats.to_dict() + + # create dataloader + from mlcolvar.data import DictLoader + + loader = DictLoader({"data": X, "target": y}, batch_size=20) + + # compute statistics of a single key of loader + key = "data" + stats = Statistics() + for batch in loader: + stats.update(batch[key]) + print(stats) + + # compute stats of all keys in dataloader + + # init a statistics object for each key + stats = {} + for batch in loader: + for key in loader.keys: + # initialize + if key not in stats: + stats[key] = Statistics(batch[key]) + # or accumulate + else: + stats[key].update(batch[key]) + + for key in loader.keys: + print(key,stats[key]) + + +if __name__ == "__main__": + test_inverse() + test_statistics() From 144674e0e5820442cb913360036473738c42a43c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 4 Jun 2024 21:18:45 +0200 Subject: [PATCH 067/267] Updated the dataset code. --- mlcolvar/graph/data/dataset.py | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 09814e3f..42e22db0 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -1,7 +1,7 @@ import torch import torch_geometric as tg import numpy as np -from typing import List +from typing import List, Union from mlcolvar.graph.data import atomic from mlcolvar.graph.data.neighborhood import get_neighborhood @@ -46,6 +46,30 @@ def __init__( self.__atomic_numbers = list(atomic_numbers) self.__cutoff = cutoff + def __getitem__( + self, + index: Union[int, slice, list, np.ndarray] + ) -> Union['GraphDataSet', tg.data.Data]: + """ + Build sub-dataset from the dataset. + + Parameters + ---------- + index : int, slice or list + Indices of the data. + """ + if (isinstance(index, Union[slice, list, np.ndarray])): + if isinstance(index, slice): + index = list(range(len(self)))[index] + data = [super(GraphDataSet, self).__getitem__(i) for i in index] + return GraphDataSet(data, self.atomic_numbers, self.cutoff) + elif np.issubdtype(type(index), np.integer): + return super(GraphDataSet, self).__getitem__(index) + else: + raise RuntimeError( + 'Could only indexing a GraphDataSet by an int, slice or list!' + ) + def __repr__(self) -> str: result = 'GRAPHDATASET [ ' From 9e37a3b45534e4acb02544edeaa62758fbff3481 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 4 Jun 2024 21:26:08 +0200 Subject: [PATCH 068/267] Added comments. --- mlcolvar/graph/utils/io.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 52eeffa1..71d40ccc 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -5,6 +5,10 @@ from mlcolvar.graph import data as gdata +""" +Some I/O things. +""" + __all__ = ['create_dataset_from_trajectories'] From 587b62c21eb4f9b28d97f70919bffeb7c951f924 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 4 Jun 2024 21:28:36 +0200 Subject: [PATCH 069/267] Added the metaphysics module. --- mlcolvar/graph/utils/__init__.py | 1 + mlcolvar/graph/utils/explain.py | 109 +++++++++++++++++++++++++++++++ 2 files changed, 110 insertions(+) create mode 100644 mlcolvar/graph/utils/explain.py diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index 0d665b88..c3aa5279 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -1,3 +1,4 @@ from . import io +from . import explain from . import progress from . import torch_tools diff --git a/mlcolvar/graph/utils/explain.py b/mlcolvar/graph/utils/explain.py new file mode 100644 index 00000000..973bd5b5 --- /dev/null +++ b/mlcolvar/graph/utils/explain.py @@ -0,0 +1,109 @@ +import torch +import numpy as np +from typing import List, Dict + +from .progress import pbar +from mlcolvar.graph import data as gdata +from mlcolvar.graph import cvs as gcvs + +""" +Metaphysics. +""" + +__all__ = ['graph_node_sensitivity_analysis'] + + +def graph_node_sensitivity_analysis( + model: gcvs.GraphBaseCV, + dataset: gdata.GraphDataSet, + node_indices: List[int] = None, + data_indices: List[int] = None, + per_class: bool = False, + device: str = 'cpu', + show_progress: bool = True +) -> Dict[str, np.ndarray]: + """ + Perform a sensitivity analysis by masking nodes. This allows us to measure + which atom is most important to the CV. + + See also + -------- + mlcolvar.utils.explain.sensitivity_analysis + Perform the sensitivity analysis of a feedforward model. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + node_indices: List[int] + Indices of the nodes to analysis. + data_indices: List[int] + Indices of the data to analysis. + per_class: bool + If the dataset has labels, compute also the sensitivity per class. + device: str + Name of the device. + show_progress: bool + If show the progress bar. + + Returns + ------- + results: dictionary + Results of the sensitivity analysis, containing 'node_indices' and the + 'sensitivity', ordered according to the node indices. + """ + if node_indices is None: + n_nodes = [len(d.positions) for d in dataset] + node_indices = list(range(max(n_nodes))) + + if data_indices is None: + data_indices = list(range(len(dataset))) + + sensitivities = [] + model = model.to(device) + + for node, i in enumerate(node_indices): + + node_results = [] + n_nodes = len(node_indices) + if show_progress: + items = pbar( + dataset[data_indices], + frequency=0.0001, + prefix='Node {:d}/{:d}'.format((i + 1), n_nodes) + ) + else: + items = dataset[data_indices] + + with torch.no_grad(): + for data in items: + data = data.to(device) + data = data.to_dict() + batch_id = torch.tensor( + [0] * len(data['positions']), dtype=torch.long + ).to(device) + data['batch'] = batch_id + + cv_org = model(data) + + mask = data['edge_index'] != node + mask = mask[0] & mask[1] + shifts_masked = data['shifts'][mask.T] + edge_index_masked = data['edge_index'][:, mask] + data['shifts'] = shifts_masked + data['edge_index'] = edge_index_masked + + cv_masked = model(data) + + delta = (cv_org - cv_masked).cpu().numpy().tolist() + node_results.extend(delta) + + sensitivities.append(np.mean(np.abs(node_results), axis=0)) + + results = {} + results['node_indices'] = node_indices + results['sensitivity'] = np.array(sensitivities) + + return results From f06c5f6c81322bcb89aaa75d59fa0763e67bb0b2 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 4 Jun 2024 23:59:44 +0200 Subject: [PATCH 070/267] Allowed to index dataset with a range. --- mlcolvar/graph/data/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 42e22db0..270cdbb4 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -48,7 +48,7 @@ def __init__( def __getitem__( self, - index: Union[int, slice, list, np.ndarray] + index: Union[int, slice, list, range, np.ndarray] ) -> Union['GraphDataSet', tg.data.Data]: """ Build sub-dataset from the dataset. @@ -58,7 +58,7 @@ def __getitem__( index : int, slice or list Indices of the data. """ - if (isinstance(index, Union[slice, list, np.ndarray])): + if type(index) in [slice, list, np.ndarray, range]: if isinstance(index, slice): index = list(range(len(self)))[index] data = [super(GraphDataSet, self).__getitem__(i) for i in index] From a0653be64a1e7797f24f73e8d81147269365f592 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 5 Jun 2024 01:50:25 +0200 Subject: [PATCH 071/267] Added components to the metaphysics module. --- mlcolvar/graph/utils/explain.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/mlcolvar/graph/utils/explain.py b/mlcolvar/graph/utils/explain.py index 973bd5b5..59945bf3 100644 --- a/mlcolvar/graph/utils/explain.py +++ b/mlcolvar/graph/utils/explain.py @@ -17,7 +17,6 @@ def graph_node_sensitivity_analysis( model: gcvs.GraphBaseCV, dataset: gdata.GraphDataSet, node_indices: List[int] = None, - data_indices: List[int] = None, per_class: bool = False, device: str = 'cpu', show_progress: bool = True @@ -39,8 +38,6 @@ def graph_node_sensitivity_analysis( Dataset on which to compute the sensitivity analysis. node_indices: List[int] Indices of the nodes to analysis. - data_indices: List[int] - Indices of the data to analysis. per_class: bool If the dataset has labels, compute also the sensitivity per class. device: str @@ -58,10 +55,8 @@ def graph_node_sensitivity_analysis( n_nodes = [len(d.positions) for d in dataset] node_indices = list(range(max(n_nodes))) - if data_indices is None: - data_indices = list(range(len(dataset))) - sensitivities = [] + sensitivities_component = [] model = model.to(device) for node, i in enumerate(node_indices): @@ -70,12 +65,12 @@ def graph_node_sensitivity_analysis( n_nodes = len(node_indices) if show_progress: items = pbar( - dataset[data_indices], + dataset, frequency=0.0001, prefix='Node {:d}/{:d}'.format((i + 1), n_nodes) ) else: - items = dataset[data_indices] + items = dataset with torch.no_grad(): for data in items: @@ -100,10 +95,14 @@ def graph_node_sensitivity_analysis( delta = (cv_org - cv_masked).cpu().numpy().tolist() node_results.extend(delta) + sensitivities_component.append(np.abs(node_results)) sensitivities.append(np.mean(np.abs(node_results), axis=0)) results = {} results['node_indices'] = node_indices - results['sensitivity'] = np.array(sensitivities) + results['sensitivities'] = np.array(sensitivities) + results['sensitivities_components'] = np.array( + sensitivities_component + ).transpose(2, 1, 0)[0] return results From a292a9f64fbb68e2175bccf0b6a7b45cf9a7fe40 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 5 Jun 2024 02:13:30 +0200 Subject: [PATCH 072/267] Added more tests. --- mlcolvar/graph/data/dataset.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 270cdbb4..1e6ac111 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -383,6 +383,31 @@ def test_from_configuration() -> None: assert (data['n_receivers'] == torch.tensor([[2]])).all() assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() + config = [atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=np.array([[i]]), + edge_senders=[0], + edge_receivers=[1, 2], + ) for i in range(0, 10)] + dataset = create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) + + dataset_1 = dataset[range(0, 5, 2)] + assert dataset_1.atomic_numbers == [1, 8] + assert (dataset_1[0]['graph_labels'] == torch.tensor([[0.0]])).all() + assert (dataset_1[1]['graph_labels'] == torch.tensor([[2.0]])).all() + assert (dataset_1[2]['graph_labels'] == torch.tensor([[4.0]])).all() + + dataset_1 = dataset[np.array([0, -1])] + assert dataset_1.atomic_numbers == [1, 8] + assert (dataset_1[0]['graph_labels'] == torch.tensor([[0.0]])).all() + assert (dataset_1[1]['graph_labels'] == torch.tensor([[9.0]])).all() + if __name__ == '__main__': test_from_configuration() From 156e7d19b85f48d27d22d8077599265a6f626c8c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 5 Jun 2024 02:49:56 +0200 Subject: [PATCH 073/267] Changed import order. --- mlcolvar/graph/utils/explain.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/utils/explain.py b/mlcolvar/graph/utils/explain.py index 59945bf3..cad0930c 100644 --- a/mlcolvar/graph/utils/explain.py +++ b/mlcolvar/graph/utils/explain.py @@ -3,8 +3,8 @@ from typing import List, Dict from .progress import pbar -from mlcolvar.graph import data as gdata from mlcolvar.graph import cvs as gcvs +from mlcolvar.graph import data as gdata """ Metaphysics. From db6aea76a49ab90765a7358d68d0546046561ce1 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 5 Jun 2024 12:16:49 +0200 Subject: [PATCH 074/267] Used batchs in analysis. --- mlcolvar/graph/utils/explain.py | 71 ++++++++++++++++++++------------- 1 file changed, 44 insertions(+), 27 deletions(-) diff --git a/mlcolvar/graph/utils/explain.py b/mlcolvar/graph/utils/explain.py index cad0930c..0677a5bf 100644 --- a/mlcolvar/graph/utils/explain.py +++ b/mlcolvar/graph/utils/explain.py @@ -1,3 +1,4 @@ +import copy import torch import numpy as np from typing import List, Dict @@ -19,6 +20,7 @@ def graph_node_sensitivity_analysis( node_indices: List[int] = None, per_class: bool = False, device: str = 'cpu', + batch_size: int = None, show_progress: bool = True ) -> Dict[str, np.ndarray]: """ @@ -42,6 +44,8 @@ def graph_node_sensitivity_analysis( If the dataset has labels, compute also the sensitivity per class. device: str Name of the device. + batch_size: + Batch size used for evaluating the CV. show_progress: bool If show the progress bar. @@ -54,43 +58,56 @@ def graph_node_sensitivity_analysis( if node_indices is None: n_nodes = [len(d.positions) for d in dataset] node_indices = list(range(max(n_nodes))) + n_nodes = len(node_indices) + if show_progress: + node_indices = pbar( + node_indices, frequency=0.0001, prefix='Sensitivity' + ) sensitivities = [] sensitivities_component = [] + dataset_clone = copy.deepcopy(dataset) model = model.to(device) for node, i in enumerate(node_indices): node_results = [] - n_nodes = len(node_indices) - if show_progress: - items = pbar( - dataset, - frequency=0.0001, - prefix='Node {:d}/{:d}'.format((i + 1), n_nodes) - ) - else: - items = dataset + + for j in range(len(dataset_clone)): + mask = dataset[j]['edge_index'] != node + mask = mask[0] & mask[1] + shifts_masked = dataset[j]['shifts'][mask.T] + edge_index_masked = dataset[j]['edge_index'][:, mask] + dataset_clone[j]['shifts'] = shifts_masked + dataset_clone[j]['edge_index'] = edge_index_masked + + datamodule_org = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule_org.setup() + + datamodule_masked = gdata.GraphDataModule( + dataset_clone, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule_masked.setup() + + loaders = zip( + datamodule_org.train_dataloader(), + datamodule_masked.train_dataloader() + ) with torch.no_grad(): - for data in items: - data = data.to(device) - data = data.to_dict() - batch_id = torch.tensor( - [0] * len(data['positions']), dtype=torch.long - ).to(device) - data['batch'] = batch_id - - cv_org = model(data) - - mask = data['edge_index'] != node - mask = mask[0] & mask[1] - shifts_masked = data['shifts'][mask.T] - edge_index_masked = data['edge_index'][:, mask] - data['shifts'] = shifts_masked - data['edge_index'] = edge_index_masked - - cv_masked = model(data) + for batchs in loaders: + cv_org = model(batchs[0].to(device).to_dict()) + cv_masked = model(batchs[1].to(device).to_dict()) delta = (cv_org - cv_masked).cpu().numpy().tolist() node_results.extend(delta) From 6b5d273ed7fd66e46ce5cb7eb58b9fec83aa9e77 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 5 Jun 2024 12:27:43 +0200 Subject: [PATCH 075/267] Removed extra keywords. --- mlcolvar/graph/utils/explain.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/mlcolvar/graph/utils/explain.py b/mlcolvar/graph/utils/explain.py index 0677a5bf..ef65d667 100644 --- a/mlcolvar/graph/utils/explain.py +++ b/mlcolvar/graph/utils/explain.py @@ -18,7 +18,6 @@ def graph_node_sensitivity_analysis( model: gcvs.GraphBaseCV, dataset: gdata.GraphDataSet, node_indices: List[int] = None, - per_class: bool = False, device: str = 'cpu', batch_size: int = None, show_progress: bool = True @@ -40,8 +39,6 @@ def graph_node_sensitivity_analysis( Dataset on which to compute the sensitivity analysis. node_indices: List[int] Indices of the nodes to analysis. - per_class: bool - If the dataset has labels, compute also the sensitivity per class. device: str Name of the device. batch_size: From 3ff8326ff80be044fae36ac5971db9371d260c04 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 6 Jun 2024 12:17:16 +0200 Subject: [PATCH 076/267] Sync directory layout from upstream. --- mlcolvar/graph/__init__.py | 1 + mlcolvar/graph/explain/__init__.py | 5 +++++ .../graph/{utils/explain.py => explain/sensitivity.py} | 10 +++++----- mlcolvar/graph/utils/__init__.py | 1 - 4 files changed, 11 insertions(+), 6 deletions(-) create mode 100644 mlcolvar/graph/explain/__init__.py rename mlcolvar/graph/{utils/explain.py => explain/sensitivity.py} (95%) diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index 95c5ea56..0f8ba12f 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -4,3 +4,4 @@ from . import core from . import data from . import cvs +from . import explain diff --git a/mlcolvar/graph/explain/__init__.py b/mlcolvar/graph/explain/__init__.py new file mode 100644 index 00000000..695a9784 --- /dev/null +++ b/mlcolvar/graph/explain/__init__.py @@ -0,0 +1,5 @@ +""" +Metaphysics. +""" + +from .sensitivity import graph_node_sensitivity diff --git a/mlcolvar/graph/utils/explain.py b/mlcolvar/graph/explain/sensitivity.py similarity index 95% rename from mlcolvar/graph/utils/explain.py rename to mlcolvar/graph/explain/sensitivity.py index ef65d667..205a708e 100644 --- a/mlcolvar/graph/utils/explain.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -3,18 +3,18 @@ import numpy as np from typing import List, Dict -from .progress import pbar from mlcolvar.graph import cvs as gcvs from mlcolvar.graph import data as gdata +from mlcolvar.graph import utils as gutils """ -Metaphysics. +Sensitivity analysis. """ -__all__ = ['graph_node_sensitivity_analysis'] +__all__ = ['graph_node_sensitivity'] -def graph_node_sensitivity_analysis( +def graph_node_sensitivity( model: gcvs.GraphBaseCV, dataset: gdata.GraphDataSet, node_indices: List[int] = None, @@ -57,7 +57,7 @@ def graph_node_sensitivity_analysis( node_indices = list(range(max(n_nodes))) n_nodes = len(node_indices) if show_progress: - node_indices = pbar( + node_indices = gutils.progress.pbar( node_indices, frequency=0.0001, prefix='Sensitivity' ) diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index c3aa5279..0d665b88 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -1,4 +1,3 @@ from . import io -from . import explain from . import progress from . import torch_tools From fd26ea93294e2236fab3c61230c2b9e0a808f18d Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 6 Jun 2024 13:44:49 +0200 Subject: [PATCH 077/267] Cleaned code. --- mlcolvar/graph/explain/sensitivity.py | 66 +++++++++------------------ mlcolvar/graph/explain/utils.py | 65 ++++++++++++++++++++++++++ 2 files changed, 87 insertions(+), 44 deletions(-) create mode 100644 mlcolvar/graph/explain/utils.py diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index 205a708e..da8d2471 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -1,5 +1,4 @@ import copy -import torch import numpy as np from typing import List, Dict @@ -7,6 +6,8 @@ from mlcolvar.graph import data as gdata from mlcolvar.graph import utils as gutils +from .utils import get_dataset_cv_values + """ Sensitivity analysis. """ @@ -26,11 +27,6 @@ def graph_node_sensitivity( Perform a sensitivity analysis by masking nodes. This allows us to measure which atom is most important to the CV. - See also - -------- - mlcolvar.utils.explain.sensitivity_analysis - Perform the sensitivity analysis of a feedforward model. - Parameters ---------- model: mlcolvar.graph.cvs.GraphBaseCV @@ -49,8 +45,14 @@ def graph_node_sensitivity( Returns ------- results: dictionary - Results of the sensitivity analysis, containing 'node_indices' and the - 'sensitivity', ordered according to the node indices. + Results of the sensitivity analysis, containing 'node_indices', + 'sensitivities', and 'sensitivities_components', ordered according to + the node indices. + + See also + -------- + mlcolvar.utils.explain.sensitivity_analysis + Perform the sensitivity analysis of a feedforward model. """ if node_indices is None: n_nodes = [len(d.positions) for d in dataset] @@ -62,61 +64,37 @@ def graph_node_sensitivity( ) sensitivities = [] - sensitivities_component = [] + sensitivities_components = [] dataset_clone = copy.deepcopy(dataset) model = model.to(device) - for node, i in enumerate(node_indices): + cv_org = get_dataset_cv_values( + model, dataset, batch_size, show_progress, 'Getting base data' + ) - node_results = [] + for node in node_indices: for j in range(len(dataset_clone)): mask = dataset[j]['edge_index'] != node mask = mask[0] & mask[1] - shifts_masked = dataset[j]['shifts'][mask.T] + shifts_masked = dataset[j]['shifts'][mask.transpose(-1, 0)] edge_index_masked = dataset[j]['edge_index'][:, mask] dataset_clone[j]['shifts'] = shifts_masked dataset_clone[j]['edge_index'] = edge_index_masked - datamodule_org = gdata.GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=batch_size, - random_split=False, - shuffle=False - ) - datamodule_org.setup() - - datamodule_masked = gdata.GraphDataModule( - dataset_clone, - lengths=(1.0,), - batch_size=batch_size, - random_split=False, - shuffle=False - ) - datamodule_masked.setup() - - loaders = zip( - datamodule_org.train_dataloader(), - datamodule_masked.train_dataloader() + cv_masked = get_dataset_cv_values( + model, dataset_clone, batch_size, False, ) - with torch.no_grad(): - for batchs in loaders: - cv_org = model(batchs[0].to(device).to_dict()) - cv_masked = model(batchs[1].to(device).to_dict()) - - delta = (cv_org - cv_masked).cpu().numpy().tolist() - node_results.extend(delta) - - sensitivities_component.append(np.abs(node_results)) - sensitivities.append(np.mean(np.abs(node_results), axis=0)) + delta = cv_org - cv_masked + sensitivities_components.append(np.abs(delta)) + sensitivities.append(np.mean(np.abs(delta), axis=0)) results = {} results['node_indices'] = node_indices results['sensitivities'] = np.array(sensitivities) results['sensitivities_components'] = np.array( - sensitivities_component + sensitivities_components ).transpose(2, 1, 0)[0] return results diff --git a/mlcolvar/graph/explain/utils.py b/mlcolvar/graph/explain/utils.py new file mode 100644 index 00000000..62c250f0 --- /dev/null +++ b/mlcolvar/graph/explain/utils.py @@ -0,0 +1,65 @@ +import torch +import numpy as np +from typing import Dict + +from mlcolvar.graph import cvs as gcvs +from mlcolvar.graph import data as gdata +from mlcolvar.graph import utils as gutils + +__all__ = ['get_dataset_cv_values'] + +""" +Analysis utils. +""" + + +def get_dataset_cv_values( + model: gcvs.GraphBaseCV, + dataset: gdata.GraphDataSet, + batch_size: int = None, + show_progress: bool = True, + progress_prefix: str = 'Calculating CV values' +) -> Dict[str, np.ndarray]: + """ + Get CV values of a given dataset. The calculation will run on the device + where the model is on. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + """ + datamodule = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule.setup() + + cv_values = [] + device = next(model.parameters()).device + + if show_progress: + items = gutils.progress.pbar( + datamodule.train_dataloader(), + frequency=0.001, + prefix=progress_prefix + ) + else: + items = datamodule.train_dataloader() + + with torch.no_grad(): + for batchs in items: + outputs = model(batchs.to(device).to_dict()) + outputs = outputs.cpu().numpy() + cv_values.append(outputs) + + return np.concatenate(cv_values) From a330cc85279719cf045e7cb4760dae86683d1626 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 6 Jun 2024 14:40:16 +0200 Subject: [PATCH 078/267] Fixed node indices in output. --- mlcolvar/graph/explain/sensitivity.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index da8d2471..a6b3f689 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -57,11 +57,16 @@ def graph_node_sensitivity( if node_indices is None: n_nodes = [len(d.positions) for d in dataset] node_indices = list(range(max(n_nodes))) + else: + node_indices = [i for i in node_indices] n_nodes = len(node_indices) + if show_progress: - node_indices = gutils.progress.pbar( + items = gutils.progress.pbar( node_indices, frequency=0.0001, prefix='Sensitivity' ) + else: + items = node_indices sensitivities = [] sensitivities_components = [] @@ -72,7 +77,7 @@ def graph_node_sensitivity( model, dataset, batch_size, show_progress, 'Getting base data' ) - for node in node_indices: + for node in items: for j in range(len(dataset_clone)): mask = dataset[j]['edge_index'] != node From b5d4a2ed2a9313a1df6f43e691321eebd481a723 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 10 Jun 2024 23:47:45 +0200 Subject: [PATCH 079/267] Read `n_out` from model options. --- mlcolvar/graph/cvs/cv.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index 1c49c127..b69d86c1 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -68,13 +68,15 @@ def __init__( 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) ) - for key in ['n_out', 'cutoff', 'atomic_numbers']: + for key in ['cutoff', 'atomic_numbers']: model_options.pop(key, None) + # For DeepTICA + n_out = model_options.pop('n_out', n_cvs) if not hasattr(models, model_name): raise RuntimeError(f'Unknown model: {model_name}') self._model = eval(f'models.{model_name}')( - n_out=n_cvs, + n_out=n_out, cutoff=cutoff, atomic_numbers=atomic_numbers, **model_options From 40524663fff3f925bd59120154523a5d052f4b15 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 10 Jun 2024 23:49:45 +0200 Subject: [PATCH 080/267] Added dataset support for DeepTICA. --- mlcolvar/graph/data/__init__.py | 2 +- mlcolvar/graph/data/datamodule.py | 463 ++++++++++++++++++++++++++++- mlcolvar/graph/utils/__init__.py | 1 + mlcolvar/graph/utils/timelagged.py | 240 +++++++++++++++ 4 files changed, 703 insertions(+), 3 deletions(-) create mode 100644 mlcolvar/graph/utils/timelagged.py diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index 5d00a726..b7c536e3 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -6,4 +6,4 @@ save_dataset, load_dataset ) -from .datamodule import GraphDataModule +from .datamodule import GraphDataModule, GraphCombinedDataModule diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index 7ab9f9ba..bebe2116 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -2,7 +2,9 @@ import lightning import torch_geometric as tg import numpy as np -from typing import Sequence, Union, Optional + +from typing import Sequence, Union, Optional, Tuple +from lightning.pytorch.utilities import combined_loader from mlcolvar.graph.data import atomic, create_dataset_from_configurations @@ -10,7 +12,7 @@ The data module for lightning. """ -__all__ = ['GraphDataModule'] +__all__ = ['GraphDataModule', 'GraphCombinedDataModule'] class GraphDataModule(lightning.LightningDataModule): @@ -245,6 +247,263 @@ def _check_setup(self) -> None: ) +class GraphCombinedDataModule(lightning.LightningDataModule): + """ + Lightning DataModule constructed for `torch_geometric.data.Data`. This data + module automatically splits the input graphs into training, validation, + and (optionally) test sets. + Being differnet from `GraphDataModule`, this class takes two different + datasets as input, and evaluates them at the same time during the training. + + Parameters + ---------- + datasets: Tuple[ + List[torch_geometric.data.Data], List[torch_geometric.data.Data] + ] + Lists of graph data. + lengths: List[int] + Lengths of the training, validation, and (optionally) test datasets. + This must be a list of (float) fractions summing to 1. + batch_size : Union[int, List[int]] + The batch size. + random_split: bool + Whether to randomly split train/valid/test or sequentially. + seed: int + The random seed used to split the dataset. + """ + + def __init__( + self, + datasets: Tuple[Sequence[tg.data.Data], Sequence[tg.data.Data]], + lengths: Sequence = (0.8, 0.2), + batch_size: Union[int, Sequence] = None, + random_split: bool = True, + seed: Optional[int] = None, + ) -> None: + super().__init__() + + self._datasets = datasets + self._lengths = lengths + self._n_total = len(datasets[0]) + + assert self._n_total > 0 + assert self._n_total == len(datasets[1]) + assert len(lengths) in [1, 2, 3] + assert np.abs(1 - sum(lengths)) < 1e-12 + + if self._n_total == 1: + self._n_train = 1 + else: + self._n_train = int(lengths[0] * self._n_total) + if len(lengths) == 3: + self._n_validation = int(lengths[1] * self._n_total) + self._n_test = self._n_total - self._n_train - self._n_validation + elif len(lengths) == 2: + self._n_validation = self._n_total - self._n_train + self._n_test = 0 + + indices = list(range(self._n_total)) + if random_split: + rng = np.random.default_rng(seed) + rng.shuffle(indices) + indices_train = indices[0:self._n_train] + if len(lengths) == 3: + indices_validation = indices[ + self._n_train:(self._n_train + self._n_validation) + ] + indices_test = indices[ + (self._n_train + self._n_validation):self._n_total + ] + elif len(lengths) == 2: + indices_validation = indices[ + self._n_train:(self._n_train + self._n_validation) + ] + indices_test = [] + else: + indices_validation = [] + indices_test = [] + self._dataset_indices = [ + indices_train, + indices_validation, + indices_test, + ] + + # Make sure batch_size and shuffle are lists. + if batch_size is None: + batch_size = self._n_total + if isinstance(batch_size, int): + self.batch_size = [batch_size for _ in lengths] + else: + self.batch_size = batch_size + + # This is initialized in setup(). + self._dataset_splits = None + + # dataloaders + self.train_loader = None + self.valid_loader = None + self.test_loader = None + + def setup(self, stage: Optional[str] = None) -> None: + """ + Set up the datasets. + """ + if self._dataset_splits is None: + dataset_splits = [None] * 2 + dataset_splits[0] = [ + [self._datasets[0][j] for j in i] + for i in self._dataset_indices + ] + dataset_splits[1] = [ + [self._datasets[1][j] for j in i] + for i in self._dataset_indices + ] + self._dataset_splits = dataset_splits + + def train_dataloader(self) -> tg.loader.DataLoader: + """ + Return training dataloader. + """ + self._check_setup() + if self.train_loader is None: + train_loader_1 = tg.loader.DataLoader( + self._dataset_splits[0][0], + batch_size=self.batch_size[0], + shuffle=False, + ) + train_loader_2 = tg.loader.DataLoader( + self._dataset_splits[1][0], + batch_size=self.batch_size[0], + shuffle=False, + ) + self.train_loader = combined_loader.CombinedLoader( + {'dataset_1': train_loader_1, 'dataset_2': train_loader_2}, + mode='min_size' + ) + return self.train_loader + + def val_dataloader(self) -> tg.loader.DataLoader: + """ + Return validation dataloader. + """ + self._check_setup() + if len(self._dataset_indices[1]) == 0: + raise NotImplementedError( + 'Validation dataset not available, you need to pass two ' + + 'lengths to datamodule.' + ) + if self.valid_loader is None: + valid_loader_1 = tg.loader.DataLoader( + self._dataset_splits[0][1], + batch_size=self.batch_size[1], + shuffle=False, + ) + valid_loader_2 = tg.loader.DataLoader( + self._dataset_splits[1][1], + batch_size=self.batch_size[1], + shuffle=False, + ) + self.valid_loader = combined_loader.CombinedLoader( + {'dataset_1': valid_loader_1, 'dataset_2': valid_loader_2}, + mode='min_size' + ) + return self.valid_loader + + def test_dataloader(self) -> tg.loader.DataLoader: + """ + Return test dataloader. + """ + self._check_setup() + if len(self._dataset_indices[2]) == 0: + raise NotImplementedError( + 'Test dataset not available, you need to pass three ' + + 'lengths to datamodule.' + ) + if self.test_loader is None: + test_loader_1 = tg.loader.DataLoader( + self._dataset_splits[0][2], + batch_size=self.batch_size[2], + shuffle=False, + ) + test_loader_2 = tg.loader.DataLoader( + self._dataset_splits[1][2], + batch_size=self.batch_size[2], + shuffle=False, + ) + self.test_loader = combined_loader.CombinedLoader( + {'dataset_1': test_loader_1, 'dataset_2': test_loader_2}, + mode='min_size' + ) + return self.test_loader + + def predict_dataloader(self) -> tg.loader.DataLoader: + """ + Return predict dataloader. + """ + raise NotImplementedError() + + def teardown(self, stage: str) -> None: + pass + + def __repr__(self) -> str: + result = '' + n_digits = len(str(self._n_total)) + data_string_1 = '[ \033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' + data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' + shuffle_string = '|\033[36m  \033[0m ]' + + prefix = '\033[1m\033[34m BASEDATA \033[0m: ' + result += ( + prefix + self._datasets[0].__repr__().split('GRAPHDATASET ')[1] + + '\n' + + prefix + self._datasets[1].__repr__().split('GRAPHDATASET ')[1] + + '\n' + ) + prefix = '\033[1m\033[34m TRAINING \033[0m: ' + string = prefix + data_string_1.format(n_digits) + result += string.format( + self._n_train * 2, self._n_train / self._n_total * 100 + ) + string = data_string_2.format(n_digits) + result += string.format(self.batch_size[0]) + result += shuffle_string + + if self._n_validation > 0: + result += '\n' + prefix = '\033[1m\033[34m VALIDATION \033[0m: ' + string = prefix + data_string_1.format(n_digits) + result += string.format( + self._n_validation * 2, + self._n_validation / self._n_total * 100 + ) + string = data_string_2.format(n_digits) + result += string.format(self.batch_size[1]) + result += shuffle_string + + if self._n_test > 0: + result += '\n' + prefix = '\033[1m\033[34m TEST \033[0m: ' + string = prefix + data_string_1.format(n_digits) + result += string.format( + self._n_test * 2, self._n_test / self._n_total * 100 + ) + string = data_string_2.format(n_digits) + result += string.format(self.batch_size[2]) + result += shuffle_string + return result + + def _check_setup(self) -> None: + """ + Raise an error if setup() has not been called. + """ + if self._dataset_splits is None: + raise AttributeError( + 'The datamodule has not been set up yet. To get the ' + + 'dataloaders outside a Lightning trainer please call ' + + '.setup() first.' + ) + + def test_datamodule() -> None: numbers = [8, 1, 1] positions = np.array( @@ -411,5 +670,205 @@ def test_datamodule() -> None: ).all() +def test_combined_datamodule() -> None: + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + dataset = create_dataset_from_configurations( + [config] * 10, z_table, 0.1, show_progress=False + ) + for i, d in enumerate(dataset): + d['graph_labels'][0][0] = i + + loader = GraphCombinedDataModule( + (dataset, dataset), + lengths=(1.0,), + batch_size=10, + seed=1 + ) + loader.setup() + assert len(loader._dataset_indices[0]) == 10 + assert len(loader._dataset_indices[1]) == 0 + assert len(loader._dataset_indices[2]) == 0 + + loader = GraphCombinedDataModule( + (dataset, dataset), + lengths=(0.8, 0.2), + batch_size=10, + seed=1 + ) + loader.setup() + assert len(loader._dataset_indices[0]) == 8 + assert len(loader._dataset_indices[1]) == 2 + assert len(loader._dataset_indices[2]) == 0 + + loader = GraphCombinedDataModule( + (dataset, dataset), + lengths=(0.6, 0.3, 0.1), + batch_size=10, + seed=1 + ) + loader.setup() + assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] + + batch = next(iter(loader.train_dataloader()))[0] + data_dict_1 = batch['dataset_1'].to_dict() + data_dict_2 = batch['dataset_2'].to_dict() + + assert data_dict_1['edge_index'].shape == (2, 36) + assert data_dict_2['edge_index'].shape == (2, 36) + assert ( + data_dict_1['graph_labels'] == torch.tensor( + [[8], [4], [7], [0], [1], [2]] + ) + ).all() + assert ( + data_dict_2['graph_labels'] == torch.tensor( + [[8], [4], [7], [0], [1], [2]] + ) + ).all() + assert (data_dict_1['n_receivers'] == torch.tensor([[3]] * 6)).all() + assert (data_dict_2['n_receivers'] == torch.tensor([[3]] * 6)).all() + + batch = next(iter(loader.val_dataloader()))[0] + data_dict_1 = batch['dataset_1'].to_dict() + data_dict_2 = batch['dataset_2'].to_dict() + assert ( + data_dict_1['edge_index'] == torch.tensor([ + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], + [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] + ]) + ).all() + assert ( + data_dict_2['edge_index'] == torch.tensor([ + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], + [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] + ]) + ).all() + assert ( + data_dict_1['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) + ).all() + assert ( + data_dict_2['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) + ).all() + assert (data_dict_1['graph_labels'] == torch.tensor([[5], [9], [6]])).all() + assert (data_dict_2['graph_labels'] == torch.tensor([[5], [9], [6]])).all() + assert (data_dict_1['ptr'] == torch.tensor([0, 3, 6, 9])).all() + assert (data_dict_2['ptr'] == torch.tensor([0, 3, 6, 9])).all() + assert (data_dict_1['n_receivers'] == torch.tensor([[3]] * 3)).all() + assert (data_dict_2['n_receivers'] == torch.tensor([[3]] * 3)).all() + + batch = next(iter(loader.test_dataloader()))[0] + data_dict_1 = batch['dataset_1'].to_dict() + data_dict_2 = batch['dataset_2'].to_dict() + assert (data_dict_1['graph_labels'] == torch.tensor([[3]])).all() + assert (data_dict_2['graph_labels'] == torch.tensor([[3]])).all() + assert ( + data_dict_1['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + assert ( + data_dict_2['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + assert ( + data_dict_1['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data_dict_2['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data_dict_1['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data_dict_2['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data_dict_1['positions'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0], + ]) + ).all() + assert ( + data_dict_2['positions'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0], + ]) + ).all() + assert ( + data_dict_1['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + assert ( + data_dict_2['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + assert ( + data_dict_1['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + assert ( + data_dict_2['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + assert (data_dict_1['n_receivers'] == torch.tensor([[3]])).all() + assert (data_dict_2['n_receivers'] == torch.tensor([[3]])).all() + + if __name__ == '__main__': test_datamodule() + test_combined_datamodule() diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py index 0d665b88..c6fe1591 100644 --- a/mlcolvar/graph/utils/__init__.py +++ b/mlcolvar/graph/utils/__init__.py @@ -1,3 +1,4 @@ from . import io from . import progress from . import torch_tools +from . import timelagged diff --git a/mlcolvar/graph/utils/timelagged.py b/mlcolvar/graph/utils/timelagged.py new file mode 100644 index 00000000..f8334b6b --- /dev/null +++ b/mlcolvar/graph/utils/timelagged.py @@ -0,0 +1,240 @@ +import torch +import numpy as np +from typing import Tuple + +from mlcolvar.graph import data as gdata +from mlcolvar.utils.timelagged import ( + tprime_evaluation, + find_timelagged_configurations +) + +try: + import pandas as pd + + PANDAS_IS_INSTALLED = True +except ImportError: + PANDAS_IS_INSTALLED = False +try: + from tqdm import tqdm + + TQDM_IS_INSTALLED = True +except ImportError: + TQDM_IS_INSTALLED = False + +""" +Build time-lagged dataset for GNNs. +""" + +__all__ = ['create_timelagged_datasets'] + + +def create_timelagged_datasets( + dataset: gdata.GraphDataSet, + t: torch.Tensor = None, + lag_time: float = 1, + reweight_mode: str = None, + logweights: torch.Tensor = None, + tprime: torch.Tensor = None, + interval: list = None, + progress_bar: bool = False, +) -> Tuple[gdata.GraphDataSet, gdata.GraphDataSet]: + """ + Create two GraphDataSets of time-lagged configurations. + + In case of biased simulations the reweight can be performed in two + different ways (``reweight_mode``): + + 1. ``rescale_time`` : the search for time-lagged pairs is performed in the + accelerated time (dt' = dt*exp(logweights)), as described in [1]_ . + 2. ``weights_t`` : the weight of each pair of configurations (t,t+lag_time) + depends only on time t (logweights(t)), as done in [2]_ , [3]_ . + + If reweighting is None and tprime is given the `rescale_time` mode is used. + If instead only the logweights are specified the user needs to choose the + reweighting mode. + + References + ---------- + .. [1] Y. I. Yang and M. Parrinello, "Refining collective coordinates and + improving free energy representation in variational enhanced sampling." + JCTC 14, 2889–2894 (2018). + .. [2] J. McCarty and M. Parrinello, "A variational conformational dynamics + approach to the selection of collective variables in meta-dynamics." + JCP 147, 204109 (2017). + .. [3] H. Wu, et al. "Variational Koopman models: Slow collective variables + and molecular kinetics from short off-equilibrium simulations." + JCP 146.15 (2017). + + Parameters + ---------- + dataset : mlcolvar.graph.data.GraphDataSet + The reference dataset. + t : array-like, optional + Time series, by default np.arange(len(X)) + reweight_mode: str, optional + How to do the reweighting, see documentation, by default none + lag_time: float, optional + Lag between configurations, by default = 10 + logweights : array-like,optional + Logweight of each configuration (typically beta*bias) + tprime : array-like,optional + Rescaled time estimated from the simulation. If not given and + `reweighting_mode`=`rescale_time` then + `tprime_evaluation(t,logweights)` is used + interval : list or np.array or tuple, optional + Range for slicing the returned dataset. Useful to work with batches of + same sizes. Recall that with different lag_times one obtains different + datasets, with different lengths + progress_bar: bool + If display progress bar with tqdm. + + Returns + ------- + datasets: Tuple[GraphDataSet, GraphDataSet] + Datasets at time t and t+lag. + """ + + if PANDAS_IS_INSTALLED and isinstance(t, pd.core.frame.DataFrame): + t = t.values + + # check reweigthing mode if logweights are given: + # 1) if rescaled time tprime is given + if tprime is not None: + if reweight_mode is None: + reweight_mode = 'rescale_time' + elif reweight_mode != 'rescale_time': + raise ValueError( + 'The `reweighting_mode` needs to be equal to `rescale_time`, ' + + 'and not {reweight_mode} if the rescale time `tprime` is ' + + 'given.' + ) + # 2) if logweights are given + elif logweights is not None: + if reweight_mode is None: + reweight_mode = 'rescale_time' + # TODO output warning or error if mode not specified? + # warnings.warn( + # '`reweight_mode` not specified, setting it to `rescale_time`.' + # ) + + # define time if not given + if t is None: + t = torch.arange(0, len(dataset)) + else: + if len(t) != len(dataset): + message = ( + 'The length of t ({:d}) is different from the one of ' + + 'dataset ({:d}).' + ) + raise ValueError(message.format(len(t), len(dataset))) + + # define tprime if not given: + if reweight_mode == 'rescale_time': + if tprime is None: + tprime = tprime_evaluation(t, logweights) + else: + tprime = t + + # find pairs of configurations separated by lag_time + index = torch.arange(len(dataset), dtype=torch.long) + x_t, x_lag, w_t, w_lag = find_timelagged_configurations( + index, + tprime, + lag_time=lag_time, + logweights=logweights if reweight_mode == 'weights_t' else None, + progress_bar=progress_bar, + ) + + # return only a slice of the data (N. Pedrani) + if interval is not None: + # convert to a list + data = list(x_t, x_lag, w_t, w_lag) + # assert dimension of interval + assert len(interval) == 2 + # modifies the content of data by slicing + for i in range(len(data)): + data[i] = data[i][interval[0]:interval[1]] + x_t, x_lag, w_t, w_lag = data + + assert len(x_t) == len(w_t) + assert len(x_t) == len(x_lag) + assert len(w_t) == len(w_lag) + + # assign weights + dataset_t = dataset[x_t.numpy().tolist()] + dataset_lag = dataset[x_lag.numpy().tolist()] + + for i in range(len(x_t)): + dataset_t[i]['weight'] = w_t[i] + dataset_lag[i]['weight'] = w_lag[i] + + return (dataset_t, dataset_lag) + + +def test_timelagged() -> None: + from mlcolvar.utils.timelagged import create_timelagged_dataset as _ctd + + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + + config = gdata.atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + dataset = gdata.create_dataset_from_configurations( + [config] * 10, z_table, 0.1, show_progress=False + ) + for i in range(len(dataset)): + dataset[i]['graph_labels'] *= i + + datasets = create_timelagged_datasets(dataset) + data_reference = _ctd(torch.arange(10)) + + assert len(datasets[0]) == len(data_reference['data']) + assert len(datasets[1]) == len(data_reference['data_lag']) + + for i in range(len(datasets[0])): + d_0 = datasets[0] + d_ref = data_reference['data'] + w_ref = data_reference['weights'] + assert d_0[i]['weight'] == w_ref[i] + assert d_0[i]['graph_labels'][0, 0] == d_ref[i] + for i in range(len(datasets[1])): + d_1 = datasets[1] + d_ref = data_reference['data_lag'] + w_ref = data_reference['weights_lag'] + assert d_1[i]['weight'] == w_ref[i] + assert d_1[i]['graph_labels'][0, 0] == d_ref[i] + + datasets = create_timelagged_datasets(dataset, lag_time=2) + data_reference = _ctd(torch.arange(10), lag_time=2) + + assert len(datasets[0]) == len(data_reference['data']) + assert len(datasets[1]) == len(data_reference['data_lag']) + + for i in range(len(datasets[0])): + d_0 = datasets[0] + d_ref = data_reference['data'] + w_ref = data_reference['weights'] + assert d_0[i]['weight'] == w_ref[i] + assert d_0[i]['graph_labels'][0, 0] == d_ref[i] + for i in range(len(datasets[1])): + d_1 = datasets[1] + d_ref = data_reference['data_lag'] + w_ref = data_reference['weights_lag'] + assert d_1[i]['weight'] == w_ref[i] + assert d_1[i]['graph_labels'][0, 0] == d_ref[i] + + +if __name__ == '__main__': + test_timelagged() From dc6e13295a5e5b7bec565fa7b8b920446a41e177 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 11 Jun 2024 11:32:57 +0200 Subject: [PATCH 081/267] Make more parameter tests. --- mlcolvar/graph/cvs/supervised/deeptda.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 2f655efd..67f7c44c 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -69,6 +69,10 @@ def __init__( optimizer_options: Dict[Any, Any] = {}, **kwargs, ) -> None: + if model_options.pop('n_out', None) is not None: + raise RuntimeError( + 'The `n_out` key of parameter `model_options` will be ignored!' + ) if optimizer_options != {}: kwargs['optimizer_options'] = optimizer_options From 0b6ee020541f7b7ab280fb5994b361b0090926ae Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 11 Jun 2024 16:29:51 +0200 Subject: [PATCH 082/267] Added GraphDeepTICA. --- mlcolvar/graph/cvs/__init__.py | 1 + mlcolvar/graph/cvs/timelagged/__init__.py | 1 + mlcolvar/graph/cvs/timelagged/deeptica.py | 193 ++++++++++++++++++++++ 3 files changed, 195 insertions(+) create mode 100644 mlcolvar/graph/cvs/timelagged/__init__.py create mode 100644 mlcolvar/graph/cvs/timelagged/deeptica.py diff --git a/mlcolvar/graph/cvs/__init__.py b/mlcolvar/graph/cvs/__init__.py index c9eb3530..6dff8e4e 100644 --- a/mlcolvar/graph/cvs/__init__.py +++ b/mlcolvar/graph/cvs/__init__.py @@ -1,2 +1,3 @@ from .cv import GraphBaseCV from .supervised import GraphDeepTDA +from .timelagged import GraphDeepTICA diff --git a/mlcolvar/graph/cvs/timelagged/__init__.py b/mlcolvar/graph/cvs/timelagged/__init__.py new file mode 100644 index 00000000..33e71a3d --- /dev/null +++ b/mlcolvar/graph/cvs/timelagged/__init__.py @@ -0,0 +1 @@ +from .deeptica import GraphDeepTICA diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py new file mode 100644 index 00000000..80cc17d3 --- /dev/null +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -0,0 +1,193 @@ +import torch +import torch_geometric as tg +import numpy as np +from typing import Dict, Any, List + +from mlcolvar.core.stats import TICA +from mlcolvar.core.loss import ReduceEigenvaluesLoss +from mlcolvar.graph.cvs import GraphBaseCV +from mlcolvar.graph.cvs.cv import test_get_data +from mlcolvar.graph import data as gdata +from mlcolvar.graph.utils import torch_tools + +""" +The Deep time-lagged independent component analysis (Deep-TICA) CV based on +Graph Neural Networks (GNN). +""" + +__all__ = ['GraphDeepTICA'] + + +class GraphDeepTICA(GraphBaseCV): + """ + Graph neural network-based time-lagged independent component analysis + (Deep-TICA). + + It is a non-linear generalization of TICA in which a feature map is learned + by a neural network optimized as to maximize the eigenvalues of the + transfer operator, approximated by TICA. The method is described in [1]_. + Note that from the point of view of the architecture DeepTICA is similar to + the SRV [2]_ method. + + Parameters + ---------- + n_cvs: int + Number of components of the CV. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_cvs : int + Number of collective variables to be trained + model_name: str + Name of the GNN model. + model_options: Dict[Any, Any] + Model options. Note that the `n_out` key of this dict is REQUIRED, + which stands for the dimension of the output of the network. + optimizer_options: Dict[Any, Any] + Optimizer options. + + References + ---------- + .. [1] L. Bonati, G. Piccini, and M. Parrinello, "Deep learning the slow + modes for rare events sampling." PNAS USA 118, e2113533118 (2021) + .. [2] W. Chen, H. Sidky, and A. L. Ferguson, "Nonlinear discovery of slow + molecular modes using state-free reversible vampnets." + JCP 150, 214114 (2019). + + See also + -------- + mlcolvar.core.stats.TICA + Time Lagged Indipendent Component Analysis + mlcolvar.core.loss.ReduceEigenvalueLoss + Eigenvalue reduction to a scalar quantity + mlcolvar.utils.timelagged.create_timelagged_dataset + Create dataset of time-lagged data. + """ + def __init__( + self, + n_cvs: int, + cutoff: float, + atomic_numbers: List[int], + model_name: str = 'GVPModel', + model_options: Dict[Any, Any] = {'n_out': 6}, + optimizer_options: Dict[Any, Any] = {}, + **kwargs, + ) -> None: + if 'n_out' not in model_options.keys(): + raise RuntimeError( + 'The `n_out` key of parameter `model_options` is required!' + ) + model_options['drop_rate'] = 0 + n_out = model_options['n_out'] + + if optimizer_options != {}: + kwargs['optimizer_options'] = optimizer_options + + super().__init__( + n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs + ) + + self.loss_fn = ReduceEigenvaluesLoss(mode='sum2') + + self.tica = TICA(n_out, n_cvs) + + def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + """ + nn_outputs = super(GraphDeepTICA, self).forward(data) + outputs = self.tica(nn_outputs) + + return outputs + + def training_step( + self, + train_batch: Dict[str, tg.data.Batch], + *args, + **kwargs + ) -> torch.Tensor: + """ + Compute and return the training loss and record metrics. + + Parameters + ---------- + train_batch: Tuple[Dict[str, torch_geometric.data.Batch], int, int] + The data batch. + """ + data_t = train_batch['dataset_1'].to_dict() + data_lag = train_batch['dataset_2'].to_dict() + + nn_outputs_t = super(GraphDeepTICA, self).forward(data_t) + nn_outputs_lag = super(GraphDeepTICA, self).forward(data_lag) + + eigvals, _ = self.tica.compute( + data=[nn_outputs_t, nn_outputs_lag], + weights=[data_t['weight'], data_lag['weight']], + save_params=True + ) + + loss = self.loss_fn(eigvals) + name = 'train' if self.training else 'valid' + loss_dict = {f'{name}_loss': loss} + eig_dict = { + f'{name}_eigval_{i+1}': eigvals[i] for i in range(len(eigvals)) + } + self.log_dict(dict(loss_dict, **eig_dict), on_step=True, on_epoch=True) + return loss + + def set_regularization(self, c0_reg=1e-6) -> None: + """ + Add identity matrix multiplied by `c0_reg` to correlation matrix C(0) + to avoid instabilities in performin Cholesky. + + Parameters + ---------- + c0_reg : float + Regularization value for C_0. + """ + self.tica.reg_C_0 = c0_reg + + @property + def example_input_array(self) -> Dict[str, torch.Tensor]: + """ + Example data. + """ + numbers = self._model.atomic_numbers.cpu().numpy().tolist() + positions = np.random.randn(2, len(numbers), 3) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[[0]], [[1]]]) + node_labels = np.array([[0]] * len(numbers)) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + + config = [ + gdata.atomic.Configuration( + atomic_numbers=numbers, + positions=positions[i], + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels[i], + ) for i in range(2) + ] + dataset = gdata.create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) + + loader = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + loader.setup() + + return next(iter(loader.train_dataloader())) From 77c52b7b80884bb04f083af39a1c3189d5eb4d01 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 11 Jun 2024 16:50:33 +0200 Subject: [PATCH 083/267] Changed data type. --- mlcolvar/graph/cvs/timelagged/deeptica.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index 80cc17d3..84afafc3 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -80,7 +80,7 @@ def __init__( raise RuntimeError( 'The `n_out` key of parameter `model_options` is required!' ) - model_options['drop_rate'] = 0 + model_options['drop_rate'] = 0.0 n_out = model_options['n_out'] if optimizer_options != {}: From e6c9aa4299eea7c62753a237151879fe79e84b2c Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 11 Jun 2024 17:27:23 +0200 Subject: [PATCH 084/267] Made `GraphDeepTICA` compilable. --- mlcolvar/core/loss/eigvals.py | 2 +- mlcolvar/core/transform/tools/utils.py | 4 ++-- mlcolvar/graph/cvs/timelagged/deeptica.py | 27 ++++++++++++++++++++--- 3 files changed, 27 insertions(+), 6 deletions(-) diff --git a/mlcolvar/core/loss/eigvals.py b/mlcolvar/core/loss/eigvals.py index e0c84345..4fc2c826 100644 --- a/mlcolvar/core/loss/eigvals.py +++ b/mlcolvar/core/loss/eigvals.py @@ -129,7 +129,7 @@ def reduce_eigenvalues_loss( else: n_eig = len(evals) - loss = None + loss = torch.zeros(1, dtype=evals.dtype, device=evals.device) if mode == "sum": loss = torch.sum(evals[:n_eig]) diff --git a/mlcolvar/core/transform/tools/utils.py b/mlcolvar/core/transform/tools/utils.py index 6705ffff..c8d9a51c 100644 --- a/mlcolvar/core/transform/tools/utils.py +++ b/mlcolvar/core/transform/tools/utils.py @@ -3,7 +3,7 @@ from typing import Union, List -def batch_reshape(t: torch.Tensor, size: torch.Size) -> torch.Tensor: +def batch_reshape(t: torch.Tensor, size: List[int]) -> torch.Tensor: """Return value reshaped according to size. In case of batch unsqueeze and expand along the first dimension. For single inputs just pass. @@ -90,4 +90,4 @@ def easy_KDE(x : torch.Tensor, if return_bins: return out, bins else: - return out \ No newline at end of file + return out diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index 84afafc3..a9b8f09c 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -94,6 +94,27 @@ def __init__( self.tica = TICA(n_out, n_cvs) + def forward_nn( + self, + data: Dict[str, torch.Tensor], + token: bool = False + ) -> torch.Tensor: + """ + The forward pass for the NN. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + token: bool + To be used. + """ + data['positions'].requires_grad_(True) + data['node_attrs'].requires_grad_(True) + + return self._model(data) + def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: """ The forward pass. @@ -104,7 +125,7 @@ def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. """ - nn_outputs = super(GraphDeepTICA, self).forward(data) + nn_outputs = self.forward_nn(data) outputs = self.tica(nn_outputs) return outputs @@ -126,8 +147,8 @@ def training_step( data_t = train_batch['dataset_1'].to_dict() data_lag = train_batch['dataset_2'].to_dict() - nn_outputs_t = super(GraphDeepTICA, self).forward(data_t) - nn_outputs_lag = super(GraphDeepTICA, self).forward(data_lag) + nn_outputs_t = self.forward_nn(data_t) + nn_outputs_lag = self.forward_nn(data_lag) eigvals, _ = self.tica.compute( data=[nn_outputs_t, nn_outputs_lag], From 94937ad368d79ab2f2ff683814acacc807262fec Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 11 Jun 2024 17:58:22 +0200 Subject: [PATCH 085/267] Fixed for PLUMED. --- mlcolvar/graph/cvs/timelagged/deeptica.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index a9b8f09c..9c6adbe4 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -115,7 +115,11 @@ def forward_nn( return self._model(data) - def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: + def forward( + self, + data: Dict[str, torch.Tensor], + token: bool = False + ) -> torch.Tensor: """ The forward pass. @@ -124,6 +128,8 @@ def forward(self, data: Dict[str, torch.Tensor]) -> torch.Tensor: data: Dict[str, torch.Tensor] The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. + token: bool + To be used. """ nn_outputs = self.forward_nn(data) outputs = self.tica(nn_outputs) From c501e8e5bf8c7062dcdad1d348244584d9f58bca Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:24:24 +0200 Subject: [PATCH 086/267] Set default dtype. --- mlcolvar/graph/__init__.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index 0f8ba12f..44f37b44 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -1,7 +1,9 @@ """The graph neural network module.""" from . import utils -from . import core -from . import data +from . import core +from . import data from . import cvs from . import explain + +utils.torch_tools.set_default_dtype('float32') From aea9d5ae7dc8b6720acb823f91aa20313fe89cad Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:35:29 +0200 Subject: [PATCH 087/267] Added tests. --- mlcolvar/graph/cvs/timelagged/deeptica.py | 42 +++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index 9c6adbe4..94bada14 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -218,3 +218,45 @@ def example_input_array(self) -> Dict[str, torch.Tensor]: loader.setup() return next(iter(loader.train_dataloader())) + + +def test_deep_tica(): + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + cv = GraphDeepTICA( + 2, + 0.1, + [1, 8], + model_options={ + 'n_out': 6, + 'n_bases': 6, + 'n_polynomials': 6, + 'n_layers': 2, + 'n_messages': 2, + 'n_feedforwards': 1, + 'n_scalars_node': 16, + 'n_vectors_node': 8, + 'n_scalars_edge': 16, + 'drop_rate': 0, + 'activation': 'Tanh', + } + ) + + data = test_get_data() + + assert ( + torch.abs( + cv(data) + - torch.tensor([[0.4301124873647384, -0.3866279366944752]] * 6) + ) < 1E-12 + ).all() + + assert torch.abs( + cv.training_step({'dataset_1': data, 'dataset_2': data}) + - torch.tensor(0) + ) < 1E-12 + + +if __name__ == '__main__': + test_deep_tica() From bb5507f3ffb7d59d74015ed8c73a78372df93340 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:47:36 +0200 Subject: [PATCH 088/267] Added a logo. --- mlcolvar/graph/.github/logo.png | Bin 0 -> 81529 bytes mlcolvar/graph/README.md | 2 ++ 2 files changed, 2 insertions(+) create mode 100644 mlcolvar/graph/.github/logo.png diff --git a/mlcolvar/graph/.github/logo.png b/mlcolvar/graph/.github/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..8472de71dd3bd2dfdaf34d2c4d172efff032b426 GIT binary patch literal 81529 zcmeFXg;$i__dYy?pi(MGC`dSfgn)F1h%n?sNS8`?BMk;f!wlV$(lAJ;K@6SJ-O}Cg zJIJHY=P!8Ide{28bS)On+-IM?uYFzD+2{8EM_v-|=DnK`2m}u%^-Ku@!S04YFmxf;3>{z6Z*F)Lmg(lG#jR(?=aa;(8|fKTrgU7Bi|}ue 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zJf<68gOKa55Rpp#q8wvCnfCM2(NaShEY;|{V46Lr?>Mq0^(d=L|C7fB@KD9Q(mx~?c2$;8u*6qXZ*Dusf?P?7f(Vr+H6GzTY zKUgnlnrRVr$~ + ## INTRODUCTION WIP From b1c6b29f5a8df6c9bd0e3325b9aab2225c5cfacb Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:48:57 +0200 Subject: [PATCH 089/267] Centered logo. --- mlcolvar/graph/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/README.md b/mlcolvar/graph/README.md index 71c45e90..5f4e787e 100644 --- a/mlcolvar/graph/README.md +++ b/mlcolvar/graph/README.md @@ -1,6 +1,6 @@ # The Graph Neural Network (GNN) Module in MLCOLVAR. -
+
## INTRODUCTION From 8b28254decb3f3c878e87767aaa9abf89286f8dd Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:50:06 +0200 Subject: [PATCH 090/267] Centered logo. --- mlcolvar/graph/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/README.md b/mlcolvar/graph/README.md index 5f4e787e..f5c49874 100644 --- a/mlcolvar/graph/README.md +++ b/mlcolvar/graph/README.md @@ -1,6 +1,6 @@ # The Graph Neural Network (GNN) Module in MLCOLVAR. -
+ ## INTRODUCTION From 882d73223fe6c4efc43385d07e6486867b4d590f Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:51:48 +0200 Subject: [PATCH 091/267] Centered logo. --- mlcolvar/graph/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/README.md b/mlcolvar/graph/README.md index f5c49874..17b640c0 100644 --- a/mlcolvar/graph/README.md +++ b/mlcolvar/graph/README.md @@ -1,6 +1,8 @@ # The Graph Neural Network (GNN) Module in MLCOLVAR. - +

+ +

## INTRODUCTION From c84dd46205c0c7ca0ac71dcf6b8b9b0b3a6c7788 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 14 Jun 2024 16:26:46 +0200 Subject: [PATCH 092/267] Added a normalizing option. --- mlcolvar/graph/explain/sensitivity.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index a6b3f689..916bba23 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -19,6 +19,7 @@ def graph_node_sensitivity( model: gcvs.GraphBaseCV, dataset: gdata.GraphDataSet, node_indices: List[int] = None, + normalizing_method: str = 'std', device: str = 'cpu', batch_size: int = None, show_progress: bool = True @@ -35,6 +36,11 @@ def graph_node_sensitivity( Dataset on which to compute the sensitivity analysis. node_indices: List[int] Indices of the nodes to analysis. + normalizing_method: str + Method of calculating the normalizing constant of the sensitivit. + - `None`: do not perform the normalization. + - `std`: normalize with six times of the standard deviation of the CV. + - `range`: normalize with the range of the CV. device: str Name of the device. batch_size: @@ -76,6 +82,18 @@ def graph_node_sensitivity( cv_org = get_dataset_cv_values( model, dataset, batch_size, show_progress, 'Getting base data' ) + if normalizing_method is None: + normalizing_constant = 1 + elif normalizing_method == 'std': + normalizing_constant = cv_org.std() * 6 + elif normalizing_method == 'range': + normalizing_constant = cv_org.max() - cv_org.min() + else: + raise KeyError( + 'Value of the `normalizing_method` parameter should be ' + + '`std` or `range`!' + ) + normalizing_constant = abs(normalizing_constant) for node in items: @@ -92,6 +110,9 @@ def graph_node_sensitivity( ) delta = cv_org - cv_masked + if normalizing_method is not None: + delta = delta / normalizing_constant + sensitivities_components.append(np.abs(delta)) sensitivities.append(np.mean(np.abs(delta), axis=0)) From e7a3d10fccfad930cbb222443c639dbd5a95fec9 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 14 Jun 2024 17:37:29 +0200 Subject: [PATCH 093/267] Changed default parameter. --- mlcolvar/graph/explain/sensitivity.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index 916bba23..81b3a4c4 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -39,7 +39,7 @@ def graph_node_sensitivity( normalizing_method: str Method of calculating the normalizing constant of the sensitivit. - `None`: do not perform the normalization. - - `std`: normalize with six times of the standard deviation of the CV. + - `std`: normalize with four times of the standard deviation of the CV. - `range`: normalize with the range of the CV. device: str Name of the device. @@ -85,7 +85,7 @@ def graph_node_sensitivity( if normalizing_method is None: normalizing_constant = 1 elif normalizing_method == 'std': - normalizing_constant = cv_org.std() * 6 + normalizing_constant = cv_org.std() * 4 elif normalizing_method == 'range': normalizing_constant = cv_org.max() - cv_org.min() else: From 764c3c4aa93049b1737869e8cfd1a042a3eac2e7 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sun, 16 Jun 2024 01:26:39 +0200 Subject: [PATCH 094/267] Changed image width. --- mlcolvar/graph/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/README.md b/mlcolvar/graph/README.md index 17b640c0..4d40c7e0 100644 --- a/mlcolvar/graph/README.md +++ b/mlcolvar/graph/README.md @@ -1,7 +1,7 @@ # The Graph Neural Network (GNN) Module in MLCOLVAR.

- +

## INTRODUCTION From 62e0e23d2987dbe63df1017921a20e0e5aa5ddda Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 17 Jun 2024 00:44:51 +0200 Subject: [PATCH 095/267] Also mask node features. --- mlcolvar/graph/explain/sensitivity.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index 81b3a4c4..2c959602 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -1,4 +1,5 @@ import copy +import torch import numpy as np from typing import List, Dict @@ -104,6 +105,11 @@ def graph_node_sensitivity( edge_index_masked = dataset[j]['edge_index'][:, mask] dataset_clone[j]['shifts'] = shifts_masked dataset_clone[j]['edge_index'] = edge_index_masked + n_nodes = len(dataset_clone[j]['positions']) + node_masks = torch.ones((n_nodes, 1), dtype=torch.bool) + node_masks[node, 0] = False + dataset_clone[j]['receiver_masks'] = node_masks + dataset_clone[j]['n_receivers'][0, 0] = n_nodes - 1 cv_masked = get_dataset_cv_values( model, dataset_clone, batch_size, False, From 6bfb902d79dda7479d6df5a2425073f52139f960 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 1 Jul 2024 12:38:06 +0200 Subject: [PATCH 096/267] Added extra loss function options. --- mlcolvar/graph/cvs/supervised/deeptda.py | 4 ++++ mlcolvar/graph/cvs/timelagged/deeptica.py | 5 ++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 67f7c44c..36324759 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -42,6 +42,8 @@ class GraphDeepTDA(GraphBaseCV): Name of the GNN model. model_options: Dict[Any, Any] Model options. + extra_loss_options: Dict[Any, Any] + Extra loss function options. optimizer_options: Dict[Any, Any] Optimizer options. @@ -66,6 +68,7 @@ def __init__( target_sigmas: Union[List[float], List[List[float]]], model_name: str = 'GVPModel', model_options: Dict[Any, Any] = {}, + extra_loss_options: Dict[Any, Any] = {'alpha': 1.0, 'beta': 100.0}, optimizer_options: Dict[Any, Any] = {}, **kwargs, ) -> None: @@ -115,6 +118,7 @@ def __init__( n_states=target_centers.shape[0], target_centers=target_centers, target_sigmas=target_sigmas, + **extra_loss_options ) def training_step( diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index 94bada14..2103f2e6 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -46,6 +46,8 @@ class GraphDeepTICA(GraphBaseCV): model_options: Dict[Any, Any] Model options. Note that the `n_out` key of this dict is REQUIRED, which stands for the dimension of the output of the network. + extra_loss_options: Dict[Any, Any] + Extra loss function options. optimizer_options: Dict[Any, Any] Optimizer options. @@ -73,6 +75,7 @@ def __init__( atomic_numbers: List[int], model_name: str = 'GVPModel', model_options: Dict[Any, Any] = {'n_out': 6}, + extra_loss_options: Dict[Any, Any] = {'mode': 'sum2', 'n_eig': 0}, optimizer_options: Dict[Any, Any] = {}, **kwargs, ) -> None: @@ -90,7 +93,7 @@ def __init__( n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs ) - self.loss_fn = ReduceEigenvaluesLoss(mode='sum2') + self.loss_fn = ReduceEigenvaluesLoss(**extra_loss_options) self.tica = TICA(n_out, n_cvs) From e923576aa4b12467d22aa9c9660e717da35317d3 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 5 Jul 2024 02:31:48 +0200 Subject: [PATCH 097/267] Fixed multiple CV sensitivity. --- mlcolvar/graph/explain/sensitivity.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index 2c959602..3409da39 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -86,9 +86,9 @@ def graph_node_sensitivity( if normalizing_method is None: normalizing_constant = 1 elif normalizing_method == 'std': - normalizing_constant = cv_org.std() * 4 + normalizing_constant = cv_org.std(axis=0) * 4 elif normalizing_method == 'range': - normalizing_constant = cv_org.max() - cv_org.min() + normalizing_constant = cv_org.max(axis=0) - cv_org.min(axis=0) else: raise KeyError( 'Value of the `normalizing_method` parameter should be ' @@ -127,6 +127,6 @@ def graph_node_sensitivity( results['sensitivities'] = np.array(sensitivities) results['sensitivities_components'] = np.array( sensitivities_components - ).transpose(2, 1, 0)[0] + ).transpose(2, 1, 0) return results From fc5d69295c443a44ace6e6386dc96e127aa1b827 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sun, 7 Jul 2024 22:34:49 +0200 Subject: [PATCH 098/267] Modified default analysis method. --- mlcolvar/graph/explain/sensitivity.py | 63 +++++++++++++++++++++-- mlcolvar/graph/explain/utils.py | 73 +++++++++++++++++++++++++-- 2 files changed, 130 insertions(+), 6 deletions(-) diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index 3409da39..fa9d93b8 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -7,16 +7,16 @@ from mlcolvar.graph import data as gdata from mlcolvar.graph import utils as gutils -from .utils import get_dataset_cv_values +from .utils import get_dataset_cv_values, get_dataset_cv_gradients """ Sensitivity analysis. """ -__all__ = ['graph_node_sensitivity'] +__all__ = ['graph_node_sensitivity', 'graph_node_sensitivity_mask'] -def graph_node_sensitivity( +def graph_node_sensitivity_mask( model: gcvs.GraphBaseCV, dataset: gdata.GraphDataSet, node_indices: List[int] = None, @@ -130,3 +130,60 @@ def graph_node_sensitivity( ).transpose(2, 1, 0) return results + + +def graph_node_sensitivity( + model: gcvs.GraphBaseCV, + dataset: gdata.GraphDataSet, + component: int = 0, + device: str = 'cpu', + batch_size: int = None, + show_progress: bool = True +) -> Dict[str, np.ndarray]: + """ + Perform a sensitivity analysis by calculating CV gradient w.r.t. nodes' + positions. This allows us to measure which atom is most important to the + CV. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + device: str + Name of the device. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + + Returns + ------- + results: dictionary + Results of the sensitivity analysis, containing 'node_indices', + 'sensitivities', and 'sensitivities_components', ordered according to + the node indices. + + See also + -------- + mlcolvar.utils.explain.sensitivity_analysis + Perform the sensitivity analysis of a feedforward model. + """ + model = model.to(device) + + gradients = get_dataset_cv_gradients( + model, + dataset, + component, + batch_size, + show_progress, + 'Getting gradients' + ) + sensitivities_components = np.linalg.norm(gradients, axis=-1) + + results = {} + results['sensitivities'] = sensitivities_components.mean(axis=0) + results['sensitivities_components'] = sensitivities_components + + return results diff --git a/mlcolvar/graph/explain/utils.py b/mlcolvar/graph/explain/utils.py index 62c250f0..138941b9 100644 --- a/mlcolvar/graph/explain/utils.py +++ b/mlcolvar/graph/explain/utils.py @@ -1,12 +1,11 @@ import torch import numpy as np -from typing import Dict from mlcolvar.graph import cvs as gcvs from mlcolvar.graph import data as gdata from mlcolvar.graph import utils as gutils -__all__ = ['get_dataset_cv_values'] +__all__ = ['get_dataset_cv_values', 'get_dataset_cv_gradients'] """ Analysis utils. @@ -19,7 +18,7 @@ def get_dataset_cv_values( batch_size: int = None, show_progress: bool = True, progress_prefix: str = 'Calculating CV values' -) -> Dict[str, np.ndarray]: +) -> np.ndarray: """ Get CV values of a given dataset. The calculation will run on the device where the model is on. @@ -63,3 +62,71 @@ def get_dataset_cv_values( cv_values.append(outputs) return np.concatenate(cv_values) + + +def get_dataset_cv_gradients( + model: gcvs.GraphBaseCV, + dataset: gdata.GraphDataSet, + component: int = 0, + batch_size: int = None, + show_progress: bool = True, + progress_prefix: str = 'Calculating CV gradients' +) -> np.ndarray: + """ + Get gradients of the CV w.r.t. node positions in a given dataset. The + calculation will run on the device where the model is on. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + component: int + Component of the CV to analysis. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + """ + datamodule = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule.setup() + + cv_value_gradients = [] + device = next(model.parameters()).device + + if show_progress: + items = gutils.progress.pbar( + datamodule.train_dataloader(), + frequency=0.001, + prefix=progress_prefix + ) + else: + items = datamodule.train_dataloader() + + for batchs in items: + batch_dict = batchs.to(device).to_dict() + cv_values = model(batch_dict) + cv_values = cv_values[:, component] + grad_outputs = [torch.ones_like(cv_values, device=device)] + gradients = torch.autograd.grad( + outputs=[cv_values], + inputs=[batch_dict['positions']], + grad_outputs=grad_outputs, + retain_graph=False, + create_graph=False, + ) + graph_sizes = batch_dict['ptr'][1:] - batch_dict['ptr'][:-1] + gradients = torch.split( + gradients[0].detach(), graph_sizes.cpu().numpy().tolist() + ) + gradients = [g.cpu().numpy() for g in gradients] + cv_value_gradients.extend(gradients) + + return np.array(cv_value_gradients) From 1bfb626a4057e3d8f08d8bad389be75e46c3cc48 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 8 Jul 2024 02:17:18 +0200 Subject: [PATCH 099/267] Fixed import --- mlcolvar/graph/explain/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/explain/__init__.py b/mlcolvar/graph/explain/__init__.py index 695a9784..60611d37 100644 --- a/mlcolvar/graph/explain/__init__.py +++ b/mlcolvar/graph/explain/__init__.py @@ -2,4 +2,4 @@ Metaphysics. """ -from .sensitivity import graph_node_sensitivity +from .sensitivity import graph_node_sensitivity, graph_node_sensitivity_mask From f602acbd7acba3c89e3729e92c6c5924e316d1be Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 5 Aug 2024 12:06:53 +0200 Subject: [PATCH 100/267] Added the Gaussian basis function. --- mlcolvar/graph/core/nn/radial.py | 112 +++++++++++++++++++++++++++---- 1 file changed, 100 insertions(+), 12 deletions(-) diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py index f2f2fe9f..e2b03696 100644 --- a/mlcolvar/graph/core/nn/radial.py +++ b/mlcolvar/graph/core/nn/radial.py @@ -9,6 +9,52 @@ __all__ = ['RadialEmbeddingBlock'] +class GaussianBasis(torch.nn.Module): + """ + The Gaussian basis functions. + + Parameters + ---------- + cutoff: float + The cutoff radius. + n_bases: int + Size of the basis set. + """ + def __init__(self, cutoff: float, n_bases=32) -> None: + super().__init__() + + offset = torch.linspace( + start=0.0, + end=cutoff, + steps=n_bases, + dtype=torch.get_default_dtype(), + ) + coeff = -0.5 / (offset[1] - offset[0]).item() ** 2 + self.register_buffer( + 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) + ) + self.register_buffer( + 'coeff', torch.tensor(coeff, dtype=torch.get_default_dtype()) + ) + self.register_buffer('offset', offset) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + dist = x.view(-1, 1) - self.offset.view(1, -1) + return torch.exp(self.coeff * torch.pow(dist, 2))[0] + + def __repr__(self) -> str: + result = 'GAUSSIANBASIS [ ' + + data_string = '\033[32m{:d}\033[0m\033[36m 󰯰 \033[0m' + result = result + data_string.format(len(self.offset)) + result = result + '| ' + data_string = '\033[32m{:f}\033[0m\033[36m 󰳁 \033[0m' + result = result + data_string.format(self.cutoff) + result = result + ']' + + return result + + class BesselBasis(torch.nn.Module): """ The Bessel radial basis functions (equation (7) in [1]). @@ -44,13 +90,13 @@ def __init__(self, cutoff: float, n_bases=8, trainable=False) -> None: if trainable: self.bessel_weights = torch.nn.Parameter(bessel_weights) else: - self.register_buffer("bessel_weights", bessel_weights) + self.register_buffer('bessel_weights', bessel_weights) self.register_buffer( - "cutoff", torch.tensor(cutoff, dtype=torch.get_default_dtype()) + 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) ) self.register_buffer( - "prefactor", + 'prefactor', torch.tensor( np.sqrt(2.0 / cutoff), dtype=torch.get_default_dtype() ) @@ -97,10 +143,10 @@ class PolynomialCutoff(torch.nn.Module): def __init__(self, cutoff: float, p: int = 6) -> None: super().__init__() self.register_buffer( - "p", torch.tensor(p, dtype=torch.get_default_dtype()) + 'p', torch.tensor(p, dtype=torch.get_default_dtype()) ) self.register_buffer( - "cutoff", torch.tensor(cutoff, dtype=torch.get_default_dtype()) + 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) ) def forward(self, x: torch.Tensor) -> torch.Tensor: @@ -134,8 +180,7 @@ def __repr__(self) -> str: class RadialEmbeddingBlock(torch.nn.Module): """ - The radial embedding block using a Bessel basis set and a smooth cutoff - function [1]. + The radial embedding block [1]. Parameters ---------- @@ -145,6 +190,8 @@ class RadialEmbeddingBlock(torch.nn.Module): Size of the basis set. n_polynomials: bool Order of the polynomial. + basis_type: str + Type of the basis function. References ---------- @@ -157,11 +204,20 @@ def __init__( cutoff: float, n_bases: int = 8, n_polynomials: int = 6, + basis_type: str = 'bessel', ) -> None: super().__init__() self.n_out = n_bases - self.bessel_fn = BesselBasis(cutoff=cutoff, n_bases=n_bases) - self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomials) + if basis_type == 'bessel': + self.basis_fn = BesselBasis(cutoff=cutoff, n_bases=n_bases) + self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomials) + elif basis_type == 'gaussian': + self.basis_fn = GaussianBasis(cutoff=cutoff, n_bases=n_bases) + self.cutoff_fn = None + else: + raise RuntimeError( + 'Unknown basis function type "{:s}" !'.format(basis_type) + ) def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: """ @@ -177,9 +233,12 @@ def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: edge_embedding: torch.Tensor (shape: [n_edges, n_bases]) The radial edge embedding. """ - r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_bases] - c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] - return r * c + r = self.basis_fn(edge_lengths) # shape: [n_edges, n_bases] + if self.cutoff_fn is not None: + c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] + return r * c + else: + return r def test_bessel_basis() -> None: @@ -210,6 +269,34 @@ def test_bessel_basis() -> None: torch.set_default_dtype(dtype) +def test_gaussian_basis() -> None: + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = torch.tensor([ + [0.9998611207557263, 0.6166385641763439], + [0.9950124791926823, 0.6669768108584744], + [0.9833348700493460, 0.7164317992468783], + [0.9650691177896804, 0.7642281651714904], + [0.9405880633643421, 0.8095716486678869], + [0.9103839103891423, 0.8516705072294410], + [0.8750517756337902, 0.8897581848801761], + [0.8352702114112720, 0.9231163463866358], + [0.7917795893122607, 0.9510973184771084], + [0.7453593045429805, 0.9731449630580510], + ]) + + rbf = GaussianBasis(6.0, 2) + + data_new = torch.stack( + [rbf(torch.ones(1) * i * 0.5 + 0.1) for i in range(0, 10)] + ) + + assert (torch.abs(data - data_new) < 1E-12).all() + + torch.set_default_dtype(dtype) + + def test_polynomial_cutoff() -> None: dtype = torch.get_default_dtype() torch.set_default_dtype(torch.float64) @@ -268,5 +355,6 @@ def test_radial_embedding_block(): if __name__ == '__main__': test_bessel_basis() + test_gaussian_basis() test_polynomial_cutoff() test_radial_embedding_block() From 2b207f1a0732f247124f7e239b4e0d5954ee5af2 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 5 Aug 2024 12:48:26 +0200 Subject: [PATCH 101/267] Fixed dim. --- mlcolvar/graph/core/nn/radial.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py index e2b03696..c208acee 100644 --- a/mlcolvar/graph/core/nn/radial.py +++ b/mlcolvar/graph/core/nn/radial.py @@ -40,7 +40,7 @@ def __init__(self, cutoff: float, n_bases=32) -> None: def forward(self, x: torch.Tensor) -> torch.Tensor: dist = x.view(-1, 1) - self.offset.view(1, -1) - return torch.exp(self.coeff * torch.pow(dist, 2))[0] + return torch.exp(self.coeff * torch.pow(dist, 2)) def __repr__(self) -> str: result = 'GAUSSIANBASIS [ ' @@ -283,13 +283,13 @@ def test_gaussian_basis() -> None: [0.8750517756337902, 0.8897581848801761], [0.8352702114112720, 0.9231163463866358], [0.7917795893122607, 0.9510973184771084], - [0.7453593045429805, 0.9731449630580510], + [0.7453593045429805, 0.9731449630580510] ]) rbf = GaussianBasis(6.0, 2) data_new = torch.stack( - [rbf(torch.ones(1) * i * 0.5 + 0.1) for i in range(0, 10)] + [rbf(torch.ones(1) * i * 0.5 + 0.1)[0] for i in range(0, 10)] ) assert (torch.abs(data - data_new) < 1E-12).all() From 1cbc7f0d8a5a547cbefabd503ca9f967d8db852e Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 5 Aug 2024 13:08:55 +0200 Subject: [PATCH 102/267] Changed some variable names to be compatible with old models. --- mlcolvar/graph/core/nn/radial.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/graph/core/nn/radial.py index c208acee..e43193fd 100644 --- a/mlcolvar/graph/core/nn/radial.py +++ b/mlcolvar/graph/core/nn/radial.py @@ -209,10 +209,10 @@ def __init__( super().__init__() self.n_out = n_bases if basis_type == 'bessel': - self.basis_fn = BesselBasis(cutoff=cutoff, n_bases=n_bases) + self.bessel_fn = BesselBasis(cutoff=cutoff, n_bases=n_bases) self.cutoff_fn = PolynomialCutoff(cutoff=cutoff, p=n_polynomials) elif basis_type == 'gaussian': - self.basis_fn = GaussianBasis(cutoff=cutoff, n_bases=n_bases) + self.bessel_fn = GaussianBasis(cutoff=cutoff, n_bases=n_bases) self.cutoff_fn = None else: raise RuntimeError( @@ -233,7 +233,7 @@ def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: edge_embedding: torch.Tensor (shape: [n_edges, n_bases]) The radial edge embedding. """ - r = self.basis_fn(edge_lengths) # shape: [n_edges, n_bases] + r = self.bessel_fn(edge_lengths) # shape: [n_edges, n_bases] if self.cutoff_fn is not None: c = self.cutoff_fn(edge_lengths) # shape: [n_edges, 1] return r * c From a535a5fe6092abb27adeb7f9f4a532287139260e Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 7 Aug 2024 23:03:46 +0200 Subject: [PATCH 103/267] Allowed to switch basis function types. --- mlcolvar/graph/core/nn/models.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index d75bfe42..edaf6bac 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -34,6 +34,8 @@ class BaseModel(torch.nn.Module): Size of the basis set. n_polynomials: bool Order of the polynomials in the basis functions. + basis_type: str + Type of the basis function. """ def __init__( @@ -43,11 +45,12 @@ def __init__( atomic_numbers: List[int], n_bases: int = 6, n_polynomials: int = 6, + basis_type: str = 'bessel' ) -> None: super().__init__() self._n_out = n_out self._radial_embedding = radial.RadialEmbeddingBlock( - cutoff, n_bases, n_polynomials + cutoff, n_bases, n_polynomials, basis_type ) self.register_buffer( 'n_out', torch.tensor(n_out, dtype=torch.int64) @@ -123,6 +126,8 @@ class GVPModel(BaseModel): Drop probability in all dropout layers. activation: str Name of the activation function to use in the GVPs (case sensitive). + basis_type: str + Type of the basis function. References ---------- @@ -148,8 +153,11 @@ def __init__( n_scalars_edge: int = 8, drop_rate: int = 0.1, activation: str = 'SiLU', + basis_type: str = 'bessel' ) -> None: - super().__init__(n_out, cutoff, atomic_numbers, n_bases, n_polynomials) + super().__init__( + n_out, cutoff, atomic_numbers, n_bases, n_polynomials, basis_type + ) self.W_e = nn.ModuleList([ gvp_layer.LayerNorm((n_bases, 1)), From 0373b6c0a60b25c6a3768898dd59b49f7b1a34a8 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 13 Aug 2024 00:57:37 +0200 Subject: [PATCH 104/267] Added SchNet. --- mlcolvar/graph/core/nn/__init__.py | 2 +- mlcolvar/graph/core/nn/models.py | 163 +++++++++++++++++++++++++++-- mlcolvar/graph/core/nn/schnet.py | 105 +++++++++++++++++++ 3 files changed, 262 insertions(+), 8 deletions(-) create mode 100644 mlcolvar/graph/core/nn/schnet.py diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py index 32523f36..c92aa4ec 100644 --- a/mlcolvar/graph/core/nn/__init__.py +++ b/mlcolvar/graph/core/nn/__init__.py @@ -1 +1 @@ -from .models import BaseModel, GVPModel +from .models import BaseModel, GVPModel, SchNetModel diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index edaf6bac..9e4c6c08 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -7,6 +7,7 @@ from mlcolvar.graph import data as gdata from mlcolvar.graph.utils import torch_tools from mlcolvar.graph.core.nn import radial +from mlcolvar.graph.core.nn import schnet from mlcolvar.graph.core.nn import gvp_layer """ @@ -16,7 +17,7 @@ __all__ = ['BaseModel', 'GVPModel'] -class BaseModel(torch.nn.Module): +class BaseModel(nn.Module): """ The commen GNN interface for mlcolvar. @@ -64,7 +65,7 @@ def __init__( def embed_edge( self, data: Dict[str, torch.Tensor], normalize: bool = True - ) -> Tuple[torch.Tensor, torch.Tensor]: + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Perform the edge embedding. @@ -78,18 +79,20 @@ def embed_edge( Returns ------- + edge_lengths: torch.Tensor (shape: [n_edges, 1]) + The edge lengths. edge_length_embeddings: torch.Tensor (shape: [n_edges, n_bases]) The edge length embeddings. edge_unit_vectors: torch.Tensor (shape: [n_edges, 3]) The normalized edge vectors. """ vectors, lengths = torch_tools.get_edge_vectors_and_lengths( - positions=data["positions"], - edge_index=data["edge_index"], - shifts=data["shifts"], + positions=data['positions'], + edge_index=data['edge_index'], + shifts=data['shifts'], normalize=normalize, ) - return self._radial_embedding(lengths), vectors + return lengths, self._radial_embedding(lengths), vectors class GVPModel(BaseModel): @@ -224,7 +227,7 @@ def forward( h_V = (h_V_1, h_V_2) h_E = self.embed_edge(data) - h_E = (h_E[0], h_E[1].unsqueeze(-2)) + h_E = (h_E[1], h_E[2].unsqueeze(-2)) for w in self.W_e: h_E = w(h_E) h_E_1, h_E_2 = h_E @@ -260,6 +263,128 @@ def forward( return out +class SchNetModel(BaseModel): + """ + The SchNet [1] model. This implementation is taken from torch_geometric: + https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py + + Parameters + ---------- + n_out: int + Size of the output node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_bases: int + Size of the basis set. + n_layers: int + Number of the graph convolution layers. + n_filters: int + Number of filters. + n_hidden_channels: int + Size of hidden embeddings. + + References + ---------- + .. [1] Schütt, Kristof T., et al. "Schnet–a deep learning architecture for + molecules and materials." The Journal of Chemical Physics 148.24 + (2018). + """ + + def __init__( + self, + n_out: int, + cutoff: float, + atomic_numbers: List[int], + n_bases: int = 16, + n_layers: int = 2, + n_filters: int = 16, + n_hidden_channels: int = 16, + drop_rate: int = 0 + ) -> None: + + super().__init__( + n_out, cutoff, atomic_numbers, n_bases, 0, 'gaussian' + ) + + self.W_v = nn.Linear( + len(atomic_numbers), n_hidden_channels, bias=False + ) + + self.layers = nn.ModuleList([ + schnet.InteractionBlock( + n_hidden_channels, n_bases, n_filters, cutoff + ) + for _ in range(n_layers) + ]) + + self.W_out = nn.ModuleList([ + nn.Linear(n_hidden_channels, n_hidden_channels // 2), + schnet.ShiftedSoftplus(), + nn.Linear(n_hidden_channels // 2, n_out) + ]) + + self.reset_parameters() + + def reset_parameters(self) -> None: + """ + Resets all learnable parameters of the module. + """ + self.W_v.reset_parameters() + + for layer in self.layers: + layer.reset_parameters() + + nn.init.xavier_uniform_(self.W_out[0].weight) + self.W_out[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.W_out[2].weight) + self.W_out[2].bias.data.fill_(0) + + def forward( + self, data: Dict[str, torch.Tensor], scatter_mean: bool = True + ) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + scatter_mean: bool + If perform the scatter mean to the model output. + """ + + h_E = self.embed_edge(data) + h_V = self.W_v(data['node_attrs']) + + batch_id = data['batch'] + if data.get('receiver_masks') is not None: + receiver_masks = data['receiver_masks'].squeeze(-1) + else: + receiver_masks = None + + for layer in self.layers: + h_V = h_V + layer(h_V, data['edge_index'], h_E[0], h_E[1]) + + for w in self.W_out: + h_V = w(h_V) + out = h_V + + if scatter_mean: + if receiver_masks is None: + out = torch_tools.scatter_mean(out, batch_id, dim=0) + else: + out = out * data['receiver_masks'] + out = torch_tools.scatter_sum(out, batch_id, dim=0) + out = out / data['n_receivers'] + + return out + + def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. # Maybe should change its name. @@ -346,5 +471,29 @@ def test_gvp() -> None: ).all() +def test_schnet() -> None: + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + model = SchNetModel( + n_out=2, + cutoff=0.1, + atomic_numbers=[1, 8], + n_bases=6, + n_layers=2, + n_filters=16, + n_hidden_channels=16 + ) + + data = test_get_data().to_dict() + assert ( + torch.abs( + model(data) - + torch.tensor([[0.409843614955313, -0.12684721733013735]] * 6) + ) < 1E-12 + ).all() + + if __name__ == '__main__': test_gvp() + test_schnet() diff --git a/mlcolvar/graph/core/nn/schnet.py b/mlcolvar/graph/core/nn/schnet.py new file mode 100644 index 00000000..128049a6 --- /dev/null +++ b/mlcolvar/graph/core/nn/schnet.py @@ -0,0 +1,105 @@ +import math +import torch +from torch import nn +from torch_geometric.nn import MessagePassing + +""" +The SchNet components. This module is taken from the pgy package: +https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py +""" + +__all__ = ['InteractionBlock', 'ShiftedSoftplus'] + + +class InteractionBlock(nn.Module): + def __init__( + self, + hidden_channels: int, + num_gaussians: int, + num_filters: int, + cutoff: float + ) -> None: + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(num_gaussians, num_filters), + ShiftedSoftplus(), + nn.Linear(num_filters, num_filters), + ) + self.conv = CFConv( + hidden_channels, hidden_channels, num_filters, self.mlp, cutoff + ) + self.act = ShiftedSoftplus() + self.lin = nn.Linear(hidden_channels, hidden_channels) + + self.reset_parameters() + + def reset_parameters(self) -> None: + nn.init.xavier_uniform_(self.mlp[0].weight) + self.mlp[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.mlp[2].weight) + self.mlp[2].bias.data.fill_(0) + self.conv.reset_parameters() + nn.init.xavier_uniform_(self.lin.weight) + self.lin.bias.data.fill_(0) + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + edge_weight: torch.Tensor, + edge_attr: torch.Tensor, + ) -> torch.Tensor: + x = self.conv(x, edge_index, edge_weight, edge_attr) + x = self.act(x) + x = self.lin(x) + return x + + +class CFConv(MessagePassing): + def __init__( + self, + in_channels: int, + out_channels: int, + num_filters: int, + network: nn.Sequential, + cutoff: float, + ) -> None: + super().__init__(aggr='add') + self.lin1 = nn.Linear(in_channels, num_filters, bias=False) + self.lin2 = nn.Linear(num_filters, out_channels) + self.network = network + self.cutoff = cutoff + + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.lin1.weight) + nn.init.xavier_uniform_(self.lin2.weight) + self.lin2.bias.data.fill_(0) + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + edge_weight: torch.Tensor, + edge_attr: torch.Tensor, + ) -> torch.Tensor: + C = 0.5 * (torch.cos(edge_weight * math.pi / self.cutoff) + 1.0) + W = self.network(edge_attr) * C.view(-1, 1) + + x = self.lin1(x) + x = self.propagate(edge_index, x=x, W=W) + x = self.lin2(x) + return x + + def message(self, x_j: torch.Tensor, W: torch.Tensor) -> torch.Tensor: + return x_j * W + + +class ShiftedSoftplus(nn.Module): + def __init__(self) -> None: + super().__init__() + self.shift = torch.log(torch.tensor(2.0)).item() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return nn.functional.softplus(x) - self.shift From 5158a108fd0b3436305d5c9c9b3d668cba864882 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 13 Aug 2024 01:21:54 +0200 Subject: [PATCH 105/267] Fixed attributes. --- mlcolvar/graph/core/nn/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 9e4c6c08..50bd5f72 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -14,7 +14,7 @@ GNN models. """ -__all__ = ['BaseModel', 'GVPModel'] +__all__ = ['BaseModel', 'GVPModel', 'SchNetModel'] class BaseModel(nn.Module): From c2f6c929123a2acd832a3043c0dc5df141ee21a8 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 13 Aug 2024 11:37:35 +0200 Subject: [PATCH 106/267] Fixed import. --- mlcolvar/graph/core/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/core/__init__.py b/mlcolvar/graph/core/__init__.py index a8893db1..f0718b2d 100644 --- a/mlcolvar/graph/core/__init__.py +++ b/mlcolvar/graph/core/__init__.py @@ -1 +1 @@ -from .nn import BaseModel, GVPModel +from .nn import BaseModel, GVPModel, SchNetModel From 50b04a34570d2f0a3d56122b8f3e11b999b0286a Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 13 Aug 2024 16:21:22 +0200 Subject: [PATCH 107/267] Updated one link. --- mlcolvar/graph/core/nn/gvp_layer.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py index 2098b58a..2f855342 100644 --- a/mlcolvar/graph/core/nn/gvp_layer.py +++ b/mlcolvar/graph/core/nn/gvp_layer.py @@ -5,8 +5,9 @@ from typing import Tuple, Callable, Optional """ -The Geometric Vector Perceptron (GVP) layer. This module is taken from the -orginal repo: https://github.com/drorlab/gvp-pytorch, and made compilable. +The Geometric Vector Perceptron (GVP) layer. This module is taken from: +https://github.com/chaitjo/geometric-gnn-dojo/blob/main/models/layers/gvp_layer.py, +and made compilable. """ __all__ = ['GVPConvLayer', 'LayerNorm', 'Dropout'] From 5ae9707bc5c8e55186c34b46b98e0790b05483a9 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 21 Aug 2024 12:00:57 +0200 Subject: [PATCH 108/267] Moved example inputs to the base class. --- mlcolvar/graph/cvs/cv.py | 36 +++++++++++++++++++++++ mlcolvar/graph/cvs/supervised/deeptda.py | 36 ----------------------- mlcolvar/graph/cvs/timelagged/deeptica.py | 36 ----------------------- 3 files changed, 36 insertions(+), 72 deletions(-) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index b69d86c1..018b2193 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -199,6 +199,42 @@ def configure_optimizers(self) -> Union[ else: return optimizer + @property + def example_input_array(self) -> Dict[str, torch.Tensor]: + """ + Example data. + """ + numbers = self._model.atomic_numbers.cpu().numpy().tolist() + positions = np.random.randn(2, len(numbers), 3) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[[0]], [[1]]]) + node_labels = np.array([[0]] * len(numbers)) + z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + + config = [ + gdata.atomic.Configuration( + atomic_numbers=numbers, + positions=positions[i], + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels[i], + ) for i in range(2) + ] + dataset = gdata.create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) + + loader = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + loader.setup() + + return next(iter(loader.train_dataloader())) + def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 36324759..6c70df7b 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -146,42 +146,6 @@ def training_step( self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) return loss - @property - def example_input_array(self) -> Dict[str, torch.Tensor]: - """ - Example data. - """ - numbers = self._model.atomic_numbers.cpu().numpy().tolist() - positions = np.random.randn(2, len(numbers), 3) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[[0]], [[1]]]) - node_labels = np.array([[0]] * len(numbers)) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = [ - gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=positions[i], - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels[i], - ) for i in range(2) - ] - dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False - ) - - loader = gdata.GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=10, - shuffle=False, - ) - loader.setup() - - return next(iter(loader.train_dataloader())) - def test_deep_tda(): torch.manual_seed(0) diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index 2103f2e6..004aa895 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -186,42 +186,6 @@ def set_regularization(self, c0_reg=1e-6) -> None: """ self.tica.reg_C_0 = c0_reg - @property - def example_input_array(self) -> Dict[str, torch.Tensor]: - """ - Example data. - """ - numbers = self._model.atomic_numbers.cpu().numpy().tolist() - positions = np.random.randn(2, len(numbers), 3) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[[0]], [[1]]]) - node_labels = np.array([[0]] * len(numbers)) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = [ - gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=positions[i], - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels[i], - ) for i in range(2) - ] - dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False - ) - - loader = gdata.GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=10, - shuffle=False, - ) - loader.setup() - - return next(iter(loader.train_dataloader())) - def test_deep_tica(): torch.manual_seed(0) From 401523da32f367f6737657c2831fd2b38172c649 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 21 Aug 2024 12:04:14 +0200 Subject: [PATCH 109/267] Removed unused imports. --- mlcolvar/graph/cvs/supervised/deeptda.py | 2 -- mlcolvar/graph/cvs/timelagged/deeptica.py | 2 -- 2 files changed, 4 deletions(-) diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py index 6c70df7b..cade3fcf 100644 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ b/mlcolvar/graph/cvs/supervised/deeptda.py @@ -1,12 +1,10 @@ import torch import torch_geometric as tg -import numpy as np from typing import Dict, Any, List, Union from mlcolvar.core.loss import TDALoss from mlcolvar.graph.cvs import GraphBaseCV from mlcolvar.graph.cvs.cv import test_get_data -from mlcolvar.graph import data as gdata from mlcolvar.graph.utils import torch_tools """ diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py index 004aa895..be78d5b9 100644 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ b/mlcolvar/graph/cvs/timelagged/deeptica.py @@ -1,13 +1,11 @@ import torch import torch_geometric as tg -import numpy as np from typing import Dict, Any, List from mlcolvar.core.stats import TICA from mlcolvar.core.loss import ReduceEigenvaluesLoss from mlcolvar.graph.cvs import GraphBaseCV from mlcolvar.graph.cvs.cv import test_get_data -from mlcolvar.graph import data as gdata from mlcolvar.graph.utils import torch_tools """ From 97d609720dadfae5b58a6b875fbc37f87c19dee1 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Fri, 23 Aug 2024 16:57:02 +0200 Subject: [PATCH 110/267] Changed the behavior of building graphs. --- mlcolvar/graph/core/nn/models.py | 40 +---- mlcolvar/graph/cvs/cv.py | 3 +- mlcolvar/graph/data/atomic.py | 6 +- mlcolvar/graph/data/datamodule.py | 28 ++-- mlcolvar/graph/data/dataset.py | 62 ++----- mlcolvar/graph/data/neighborhood.py | 137 ++++++++++----- mlcolvar/graph/explain/__init__.py | 2 +- mlcolvar/graph/explain/sensitivity.py | 125 +------------- mlcolvar/graph/utils/io.py | 232 +++++++++++++------------- 9 files changed, 252 insertions(+), 383 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 50bd5f72..71e33872 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -235,30 +235,16 @@ def forward( h_E = (h_E_1, h_E_2) batch_id = data['batch'] - if data.get('receiver_masks') is not None: - receiver_masks = data['receiver_masks'].squeeze(-1) - else: - receiver_masks = None for layer in self.layers: - h_V = layer( - h_V, - data['edge_index'], - h_E, - node_mask=receiver_masks - ) + h_V = layer(h_V, data['edge_index'], h_E) for w in self.W_out: h_V = w(h_V) out = h_V[0] if scatter_mean: - if receiver_masks is None: - out = torch_tools.scatter_mean(out, batch_id, dim=0) - else: - out = out * data['receiver_masks'] - out = torch_tools.scatter_sum(out, batch_id, dim=0) - out = out / data['n_receivers'] + out = torch_tools.scatter_mean(out, batch_id, dim=0) return out @@ -362,10 +348,6 @@ def forward( h_V = self.W_v(data['node_attrs']) batch_id = data['batch'] - if data.get('receiver_masks') is not None: - receiver_masks = data['receiver_masks'].squeeze(-1) - else: - receiver_masks = None for layer in self.layers: h_V = h_V + layer(h_V, data['edge_index'], h_E[0], h_E[1]) @@ -375,17 +357,12 @@ def forward( out = h_V if scatter_mean: - if receiver_masks is None: - out = torch_tools.scatter_mean(out, batch_id, dim=0) - else: - out = out * data['receiver_masks'] - out = torch_tools.scatter_sum(out, batch_id, dim=0) - out = out / data['n_receivers'] + out = torch_tools.scatter_mean(out, batch_id, dim=0) return out -def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: +def test_get_data() -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. # Maybe should change its name. torch.manual_seed(0) @@ -416,7 +393,6 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels, - edge_receivers=receivers, ) for p in positions ] dataset = gdata.create_dataset_from_configurations( @@ -462,14 +438,6 @@ def test_gvp() -> None: ) < 1E-12 ).all() - data = test_get_data([0]).to_dict() - assert ( - torch.abs( - model(data) - - torch.tensor([[0.6049081358540733, -0.2549507187584082]] * 6) - ) < 1E-12 - ).all() - def test_schnet() -> None: torch.manual_seed(0) diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py index 018b2193..beeef02a 100644 --- a/mlcolvar/graph/cvs/cv.py +++ b/mlcolvar/graph/cvs/cv.py @@ -236,7 +236,7 @@ def example_input_array(self) -> Dict[str, torch.Tensor]: return next(iter(loader.train_dataloader())) -def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: +def test_get_data() -> tg.data.Batch: # TODO: This is not a real test, but a helper function for other tests. # Maybe should change its name. @@ -265,7 +265,6 @@ def test_get_data(receivers: List[int] = [0, 1, 2]) -> tg.data.Batch: pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels[i], - edge_receivers=receivers, ) for i in range(0, 6) ] dataset = gdata.create_dataset_from_configurations( diff --git a/mlcolvar/graph/data/atomic.py b/mlcolvar/graph/data/atomic.py index 1a1d4cd7..662b97e3 100644 --- a/mlcolvar/graph/data/atomic.py +++ b/mlcolvar/graph/data/atomic.py @@ -95,9 +95,9 @@ class Configuration: pbc: Optional[tuple] # shape: [3] node_labels: Optional[np.ndarray] # shape: [n_atoms, n_node_labels] graph_labels: Optional[np.ndarray] # shape: [n_graph_labels, 1] - edge_senders: Optional[np.ndarray] = None # shape: [n_senders] - edge_receivers: Optional[np.ndarray] = None # shape: [n_receivers] - weight: Optional[float] = 1.0 # shape: [] + weight: Optional[float] = 1.0 # shape: [] + system: Optional[np.ndarray] = None # shape: [n_system_atoms] + environment: Optional[np.ndarray] = None # shape: [n_environment_atoms] Configurations = List[Configuration] diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/graph/data/datamodule.py index bebe2116..dec96528 100644 --- a/mlcolvar/graph/data/datamodule.py +++ b/mlcolvar/graph/data/datamodule.py @@ -568,7 +568,6 @@ def test_datamodule() -> None: [[8], [4], [7], [0], [1], [2]] ) ).all() - assert (data_dict['n_receivers'] == torch.tensor([[3]] * 6)).all() data_dict = next(iter(loader.val_dataloader())).to_dict() assert ( @@ -582,7 +581,6 @@ def test_datamodule() -> None: ).all() assert (data_dict['graph_labels'] == torch.tensor([[5], [9], [6]])).all() assert (data_dict['ptr'] == torch.tensor([0, 3, 6, 9])).all() - assert (data_dict['n_receivers'] == torch.tensor([[3]] * 3)).all() data_dict = next(iter(loader.test_dataloader())).to_dict() assert (data_dict['graph_labels'] == torch.tensor([[3]])).all() @@ -630,16 +628,16 @@ def test_datamodule() -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data_dict['n_receivers'] == torch.tensor([[3]])).all() config = atomic.Configuration( atomic_numbers=numbers, positions=positions, cell=cell, pbc=[True] * 3, - edge_receivers=[0], node_labels=node_labels, graph_labels=graph_labels, + system=[0], + environment=[1, 2] ) dataset = create_dataset_from_configurations( [config] * 10, z_table, 0.1, show_progress=False @@ -655,18 +653,20 @@ def test_datamodule() -> None: loader.setup() assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] - data_dict = next(iter(loader.train_dataloader())).to_dict() - assert (data_dict['n_receivers'] == torch.tensor([[1]] * 6)).all() + data_dict = next(iter(loader.val_dataloader())).to_dict() assert ( - data_dict['receiver_masks'] == torch.tensor([[1], [0], [0]] * 6) + data_dict['edge_index'] == torch.tensor([ + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], + [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] + ]) ).all() - data_dict = next(iter(loader.val_dataloader())).to_dict() - - assert (data_dict['n_receivers'] == torch.tensor([[1]] * 3)).all() + data_dict = next(iter(loader.test_dataloader())).to_dict() assert ( - data_dict['receiver_masks'] == torch.tensor([[1], [0], [0]] * 3) + data_dict['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) ).all() @@ -741,8 +741,6 @@ def test_combined_datamodule() -> None: [[8], [4], [7], [0], [1], [2]] ) ).all() - assert (data_dict_1['n_receivers'] == torch.tensor([[3]] * 6)).all() - assert (data_dict_2['n_receivers'] == torch.tensor([[3]] * 6)).all() batch = next(iter(loader.val_dataloader()))[0] data_dict_1 = batch['dataset_1'].to_dict() @@ -769,8 +767,6 @@ def test_combined_datamodule() -> None: assert (data_dict_2['graph_labels'] == torch.tensor([[5], [9], [6]])).all() assert (data_dict_1['ptr'] == torch.tensor([0, 3, 6, 9])).all() assert (data_dict_2['ptr'] == torch.tensor([0, 3, 6, 9])).all() - assert (data_dict_1['n_receivers'] == torch.tensor([[3]] * 3)).all() - assert (data_dict_2['n_receivers'] == torch.tensor([[3]] * 3)).all() batch = next(iter(loader.test_dataloader()))[0] data_dict_1 = batch['dataset_1'].to_dict() @@ -865,8 +861,6 @@ def test_combined_datamodule() -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data_dict_1['n_receivers'] == torch.tensor([[3]])).all() - assert (data_dict_2['n_receivers'] == torch.tensor([[3]])).all() if __name__ == '__main__': diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 1e6ac111..63f44c50 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -127,8 +127,8 @@ def _create_dataset_from_configuration( cutoff=cutoff, cell=config.cell, pbc=config.pbc, - sender_indices=config.edge_senders, - receiver_indices=config.edge_receivers, + system_indices=config.system, + environment_indices=config.environment, ) edge_index = torch.tensor(edge_index, dtype=torch.long) shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) @@ -165,21 +165,6 @@ def _create_dataset_from_configuration( else 1 ) - n_receivers = ( - torch.tensor( - [[len(config.edge_receivers)]], dtype=torch.get_default_dtype() - ) if config.edge_receivers is not None - else torch.tensor( - [[one_hot.shape[0]]], dtype=torch.get_default_dtype() - ) - ) - - if config.edge_receivers is not None: - receiver_masks = torch.zeros((one_hot.shape[0], 1), dtype=torch.bool) - receiver_masks[config.edge_receivers, 0] = 1 - else: - receiver_masks = None - return tg.data.Data( edge_index=edge_index, shifts=shifts, @@ -189,8 +174,6 @@ def _create_dataset_from_configuration( node_attrs=one_hot, node_labels=node_labels, graph_labels=graph_labels, - n_receivers=n_receivers, - receiver_masks=receiver_masks, weight=weight, ) @@ -334,7 +317,6 @@ def test_from_configuration() -> None: ).all() assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() assert (data['graph_labels'] == torch.tensor([[1.0]])).all() - assert (data['n_receivers'] == torch.tensor([[3]])).all() assert data['weight'] == 1.0 config = atomic.Configuration( @@ -344,29 +326,21 @@ def test_from_configuration() -> None: pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels, - edge_senders=[0], + system=[1], + environment=[2] ) data = _create_dataset_from_configuration(config, z_table, 0.1) assert ( - data['edge_index'] == torch.tensor([[0, 0], [2, 1]]) + data['edge_index'] == torch.tensor([[1, 2], [2, 1]]) ).all() - assert (data['n_receivers'] == torch.tensor([[3]])).all() - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - edge_receivers=[1, 2], - ) - data = _create_dataset_from_configuration(config, z_table, 0.1) assert ( - data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) + data['shifts'] == torch.tensor([[0.0, 0.2, 0.0], [0.0, -0.2, 0.0]]) + ).all() + assert ( + data['unit_shifts'] == torch.tensor( + [[0.0, 1.0, 0.0], [0.0, -1.0, 0.0]] + ) ).all() - assert (data['n_receivers'] == torch.tensor([[2]])).all() - assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() config = atomic.Configuration( atomic_numbers=numbers, @@ -375,13 +349,15 @@ def test_from_configuration() -> None: pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels, - edge_senders=[0], - edge_receivers=[1, 2], + system=[0], + environment=[1, 2] ) data = _create_dataset_from_configuration(config, z_table, 0.1) - assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() - assert (data['n_receivers'] == torch.tensor([[2]])).all() - assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() + assert ( + data['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() config = [atomic.Configuration( atomic_numbers=numbers, @@ -390,8 +366,6 @@ def test_from_configuration() -> None: pbc=[True] * 3, node_labels=node_labels, graph_labels=np.array([[i]]), - edge_senders=[0], - edge_receivers=[1, 2], ) for i in range(0, 10)] dataset = create_dataset_from_configurations( config, z_table, 0.1, show_progress=False diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py index e534b2cd..4765f45b 100644 --- a/mlcolvar/graph/data/neighborhood.py +++ b/mlcolvar/graph/data/neighborhood.py @@ -16,8 +16,8 @@ def get_neighborhood( pbc: Optional[Tuple[bool, bool, bool]] = None, cell: Optional[np.ndarray] = None, # [3, 3] true_self_interaction: Optional[bool] = False, - sender_indices: Optional[List[int]] = None, - receiver_indices: Optional[List[int]] = None, + system_indices: Optional[List[int]] = None, + environment_indices: Optional[List[int]] = None, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Get the neighbor list of a given set atoms. @@ -34,12 +34,10 @@ def get_neighborhood( The lattice vectors. true_self_interaction: bool If keep self-edges that don't cross periodic boundaries. - sender_indices: List[int] - Indices of senders. If given, only edges sent by these atoms will be - kept in the graph. - receiver_indices: List[int] - Indices of receiver. If given, only edges received by these atoms will - be kept in the graph. + system_indices: List[int] + Indices of the system atoms. + environment_indices: List[int] + Indices of the environment atoms. Returns ------- @@ -49,7 +47,23 @@ def get_neighborhood( The shift vectors (unit_shifts * cell_lengths). unit_shifts: numpy.ndarray (shape: [n_edges, 3]) The unit shift vectors (number of PBC croessed by the edges). + + Notes + ----- + Arguments `system_indices` and `environment_indices` must presnet at the + same time. When these arguments are given, only edges in the [subsystem] + formed by [the systems atoms] and [the environment atoms within the cutoff + radius of the systems atoms] will be kept. + Besides, these two lists could not contain common atoms. """ + + if system_indices is not None or environment_indices is not None: + assert system_indices is not None and environment_indices is not None + + system_indices = np.array(system_indices) + environment_indices = np.array(environment_indices) + assert np.intersect1d(system_indices, environment_indices).size == 0 + if pbc is None: pbc = (False, False, False) @@ -96,16 +110,16 @@ def get_neighborhood( receiver = receiver[keep_edge] unit_shifts = unit_shifts[keep_edge] - if sender_indices is not None: - sender_indices = np.array(sender_indices, dtype=int) - keep_edge = np.where(np.in1d(sender, sender_indices))[0] - sender = sender[keep_edge] - receiver = receiver[keep_edge] - unit_shifts = unit_shifts[keep_edge] - - if receiver_indices is not None: - receiver_indices = np.array(receiver_indices, dtype=int) - keep_edge = np.where(np.in1d(receiver, receiver_indices))[0] + if system_indices is not None: + # Get environment atoms that are neighbors of the system. + keep_edge = np.where(np.in1d(receiver, system_indices))[0] + keep_sender = np.intersect1d(sender[keep_edge], environment_indices) + keep_atom = np.concatenate((system_indices, np.unique(keep_sender))) + # Get the edges in the subsystem. + keep_sender = np.where(np.in1d(sender, keep_atom))[0] + keep_receiver = np.where(np.in1d(receiver, keep_atom))[0] + keep_edge = np.intersect1d(keep_sender, keep_receiver) + # Get the edges sender = sender[keep_edge] receiver = receiver[keep_edge] unit_shifts = unit_shifts[keep_edge] @@ -125,7 +139,7 @@ def test_get_neighborhood() -> None: positions = np.array( [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3]], dtype=float ) - cell = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]], dtype=float) + cell = np.array([[4, 0, 0], [0, 4, 0], [0, 0, 4]], dtype=float) n, s, u = get_neighborhood(positions, cutoff=5.0) assert ( @@ -135,53 +149,94 @@ def test_get_neighborhood() -> None: ) ).all() - n, s, u = get_neighborhood(positions, cutoff=5.0, receiver_indices=[0, 1]) - assert ( - n == np.array([[0, 1, 2, 2, 3], [1, 0, 0, 1, 1]], dtype=int) - ).all() - - n, s, u = get_neighborhood(positions, cutoff=5.0, sender_indices=[0, 1]) - assert ( - n == np.array([[0, 0, 1, 1, 1], [1, 2, 0, 2, 3]], dtype=int) - ).all() - n, s, u = get_neighborhood(positions, cutoff=2.0) assert ( n == np.array([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]], dtype=int) ).all() n, s, u = get_neighborhood( - positions, cutoff=1.0, pbc=[True] * 3, cell=cell + positions, cutoff=2.0, pbc=[True] * 3, cell=cell ) assert ( - n == np.array([[0, 1, 2, 3], [2, 3, 0, 1]], dtype=int) + n == np.array( + [[0, 0, 1, 1, 2, 2, 3, 3], [3, 1, 0, 2, 1, 3, 2, 0]], dtype=int + ) ).all() assert ( s == np.array( - [[-2, -2, -2], [-2, -2, -2], [2, 2, 2], [2, 2, 2]], + [ + [-4.0, -4.0, -4.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [4.0, 4.0, 4.0] + ], dtype=float ) ).all() assert ( u == np.array( - [[-1, -1, -1], [-1, -1, -1], [1, 1, 1], [1, 1, 1]], + [ + [-1, -1, -1], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [1, 1, 1] + ], dtype=int ) ).all() n, s, u = get_neighborhood( - positions, cutoff=1.0, pbc=[True] * 3, cell=cell, receiver_indices=[0] + positions, + cutoff=2.0, + pbc=[True] * 3, + cell=cell, + system_indices=[0, 1], + environment_indices=[2, 3] ) - assert (n == np.array([[2], [0]], dtype=int)).all() - assert (s == np.array([[2, 2, 2]], dtype=float)).all() - assert (u == np.array([[1, 1, 1]], dtype=int)).all() + assert ( + n == np.array( + [[0, 0, 1, 1, 2, 2, 3, 3], [3, 1, 0, 2, 1, 3, 2, 0]], dtype=int + ) + ).all() n, s, u = get_neighborhood( - positions, cutoff=1.0, pbc=[True] * 3, cell=cell, sender_indices=[0] + positions, + cutoff=2.0, + pbc=[True] * 3, + cell=cell, + system_indices=[0], + environment_indices=[1, 2, 3] ) - assert (n == np.array([[0], [2]], dtype=int)).all() - assert (s == np.array([[-2, -2, -2]], dtype=float)).all() - assert (u == np.array([[-1, -1, -1]], dtype=int)).all() + assert ( + n == np.array( + [[0, 0, 1, 3], [3, 1, 0, 0]], dtype=int + ) + ).all() + assert ( + s == np.array( + [ + [-4.0, -4.0, -4.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [4.0, 4.0, 4.0] + ], + dtype=float + ) + ).all() + assert ( + u == np.array( + [[-1, -1, -1], [0, 0, 0], [0, 0, 0], [1, 1, 1]], + dtype=int + ) + ).all() if __name__ == "__main__": diff --git a/mlcolvar/graph/explain/__init__.py b/mlcolvar/graph/explain/__init__.py index 60611d37..695a9784 100644 --- a/mlcolvar/graph/explain/__init__.py +++ b/mlcolvar/graph/explain/__init__.py @@ -2,4 +2,4 @@ Metaphysics. """ -from .sensitivity import graph_node_sensitivity, graph_node_sensitivity_mask +from .sensitivity import graph_node_sensitivity diff --git a/mlcolvar/graph/explain/sensitivity.py b/mlcolvar/graph/explain/sensitivity.py index fa9d93b8..fd41067d 100644 --- a/mlcolvar/graph/explain/sensitivity.py +++ b/mlcolvar/graph/explain/sensitivity.py @@ -1,135 +1,16 @@ -import copy -import torch import numpy as np -from typing import List, Dict +from typing import Dict from mlcolvar.graph import cvs as gcvs from mlcolvar.graph import data as gdata -from mlcolvar.graph import utils as gutils -from .utils import get_dataset_cv_values, get_dataset_cv_gradients +from .utils import get_dataset_cv_gradients """ Sensitivity analysis. """ -__all__ = ['graph_node_sensitivity', 'graph_node_sensitivity_mask'] - - -def graph_node_sensitivity_mask( - model: gcvs.GraphBaseCV, - dataset: gdata.GraphDataSet, - node_indices: List[int] = None, - normalizing_method: str = 'std', - device: str = 'cpu', - batch_size: int = None, - show_progress: bool = True -) -> Dict[str, np.ndarray]: - """ - Perform a sensitivity analysis by masking nodes. This allows us to measure - which atom is most important to the CV. - - Parameters - ---------- - model: mlcolvar.graph.cvs.GraphBaseCV - Collective variable model. - dataset: mlcovar.graph.data.GraphDataSet - Dataset on which to compute the sensitivity analysis. - node_indices: List[int] - Indices of the nodes to analysis. - normalizing_method: str - Method of calculating the normalizing constant of the sensitivit. - - `None`: do not perform the normalization. - - `std`: normalize with four times of the standard deviation of the CV. - - `range`: normalize with the range of the CV. - device: str - Name of the device. - batch_size: - Batch size used for evaluating the CV. - show_progress: bool - If show the progress bar. - - Returns - ------- - results: dictionary - Results of the sensitivity analysis, containing 'node_indices', - 'sensitivities', and 'sensitivities_components', ordered according to - the node indices. - - See also - -------- - mlcolvar.utils.explain.sensitivity_analysis - Perform the sensitivity analysis of a feedforward model. - """ - if node_indices is None: - n_nodes = [len(d.positions) for d in dataset] - node_indices = list(range(max(n_nodes))) - else: - node_indices = [i for i in node_indices] - n_nodes = len(node_indices) - - if show_progress: - items = gutils.progress.pbar( - node_indices, frequency=0.0001, prefix='Sensitivity' - ) - else: - items = node_indices - - sensitivities = [] - sensitivities_components = [] - dataset_clone = copy.deepcopy(dataset) - model = model.to(device) - - cv_org = get_dataset_cv_values( - model, dataset, batch_size, show_progress, 'Getting base data' - ) - if normalizing_method is None: - normalizing_constant = 1 - elif normalizing_method == 'std': - normalizing_constant = cv_org.std(axis=0) * 4 - elif normalizing_method == 'range': - normalizing_constant = cv_org.max(axis=0) - cv_org.min(axis=0) - else: - raise KeyError( - 'Value of the `normalizing_method` parameter should be ' - + '`std` or `range`!' - ) - normalizing_constant = abs(normalizing_constant) - - for node in items: - - for j in range(len(dataset_clone)): - mask = dataset[j]['edge_index'] != node - mask = mask[0] & mask[1] - shifts_masked = dataset[j]['shifts'][mask.transpose(-1, 0)] - edge_index_masked = dataset[j]['edge_index'][:, mask] - dataset_clone[j]['shifts'] = shifts_masked - dataset_clone[j]['edge_index'] = edge_index_masked - n_nodes = len(dataset_clone[j]['positions']) - node_masks = torch.ones((n_nodes, 1), dtype=torch.bool) - node_masks[node, 0] = False - dataset_clone[j]['receiver_masks'] = node_masks - dataset_clone[j]['n_receivers'][0, 0] = n_nodes - 1 - - cv_masked = get_dataset_cv_values( - model, dataset_clone, batch_size, False, - ) - - delta = cv_org - cv_masked - if normalizing_method is not None: - delta = delta / normalizing_constant - - sensitivities_components.append(np.abs(delta)) - sensitivities.append(np.mean(np.abs(delta), axis=0)) - - results = {} - results['node_indices'] = node_indices - results['sensitivities'] = np.array(sensitivities) - results['sensitivities_components'] = np.array( - sensitivities_components - ).transpose(2, 1, 0) - - return results +__all__ = ['graph_node_sensitivity'] def graph_node_sensitivity( diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 71d40ccc..83eb7497 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -20,8 +20,7 @@ def create_dataset_from_trajectories( folder: str = None, create_labels: bool = None, system_selection: str = None, - edge_sender_selection: str = None, - edge_receiver_selection: str = None, + environment_selection: str = None, return_trajectories: bool = False, remove_isolated_nodes: bool = True, show_progress: bool = True @@ -53,15 +52,13 @@ def create_dataset_from_trajectories( Assign a label to each file, default True if more than one set of files is given, otherwise False. system_selection: str - MDTraj style atom selections [1] of the edge senders. If given, only + MDTraj style atom selections [1] of the system atoms. If given, only selected atoms will be loaded from the trajectories. This option may increase the speed of building graphs. - edge_sender_selection: str - MDTraj style atom selections [1] of the edge senders. If given, only - edges sent by these atoms will be kept in the graph. - edge_receiver_selection: str - MDTraj style atom selections [1] of the edge receivers. If given, only - edges received by these atoms will be kept in the graph. + environment_selection: str + MDTraj style atom selections [1] of the environment atoms. If given, + only the system atoms and [the environment atoms within the cutoff + radius of the system atoms] will be kept in the graph. return_trajectories: bool If also return the loaded trajectory objects. remove_isolated_nodes: bool @@ -78,15 +75,32 @@ def create_dataset_from_trajectories( Notes ----- - The selections `edge_sender_selection` and `edge_receiver_selection` - will be made after the `system_selection` was made. Thus, absolute index - based edge selections may lead to unwanted results when `system_selection` - is given. + The login behind this method is like the follows: + 1. If only `system_selection` is given, the method will only load atoms + selected by this selection, from the trajectories. + 2. If both `system_selection` and `environment_selection` are given, + the method will load the atoms select by both selections, but will + build graphs using [the system atoms] and [the environment atoms within + the cutoff radius of the system atoms]. References ---------- .. [1] https://www.mdtraj.org/1.9.8.dev0/atom_selection.html """ + + if environment_selection is not None: + assert system_selection is not None, ( + 'the `environment_selection` argument requires the' + + '`system_selection` argument to be defined!' + ) + selection = '({:s}) or ({:s})'.format( + system_selection, environment_selection + ) + elif system_selection is not None: + selection = system_selection + else: + selection = None + # fmt: off assert type(trajectories) is type(top), ( 'The `trajectories` and `top` parameters should have the same type!' @@ -133,12 +147,12 @@ def create_dataset_from_trajectories( ] for t in traj: t.top = md.core.trajectory.load_topology(top[i][j]) - if system_selection is not None: + if selection is not None: for j in range(len(traj)): - subset = traj[j].top.select(system_selection) + subset = traj[j].top.select(selection) assert len(subset) > 0, ( - 'No atoms will be selected with system selection ' - + '"{:s}"!'.format(system_selection) + 'No atoms will be selected with selection string ' + + '"{:s}"!'.format(selection) ) traj[j] = traj[j].atom_slice(subset) trajectories_in_memory.append(traj) @@ -146,11 +160,11 @@ def create_dataset_from_trajectories( else: traj = md.load(trajectories[i], top=top[i]) traj.top = md.core.trajectory.load_topology(top[i]) - if system_selection is not None: - subset = traj.top.select(system_selection) + if selection is not None: + subset = traj.top.select(selection) assert len(subset) > 0, ( - 'No atoms will be selected with system selection ' - + '"{:s}"!'.format(system_selection) + 'No atoms will be selected with selection string ' + + '"{:s}"!'.format(selection) ) traj = traj.atom_slice(subset) trajectories_in_memory.append(traj) @@ -166,16 +180,16 @@ def create_dataset_from_trajectories( configuration = _configures_from_trajectory( trajectories_in_memory[i][j], i, # NOTE: all these configurations have a label `i` - edge_sender_selection, - edge_receiver_selection, + system_selection, + environment_selection, ) configurations.extend(configuration) else: configuration = _configures_from_trajectory( trajectories_in_memory[i], i, - edge_sender_selection, - edge_receiver_selection, + system_selection, + environment_selection, ) configurations.extend(configuration) @@ -211,8 +225,8 @@ def _z_table_from_top( def _configures_from_trajectory( trajectory: md.Trajectory, label: int = None, - edge_sender_selection: str = None, - edge_receiver_selection: str = None, + system_selection: str = None, + environment_selection: str = None, ) -> gdata.atomic.Configurations: """ Create configurations from one trajectory. @@ -223,32 +237,32 @@ def _configures_from_trajectory( The MDTraj Trajectory object. label: int The graph label. - edge_sender_selection: str - MDTraj style atom selections [1] of the edge senders. If given, only - edges sent by these atoms will be kept in the graph. - edge_receiver_selection: str - MDTraj style atom selections [1] of the edge receivers. If given, only - edges received by these atoms will be kept in the graph. + system_selection: str + MDTraj style atom selections of the system atoms. If given, only + selected atoms will be loaded from the trajectories. This option may + increase the speed of building graphs. + environment_selection: str + MDTraj style atom selections of the environment atoms. If given, + only the system atoms and [the environment atoms within the cutoff + radius of the system atoms] will be kept in the graph. """ if label is not None: label = np.array([[label]]) - if edge_sender_selection is not None: - edge_senders = trajectory.top.select(edge_sender_selection) - assert len(edge_senders) > 0, ( - 'No atoms will be selected with edge_sender_selection selection ' - + '"{:s}"!'.format(edge_sender_selection) + if system_selection is not None and environment_selection is not None: + system_atoms = trajectory.top.select(system_selection) + assert len(system_selection) > 0, ( + 'No atoms will be selected with `system_selection`: ' + + '"{:s}"!'.format(system_selection) ) - else: - edge_senders = None - if edge_receiver_selection is not None: - edge_receivers = trajectory.top.select(edge_receiver_selection) - assert len(edge_receivers) > 0, ( - 'No atoms will be selected with edge_receiver_selection selection ' - + '"{:s}"!'.format(edge_receiver_selection) + environment_atoms = trajectory.top.select(environment_selection) + assert len(environment_atoms) > 0, ( + 'No atoms will be selected with `environment_selection`: ' + + '"{:s}"!'.format(environment_selection) ) else: - edge_receivers = None + system_atoms = None + environment_atoms = None atomic_numbers = [a.element.number for a in trajectory.top.atoms] if trajectory.unitcell_vectors is not None: @@ -267,8 +281,8 @@ def _configures_from_trajectory( pbc=pbc, graph_labels=label, node_labels=None, # TODO: Add supports for per-node labels. - edge_senders=edge_senders, - edge_receivers=edge_receivers, + system=system_atoms, + environment=environment_atoms ) configurations.append(configuration) @@ -351,81 +365,65 @@ def check_data_1(data) -> None: [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] ]) ).all() - assert (data['n_receivers'] == torch.tensor([[3]])).all() for i in range(6): check_data_1(dataset[i]) - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection=system_selection, - edge_sender_selection='element O', - show_progress=False - ) - - def check_data_2(data) -> None: - assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() - assert ( - data['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - # NOTE: This is a very bad testing case: as you can see, the oxygen - # is one of the receivers, however no edge is pointing to it. - # This is the risk when you have too few edge senders. - assert (data['n_receivers'] == torch.tensor([[3]])).all() - - for i in range(6): - check_data_2(dataset[i]) - - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection=system_selection, - edge_receiver_selection='element H', - show_progress=False - ) - - def check_data_3(data) -> None: - assert ( - data['edge_index'] == torch.tensor([[0, 0, 1, 2], [2, 1, 2, 1]]) - ).all() - assert ( - data['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - assert (data['n_receivers'] == torch.tensor([[2]])).all() - assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() + if system_selection is not None: - for i in range(6): - check_data_3(dataset[i]) + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + system_selection='type O and {:s}'.format(system_selection), + environment_selection='type H and {:s}'.format(system_selection), + show_progress=False + ) - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection=system_selection, - edge_sender_selection='element O', - edge_receiver_selection='element H', - show_progress=False - ) + for i in range(6): + check_data_1(dataset[i]) - def check_data_4(data) -> None: - assert (data['edge_index'] == torch.tensor([[0, 0], [2, 1]])).all() - assert ( - data['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - assert (data['n_receivers'] == torch.tensor([[2]])).all() - assert (data['receiver_masks'] == torch.tensor([[0], [1], [1]])).all() + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + system_selection='name H1 and {:s}'.format(system_selection), + environment_selection='name H2 and {:s}'.format(system_selection), + show_progress=False + ) - for i in range(6): - check_data_4(dataset[i]) + def check_data_2(data) -> None: + assert (data['edge_index'] == torch.tensor([[0, 1], [1, 0]])).all() + assert ( + data['shifts'] == torch.tensor([ + [0.0, 2.0, 0.0], [0.0, -2.0, 0.0] + ]) + ).all() + assert ( + data['unit_shifts'] == torch.tensor([ + [0.0, 1.0, 0.0], [0.0, -1.0, 0.0], + ]) + ).all() + assert ( + data['positions'] == torch.tensor([ + [0.7, 0.7, 0.0], [0.7, -0.7, 0.0], + ]) + ).all() + assert ( + data['cell'] == torch.tensor([ + [2.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, 0.0, 2.0], + ]) + ).all() + assert ( + data['node_attrs'] == torch.tensor([ + [1.0], [1.0] + ]) + ).all() + + for i in range(6): + check_data_2(dataset[i]) __import__('os').remove('test_dataset.pdb') @@ -461,7 +459,7 @@ def check_data_4(data) -> None: ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H END """ - test_create_dataset_from_trajectories(text, "not resname XXXX") + test_create_dataset_from_trajectories(text, 'not resname XXXX') text = """ CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 @@ -480,4 +478,4 @@ def check_data_4(data) -> None: ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H END """ - test_create_dataset_from_trajectories(text, "not resname XXXX") + test_create_dataset_from_trajectories(text, 'not resname XXXX') From 93e9ca48e0c6621147f4269228e4a8a755323975 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sat, 24 Aug 2024 15:40:32 +0200 Subject: [PATCH 111/267] Fixed typo. --- mlcolvar/graph/utils/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 83eb7497..3a11355f 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -251,7 +251,7 @@ def _configures_from_trajectory( if system_selection is not None and environment_selection is not None: system_atoms = trajectory.top.select(system_selection) - assert len(system_selection) > 0, ( + assert len(system_atoms) > 0, ( 'No atoms will be selected with `system_selection`: ' + '"{:s}"!'.format(system_selection) ) From f79bdeb4116307bbd849f66f6dc4145525f383d4 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Sat, 24 Aug 2024 19:40:08 +0200 Subject: [PATCH 112/267] Added a method to dump dataset. --- mlcolvar/graph/data/__init__.py | 1 + mlcolvar/graph/data/atomic.py | 12 ++++++++++++ mlcolvar/graph/data/dataset.py | 33 +++++++++++++++++++++++++++++++++ 3 files changed, 46 insertions(+) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/graph/data/__init__.py index b7c536e3..c6e95ad8 100644 --- a/mlcolvar/graph/data/__init__.py +++ b/mlcolvar/graph/data/__init__.py @@ -4,6 +4,7 @@ GraphDataSet, create_dataset_from_configurations, save_dataset, + save_dataset_as_exyz, load_dataset ) from .datamodule import GraphDataModule, GraphCombinedDataModule diff --git a/mlcolvar/graph/data/atomic.py b/mlcolvar/graph/data/atomic.py index 662b97e3..96bdcb06 100644 --- a/mlcolvar/graph/data/atomic.py +++ b/mlcolvar/graph/data/atomic.py @@ -1,4 +1,5 @@ import numpy as np +import mdtraj as md from dataclasses import dataclass from typing import List, Iterable, Optional @@ -45,6 +46,17 @@ def index_to_z(self, index: int) -> int: """ return self.zs[index] + def index_to_symbol(self, index: int) -> str: + """ + Map the encoding to the atomic symbol. + + Parameters + ---------- + index: int + The encoding. + """ + return md.element.Element.getByAtomicNumber(self.zs[index]).symbol + def z_to_index(self, atomic_number: int) -> int: """ Map an atomic number to the encoding. diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 63f44c50..4d9803a0 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -17,6 +17,7 @@ 'GraphDataSet', 'create_dataset_from_configurations', 'save_dataset', + 'save_dataset_as_exyz', 'load_dataset' ] @@ -251,6 +252,38 @@ def load_dataset(file_name: str) -> GraphDataSet: return dataset +def save_dataset_as_exyz(dataset: GraphDataSet, file_name: str) -> None: + """ + Save a dataset to disk in the extxyz format. + + Parameters + ---------- + dataset: GraphDataSet + The dataset. + file_name: str + The filename. + """ + z_table = atomic.AtomicNumberTable.from_zs(dataset.atomic_numbers) + + fp = open(file_name, 'w') + + for d in dataset: + print(len(d['positions']), file=fp) + line = ( + 'Lattice="{:s}" '.format((r'{:.5f} ' * 9).strip()) + + 'Properties=species:S:1:pos:R:3 pbc="T T T"' + ) + cell = [c.item() for c in d['cell'].flatten()] + print(line.format(*cell), file=fp) + for i in range(0, len(d['positions'])): + s = z_table.index_to_symbol(np.where(d['node_attrs'][i])[0][0]) + print('{:2s}'.format(s), file=fp, end=' ') + positions = [p.item() for p in d['positions'][i]] + print('{:10.5f} {:10.5f} {:10.5f}'.format(*positions), file=fp) + + fp.close() + + def test_from_configuration() -> None: numbers = [8, 1, 1] positions = np.array( From 58853c30f391e91cb1ea04601a61dacd44347dda Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 26 Aug 2024 02:43:15 +0200 Subject: [PATCH 113/267] Made the dataset function more robust. --- mlcolvar/graph/data/dataset.py | 128 +++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index 4d9803a0..d24551c8 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -123,6 +123,10 @@ def _create_dataset_from_configuration( assert config.graph_labels is None or len(config.graph_labels.shape) == 2 + # NOTE: here we do not take care about the nodes that are not taking part + # the graph, like, we don't even change the node indices in `edge_index`. + # Here we simply ignore them, and rely on the `RemoveIsolatedNodes` method + # that will be called later (in `create_dataset_from_configurations`). edge_index, shifts, unit_shifts = get_neighborhood( positions=config.positions, cutoff=cutoff, @@ -212,8 +216,18 @@ def create_dataset_from_configurations( ] if remove_isolated_nodes: + # TODO: not the worst way to fake the `is_node_attr` method of + # `tg.data.storage.GlobalStorage` ... + # I mean, when there are exact three atoms in the graph, the + # `RemoveIsolatedNodes` method will remove the cell vectors that + # correspond to the isolated node ... This is a consequence of that + # pyg regarding the cell vectors as some kind of node features. + # So here we first remove the isolated nodes, then set the cell back. + cell_list = [d.cell.clone() for d in data_list] transform = tg.transforms.remove_isolated_nodes.RemoveIsolatedNodes() data_list = [transform(d) for d in data_list] + for i in range(len(data_list)): + data_list[i].cell = cell_list[i] dataset = GraphDataSet(data_list, z_table.zs, cutoff) @@ -416,5 +430,119 @@ def test_from_configuration() -> None: assert (dataset_1[1]['graph_labels'] == torch.tensor([[9.0]])).all() +def test_from_configurations() -> None: + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + data = create_dataset_from_configurations( + [config], z_table, 0.1, remove_isolated_nodes=True, show_progress=False + )[0] + assert ( + data['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + assert ( + data['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + ]) + ).all() + assert ( + data['positions'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0], + ]) + ).all() + assert ( + data['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + assert ( + data['node_attrs'] == torch.tensor([ + [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() + assert (data['graph_labels'] == torch.tensor([[1.0]])).all() + assert data['weight'] == 1.0 + + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + system=[1], + environment=[2] + ) + data = create_dataset_from_configurations( + [config], z_table, 0.1, remove_isolated_nodes=True, show_progress=False + )[0] + assert ( + data['positions'] == torch.tensor([ + [0.07, 0.07, 0.0], [0.07, -0.07, 0.0], + ]) + ).all() + assert ( + data['cell'] == torch.tensor([ + [0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2], + ]) + ).all() + assert ( + data['node_attrs'] == torch.tensor([ + [1.0, 0.0], [1.0, 0.0] + ]) + ).all() + assert ( + data['edge_index'] == torch.tensor([[0, 1], [1, 0]]) + ).all() + assert ( + data['shifts'] == torch.tensor([[0.0, 0.2, 0.0], [0.0, -0.2, 0.0]]) + ).all() + assert ( + data['unit_shifts'] == torch.tensor( + [[0.0, 1.0, 0.0], [0.0, -1.0, 0.0]] + ) + ).all() + + if __name__ == '__main__': test_from_configuration() + test_from_configurations() From 1bb36d8bbabd72895a765e9920f2201c03cbefd8 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:12:36 +0200 Subject: [PATCH 114/267] Added buffer region support. --- mlcolvar/graph/data/dataset.py | 58 ++++++++++++++++++++++++++++- mlcolvar/graph/data/neighborhood.py | 14 +++++-- mlcolvar/graph/utils/io.py | 15 +++++++- 3 files changed, 82 insertions(+), 5 deletions(-) diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index d24551c8..fb1ad33d 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -107,6 +107,7 @@ def _create_dataset_from_configuration( config: atomic.Configuration, z_table: atomic.AtomicNumberTable, cutoff: float, + buffer: float = 0.0 ) -> tg.data.Data: """ Build the graph data object from a configuration. @@ -119,6 +120,8 @@ def _create_dataset_from_configuration( The atomic number table used to build the node attributes. cutoff: float The graph cutoff radius. + buffer: float + Buffer size used in finding active environment atoms. """ assert config.graph_labels is None or len(config.graph_labels.shape) == 2 @@ -134,6 +137,7 @@ def _create_dataset_from_configuration( pbc=config.pbc, system_indices=config.system, environment_indices=config.environment, + buffer=buffer ) edge_index = torch.tensor(edge_index, dtype=torch.long) shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) @@ -187,6 +191,7 @@ def create_dataset_from_configurations( config: atomic.Configurations, z_table: atomic.AtomicNumberTable, cutoff: float, + buffer: float = 0.0, remove_isolated_nodes: bool = False, show_progress: bool = True ) -> GraphDataSet: @@ -201,6 +206,8 @@ def create_dataset_from_configurations( The atomic number table used to build the node attributes. cutoff: float The graph cutoff radius. + buffer: float + Buffer size used in finding active environment atoms. remove_isolated_nodes: bool If remove isolated nodes from the dataset. show_progress: bool @@ -212,7 +219,9 @@ def create_dataset_from_configurations( items = config data_list = [ - _create_dataset_from_configuration(c, z_table, cutoff) for c in items + _create_dataset_from_configuration( + c, z_table, cutoff, buffer + ) for c in items ] if remove_isolated_nodes: @@ -406,6 +415,53 @@ def test_from_configuration() -> None: ) ).all() + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.08, 0.0]], + dtype=float + ) + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + system=[0], + environment=[1, 2] + ) + data = _create_dataset_from_configuration(config, z_table, 0.1) + assert ( + data['edge_index'] == torch.tensor([[0, 1], [1, 0]]) + ).all() + data = _create_dataset_from_configuration(config, z_table, 0.11) + assert ( + data['edge_index'] == torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + ).all() + data = _create_dataset_from_configuration(config, z_table, 0.1, 0.01) + assert ( + data['edge_index'] == torch.tensor( + [[0, 1, 1, 2], [1, 0, 2, 1]] + ) + ).all() + assert ( + data['shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0] + ]) + ).all() + assert ( + data['unit_shifts'] == torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0] + ]) + ).all() + config = [atomic.Configuration( atomic_numbers=numbers, positions=positions, diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/graph/data/neighborhood.py index 4765f45b..077b88df 100644 --- a/mlcolvar/graph/data/neighborhood.py +++ b/mlcolvar/graph/data/neighborhood.py @@ -18,6 +18,7 @@ def get_neighborhood( true_self_interaction: Optional[bool] = False, system_indices: Optional[List[int]] = None, environment_indices: Optional[List[int]] = None, + buffer: float = 0.0 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Get the neighbor list of a given set atoms. @@ -38,6 +39,8 @@ def get_neighborhood( Indices of the system atoms. environment_indices: List[int] Indices of the environment atoms. + buffer: float + Buffer size used in finding active environment atoms. Returns ------- @@ -63,6 +66,8 @@ def get_neighborhood( system_indices = np.array(system_indices) environment_indices = np.array(environment_indices) assert np.intersect1d(system_indices, environment_indices).size == 0 + else: + assert buffer == 0.0 if pbc is None: pbc = (False, False, False) @@ -88,12 +93,12 @@ def get_neighborhood( if not pbc_z: cell[:, 2] = max_positions * 5 * cutoff * identity[:, 2] - sender, receiver, unit_shifts = neighbour_list( - quantities='ijS', + sender, receiver, unit_shifts, distances = neighbour_list( + quantities='ijSd', pbc=pbc, cell=cell, positions=positions, - cutoff=float(cutoff), + cutoff=float(cutoff + buffer), # self_interaction=True, # we want edges from atom to itself in different periodic images # use_scaled_positions=False, # positions are not scaled positions ) @@ -109,6 +114,7 @@ def get_neighborhood( sender = sender[keep_edge] receiver = receiver[keep_edge] unit_shifts = unit_shifts[keep_edge] + distances = distances[keep_edge] if system_indices is not None: # Get environment atoms that are neighbors of the system. @@ -119,6 +125,8 @@ def get_neighborhood( keep_sender = np.where(np.in1d(sender, keep_atom))[0] keep_receiver = np.where(np.in1d(receiver, keep_atom))[0] keep_edge = np.intersect1d(keep_sender, keep_receiver) + keep_edge_distance = np.where(distances <= cutoff)[0] + keep_edge = np.intersect1d(keep_edge, keep_edge_distance) # Get the edges sender = sender[keep_edge] receiver = receiver[keep_edge] diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 3a11355f..0d5adb5f 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -16,6 +16,7 @@ def create_dataset_from_trajectories( trajectories: Union[List[List[str]], List[str], str], top: Union[List[List[str]], List[str], str], cutoff: float, + buffer: float = 0.0, z_table: gdata.atomic.AtomicNumberTable = None, folder: str = None, create_labels: bool = None, @@ -42,6 +43,8 @@ def create_dataset_from_trajectories( Names of topology files. cutoff: float (units: Ang) The graph cutoff radius. + buffer: float + Buffer size used in finding active environment atoms. z_table: mlcolvar.graph.data.atomic.AtomicNumberTable The atomic number table used to build the node attributes. If not given, it will be created from the given trajectories. @@ -101,6 +104,11 @@ def create_dataset_from_trajectories( else: selection = None + if environment_selection is None: + assert buffer == 0, ( + 'Not `environment_selection` given! Cannot define buffer size!' + ) + # fmt: off assert type(trajectories) is type(top), ( 'The `trajectories` and `top` parameters should have the same type!' @@ -194,7 +202,12 @@ def create_dataset_from_trajectories( configurations.extend(configuration) dataset = gdata.create_dataset_from_configurations( - configurations, z_table, cutoff, remove_isolated_nodes, show_progress + configurations, + z_table, + cutoff, + buffer, + remove_isolated_nodes, + show_progress ) if return_trajectories: From 3e9b42ce1019de608984fbf57e360f8d1b45e7a7 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 2 Sep 2024 23:09:59 +0200 Subject: [PATCH 115/267] Changed the default aggr operation. --- mlcolvar/graph/core/nn/schnet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/core/nn/schnet.py b/mlcolvar/graph/core/nn/schnet.py index 128049a6..afdc0e2f 100644 --- a/mlcolvar/graph/core/nn/schnet.py +++ b/mlcolvar/graph/core/nn/schnet.py @@ -64,7 +64,7 @@ def __init__( network: nn.Sequential, cutoff: float, ) -> None: - super().__init__(aggr='add') + super().__init__(aggr='mean') self.lin1 = nn.Linear(in_channels, num_filters, bias=False) self.lin2 = nn.Linear(num_filters, out_channels) self.network = network From 205f5fe1b64ef197fd0156d9002d98e944517d5a Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 3 Sep 2024 22:32:08 +0200 Subject: [PATCH 116/267] Only use system node features as CV values. --- mlcolvar/graph/core/nn/models.py | 16 +++++++++++++--- mlcolvar/graph/data/dataset.py | 17 +++++++++++++++++ 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 71e33872..9a790b59 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -5,10 +5,10 @@ from typing import List, Dict, Tuple from mlcolvar.graph import data as gdata -from mlcolvar.graph.utils import torch_tools from mlcolvar.graph.core.nn import radial from mlcolvar.graph.core.nn import schnet from mlcolvar.graph.core.nn import gvp_layer +from mlcolvar.graph.utils import torch_tools """ GNN models. @@ -244,7 +244,12 @@ def forward( out = h_V[0] if scatter_mean: - out = torch_tools.scatter_mean(out, batch_id, dim=0) + if 'system_masks' not in data.keys(): + out = torch_tools.scatter_mean(out, batch_id, dim=0) + else: + out = out * data['system_masks'] + out = torch_tools.scatter_sum(out, batch_id, dim=0) + out = out / data['n_system'] return out @@ -357,7 +362,12 @@ def forward( out = h_V if scatter_mean: - out = torch_tools.scatter_mean(out, batch_id, dim=0) + if 'system_masks' not in data.keys(): + out = torch_tools.scatter_mean(out, batch_id, dim=0) + else: + out = out * data['system_masks'] + out = torch_tools.scatter_sum(out, batch_id, dim=0) + out = out / data['n_system'] return out diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/graph/data/dataset.py index fb1ad33d..8daaa9aa 100644 --- a/mlcolvar/graph/data/dataset.py +++ b/mlcolvar/graph/data/dataset.py @@ -174,6 +174,21 @@ def _create_dataset_from_configuration( else 1 ) + n_system = ( + torch.tensor( + [[len(config.system)]], dtype=torch.get_default_dtype() + ) if config.system is not None + else torch.tensor( + [[one_hot.shape[0]]], dtype=torch.get_default_dtype() + ) + ) + + if config.system is not None: + system_masks = torch.zeros((one_hot.shape[0], 1), dtype=torch.bool) + system_masks[config.system, 0] = 1 + else: + system_masks = None + return tg.data.Data( edge_index=edge_index, shifts=shifts, @@ -183,6 +198,8 @@ def _create_dataset_from_configuration( node_attrs=one_hot, node_labels=node_labels, graph_labels=graph_labels, + n_system=n_system, + system_masks=system_masks, weight=weight, ) From c6aa16cfdbc831a4c4e3ee4e07512e8348c2bdb1 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 3 Sep 2024 22:43:59 +0200 Subject: [PATCH 117/267] Fixed tests. --- mlcolvar/graph/core/nn/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 9a790b59..3bfe828d 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -467,7 +467,7 @@ def test_schnet() -> None: assert ( torch.abs( model(data) - - torch.tensor([[0.409843614955313, -0.12684721733013735]] * 6) + torch.tensor([[0.40384621527953063, -0.1257513365138969]] * 6) ) < 1E-12 ).all() From 21d807a6cbce9229b85391427b632a2d259bf808 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Thu, 12 Sep 2024 14:23:12 +0200 Subject: [PATCH 118/267] Do not compile `torch_scatter`. --- mlcolvar/graph/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py index 44f37b44..1e9f9239 100644 --- a/mlcolvar/graph/__init__.py +++ b/mlcolvar/graph/__init__.py @@ -7,3 +7,5 @@ from . import explain utils.torch_tools.set_default_dtype('float32') +# torch_scatter will compile problems. +__import__('torch_geometric').typing.WITH_TORCH_SCATTER = False From c1b9418d1571b8d7ecf10ba2ab8d7a47cc72e13a Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 16 Sep 2024 20:23:48 +0200 Subject: [PATCH 119/267] Changed the behavior of `create_label`. --- mlcolvar/graph/utils/io.py | 27 ++++++++++++++++++++++----- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py index 0d5adb5f..c0b5116b 100644 --- a/mlcolvar/graph/utils/io.py +++ b/mlcolvar/graph/utils/io.py @@ -19,7 +19,7 @@ def create_dataset_from_trajectories( buffer: float = 0.0, z_table: gdata.atomic.AtomicNumberTable = None, folder: str = None, - create_labels: bool = None, + create_labels: bool = True, system_selection: str = None, environment_selection: str = None, return_trajectories: bool = False, @@ -52,8 +52,8 @@ def create_dataset_from_trajectories( Common path for the files to be imported. If set, filenames become `folder/file_name`. create_labels: bool - Assign a label to each file, default True if more than one set of files - is given, otherwise False. + Assign a label to each file according to the total number of files. + If False, labels of all files will be `-1`. system_selection: str MDTraj style atom selections [1] of the system atoms. If given, only selected atoms will be loaded from the trajectories. This option may @@ -187,7 +187,7 @@ def create_dataset_from_trajectories( for j in range(len(trajectories_in_memory[i])): configuration = _configures_from_trajectory( trajectories_in_memory[i][j], - i, # NOTE: all these configurations have a label `i` + i if create_labels else -1, # NOTE: all these configurations have a label `i` system_selection, environment_selection, ) @@ -195,7 +195,7 @@ def create_dataset_from_trajectories( else: configuration = _configures_from_trajectory( trajectories_in_memory[i], - i, + i if create_labels else -1, system_selection, environment_selection, ) @@ -333,6 +333,23 @@ def test_create_dataset_from_trajectories( assert dataset[4]['graph_labels'] == torch.tensor([[1.0]]) assert dataset[5]['graph_labels'] == torch.tensor([[1.0]]) + dataset, trajectories = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + create_labels=False, + system_selection=system_selection, + return_trajectories=True, + show_progress=False + ) + + assert dataset[0]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[1]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[2]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[3]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[4]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[5]['graph_labels'] == torch.tensor([[-1.0]]) + def check_data_1(data) -> None: assert ( data['edge_index'] == torch.tensor( From b5271075465b0076760c30acd2e80e75356a642e Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 16 Sep 2024 22:53:03 +0200 Subject: [PATCH 120/267] Added a mass function. --- mlcolvar/graph/data/atomic.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/mlcolvar/graph/data/atomic.py b/mlcolvar/graph/data/atomic.py index 96bdcb06..8fe5a573 100644 --- a/mlcolvar/graph/data/atomic.py +++ b/mlcolvar/graph/data/atomic.py @@ -1,3 +1,4 @@ +import warnings import numpy as np import mdtraj as md from dataclasses import dataclass @@ -25,6 +26,15 @@ class AtomicNumberTable: def __init__(self, zs: List[int]) -> None: self.zs = zs + self.masses = [1.0] * len(zs) + for i in range(len(zs)): + try: + m = md.element.Element.getByAtomicNumber(zs[i]).mass + self.masses[i] = m + except Exception: + warnings.warn( + 'Can not assign mass for atom number: {:d}'.format(zs[i]) + ) def __len__(self) -> int: """ @@ -96,6 +106,18 @@ def from_zs(cls, atomic_numbers: Iterable[int]) -> 'AtomicNumberTable': return cls(sorted(list(z_set))) +def get_masses(atomic_numbers: Iterable[int]) -> List[float]: + """ + Get atomic masses from atomic numbers. + + Parameters + ---------- + atomic_numbers: Iterable[int] + The atomic numbers. + """ + return AtomicNumberTable.from_zs(atomic_numbers).masses.copy() + + @dataclass class Configuration: """ From a2c4a7ac0a54b468c48b20fe871fb0d6c9cd3fc6 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 17:38:30 +0100 Subject: [PATCH 121/267] Create graph module in nn --- mlcolvar/core/nn/graph/gnn.py | 178 ++++++++++++++ .../core/nn => core/nn/graph}/radial.py | 0 mlcolvar/core/nn/graph/schnet.py | 225 ++++++++++++++++++ 3 files changed, 403 insertions(+) create mode 100644 mlcolvar/core/nn/graph/gnn.py rename mlcolvar/{graph/core/nn => core/nn/graph}/radial.py (100%) create mode 100644 mlcolvar/core/nn/graph/schnet.py diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py new file mode 100644 index 00000000..0f7d4713 --- /dev/null +++ b/mlcolvar/core/nn/graph/gnn.py @@ -0,0 +1,178 @@ +import torch +from torch import nn +import numpy as np +import torch_geometric as tg +from typing import List, Dict, Tuple + +# from mlcolvar.graph import data as gdata +from mlcolvar.core.nn.graph import radial +# from mlcolvar.graph.core.nn import schnet +# from mlcolvar.graph.core.nn import gvp_layer +# from mlcolvar.graph.utils import torch_tools + +""" +GNN models. +""" + +__all__ = ['BaseGNN'] + + +class BaseGNN(nn.Module): + """ + The commen GNN interface for mlcolvar. + Parameters + ---------- + n_out: int + Size of the output node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_bases: int + Size of the basis set. + n_polynomials: bool + Order of the polynomials in the basis functions. + basis_type: str + Type of the basis function. + """ + + def __init__( + self, + n_out: int, + cutoff: float, + atomic_numbers: List[int], + n_bases: int = 6, + n_polynomials: int = 6, + basis_type: str = 'bessel' + ) -> None: + super().__init__() + self._model_type = 'gnn' + + self._n_out = n_out + self._radial_embedding = radial.RadialEmbeddingBlock( + cutoff, n_bases, n_polynomials, basis_type + ) + self.register_buffer( + 'n_out', torch.tensor(n_out, dtype=torch.int64) + ) + self.register_buffer( + 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) + ) + self.register_buffer( + 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) + ) + + def embed_edge( + self, data: Dict[str, torch.Tensor], normalize: bool = True + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Perform the edge embedding. + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + normalize: bool + If return the normalized distance vectors. + Returns + ------- + edge_lengths: torch.Tensor (shape: [n_edges, 1]) + The edge lengths. + edge_length_embeddings: torch.Tensor (shape: [n_edges, n_bases]) + The edge length embeddings. + edge_unit_vectors: torch.Tensor (shape: [n_edges, 3]) + The normalized edge vectors. + """ + vectors, lengths = get_edge_vectors_and_lengths( + positions=data['positions'], + edge_index=data['edge_index'], + shifts=data['shifts'], + normalize=normalize, + ) + return lengths, self._radial_embedding(lengths), vectors + +def get_edge_vectors_and_lengths( + positions: torch.Tensor, + edge_index: torch.Tensor, + shifts: torch.Tensor, + normalize: bool = True, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate edge vectors and lengths by indices and shift vectors. + Parameters + ---------- + position: torch.Tensor (shape: [n_atoms, 3]) + The position vector. + edge_index: torch.Tensor (shape: [2, n_edges]) + The edge indices. + shifts: torch.Tensor (shape: [n_edges, 3]) + The shift vector. + normalize: bool + If return the normalized distance vectors. + Returns + ------- + vectors: torch.Tensor (shape: [n_edges, 3]) + The distance vectors. + lengths: torch.Tensor (shape: [n_edges, 1]) + The edge lengths. + """ + sender = edge_index[0] + receiver = edge_index[1] + vectors = positions[receiver] - positions[sender] + shifts # [n_edges, 3] + lengths = torch.linalg.norm(vectors, dim=-1, keepdim=True) # [n_edges, 1] + + if normalize: + vectors = torch.nan_to_num(torch.div(vectors, lengths)) + + return vectors, lengths + + +# def test_get_data() -> tg.data.Batch: +# # TODO: This is not a real test, but a helper function for other tests. +# # Maybe should change its name. +# torch.manual_seed(0) +# torch_tools.set_default_dtype('float64') + +# numbers = [8, 1, 1] +# positions = np.array( +# [ +# [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], +# [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], +# [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], +# [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], +# [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], +# [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], +# ], +# dtype=np.float64 +# ) +# cell = np.identity(3, dtype=float) * 0.2 +# graph_labels = np.array([[1]]) +# node_labels = np.array([[0], [1], [1]]) +# z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) + +# config = [ +# gdata.atomic.Configuration( +# atomic_numbers=numbers, +# positions=p, +# cell=cell, +# pbc=[True] * 3, +# node_labels=node_labels, +# graph_labels=graph_labels, +# ) for p in positions +# ] +# dataset = gdata.create_dataset_from_configurations( +# config, z_table, 0.1, show_progress=False +# ) + +# loader = gdata.GraphDataModule( +# dataset, +# lengths=(1.0,), +# batch_size=10, +# shuffle=False, +# ) +# loader.setup() + +# return next(iter(loader.train_dataloader())) + diff --git a/mlcolvar/graph/core/nn/radial.py b/mlcolvar/core/nn/graph/radial.py similarity index 100% rename from mlcolvar/graph/core/nn/radial.py rename to mlcolvar/core/nn/graph/radial.py diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py new file mode 100644 index 00000000..0b755d13 --- /dev/null +++ b/mlcolvar/core/nn/graph/schnet.py @@ -0,0 +1,225 @@ +import math +import torch +import torch_scatter # TODO check this is equivalent in torch scatter +from torch import nn +from torch_geometric.nn import MessagePassing + +from mlcolvar.core.nn.graph.gnn import BaseGNN + +from typing import List, Dict, Tuple + +""" +The SchNet components. This module is taken from the pgy package: +https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py +""" + +__all__ = ['InteractionBlock', 'ShiftedSoftplus'] + +class SchNetModel(BaseGNN): + """ + The SchNet [1] model. This implementation is taken from torch_geometric: + https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py + Parameters + ---------- + n_out: int + Size of the output node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_bases: int + Size of the basis set. + n_layers: int + Number of the graph convolution layers. + n_filters: int + Number of filters. + n_hidden_channels: int + Size of hidden embeddings. + References + ---------- + .. [1] Schütt, Kristof T., et al. "Schnet–a deep learning architecture for + molecules and materials." The Journal of Chemical Physics 148.24 + (2018). + """ + + def __init__( + self, + n_out: int, + cutoff: float, + atomic_numbers: List[int], + n_bases: int = 16, + n_layers: int = 2, + n_filters: int = 16, + n_hidden_channels: int = 16, + drop_rate: int = 0 + ) -> None: + + super().__init__( + n_out, cutoff, atomic_numbers, n_bases, 0, 'gaussian' + ) + + self.W_v = nn.Linear( + len(atomic_numbers), n_hidden_channels, bias=False + ) + + self.layers = nn.ModuleList([ + InteractionBlock( + n_hidden_channels, n_bases, n_filters, cutoff + ) + for _ in range(n_layers) + ]) + + self.W_out = nn.ModuleList([ + nn.Linear(n_hidden_channels, n_hidden_channels // 2), + ShiftedSoftplus(), + nn.Linear(n_hidden_channels // 2, n_out) + ]) + + self.reset_parameters() + + def reset_parameters(self) -> None: + """ + Resets all learnable parameters of the module. + """ + self.W_v.reset_parameters() + + for layer in self.layers: + layer.reset_parameters() + + nn.init.xavier_uniform_(self.W_out[0].weight) + self.W_out[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.W_out[2].weight) + self.W_out[2].bias.data.fill_(0) + + def forward( + self, data: Dict[str, torch.Tensor], scatter_mean: bool = True + ) -> torch.Tensor: + """ + The forward pass. + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + scatter_mean: bool + If perform the scatter mean to the model output. + """ + + h_E = self.embed_edge(data) + h_V = self.W_v(data['node_attrs']) + + batch_id = data['batch'] + + for layer in self.layers: + h_V = h_V + layer(h_V, data['edge_index'], h_E[0], h_E[1]) + + for w in self.W_out: + h_V = w(h_V) + out = h_V + + if scatter_mean: + if 'system_masks' not in data.keys(): + # TODO check this is equivalent in torch scatter + out = torch_scatter.scatter_mean(out, batch_id, dim=0) + else: + out = out * data['system_masks'] + # TODO check this is equivalent in torch scatter + out = torch_scatter.scatter_sum(out, batch_id, dim=0) + out = out / data['n_system'] + + return out + +class InteractionBlock(nn.Module): + def __init__( + self, + hidden_channels: int, + num_gaussians: int, + num_filters: int, + cutoff: float + ) -> None: + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(num_gaussians, num_filters), + ShiftedSoftplus(), + nn.Linear(num_filters, num_filters), + ) + self.conv = CFConv( + hidden_channels, hidden_channels, num_filters, self.mlp, cutoff + ) + self.act = ShiftedSoftplus() + self.lin = nn.Linear(hidden_channels, hidden_channels) + + self.reset_parameters() + + def reset_parameters(self) -> None: + nn.init.xavier_uniform_(self.mlp[0].weight) + self.mlp[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.mlp[2].weight) + self.mlp[2].bias.data.fill_(0) + self.conv.reset_parameters() + nn.init.xavier_uniform_(self.lin.weight) + self.lin.bias.data.fill_(0) + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + edge_weight: torch.Tensor, + edge_attr: torch.Tensor, + ) -> torch.Tensor: + x = self.conv(x, edge_index, edge_weight, edge_attr) + x = self.act(x) + x = self.lin(x) + return x + + +class CFConv(MessagePassing): + def __init__( + self, + in_channels: int, + out_channels: int, + num_filters: int, + network: nn.Sequential, + cutoff: float, + ) -> None: + super().__init__(aggr='mean') + self.lin1 = nn.Linear(in_channels, num_filters, bias=False) + self.lin2 = nn.Linear(num_filters, out_channels) + self.network = network + self.cutoff = cutoff + + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.lin1.weight) + nn.init.xavier_uniform_(self.lin2.weight) + self.lin2.bias.data.fill_(0) + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + edge_weight: torch.Tensor, + edge_attr: torch.Tensor, + ) -> torch.Tensor: + C = 0.5 * (torch.cos(edge_weight * math.pi / self.cutoff) + 1.0) + W = self.network(edge_attr) * C.view(-1, 1) + + x = self.lin1(x) + x = self.propagate(edge_index, x=x, W=W) + x = self.lin2(x) + return x + + def message(self, x_j: torch.Tensor, W: torch.Tensor) -> torch.Tensor: + return x_j * W + +# TODO maybe remove and use the common one +class ShiftedSoftplus(nn.Module): + def __init__(self) -> None: + super().__init__() + self.shift = torch.log(torch.tensor(2.0)).item() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return nn.functional.softplus(x) - self.shift \ No newline at end of file From c84f59d9a7c9462238e9563896cacd2ab313fd09 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 17:42:12 +0100 Subject: [PATCH 122/267] Added tda graph and tda merged as working examples --- mlcolvar/cvs/supervised/deeptda_graph.py | 201 ++++++++++++++++++ mlcolvar/cvs/supervised/deeptda_merged.py | 243 ++++++++++++++++++++++ 2 files changed, 444 insertions(+) create mode 100644 mlcolvar/cvs/supervised/deeptda_graph.py create mode 100644 mlcolvar/cvs/supervised/deeptda_merged.py diff --git a/mlcolvar/cvs/supervised/deeptda_graph.py b/mlcolvar/cvs/supervised/deeptda_graph.py new file mode 100644 index 00000000..cade3fcf --- /dev/null +++ b/mlcolvar/cvs/supervised/deeptda_graph.py @@ -0,0 +1,201 @@ +import torch +import torch_geometric as tg +from typing import Dict, Any, List, Union + +from mlcolvar.core.loss import TDALoss +from mlcolvar.graph.cvs import GraphBaseCV +from mlcolvar.graph.cvs.cv import test_get_data +from mlcolvar.graph.utils import torch_tools + +""" +The Deep Targeted Discriminant Analysis (Deep-TDA) CV based on Graph Neural +Networks (GNN). +""" + +__all__ = ['GraphDeepTDA'] + + +class GraphDeepTDA(GraphBaseCV): + """ + The Deep Targeted Discriminant Analysis (Deep-TDA) CV [1] based on Graph + Neural Networks (GNN). + + Parameters + ---------- + n_cvs: int + Number of components of the CV. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_cvs : int + Number of collective variables to be trained + target_centers : list + Centers of the Gaussian targets + target_sigmas : list + Standard deviations of the Gaussian targets + model_name: str + Name of the GNN model. + model_options: Dict[Any, Any] + Model options. + extra_loss_options: Dict[Any, Any] + Extra loss function options. + optimizer_options: Dict[Any, Any] + Optimizer options. + + References + ---------- + .. [1] E. Trizio and M. Parrinello, + 'From enhanced sampling to reaction profiles', + The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). + + See also + -------- + mlcolvar.core.loss.TDALoss + Distance from a simple Gaussian target distribution. + """ + + def __init__( + self, + n_cvs: int, + cutoff: float, + atomic_numbers: List[int], + target_centers: Union[List[float], List[List[float]]], + target_sigmas: Union[List[float], List[List[float]]], + model_name: str = 'GVPModel', + model_options: Dict[Any, Any] = {}, + extra_loss_options: Dict[Any, Any] = {'alpha': 1.0, 'beta': 100.0}, + optimizer_options: Dict[Any, Any] = {}, + **kwargs, + ) -> None: + if model_options.pop('n_out', None) is not None: + raise RuntimeError( + 'The `n_out` key of parameter `model_options` will be ignored!' + ) + if optimizer_options != {}: + kwargs['optimizer_options'] = optimizer_options + + super().__init__( + n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs + ) + + # check size and type of targets + if not isinstance(target_centers, torch.Tensor): + target_centers = torch.tensor( + target_centers, dtype=torch.get_default_dtype() + ) + if not isinstance(target_sigmas, torch.Tensor): + target_sigmas = torch.tensor( + target_sigmas, dtype=torch.get_default_dtype() + ) + + self._n_states = target_centers.shape[0] + if target_centers.shape != target_sigmas.shape: + raise ValueError( + 'Size of target_centers and target_sigmas should be the same!' + ) + if len(target_centers.shape) == 1: + if n_cvs != 1: + raise ValueError( + 'Size of target_centers at dimension 1 should match the ' + + f'number of cvs! Expected 1 found {n_cvs}' + ) + elif len(target_centers.shape) == 2: + if n_cvs != target_centers.shape[1]: + raise ValueError( + 'Size of target_centers at dimension 1 should match the ' + + f'number of cvs! Expected {n_cvs} found ' + + f'{target_centers.shape[1]}' + ) + elif len(target_centers.shape) > 2: + raise ValueError('Too much target_centers dimensions!') + + self.loss_fn = TDALoss( + n_states=target_centers.shape[0], + target_centers=target_centers, + target_sigmas=target_sigmas, + **extra_loss_options + ) + + def training_step( + self, train_batch: tg.data.Batch, *args, **kwargs + ) -> torch.Tensor: + """ + Compute and return the training loss and record metrics. + + Parameters + ---------- + train_batch: torch_geometric.data.Batch + The data batch. + """ + output = self.forward(train_batch.to_dict()) + + loss, loss_centers, loss_sigmas = self.loss_fn( + output, + train_batch.graph_labels.squeeze(), + return_loss_terms=True + ) + + name = 'train' if self.training else 'valid' + self.log(f'{name}_loss', loss, on_epoch=True) + self.log(f'{name}_loss_centers', loss_centers, on_epoch=True) + self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) + return loss + + +def test_deep_tda(): + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + cv = GraphDeepTDA( + 2, + 0.1, + [1, 8], + [[-1, -1], [1, 1]], + [[1, 1], [1, 1]], + model_options={ + 'n_bases': 6, + 'n_polynomials': 6, + 'n_layers': 2, + 'n_messages': 2, + 'n_feedforwards': 1, + 'n_scalars_node': 16, + 'n_vectors_node': 8, + 'n_scalars_edge': 16, + 'drop_rate': 0, + 'activation': 'SiLU', + } + ) + + data = test_get_data() + + assert ( + torch.abs( + cv(data) + - torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) + ) < 1E-12 + ).all() + + assert torch.abs( + cv.training_step(data) - torch.tensor(404.8752553674548) + ) < 1E-12 + + try: + cv = GraphDeepTDA(2, 0.1, [1, 8], [-1, 1], [1, 1]) + except ValueError: + pass + else: + raise RuntimeError + + try: + cv = GraphDeepTDA(2, 0.1, [1, 8], [[-1, -1], [1, 1]], [1, 1]) + except ValueError: + pass + else: + raise RuntimeError + + +if __name__ == '__main__': + test_deep_tda() diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py new file mode 100644 index 00000000..afac496d --- /dev/null +++ b/mlcolvar/cvs/supervised/deeptda_merged.py @@ -0,0 +1,243 @@ +import torch +import lightning +from mlcolvar.cvs import BaseCV +from mlcolvar.core import FeedForward, Normalization +from mlcolvar.core.loss import TDALoss +from mlcolvar.data import DictModule + +import torch_geometric + + +__all__ = ["DeepTDA"] + + +class DeepTDA(BaseCV, lightning.LightningModule): + """ + Deep Targeted Discriminant Analysis (Deep-TDA) CV. + Combine the inputs with a neural-network and optimize it in a way such that + the data are distributed accordingly to a mixture of Gaussians. The method is described in [1]_. + **Data**: for training it requires a DictDataset with the keys 'data' and 'labels'. + **Loss**: distance of the samples of each class from a set of Gaussians (TDALoss) + References + ---------- + .. [1] E. Trizio and M. Parrinello, "From enhanced sampling to reaction profiles", + The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). + See also + -------- + mlcolvar.core.loss.TDALoss + Distance from a simple Gaussian target distribution. + """ + + BLOCKS = ["norm_in", "nn"] + + # TODO n_states optional? + def __init__( + self, + n_states: int, + n_cvs: int, + target_centers: list, + target_sigmas: list, + layers: list, + gnn_model=None, + options: dict = None, + **kwargs, + ): + """ + Define Deep Targeted Discriminant Analysis (Deep-TDA) CV composed by a neural network module. + By default a module standardizing the inputs is also used. + Parameters + ---------- + n_states : int + Number of states for the training + n_cvs : int + Number of collective variables to be trained + target_centers : list + Centers of the Gaussian targets + target_sigmas : list + Standard deviations of the Gaussian targets + layers : list + Number of neurons per layer + options : dict[str, Any], optional + Options for the building blocks of the model, by default {}. + Available blocks: ['norm_in', 'nn']. + Set 'block_name' = None or False to turn off that block + """ + + super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) + self.gnn_model = gnn_model + + # ======= LOSS ======= + self.loss_fn = TDALoss( + n_states=n_states, + target_centers=target_centers, + target_sigmas=target_sigmas, + ) + + # ======= OPTIONS ======= + # parse and sanitize + options = self.parse_options(options) + # Save n_states + self.n_states = n_states + if self.out_features != n_cvs: + raise ValueError( + "Number of neurons of last layer should match the number of CVs!" + ) + + # check size and type of targets + if not isinstance(target_centers, torch.Tensor): + target_centers = torch.Tensor(target_centers) + if not isinstance(target_sigmas, torch.Tensor): + target_sigmas = torch.Tensor(target_sigmas) + + if target_centers.shape != target_sigmas.shape: + raise ValueError( + f"Size of target_centers and target_sigmas should be the same!" + ) + if n_states != target_centers.shape[0]: + raise ValueError( + f"Size of target_centers at dimension 0 should match the number of states! Expected {n_states} found {target_centers.shape[0]}" + ) + if len(target_centers.shape) == 2: + if n_cvs != target_centers.shape[1]: + raise ValueError( + ( + f"Size of target_centers at dimension 1 should match the number of cvs! Expected {n_cvs} found {target_centers.shape[1]}" + ) + ) + + # ======= BLOCKS ======= + # Initialize norm_in + o = "norm_in" + if (options[o] is not False) and (options[o] is not None and gnn_model is None): + self.norm_in = Normalization(self.in_features, **options[o]) + + # initialize NN + o = "nn" + if gnn_model is None: + self.nn = FeedForward(layers, **options[o]) + else: + self.nn = gnn_model + + def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: + if self.gnn_model is None: + # =================get data=================== + x = train_batch["data"] + labels = train_batch["labels"] + # =================forward==================== + z = self.forward_cv(x) + # ===================loss===================== + loss, loss_centers, loss_sigmas = self.loss_fn(z, + labels, + return_loss_terms=True + ) + elif self.gnn_model._model_type=='gnn': + # data = train_batch.to_dict() + data = train_batch['data_list'] + data['positions'].requires_grad_(True) + data['node_attrs'].requires_grad_(True) + + output = self.forward(data) + + loss, loss_centers, loss_sigmas = self.loss_fn(output, + data["graph_labels"].squeeze(), + return_loss_terms=True + ) + + # ====================log=====================+ + name = "train" if self.training else "valid" + self.log(f"{name}_loss", loss, on_epoch=True) + self.log(f"{name}_loss_centers", loss_centers, on_epoch=True) + self.log(f"{name}_loss_sigmas", loss_sigmas, on_epoch=True) + + return loss + + # def training_step( + # self, train_batch: torch_geometric.data.Batch, *args, **kwargs + # ) -> torch.Tensor: + # """ + # Compute and return the training loss and record metrics. + + # Parameters + # ---------- + # train_batch: torch_geometric.data.Batch + # The data batch. + # """ + # data = train_batch.to_dict() + # data['positions'].requires_grad_(True) + # data['node_attrs'].requires_grad_(True) + + # output = self.forward(data) + + # loss, loss_centers, loss_sigmas = self.loss_fn( + # output, + # train_batch.graph_labels.squeeze(), + # return_loss_terms=True + # ) + + # name = 'train' if self.training else 'valid' + # self.log(f'{name}_loss', loss, on_epoch=True) + # self.log(f'{name}_loss_centers', loss_centers, on_epoch=True) + # self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) + # return loss + + +# TODO signature of tests? +import numpy as np + + +def test_deeptda_cv(): + from mlcolvar.data import DictDataset + + for states_and_cvs in [[2, 1], [3, 1], [3, 2], [5, 4]]: + # get the number of states and cvs for the test run + n_states = states_and_cvs[0] + n_cvs = states_and_cvs[1] + + in_features, out_features = 2, n_cvs + layers = [in_features, 4, 2, out_features] + target_centers = np.random.randn(n_states, n_cvs) + target_sigmas = np.random.randn(n_states, n_cvs) + + # test initialize via dictionary + options = {"nn": {"activation": "relu"}} + + model = DeepTDA( + n_states=n_states, + n_cvs=n_cvs, + target_centers=target_centers, + target_sigmas=target_sigmas, + layers=layers, + options=options, + ) + + print("----------") + print(model) + + # create dataset + samples = 100 + X = torch.randn((samples * n_states, 2)) + + # create labels + y = torch.zeros(X.shape[0]) + for i in range(1, n_states): + y[samples * i :] += 1 + + dataset = DictDataset({"data": X, "labels": y}) + datamodule = DictModule(dataset, lengths=[0.75, 0.2, 0.05], batch_size=samples) + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False + ) + trainer.fit(model, datamodule) + + # trace model + traced_model = model.to_torchscript( + file_path=None, method="trace", example_inputs=X[0] + ) + model.eval() + assert torch.allclose(model(X), traced_model(X)) + + +if __name__ == "__main__": + test_deeptda_cv() + From 2e363711b801c85771627ca48cc770af726b3b65 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 17:51:45 +0100 Subject: [PATCH 123/267] Moved graph.data into data.graph --- mlcolvar/{graph/data => data/graph}/__init__.py | 0 mlcolvar/{graph/data => data/graph}/atomic.py | 9 +-------- mlcolvar/{graph/data => data/graph}/datamodule.py | 0 mlcolvar/{graph/data => data/graph}/dataset.py | 0 mlcolvar/{graph/data => data/graph}/neighborhood.py | 0 5 files changed, 1 insertion(+), 8 deletions(-) rename mlcolvar/{graph/data => data/graph}/__init__.py (100%) rename mlcolvar/{graph/data => data/graph}/atomic.py (98%) rename mlcolvar/{graph/data => data/graph}/datamodule.py (100%) rename mlcolvar/{graph/data => data/graph}/dataset.py (100%) rename mlcolvar/{graph/data => data/graph}/neighborhood.py (100%) diff --git a/mlcolvar/graph/data/__init__.py b/mlcolvar/data/graph/__init__.py similarity index 100% rename from mlcolvar/graph/data/__init__.py rename to mlcolvar/data/graph/__init__.py diff --git a/mlcolvar/graph/data/atomic.py b/mlcolvar/data/graph/atomic.py similarity index 98% rename from mlcolvar/graph/data/atomic.py rename to mlcolvar/data/graph/atomic.py index 8fe5a573..11a0630e 100644 --- a/mlcolvar/graph/data/atomic.py +++ b/mlcolvar/data/graph/atomic.py @@ -1,6 +1,6 @@ import warnings import numpy as np -import mdtraj as md +import mdtraj as md # TODO re-write the two functions from dataclasses import dataclass from typing import List, Iterable, Optional @@ -17,7 +17,6 @@ class AtomicNumberTable: """ The atomic number table. Used to map between one hot encodings and a given set of actual atomic numbers. - Parameters ---------- zs: List[int] @@ -48,7 +47,6 @@ def __str__(self) -> str: def index_to_z(self, index: int) -> int: """ Map the encoding to the actual atomic number. - Parameters ---------- index: int @@ -59,7 +57,6 @@ def index_to_z(self, index: int) -> int: def index_to_symbol(self, index: int) -> str: """ Map the encoding to the atomic symbol. - Parameters ---------- index: int @@ -70,7 +67,6 @@ def index_to_symbol(self, index: int) -> str: def z_to_index(self, atomic_number: int) -> int: """ Map an atomic number to the encoding. - Parameters ---------- atomic_number: int @@ -81,7 +77,6 @@ def z_to_index(self, atomic_number: int) -> int: def zs_to_indices(self, atomic_numbers: np.ndarray) -> np.ndarray: """ Map an array of atomic number to the encodings. - Parameters ---------- atomic_numbers: numpy.ndarray @@ -94,7 +89,6 @@ def zs_to_indices(self, atomic_numbers: np.ndarray) -> np.ndarray: def from_zs(cls, atomic_numbers: Iterable[int]) -> 'AtomicNumberTable': """ Build the table from an array atomic numbers. - Parameters ---------- atomic_numbers: Iterable[int] @@ -109,7 +103,6 @@ def from_zs(cls, atomic_numbers: Iterable[int]) -> 'AtomicNumberTable': def get_masses(atomic_numbers: Iterable[int]) -> List[float]: """ Get atomic masses from atomic numbers. - Parameters ---------- atomic_numbers: Iterable[int] diff --git a/mlcolvar/graph/data/datamodule.py b/mlcolvar/data/graph/datamodule.py similarity index 100% rename from mlcolvar/graph/data/datamodule.py rename to mlcolvar/data/graph/datamodule.py diff --git a/mlcolvar/graph/data/dataset.py b/mlcolvar/data/graph/dataset.py similarity index 100% rename from mlcolvar/graph/data/dataset.py rename to mlcolvar/data/graph/dataset.py diff --git a/mlcolvar/graph/data/neighborhood.py b/mlcolvar/data/graph/neighborhood.py similarity index 100% rename from mlcolvar/graph/data/neighborhood.py rename to mlcolvar/data/graph/neighborhood.py From 9d720f0fe44bdf67154b154a4a7bba7aeb3f4368 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 17:54:55 +0100 Subject: [PATCH 124/267] modified DictDataset to include metadata and handle graph data --- mlcolvar/data/dataset.py | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index 4dfdb15f..72b76928 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -2,6 +2,8 @@ import numpy as np from mlcolvar.core.transform.utils import Statistics from torch.utils.data import Dataset +import torch_geometric +from mlcolvar.data.graph.atomic import AtomicNumberTable __all__ = ["DictDataset"] @@ -14,7 +16,7 @@ class DictDataset(Dataset): 'weights' : np.asarray([0.5,1.5,1.5,0.5]) } """ - def __init__(self, dictionary: dict = None, feature_names=None, **kwargs): + def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = None, **kwargs): """Create a Dataset from a dictionary or from a list of kwargs. Parameters @@ -30,7 +32,12 @@ def __init__(self, dictionary: dict = None, feature_names=None, **kwargs): raise TypeError( f"DictDataset requires a dictionary , not {type(dictionary)}." ) - + + if (metadata is not None) and (not isinstance(metadata, dict)): + raise TypeError( + f"DictDataset metadata requires a dictionary , not {type(metadata)}." + ) + # Add kwargs to dict if dictionary is None: dictionary = {} @@ -41,7 +48,10 @@ def __init__(self, dictionary: dict = None, feature_names=None, **kwargs): # convert to torch.Tensors for key, val in dictionary.items(): if not isinstance(val, torch.Tensor): - dictionary[key] = torch.Tensor(val) + if key =="data_list": + dictionary[key] = val + else: + dictionary[key] = torch.Tensor(val) # save dictionary self._dictionary = dictionary @@ -49,10 +59,13 @@ def __init__(self, dictionary: dict = None, feature_names=None, **kwargs): # save feature names self.feature_names = feature_names + # save metadata + self.metadata = metadata + # check that all elements of dict have same length it = iter(dictionary.values()) self.length = len(next(it)) - if not all(len(l) == self.length for l in it): + if not all([len(l)==self.length for l in it]): raise ValueError("not all arrays in dictionary have same length!") def __getitem__(self, index): @@ -70,7 +83,7 @@ def __setitem__(self, index, value): # check lengths if len(value) != len(self): raise ValueError( - f"length of value ({len(value)}) != length of dataset ({len(self)})." + f"length of value ({len(value)}) != length of dataset ({len(self)})." ) self._dictionary[index] = value else: @@ -98,7 +111,12 @@ def get_stats(self): def __repr__(self) -> str: string = "DictDataset(" for key, val in self._dictionary.items(): - string += f' "{key}": {list(val.shape)},' + if key=="data_list": + string += f' "{key}": {len(val)},' + else: + string += f' "{key}": {list(val.shape)},' + for key, val in self.metadata.items(): + string += f' "{key}": {val},' string = string[:-1] + " )" return string @@ -148,4 +166,4 @@ def test_DictDataset(): if __name__ == "__main__": - test_DictDataset() + test_DictDataset() \ No newline at end of file From 674be2795bfcdeb9b0e94f6e7d15482e2d2c3c3a Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 17:55:19 +0100 Subject: [PATCH 125/267] First test to unify data hadling on ff and gnn --- mlcolvar/data/graph/__init__.py | 18 +++-- mlcolvar/data/graph/atomic.py | 2 +- mlcolvar/data/graph/datamodule.py | 117 +++++++++++++++------------- mlcolvar/data/graph/dataset.py | 45 +++++++++-- mlcolvar/data/graph/neighborhood.py | 3 - 5 files changed, 112 insertions(+), 73 deletions(-) diff --git a/mlcolvar/data/graph/__init__.py b/mlcolvar/data/graph/__init__.py index c6e95ad8..09234aca 100644 --- a/mlcolvar/data/graph/__init__.py +++ b/mlcolvar/data/graph/__init__.py @@ -1,10 +1,12 @@ +# TODO fix imports + from . import atomic from . import neighborhood -from .dataset import ( - GraphDataSet, - create_dataset_from_configurations, - save_dataset, - save_dataset_as_exyz, - load_dataset -) -from .datamodule import GraphDataModule, GraphCombinedDataModule +# from .dataset import ( +# GraphDataSet, +# create_dataset_from_configurations, +# save_dataset, +# save_dataset_as_exyz, +# load_dataset +# ) +# from .datamodule import GraphDataModule, GraphCombinedDataModule \ No newline at end of file diff --git a/mlcolvar/data/graph/atomic.py b/mlcolvar/data/graph/atomic.py index 11a0630e..567490b6 100644 --- a/mlcolvar/data/graph/atomic.py +++ b/mlcolvar/data/graph/atomic.py @@ -1,6 +1,6 @@ import warnings import numpy as np -import mdtraj as md # TODO re-write the two functions +import mdtraj as md # TODO re-write the two functions maybe from dataclasses import dataclass from typing import List, Iterable, Optional diff --git a/mlcolvar/data/graph/datamodule.py b/mlcolvar/data/graph/datamodule.py index dec96528..4de8576d 100644 --- a/mlcolvar/data/graph/datamodule.py +++ b/mlcolvar/data/graph/datamodule.py @@ -6,7 +6,8 @@ from typing import Sequence, Union, Optional, Tuple from lightning.pytorch.utilities import combined_loader -from mlcolvar.graph.data import atomic, create_dataset_from_configurations +from mlcolvar.data.graph import atomic +from mlcolvar.data.graph.dataset import create_dataset_from_configurations """ The data module for lightning. @@ -182,58 +183,68 @@ def predict_dataloader(self) -> tg.loader.DataLoader: def teardown(self, stage: str) -> None: pass - def __repr__(self) -> str: - result = '' - n_digits = len(str(self._n_total)) - data_string_1 = '[ \033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' - data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' - shuffle_string_1 = '|\033[36m  \033[0m ]' - shuffle_string_2 = '|\033[36m  \033[0m ]' - - prefix = '\033[1m\033[34m BASEDATA \033[0m: ' - result += ( - prefix + self._dataset.__repr__().split('GRAPHDATASET ')[1] + '\n' - ) - prefix = '\033[1m\033[34m TRAINING \033[0m: ' - string = prefix + data_string_1.format(n_digits) - result += string.format( - self._n_train, self._n_train / self._n_total * 100 - ) - string = data_string_2.format(n_digits) - result += string.format(self.batch_size[0]) - if self.shuffle[0]: - result += shuffle_string_1 - else: - result += shuffle_string_2 + # def __repr__(self) -> str: + # result = '' + # n_digits = len(str(self._n_total)) + # data_string_1 = '[ \033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' + # data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' + # shuffle_string_1 = '|\033[36m  \033[0m ]' + # shuffle_string_2 = '|\033[36m  \033[0m ]' + + # prefix = '\033[1m\033[34m BASEDATA \033[0m: ' + # result += ( + # prefix + self._dataset.__repr__().split('GRAPHDATASET ')[1] + '\n' + # ) + # prefix = '\033[1m\033[34m TRAINING \033[0m: ' + # string = prefix + data_string_1.format(n_digits) + # result += string.format( + # self._n_train, self._n_train / self._n_total * 100 + # ) + # string = data_string_2.format(n_digits) + # result += string.format(self.batch_size[0]) + # if self.shuffle[0]: + # result += shuffle_string_1 + # else: + # result += shuffle_string_2 + + # if self._n_validation > 0: + # result += '\n' + # prefix = '\033[1m\033[34m VALIDATION \033[0m: ' + # string = prefix + data_string_1.format(n_digits) + # result += string.format( + # self._n_validation, self._n_validation / self._n_total * 100 + # ) + # string = data_string_2.format(n_digits) + # result += string.format(self.batch_size[1]) + # if self.shuffle[1]: + # result += shuffle_string_1 + # else: + # result += shuffle_string_2 + + # if self._n_test > 0: + # result += '\n' + # prefix = '\033[1m\033[34m TEST \033[0m: ' + # string = prefix + data_string_1.format(n_digits) + # result += string.format( + # self._n_test, self._n_test / self._n_total * 100 + # ) + # string = data_string_2.format(n_digits) + # result += string.format(self.batch_size[2]) + # if self.shuffle[2]: + # result += shuffle_string_1 + # else: + # result += shuffle_string_2 + # return result - if self._n_validation > 0: - result += '\n' - prefix = '\033[1m\033[34m VALIDATION \033[0m: ' - string = prefix + data_string_1.format(n_digits) - result += string.format( - self._n_validation, self._n_validation / self._n_total * 100 - ) - string = data_string_2.format(n_digits) - result += string.format(self.batch_size[1]) - if self.shuffle[1]: - result += shuffle_string_1 - else: - result += shuffle_string_2 - - if self._n_test > 0: - result += '\n' - prefix = '\033[1m\033[34m TEST \033[0m: ' - string = prefix + data_string_1.format(n_digits) - result += string.format( - self._n_test, self._n_test / self._n_total * 100 - ) - string = data_string_2.format(n_digits) - result += string.format(self.batch_size[2]) - if self.shuffle[2]: - result += shuffle_string_1 - else: - result += shuffle_string_2 - return result + def __repr__(self) -> str: + string = f"DictModule(dataset -> {self._dataset.__repr__()}" + string += f",\n\t\t train_loader -> DictLoader(length={self._lengths[0]}, batch_size={self.batch_size[0]}, shuffle={self.shuffle[0]})" + if len(self._lengths) >= 2: + string += f",\n\t\t valid_loader -> DictLoader(length={self._lengths[1]}, batch_size={self.batch_size[1]}, shuffle={self.shuffle[1]})" + if len(self._lengths) >= 3: + string += f",\n\t\t\ttest_loader =DictLoader(length={self._lengths[2]}, batch_size={self.batch_size[2]}, shuffle={self.shuffle[2]})" + string += f")" + return string def _check_setup(self) -> None: """ @@ -865,4 +876,4 @@ def test_combined_datamodule() -> None: if __name__ == '__main__': test_datamodule() - test_combined_datamodule() + test_combined_datamodule() \ No newline at end of file diff --git a/mlcolvar/data/graph/dataset.py b/mlcolvar/data/graph/dataset.py index 8daaa9aa..6a287433 100644 --- a/mlcolvar/data/graph/dataset.py +++ b/mlcolvar/data/graph/dataset.py @@ -3,10 +3,12 @@ import numpy as np from typing import List, Union -from mlcolvar.graph.data import atomic -from mlcolvar.graph.data.neighborhood import get_neighborhood -from mlcolvar.graph.utils import torch_tools -from mlcolvar.graph.utils import progress +from mlcolvar.data.graph import atomic +from mlcolvar.data.graph.neighborhood import get_neighborhood +# from mlcolvar.data.graph.utils import torch_tools # moved here one hot +from mlcolvar.utils.plot import pbar # moved + +from mlcolvar.data.dataset import DictDataset """ Build the graph data from a configuration. This module is taken from MACE: @@ -151,7 +153,7 @@ def _create_dataset_from_configuration( cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) indices = z_table.zs_to_indices(config.atomic_numbers) - one_hot = torch_tools.to_one_hot( + one_hot = to_one_hot( torch.tensor(indices, dtype=torch.long).unsqueeze(-1), n_classes=len(z_table), ) @@ -231,7 +233,7 @@ def create_dataset_from_configurations( If show the progress bar. """ if show_progress: - items = progress.pbar(config, frequency=0.0001, prefix='Making graphs') + items = pbar(config, frequency=0.0001, prefix='Making graphs') else: items = config @@ -255,7 +257,10 @@ def create_dataset_from_configurations( for i in range(len(data_list)): data_list[i].cell = cell_list[i] - dataset = GraphDataSet(data_list, z_table.zs, cutoff) + # dataset = GraphDataSet(data_list, z_table.zs, cutoff) + dataset = DictDataset(dictionary={'data_list' : data_list}, + metadata={'z_table' : z_table.zs, + 'cutoff' : cutoff}) return dataset @@ -323,6 +328,30 @@ def save_dataset_as_exyz(dataset: GraphDataSet, file_name: str) -> None: fp.close() +def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: + """ + Generates one-hot encoding with `n_classes` classes from `indices` + + Parameters + ---------- + indices: torch.Tensor (shape: [N, 1]) + Node incices. + n_classes: int + Number of classes. + + Returns + ------- + encoding: torch.tensor (shape: [N, n_classes]) + The one-hot encoding. + """ + shape = indices.shape[:-1] + (n_classes,) + oh = torch.zeros(shape, device=indices.device).view(shape) + + # scatter_ is the in-place version of scatter + oh.scatter_(dim=-1, index=indices, value=1) + + return oh.view(*shape) + def test_from_configuration() -> None: numbers = [8, 1, 1] @@ -618,4 +647,4 @@ def test_from_configurations() -> None: if __name__ == '__main__': test_from_configuration() - test_from_configurations() + test_from_configurations() \ No newline at end of file diff --git a/mlcolvar/data/graph/neighborhood.py b/mlcolvar/data/graph/neighborhood.py index 077b88df..00dd64f8 100644 --- a/mlcolvar/data/graph/neighborhood.py +++ b/mlcolvar/data/graph/neighborhood.py @@ -22,7 +22,6 @@ def get_neighborhood( ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Get the neighbor list of a given set atoms. - Parameters ---------- positions: numpy.ndarray (shape: [N, 3]) @@ -41,7 +40,6 @@ def get_neighborhood( Indices of the environment atoms. buffer: float Buffer size used in finding active environment atoms. - Returns ------- edge_index: numpy.ndarray (shape: [2, n_edges]) @@ -50,7 +48,6 @@ def get_neighborhood( The shift vectors (unit_shifts * cell_lengths). unit_shifts: numpy.ndarray (shape: [n_edges, 3]) The unit shift vectors (number of PBC croessed by the edges). - Notes ----- Arguments `system_indices` and `environment_indices` must presnet at the From c37eebf561ea631ad1a5daa35839f7924874577d Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 18:00:23 +0100 Subject: [PATCH 126/267] Moved graph.utils.io into main.utils.io --- mlcolvar/utils/io.py | 295 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 293 insertions(+), 2 deletions(-) diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index 7bbc96db..64b7a734 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -10,7 +10,10 @@ import torch import os import urllib.request -from typing import Union +from typing import Union, List, Tuple +import mdtraj + +from mlcolvar.data import graph as gdata from mlcolvar.data import DictDataset @@ -258,6 +261,294 @@ def create_dataset_from_files( else: return dataset +def create_dataset_from_trajectories( + trajectories: Union[List[List[str]], List[str], str], + top: Union[List[List[str]], List[str], str], + cutoff: float, + buffer: float = 0.0, + z_table: gdata.atomic.AtomicNumberTable = None, + folder: str = None, + create_labels: bool = True, + system_selection: str = None, + environment_selection: str = None, + return_trajectories: bool = False, + remove_isolated_nodes: bool = True, + show_progress: bool = True +) -> Union[ + DictDataset, + Tuple[ + DictDataset, + Union[List[List[mdtraj.Trajectory]], List[mdtraj.Trajectory]] + ] +]: + """ + Create a dataset from a set of trajectory files. + + Parameters + ---------- + trajectories: Union[List[List[str]], List[str], str] + Names of trajectories files. + top: Union[List[List[str]], List[str], str] + Names of topology files. + cutoff: float (units: Ang) + The graph cutoff radius. + buffer: float + Buffer size used in finding active environment atoms. + z_table: mlcolvar.graph.data.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. If not + given, it will be created from the given trajectories. + folder: str + Common path for the files to be imported. If set, filenames become + `folder/file_name`. + create_labels: bool + Assign a label to each file according to the total number of files. + If False, labels of all files will be `-1`. + system_selection: str + MDTraj style atom selections [1] of the system atoms. If given, only + selected atoms will be loaded from the trajectories. This option may + increase the speed of building graphs. + environment_selection: str + MDTraj style atom selections [1] of the environment atoms. If given, + only the system atoms and [the environment atoms within the cutoff + radius of the system atoms] will be kept in the graph. + return_trajectories: bool + If also return the loaded trajectory objects. + remove_isolated_nodes: bool + If remove isolated nodes from the dataset. + show_progress: bool + If show the progress bar. + + Returns + ------- + dataset: mlcolvar.graph.data.GraphDataSet + The graph dataset. + trajectories: Union[List[List[mdtraj.Trajectory]], List[mdtraj.Trajectory]] + The loaded trajectory objects. + + Notes + ----- + The login behind this method is like the follows: + 1. If only `system_selection` is given, the method will only load atoms + selected by this selection, from the trajectories. + 2. If both `system_selection` and `environment_selection` are given, + the method will load the atoms select by both selections, but will + build graphs using [the system atoms] and [the environment atoms within + the cutoff radius of the system atoms]. + + References + ---------- + .. [1] https://www.mdtraj.org/1.9.8.dev0/atom_selection.html + """ + + if environment_selection is not None: + assert system_selection is not None, ( + 'the `environment_selection` argument requires the' + + '`system_selection` argument to be defined!' + ) + selection = '({:s}) or ({:s})'.format( + system_selection, environment_selection + ) + elif system_selection is not None: + selection = system_selection + else: + selection = None + + if environment_selection is None: + assert buffer == 0, ( + 'Not `environment_selection` given! Cannot define buffer size!' + ) + + # fmt: off + assert type(trajectories) is type(top), ( + 'The `trajectories` and `top` parameters should have the same type!' + ) + if isinstance(trajectories, str): + trajectories = [trajectories] + top = [top] + assert len(trajectories) == len(top), ( + 'Numbers of trajectories and topology files should be the same!' + ) + # fmt: on + + for i in range(len(trajectories)): + assert type(trajectories[i]) is type(top[i]), ( + 'Each element of `trajectories` and `top` parameters ' + + 'should have the same type!' + ) + if isinstance(trajectories[i], list): + assert len(trajectories[i]) == len(top[i]), ( + 'Numbers of trajectories and topology files should be ' + + 'the same!' + ) + for j in range(len(trajectories[i])): + if folder is not None: + trajectories[i][j] = folder + '/' + trajectories[i][j] + top[i][j] = folder + '/' + top[i][j] + assert isinstance(trajectories[i][j], str) + assert isinstance(top[i][j], str) + else: + if folder is not None: + trajectories[i] = folder + '/' + trajectories[i] + top[i] = folder + '/' + top[i] + assert isinstance(trajectories[i], str) + assert isinstance(top[i], str) + + topologies = [] + trajectories_in_memory = [] + + for i in range(len(trajectories)): + if isinstance(trajectories[i], list): + traj = [ + mdtraj.load(trajectories[i][j], top=top[i][j]) + for j in range(len(trajectories[i])) + ] + for t in traj: + t.top = mdtraj.core.trajectory.load_topology(top[i][j]) + if selection is not None: + for j in range(len(traj)): + subset = traj[j].top.select(selection) + assert len(subset) > 0, ( + 'No atoms will be selected with selection string ' + + '"{:s}"!'.format(selection) + ) + traj[j] = traj[j].atom_slice(subset) + trajectories_in_memory.append(traj) + topologies.extend([t.top for t in traj]) + else: + traj = mdtraj.load(trajectories[i], top=top[i]) + traj.top = mdtraj.core.trajectory.load_topology(top[i]) + if selection is not None: + subset = traj.top.select(selection) + assert len(subset) > 0, ( + 'No atoms will be selected with selection string ' + + '"{:s}"!'.format(selection) + ) + traj = traj.atom_slice(subset) + trajectories_in_memory.append(traj) + topologies.append(traj.top) + + if z_table is None: + z_table = _z_table_from_top(topologies) + + configurations = [] + for i in range(len(trajectories_in_memory)): + if isinstance(trajectories_in_memory[i], list): + for j in range(len(trajectories_in_memory[i])): + configuration = _configures_from_trajectory( + trajectories_in_memory[i][j], + i if create_labels else -1, # NOTE: all these configurations have a label `i` + system_selection, + environment_selection, + ) + configurations.extend(configuration) + else: + configuration = _configures_from_trajectory( + trajectories_in_memory[i], + i if create_labels else -1, + system_selection, + environment_selection, + ) + configurations.extend(configuration) + + dataset = gdata.dataset.create_dataset_from_configurations( + configurations, + z_table, + cutoff, + buffer, + remove_isolated_nodes, + show_progress + ) + + if return_trajectories: + return dataset, trajectories_in_memory + else: + return dataset + + +def _z_table_from_top( + top: List[mdtraj.Topology] +) -> gdata.atomic.AtomicNumberTable: + """ + Create an atomic number table from the topologies. + + Parameters + ---------- + top: List[mdtraj.Topology] + The topology objects. + """ + atomic_numbers = [] + for t in top: + atomic_numbers.extend([a.element.number for a in t.atoms]) + # atomic_numbers = np.array(atomic_numbers, dtype=int) + z_table = gdata.atomic.AtomicNumberTable.from_zs(atomic_numbers) + return z_table + + +def _configures_from_trajectory( + trajectory: mdtraj.Trajectory, + label: int = None, + system_selection: str = None, + environment_selection: str = None, +) -> gdata.atomic.Configurations: + """ + Create configurations from one trajectory. + + Parameters + ---------- + trajectory: mdtraj.Trajectory + The MDTraj Trajectory object. + label: int + The graph label. + system_selection: str + MDTraj style atom selections of the system atoms. If given, only + selected atoms will be loaded from the trajectories. This option may + increase the speed of building graphs. + environment_selection: str + MDTraj style atom selections of the environment atoms. If given, + only the system atoms and [the environment atoms within the cutoff + radius of the system atoms] will be kept in the graph. + """ + if label is not None: + label = np.array([[label]]) + + if system_selection is not None and environment_selection is not None: + system_atoms = trajectory.top.select(system_selection) + assert len(system_atoms) > 0, ( + 'No atoms will be selected with `system_selection`: ' + + '"{:s}"!'.format(system_selection) + ) + environment_atoms = trajectory.top.select(environment_selection) + assert len(environment_atoms) > 0, ( + 'No atoms will be selected with `environment_selection`: ' + + '"{:s}"!'.format(environment_selection) + ) + else: + system_atoms = None + environment_atoms = None + + atomic_numbers = [a.element.number for a in trajectory.top.atoms] + if trajectory.unitcell_vectors is not None: + pbc = [True] * 3 + cell = trajectory.unitcell_vectors + else: + pbc = [False] * 3 + cell = [None] * len(trajectory) + + configurations = [] + for i in range(len(trajectory)): + configuration = gdata.atomic.Configuration( + atomic_numbers=atomic_numbers, + positions=trajectory.xyz[i] * 10, + cell=cell[i] * 10, + pbc=pbc, + graph_labels=label, + node_labels=None, # TODO: Add supports for per-node labels. + system=system_atoms, + environment=environment_atoms + ) + configurations.append(configuration) + + return configurations def test_datasetFromFile(): # Test with unlabeled dataset @@ -318,4 +609,4 @@ def test_modifier(x): if __name__ == "__main__": - test_datasetFromFile() + test_datasetFromFile() \ No newline at end of file From 064d3dfcab9e7334b3d52d1d8f4941761f1cfe78 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 18:01:50 +0100 Subject: [PATCH 127/267] Moved progress bar utils to main.utils.plot --- mlcolvar/utils/plot.py | 66 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/mlcolvar/utils/plot.py b/mlcolvar/utils/plot.py index 62f1a3ee..fa941368 100644 --- a/mlcolvar/utils/plot.py +++ b/mlcolvar/utils/plot.py @@ -328,6 +328,72 @@ def plot_features_distribution(dataset, features, titles=None, axs=None): ax.set_yticks([]) ax.legend([],[],title=feat,loc='upper center',frameon=False) +import sys +import time +import typing + +""" +A simple progress bar. +""" + +__all__ = ['pbar'] + + +def pbar( + item: typing.List[int], + prefix: str = '', + size: int = 25, + frequency: int = 0.05, + use_unicode: bool = True, + file: typing.TextIO = sys.stdout +): + """ + A simple progress bar. Taken from stackoverflow: + https://stackoverflow.com/questions/3160699 + Parameters + ---------- + it : List[int] + The looped item. + prefix : str + Prefix of the bar. + size : int + Size of the bar. + frequency : float + Flush frequency of the bar. + use_unicode : bool + If use unicode char to draw the bar. + file : TextIO + The output file. + """ + if (use_unicode): + c_1 = '' + c_2 = '█' + c_3 = '━' + c_4 = '' + else: + c_1 = '|' + c_2 = '|' + c_3 = '-' + c_4 = '|' + count = len(item) + start = time.time() + interval = max(int(count * frequency), 1) + + def show(j) -> None: + x = int(size * j / count) + remaining = ((time.time() - start) / j) * (count - j) + mins, sec = divmod(remaining, 60) + time_string = f'{int(mins):02}:{sec:02.1f}' + output = f' {prefix} {c_1}{c_2 * (x - 1) + c_4}{c_3 * (size - x)} ' + \ + f'{j}/{count} Est. {time_string}' + print('\x1b[1A\x1b[2K' + output, file=file, flush=True) + + for i, it in enumerate(item): + yield it + if ((i % interval) == 0 or i in [0, (count - 1)]): + show(i + 1) + print(flush=True, file=file) + def test_utils_plot(): import matplotlib From f467b2464527eb7aac165dfb517786c844dc7c19 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 13 Nov 2024 18:04:27 +0100 Subject: [PATCH 128/267] added notebook and data for testing --- test_graphs/data/colvar_p.dat | 801 ++++++++++++++++++++++++++++++++++ test_graphs/data/colvar_r.dat | 801 ++++++++++++++++++++++++++++++++++ test_graphs/data/p.dcd | Bin 0 -> 246676 bytes test_graphs/data/p.pdb | 21 + test_graphs/data/r.dcd | Bin 0 -> 246676 bytes test_graphs/data/r.pdb | 21 + test_graphs/test_graph.ipynb | 316 ++++++++++++++ 7 files changed, 1960 insertions(+) create mode 100644 test_graphs/data/colvar_p.dat create mode 100644 test_graphs/data/colvar_r.dat create mode 100644 test_graphs/data/p.dcd create mode 100644 test_graphs/data/p.pdb create mode 100644 test_graphs/data/r.dcd create mode 100644 test_graphs/data/r.pdb create mode 100644 test_graphs/test_graph.ipynb diff --git a/test_graphs/data/colvar_p.dat b/test_graphs/data/colvar_p.dat new file mode 100644 index 00000000..82958267 --- /dev/null +++ b/test_graphs/data/colvar_p.dat @@ -0,0 +1,801 @@ +#! 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"Class 1 dataframe shape: (800, 24)\n", + "\n", + " - Loaded dataframe (1600, 24): ['time', 'd1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'd11', 'd12', 'd13', 'd14', 'd15', 'd16', 'd17', 'd18', 'd19', 'd20', 'd21', 'walker', 'labels']\n", + " - Descriptors (1600, 21): ['d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'd11', 'd12', 'd13', 'd14', 'd15', 'd16', 'd17', 'd18', 'd19', 'd20', 'd21']\n" + ] + } + ], + "source": [ + "from mlcolvar.data.graph.datamodule import GraphDataModule\n", + "from mlcolvar.utils.io import create_dataset_from_trajectories\n", + "\n", + "dataset_graph = create_dataset_from_trajectories(\n", + " trajectories=[\n", + " 'data/r.dcd',\n", + " 'data/p.dcd'\n", + " ],\n", + " top=['data/r.pdb', \n", + " 'data/p.pdb'],\n", + " cutoff=8.0, # Ang\n", + " create_labels=True,\n", + " system_selection='all and not type H',\n", + " show_progress=False\n", + ")\n", + "\n", + "datamodule_graph = GraphDataModule(dataset_graph, shuffle=[1, 0])\n", + "print(datamodule_graph)\n", + "\n", + "\n", + "from mlcolvar.utils.io import create_dataset_from_files\n", + "from mlcolvar.data import DictModule\n", + "\n", + "dataset_ff = create_dataset_from_files(file_names=['data/colvar_r.dat',\n", + " 'data/colvar_p.dat'],\n", + " filter_args={'regex': 'd'})\n", + "\n", + "datamodule_ff = DictModule(dataset_ff, lengths=[1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset_graph.metadata['cutoff'],\n", + " atomic_numbers=dataset_graph.metadata['z_table'],\n", + " n_bases=6,\n", + " n_layers=2,\n", + " n_filters=32,\n", + " n_hidden_channels=32\n", + " )\n", + "\n", + "# gnn_model = SchNetModel(n_out=1,\n", + "# cutoff=dataset_graph.cutoff,\n", + "# atomic_numbers=dataset_graph.atomic_numbers,\n", + "# n_bases=6,\n", + "# n_layers=2,\n", + "# n_filters=32,\n", + "# n_hidden_channels=32\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'gnn_model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['gnn_model'])`.\n" + ] + } + ], + "source": [ + "from mlcolvar.cvs.supervised.deeptda_merged import DeepTDA\n", + "\n", + "model_graph = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " layers=[21, 15, 10, 1],\n", + " gnn_model=gnn_model)\n", + "\n", + "model_ff = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " layers=[21, 15, 10, 1],\n", + " gnn_model=None)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2af9a70d01084bd88e864340cefb3cb1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(out_graph.detach().squeeze())\n", + "plt.hist(out_ff.detach().squeeze())\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pytorch_geometric", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fb2580025708e76697d876ce9ed9c907224b5dcd Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 14 Nov 2024 09:27:52 +0100 Subject: [PATCH 129/267] Fixed requirements and test env --- devtools/conda-envs/test_env.yaml | 6 +++++- requirements.txt | 5 ++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/devtools/conda-envs/test_env.yaml b/devtools/conda-envs/test_env.yaml index 1b848511..3b321c7f 100644 --- a/devtools/conda-envs/test_env.yaml +++ b/devtools/conda-envs/test_env.yaml @@ -3,6 +3,7 @@ channels: - pytorch - conda-forge + - pyg - defaults dependencies: @@ -24,10 +25,13 @@ dependencies: - matplotlib - scikit-learn - scipy - + - pyg + - matscipy + # Pip-only installs - pip: - KDEpy - nbmake - torch-scatter + - mdtraj diff --git a/requirements.txt b/requirements.txt index 732b44f5..e880aa22 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,7 @@ numpy<2 pandas matplotlib kdepy -torch-scatter \ No newline at end of file +torch-scatter +torch_geometric +matscipy +mdtraj \ No newline at end of file From e6bf404503b587afe1d8798a2e6eaf1743f6315b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 14 Nov 2024 09:28:17 +0100 Subject: [PATCH 130/267] update example notebook --- test_graphs/test_graph.ipynb | 95 ++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 3656035f..0b2bd1c0 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -62,6 +62,101 @@ "datamodule_ff = DictModule(dataset_ff, lengths=[1])\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_graph" + ] + }, { "cell_type": "code", "execution_count": 6, From d50c12b278ad078e147842238b1df631b045f92d Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 14 Nov 2024 09:32:14 +0100 Subject: [PATCH 131/267] Moved matscipy to pip --- devtools/conda-envs/test_env.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/devtools/conda-envs/test_env.yaml b/devtools/conda-envs/test_env.yaml index 3b321c7f..8ba6bb20 100644 --- a/devtools/conda-envs/test_env.yaml +++ b/devtools/conda-envs/test_env.yaml @@ -26,7 +26,6 @@ dependencies: - scikit-learn - scipy - pyg - - matscipy # Pip-only installs - pip: @@ -34,4 +33,6 @@ dependencies: - nbmake - torch-scatter - mdtraj + - matscipy + From af5d23892c701b858799a698bfadac746ac31ac7 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 14 Nov 2024 09:54:13 +0100 Subject: [PATCH 132/267] Fixed metadata initialization --- mlcolvar/data/dataset.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index 72b76928..1fa1bdc3 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -45,6 +45,10 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = if len(dictionary) == 0: raise ValueError("Empty datasets are not supported") + # initialize metadata as dict + if metadata is None: + metadata = {} + # convert to torch.Tensors for key, val in dictionary.items(): if not isinstance(val, torch.Tensor): From 911e213920fe1216ffc203b03d0cef94e6d484a7 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 14 Nov 2024 10:28:06 +0100 Subject: [PATCH 133/267] added data type key to dict dataset and metadata --- mlcolvar/data/dataset.py | 9 ++++++++- mlcolvar/data/graph/dataset.py | 3 ++- mlcolvar/utils/io.py | 2 +- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index 1fa1bdc3..1eb8c5bf 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -16,7 +16,7 @@ class DictDataset(Dataset): 'weights' : np.asarray([0.5,1.5,1.5,0.5]) } """ - def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = None, **kwargs): + def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = None, data_type : str = 'descriptors', **kwargs): """Create a Dataset from a dictionary or from a list of kwargs. Parameters @@ -38,6 +38,12 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = f"DictDataset metadata requires a dictionary , not {type(metadata)}." ) + # assert data_type is 'descriptors' or 'graphs' + if not data_type in ['descriptors', 'graphs']: + raise TypeError( + f"data_type expected to be either 'descriptors' or 'graph', found {data_type}" + ) + # Add kwargs to dict if dictionary is None: dictionary = {} @@ -48,6 +54,7 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = # initialize metadata as dict if metadata is None: metadata = {} + metadata['data_type'] = data_type # convert to torch.Tensors for key, val in dictionary.items(): diff --git a/mlcolvar/data/graph/dataset.py b/mlcolvar/data/graph/dataset.py index 6a287433..d0cab33e 100644 --- a/mlcolvar/data/graph/dataset.py +++ b/mlcolvar/data/graph/dataset.py @@ -260,7 +260,8 @@ def create_dataset_from_configurations( # dataset = GraphDataSet(data_list, z_table.zs, cutoff) dataset = DictDataset(dictionary={'data_list' : data_list}, metadata={'z_table' : z_table.zs, - 'cutoff' : cutoff}) + 'cutoff' : cutoff}, + data_type='graphs') return dataset diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index 64b7a734..5344e29e 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -254,7 +254,7 @@ def create_dataset_from_files( dictionary = {"data": torch.Tensor(df_data.values)} if create_labels: dictionary["labels"] = torch.Tensor(df["labels"].values) - dataset = DictDataset(dictionary, feature_names=df_data.columns.values) + dataset = DictDataset(dictionary, feature_names=df_data.columns.value, data_type='descriptors') if return_dataframe: return dataset, df From c27f2aab7628090168ef331c6f4688d48d7faa6e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 14 Nov 2024 10:37:18 +0100 Subject: [PATCH 134/267] fixed typo --- mlcolvar/utils/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index 5344e29e..3f881d75 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -254,7 +254,7 @@ def create_dataset_from_files( dictionary = {"data": torch.Tensor(df_data.values)} if create_labels: dictionary["labels"] = torch.Tensor(df["labels"].values) - dataset = DictDataset(dictionary, feature_names=df_data.columns.value, data_type='descriptors') + dataset = DictDataset(dictionary, feature_names=df_data.columns.values, data_type='descriptors') if return_dataframe: return dataset, df From 21a21033c795021911668d36b357b6da0ebbfc86 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 13:14:11 +0100 Subject: [PATCH 135/267] Added utils to save and load datasets --- mlcolvar/data/utils.py | 163 ++++++++++++++++++++++++++++++ mlcolvar/tests/test_data_utils.py | 4 + 2 files changed, 167 insertions(+) create mode 100644 mlcolvar/data/utils.py create mode 100644 mlcolvar/tests/test_data_utils.py diff --git a/mlcolvar/data/utils.py b/mlcolvar/data/utils.py new file mode 100644 index 00000000..4fc596e7 --- /dev/null +++ b/mlcolvar/data/utils.py @@ -0,0 +1,163 @@ +import torch +import numpy as np + +from mlcolvar.data import DictDataset +from mlcolvar.data.graph.atomic import AtomicNumberTable + +__all__ = ["save_dataset", "load_dataset", "save_dataset_configurations_as_extyz"] + +def save_dataset(dataset: DictDataset, file_name: str) -> None: + """ + Save a dataset to disk. + + Parameters + ---------- + dataset: DictDataset + Dataset to be saved + file_name: str + Name of the file to save to + """ + assert isinstance(dataset, DictDataset) + + torch.save(dataset, file_name) + + +def load_dataset(file_name: str) -> DictDataset: + """ + Load a dataset from disk. + + Parameters + ---------- + file_name: str + Name of the file to load the dataset from + """ + dataset = torch.load(file_name) + + assert isinstance(dataset, DictDataset) + + return dataset + + +def save_dataset_configurations_as_extyz(dataset: DictDataset, file_name: str) -> None: + """ + Save a dataset to disk in the extxyz format. + + Parameters + ---------- + dataset: DictDataset + Dataset to be saved with data_type graphs + file_name: str + Name of the file to save to + """ + # check the dataset type is 'graphs' + if not dataset.metadata["data_type"] == "graphs": + raise( + ValueError("Can only save to extxyz dataset with data_type='graphs'!") + ) + + # initialize the atomic number object + z_table = AtomicNumberTable.from_zs(dataset.metadata["z_table"]) + + # create file + fp = open(file_name, 'w') + + for d in dataset: + d = d['data_list'] + + # print number of atoms + print(len(d['positions']), file=fp) + + # header line for configuration d + # Lattice, properties, pbc + line = ( + 'Lattice="{:s}" '.format((r'{:.5f} ' * 9).strip()) + + 'Properties=species:S:1:pos:R:3 pbc="T T T"' + ) + + # cell info + cell = [c.item() for c in d['cell'].flatten()] + print(line.format(*cell), file=fp) + + # write atoms positions + for i in range(0, len(d['positions'])): + # chemical symbol + s = z_table.index_to_symbol(np.where(d['node_attrs'][i])[0][0]) + print('{:2s}'.format(s), file=fp, end=' ') + + # positions + positions = [p.item() for p in d['positions'][i]] + print('{:10.5f} {:10.5f} {:10.5f}'.format(*positions), file=fp) + fp.close() + + + + +import os + +def test_save_dataset(): + # check using descriptors dataset + dataset_dict = { + "data": torch.Tensor([[1.0], [2.0], [0.3], [0.4]]), + "labels": [0, 0, 1, 1], + "weights": np.asarray([0.5, 1.5, 1.5, 0.5]), + } + dataset = DictDataset(dataset_dict) + + # save to current working directory, so we can delete it + cwd = os.getcwd() + save_dataset(dataset=dataset, file_name=f'{cwd}/saved_dataset') + + # load and check it's ok + loaded = load_dataset(file_name=f'{cwd}/saved_dataset') + assert(torch.allclose(dataset['data'], loaded['data'])) + + # remove file + os.remove(f'{cwd}/saved_dataset') + + + + # check using graph dataset + from mlcolvar.data.graph.atomic import AtomicNumberTable, Configuration + from mlcolvar.data.graph.utils import create_dataset_from_configurations + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = AtomicNumberTable.from_zs(numbers) + + config = [Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + )] + dataset = create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) + + # save dataset + save_dataset(dataset=dataset, file_name=f'{cwd}/saved_dataset') + + # load and check it's ok + loaded = load_dataset(file_name=f'{cwd}/saved_dataset') + assert(torch.allclose(dataset['data_list'][0]['positions'], loaded['data_list'][0]['positions'])) + + # remove file + os.remove(f'{cwd}/saved_dataset') + + # save to extxyz + save_dataset_configurations_as_extyz(dataset=dataset, file_name=f'{cwd}/saved_dataset') + + # remove file + os.remove(f'{cwd}/saved_dataset') + +if __name__ == "__main__": + test_save_dataset() + + diff --git a/mlcolvar/tests/test_data_utils.py b/mlcolvar/tests/test_data_utils.py new file mode 100644 index 00000000..87c00726 --- /dev/null +++ b/mlcolvar/tests/test_data_utils.py @@ -0,0 +1,4 @@ +from mlcolvar.data.utils import test_save_dataset + +if __name__=="main": + test_save_dataset \ No newline at end of file From 2f7995e091884e3d5663f4a5edcf61ced748de72 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 13:18:36 +0100 Subject: [PATCH 136/267] Grouped utils functions for graph datasets --- mlcolvar/data/graph/utils.py | 519 ++++++++++++++++++++++++ mlcolvar/tests/test_data_graph_utils.py | 5 + 2 files changed, 524 insertions(+) create mode 100644 mlcolvar/data/graph/utils.py create mode 100644 mlcolvar/tests/test_data_graph_utils.py diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py new file mode 100644 index 00000000..6717a795 --- /dev/null +++ b/mlcolvar/data/graph/utils.py @@ -0,0 +1,519 @@ +import torch +import torch_geometric + +from mlcolvar.data import DictDataset +from mlcolvar.data.graph import atomic +from mlcolvar.data.graph.neighborhood import get_neighborhood +from mlcolvar.utils.plot import pbar + +def _create_dataset_from_configuration( + config: atomic.Configuration, + z_table: atomic.AtomicNumberTable, + cutoff: float, + buffer: float = 0.0 +) -> torch_geometric.data.Data: + """ + Build the graph data object from a configuration. + + Parameters + ---------- + config: mlcolvar.graph.utils.atomic.Configuration + The configuration. + z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. + cutoff: float + The graph cutoff radius. + buffer: float + Buffer size used in finding active environment atoms. + """ + + assert config.graph_labels is None or len(config.graph_labels.shape) == 2 + + # NOTE: here we do not take care about the nodes that are not taking part + # the graph, like, we don't even change the node indices in `edge_index`. + # Here we simply ignore them, and rely on the `RemoveIsolatedNodes` method + # that will be called later (in `create_dataset_from_configurations`). + edge_index, shifts, unit_shifts = get_neighborhood( + positions=config.positions, + cutoff=cutoff, + cell=config.cell, + pbc=config.pbc, + system_indices=config.system, + environment_indices=config.environment, + buffer=buffer + ) + edge_index = torch.tensor(edge_index, dtype=torch.long) + shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) + unit_shifts = torch.tensor( + unit_shifts, dtype=torch.get_default_dtype() + ) + + positions = torch.tensor( + config.positions, dtype=torch.get_default_dtype() + ) + cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) + + indices = z_table.zs_to_indices(config.atomic_numbers) + one_hot = to_one_hot( + torch.tensor(indices, dtype=torch.long).unsqueeze(-1), + n_classes=len(z_table), + ) + + node_labels = ( + torch.tensor(config.node_labels, dtype=torch.get_default_dtype()) + if config.node_labels is not None + else None + ) + + graph_labels = ( + torch.tensor(config.graph_labels, dtype=torch.get_default_dtype()) + if config.graph_labels is not None + else None + ) + + weight = ( + torch.tensor(config.weight, dtype=torch.get_default_dtype()) + if config.weight is not None + else 1 + ) + + n_system = ( + torch.tensor( + [[len(config.system)]], dtype=torch.get_default_dtype() + ) if config.system is not None + else torch.tensor( + [[one_hot.shape[0]]], dtype=torch.get_default_dtype() + ) + ) + + if config.system is not None: + system_masks = torch.zeros((one_hot.shape[0], 1), dtype=torch.bool) + system_masks[config.system, 0] = 1 + else: + system_masks = None + + return torch_geometric.data.Data( + edge_index=edge_index, + shifts=shifts, + unit_shifts=unit_shifts, + positions=positions, + cell=cell, + node_attrs=one_hot, + node_labels=node_labels, + graph_labels=graph_labels, + n_system=n_system, + system_masks=system_masks, + weight=weight, + ) + + +def create_dataset_from_configurations( + config: atomic.Configurations, + z_table: atomic.AtomicNumberTable, + cutoff: float, + buffer: float = 0.0, + remove_isolated_nodes: bool = False, + show_progress: bool = True +) -> DictDataset: + """ + Build graph data objects from configurations. + + Parameters + ---------- + config: mlcolvar.graph.utils.atomic.Configurations + The configurations. + z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable + The atomic number table used to build the node attributes. + cutoff: float + The graph cutoff radius. + buffer: float + Buffer size used in finding active environment atoms. + remove_isolated_nodes: bool + If remove isolated nodes from the dataset. + show_progress: bool + If show the progress bar. + """ + if show_progress: + items = pbar(config, frequency=0.0001, prefix='Making graphs') + else: + items = config + + data_list = [ + _create_dataset_from_configuration( + c, z_table, cutoff, buffer + ) for c in items + ] + + if remove_isolated_nodes: + # TODO: not the worst way to fake the `is_node_attr` method of + # `torch_geometric.data.storage.GlobalStorage` ... + # I mean, when there are exact three atoms in the graph, the + # `RemoveIsolatedNodes` method will remove the cell vectors that + # correspond to the isolated node ... This is a consequence of that + # pyg regarding the cell vectors as some kind of node features. + # So here we first remove the isolated nodes, then set the cell back. + cell_list = [d.cell.clone() for d in data_list] + transform = torch_geometric.transforms.remove_isolated_nodes.RemoveIsolatedNodes() + data_list = [transform(d) for d in data_list] + for i in range(len(data_list)): + data_list[i].cell = cell_list[i] + + dataset = DictDataset(dictionary={'data_list' : data_list}, + metadata={'z_table' : z_table.zs, + 'cutoff' : cutoff}, + data_type='graphs') + + return dataset + +def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: + """ + Generates one-hot encoding with `n_classes` classes from `indices` + + Parameters + ---------- + indices: torch.Tensor (shape: [N, 1]) + Node incices. + n_classes: int + Number of classes. + + Returns + ------- + encoding: torch.tensor (shape: [N, n_classes]) + The one-hot encoding. + """ + shape = indices.shape[:-1] + (n_classes,) + oh = torch.zeros(shape, device=indices.device).view(shape) + + # scatter_ is the in-place version of scatter + oh.scatter_(dim=-1, index=indices, value=1) + + return oh.view(*shape) + + + + +# =============================================================================== +# =============================================================================== +# ==================================== TESTS ==================================== +# =============================================================================== +# =============================================================================== + +import numpy as np + +def test_from_configuration() -> None: + # fake atomic numbers, positions, cell, graph label, node labels + numbers = [8, 1, 1] + positions = np.array([[0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0]], + dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + + # init AtomicNumber object + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + # initialize configuration using all atoms + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + + # create dataset from a configuration + data = _create_dataset_from_configuration(config, z_table, 0.1) + + # check edges and shifts are created correctly + assert(data['edge_index'] == torch.tensor([[0, 0, 1, 1, 2, 2], + [2, 1, 0, 2, 1, 0]]) + ).all() + + assert(data['shifts'] == torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0]]) + ).all() + + assert(data['unit_shifts'] == torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0]]) + ).all() + + # check correct storage + assert(data['positions'] == torch.tensor([[0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0]]) + ).all() + + assert(data['cell'] == torch.tensor([[0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2]]) + ).all() + + assert(data['node_attrs'] == torch.tensor([[0.0, 1.0], + [1.0, 0.0], + [1.0, 0.0]]) + ).all() + + assert(data['node_labels'] == torch.tensor([[0.0], + [1.0], + [1.0]]) + ).all() + + assert(data['graph_labels'] == torch.tensor([[1.0]])).all() + assert(data['weight'] == 1.0) + + # initialize configuration using two atoms (1 system, 1 env) as a subset + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + system=[1], + environment=[2] + ) + + data = _create_dataset_from_configuration(config, z_table, 0.1) + + # check edges and shift are computed correctly + assert(data['edge_index'] == torch.tensor([[1, 2], + [2, 1]]) + ).all() + assert (data['shifts'] == torch.tensor([[0.0, 0.2, 0.0], + [0.0, -0.2, 0.0]]) + ).all() + assert(data['unit_shifts'] == torch.tensor([[0.0, 1.0, 0.0], + [0.0, -1.0, 0.0]]) + ).all() + + # initialize configuration using three atoms (1 system, 2 env) as a subset and no buffer + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + system=[0], + environment=[1, 2] + ) + + data = _create_dataset_from_configuration(config, z_table, 0.1) + assert(data['edge_index'] == torch.tensor([[0, 0, 1, 1, 2, 2], + [2, 1, 0, 2, 1, 0]]) + ).all() + + + # check if pbc and cutoffs works. now the third atoms is too far + positions = np.array([[0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.08, 0.0]], + dtype=float + ) + + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + system=[0], + environment=[1, 2] + ) + # create dataset with same cutoff + data = _create_dataset_from_configuration(config, z_table, 0.1) + + # check third atom is not included anymore + assert (data['edge_index'] == torch.tensor([[0, 1], + [1, 0]]) + ).all() + + # create dataset with slightly large cutoff + data = _create_dataset_from_configuration(config, z_table, 0.11) + + # check the edge with the third atom is created once again + assert(data['edge_index'] == torch.tensor([[0, 0, 1, 1, 2, 2], + [2, 1, 0, 2, 1, 0]]) + ).all() + + # check with buffer layer + # the third atoms should be included but with no edge to the system atom + data = _create_dataset_from_configuration(config, z_table, 0.1, 0.01) + assert(data['edge_index'] == torch.tensor([[0, 1, 1, 2], + [1, 0, 2, 1]]) + ).all() + assert(data['shifts'] == torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0]]) + ).all() + assert(data['unit_shifts'] == torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0]]) + ).all() + + # create a list of configurations + config = [atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=np.array([[i]]), + ) for i in range(0, 10)] + + # create dataset from list of configurations + dataset = create_dataset_from_configurations(config, + z_table, + 0.1, + show_progress=False) + + # check if the labels of the entries are created correctly + assert dataset.metadata['z_table'] == [1, 8] + assert (dataset[0]['data_list']['graph_labels'] == torch.tensor([[0.0]])).all() + assert (dataset[2]['data_list']['graph_labels'] == torch.tensor([[2.0]])).all() + assert (dataset[4]['data_list']['graph_labels'] == torch.tensor([[4.0]])).all() + + # dataset_1 = dataset[np.array([0, -1])] + assert dataset.metadata['z_table'] == [1, 8] + assert (dataset[ 0]['data_list']['graph_labels'] == torch.tensor([[0.0]])).all() + assert (dataset[-1]['data_list']['graph_labels'] == torch.tensor([[9.0]])).all() + + + +def test_from_configurations() -> None: + # fake atomic numbers, positions, cell, graph label, node labels + numbers = [8, 1, 1] + positions = np.array([[0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0]], + dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + + # init AtomicNumber object + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + # initialize configuration using all atoms + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) + + # create dataset from a configuration, even if single is the multiple function + dataset = create_dataset_from_configurations([config], + z_table, + 0.1, + remove_isolated_nodes=True, + show_progress=False + )[0] + + # take data entry from the DictDataset + data = dataset['data_list'] + + # check edges and shifts are created correctly + assert(data['edge_index'] == torch.tensor([[0, 0, 1, 1, 2, 2], + [2, 1, 0, 2, 1, 0]]) + ).all() + assert(data['shifts'] == torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0]]) + ).all() + + assert(data['unit_shifts'] == torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0]]) + ).all() + + # check correct storage + assert(data['positions'] == torch.tensor([[0.0, 0.0, 0.0], + [0.07, 0.07, 0.0], + [0.07, -0.07, 0.0]]) + ).all() + + assert(data['cell'] == torch.tensor([[0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2]]) + ).all() + + assert(data['node_attrs'] == torch.tensor([[0.0, 1.0], + [1.0, 0.0], + [1.0, 0.0]]) + ).all() + assert(data['node_labels'] == torch.tensor([[0.0], + [1.0], + [1.0]]) + ).all() + assert(data['graph_labels'] == torch.tensor([[1.0]])).all() + assert(data['weight'] == 1.0) + + # initialize configuration using three atoms (1 system, 2 env) as a subset and no buffer + config = atomic.Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + system=[1], + environment=[2] + ) + dataset = create_dataset_from_configurations([config], + z_table, + 0.1, + remove_isolated_nodes=True, + show_progress=False + )[0] + + # take data entry from the DictDataset + data = dataset['data_list'] + + assert(data['positions'] == torch.tensor([[0.07, 0.07, 0.0], + [0.07, -0.07, 0.0]]) + ).all() + assert(data['cell'] == torch.tensor([[0.2, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, 0.0, 0.2]]) + ).all() + assert(data['node_attrs'] == torch.tensor([[1.0, 0.0], + [1.0, 0.0]]) + ).all() + assert(data['edge_index'] == torch.tensor([[0, 1], + [1, 0]]) + ).all() + assert(data['shifts'] == torch.tensor([[0.0, 0.2, 0.0], + [0.0, -0.2, 0.0]]) + ).all() + assert(data['unit_shifts'] == torch.tensor([[0.0, 1.0, 0.0], + [0.0, -1.0, 0.0]]) + ).all() + +if __name__ == '__main__': + test_from_configuration() + test_from_configurations() \ No newline at end of file diff --git a/mlcolvar/tests/test_data_graph_utils.py b/mlcolvar/tests/test_data_graph_utils.py new file mode 100644 index 00000000..98b59a5b --- /dev/null +++ b/mlcolvar/tests/test_data_graph_utils.py @@ -0,0 +1,5 @@ +from mlcolvar.data.graph.utils import test_from_configuration, test_from_configurations + +if __name__ == "main": + test_from_configuration() + test_from_configurations() \ No newline at end of file From d1e859d5bfa3a342dd1248986fb52db4b7aea3ce Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 13:18:48 +0100 Subject: [PATCH 137/267] Fixed typo --- mlcolvar/tests/test_data_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/tests/test_data_utils.py b/mlcolvar/tests/test_data_utils.py index 87c00726..6ef6eb43 100644 --- a/mlcolvar/tests/test_data_utils.py +++ b/mlcolvar/tests/test_data_utils.py @@ -1,4 +1,4 @@ from mlcolvar.data.utils import test_save_dataset if __name__=="main": - test_save_dataset \ No newline at end of file + test_save_dataset() \ No newline at end of file From 85b5672b5a49ea1dd7deca53f2066c3f959dcb30 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 14:40:42 +0100 Subject: [PATCH 138/267] Fixed imports and first test --- mlcolvar/utils/io.py | 32 +++++++++++++++++++++++--------- 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index 3f881d75..fdf23b76 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -13,9 +13,10 @@ from typing import Union, List, Tuple import mdtraj -from mlcolvar.data import graph as gdata - from mlcolvar.data import DictDataset +from mlcolvar.data.graph.atomic import AtomicNumberTable, Configuration, Configurations +from mlcolvar.data.graph.utils import create_dataset_from_configurations + __all__ = ["load_dataframe", "plumed_to_pandas", "create_dataset_from_files"] @@ -266,7 +267,7 @@ def create_dataset_from_trajectories( top: Union[List[List[str]], List[str], str], cutoff: float, buffer: float = 0.0, - z_table: gdata.atomic.AtomicNumberTable = None, + z_table: AtomicNumberTable = None, folder: str = None, create_labels: bool = True, system_selection: str = None, @@ -450,7 +451,7 @@ def create_dataset_from_trajectories( ) configurations.extend(configuration) - dataset = gdata.dataset.create_dataset_from_configurations( + dataset = create_dataset_from_configurations( configurations, z_table, cutoff, @@ -467,7 +468,7 @@ def create_dataset_from_trajectories( def _z_table_from_top( top: List[mdtraj.Topology] -) -> gdata.atomic.AtomicNumberTable: +) -> AtomicNumberTable: """ Create an atomic number table from the topologies. @@ -480,7 +481,7 @@ def _z_table_from_top( for t in top: atomic_numbers.extend([a.element.number for a in t.atoms]) # atomic_numbers = np.array(atomic_numbers, dtype=int) - z_table = gdata.atomic.AtomicNumberTable.from_zs(atomic_numbers) + z_table = AtomicNumberTable.from_zs(atomic_numbers) return z_table @@ -489,7 +490,7 @@ def _configures_from_trajectory( label: int = None, system_selection: str = None, environment_selection: str = None, -) -> gdata.atomic.Configurations: +) -> Configurations: """ Create configurations from one trajectory. @@ -536,7 +537,7 @@ def _configures_from_trajectory( configurations = [] for i in range(len(trajectory)): - configuration = gdata.atomic.Configuration( + configuration = Configuration( atomic_numbers=atomic_numbers, positions=trajectory.xyz[i] * 10, cell=cell[i] * 10, @@ -607,6 +608,19 @@ def test_modifier(x): stride=1, ) +def test_datasesetFromTrajectories(): + create_dataset_from_trajectories( + trajectories=['r.dcd', + 'p.dcd'], + top=['r.pdb', + 'p.pdb'], + folder="mlcolvar/tests/data", + cutoff=8.0, # Ang + create_labels=True, + system_selection='all and not type H', + show_progress=False + ) if __name__ == "__main__": - test_datasetFromFile() \ No newline at end of file + test_datasetFromFile() + test_datasesetFromTrajectories() \ No newline at end of file From 48eb8bca5133d46d6ec4e0da0b566c9464eb8fbe Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 14:41:07 +0100 Subject: [PATCH 139/267] Added test files for graphs --- mlcolvar/tests/data/p.dcd | Bin 0 -> 246676 bytes mlcolvar/tests/data/p.pdb | 21 +++++++++++++++++++++ mlcolvar/tests/data/r.dcd | Bin 0 -> 246676 bytes mlcolvar/tests/data/r.pdb | 21 +++++++++++++++++++++ 4 files changed, 42 insertions(+) create mode 100644 mlcolvar/tests/data/p.dcd create mode 100644 mlcolvar/tests/data/p.pdb create mode 100644 mlcolvar/tests/data/r.dcd create mode 100644 mlcolvar/tests/data/r.pdb diff --git a/mlcolvar/tests/data/p.dcd b/mlcolvar/tests/data/p.dcd new file mode 100644 index 0000000000000000000000000000000000000000..9ac637e37c31e226a9d89d81ab66b4f28a5f9012 GIT binary patch literal 246676 zcmbrmcR1DmAOCM}WG6B*UXObzNueRM 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1eb8c5bf..aa93270e 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -2,8 +2,6 @@ import numpy as np from mlcolvar.core.transform.utils import Statistics from torch.utils.data import Dataset -import torch_geometric -from mlcolvar.data.graph.atomic import AtomicNumberTable __all__ = ["DictDataset"] @@ -148,13 +146,16 @@ def feature_names(self, value): def test_DictDataset(): + # descriptors based # from list dataset_dict = { "data": torch.Tensor([[1.0], [2.0], [0.3], [0.4]]), "labels": [0, 0, 1, 1], "weights": np.asarray([0.5, 1.5, 1.5, 0.5]), } - + + # this to have the right signature in asserts + from mlcolvar.data.dataset import DictDataset dataset = DictDataset(dataset_dict) print(len(dataset)) print(dataset[0]) @@ -163,18 +164,44 @@ def test_DictDataset(): # test with dataloader from torch.utils.data import DataLoader - loader = DataLoader(dataset, batch_size=1) batch = next(iter(loader)) print(batch["data"]) # test with fastdataloader - from .dataloader import DictLoader - + from mlcolvar.data import DictLoader loader = DictLoader(dataset, batch_size=1) batch = next(iter(loader)) print(batch) + from mlcolvar.data.graph.atomic import AtomicNumberTable, Configuration + from mlcolvar.data.graph.utils import create_dataset_from_configurations + # graphs based + numbers = [8, 1, 1] + positions = np.array( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=float + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = AtomicNumberTable.from_zs(numbers) + + config = [Configuration( + atomic_numbers=numbers, + positions=positions, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + )] + dataset = create_dataset_from_configurations(config, + z_table, + 0.1, + show_progress=False + ) + print(dataset) + assert(isinstance(dataset, DictDataset)) if __name__ == "__main__": test_DictDataset() \ No newline at end of file From 34d17031d4c430376f940a2ca89c73b01fbb42ae Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 14:42:09 +0100 Subject: [PATCH 141/267] Added test for io utils --- mlcolvar/tests/test_utils_io.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mlcolvar/tests/test_utils_io.py b/mlcolvar/tests/test_utils_io.py index 629ff095..33b6c64e 100644 --- a/mlcolvar/tests/test_utils_io.py +++ b/mlcolvar/tests/test_utils_io.py @@ -2,6 +2,7 @@ import urllib from mlcolvar.utils.io import load_dataframe from mlcolvar.utils.io import test_datasetFromFile +from mlcolvar.utils.io import test_datasesetFromTrajectories example_files = { "str": "mlcolvar/tests/data/state_A.dat", @@ -24,5 +25,5 @@ def test_loadDataframe(file_type): if __name__ == "__main__": - # test_loadDataframe() test_datasetFromFile() + test_datasesetFromTrajectories() From 353b0696a9d17308928fc13f410eff7c19c805b5 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 15 Nov 2024 14:42:30 +0100 Subject: [PATCH 142/267] Merged datamodule for descritpors and graphs --- mlcolvar/data/datamodule.py | 47 +++++++++++++++++++++++++++---------- 1 file changed, 34 insertions(+), 13 deletions(-) diff --git a/mlcolvar/data/datamodule.py b/mlcolvar/data/datamodule.py index 4323b211..665f560e 100644 --- a/mlcolvar/data/datamodule.py +++ b/mlcolvar/data/datamodule.py @@ -20,6 +20,7 @@ import warnings import torch +import torch_geometric import numpy as np import lightning from torch.utils.data import random_split, Subset @@ -27,7 +28,6 @@ from mlcolvar.data import DictLoader, DictDataset - # ============================================================================= # DICTIONARY DATAMODULE CLASS # ============================================================================= @@ -122,7 +122,25 @@ def __init__( """ super().__init__() self.dataset = dataset - self.lengths = lengths + + # check the type of data we have + if not isinstance(dataset, list): + self._dataset_type = dataset.metadata['data_type'] + else: + it = iter(list(dataset)) + self._dataset_type = next(it).metadata['data_type'] + if not all(d.metadata['data_type'] for d in it): + raise ValueError("not all the dataset are of the same type!") + + + # decide which loader to use + if self._dataset_type == 'descriptors': + self.DataLoader = DictLoader + elif self._dataset_type == 'graphs': + self.DataLoader = torch_geometric.loader.DataLoader + + self._lengths = lengths + # Keeping this private for now. Changing it at runtime would # require changing dataset_split and the dataloaders. self._random_split = random_split @@ -135,6 +153,9 @@ def __init__( ) # Make sure batch_size and shuffle are lists. + + if self._dataset_type == 'graphs' and batch_size == 0: + batch_size = len(dataset) # make this explicit for torch_geometric if isinstance(batch_size, int): self.batch_size = [batch_size for _ in lengths] else: @@ -165,7 +186,7 @@ def train_dataloader(self): """Return training dataloader.""" self._check_setup() if self.train_loader is None: - self.train_loader = DictLoader( + self.train_loader = self.DataLoader( self._dataset_split[0], batch_size=self.batch_size[0], shuffle=self.shuffle[0], @@ -175,12 +196,12 @@ def train_dataloader(self): def val_dataloader(self): """Return validation dataloader.""" self._check_setup() - if len(self.lengths) < 2: + if len(self._lengths) < 2: raise NotImplementedError( "Validation dataset not available, you need to pass two lengths to datamodule." ) if self.valid_loader is None: - self.valid_loader = DictLoader( + self.valid_loader = self.DataLoader( self._dataset_split[1], batch_size=self.batch_size[1], shuffle=self.shuffle[1], @@ -190,12 +211,12 @@ def val_dataloader(self): def test_dataloader(self): """Return test dataloader.""" self._check_setup() - if len(self.lengths) < 3: + if len(self._lengths) < 3: raise NotImplementedError( "Test dataset not available, you need to pass three lengths to datamodule." ) if self.test_loader is None: - self.test_loader = DictLoader( + self.test_loader = self.DataLoader( self._dataset_split[2], batch_size=self.batch_size[2], shuffle=self.shuffle[2], @@ -210,11 +231,11 @@ def teardown(self, stage: str): def __repr__(self) -> str: string = f"DictModule(dataset -> {self.dataset.__repr__()}" - string += f",\n\t\t train_loader -> DictLoader(length={self.lengths[0]}, batch_size={self.batch_size[0]}, shuffle={self.shuffle[0]})" - if len(self.lengths) >= 2: - string += f",\n\t\t valid_loader -> DictLoader(length={self.lengths[1]}, batch_size={self.batch_size[1]}, shuffle={self.shuffle[1]})" - if len(self.lengths) >= 3: - string += f",\n\t\t\ttest_loader =DictLoader(length={self.lengths[2]}, batch_size={self.batch_size[2]}, shuffle={self.shuffle[2]})" + string += f",\n\t\t train_loader -> DictLoader(length={self._lengths[0]}, batch_size={self.batch_size[0]}, shuffle={self.shuffle[0]})" + if len(self._lengths) >= 2: + string += f",\n\t\t valid_loader -> DictLoader(length={self._lengths[1]}, batch_size={self.batch_size[1]}, shuffle={self.shuffle[1]})" + if len(self._lengths) >= 3: + string += f",\n\t\t\ttest_loader =DictLoader(length={self._lengths[2]}, batch_size={self.batch_size[2]}, shuffle={self.shuffle[2]})" string += f")" return string @@ -225,7 +246,7 @@ def _split(self, dataset): """ dataset_split = split_dataset( - dataset, self.lengths, self._random_split, self.generator + dataset, self._lengths, self._random_split, self.generator ) return dataset_split From 84bce7deba15e39629ff06b4236ad69f8a8ffa03 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 20 Nov 2024 14:04:36 +0100 Subject: [PATCH 143/267] first test scripting --- mlcolvar/cvs/supervised/deeptda_merged.py | 4 +- test_graphs/test_graph.ipynb | 399 +++++++++++++++------- 2 files changed, 271 insertions(+), 132 deletions(-) diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py index afac496d..df19372d 100644 --- a/mlcolvar/cvs/supervised/deeptda_merged.py +++ b/mlcolvar/cvs/supervised/deeptda_merged.py @@ -64,7 +64,9 @@ def __init__( """ super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) - self.gnn_model = gnn_model + + # specify that the gnn_model should be handled with scripting + self.gnn_model = torch.jit.script_if_tracing(gnn_model) # ======= LOSS ======= self.loss_fn = TDALoss( diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 0b2bd1c0..0ab33275 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -6,23 +6,20 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import torch_geometric\n", - "from mlcolvar.data import graph as gdata" + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DictModule(dataset -> DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 ),\n", - "\t\t train_loader -> DictLoader(length=0.8, batch_size=1600, shuffle=1),\n", - "\t\t valid_loader -> DictLoader(length=0.2, batch_size=1600, shuffle=0))\n", + "DictModule(dataset -> DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=1600, shuffle=True))\n", "Class 0 dataframe shape: (800, 24)\n", "Class 1 dataframe shape: (800, 24)\n", "\n", @@ -32,8 +29,10 @@ } ], "source": [ - "from mlcolvar.data.graph.datamodule import GraphDataModule\n", + "from mlcolvar.data import DictModule\n", "from mlcolvar.utils.io import create_dataset_from_trajectories\n", + "from mlcolvar.utils.io import create_dataset_from_files\n", + "\n", "\n", "dataset_graph = create_dataset_from_trajectories(\n", " trajectories=[\n", @@ -48,13 +47,10 @@ " show_progress=False\n", ")\n", "\n", - "datamodule_graph = GraphDataModule(dataset_graph, shuffle=[1, 0])\n", + "datamodule_graph = DictModule(dataset_graph, lengths=[1])\n", "print(datamodule_graph)\n", "\n", "\n", - "from mlcolvar.utils.io import create_dataset_from_files\n", - "from mlcolvar.data import DictModule\n", - "\n", "dataset_ff = create_dataset_from_files(file_names=['data/colvar_r.dat',\n", " 'data/colvar_p.dat'],\n", " filter_args={'regex': 'd'})\n", @@ -64,216 +60,286 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "dataset_graph" + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset_graph.metadata['cutoff'],\n", + " atomic_numbers=dataset_graph.metadata['z_table'],\n", + " n_bases=6,\n", + " n_layers=2,\n", + " n_filters=32,\n", + " n_hidden_channels=32\n", + " )" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'gnn_model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['gnn_model'])`.\n" + ] } ], "source": [ - "dataset_graph" + "from mlcolvar.cvs.supervised.deeptda_merged import DeepTDA\n", + "\n", + "model_graph = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " layers=[21, 15, 10, 1],\n", + " gnn_model=gnn_model)\n", + "\n", + "model_ff = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " layers=[21, 15, 10, 1],\n", + " gnn_model=None)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + ] + }, { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0ff6691d40494091b4a1126583be4354", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0 )" + "Training: | | 0/? [00:00)\n", + "tensor([[-1.2945]], grad_fn=)\n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2af9a70d01084bd88e864340cefb3cb1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00)\n", + "tensor([[-1.2945]], grad_fn=)\n", + "tensor([[-0.1347]], grad_fn=)\n", + "tensor([[-1.2945]], grad_fn=)\n", + "tensor([[-0.1347]], grad_fn=)\n", + "tensor([[-1.2945]], grad_fn=)\n" ] } ], "source": [ - "from lightning import Trainer\n", + "print(model_graph(xxx))\n", + "print(model_graph(inp_test))\n", "\n", - "trainer = Trainer(\n", - " logger=False,\n", - " enable_checkpointing=False,\n", - " accelerator='gpu',\n", - " max_epochs=5,\n", - " enable_model_summary=False, \n", - " limit_val_batches=0\n", - ")\n", + "print(traced_model(xxx))\n", + "print(traced_model(inp_test))\n", "\n", - "trainer.fit(model_graph, datamodule_graph)" + "loaded_model = torch.jit.load('test.pt')\n", + "\n", + "print(loaded_model(xxx))\n", + "print(loaded_model(inp_test))\n", + "\n", + "# EVVIVAAAAAA" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -282,7 +348,7 @@ "[{'data_list': DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])}]" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -300,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -317,7 +383,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7640eb978c0409390d64849d15f6726", + "model_id": "b5e8f7bb270543e883b4b918f7a2d461", "version_major": 2, "version_minor": 0 }, @@ -329,10 +395,45 @@ "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=5` reached.\n" + "ename": "RuntimeError", + "evalue": "expand(torch.cuda.FloatTensor{[21]}, size=[]): the number of sizes provided (0) must be greater or equal to the number of dimensions in the tensor (1)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 10\u001b[0m\n\u001b[1;32m 1\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m 2\u001b[0m logger\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 3\u001b[0m enable_checkpointing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 7\u001b[0m limit_val_batches\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 8\u001b[0m )\n\u001b[0;32m---> 10\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_ff\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule_ff\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 576\u001b[0m ckpt_path,\n\u001b[1;32m 577\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 578\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:989\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 986\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 992\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 994\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:1035\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1035\u001b[0m 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\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 202\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:359\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:136\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:240\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 239\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 240\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 242\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:187\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 180\u001b[0m closure()\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:265\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/module.py:1291\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1254\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1257\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1258\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1259\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1260\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[1;32m 1261\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \n\u001b[1;32m 1290\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1291\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/optimizer.py:151\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 151\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:230\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:117\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:373\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 370\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 76\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/adam.py:143\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 143\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 146\u001b[0m params_with_grad \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:104\u001b[0m, in \u001b[0;36mPrecision._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 93\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 94\u001b[0m optimizer: Optimizer,\n\u001b[1;32m 95\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 96\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03m hook is called.\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 102\u001b[0m \n\u001b[1;32m 103\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:140\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:126\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39menable_grad()\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 126\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:315\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[1;32m 314\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step() \u001b[38;5;66;03m# unused hook - call anyway for backward compatibility\u001b[39;00m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_result_cls\u001b[38;5;241m.\u001b[39mfrom_training_step_output(training_step_output, trainer\u001b[38;5;241m.\u001b[39maccumulate_grad_batches)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:382\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module:\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_redirection(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_step\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/cvs/supervised/deeptda_merged.py:133\u001b[0m, in \u001b[0;36mDeepTDA.training_step\u001b[0;34m(self, train_batch, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforward_cv(x)\n\u001b[1;32m 132\u001b[0m \u001b[38;5;66;03m# ===================loss=====================\u001b[39;00m\n\u001b[0;32m--> 133\u001b[0m loss, loss_centers, loss_sigmas \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_loss_terms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 136\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgnn_model\u001b[38;5;241m.\u001b[39m_model_type\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgnn\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;66;03m# data = train_batch.to_dict()\u001b[39;00m\n\u001b[1;32m 139\u001b[0m data \u001b[38;5;241m=\u001b[39m train_batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata_list\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/tda_loss.py:94\u001b[0m, in \u001b[0;36mTDALoss.forward\u001b[0;34m(self, H, labels, return_loss_terms)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28mself\u001b[39m, H: torch\u001b[38;5;241m.\u001b[39mTensor, labels: torch\u001b[38;5;241m.\u001b[39mTensor, return_loss_terms: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 69\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[torch\u001b[38;5;241m.\u001b[39mTensor, Tuple[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor]]:\n\u001b[1;32m 70\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the value of the loss function.\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \n\u001b[1;32m 72\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;124;03m loss term associated to the standard deviations of the target Gaussians.\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtda_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 95\u001b[0m \u001b[43m \u001b[49m\u001b[43mH\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_centers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_sigmas\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbeta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_loss_terms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/tda_loss.py:183\u001b[0m, in \u001b[0;36mtda_loss\u001b[0;34m(H, labels, n_states, target_centers, target_sigmas, alpha, beta, return_loss_terms)\u001b[0m\n\u001b[1;32m 180\u001b[0m sigma \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstd(H_red, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# compute loss function contributes for class i\u001b[39;00m\n\u001b[0;32m--> 183\u001b[0m \u001b[43mloss_centers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m alpha \u001b[38;5;241m*\u001b[39m (mu \u001b[38;5;241m-\u001b[39m target_centers[i])\u001b[38;5;241m.\u001b[39mpow(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 184\u001b[0m loss_sigmas[i] \u001b[38;5;241m=\u001b[39m beta \u001b[38;5;241m*\u001b[39m (sigma \u001b[38;5;241m-\u001b[39m target_sigmas[i])\u001b[38;5;241m.\u001b[39mpow(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# get total model loss\u001b[39;00m\n", + "\u001b[0;31mRuntimeError\u001b[0m: expand(torch.cuda.FloatTensor{[21]}, size=[]): the number of sizes provided (0) must be greater or equal to the number of dimensions in the tensor (1)" ] } ], @@ -351,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -364,12 +465,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -385,6 +486,42 @@ "plt.hist(out_ff.detach().squeeze())\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "\n\nforward(__torch__.mlcolvar.core.nn.graph.schnet.SchNetModel self, Dict(str, Tensor) data, bool scatter_mean=True) -> Tensor:\nExpected a value of type 'Dict[str, Tensor]' for argument 'data' but instead found type 'Union[Tensor, Dict[str, Tensor]]'.\n:\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 167\n pass\n elif self.model_type == 'gnn':\n x = self.nn(x)\n ~~~~~~~ <--- HERE\n return x\n'DeepTDA.forward_cv' is being compiled since it was called from 'DeepTDA.forward'\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 137\n x = self.preprocessing(x)\n \n x = self.forward_cv(x)\n ~~~~~~~~~~~~~~~~~~~~~ <--- HERE\n \n if self.postprocessing is not None:\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_torchscript\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest.pt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mscript\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/module.py:1452\u001b[0m, in \u001b[0;36mLightningModule.to_torchscript\u001b[0;34m(self, file_path, method, example_inputs, **kwargs)\u001b[0m\n\u001b[1;32m 1450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscript\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1451\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _jit_is_scripting():\n\u001b[0;32m-> 1452\u001b[0m torchscript_module \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscript\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1453\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrace\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1454\u001b[0m \u001b[38;5;66;03m# if no example inputs are provided, try to see if model has example_input_array set\u001b[39;00m\n\u001b[1;32m 1455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m example_inputs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_script.py:1324\u001b[0m, in \u001b[0;36mscript\u001b[0;34m(obj, optimize, _frames_up, _rcb, example_inputs)\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mModule):\n\u001b[1;32m 1323\u001b[0m obj \u001b[38;5;241m=\u001b[39m call_prepare_scriptable_func(obj)\n\u001b[0;32m-> 1324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_recursive\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_script_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1325\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_recursive\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer_methods_to_compile\u001b[49m\n\u001b[1;32m 1326\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1327\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1328\u001b[0m obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m__prepare_scriptable__() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__prepare_scriptable__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m obj \u001b[38;5;66;03m# type: ignore[operator]\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:559\u001b[0m, in \u001b[0;36mcreate_script_module\u001b[0;34m(nn_module, stubs_fn, share_types, is_tracing)\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tracing:\n\u001b[1;32m 558\u001b[0m AttributeTypeIsSupportedChecker()\u001b[38;5;241m.\u001b[39mcheck(nn_module)\n\u001b[0;32m--> 559\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcreate_script_module_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnn_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconcrete_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstubs_fn\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:636\u001b[0m, in \u001b[0;36mcreate_script_module_impl\u001b[0;34m(nn_module, concrete_type, stubs_fn)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[38;5;66;03m# Compile methods if necessary\u001b[39;00m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m concrete_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m concrete_type_store\u001b[38;5;241m.\u001b[39mmethods_compiled:\n\u001b[0;32m--> 636\u001b[0m \u001b[43mcreate_methods_and_properties_from_stubs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 637\u001b[0m \u001b[43m \u001b[49m\u001b[43mconcrete_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_stubs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproperty_stubs\u001b[49m\n\u001b[1;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 639\u001b[0m \u001b[38;5;66;03m# Create hooks after methods to ensure no name collisions between hooks and methods.\u001b[39;00m\n\u001b[1;32m 640\u001b[0m \u001b[38;5;66;03m# If done before, hooks can overshadow methods that aren't exported.\u001b[39;00m\n\u001b[1;32m 641\u001b[0m create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:469\u001b[0m, in \u001b[0;36mcreate_methods_and_properties_from_stubs\u001b[0;34m(concrete_type, method_stubs, property_stubs)\u001b[0m\n\u001b[1;32m 466\u001b[0m property_defs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mdef_ \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[1;32m 467\u001b[0m property_rcbs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mresolution_callback \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[0;32m--> 469\u001b[0m \u001b[43mconcrete_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_methods_and_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 470\u001b[0m \u001b[43m \u001b[49m\u001b[43mproperty_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproperty_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defaults\u001b[49m\n\u001b[1;32m 471\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:1047\u001b[0m, in \u001b[0;36mcompile_unbound_method\u001b[0;34m(concrete_type, fn)\u001b[0m\n\u001b[1;32m 1043\u001b[0m stub \u001b[38;5;241m=\u001b[39m make_stub(fn, fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_jit_internal\u001b[38;5;241m.\u001b[39m_disable_emit_hooks():\n\u001b[1;32m 1045\u001b[0m \u001b[38;5;66;03m# We don't want to call the hooks here since the graph that is calling\u001b[39;00m\n\u001b[1;32m 1046\u001b[0m \u001b[38;5;66;03m# this function is not yet complete\u001b[39;00m\n\u001b[0;32m-> 1047\u001b[0m \u001b[43mcreate_methods_and_properties_from_stubs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconcrete_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mstub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stub\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:469\u001b[0m, in \u001b[0;36mcreate_methods_and_properties_from_stubs\u001b[0;34m(concrete_type, method_stubs, property_stubs)\u001b[0m\n\u001b[1;32m 466\u001b[0m property_defs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mdef_ \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[1;32m 467\u001b[0m property_rcbs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mresolution_callback \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[0;32m--> 469\u001b[0m \u001b[43mconcrete_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_methods_and_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 470\u001b[0m \u001b[43m \u001b[49m\u001b[43mproperty_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproperty_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defaults\u001b[49m\n\u001b[1;32m 471\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mRuntimeError\u001b[0m: \n\nforward(__torch__.mlcolvar.core.nn.graph.schnet.SchNetModel self, Dict(str, Tensor) data, bool scatter_mean=True) -> Tensor:\nExpected a value of type 'Dict[str, Tensor]' for argument 'data' but instead found type 'Union[Tensor, Dict[str, Tensor]]'.\n:\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 167\n pass\n elif self.model_type == 'gnn':\n x = self.nn(x)\n ~~~~~~~ <--- HERE\n return x\n'DeepTDA.forward_cv' is being compiled since it was called from 'DeepTDA.forward'\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 137\n x = self.preprocessing(x)\n \n x = self.forward_cv(x)\n ~~~~~~~~~~~~~~~~~~~~~ <--- HERE\n \n if self.postprocessing is not None:\n" + ] + } + ], + "source": [ + "model_graph.to_torchscript('test.pt', method='script')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From a8f689d55c2787088521ae53b6aaccce4e1013d1 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 20 Nov 2024 14:47:52 +0100 Subject: [PATCH 144/267] Removed useless file --- mlcolvar/data/graph/dataset.py | 651 --------------------------------- 1 file changed, 651 deletions(-) delete mode 100644 mlcolvar/data/graph/dataset.py diff --git a/mlcolvar/data/graph/dataset.py b/mlcolvar/data/graph/dataset.py deleted file mode 100644 index d0cab33e..00000000 --- a/mlcolvar/data/graph/dataset.py +++ /dev/null @@ -1,651 +0,0 @@ -import torch -import torch_geometric as tg -import numpy as np -from typing import List, Union - -from mlcolvar.data.graph import atomic -from mlcolvar.data.graph.neighborhood import get_neighborhood -# from mlcolvar.data.graph.utils import torch_tools # moved here one hot -from mlcolvar.utils.plot import pbar # moved - -from mlcolvar.data.dataset import DictDataset - -""" -Build the graph data from a configuration. This module is taken from MACE: -https://github.com/ACEsuit/mace/blob/main/mace/data/atomic_data.py -""" - -__all__ = [ - 'GraphDataSet', - 'create_dataset_from_configurations', - 'save_dataset', - 'save_dataset_as_exyz', - 'load_dataset' -] - - -class GraphDataSet(list): - """ - A very simple graph dataset class. - - Parameters - ---------- - data: List[torch_geometric.data.Data] - The data. - atomic_numbers: List[int] - The atomic numbers used to build the node attributes. - cutoff: float - The graph cutoff radius. - """ - - def __init__( - self, - data: List[tg.data.Data], - atomic_numbers: List[int], - cutoff: float - ) -> None: - super().__init__() - self.extend(data) - self.__atomic_numbers = list(atomic_numbers) - self.__cutoff = cutoff - - def __getitem__( - self, - index: Union[int, slice, list, range, np.ndarray] - ) -> Union['GraphDataSet', tg.data.Data]: - """ - Build sub-dataset from the dataset. - - Parameters - ---------- - index : int, slice or list - Indices of the data. - """ - if type(index) in [slice, list, np.ndarray, range]: - if isinstance(index, slice): - index = list(range(len(self)))[index] - data = [super(GraphDataSet, self).__getitem__(i) for i in index] - return GraphDataSet(data, self.atomic_numbers, self.cutoff) - elif np.issubdtype(type(index), np.integer): - return super(GraphDataSet, self).__getitem__(index) - else: - raise RuntimeError( - 'Could only indexing a GraphDataSet by an int, slice or list!' - ) - - def __repr__(self) -> str: - result = 'GRAPHDATASET [ ' - - data_string = '\033[32m{:d}\033[0m\033[36m 󰡷 \033[0m' - result = result + data_string.format(len(self)) - result = result + '| ' - data_string = '[\033[32m{}\033[0m]\033[36m 󰝨 \033[0m' - result = result + data_string.format( - ('{:d} ' * len(self.atomic_numbers)).strip() - ).format(*self.atomic_numbers) - result = result + '| ' - data_string = '\033[32m{:f}\033[0m\033[36m 󰳁 \033[0m' - result = result + data_string.format(self.cutoff) - result = result + ']' - - return result - - @property - def cutoff(self) -> float: - """ - The graph cutoff radius. - """ - return self.__cutoff - - @property - def atomic_numbers(self) -> List[int]: - """ - The atomic numbers used to build the node attributes. - """ - return self.__atomic_numbers.copy() - - -def _create_dataset_from_configuration( - config: atomic.Configuration, - z_table: atomic.AtomicNumberTable, - cutoff: float, - buffer: float = 0.0 -) -> tg.data.Data: - """ - Build the graph data object from a configuration. - - Parameters - ---------- - config: mlcolvar.graph.utils.atomic.Configuration - The configuration. - z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable - The atomic number table used to build the node attributes. - cutoff: float - The graph cutoff radius. - buffer: float - Buffer size used in finding active environment atoms. - """ - - assert config.graph_labels is None or len(config.graph_labels.shape) == 2 - - # NOTE: here we do not take care about the nodes that are not taking part - # the graph, like, we don't even change the node indices in `edge_index`. - # Here we simply ignore them, and rely on the `RemoveIsolatedNodes` method - # that will be called later (in `create_dataset_from_configurations`). - edge_index, shifts, unit_shifts = get_neighborhood( - positions=config.positions, - cutoff=cutoff, - cell=config.cell, - pbc=config.pbc, - system_indices=config.system, - environment_indices=config.environment, - buffer=buffer - ) - edge_index = torch.tensor(edge_index, dtype=torch.long) - shifts = torch.tensor(shifts, dtype=torch.get_default_dtype()) - unit_shifts = torch.tensor( - unit_shifts, dtype=torch.get_default_dtype() - ) - - positions = torch.tensor( - config.positions, dtype=torch.get_default_dtype() - ) - cell = torch.tensor(config.cell, dtype=torch.get_default_dtype()) - - indices = z_table.zs_to_indices(config.atomic_numbers) - one_hot = to_one_hot( - torch.tensor(indices, dtype=torch.long).unsqueeze(-1), - n_classes=len(z_table), - ) - - node_labels = ( - torch.tensor(config.node_labels, dtype=torch.get_default_dtype()) - if config.node_labels is not None - else None - ) - - graph_labels = ( - torch.tensor(config.graph_labels, dtype=torch.get_default_dtype()) - if config.graph_labels is not None - else None - ) - - weight = ( - torch.tensor(config.weight, dtype=torch.get_default_dtype()) - if config.weight is not None - else 1 - ) - - n_system = ( - torch.tensor( - [[len(config.system)]], dtype=torch.get_default_dtype() - ) if config.system is not None - else torch.tensor( - [[one_hot.shape[0]]], dtype=torch.get_default_dtype() - ) - ) - - if config.system is not None: - system_masks = torch.zeros((one_hot.shape[0], 1), dtype=torch.bool) - system_masks[config.system, 0] = 1 - else: - system_masks = None - - return tg.data.Data( - edge_index=edge_index, - shifts=shifts, - unit_shifts=unit_shifts, - positions=positions, - cell=cell, - node_attrs=one_hot, - node_labels=node_labels, - graph_labels=graph_labels, - n_system=n_system, - system_masks=system_masks, - weight=weight, - ) - - -def create_dataset_from_configurations( - config: atomic.Configurations, - z_table: atomic.AtomicNumberTable, - cutoff: float, - buffer: float = 0.0, - remove_isolated_nodes: bool = False, - show_progress: bool = True -) -> GraphDataSet: - """ - Build graph data objects from configurations. - - Parameters - ---------- - config: mlcolvar.graph.utils.atomic.Configurations - The configurations. - z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable - The atomic number table used to build the node attributes. - cutoff: float - The graph cutoff radius. - buffer: float - Buffer size used in finding active environment atoms. - remove_isolated_nodes: bool - If remove isolated nodes from the dataset. - show_progress: bool - If show the progress bar. - """ - if show_progress: - items = pbar(config, frequency=0.0001, prefix='Making graphs') - else: - items = config - - data_list = [ - _create_dataset_from_configuration( - c, z_table, cutoff, buffer - ) for c in items - ] - - if remove_isolated_nodes: - # TODO: not the worst way to fake the `is_node_attr` method of - # `tg.data.storage.GlobalStorage` ... - # I mean, when there are exact three atoms in the graph, the - # `RemoveIsolatedNodes` method will remove the cell vectors that - # correspond to the isolated node ... This is a consequence of that - # pyg regarding the cell vectors as some kind of node features. - # So here we first remove the isolated nodes, then set the cell back. - cell_list = [d.cell.clone() for d in data_list] - transform = tg.transforms.remove_isolated_nodes.RemoveIsolatedNodes() - data_list = [transform(d) for d in data_list] - for i in range(len(data_list)): - data_list[i].cell = cell_list[i] - - # dataset = GraphDataSet(data_list, z_table.zs, cutoff) - dataset = DictDataset(dictionary={'data_list' : data_list}, - metadata={'z_table' : z_table.zs, - 'cutoff' : cutoff}, - data_type='graphs') - - return dataset - - -def save_dataset(dataset: GraphDataSet, file_name: str) -> None: - """ - Save a dataset to disk. - - Parameters - ---------- - dataset: GraphDataSet - The dataset. - file_name: str - The filename. - """ - assert isinstance(dataset, GraphDataSet) - - torch.save(dataset, file_name) # super torch magic go brrrrrrrrr - - -def load_dataset(file_name: str) -> GraphDataSet: - """ - Load a dataset from disk. - - Parameters - ---------- - file_name: str - The filename. - """ - dataset = torch.load(file_name) - - assert isinstance(dataset, GraphDataSet) - - return dataset - - -def save_dataset_as_exyz(dataset: GraphDataSet, file_name: str) -> None: - """ - Save a dataset to disk in the extxyz format. - - Parameters - ---------- - dataset: GraphDataSet - The dataset. - file_name: str - The filename. - """ - z_table = atomic.AtomicNumberTable.from_zs(dataset.atomic_numbers) - - fp = open(file_name, 'w') - - for d in dataset: - print(len(d['positions']), file=fp) - line = ( - 'Lattice="{:s}" '.format((r'{:.5f} ' * 9).strip()) - + 'Properties=species:S:1:pos:R:3 pbc="T T T"' - ) - cell = [c.item() for c in d['cell'].flatten()] - print(line.format(*cell), file=fp) - for i in range(0, len(d['positions'])): - s = z_table.index_to_symbol(np.where(d['node_attrs'][i])[0][0]) - print('{:2s}'.format(s), file=fp, end=' ') - positions = [p.item() for p in d['positions'][i]] - print('{:10.5f} {:10.5f} {:10.5f}'.format(*positions), file=fp) - - fp.close() - -def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: - """ - Generates one-hot encoding with `n_classes` classes from `indices` - - Parameters - ---------- - indices: torch.Tensor (shape: [N, 1]) - Node incices. - n_classes: int - Number of classes. - - Returns - ------- - encoding: torch.tensor (shape: [N, n_classes]) - The one-hot encoding. - """ - shape = indices.shape[:-1] + (n_classes,) - oh = torch.zeros(shape, device=indices.device).view(shape) - - # scatter_ is the in-place version of scatter - oh.scatter_(dim=-1, index=indices, value=1) - - return oh.view(*shape) - - -def test_from_configuration() -> None: - numbers = [8, 1, 1] - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - dtype=float - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = atomic.AtomicNumberTable.from_zs(numbers) - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - ) - data = _create_dataset_from_configuration(config, z_table, 0.1) - assert ( - data['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - assert ( - data['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data['positions'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.07, 0.07, 0.0], - [0.07, -0.07, 0.0], - ]) - ).all() - assert ( - data['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], - ]) - ).all() - assert ( - data['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() - assert (data['graph_labels'] == torch.tensor([[1.0]])).all() - assert data['weight'] == 1.0 - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - system=[1], - environment=[2] - ) - data = _create_dataset_from_configuration(config, z_table, 0.1) - assert ( - data['edge_index'] == torch.tensor([[1, 2], [2, 1]]) - ).all() - assert ( - data['shifts'] == torch.tensor([[0.0, 0.2, 0.0], [0.0, -0.2, 0.0]]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor( - [[0.0, 1.0, 0.0], [0.0, -1.0, 0.0]] - ) - ).all() - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - system=[0], - environment=[1, 2] - ) - data = _create_dataset_from_configuration(config, z_table, 0.1) - assert ( - data['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.08, 0.0]], - dtype=float - ) - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - system=[0], - environment=[1, 2] - ) - data = _create_dataset_from_configuration(config, z_table, 0.1) - assert ( - data['edge_index'] == torch.tensor([[0, 1], [1, 0]]) - ).all() - data = _create_dataset_from_configuration(config, z_table, 0.11) - assert ( - data['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - data = _create_dataset_from_configuration(config, z_table, 0.1, 0.01) - assert ( - data['edge_index'] == torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]] - ) - ).all() - assert ( - data['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0] - ]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0] - ]) - ).all() - - config = [atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=np.array([[i]]), - ) for i in range(0, 10)] - dataset = create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False - ) - - dataset_1 = dataset[range(0, 5, 2)] - assert dataset_1.atomic_numbers == [1, 8] - assert (dataset_1[0]['graph_labels'] == torch.tensor([[0.0]])).all() - assert (dataset_1[1]['graph_labels'] == torch.tensor([[2.0]])).all() - assert (dataset_1[2]['graph_labels'] == torch.tensor([[4.0]])).all() - - dataset_1 = dataset[np.array([0, -1])] - assert dataset_1.atomic_numbers == [1, 8] - assert (dataset_1[0]['graph_labels'] == torch.tensor([[0.0]])).all() - assert (dataset_1[1]['graph_labels'] == torch.tensor([[9.0]])).all() - - -def test_from_configurations() -> None: - numbers = [8, 1, 1] - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - dtype=float - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = atomic.AtomicNumberTable.from_zs(numbers) - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - ) - data = create_dataset_from_configurations( - [config], z_table, 0.1, remove_isolated_nodes=True, show_progress=False - )[0] - assert ( - data['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - assert ( - data['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data['positions'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.07, 0.07, 0.0], - [0.07, -0.07, 0.0], - ]) - ).all() - assert ( - data['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], - ]) - ).all() - assert ( - data['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - assert (data['node_labels'] == torch.tensor([[0.0], [1.0], [1.0]])).all() - assert (data['graph_labels'] == torch.tensor([[1.0]])).all() - assert data['weight'] == 1.0 - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - system=[1], - environment=[2] - ) - data = create_dataset_from_configurations( - [config], z_table, 0.1, remove_isolated_nodes=True, show_progress=False - )[0] - assert ( - data['positions'] == torch.tensor([ - [0.07, 0.07, 0.0], [0.07, -0.07, 0.0], - ]) - ).all() - assert ( - data['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], - ]) - ).all() - assert ( - data['node_attrs'] == torch.tensor([ - [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - assert ( - data['edge_index'] == torch.tensor([[0, 1], [1, 0]]) - ).all() - assert ( - data['shifts'] == torch.tensor([[0.0, 0.2, 0.0], [0.0, -0.2, 0.0]]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor( - [[0.0, 1.0, 0.0], [0.0, -1.0, 0.0]] - ) - ).all() - - -if __name__ == '__main__': - test_from_configuration() - test_from_configurations() \ No newline at end of file From ddcded49eb197751bb61894bdeb4f12b5b0b0a6c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 20 Nov 2024 17:57:23 +0100 Subject: [PATCH 145/267] Fixed imports --- mlcolvar/core/nn/__init__.py | 3 ++- mlcolvar/core/nn/graph/__init__.py | 4 ++++ 2 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 mlcolvar/core/nn/graph/__init__.py diff --git a/mlcolvar/core/nn/__init__.py b/mlcolvar/core/nn/__init__.py index 4ccf68d1..e8414cfc 100644 --- a/mlcolvar/core/nn/__init__.py +++ b/mlcolvar/core/nn/__init__.py @@ -1,3 +1,4 @@ -__all__ = ["FeedForward"] +__all__ = ["FeedForward", "BaseGNN", "SchNetModel"] from .feedforward import * +from .graph import * \ No newline at end of file diff --git a/mlcolvar/core/nn/graph/__init__.py b/mlcolvar/core/nn/graph/__init__.py new file mode 100644 index 00000000..ae4642f9 --- /dev/null +++ b/mlcolvar/core/nn/graph/__init__.py @@ -0,0 +1,4 @@ +__all__ = ["BaseGNN", "SchNetModel"] + +from .gnn import BaseGNN +from .schnet import SchNetModel \ No newline at end of file From f0dfeb09e8503128d1899b9eb30634be3d97d27c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 20 Nov 2024 17:58:02 +0100 Subject: [PATCH 146/267] Cleaned and fixed useless imports --- mlcolvar/core/nn/graph/gnn.py | 6 ------ mlcolvar/core/nn/graph/schnet.py | 4 ++-- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py index 0f7d4713..00bcc3f3 100644 --- a/mlcolvar/core/nn/graph/gnn.py +++ b/mlcolvar/core/nn/graph/gnn.py @@ -1,14 +1,8 @@ import torch from torch import nn -import numpy as np -import torch_geometric as tg from typing import List, Dict, Tuple -# from mlcolvar.graph import data as gdata from mlcolvar.core.nn.graph import radial -# from mlcolvar.graph.core.nn import schnet -# from mlcolvar.graph.core.nn import gvp_layer -# from mlcolvar.graph.utils import torch_tools """ GNN models. diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index 0b755d13..4a08cbcb 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -6,14 +6,14 @@ from mlcolvar.core.nn.graph.gnn import BaseGNN -from typing import List, Dict, Tuple +from typing import List, Dict """ The SchNet components. This module is taken from the pgy package: https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py """ -__all__ = ['InteractionBlock', 'ShiftedSoftplus'] +__all__ = ["SchNetModel", "InteractionBlock", "ShiftedSoftplus"] class SchNetModel(BaseGNN): """ From 5925c86047f3e767fcb06414f96284f7e7ba6e95 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 20 Nov 2024 17:58:27 +0100 Subject: [PATCH 147/267] Test changing layers to model --- mlcolvar/cvs/cv.py | 30 ++- mlcolvar/cvs/supervised/deeptda_merged.py | 77 ++----- test_graphs/test_graph.ipynb | 240 ++++++++++------------ 3 files changed, 159 insertions(+), 188 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 7bfd95c5..78705db5 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -1,5 +1,7 @@ import torch from mlcolvar.core.transform import Transform +from typing import Union +from mlcolvar.core.nn import FeedForward, BaseGNN class BaseCV: @@ -9,6 +11,8 @@ class BaseCV: To inherit from this class, the class must define a BLOCKS class attribute. """ + DEFAULT_BLOCKS = [] + def __init__( self, in_features, @@ -66,6 +70,24 @@ def example_input_array(self): else self.preprocessing.in_features ) + def parse_model(self, model: Union[list[int], FeedForward, BaseGNN]): + if isinstance(model, list): + self.layers = model + self.BLOCKS = self.DEFAULT_BLOCKS + self._override_model = False + elif isinstance(model, FeedForward) or isinstance(model, BaseGNN): + self.BLOCKS = ['nn'] + self._override_model = True + if isinstance(model, FeedForward): + self.nn = model + elif isinstance(model, BaseGNN): + # GNN models need to be scripted! + self.nn = torch.jit.script_if_tracing(model) + else: + raise ValueError( + "Ma belin se scemo?" + ) + def parse_options(self, options: dict = None): """ Sanitize options and create defaults ({}) if not in options. @@ -78,7 +100,13 @@ def parse_options(self, options: dict = None): """ if options is None: options = {} - + else: + for o in options.keys(): + if o in self.DEFAULT_BLOCKS and self._override_model: + raise ValueError( + "Options on blocks are disabled if a model is provided!" + ) + for b in self.BLOCKS: options.setdefault(b, {}) diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py index df19372d..ea1a28b9 100644 --- a/mlcolvar/cvs/supervised/deeptda_merged.py +++ b/mlcolvar/cvs/supervised/deeptda_merged.py @@ -1,16 +1,13 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward, Normalization +from mlcolvar.core import FeedForward, BaseGNN, Normalization from mlcolvar.core.loss import TDALoss from mlcolvar.data import DictModule - -import torch_geometric - +from typing import Union __all__ = ["DeepTDA"] - class DeepTDA(BaseCV, lightning.LightningModule): """ Deep Targeted Discriminant Analysis (Deep-TDA) CV. @@ -28,7 +25,7 @@ class DeepTDA(BaseCV, lightning.LightningModule): Distance from a simple Gaussian target distribution. """ - BLOCKS = ["norm_in", "nn"] + DEFAULT_BLOCKS = ["norm_in", "nn"] # TODO n_states optional? def __init__( @@ -37,8 +34,7 @@ def __init__( n_cvs: int, target_centers: list, target_sigmas: list, - layers: list, - gnn_model=None, + model: Union[list[int], FeedForward, BaseGNN], options: dict = None, **kwargs, ): @@ -62,11 +58,11 @@ def __init__( Available blocks: ['norm_in', 'nn']. Set 'block_name' = None or False to turn off that block """ + # check what model is + self.parse_model(model=model) - super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) - - # specify that the gnn_model should be handled with scripting - self.gnn_model = torch.jit.script_if_tracing(gnn_model) + # TODO in_features and out_features?? + super().__init__(in_features=0, out_features=n_cvs, **kwargs) # ======= LOSS ======= self.loss_fn = TDALoss( @@ -77,6 +73,7 @@ def __init__( # ======= OPTIONS ======= # parse and sanitize + # TODO make here the checks on options? options = self.parse_options(options) # Save n_states self.n_states = n_states @@ -108,20 +105,20 @@ def __init__( ) # ======= BLOCKS ======= - # Initialize norm_in - o = "norm_in" - if (options[o] is not False) and (options[o] is not None and gnn_model is None): - self.norm_in = Normalization(self.in_features, **options[o]) - - # initialize NN - o = "nn" - if gnn_model is None: - self.nn = FeedForward(layers, **options[o]) - else: - self.nn = gnn_model + if not self._override_model: + # Initialize norm_in + o = "norm_in" + if (options[o] is not False) and (options[o] is not None): + self.norm_in = Normalization(self.in_features, **options[o]) + + # initialize NN + o = "nn" + self.nn = FeedForward(self.layers, **options[o]) + elif self._override_model: + self.nn = model def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: - if self.gnn_model is None: + if isinstance(self.nn, FeedForward): # =================get data=================== x = train_batch["data"] labels = train_batch["labels"] @@ -132,7 +129,7 @@ def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: labels, return_loss_terms=True ) - elif self.gnn_model._model_type=='gnn': + elif isinstance(self.nn, BaseGNN): # data = train_batch.to_dict() data = train_batch['data_list'] data['positions'].requires_grad_(True) @@ -153,36 +150,6 @@ def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: return loss - # def training_step( - # self, train_batch: torch_geometric.data.Batch, *args, **kwargs - # ) -> torch.Tensor: - # """ - # Compute and return the training loss and record metrics. - - # Parameters - # ---------- - # train_batch: torch_geometric.data.Batch - # The data batch. - # """ - # data = train_batch.to_dict() - # data['positions'].requires_grad_(True) - # data['node_attrs'].requires_grad_(True) - - # output = self.forward(data) - - # loss, loss_centers, loss_sigmas = self.loss_fn( - # output, - # train_batch.graph_labels.squeeze(), - # return_loss_terms=True - # ) - - # name = 'train' if self.training else 'valid' - # self.log(f'{name}_loss', loss, on_epoch=True) - # self.log(f'{name}_loss_centers', loss_centers, on_epoch=True) - # self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) - # return loss - - # TODO signature of tests? import numpy as np diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 0ab33275..1ce347df 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -78,17 +78,48 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + "None\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'gnn_model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['gnn_model'])`.\n" + "/home/etrizio/Bin/miniconda3/envs/mlcolvar_dev_graphs/lib/python3.10/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" ] } ], + "source": [ + "from mlcolvar.cvs.supervised.deeptda_merged import DeepTDA\n", + "\n", + "model_graph = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " model=gnn_model)\n", + "\n", + "model_ff = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " model=[21, 15, 10, 1])" + ] + }, + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "from mlcolvar.cvs.supervised.deeptda_merged import DeepTDA\n", "\n", @@ -110,42 +141,53 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: False\n", + "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + "/home/etrizio/Bin/miniconda3/envs/mlcolvar_dev_graphs/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0ff6691d40494091b4a1126583be4354", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00)\n", - "tensor([[-1.2945]], grad_fn=)\n" + "tensor([[0.7130]], grad_fn=)\n", + "tensor([[1.1350]], grad_fn=)\n" ] } ], @@ -306,19 +348,19 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[-0.1347]], grad_fn=)\n", - "tensor([[-1.2945]], grad_fn=)\n", - "tensor([[-0.1347]], grad_fn=)\n", - "tensor([[-1.2945]], grad_fn=)\n", - "tensor([[-0.1347]], grad_fn=)\n", - "tensor([[-1.2945]], grad_fn=)\n" + "tensor([[0.7130]], grad_fn=)\n", + "tensor([[1.1350]], grad_fn=)\n", + "tensor([[0.7130]], grad_fn=)\n", + "tensor([[1.1350]], grad_fn=)\n", + "tensor([[0.7130]], grad_fn=)\n", + "tensor([[1.1350]], grad_fn=)\n" ] } ], @@ -339,16 +381,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'data_list': DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])}]" + "[{'data_list': DataBatch(edge_index=[2, 67200], shifts=[67200, 3], unit_shifts=[67200, 3], positions=[11200, 3], cell=[4800, 3], node_attrs=[11200, 2], graph_labels=[1600, 1], n_system=[1600, 1], weight=[1600], batch=[11200], ptr=[1601])}]" ] }, - "execution_count": 7, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -366,74 +408,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: True\n", + "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + "HPU available: False, using: 0 HPUs\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b5e8f7bb270543e883b4b918f7a2d461", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00 10\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_ff\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule_ff\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 576\u001b[0m ckpt_path,\n\u001b[1;32m 577\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 578\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:989\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 986\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 992\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 994\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:1035\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1035\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:202\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 202\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:359\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:136\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:240\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 239\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 240\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 242\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:187\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 180\u001b[0m closure()\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:265\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/module.py:1291\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1254\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1257\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1258\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1259\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1260\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[1;32m 1261\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \n\u001b[1;32m 1290\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1291\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/optimizer.py:151\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 151\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:230\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:117\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:373\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 370\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 76\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/adam.py:143\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 143\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 146\u001b[0m params_with_grad \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:104\u001b[0m, in \u001b[0;36mPrecision._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 93\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 94\u001b[0m optimizer: Optimizer,\n\u001b[1;32m 95\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 96\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03m hook is called.\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 102\u001b[0m \n\u001b[1;32m 103\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:140\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:126\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39menable_grad()\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 126\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:315\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[1;32m 314\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step() \u001b[38;5;66;03m# unused hook - call anyway for backward compatibility\u001b[39;00m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_result_cls\u001b[38;5;241m.\u001b[39mfrom_training_step_output(training_step_output, trainer\u001b[38;5;241m.\u001b[39maccumulate_grad_batches)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:382\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module:\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_redirection(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_step\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/cvs/supervised/deeptda_merged.py:133\u001b[0m, in \u001b[0;36mDeepTDA.training_step\u001b[0;34m(self, train_batch, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforward_cv(x)\n\u001b[1;32m 132\u001b[0m \u001b[38;5;66;03m# ===================loss=====================\u001b[39;00m\n\u001b[0;32m--> 133\u001b[0m loss, loss_centers, loss_sigmas \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_loss_terms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 136\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgnn_model\u001b[38;5;241m.\u001b[39m_model_type\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgnn\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;66;03m# data = train_batch.to_dict()\u001b[39;00m\n\u001b[1;32m 139\u001b[0m data \u001b[38;5;241m=\u001b[39m train_batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata_list\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/tda_loss.py:94\u001b[0m, in \u001b[0;36mTDALoss.forward\u001b[0;34m(self, H, labels, return_loss_terms)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28mself\u001b[39m, H: torch\u001b[38;5;241m.\u001b[39mTensor, labels: torch\u001b[38;5;241m.\u001b[39mTensor, return_loss_terms: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 69\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[torch\u001b[38;5;241m.\u001b[39mTensor, Tuple[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor]]:\n\u001b[1;32m 70\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the value of the loss function.\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \n\u001b[1;32m 72\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;124;03m loss term associated to the standard deviations of the target Gaussians.\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtda_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 95\u001b[0m \u001b[43m \u001b[49m\u001b[43mH\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_centers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_sigmas\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbeta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_loss_terms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/tda_loss.py:183\u001b[0m, in \u001b[0;36mtda_loss\u001b[0;34m(H, labels, n_states, target_centers, target_sigmas, alpha, beta, return_loss_terms)\u001b[0m\n\u001b[1;32m 180\u001b[0m sigma \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstd(H_red, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# compute loss function contributes for class i\u001b[39;00m\n\u001b[0;32m--> 183\u001b[0m \u001b[43mloss_centers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m alpha \u001b[38;5;241m*\u001b[39m (mu \u001b[38;5;241m-\u001b[39m target_centers[i])\u001b[38;5;241m.\u001b[39mpow(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 184\u001b[0m loss_sigmas[i] \u001b[38;5;241m=\u001b[39m beta \u001b[38;5;241m*\u001b[39m (sigma \u001b[38;5;241m-\u001b[39m target_sigmas[i])\u001b[38;5;241m.\u001b[39mpow(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# get total model loss\u001b[39;00m\n", - "\u001b[0;31mRuntimeError\u001b[0m: expand(torch.cuda.FloatTensor{[21]}, size=[]): the number of sizes provided (0) must be greater or equal to the number of dimensions in the tensor (1)" + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=5` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4: 100%|██████████| 1/1 [00:00<00:00, 61.72it/s]\n" ] } ], @@ -441,7 +446,7 @@ "trainer = Trainer(\n", " logger=False,\n", " enable_checkpointing=False,\n", - " accelerator='gpu',\n", + " accelerator='cpu',\n", " max_epochs=5,\n", " enable_model_summary=False, \n", " limit_val_batches=0\n", @@ -452,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -465,12 +470,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": 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", 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", 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" ] @@ -487,35 +492,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "\n\nforward(__torch__.mlcolvar.core.nn.graph.schnet.SchNetModel self, Dict(str, Tensor) data, bool scatter_mean=True) -> Tensor:\nExpected a value of type 'Dict[str, Tensor]' for argument 'data' but instead found type 'Union[Tensor, Dict[str, Tensor]]'.\n:\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 167\n pass\n elif self.model_type == 'gnn':\n x = self.nn(x)\n ~~~~~~~ <--- HERE\n return x\n'DeepTDA.forward_cv' is being compiled since it was called from 'DeepTDA.forward'\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 137\n x = self.preprocessing(x)\n \n x = self.forward_cv(x)\n ~~~~~~~~~~~~~~~~~~~~~ <--- HERE\n \n if self.postprocessing is not None:\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_torchscript\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest.pt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mscript\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/module.py:1452\u001b[0m, in \u001b[0;36mLightningModule.to_torchscript\u001b[0;34m(self, file_path, method, example_inputs, **kwargs)\u001b[0m\n\u001b[1;32m 1450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscript\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1451\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _jit_is_scripting():\n\u001b[0;32m-> 1452\u001b[0m torchscript_module \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscript\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1453\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrace\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1454\u001b[0m \u001b[38;5;66;03m# if no example inputs are provided, try to see if model has example_input_array set\u001b[39;00m\n\u001b[1;32m 1455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m example_inputs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_script.py:1324\u001b[0m, in \u001b[0;36mscript\u001b[0;34m(obj, optimize, _frames_up, _rcb, example_inputs)\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mModule):\n\u001b[1;32m 1323\u001b[0m obj \u001b[38;5;241m=\u001b[39m call_prepare_scriptable_func(obj)\n\u001b[0;32m-> 1324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_recursive\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_script_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1325\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_recursive\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer_methods_to_compile\u001b[49m\n\u001b[1;32m 1326\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1327\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1328\u001b[0m obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m__prepare_scriptable__() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__prepare_scriptable__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m obj \u001b[38;5;66;03m# type: ignore[operator]\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:559\u001b[0m, in \u001b[0;36mcreate_script_module\u001b[0;34m(nn_module, stubs_fn, share_types, is_tracing)\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tracing:\n\u001b[1;32m 558\u001b[0m AttributeTypeIsSupportedChecker()\u001b[38;5;241m.\u001b[39mcheck(nn_module)\n\u001b[0;32m--> 559\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcreate_script_module_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnn_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconcrete_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstubs_fn\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:636\u001b[0m, in \u001b[0;36mcreate_script_module_impl\u001b[0;34m(nn_module, concrete_type, stubs_fn)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[38;5;66;03m# Compile methods if necessary\u001b[39;00m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m concrete_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m concrete_type_store\u001b[38;5;241m.\u001b[39mmethods_compiled:\n\u001b[0;32m--> 636\u001b[0m \u001b[43mcreate_methods_and_properties_from_stubs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 637\u001b[0m \u001b[43m \u001b[49m\u001b[43mconcrete_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_stubs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproperty_stubs\u001b[49m\n\u001b[1;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 639\u001b[0m \u001b[38;5;66;03m# Create hooks after methods to ensure no name collisions between hooks and methods.\u001b[39;00m\n\u001b[1;32m 640\u001b[0m \u001b[38;5;66;03m# If done before, hooks can overshadow methods that aren't exported.\u001b[39;00m\n\u001b[1;32m 641\u001b[0m create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:469\u001b[0m, in \u001b[0;36mcreate_methods_and_properties_from_stubs\u001b[0;34m(concrete_type, method_stubs, property_stubs)\u001b[0m\n\u001b[1;32m 466\u001b[0m property_defs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mdef_ \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[1;32m 467\u001b[0m property_rcbs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mresolution_callback \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[0;32m--> 469\u001b[0m \u001b[43mconcrete_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_methods_and_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 470\u001b[0m \u001b[43m \u001b[49m\u001b[43mproperty_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproperty_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defaults\u001b[49m\n\u001b[1;32m 471\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:1047\u001b[0m, in \u001b[0;36mcompile_unbound_method\u001b[0;34m(concrete_type, fn)\u001b[0m\n\u001b[1;32m 1043\u001b[0m stub \u001b[38;5;241m=\u001b[39m make_stub(fn, fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_jit_internal\u001b[38;5;241m.\u001b[39m_disable_emit_hooks():\n\u001b[1;32m 1045\u001b[0m \u001b[38;5;66;03m# We don't want to call the hooks here since the graph that is calling\u001b[39;00m\n\u001b[1;32m 1046\u001b[0m \u001b[38;5;66;03m# this function is not yet complete\u001b[39;00m\n\u001b[0;32m-> 1047\u001b[0m \u001b[43mcreate_methods_and_properties_from_stubs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconcrete_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mstub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stub\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/jit/_recursive.py:469\u001b[0m, in \u001b[0;36mcreate_methods_and_properties_from_stubs\u001b[0;34m(concrete_type, method_stubs, property_stubs)\u001b[0m\n\u001b[1;32m 466\u001b[0m property_defs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mdef_ \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[1;32m 467\u001b[0m property_rcbs \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mresolution_callback \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m property_stubs]\n\u001b[0;32m--> 469\u001b[0m \u001b[43mconcrete_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_methods_and_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 470\u001b[0m \u001b[43m \u001b[49m\u001b[43mproperty_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproperty_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_rcbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_defaults\u001b[49m\n\u001b[1;32m 471\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mRuntimeError\u001b[0m: \n\nforward(__torch__.mlcolvar.core.nn.graph.schnet.SchNetModel self, Dict(str, Tensor) data, bool scatter_mean=True) -> Tensor:\nExpected a value of type 'Dict[str, Tensor]' for argument 'data' but instead found type 'Union[Tensor, Dict[str, Tensor]]'.\n:\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 167\n pass\n elif self.model_type == 'gnn':\n x = self.nn(x)\n ~~~~~~~ <--- HERE\n return x\n'DeepTDA.forward_cv' is being compiled since it was called from 'DeepTDA.forward'\n File \"/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/cvs/cv.py\", line 137\n x = self.preprocessing(x)\n \n x = self.forward_cv(x)\n ~~~~~~~~~~~~~~~~~~~~~ <--- HERE\n \n if self.postprocessing is not None:\n" - ] - } - ], - "source": [ - "model_graph.to_torchscript('test.pt', method='script')" - ] - }, { "cell_type": "code", "execution_count": null, From 4b39bc30454127a51fd34f0df2bf3207a4c237cf Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 21 Nov 2024 10:36:22 +0100 Subject: [PATCH 148/267] Fixed typings --- mlcolvar/cvs/cv.py | 4 ++-- mlcolvar/cvs/supervised/deeptda_merged.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 78705db5..58d95764 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -1,6 +1,6 @@ import torch from mlcolvar.core.transform import Transform -from typing import Union +from typing import Union, List from mlcolvar.core.nn import FeedForward, BaseGNN @@ -70,7 +70,7 @@ def example_input_array(self): else self.preprocessing.in_features ) - def parse_model(self, model: Union[list[int], FeedForward, BaseGNN]): + def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): if isinstance(model, list): self.layers = model self.BLOCKS = self.DEFAULT_BLOCKS diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py index ea1a28b9..b57d0921 100644 --- a/mlcolvar/cvs/supervised/deeptda_merged.py +++ b/mlcolvar/cvs/supervised/deeptda_merged.py @@ -4,7 +4,7 @@ from mlcolvar.core import FeedForward, BaseGNN, Normalization from mlcolvar.core.loss import TDALoss from mlcolvar.data import DictModule -from typing import Union +from typing import Union, List __all__ = ["DeepTDA"] @@ -34,7 +34,7 @@ def __init__( n_cvs: int, target_centers: list, target_sigmas: list, - model: Union[list[int], FeedForward, BaseGNN], + model: Union[List[int], FeedForward, BaseGNN], options: dict = None, **kwargs, ): From 6c8d539ab6e40bc613d90dd177534096f584e6b7 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 18 Sep 2024 01:25:59 +0200 Subject: [PATCH 149/267] First attmpt of committor. --- mlcolvar/graph/cvs/__init__.py | 1 + mlcolvar/graph/cvs/committor/__init__.py | 3 + mlcolvar/graph/cvs/committor/committor.py | 172 +++++++++++ mlcolvar/graph/cvs/committor/utils.py | 345 ++++++++++++++++++++++ 4 files changed, 521 insertions(+) create mode 100644 mlcolvar/graph/cvs/committor/__init__.py create mode 100644 mlcolvar/graph/cvs/committor/committor.py create mode 100644 mlcolvar/graph/cvs/committor/utils.py diff --git a/mlcolvar/graph/cvs/__init__.py b/mlcolvar/graph/cvs/__init__.py index 6dff8e4e..7833d8aa 100644 --- a/mlcolvar/graph/cvs/__init__.py +++ b/mlcolvar/graph/cvs/__init__.py @@ -1,3 +1,4 @@ from .cv import GraphBaseCV from .supervised import GraphDeepTDA from .timelagged import GraphDeepTICA +from .committor import GraphCommittor diff --git a/mlcolvar/graph/cvs/committor/__init__.py b/mlcolvar/graph/cvs/committor/__init__.py new file mode 100644 index 00000000..3af5089f --- /dev/null +++ b/mlcolvar/graph/cvs/committor/__init__.py @@ -0,0 +1,3 @@ +from . import utils +from .utils import get_dataset_kolmogorov_bias, compute_committor_weights +from .committor import GraphCommittor diff --git a/mlcolvar/graph/cvs/committor/committor.py b/mlcolvar/graph/cvs/committor/committor.py new file mode 100644 index 00000000..a8ffe427 --- /dev/null +++ b/mlcolvar/graph/cvs/committor/committor.py @@ -0,0 +1,172 @@ + +import torch +import torch_geometric as tg +from typing import Dict, Any, List + +from mlcolvar.core.nn.utils import Custom_Sigmoid + +from mlcolvar.graph.cvs import GraphBaseCV +from mlcolvar.graph.cvs.cv import test_get_data +from mlcolvar.graph.cvs.committor.utils import GraphCommittorLoss +from mlcolvar.graph.utils import torch_tools + +""" +Data-driven learning of committor function, based on Graph Neural Networks +(GNNs). +""" + +__all__ = ['GraphCommittor'] + + +class GraphCommittor(GraphBaseCV): + """ + Data-driven learning of committor function, based on GNNs. + + The committor function q is expressed as the output of a neural network + optimized with a self-consistent approach based on the Kolmogorov's + variational principle for the committor and on the imposition of its + boundary conditions. + + Parameters + ---------- + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + atomic_masses : List[float] + List of masses of all the atoms we are using. + model_name: str + Name of the GNN model. + model_options: Dict[Any, Any] + Model options. + extra_loss_options: Dict[Any, Any] + Extra loss function options. + optimizer_options: Dict[Any, Any] + Optimizer options. + + References + ---------- + .. [*] P. Kang, E. Trizio, and M. Parrinello, "Computing the committor + using the committor to study the transition state ensemble", + Nat. Comput. Sci., 2024, DOI: 10.1038/s43588-024-00645-0 + + See also + -------- + mlcolvar.cvs.committor.Committor + The feedforward NN based ML committor module. + mlcolvar.graph.cvs.committor.utils.GraphCommittorLoss + Kolmogorov's variational optimization of committor and imposition of + boundary conditions. + mlcolvar.graph.cvs.committor.utils.compute_committor_weights + Utils to compute the appropriate weights for the training set + """ + def __init__( + self, + cutoff: float, + atomic_numbers: List[int], + atomic_masses: List[float], + model_name: str = 'GVPModel', + model_options: Dict[Any, Any] = {}, + extra_loss_options: Dict[Any, Any] = { + 'alpha': 1.0, 'gamma': 100.0, 'delta_f': 0.0, 'sigmoid_p': 3.0 + }, + optimizer_options: Dict[Any, Any] = {}, + **kwargs, + ) -> None: + if model_options.pop('n_out', None) is not None: + raise RuntimeError( + 'The `n_out` key of parameter `model_options` will be ignored!' + ) + if optimizer_options != {}: + kwargs['optimizer_options'] = optimizer_options + + super().__init__( + 1, cutoff, atomic_numbers, model_name, model_options, **kwargs + ) + + atomic_masses = torch.tensor( + atomic_masses, dtype=torch.get_default_dtype() + ) + self.register_buffer('atomic_masses', atomic_masses) + self.register_buffer('is_committor', torch.tensor(1, dtype=int)) + + self.sigmoid = Custom_Sigmoid(extra_loss_options.get('sigmoid_p', 3.0)) + + self.loss_fn = GraphCommittorLoss( + atomic_masses, + alpha=float(extra_loss_options.get('alpha', 1.0)), + gamma=float(extra_loss_options.get('gamma', 10000.0)), + delta_f=float(extra_loss_options.get('delta_f', 0.0)), + ) + + def forward_nn( + self, + data: Dict[str, torch.Tensor], + token: bool = False + ) -> torch.Tensor: + """ + The forward pass for the NN. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + token: bool + To be used. + """ + data['positions'].requires_grad_(True) + data['node_attrs'].requires_grad_(True) + + return self._model(data) + + def forward( + self, + data: Dict[str, torch.Tensor], + token: bool = False + ) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + token: bool + To be used. + """ + z = self.forward_nn(data) + q = self.sigmoid(z) + + return torch.hstack([z, q]) + + def training_step( + self, train_batch: tg.data.Batch, *args, **kwargs + ) -> torch.Tensor: + """ + Compute and return the training loss and record metrics. + + Parameters + ---------- + train_batch: torch_geometric.data.Batch + The data batch. + """ + torch.set_grad_enabled(True) + + batch_dict = train_batch.to_dict() + z = self.forward_nn(batch_dict) + q = self.sigmoid(z) + + loss, loss_var, loss_bound_A, loss_bound_B = self.loss_fn( + batch_dict, q + ) + + name = 'train' if self.training else 'valid' + self.log(f'{name}_loss', loss, on_epoch=True) + self.log(f'{name}_loss_variational', loss_var, on_epoch=True) + self.log(f'{name}_loss_boundary_A', loss_bound_A, on_epoch=True) + self.log(f'{name}_loss_boundary_B', loss_bound_B, on_epoch=True) + return loss diff --git a/mlcolvar/graph/cvs/committor/utils.py b/mlcolvar/graph/cvs/committor/utils.py new file mode 100644 index 00000000..779d3b60 --- /dev/null +++ b/mlcolvar/graph/cvs/committor/utils.py @@ -0,0 +1,345 @@ +import torch +import numpy as np + +from typing import Tuple, Dict, Optional, List +from mlcolvar.graph.cvs.cv import GraphBaseCV +from mlcolvar.graph import data as gdata +from mlcolvar.graph import utils as gutils + +""" +GNN committor utils. +""" + +__all__ = [ + 'GraphCommittorLoss', + 'get_dataset_kolmogorov_bias', + 'compute_committor_weights' +] + + +class GraphCommittorLoss(torch.nn.Module): + """ + Compute Kolmogorov's variational principle loss and impose boundary + conditions on the metastable states. Modified for Graph Neural Networks + (GNNs). + + Parameters + ---------- + atomic_masses : torch.Tensor + Atomic masses of the atoms in the system. + alpha : float + Hyperparamer that scales the boundary conditions contribution to loss, + i.e. alpha*(loss_bound_A + loss_bound_B) + gamma : float + Hyperparamer that scales the whole loss to avoid too small numbers, + i.e. gamma*(loss_var + loss_bound), by default 10000 + delta_f : float + Delta free energy between A (label 0) and B (label 1), units is kBT, + by default 0. State B is supposed to be higher in energy. + + See Also + -------- + mlcolvar.core.loss.committor.CommittorLoss + The original `CommittorLoss` module. + """ + + def __init__( + self, + atomic_masses: torch.Tensor, + alpha: float, + gamma: float = 10000.0, + delta_f: float = 0.0 + ) -> None: + super().__init__() + atomic_masses = torch.tensor( + atomic_masses, dtype=torch.get_default_dtype() + ) + self.register_buffer('atomic_masses', atomic_masses) + self.alpha = alpha + self.gamma = gamma + self.delta_f = delta_f + + def forward( + self, + data: Dict[str, torch.Tensor], + q: torch.Tensor, + create_graph: bool = True + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data batch. + q : torch.Tensor + Committor quess q(x), it is the output of NN + """ + return graph_committor_loss( + data=data, + q=q, + atomic_masses=self.atomic_masses, + alpha=self.alpha, + gamma=self.gamma, + delta_f=self.delta_f, + create_graph=create_graph + ) + + +def graph_committor_loss( + data: Dict[str, torch.Tensor], + q: torch.Tensor, + atomic_masses: torch.Tensor, + alpha: float, + gamma: float = 10000.0, + delta_f: float = 0.0, + create_graph: bool = True, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Compute variational loss for committor optimization with boundary + conditions.Modified for Graph Neural Networks (GNNs). + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data batch. + q : torch.Tensor + Committor quess q(x), it is the output of NN + atomic_masses : torch.Tensor + List of masses of all the atoms we are using. + alpha : float + Hyperparamer that scales the boundary conditions contribution to loss, + i.e. alpha*(loss_bound_A + loss_bound_B) + gamma : float + Hyperparamer that scales the whole loss to avoid too small numbers, + i.e. gamma*(loss_var + loss_bound) + delta_f : float + Delta free energy between A (label 0) and B (label 1), units is kBT. + create_graph : bool + Make loss backwardable, deactivate for validation to save memory. + + Returns + ------- + loss : torch.Tensor + Loss value. + gamma*loss_var : torch.Tensor + The variational loss term + gamma*alpha*loss_a : torch.Tensor + The boundary loss term on basin A + gamma*alpha*loss_b : torch.Tensor + The boundary loss term on basin B + + See Also + -------- + mlcolvar.core.loss.committor.committor_loss + The original `committor_loss` function. + """ + # inherit right device and dtpye + dtype = data['positions'].dtype + device = data['positions'].device + + atomic_masses = atomic_masses.to(dtype).to(device) + + # Create masks to access different states data + labels = data['graph_labels'].long().squeeze() + mask_a = labels == 0 + mask_b = labels == 1 + mask_t = labels > 1 + + # Update weights of basin B using the information on the delta_f + factor = torch.exp(torch.tensor([delta_f], dtype=dtype, device=device)) + weights = data['weight'].clone() + if delta_f < 0: # B higher in energy --> A-B < 0 + weights[mask_b] = weights[mask_b] * factor + if delta_f > 0: # A higher in energy --> A-B > 0 + weights[mask_a] = weights[mask_a] * factor + + # Each loss contribution is scaled by the number of samples + + # We need the gradient of q(x) + grad_outputs: Optional[List[Optional[torch.Tensor]]] = [ + torch.ones_like(q, device=device) + ] + gradients = torch.autograd.grad( + [q], + [data['positions']], + grad_outputs=grad_outputs, + retain_graph=True, + create_graph=create_graph + )[0] # [n_nodes, 3] + assert gradients is not None + + # we sanitize the shapes of mass and weights tensors + node_types = torch.where(data['node_attrs'])[1] # [n_graphs, 1] + atomic_masses = atomic_masses[node_types].unsqueeze(-1) # [n_nodes, 1] + weights = weights.unsqueeze(-1) # [n_graphs, 1] + + # square, do the mass-weight, and sum over Cartesian dims + gradients_atomic = torch.pow(gradients, 2) / atomic_masses # [n_nodes, 3] + gradients_atomic = torch.sum( + gradients_atomic, dim=1, keepdim=True + ) # [n_nodes, 1] + # sum over batchs + gradients_batch = gutils.torch_tools.scatter_sum( + gradients_atomic, data['batch'], dim=0 + ) # [n_graphs, 1] + # ensemble avg. + loss_v = torch.mean((gradients_batch * weights)[mask_t]) # [,] + + # boundary conditions + q_a = q[mask_a] + q_b = q[mask_b] + loss_a = torch.mean(torch.pow(q_a, 2)) + loss_b = torch.mean(torch.pow((q_b - 1.0), 2)) + + loss = gamma * (loss_v + alpha * (loss_a + loss_b)) + + return ( + loss, gamma * loss_v, alpha * gamma * loss_a, alpha * gamma * loss_b + ) + + +def get_dataset_kolmogorov_bias( + model: GraphBaseCV, + dataset: gdata.GraphDataSet, + beta: float, + epsilon: float = 1E-6, + lambd: float = 0.0, + batch_size: int = None, + device: str = 'cpu', + show_progress: bool = True, + progress_prefix: str = 'Calculating KM Bias' +) -> np.ndarray: + """ + Wrappper class to compute the Kolmogorov bias V_K from a GNN-based + committor model. + + Parameters + ---------- + input_model : torch.nn.Module + Model to compute the bias from. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the bias. + beta: float + Inverse temperature in the right energy units, i.e. 1/(k_B*T) + epsilon : float + Regularization term in the logarithm. + lambd : float + Multiplicative term for the whole bias. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + """ + epsilon = torch.tensor(epsilon, dtype=torch.float64) + + datamodule = gdata.GraphDataModule( + dataset, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule.setup() + + gradients_list = [] + + if show_progress: + items = gutils.progress.pbar( + datamodule.train_dataloader(), + frequency=0.001, + prefix=progress_prefix + ) + else: + items = datamodule.train_dataloader() + + for batchs in items: + batch_dict = batchs.to(device).to_dict() + q = model(batch_dict) + grad_outputs: Optional[List[Optional[torch.Tensor]]] = [ + torch.ones_like(q, device=device) + ] + gradients = torch.autograd.grad( + outputs=[q], + inputs=[batch_dict['positions']], + grad_outputs=grad_outputs, + retain_graph=False, + create_graph=False, + )[0] + + # square and sum over Cartesian dims + gradients_atomic = torch.pow(gradients, 2) # [n_nodes, 3] + gradients_atomic = torch.sum( + gradients_atomic, dim=1, keepdim=True + ) # [n_nodes, 1] + # sum over batchs + gradients_batch = gutils.torch_tools.scatter_sum( + gradients_atomic, batch_dict['batch'], dim=0 + ) # [n_graphs, 1] + + gradients_list.append(gradients_batch) + + gradients = torch.vstack(gradients_list) + bias = -lambd * (1 / beta) * ( + torch.log(gradients + epsilon) - torch.log(epsilon) + ) + + return bias.cpu().numpy() + + +def compute_committor_weights( + dataset: gdata.GraphDataSet, + bias: torch.Tensor, + beta: float +) -> gdata.GraphDataSet: + """ + Utils to update a `GraphDataSet` object with the appropriate weights for + the training set for the learning of committor function. + + Parameters + ---------- + dataset: mlcovar.graph.data.GraphDataSet + The graph dataset. + bias : torch.Tensor + Bias values for the data in the dataset, usually it should be the + committor-based bias. + beta : float + Inverse temperature in the right energy units + + Returns + ------- + dataset: mlcovar.graph.data.GraphDataSet + Updated dataset with weights and updated labels. + """ + assert len(dataset) == len(bias) + + bias = torch.tensor(bias, dtype=torch.get_default_dtype()) + if bias.isnan().any(): + raise ValueError( + 'Found Nan(s) in bias tensor. Check before proceeding! ' + + 'If no bias was applied replace Nan with zero!' + ) + + # TODO sign if not from committor bias + weights = torch.exp(beta * bias) + labels = torch.tensor( + [d['graph_labels'][0, 0] for d in dataset] + ).long() + + for i in np.unique(labels.cpu().numpy()): + # compute average of exp(beta*V) on this simualtions + coeff = 1 / torch.mean( + weights[torch.nonzero(labels == i, as_tuple=True)] + ) + # update the weights + weights[ + torch.nonzero(labels == i, as_tuple=True) + ] = coeff * weights[ + torch.nonzero(labels == i, as_tuple=True) + ] + + # update dataset + for i in range(len(dataset)): + dataset[i]['weight'] = weights[i] + + return dataset From 9e2a1a1c8f50ea4cc37da3453268b850f7f14363 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 23 Sep 2024 17:29:14 +0200 Subject: [PATCH 150/267] Changed loss function expressions. --- mlcolvar/graph/cvs/committor/utils.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/mlcolvar/graph/cvs/committor/utils.py b/mlcolvar/graph/cvs/committor/utils.py index 779d3b60..0d4470d8 100644 --- a/mlcolvar/graph/cvs/committor/utils.py +++ b/mlcolvar/graph/cvs/committor/utils.py @@ -187,15 +187,13 @@ def graph_committor_loss( loss_v = torch.mean((gradients_batch * weights)[mask_t]) # [,] # boundary conditions - q_a = q[mask_a] - q_b = q[mask_b] - loss_a = torch.mean(torch.pow(q_a, 2)) - loss_b = torch.mean(torch.pow((q_b - 1.0), 2)) + loss_a = torch.mean(torch.pow(q[mask_a], 2)) + loss_b = torch.mean(torch.pow((q[mask_b] - 1.0), 2)) - loss = gamma * (loss_v + alpha * (loss_a + loss_b)) + loss = loss_v.log() + gamma * (alpha * (loss_a + loss_b)) return ( - loss, gamma * loss_v, alpha * gamma * loss_a, alpha * gamma * loss_b + loss, loss_v.log(), alpha * gamma * loss_a, alpha * gamma * loss_b ) From 4b45a9ded1f7da46cc1a0eac091afee340230929 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Mon, 23 Sep 2024 18:28:27 +0200 Subject: [PATCH 151/267] Added two model options. --- mlcolvar/graph/core/nn/models.py | 51 ++++++++++++++++++++++++++++---- mlcolvar/graph/core/nn/schnet.py | 13 ++++++-- 2 files changed, 55 insertions(+), 9 deletions(-) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index 3bfe828d..df67b842 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -277,6 +277,10 @@ class SchNetModel(BaseModel): Number of filters. n_hidden_channels: int Size of hidden embeddings. + aggr: str + Type of the GNN aggr function. + w_out_after_sum: bool + If apply the readout MLP layer after the scatter sum. References ---------- @@ -294,7 +298,9 @@ def __init__( n_layers: int = 2, n_filters: int = 16, n_hidden_channels: int = 16, - drop_rate: int = 0 + drop_rate: int = 0, + aggr: str = 'mean', + w_out_after_sum: bool = False ) -> None: super().__init__( @@ -307,7 +313,7 @@ def __init__( self.layers = nn.ModuleList([ schnet.InteractionBlock( - n_hidden_channels, n_bases, n_filters, cutoff + n_hidden_channels, n_bases, n_filters, cutoff, aggr ) for _ in range(n_layers) ]) @@ -318,6 +324,8 @@ def __init__( nn.Linear(n_hidden_channels // 2, n_out) ]) + self._w_out_after_sum = w_out_after_sum + self.reset_parameters() def reset_parameters(self) -> None: @@ -357,8 +365,9 @@ def forward( for layer in self.layers: h_V = h_V + layer(h_V, data['edge_index'], h_E[0], h_E[1]) - for w in self.W_out: - h_V = w(h_V) + if not self._w_out_after_sum: + for w in self.W_out: + h_V = w(h_V) out = h_V if scatter_mean: @@ -369,6 +378,10 @@ def forward( out = torch_tools.scatter_sum(out, batch_id, dim=0) out = out / data['n_system'] + if self._w_out_after_sum: + for w in self.W_out: + out = w(out) + return out @@ -449,7 +462,7 @@ def test_gvp() -> None: ).all() -def test_schnet() -> None: +def test_schnet_1() -> None: torch.manual_seed(0) torch_tools.set_default_dtype('float64') @@ -472,6 +485,32 @@ def test_schnet() -> None: ).all() +def test_schnet_2() -> None: + torch.manual_seed(0) + torch_tools.set_default_dtype('float64') + + model = SchNetModel( + n_out=2, + cutoff=0.1, + atomic_numbers=[1, 8], + n_bases=6, + n_layers=2, + n_filters=16, + n_hidden_channels=16, + aggr='min', + w_out_after_sum=True + ) + + data = test_get_data().to_dict() + assert ( + torch.abs( + model(data) - + torch.tensor([[0.3654537816221449, -0.0748265132499575]] * 6) + ) < 1E-12 + ).all() + + if __name__ == '__main__': test_gvp() - test_schnet() + test_schnet_1() + test_schnet_2() diff --git a/mlcolvar/graph/core/nn/schnet.py b/mlcolvar/graph/core/nn/schnet.py index afdc0e2f..5d1ef645 100644 --- a/mlcolvar/graph/core/nn/schnet.py +++ b/mlcolvar/graph/core/nn/schnet.py @@ -17,7 +17,8 @@ def __init__( hidden_channels: int, num_gaussians: int, num_filters: int, - cutoff: float + cutoff: float, + aggr: str = 'mean' ) -> None: super().__init__() self.mlp = nn.Sequential( @@ -26,7 +27,12 @@ def __init__( nn.Linear(num_filters, num_filters), ) self.conv = CFConv( - hidden_channels, hidden_channels, num_filters, self.mlp, cutoff + hidden_channels, + hidden_channels, + num_filters, + self.mlp, + cutoff, + aggr ) self.act = ShiftedSoftplus() self.lin = nn.Linear(hidden_channels, hidden_channels) @@ -63,8 +69,9 @@ def __init__( num_filters: int, network: nn.Sequential, cutoff: float, + aggr: str = 'mean' ) -> None: - super().__init__(aggr='mean') + super().__init__(aggr=aggr) self.lin1 = nn.Linear(in_channels, num_filters, bias=False) self.lin2 = nn.Linear(num_filters, out_channels) self.network = network From 1382fc338c776a6eabbe5d9b4bb42d19bf291f80 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 24 Sep 2024 14:22:45 +0200 Subject: [PATCH 152/267] Fixed Kolmogorov bias. --- mlcolvar/graph/cvs/committor/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/committor/utils.py b/mlcolvar/graph/cvs/committor/utils.py index 0d4470d8..02d07f1d 100644 --- a/mlcolvar/graph/cvs/committor/utils.py +++ b/mlcolvar/graph/cvs/committor/utils.py @@ -253,7 +253,7 @@ def get_dataset_kolmogorov_bias( for batchs in items: batch_dict = batchs.to(device).to_dict() - q = model(batch_dict) + q = model(batch_dict)[:, 1].unsqueeze(-1) grad_outputs: Optional[List[Optional[torch.Tensor]]] = [ torch.ones_like(q, device=device) ] From dc4e1d47db04c12fc9481609430730ba183c66ed Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 24 Sep 2024 16:18:41 +0200 Subject: [PATCH 153/267] Fixed output dim. --- mlcolvar/graph/cvs/committor/committor.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/committor/committor.py b/mlcolvar/graph/cvs/committor/committor.py index a8ffe427..b38d5cd3 100644 --- a/mlcolvar/graph/cvs/committor/committor.py +++ b/mlcolvar/graph/cvs/committor/committor.py @@ -79,11 +79,12 @@ def __init__( raise RuntimeError( 'The `n_out` key of parameter `model_options` will be ignored!' ) + model_options['n_out'] = 1 if optimizer_options != {}: kwargs['optimizer_options'] = optimizer_options super().__init__( - 1, cutoff, atomic_numbers, model_name, model_options, **kwargs + 2, cutoff, atomic_numbers, model_name, model_options, **kwargs ) atomic_masses = torch.tensor( From b3bcd6c38f8d3008d2cb99805b2ff945bc043772 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 25 Sep 2024 03:38:50 +0200 Subject: [PATCH 154/267] Fixed training. --- mlcolvar/graph/cvs/committor/utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mlcolvar/graph/cvs/committor/utils.py b/mlcolvar/graph/cvs/committor/utils.py index 02d07f1d..68bf3e38 100644 --- a/mlcolvar/graph/cvs/committor/utils.py +++ b/mlcolvar/graph/cvs/committor/utils.py @@ -144,7 +144,6 @@ def graph_committor_loss( labels = data['graph_labels'].long().squeeze() mask_a = labels == 0 mask_b = labels == 1 - mask_t = labels > 1 # Update weights of basin B using the information on the delta_f factor = torch.exp(torch.tensor([delta_f], dtype=dtype, device=device)) @@ -184,7 +183,7 @@ def graph_committor_loss( gradients_atomic, data['batch'], dim=0 ) # [n_graphs, 1] # ensemble avg. - loss_v = torch.mean((gradients_batch * weights)[mask_t]) # [,] + loss_v = torch.mean((gradients_batch * weights)) # [,] # boundary conditions loss_a = torch.mean(torch.pow(q[mask_a], 2)) From f11d1c47379092c1b25279a8b2285ec005cc5d1f Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 2 Oct 2024 18:08:35 +0200 Subject: [PATCH 155/267] Fixed deltaF bug. --- mlcolvar/graph/cvs/committor/utils.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/mlcolvar/graph/cvs/committor/utils.py b/mlcolvar/graph/cvs/committor/utils.py index 68bf3e38..2253c883 100644 --- a/mlcolvar/graph/cvs/committor/utils.py +++ b/mlcolvar/graph/cvs/committor/utils.py @@ -146,11 +146,16 @@ def graph_committor_loss( mask_b = labels == 1 # Update weights of basin B using the information on the delta_f - factor = torch.exp(torch.tensor([delta_f], dtype=dtype, device=device)) weights = data['weight'].clone() if delta_f < 0: # B higher in energy --> A-B < 0 + factor = torch.exp( + torch.tensor([delta_f], dtype=dtype, device=device) + ) weights[mask_b] = weights[mask_b] * factor if delta_f > 0: # A higher in energy --> A-B > 0 + factor = torch.exp( + torch.tensor([delta_f], dtype=dtype, device=device) * -1 + ) weights[mask_a] = weights[mask_a] * factor # Each loss contribution is scaled by the number of samples From 4b4ebac6e451f34c5c0cd8eaddfcd81070c73822 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Tue, 15 Oct 2024 02:56:03 +0200 Subject: [PATCH 156/267] Added a loss term. --- mlcolvar/graph/cvs/committor/committor.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mlcolvar/graph/cvs/committor/committor.py b/mlcolvar/graph/cvs/committor/committor.py index b38d5cd3..7478c326 100644 --- a/mlcolvar/graph/cvs/committor/committor.py +++ b/mlcolvar/graph/cvs/committor/committor.py @@ -164,10 +164,13 @@ def training_step( loss, loss_var, loss_bound_A, loss_bound_B = self.loss_fn( batch_dict, q ) + loss_z_diff = (z.max().abs() - z.min().abs()).pow(2) + loss = loss + loss_z_diff name = 'train' if self.training else 'valid' self.log(f'{name}_loss', loss, on_epoch=True) self.log(f'{name}_loss_variational', loss_var, on_epoch=True) self.log(f'{name}_loss_boundary_A', loss_bound_A, on_epoch=True) self.log(f'{name}_loss_boundary_B', loss_bound_B, on_epoch=True) + self.log(f'{name}_loss_z_diff', loss_z_diff, on_epoch=True) return loss From 0341bcfb57918e62ae2603160b4027bd749d6a83 Mon Sep 17 00:00:00 2001 From: jintuzhang <119382168+jintuzhang@users.noreply.github.com> Date: Wed, 13 Nov 2024 14:30:39 +0100 Subject: [PATCH 157/267] Smooth GVP. --- mlcolvar/graph/core/nn/gvp_layer.py | 29 ++++++++++++++++++++++------- mlcolvar/graph/core/nn/models.py | 9 +++++++-- 2 files changed, 29 insertions(+), 9 deletions(-) diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py index 2f855342..5ace06a8 100644 --- a/mlcolvar/graph/core/nn/gvp_layer.py +++ b/mlcolvar/graph/core/nn/gvp_layer.py @@ -1,5 +1,6 @@ import functools import torch +import math from torch import nn from torch_geometric.nn import MessagePassing from typing import Tuple, Callable, Optional @@ -134,7 +135,8 @@ class GVPConv(MessagePassing): 's': torch.Tensor, 'v': torch.Tensor, 'edge_attr_s': torch.Tensor, - 'edge_attr_v': torch.Tensor + 'edge_attr_v': torch.Tensor, + 'edge_lengths': torch.Tensor, } def __init__( @@ -146,11 +148,13 @@ def __init__( aggr='mean', activations=(nn.functional.relu, torch.sigmoid), vector_gate=True, + cutoff: float = -1.0, ) -> None: super(GVPConv, self).__init__(aggr=aggr) self.si, self.vi = in_dims self.so, self.vo = out_dims self.se, self.ve = edge_dims + self.cutoff = cutoff GVP_ = functools.partial( GVP, activations=activations, vector_gate=vector_gate @@ -182,7 +186,8 @@ def forward( self, x: Tuple[torch.Tensor, torch.Tensor], edge_index: torch.Tensor, - edge_attr: Tuple[torch.Tensor, torch.Tensor] + edge_attr: Tuple[torch.Tensor, torch.Tensor], + edge_lengths: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: """ :param x: tuple (s, V) of `torch.Tensor` @@ -196,7 +201,8 @@ def forward( s=x_s, v=x_v.contiguous().view(x_v.shape[0], x_v.shape[1] * 3), edge_attr_s=edge_attr[0], - edge_attr_v=edge_attr[1] + edge_attr_v=edge_attr[1], + edge_lengths=edge_lengths, ) return _split(message, self.vo) @@ -207,7 +213,8 @@ def message( s_j: torch.Tensor, v_j: torch.Tensor, edge_attr_s: torch.Tensor, - edge_attr_v: torch.Tensor + edge_attr_v: torch.Tensor, + edge_lengths: torch.Tensor, ) -> torch.Tensor: assert edge_attr_s is not None assert edge_attr_v is not None @@ -217,7 +224,12 @@ def message( (s_j, v_j), (edge_attr_s, edge_attr_v), (s_i, v_i) ) message = self.message_func(message) - return _merge(*message) + message_merged = _merge(*message) + if self.cutoff > 0: + # apply SchNet-style cutoff function + c = 0.5 * (torch.cos(edge_lengths * math.pi / self.cutoff) + 1.0) + message_merged = message_merged * c.view(-1, 1) + return message_merged def message_func( self, x: Tuple[torch.Tensor, torch.Tensor] @@ -259,6 +271,7 @@ def __init__( activations=(nn.functional.relu, torch.sigmoid), vector_gate=True, residual=True, + cutoff: float = -1.0, ) -> None: super(GVPConvLayer, self).__init__() self.conv = GVPConv( @@ -269,6 +282,7 @@ def __init__( aggr='mean', activations=activations, vector_gate=vector_gate, + cutoff=cutoff, ) GVP_ = functools.partial( GVP, activations=activations, vector_gate=vector_gate @@ -297,7 +311,8 @@ def forward( x: Tuple[torch.Tensor, torch.Tensor], edge_index: torch.Tensor, edge_attr: Tuple[torch.Tensor, torch.Tensor], - node_mask: Optional[torch.Tensor] = None + edge_lengths: torch.Tensor, + node_mask: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ :param x: tuple (s, V) of `torch.Tensor` @@ -308,7 +323,7 @@ def forward( these nodes will be updated. """ - dh = self.conv(x, edge_index, edge_attr) + dh = self.conv(x, edge_index, edge_attr, edge_lengths) x_ = x if node_mask is not None: diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py index df67b842..ad8b8125 100644 --- a/mlcolvar/graph/core/nn/models.py +++ b/mlcolvar/graph/core/nn/models.py @@ -131,6 +131,8 @@ class GVPModel(BaseModel): Name of the activation function to use in the GVPs (case sensitive). basis_type: str Type of the basis function. + smooth: bool + If use the smoothed GVPConv. References ---------- @@ -156,7 +158,8 @@ def __init__( n_scalars_edge: int = 8, drop_rate: int = 0.1, activation: str = 'SiLU', - basis_type: str = 'bessel' + basis_type: str = 'bessel', + smooth: bool = False, ) -> None: super().__init__( n_out, cutoff, atomic_numbers, n_bases, n_polynomials, basis_type @@ -191,6 +194,7 @@ def __init__( drop_rate=drop_rate, activations=(eval(f'torch.nn.{activation}')(), None), vector_gate=True, + cutoff=(cutoff if smooth else -1), ) for _ in range(n_layers) ) @@ -227,6 +231,7 @@ def forward( h_V = (h_V_1, h_V_2) h_E = self.embed_edge(data) + lengths = h_E[0] h_E = (h_E[1], h_E[2].unsqueeze(-2)) for w in self.W_e: h_E = w(h_E) @@ -237,7 +242,7 @@ def forward( batch_id = data['batch'] for layer in self.layers: - h_V = layer(h_V, data['edge_index'], h_E) + h_V = layer(h_V, data['edge_index'], h_E, lengths) for w in self.W_out: h_V = w(h_V) From cba40ebc9eb6b82e3824d55ccc95380a4fcc0fd2 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 21 Nov 2024 17:56:21 +0100 Subject: [PATCH 158/267] Added utils to create graph example input for tests --- mlcolvar/data/graph/utils.py | 46 +++++++++++++++++++++++++++++++++++- 1 file changed, 45 insertions(+), 1 deletion(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 6717a795..7049e266 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -1,7 +1,7 @@ import torch import torch_geometric -from mlcolvar.data import DictDataset +from mlcolvar.data import DictDataset, DictModule from mlcolvar.data.graph import atomic from mlcolvar.data.graph.neighborhood import get_neighborhood from mlcolvar.utils.plot import pbar @@ -189,7 +189,51 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: return oh.view(*shape) +def create_test_graph_input(get_example: bool = False) -> torch_geometric.data.Batch: + numbers = [8, 1, 1] + positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], + [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], + ], + dtype=np.float64 + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[[0]], [[1]]] * 3) + node_labels = np.array([[0], [1], [1]]) + z_table = atomic.AtomicNumberTable.from_zs(numbers) + config = [ + atomic.Configuration( + atomic_numbers=numbers, + positions=positions[i], + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels[i], + ) for i in range(0, 6) + ] + dataset = create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) + + loader = DictModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + loader.setup() + if get_example: + out = next(iter(loader.train_dataloader()))['data_list'].get_example(0) + out['batch'] = torch.tensor([0], dtype=torch.int64) + return out + else: + return next(iter(loader.train_dataloader())) # =============================================================================== From 67b96eeefa60625c0f9bad315e4d6674c487affe Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 22 Nov 2024 17:01:09 +0100 Subject: [PATCH 159/267] Intermediate test CV models --- mlcolvar/core/nn/feedforward.py | 2 +- mlcolvar/cvs/cv.py | 29 +- mlcolvar/cvs/supervised/deeptda_merged.py | 70 +++- mlcolvar/data/graph/utils.py | 53 ++- test_graphs/test_graph.ipynb | 465 ++++++++++++++++++---- 5 files changed, 508 insertions(+), 111 deletions(-) diff --git a/mlcolvar/core/nn/feedforward.py b/mlcolvar/core/nn/feedforward.py index f84596dd..d5566df8 100644 --- a/mlcolvar/core/nn/feedforward.py +++ b/mlcolvar/core/nn/feedforward.py @@ -27,7 +27,7 @@ # ============================================================================= -class FeedForward(lightning.LightningModule): +class FeedForward(torch.nn.Module): """Define a feedforward neural network given the list of layers. Optionally dropout and batchnorm can be applied (the order is activation -> dropout -> batchnorm). diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 58d95764..843bb404 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -2,6 +2,7 @@ from mlcolvar.core.transform import Transform from typing import Union, List from mlcolvar.core.nn import FeedForward, BaseGNN +from mlcolvar.data.graph.utils import create_test_graph_input class BaseCV: @@ -15,6 +16,7 @@ class BaseCV: def __init__( self, + model: Union[List[int], FeedForward, BaseGNN], in_features, out_features, preprocessing: torch.nn.Module = None, @@ -43,8 +45,9 @@ def __init__( self.save_hyperparameters(ignore=['in_features', 'out_features']) # MODEL + self.parse_model(model=model) self.initialize_blocks() - self.in_features = in_features + # self.in_features = in_features self.out_features = out_features # OPTIM @@ -63,29 +66,37 @@ def n_cvs(self): @property def example_input_array(self): - return torch.randn( - (1,self.in_features) - if self.preprocessing is None - or not hasattr(self.preprocessing, "in_features") - else self.preprocessing.in_features - ) + if self.in_features is not None: + return torch.randn( + (1,self.in_features) + if self.preprocessing is None + or not hasattr(self.preprocessing, "in_features") + else self.preprocessing.in_features + ) + else: + return create_test_graph_input(output_type='example', n_samples=1, n_states=1) + + def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): if isinstance(model, list): self.layers = model self.BLOCKS = self.DEFAULT_BLOCKS self._override_model = False + self.in_features = self.layers[0] elif isinstance(model, FeedForward) or isinstance(model, BaseGNN): self.BLOCKS = ['nn'] self._override_model = True if isinstance(model, FeedForward): - self.nn = model + self.nn = model + self.in_features = self.nn.in_features elif isinstance(model, BaseGNN): # GNN models need to be scripted! self.nn = torch.jit.script_if_tracing(model) + self.in_features = None else: raise ValueError( - "Ma belin se scemo?" + "Ma belin sei scemo?" ) def parse_options(self, options: dict = None): diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py index b57d0921..3765ee50 100644 --- a/mlcolvar/cvs/supervised/deeptda_merged.py +++ b/mlcolvar/cvs/supervised/deeptda_merged.py @@ -59,10 +59,11 @@ def __init__( Set 'block_name' = None or False to turn off that block """ # check what model is - self.parse_model(model=model) - + # self.parse_model(model=model) + # TODO in_features and out_features?? - super().__init__(in_features=0, out_features=n_cvs, **kwargs) + super().__init__(model, in_features=2, out_features=n_cvs, **kwargs) + self.save_hyperparameters(ignore=['model']) # ======= LOSS ======= self.loss_fn = TDALoss( @@ -157,7 +158,9 @@ def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: def test_deeptda_cv(): from mlcolvar.data import DictDataset + # feedforward with layers for states_and_cvs in [[2, 1], [3, 1], [3, 2], [5, 4]]: + print(states_and_cvs) # get the number of states and cvs for the test run n_states = states_and_cvs[0] n_cvs = states_and_cvs[1] @@ -175,13 +178,10 @@ def test_deeptda_cv(): n_cvs=n_cvs, target_centers=target_centers, target_sigmas=target_sigmas, - layers=layers, + model=layers, options=options, ) - print("----------") - print(model) - # create dataset samples = 100 X = torch.randn((samples * n_states, 2)) @@ -195,7 +195,31 @@ def test_deeptda_cv(): datamodule = DictModule(dataset, lengths=[0.75, 0.2, 0.05], batch_size=samples) # train model trainer = lightning.Trainer( - accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False + accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + + # trace model + traced_model = model.to_torchscript( + file_path=None, method="trace", example_inputs=X[0] + ) + model.eval() + assert torch.allclose(model(X), traced_model(X)) + + + # feedforward external + ff_model = FeedForward(layers=layers) + model = DeepTDA( + n_states=n_states, + n_cvs=n_cvs, + target_centers=target_centers, + target_sigmas=target_sigmas, + model=ff_model + ) + + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False, enable_model_summary=False ) trainer.fit(model, datamodule) @@ -207,6 +231,36 @@ def test_deeptda_cv(): assert torch.allclose(model(X), traced_model(X)) + + # gnn external + from mlcolvar.core.nn.graph.schnet import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(1, 5, [1, 8]) + model = DeepTDA( + n_states=n_states, + n_cvs=n_cvs, + target_centers=target_centers, + target_sigmas=target_sigmas, + model=gnn_model + ) + datamodule = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=n_states) + + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=2, logger=False, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + + # trace model + example_input_graph = create_test_graph_input(output_type='tracing_example', n_samples=10, n_states=1) + traced_model = model.to_torchscript( + file_path=None, method="trace", example_inputs=example_input_graph + ) + model.eval() + assert torch.allclose(model(X), traced_model(X)) + + + if __name__ == "__main__": test_deeptda_cv() diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 7049e266..78a657e6 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -189,9 +189,12 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: return oh.view(*shape) -def create_test_graph_input(get_example: bool = False) -> torch_geometric.data.Batch: +def create_test_graph_input(output_type: str, + n_samples: int = 60, + n_states: int = 2) -> torch_geometric.data.Batch: + n_atoms = 3 numbers = [8, 1, 1] - positions = np.array( + _ref_positions = np.array( [ [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], @@ -202,8 +205,14 @@ def create_test_graph_input(get_example: bool = False) -> torch_geometric.data.B ], dtype=np.float64 ) + + idx = np.random.randint(low=0, high=6, size=(n_samples*n_states)) + positions = _ref_positions[idx, :, :] + cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[[0]], [[1]]] * 3) + graph_labels = torch.zeros((n_samples*n_states, 1, 1)) + for i in range(1, n_states): + graph_labels[n_samples * i :] += 1 node_labels = np.array([[0], [1], [1]]) z_table = atomic.AtomicNumberTable.from_zs(numbers) @@ -215,25 +224,41 @@ def create_test_graph_input(get_example: bool = False) -> torch_geometric.data.B pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels[i], - ) for i in range(0, 6) + ) for i in range(0, n_samples*n_states) ] + + if output_type == 'configuration': + return config[0] + if output_type == 'configurations': + return config + dataset = create_dataset_from_configurations( config, z_table, 0.1, show_progress=False ) - loader = DictModule( + if output_type == 'dataset': + return dataset + + datamodule = DictModule( dataset, - lengths=(1.0,), - batch_size=10, + lengths=(0.8, 0.2), + batch_size=0, shuffle=False, ) - loader.setup() - if get_example: - out = next(iter(loader.train_dataloader()))['data_list'].get_example(0) - out['batch'] = torch.tensor([0], dtype=torch.int64) - return out - else: - return next(iter(loader.train_dataloader())) + + if output_type == 'datamodule': + return datamodule + + datamodule.setup() + batch = next(iter(datamodule.train_dataloader())) + if output_type == 'batch': + return batch + example = batch['data_list'].get_example(0) + example['batch'] = torch.tensor([0], dtype=torch.int64) + if output_type == 'example': + return example + if output_type == 'tracing_example': + return example.to_dict() # =============================================================================== diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 1ce347df..4ff38bd8 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -11,7 +11,31 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DictModule(dataset -> DictDataset( \"data_list\": 6, \"z_table\": [1, 8], \"cutoff\": 0.1, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1.0, batch_size=6, shuffle=False))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlcolvar.data.graph.utils import create_test_graph_input\n", + "\n", + "sss = create_test_graph_input(output_type='datamodule', n_samples=3, n_states=2)\n", + "sss" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -19,7 +43,8 @@ "output_type": "stream", "text": [ "DictModule(dataset -> DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0, \"data_type\": graphs ),\n", - "\t\t train_loader -> DictLoader(length=1, batch_size=1600, shuffle=True))\n", + "\t\t train_loader -> DictLoader(length=0.8, batch_size=1600, shuffle=True),\n", + "\t\t valid_loader -> DictLoader(length=0.2, batch_size=1600, shuffle=True))\n", "Class 0 dataframe shape: (800, 24)\n", "Class 1 dataframe shape: (800, 24)\n", "\n", @@ -47,7 +72,7 @@ " show_progress=False\n", ")\n", "\n", - "datamodule_graph = DictModule(dataset_graph, lengths=[1])\n", + "datamodule_graph = DictModule(dataset_graph, lengths=[0.8, 0.2])\n", "print(datamodule_graph)\n", "\n", "\n", @@ -55,12 +80,12 @@ " 'data/colvar_p.dat'],\n", " filter_args={'regex': 'd'})\n", "\n", - "datamodule_ff = DictModule(dataset_ff, lengths=[1])\n" + "datamodule_ff = DictModule(dataset_ff, lengths=[0.8, 0.2])\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -78,22 +103,77 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n", - "None\n" - ] - }, + "data": { + "text/plain": [ + "(6, 1, 1)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_samples=10\n", + "n_states=3\n", + "n_atoms = 3\n", + "numbers = [8, 1, 1]\n", + "_ref_positions = np.array(\n", + " [\n", + " [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]],\n", + " [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]],\n", + " [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]],\n", + " [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]],\n", + " [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]],\n", + " [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]],\n", + " ],\n", + " dtype=np.float64\n", + ")\n", + "\n", + "# positions = np.zeros((n_samples*n_states, 3, 3))\n", + "idx = np.random.randint(low=0, high=6, size=(n_samples*n_states))\n", + "\n", + "positions = _ref_positions[idx, :, :]\n", + "positions.shape\n", + "\n", + "graph_labels = np.array([[[0]], [[1]]] * 3)\n", + "graph_labels.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[6, 9]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_graph.metadata['z_table']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/etrizio/Bin/miniconda3/envs/mlcolvar_dev_graphs/lib/python3.10/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" ] } ], @@ -106,11 +186,54 @@ " target_sigmas=[0.2, 0.2],\n", " model=gnn_model)\n", "\n", + "from mlcolvar.core.nn import FeedForward\n", + "\n", + "ff_model = FeedForward(layers=[21, 15, 10, 1])\n", "model_ff = DeepTDA(n_states=2,\n", " n_cvs=1,\n", " target_centers=[-7, 7],\n", " target_sigmas=[0.2, 0.2],\n", - " model=[21, 15, 10, 1])" + " model=ff_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[FeedForward(\n", + " (nn): Sequential(\n", + " (0): Linear(in_features=21, out_features=15, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=15, out_features=10, bias=True)\n", + " (3): ReLU(inplace=True)\n", + " (4): Linear(in_features=10, out_features=1, bias=True)\n", + " )\n", + " ),\n", + " Sequential(\n", + " (0): Linear(in_features=21, out_features=15, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=15, out_features=10, bias=True)\n", + " (3): ReLU(inplace=True)\n", + " (4): Linear(in_features=10, out_features=1, bias=True)\n", + " ),\n", + " Linear(in_features=21, out_features=15, bias=True),\n", + " ReLU(inplace=True),\n", + " Linear(in_features=15, out_features=10, bias=True),\n", + " ReLU(inplace=True),\n", + " Linear(in_features=10, out_features=1, bias=True)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(ff_model.modules())" ] }, { @@ -141,52 +264,132 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (cuda), used: False\n", "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", - "/home/etrizio/Bin/miniconda3/envs/mlcolvar_dev_graphs/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n" + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0: 0%| | 0/1 [00:00)" ] }, - "execution_count": 6, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_graph(sss)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])" + ] + }, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -241,7 +463,7 @@ "Data(edge_index=[2, 42], shifts=[42, 3], unit_shifts=[42, 3], positions=[7, 3], cell=[3, 3], node_attrs=[7, 2], graph_labels=[1, 1], n_system=[1, 1], weight=[1], batch=[1])" ] }, - "execution_count": 7, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -255,17 +477,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# model_graph.eval()\n", - "traced_model = model_graph.to_torchscript('test.pt', method='trace', example_inputs=xxx.to_dict())" + "traced_model = model_graph.to_torchscript('test.pt', method='trace', example_inputs=sss.to_dict())" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -307,21 +529,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DataBatch(edge_index=[2, 4], shifts=[4, 3], unit_shifts=[4, 3], positions=[4, 3], cell=[6, 3], node_attrs=[4, 2], node_labels=[4, 1], graph_labels=[2, 1], n_system=[2, 1], weight=[2], batch=[1], ptr=[3])" + "DataBatch(edge_index=[2, 2], shifts=[2, 3], unit_shifts=[2, 3], positions=[4, 3], cell=[6, 3], node_attrs=[4, 2], node_labels=[4, 1], graph_labels=[2, 1], n_system=[2, 1], weight=[2], batch=[1], ptr=[3])" ] }, - "execution_count": 10, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import torch\n", "inp_test.batch = torch.tensor([0], dtype=torch.long)\n", "\n", "inp_test" @@ -329,15 +552,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[0.7130]], grad_fn=)\n", - "tensor([[1.1350]], grad_fn=)\n" + "tensor([[0.4284]], grad_fn=)\n", + "tensor([[0.6660]], grad_fn=)\n" ] } ], @@ -348,19 +571,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[0.7130]], grad_fn=)\n", - "tensor([[1.1350]], grad_fn=)\n", - "tensor([[0.7130]], grad_fn=)\n", - "tensor([[1.1350]], grad_fn=)\n", - "tensor([[0.7130]], grad_fn=)\n", - "tensor([[1.1350]], grad_fn=)\n" + "tensor([[0.4284]], grad_fn=)\n", + "tensor([[0.6660]], grad_fn=)\n", + "tensor([[0.4284]], grad_fn=)\n", + "tensor([[0.6660]], grad_fn=)\n", + "tensor([[0.4284]], grad_fn=)\n", + "tensor([[0.6660]], grad_fn=)\n" ] } ], @@ -381,16 +604,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'data_list': DataBatch(edge_index=[2, 67200], shifts=[67200, 3], unit_shifts=[67200, 3], positions=[11200, 3], cell=[4800, 3], node_attrs=[11200, 2], graph_labels=[1600, 1], n_system=[1600, 1], weight=[1600], batch=[11200], ptr=[1601])}]" + "[{'data_list': DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])}]" ] }, - "execution_count": 13, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -408,37 +631,122 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (cuda), used: False\n", "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4: 100%|██████████| 1/1 [00:00<00:00, 71.21it/s] " - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6f561fd44ecc422fbb5ffa5efdc799d6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00 Date: Mon, 25 Nov 2024 11:42:03 +0100 Subject: [PATCH 160/267] Fixed test --- mlcolvar/cvs/supervised/deeptda_merged.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py index 3765ee50..a96241a9 100644 --- a/mlcolvar/cvs/supervised/deeptda_merged.py +++ b/mlcolvar/cvs/supervised/deeptda_merged.py @@ -231,11 +231,10 @@ def test_deeptda_cv(): assert torch.allclose(model(X), traced_model(X)) - # gnn external from mlcolvar.core.nn.graph.schnet import SchNetModel from mlcolvar.data.graph.utils import create_test_graph_input - gnn_model = SchNetModel(1, 5, [1, 8]) + gnn_model = SchNetModel(1, 0.1, [1, 8]) model = DeepTDA( n_states=n_states, n_cvs=n_cvs, @@ -252,14 +251,13 @@ def test_deeptda_cv(): trainer.fit(model, datamodule) # trace model - example_input_graph = create_test_graph_input(output_type='tracing_example', n_samples=10, n_states=1) + example_input_graph = create_test_graph_input(output_type='tracing_example', n_samples=3, n_states=2) + traced_model = model.to_torchscript( file_path=None, method="trace", example_inputs=example_input_graph ) model.eval() - assert torch.allclose(model(X), traced_model(X)) - - + assert torch.allclose(model(example_input_graph), traced_model(example_input_graph)) if __name__ == "__main__": test_deeptda_cv() From 1700a6b01f20f6d8e04f00b8b6489d9a3f0a276e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 28 Nov 2024 12:29:29 +0100 Subject: [PATCH 161/267] added out_features property --- mlcolvar/core/nn/graph/gnn.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py index 00bcc3f3..8955574b 100644 --- a/mlcolvar/core/nn/graph/gnn.py +++ b/mlcolvar/core/nn/graph/gnn.py @@ -58,6 +58,10 @@ def __init__( 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) ) + @property + def out_features(self): + return self._n_out + def embed_edge( self, data: Dict[str, torch.Tensor], normalize: bool = True ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: From 1343836145eeb458b8040d540c45ba31ccece540 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 10:54:40 +0100 Subject: [PATCH 162/267] removed if_tracing and in_features, added setup and model_blocks --- mlcolvar/cvs/cv.py | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 843bb404..6e7d4082 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -13,11 +13,11 @@ class BaseCV: """ DEFAULT_BLOCKS = [] + MODEL_BLOCKS = [] def __init__( self, model: Union[List[int], FeedForward, BaseGNN], - in_features, out_features, preprocessing: torch.nn.Module = None, postprocessing: torch.nn.Module = None, @@ -47,7 +47,6 @@ def __init__( # MODEL self.parse_model(model=model) self.initialize_blocks() - # self.in_features = in_features self.out_features = out_features # OPTIM @@ -74,10 +73,10 @@ def example_input_array(self): else self.preprocessing.in_features ) else: - return create_test_graph_input(output_type='example', n_samples=1, n_states=1) - + return create_test_graph_input(output_type='tracing_example', n_atoms=3, n_samples=1, n_states=1) + # TODO add general torch.nn.Module def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): if isinstance(model, list): self.layers = model @@ -85,18 +84,19 @@ def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): self._override_model = False self.in_features = self.layers[0] elif isinstance(model, FeedForward) or isinstance(model, BaseGNN): - self.BLOCKS = ['nn'] + self.BLOCKS = self.MODEL_BLOCKS self._override_model = True if isinstance(model, FeedForward): - self.nn = model - self.in_features = self.nn.in_features + # self.nn = model + self.in_features = model.in_features elif isinstance(model, BaseGNN): # GNN models need to be scripted! - self.nn = torch.jit.script_if_tracing(model) + # self.nn = torch.jit.script_if_tracing(model) + # self.nn = model self.in_features = None else: raise ValueError( - "Ma belin sei scemo?" + f"Keyword model can either accept type list, FeedForward or BaseGNN. Found {type(model)}" ) def parse_options(self, options: dict = None): @@ -139,6 +139,7 @@ def initialize_blocks(self): Initialize the blocks as attributes of the CV class. """ for b in self.BLOCKS: + #if not self._override_model and b!='nn': self.__setattr__(b, None) def setup(self, stage=None): @@ -264,3 +265,9 @@ def __setattr__(self, key, value): if (key == "loss_fn") and ("cannot assign" in str(e)): del self.loss_fn super().__setattr__(key, value) + + def _setup_graph_data(self, train_batch): + data = train_batch['data_list'] + data['positions'].requires_grad_(True) + data['node_attrs'].requires_grad_(True) + return data \ No newline at end of file From 52d3886b8581fdd784e986d60d0b2fd3a0b3682e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 10:55:25 +0100 Subject: [PATCH 163/267] Improved test data utils with number of atoms --- mlcolvar/data/graph/utils.py | 47 ++++++++++++++++++++++++------------ 1 file changed, 32 insertions(+), 15 deletions(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 78a657e6..42386290 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -190,30 +190,47 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: return oh.view(*shape) def create_test_graph_input(output_type: str, + n_atoms: int = 3, n_samples: int = 60, n_states: int = 2) -> torch_geometric.data.Batch: - n_atoms = 3 - numbers = [8, 1, 1] - _ref_positions = np.array( - [ - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], - [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], - [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], - [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], - [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], - ], - dtype=np.float64 - ) + if n_atoms == 3: + numbers = [8, 1, 1] + node_labels = np.array([[0], [1], [1]]) + _ref_positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], + [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], + ], + dtype=np.float64 + ) + + if n_atoms == 4: + numbers = [8, 1, 1, 8] + node_labels = np.array([[0], [1], [1], [0]]) + _ref_positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0] , [0.07, -0.07, 0.0], [0.05, -0.05, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0], [0.05, 0.05, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0], [0.05, 0.05, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07], [0.0, 0.05, 0.05]], + [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07] , [-0.07, 0.0, 0.07], [-0.05, 0.0, 0.05]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1] , [0.17, -0.07, 1.1], [0.15, -0.05, 1.1]], + ], + dtype=np.float64 + ) + idx = np.random.randint(low=0, high=6, size=(n_samples*n_states)) positions = _ref_positions[idx, :, :] cell = np.identity(3, dtype=float) * 0.2 - graph_labels = torch.zeros((n_samples*n_states, 1, 1)) + graph_labels = np.zeros((n_samples*n_states, 1, 1)) for i in range(1, n_states): graph_labels[n_samples * i :] += 1 - node_labels = np.array([[0], [1], [1]]) z_table = atomic.AtomicNumberTable.from_zs(numbers) config = [ From d3e7b89502866869bebeac9b61c06750ef94c170 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 10:55:40 +0100 Subject: [PATCH 164/267] Updated DeepTDA --- mlcolvar/cvs/supervised/deeptda.py | 137 +++++++---- mlcolvar/cvs/supervised/deeptda_graph.py | 201 ---------------- mlcolvar/cvs/supervised/deeptda_merged.py | 264 ---------------------- 3 files changed, 98 insertions(+), 504 deletions(-) delete mode 100644 mlcolvar/cvs/supervised/deeptda_graph.py delete mode 100644 mlcolvar/cvs/supervised/deeptda_merged.py diff --git a/mlcolvar/cvs/supervised/deeptda.py b/mlcolvar/cvs/supervised/deeptda.py index e58f215c..3b323aa3 100644 --- a/mlcolvar/cvs/supervised/deeptda.py +++ b/mlcolvar/cvs/supervised/deeptda.py @@ -1,35 +1,33 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward, Normalization +from mlcolvar.core import FeedForward, BaseGNN, Normalization from mlcolvar.core.loss import TDALoss from mlcolvar.data import DictModule +from typing import Union, List __all__ = ["DeepTDA"] - class DeepTDA(BaseCV, lightning.LightningModule): """ Deep Targeted Discriminant Analysis (Deep-TDA) CV. Combine the inputs with a neural-network and optimize it in a way such that the data are distributed accordingly to a mixture of Gaussians. The method is described in [1]_. - **Data**: for training it requires a DictDataset with the keys 'data' and 'labels'. - **Loss**: distance of the samples of each class from a set of Gaussians (TDALoss) - References ---------- .. [1] E. Trizio and M. Parrinello, "From enhanced sampling to reaction profiles", The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). - See also -------- mlcolvar.core.loss.TDALoss Distance from a simple Gaussian target distribution. """ - BLOCKS = ["norm_in", "nn"] + DEFAULT_BLOCKS = ["norm_in", "nn"] + MODEL_BLOCKS = ["nn"] + # TODO n_states optional? def __init__( @@ -38,14 +36,13 @@ def __init__( n_cvs: int, target_centers: list, target_sigmas: list, - layers: list, + model: Union[List[int], FeedForward, BaseGNN], options: dict = None, **kwargs, ): """ Define Deep Targeted Discriminant Analysis (Deep-TDA) CV composed by a neural network module. By default a module standardizing the inputs is also used. - Parameters ---------- n_states : int @@ -62,9 +59,9 @@ def __init__( Options for the building blocks of the model, by default {}. Available blocks: ['norm_in', 'nn']. Set 'block_name' = None or False to turn off that block - """ - - super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) + """ + super().__init__(model, out_features=n_cvs, **kwargs) + self.save_hyperparameters(ignore=['model']) # ======= LOSS ======= self.loss_fn = TDALoss( @@ -106,34 +103,47 @@ def __init__( ) # ======= BLOCKS ======= - - # Initialize norm_in - o = "norm_in" - if (options[o] is not False) and (options[o] is not None): - self.norm_in = Normalization(self.in_features, **options[o]) - - # initialize NN - o = "nn" - self.nn = FeedForward(layers, **options[o]) - - def training_step(self, train_batch, batch_idx): + if not self._override_model: + # Initialize norm_in + o = "norm_in" + if (options[o] is not False) and (options[o] is not None): + self.norm_in = Normalization(self.in_features, **options[o]) + + # initialize NN + o = "nn" + self.nn = FeedForward(self.layers, **options[o]) + elif self._override_model: + self.nn = model + + def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: """Compute and return the training loss and record metrics.""" - # =================get data=================== - x = train_batch["data"] - labels = train_batch["labels"] - # =================forward==================== - z = self.forward_cv(x) + if isinstance(self.nn, FeedForward): + # =================get data=================== + x = train_batch["data"] + labels = train_batch["labels"] + # =================forward==================== + z = self.forward_cv(x) + + elif isinstance(self.nn, BaseGNN): + # =================get data=================== + data = self._setup_graph_data(train_batch) + labels = data['graph_labels'].squeeze() + # =================forward==================== + z = self.forward(data) + # ===================loss===================== - loss, loss_centers, loss_sigmas = self.loss_fn( - z, labels, return_loss_terms=True - ) - # ====================log=====================+ + loss, loss_centers, loss_sigmas = self.loss_fn(z, + labels, + return_loss_terms=True + ) + + # ====================log===================== name = "train" if self.training else "valid" self.log(f"{name}_loss", loss, on_epoch=True) self.log(f"{name}_loss_centers", loss_centers, on_epoch=True) self.log(f"{name}_loss_sigmas", loss_sigmas, on_epoch=True) - return loss + return loss # TODO signature of tests? import numpy as np @@ -142,7 +152,9 @@ def training_step(self, train_batch, batch_idx): def test_deeptda_cv(): from mlcolvar.data import DictDataset + # feedforward with layers for states_and_cvs in [[2, 1], [3, 1], [3, 2], [5, 4]]: + print(states_and_cvs) # get the number of states and cvs for the test run n_states = states_and_cvs[0] n_cvs = states_and_cvs[1] @@ -160,13 +172,10 @@ def test_deeptda_cv(): n_cvs=n_cvs, target_centers=target_centers, target_sigmas=target_sigmas, - layers=layers, + model=layers, options=options, ) - print("----------") - print(model) - # create dataset samples = 100 X = torch.randn((samples * n_states, 2)) @@ -180,17 +189,67 @@ def test_deeptda_cv(): datamodule = DictModule(dataset, lengths=[0.75, 0.2, 0.05], batch_size=samples) # train model trainer = lightning.Trainer( - accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False + accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False, enable_model_summary=False ) trainer.fit(model, datamodule) # trace model traced_model = model.to_torchscript( - file_path=None, method="trace", example_inputs=X[0] + file_path=None, method="trace") + model.eval() + assert torch.allclose(model(X), traced_model(X)) + + + # feedforward external + ff_model = FeedForward(layers=layers) + model = DeepTDA( + n_states=n_states, + n_cvs=n_cvs, + target_centers=target_centers, + target_sigmas=target_sigmas, + model=ff_model ) + + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + + # trace model + traced_model = model.to_torchscript( + file_path=None, method="trace") model.eval() assert torch.allclose(model(X), traced_model(X)) + # gnn external + from mlcolvar.core.nn.graph.schnet import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(1, 0.1, [1, 8]) + model = DeepTDA( + n_states=n_states, + n_cvs=n_cvs, + target_centers=target_centers, + target_sigmas=target_sigmas, + model=gnn_model + ) + datamodule = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=n_states) + + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=2, logger=False, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + + # trace model + traced_model = model.to_torchscript( + file_path=None, method="trace") + + # check on a different number of atoms + example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=2) + assert torch.allclose(model(example_input_graph_test), traced_model(example_input_graph_test)) + if __name__ == "__main__": test_deeptda_cv() + diff --git a/mlcolvar/cvs/supervised/deeptda_graph.py b/mlcolvar/cvs/supervised/deeptda_graph.py deleted file mode 100644 index cade3fcf..00000000 --- a/mlcolvar/cvs/supervised/deeptda_graph.py +++ /dev/null @@ -1,201 +0,0 @@ -import torch -import torch_geometric as tg -from typing import Dict, Any, List, Union - -from mlcolvar.core.loss import TDALoss -from mlcolvar.graph.cvs import GraphBaseCV -from mlcolvar.graph.cvs.cv import test_get_data -from mlcolvar.graph.utils import torch_tools - -""" -The Deep Targeted Discriminant Analysis (Deep-TDA) CV based on Graph Neural -Networks (GNN). -""" - -__all__ = ['GraphDeepTDA'] - - -class GraphDeepTDA(GraphBaseCV): - """ - The Deep Targeted Discriminant Analysis (Deep-TDA) CV [1] based on Graph - Neural Networks (GNN). - - Parameters - ---------- - n_cvs: int - Number of components of the CV. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_cvs : int - Number of collective variables to be trained - target_centers : list - Centers of the Gaussian targets - target_sigmas : list - Standard deviations of the Gaussian targets - model_name: str - Name of the GNN model. - model_options: Dict[Any, Any] - Model options. - extra_loss_options: Dict[Any, Any] - Extra loss function options. - optimizer_options: Dict[Any, Any] - Optimizer options. - - References - ---------- - .. [1] E. Trizio and M. Parrinello, - 'From enhanced sampling to reaction profiles', - The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). - - See also - -------- - mlcolvar.core.loss.TDALoss - Distance from a simple Gaussian target distribution. - """ - - def __init__( - self, - n_cvs: int, - cutoff: float, - atomic_numbers: List[int], - target_centers: Union[List[float], List[List[float]]], - target_sigmas: Union[List[float], List[List[float]]], - model_name: str = 'GVPModel', - model_options: Dict[Any, Any] = {}, - extra_loss_options: Dict[Any, Any] = {'alpha': 1.0, 'beta': 100.0}, - optimizer_options: Dict[Any, Any] = {}, - **kwargs, - ) -> None: - if model_options.pop('n_out', None) is not None: - raise RuntimeError( - 'The `n_out` key of parameter `model_options` will be ignored!' - ) - if optimizer_options != {}: - kwargs['optimizer_options'] = optimizer_options - - super().__init__( - n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs - ) - - # check size and type of targets - if not isinstance(target_centers, torch.Tensor): - target_centers = torch.tensor( - target_centers, dtype=torch.get_default_dtype() - ) - if not isinstance(target_sigmas, torch.Tensor): - target_sigmas = torch.tensor( - target_sigmas, dtype=torch.get_default_dtype() - ) - - self._n_states = target_centers.shape[0] - if target_centers.shape != target_sigmas.shape: - raise ValueError( - 'Size of target_centers and target_sigmas should be the same!' - ) - if len(target_centers.shape) == 1: - if n_cvs != 1: - raise ValueError( - 'Size of target_centers at dimension 1 should match the ' - + f'number of cvs! Expected 1 found {n_cvs}' - ) - elif len(target_centers.shape) == 2: - if n_cvs != target_centers.shape[1]: - raise ValueError( - 'Size of target_centers at dimension 1 should match the ' - + f'number of cvs! Expected {n_cvs} found ' - + f'{target_centers.shape[1]}' - ) - elif len(target_centers.shape) > 2: - raise ValueError('Too much target_centers dimensions!') - - self.loss_fn = TDALoss( - n_states=target_centers.shape[0], - target_centers=target_centers, - target_sigmas=target_sigmas, - **extra_loss_options - ) - - def training_step( - self, train_batch: tg.data.Batch, *args, **kwargs - ) -> torch.Tensor: - """ - Compute and return the training loss and record metrics. - - Parameters - ---------- - train_batch: torch_geometric.data.Batch - The data batch. - """ - output = self.forward(train_batch.to_dict()) - - loss, loss_centers, loss_sigmas = self.loss_fn( - output, - train_batch.graph_labels.squeeze(), - return_loss_terms=True - ) - - name = 'train' if self.training else 'valid' - self.log(f'{name}_loss', loss, on_epoch=True) - self.log(f'{name}_loss_centers', loss_centers, on_epoch=True) - self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) - return loss - - -def test_deep_tda(): - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - cv = GraphDeepTDA( - 2, - 0.1, - [1, 8], - [[-1, -1], [1, 1]], - [[1, 1], [1, 1]], - model_options={ - 'n_bases': 6, - 'n_polynomials': 6, - 'n_layers': 2, - 'n_messages': 2, - 'n_feedforwards': 1, - 'n_scalars_node': 16, - 'n_vectors_node': 8, - 'n_scalars_edge': 16, - 'drop_rate': 0, - 'activation': 'SiLU', - } - ) - - data = test_get_data() - - assert ( - torch.abs( - cv(data) - - torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) - ) < 1E-12 - ).all() - - assert torch.abs( - cv.training_step(data) - torch.tensor(404.8752553674548) - ) < 1E-12 - - try: - cv = GraphDeepTDA(2, 0.1, [1, 8], [-1, 1], [1, 1]) - except ValueError: - pass - else: - raise RuntimeError - - try: - cv = GraphDeepTDA(2, 0.1, [1, 8], [[-1, -1], [1, 1]], [1, 1]) - except ValueError: - pass - else: - raise RuntimeError - - -if __name__ == '__main__': - test_deep_tda() diff --git a/mlcolvar/cvs/supervised/deeptda_merged.py b/mlcolvar/cvs/supervised/deeptda_merged.py deleted file mode 100644 index a96241a9..00000000 --- a/mlcolvar/cvs/supervised/deeptda_merged.py +++ /dev/null @@ -1,264 +0,0 @@ -import torch -import lightning -from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward, BaseGNN, Normalization -from mlcolvar.core.loss import TDALoss -from mlcolvar.data import DictModule -from typing import Union, List - -__all__ = ["DeepTDA"] - -class DeepTDA(BaseCV, lightning.LightningModule): - """ - Deep Targeted Discriminant Analysis (Deep-TDA) CV. - Combine the inputs with a neural-network and optimize it in a way such that - the data are distributed accordingly to a mixture of Gaussians. The method is described in [1]_. - **Data**: for training it requires a DictDataset with the keys 'data' and 'labels'. - **Loss**: distance of the samples of each class from a set of Gaussians (TDALoss) - References - ---------- - .. [1] E. Trizio and M. Parrinello, "From enhanced sampling to reaction profiles", - The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). - See also - -------- - mlcolvar.core.loss.TDALoss - Distance from a simple Gaussian target distribution. - """ - - DEFAULT_BLOCKS = ["norm_in", "nn"] - - # TODO n_states optional? - def __init__( - self, - n_states: int, - n_cvs: int, - target_centers: list, - target_sigmas: list, - model: Union[List[int], FeedForward, BaseGNN], - options: dict = None, - **kwargs, - ): - """ - Define Deep Targeted Discriminant Analysis (Deep-TDA) CV composed by a neural network module. - By default a module standardizing the inputs is also used. - Parameters - ---------- - n_states : int - Number of states for the training - n_cvs : int - Number of collective variables to be trained - target_centers : list - Centers of the Gaussian targets - target_sigmas : list - Standard deviations of the Gaussian targets - layers : list - Number of neurons per layer - options : dict[str, Any], optional - Options for the building blocks of the model, by default {}. - Available blocks: ['norm_in', 'nn']. - Set 'block_name' = None or False to turn off that block - """ - # check what model is - # self.parse_model(model=model) - - # TODO in_features and out_features?? - super().__init__(model, in_features=2, out_features=n_cvs, **kwargs) - self.save_hyperparameters(ignore=['model']) - - # ======= LOSS ======= - self.loss_fn = TDALoss( - n_states=n_states, - target_centers=target_centers, - target_sigmas=target_sigmas, - ) - - # ======= OPTIONS ======= - # parse and sanitize - # TODO make here the checks on options? - options = self.parse_options(options) - # Save n_states - self.n_states = n_states - if self.out_features != n_cvs: - raise ValueError( - "Number of neurons of last layer should match the number of CVs!" - ) - - # check size and type of targets - if not isinstance(target_centers, torch.Tensor): - target_centers = torch.Tensor(target_centers) - if not isinstance(target_sigmas, torch.Tensor): - target_sigmas = torch.Tensor(target_sigmas) - - if target_centers.shape != target_sigmas.shape: - raise ValueError( - f"Size of target_centers and target_sigmas should be the same!" - ) - if n_states != target_centers.shape[0]: - raise ValueError( - f"Size of target_centers at dimension 0 should match the number of states! Expected {n_states} found {target_centers.shape[0]}" - ) - if len(target_centers.shape) == 2: - if n_cvs != target_centers.shape[1]: - raise ValueError( - ( - f"Size of target_centers at dimension 1 should match the number of cvs! Expected {n_cvs} found {target_centers.shape[1]}" - ) - ) - - # ======= BLOCKS ======= - if not self._override_model: - # Initialize norm_in - o = "norm_in" - if (options[o] is not False) and (options[o] is not None): - self.norm_in = Normalization(self.in_features, **options[o]) - - # initialize NN - o = "nn" - self.nn = FeedForward(self.layers, **options[o]) - elif self._override_model: - self.nn = model - - def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: - if isinstance(self.nn, FeedForward): - # =================get data=================== - x = train_batch["data"] - labels = train_batch["labels"] - # =================forward==================== - z = self.forward_cv(x) - # ===================loss===================== - loss, loss_centers, loss_sigmas = self.loss_fn(z, - labels, - return_loss_terms=True - ) - elif isinstance(self.nn, BaseGNN): - # data = train_batch.to_dict() - data = train_batch['data_list'] - data['positions'].requires_grad_(True) - data['node_attrs'].requires_grad_(True) - - output = self.forward(data) - - loss, loss_centers, loss_sigmas = self.loss_fn(output, - data["graph_labels"].squeeze(), - return_loss_terms=True - ) - - # ====================log=====================+ - name = "train" if self.training else "valid" - self.log(f"{name}_loss", loss, on_epoch=True) - self.log(f"{name}_loss_centers", loss_centers, on_epoch=True) - self.log(f"{name}_loss_sigmas", loss_sigmas, on_epoch=True) - - return loss - -# TODO signature of tests? -import numpy as np - - -def test_deeptda_cv(): - from mlcolvar.data import DictDataset - - # feedforward with layers - for states_and_cvs in [[2, 1], [3, 1], [3, 2], [5, 4]]: - print(states_and_cvs) - # get the number of states and cvs for the test run - n_states = states_and_cvs[0] - n_cvs = states_and_cvs[1] - - in_features, out_features = 2, n_cvs - layers = [in_features, 4, 2, out_features] - target_centers = np.random.randn(n_states, n_cvs) - target_sigmas = np.random.randn(n_states, n_cvs) - - # test initialize via dictionary - options = {"nn": {"activation": "relu"}} - - model = DeepTDA( - n_states=n_states, - n_cvs=n_cvs, - target_centers=target_centers, - target_sigmas=target_sigmas, - model=layers, - options=options, - ) - - # create dataset - samples = 100 - X = torch.randn((samples * n_states, 2)) - - # create labels - y = torch.zeros(X.shape[0]) - for i in range(1, n_states): - y[samples * i :] += 1 - - dataset = DictDataset({"data": X, "labels": y}) - datamodule = DictModule(dataset, lengths=[0.75, 0.2, 0.05], batch_size=samples) - # train model - trainer = lightning.Trainer( - accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False, enable_model_summary=False - ) - trainer.fit(model, datamodule) - - # trace model - traced_model = model.to_torchscript( - file_path=None, method="trace", example_inputs=X[0] - ) - model.eval() - assert torch.allclose(model(X), traced_model(X)) - - - # feedforward external - ff_model = FeedForward(layers=layers) - model = DeepTDA( - n_states=n_states, - n_cvs=n_cvs, - target_centers=target_centers, - target_sigmas=target_sigmas, - model=ff_model - ) - - # train model - trainer = lightning.Trainer( - accelerator="cpu", max_epochs=2, logger=None, enable_checkpointing=False, enable_model_summary=False - ) - trainer.fit(model, datamodule) - - # trace model - traced_model = model.to_torchscript( - file_path=None, method="trace", example_inputs=X[0] - ) - model.eval() - assert torch.allclose(model(X), traced_model(X)) - - - # gnn external - from mlcolvar.core.nn.graph.schnet import SchNetModel - from mlcolvar.data.graph.utils import create_test_graph_input - gnn_model = SchNetModel(1, 0.1, [1, 8]) - model = DeepTDA( - n_states=n_states, - n_cvs=n_cvs, - target_centers=target_centers, - target_sigmas=target_sigmas, - model=gnn_model - ) - datamodule = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=n_states) - - # train model - trainer = lightning.Trainer( - accelerator="cpu", max_epochs=2, logger=False, enable_checkpointing=False, enable_model_summary=False - ) - trainer.fit(model, datamodule) - - # trace model - example_input_graph = create_test_graph_input(output_type='tracing_example', n_samples=3, n_states=2) - - traced_model = model.to_torchscript( - file_path=None, method="trace", example_inputs=example_input_graph - ) - model.eval() - assert torch.allclose(model(example_input_graph), traced_model(example_input_graph)) - -if __name__ == "__main__": - test_deeptda_cv() - From a4f76c1890392a4401da8fa3a24a1ec2188532d7 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 10:55:57 +0100 Subject: [PATCH 165/267] Updated DeepLDA --- mlcolvar/cvs/supervised/deeplda.py | 113 ++++++++++++++++++++++++----- 1 file changed, 93 insertions(+), 20 deletions(-) diff --git a/mlcolvar/cvs/supervised/deeplda.py b/mlcolvar/cvs/supervised/deeplda.py index 55d949ff..159a1843 100644 --- a/mlcolvar/cvs/supervised/deeplda.py +++ b/mlcolvar/cvs/supervised/deeplda.py @@ -1,10 +1,12 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward, Normalization +from mlcolvar.core import FeedForward, BaseGNN, Normalization from mlcolvar.data import DictModule from mlcolvar.core.stats import LDA from mlcolvar.core.loss import ReduceEigenvaluesLoss +from typing import Union, List + __all__ = ["DeepLDA"] @@ -31,9 +33,14 @@ class DeepLDA(BaseCV, lightning.LightningModule): Eigenvalue reduction to a scalar quantity """ - BLOCKS = ["norm_in", "nn", "lda"] + DEFAULT_BLOCKS = ["norm_in", "nn", "lda"] + MODEL_BLOCKS = ["nn", "lda"] - def __init__(self, layers: list, n_states: int, options: dict = None, **kwargs): + def __init__(self, + model: Union[List[int], FeedForward, BaseGNN], + n_states: int, + options: dict = None, + **kwargs): """ Define a Deep Linear Discriminant Analysis (Deep-LDA) CV composed by a neural network module and a LDA object. @@ -50,7 +57,8 @@ def __init__(self, layers: list, n_states: int, options: dict = None, **kwargs): Available blocks: ['norm_in','nn','lda'] . Set 'block_name' = None or False to turn off that block """ - super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) + super().__init__(model=model, out_features=n_states-1, **kwargs) + self.save_hyperparameters(ignore=['model']) # ======= LOSS ======= # Maximize the sum of all the LDA eigenvalues. @@ -66,25 +74,30 @@ def __init__(self, layers: list, n_states: int, options: dict = None, **kwargs): # ======= BLOCKS ======= # initialize norm_in - o = "norm_in" - if (options[o] is not False) and (options[o] is not None): - self.norm_in = Normalization(self.in_features, **options[o]) + if not self._override_model: + o = "norm_in" + if (options[o] is not False) and (options[o] is not None): + self.norm_in = Normalization(self.in_features, **options[o]) + + # initialize nn + o = "nn" + self.nn = FeedForward(self.layers, **options[o]) - # initialize nn - o = "nn" - self.nn = FeedForward(layers, **options[o]) + elif self._override_model: + self.nn = model # initialize lda o = "lda" - self.lda = LDA(layers[-1], n_states, **options[o]) + self.lda = LDA(self.nn.out_features, n_states, **options[o]) # regularization self.lorentzian_reg = 40 # == 2/sw_reg, see set_regularization self.set_regularization(sw_reg=0.05) def forward_nn(self, x: torch.Tensor) -> torch.Tensor: - if self.norm_in is not None: - x = self.norm_in(x) + if not self._override_model: + if self.norm_in is not None: + x = self.norm_in(x) x = self.nn(x) return x @@ -136,14 +149,21 @@ def regularization_lorentzian(self, x): def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" - # =================get data=================== - x = train_batch["data"] - y = train_batch["labels"] - # =================forward==================== - h = self.forward_nn(x) + if isinstance(self.nn, FeedForward): + # =================get data=================== + x = train_batch["data"] + labels = train_batch["labels"] + # =================forward==================== + h = self.forward_nn(x) + + elif isinstance(self.nn, BaseGNN): + data = self._setup_graph_data(train_batch) + labels = data['graph_labels'].squeeze() + h = self.forward_nn(data) + # ===================lda====================== eigvals, _ = self.lda.compute( - h, y, save_params=True if self.training else False + h, labels, save_params=True if self.training else False ) # ===================loss===================== loss = self.loss_fn(eigvals) @@ -151,6 +171,7 @@ def training_step(self, train_batch, batch_idx): s = self.lda(h) lorentzian_reg = self.regularization_lorentzian(s) loss += lorentzian_reg + # ====================log===================== name = "train" if self.training else "valid" loss_dict = {f"{name}_loss": loss, f"{name}_lorentzian_reg": lorentzian_reg} @@ -164,7 +185,7 @@ def test_deeplda(n_states=2): in_features, out_features = 2, n_states - 1 layers = [in_features, 50, 50, out_features] - + # create dataset n_points = 500 X, y = [], [] @@ -187,6 +208,9 @@ def test_deeplda(n_states=2): "nn": {"activation": "relu"}, "lda": {}, } + print() + print('NORMAL') + print() model = DeepLDA(layers, n_states, options=opts) # create trainer and fit @@ -200,6 +224,55 @@ def test_deeplda(n_states=2): with torch.no_grad(): s = model(X).numpy() + + # feedforward external + ff_model = FeedForward(layers=layers) + print() + print('EXTERNAL') + print() + model = DeepLDA(ff_model, n_states) + + # create trainer and fit + trainer = lightning.Trainer( + max_epochs=1, log_every_n_steps=2, logger=None, enable_checkpointing=False + ) + trainer.fit(model, datamodule) + + # eval + model.eval() + with torch.no_grad(): + s = model(X).numpy() + + + # gnn external + from mlcolvar.core.nn.graph.schnet import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(2, 0.1, [1, 8]) + print() + print('GNN') + print() + model = DeepLDA(gnn_model, n_states) + + datamodule = create_test_graph_input(output_type='datamodule', n_samples=200, n_states=n_states) + + # create trainer and fit + trainer = lightning.Trainer( + max_epochs=1, log_every_n_steps=2, logger=False, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + + traced_model = model.to_torchscript( + file_path=None, method="trace") + + example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=n_states) + assert torch.allclose(model(example_input_graph_test), traced_model(example_input_graph_test)) + + # eval + model.eval() + with torch.no_grad(): + s = model(example_input_graph_test).numpy() + + if __name__ == "__main__": test_deeplda(n_states=2) From c47d7e3c7838d6164ec06d187850c407c4fa6156 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 10:56:09 +0100 Subject: [PATCH 166/267] Updated test notebook --- test_graphs/test_graph.ipynb | 435 +++++++++++++---------------------- 1 file changed, 161 insertions(+), 274 deletions(-) diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 4ff38bd8..908189bc 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -2,40 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DictModule(dataset -> DictDataset( \"data_list\": 6, \"z_table\": [1, 8], \"cutoff\": 0.1, \"data_type\": graphs ),\n", - "\t\t train_loader -> DictLoader(length=1.0, batch_size=6, shuffle=False))" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlcolvar.data.graph.utils import create_test_graph_input\n", - "\n", - "sss = create_test_graph_input(output_type='datamodule', n_samples=3, n_states=2)\n", - "sss" - ] - }, - { - "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -43,8 +10,8 @@ "output_type": "stream", "text": [ "DictModule(dataset -> DictDataset( \"data_list\": 1600, \"z_table\": [6, 9], \"cutoff\": 8.0, \"data_type\": graphs ),\n", - "\t\t train_loader -> DictLoader(length=0.8, batch_size=1600, shuffle=True),\n", - "\t\t valid_loader -> DictLoader(length=0.2, batch_size=1600, shuffle=True))\n", + "\t\t train_loader -> DictLoader(length=0.8, batch_size=1600, shuffle=1),\n", + "\t\t valid_loader -> DictLoader(length=0.2, batch_size=1600, shuffle=0))\n", "Class 0 dataframe shape: (800, 24)\n", "Class 1 dataframe shape: (800, 24)\n", "\n", @@ -72,7 +39,7 @@ " show_progress=False\n", ")\n", "\n", - "datamodule_graph = DictModule(dataset_graph, lengths=[0.8, 0.2])\n", + "datamodule_graph = DictModule(dataset_graph, lengths=[0.8, 0.2], shuffle=[1, 0])\n", "print(datamodule_graph)\n", "\n", "\n", @@ -80,12 +47,12 @@ " 'data/colvar_p.dat'],\n", " filter_args={'regex': 'd'})\n", "\n", - "datamodule_ff = DictModule(dataset_ff, lengths=[0.8, 0.2])\n" + "datamodule_ff = DictModule(dataset_ff, lengths=[0.8, 0.2], shuffle=[1, 0])\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -103,70 +70,7 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6, 1, 1)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_samples=10\n", - "n_states=3\n", - "n_atoms = 3\n", - "numbers = [8, 1, 1]\n", - "_ref_positions = np.array(\n", - " [\n", - " [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]],\n", - " [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]],\n", - " [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]],\n", - " [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]],\n", - " [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]],\n", - " [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]],\n", - " ],\n", - " dtype=np.float64\n", - ")\n", - "\n", - "# positions = np.zeros((n_samples*n_states, 3, 3))\n", - "idx = np.random.randint(low=0, high=6, size=(n_samples*n_states))\n", - "\n", - "positions = _ref_positions[idx, :, :]\n", - "positions.shape\n", - "\n", - "graph_labels = np.array([[[0]], [[1]]] * 3)\n", - "graph_labels.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[6, 9]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_graph.metadata['z_table']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -178,7 +82,7 @@ } ], "source": [ - "from mlcolvar.cvs.supervised.deeptda_merged import DeepTDA\n", + "from mlcolvar.cvs.supervised.deeptda import DeepTDA\n", "\n", "model_graph = DeepTDA(n_states=2,\n", " n_cvs=1,\n", @@ -204,27 +108,7 @@ { "data": { "text/plain": [ - "[FeedForward(\n", - " (nn): Sequential(\n", - " (0): Linear(in_features=21, out_features=15, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=15, out_features=10, bias=True)\n", - " (3): ReLU(inplace=True)\n", - " (4): Linear(in_features=10, out_features=1, bias=True)\n", - " )\n", - " ),\n", - " Sequential(\n", - " (0): Linear(in_features=21, out_features=15, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=15, out_features=10, bias=True)\n", - " (3): ReLU(inplace=True)\n", - " (4): Linear(in_features=10, out_features=1, bias=True)\n", - " ),\n", - " Linear(in_features=21, out_features=15, bias=True),\n", - " ReLU(inplace=True),\n", - " Linear(in_features=15, out_features=10, bias=True),\n", - " ReLU(inplace=True),\n", - " Linear(in_features=10, out_features=1, bias=True)]" + "False" ] }, "execution_count": 12, @@ -233,55 +117,30 @@ } ], "source": [ - "list(ff_model.modules())" - ] - }, - { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "from mlcolvar.cvs.supervised.deeptda_merged import DeepTDA\n", - "\n", - "model_graph = DeepTDA(n_states=2,\n", - " n_cvs=1,\n", - " target_centers=[-7, 7],\n", - " target_sigmas=[0.2, 0.2],\n", - " layers=[21, 15, 10, 1],\n", - " gnn_model=gnn_model)\n", - "\n", - "model_ff = DeepTDA(n_states=2,\n", - " n_cvs=1,\n", - " target_centers=[-7, 7],\n", - " target_sigmas=[0.2, 0.2],\n", - " layers=[21, 15, 10, 1],\n", - " gnn_model=None)\n", - "\n" + "model_graph.eval()\n", + "model_graph.training" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: False\n", + "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2f46b24cd0a4b3dbc216d11070cf605", + "model_id": "7cc2ca5515fd41cebfd86c74155a2308", "version_major": 2, "version_minor": 0 }, @@ -296,7 +155,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:492: Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n", "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n", "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" ] @@ -304,7 +162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0deda85d8c8d4aeb9b727a23763da446", + "model_id": "efaed37c2205450392f2e1d63b904c70", "version_major": 2, "version_minor": 0 }, @@ -318,7 +176,77 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a44759e1135481c90201e60a78d4395", + "model_id": "063d6a064ab742fbb81a595abfd9e76d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: | | 0/? [00:00)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "model_graph(sss)" + "gnn_model.ou" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -438,7 +355,7 @@ "DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])" ] }, - "execution_count": 23, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -454,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -463,7 +380,7 @@ "Data(edge_index=[2, 42], shifts=[42, 3], unit_shifts=[42, 3], positions=[7, 3], cell=[3, 3], node_attrs=[7, 2], graph_labels=[1, 1], n_system=[1, 1], weight=[1], batch=[1])" ] }, - "execution_count": 24, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -477,134 +394,103 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/data/graph/utils.py:69: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " torch.tensor(config.graph_labels, dtype=torch.get_default_dtype())\n", + "/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/data/datamodule.py:301: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# model_graph.eval()\n", - "traced_model = model_graph.to_torchscript('test.pt', method='trace', example_inputs=sss.to_dict())" + "traced_model = model_graph.to_torchscript('test.pt', method='trace')" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0.3392]], grad_fn=)\n" + ] + } + ], "source": [ - "from mlcolvar.data.graph.utils import create_dataset_from_configurations\n", - "from mlcolvar.data.graph.atomic import AtomicNumberTable, Configuration\n", - "\n", - "numbers = gnn_model.atomic_numbers.cpu().numpy().tolist()\n", - "positions = np.random.randn(2, len(numbers), 3)\n", - "cell = np.identity(3, dtype=float) * 0.2\n", - "graph_labels = np.array([[[0]], [[1]]])\n", - "node_labels = np.array([[0]] * len(numbers))\n", - "z_table = AtomicNumberTable.from_zs(numbers)\n", - "\n", - "config = [\n", - " Configuration(\n", - " atomic_numbers=numbers,\n", - " positions=positions[i],\n", - " cell=cell,\n", - " pbc=[True] * 3,\n", - " node_labels=node_labels,\n", - " graph_labels=graph_labels[i],\n", - " ) for i in range(2)\n", - "]\n", - "dataset = create_dataset_from_configurations(\n", - " config, z_table, 0.1, show_progress=False\n", - ")\n", - "\n", - "loader = DictModule(\n", - " dataset,\n", - " lengths=(1.0,),\n", - " batch_size=10,\n", - " shuffle=False,\n", - ")\n", - "loader.setup()\n", - "\n", - "please = next(iter(loader.train_dataloader()))\n", - "inp_test = please['data_list']" + "print(model_graph(xxx))" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DataBatch(edge_index=[2, 2], shifts=[2, 3], unit_shifts=[2, 3], positions=[4, 3], cell=[6, 3], node_attrs=[4, 2], node_labels=[4, 1], graph_labels=[2, 1], n_system=[2, 1], weight=[2], batch=[1], ptr=[3])" + "torch_geometric.data.batch.DataBatch" ] }, - "execution_count": 27, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import torch\n", - "inp_test.batch = torch.tensor([0], dtype=torch.long)\n", - "\n", - "inp_test" + "type(inp)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[0.4284]], grad_fn=)\n", - "tensor([[0.6660]], grad_fn=)\n" - ] - } - ], - "source": [ - "print(model_graph(xxx))\n", - "print(model_graph(inp_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.4284]], grad_fn=)\n", - "tensor([[0.6660]], grad_fn=)\n", - "tensor([[0.4284]], grad_fn=)\n", - "tensor([[0.6660]], grad_fn=)\n", - "tensor([[0.4284]], grad_fn=)\n", - "tensor([[0.6660]], grad_fn=)\n" + "tensor([[0.3392]], grad_fn=)\n", + "tensor([[0.2707]], grad_fn=)\n", + "tensor([[0.3392]], grad_fn=)\n", + "tensor([[0.2707]], grad_fn=)\n", + "tensor([[0.3392]], grad_fn=)\n", + "tensor([[0.2707]], grad_fn=)\n" ] } ], "source": [ + "test = inp.get_example(100)\n", + "test.batch = torch.tensor([0], dtype = torch.long)\n", + "\n", "print(model_graph(xxx))\n", - "print(model_graph(inp_test))\n", + "print(model_graph(test))\n", "\n", "print(traced_model(xxx))\n", - "print(traced_model(inp_test))\n", + "print(traced_model(test))\n", + "\n", "\n", "loaded_model = torch.jit.load('test.pt')\n", "\n", "print(loaded_model(xxx))\n", - "print(loaded_model(inp_test))\n", + "print(loaded_model(test))\n", + "\n", "\n", "# EVVIVAAAAAA" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -613,7 +499,7 @@ "[{'data_list': DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])}]" ] }, - "execution_count": 30, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -631,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -641,13 +527,14 @@ "GPU available: True (cuda), used: False\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" + "HPU available: False, using: 0 HPUs\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f561fd44ecc422fbb5ffa5efdc799d6", + "model_id": "93c79dd966284476b9c70b5baaf81a76", "version_major": 2, "version_minor": 0 }, @@ -661,7 +548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "23ad71a286e94f59b867e224d1b307cf", + "model_id": "f65ea81b4e2245eca9b7c8ce38044a28", "version_major": 2, "version_minor": 0 }, @@ -675,7 +562,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d15caf10f1974143be92483e4a9f23ab", + "model_id": "a0c3ddc9056b4da5946c14af2ecb85dc", "version_major": 2, "version_minor": 0 }, @@ -689,7 +576,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab18049add364a10aa56a4241a5be79e", + "model_id": "e6e1424a81f5461687a01759282f4f68", "version_major": 2, "version_minor": 0 }, @@ -703,7 +590,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f5443851c7f4426ab82c797991734255", + "model_id": "69ca412e3b004d21b5bb2103fea86cb0", "version_major": 2, "version_minor": 0 }, @@ -717,7 +604,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "864a4cf9b865444da4f089918d4f9a08", + "model_id": "1715c5b23bbe461fa34c3ff16e33f042", "version_major": 2, "version_minor": 0 }, @@ -731,7 +618,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00ecacf424cb48d78ef2b68260dc25a0", + "model_id": "2abb55cc1e6442359ef18e970a03d15c", "version_major": 2, "version_minor": 0 }, @@ -764,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -777,12 +664,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -809,7 +696,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pytorch_geometric", + "display_name": "graph_mlcolvar_test", "language": "python", "name": "python3" }, From b072ec6aed4863612ea3a511c6858553beb9f5be Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 12:29:04 +0100 Subject: [PATCH 167/267] Added backward method --- mlcolvar/core/nn/feedforward.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mlcolvar/core/nn/feedforward.py b/mlcolvar/core/nn/feedforward.py index d5566df8..19c233bb 100644 --- a/mlcolvar/core/nn/feedforward.py +++ b/mlcolvar/core/nn/feedforward.py @@ -15,10 +15,9 @@ # GLOBAL IMPORTS # ============================================================================= -from typing import Optional, Union +from typing import Optional, Union, Any import torch -import lightning from mlcolvar.core.nn.utils import get_activation, parse_nn_options @@ -110,3 +109,6 @@ def __init__( def forward(self, x: torch.Tensor) -> torch.Tensor: return self.nn(x) + + def backward(self, loss: torch.Tensor, *args: Any, **kwargs: Any): + return loss.backward() From 0debbccdec7508524aa734cd16c78f4ca8d6460c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 12:37:42 +0100 Subject: [PATCH 168/267] removed out_features keyword --- mlcolvar/cvs/cv.py | 5 +++-- mlcolvar/cvs/supervised/deeplda.py | 2 +- mlcolvar/cvs/supervised/deeptda.py | 15 +++++++++++---- 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 6e7d4082..638a20cb 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -18,7 +18,6 @@ class BaseCV: def __init__( self, model: Union[List[int], FeedForward, BaseGNN], - out_features, preprocessing: torch.nn.Module = None, postprocessing: torch.nn.Module = None, *args, @@ -47,7 +46,6 @@ def __init__( # MODEL self.parse_model(model=model) self.initialize_blocks() - self.out_features = out_features # OPTIM self._optimizer_name = "Adam" @@ -83,17 +81,20 @@ def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): self.BLOCKS = self.DEFAULT_BLOCKS self._override_model = False self.in_features = self.layers[0] + self.out_features = self.layers[-1] elif isinstance(model, FeedForward) or isinstance(model, BaseGNN): self.BLOCKS = self.MODEL_BLOCKS self._override_model = True if isinstance(model, FeedForward): # self.nn = model self.in_features = model.in_features + self.out_features = model.out_features elif isinstance(model, BaseGNN): # GNN models need to be scripted! # self.nn = torch.jit.script_if_tracing(model) # self.nn = model self.in_features = None + self.out_features = model.out_features else: raise ValueError( f"Keyword model can either accept type list, FeedForward or BaseGNN. Found {type(model)}" diff --git a/mlcolvar/cvs/supervised/deeplda.py b/mlcolvar/cvs/supervised/deeplda.py index 159a1843..c6280abe 100644 --- a/mlcolvar/cvs/supervised/deeplda.py +++ b/mlcolvar/cvs/supervised/deeplda.py @@ -57,7 +57,7 @@ def __init__(self, Available blocks: ['norm_in','nn','lda'] . Set 'block_name' = None or False to turn off that block """ - super().__init__(model=model, out_features=n_states-1, **kwargs) + super().__init__(model=model, **kwargs) self.save_hyperparameters(ignore=['model']) # ======= LOSS ======= diff --git a/mlcolvar/cvs/supervised/deeptda.py b/mlcolvar/cvs/supervised/deeptda.py index 3b323aa3..d50b8ed0 100644 --- a/mlcolvar/cvs/supervised/deeptda.py +++ b/mlcolvar/cvs/supervised/deeptda.py @@ -60,7 +60,7 @@ def __init__( Available blocks: ['norm_in', 'nn']. Set 'block_name' = None or False to turn off that block """ - super().__init__(model, out_features=n_cvs, **kwargs) + super().__init__(model, **kwargs) self.save_hyperparameters(ignore=['model']) # ======= LOSS ======= @@ -167,6 +167,9 @@ def test_deeptda_cv(): # test initialize via dictionary options = {"nn": {"activation": "relu"}} + print() + print('NORMAL') + print() model = DeepTDA( n_states=n_states, n_cvs=n_cvs, @@ -199,7 +202,9 @@ def test_deeptda_cv(): model.eval() assert torch.allclose(model(X), traced_model(X)) - + print() + print('EXTERNAL FEEDFORWARD') + print() # feedforward external ff_model = FeedForward(layers=layers) model = DeepTDA( @@ -222,11 +227,13 @@ def test_deeptda_cv(): model.eval() assert torch.allclose(model(X), traced_model(X)) - + print() + print('EXTERNAL GNN') + print() # gnn external from mlcolvar.core.nn.graph.schnet import SchNetModel from mlcolvar.data.graph.utils import create_test_graph_input - gnn_model = SchNetModel(1, 0.1, [1, 8]) + gnn_model = SchNetModel(n_cvs, 0.1, [1, 8]) model = DeepTDA( n_states=n_states, n_cvs=n_cvs, From fe638942b328baa1315a5a59a5eda968f902aeac Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 12:46:21 +0100 Subject: [PATCH 169/267] simplified training_step --- mlcolvar/cvs/supervised/deeplda.py | 13 ++++++------- mlcolvar/cvs/supervised/deeptda.py | 15 ++++++--------- 2 files changed, 12 insertions(+), 16 deletions(-) diff --git a/mlcolvar/cvs/supervised/deeplda.py b/mlcolvar/cvs/supervised/deeplda.py index c6280abe..a1e8ffdc 100644 --- a/mlcolvar/cvs/supervised/deeplda.py +++ b/mlcolvar/cvs/supervised/deeplda.py @@ -149,17 +149,16 @@ def regularization_lorentzian(self, x): def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" + # =================get data=================== if isinstance(self.nn, FeedForward): - # =================get data=================== x = train_batch["data"] labels = train_batch["labels"] - # =================forward==================== - h = self.forward_nn(x) - elif isinstance(self.nn, BaseGNN): - data = self._setup_graph_data(train_batch) - labels = data['graph_labels'].squeeze() - h = self.forward_nn(data) + x = self._setup_graph_data(train_batch) + labels = x['graph_labels'].squeeze() + + # =================forward==================== + h = self.forward_nn(x) # ===================lda====================== eigvals, _ = self.lda.compute( diff --git a/mlcolvar/cvs/supervised/deeptda.py b/mlcolvar/cvs/supervised/deeptda.py index d50b8ed0..549ec648 100644 --- a/mlcolvar/cvs/supervised/deeptda.py +++ b/mlcolvar/cvs/supervised/deeptda.py @@ -117,19 +117,16 @@ def __init__( def training_step(self, train_batch, *args, **kwargs) -> torch.Tensor: """Compute and return the training loss and record metrics.""" + # =================get data=================== if isinstance(self.nn, FeedForward): - # =================get data=================== x = train_batch["data"] labels = train_batch["labels"] - # =================forward==================== - z = self.forward_cv(x) - elif isinstance(self.nn, BaseGNN): - # =================get data=================== - data = self._setup_graph_data(train_batch) - labels = data['graph_labels'].squeeze() - # =================forward==================== - z = self.forward(data) + x = self._setup_graph_data(train_batch) + labels = x['graph_labels'].squeeze() + + # =================forward==================== + z = self.forward_cv(x) # ===================loss===================== loss, loss_centers, loss_sigmas = self.loss_fn(z, From 5338da9f17e4f7cb978b9b95d1e20591b212b891 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 13:17:49 +0100 Subject: [PATCH 170/267] Added random weights keyword --- mlcolvar/data/graph/utils.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 42386290..9763d7a2 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -192,7 +192,8 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: def create_test_graph_input(output_type: str, n_atoms: int = 3, n_samples: int = 60, - n_states: int = 2) -> torch_geometric.data.Batch: + n_states: int = 2, + random_weights = False ) -> torch_geometric.data.Batch: if n_atoms == 3: numbers = [8, 1, 1] node_labels = np.array([[0], [1], [1]]) @@ -233,6 +234,10 @@ def create_test_graph_input(output_type: str, graph_labels[n_samples * i :] += 1 z_table = atomic.AtomicNumberTable.from_zs(numbers) + if random_weights: + weights = np.random.random_sample((n_samples*n_states, 1, 1)) + else: + weights = np.ones((n_samples*n_states, 1, 1)) config = [ atomic.Configuration( atomic_numbers=numbers, @@ -241,6 +246,7 @@ def create_test_graph_input(output_type: str, pbc=[True] * 3, node_labels=node_labels, graph_labels=graph_labels[i], + weight=weights[i] ) for i in range(0, n_samples*n_states) ] From 41eba2062ef30473d69ba280876bc64560e0c02e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 13:17:58 +0100 Subject: [PATCH 171/267] Updated regression CV --- mlcolvar/cvs/supervised/regression.py | 141 ++++++++++++++++++++++---- 1 file changed, 124 insertions(+), 17 deletions(-) diff --git a/mlcolvar/cvs/supervised/regression.py b/mlcolvar/cvs/supervised/regression.py index 1ea52331..c180947b 100644 --- a/mlcolvar/cvs/supervised/regression.py +++ b/mlcolvar/cvs/supervised/regression.py @@ -1,8 +1,10 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward, Normalization +from mlcolvar.core import FeedForward, Normalization, BaseGNN from mlcolvar.core.loss import MSELoss +from typing import Union, List + __all__ = ["RegressionCV"] @@ -22,9 +24,14 @@ class RegressionCV(BaseCV, lightning.LightningModule): (weighted) Mean Squared Error (MSE) loss function. """ - BLOCKS = ["norm_in", "nn"] + DEFAULT_BLOCKS = ["norm_in", "nn"] + MODEL_BLOCKS = ["nn"] + - def __init__(self, layers: list, options: dict = None, **kwargs): + def __init__(self, + model: Union[List[int], FeedForward, BaseGNN], + options: dict = None, + **kwargs): """Example of collective variable obtained with a regression task. By default a module standardizing the inputs is used. @@ -37,7 +44,9 @@ def __init__(self, layers: list, options: dict = None, **kwargs): Available blocks: ['norm_in', 'nn']. Set 'block_name' = None or False to turn off that block """ - super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) + super().__init__(model, **kwargs) + self.save_hyperparameters(ignore=['model']) + # ======= LOSS ======= self.loss_fn = MSELoss() @@ -46,25 +55,38 @@ def __init__(self, layers: list, options: dict = None, **kwargs): # parse and sanitize options = self.parse_options(options) - # Initialize norm_in - o = "norm_in" - if (options[o] is not False) and (options[o] is not None): - self.norm_in = Normalization(self.in_features, **options[o]) + # ======= BLOCKS ======= + if not self._override_model: + # Initialize norm_in + o = "norm_in" + if (options[o] is not False) and (options[o] is not None): + self.norm_in = Normalization(self.in_features, **options[o]) - # initialize NN - o = "nn" - self.nn = FeedForward(layers, **options[o]) + # initialize NN + o = "nn" + self.nn = FeedForward(self.layers, **options[o]) + elif self._override_model: + self.nn = model def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" # =================get data=================== - x = train_batch["data"] - labels = train_batch["target"] loss_kwargs = {} - if "weights" in train_batch: - loss_kwargs["weights"] = train_batch["weights"] + if isinstance(self.nn, FeedForward): + x = train_batch["data"] + labels = train_batch["target"] + if "weights" in train_batch: + loss_kwargs["weights"] = train_batch["weights"] + elif isinstance(self.nn, BaseGNN): + x = self._setup_graph_data(train_batch) + # TODO maybe add an external key like target? + labels = x['graph_labels'].squeeze() + if "weights" in x: + loss_kwargs["weights"] = x["weights"] + # =================forward==================== y = self.forward_cv(x) + # ===================loss===================== loss = self.loss_fn(y, labels, **loss_kwargs) # ====================log===================== @@ -82,10 +104,13 @@ def test_regression_cv(): in_features, out_features = 2, 1 layers = [in_features, 5, 10, out_features] + print() + print('NORMAL') + print() # initialize via dictionary options = {"nn": {"activation": "relu"}} - model = RegressionCV(layers=layers, options=options) + model = RegressionCV(model=layers, options=options) print("----------") print(model) @@ -123,7 +148,89 @@ def test_regression_cv(): accelerator="cpu", max_epochs=1, logger=None, enable_checkpointing=False ) - model = RegressionCV(layers=[2, 10, 10, 1]) + model = RegressionCV(model=[2, 10, 10, 1]) + model.loss_fn = lambda y, y_ref: (y - y_ref).abs().mean() + trainer.fit(model, datamodule) + + print() + print('EXTERNAL FEEDFORWARD') + print() + ff_model = FeedForward(layers=layers) + # create model + model = RegressionCV(model=ff_model) + + # create dataset + X = torch.randn((100, 2)) + y = X.square().sum(1) + dataset = DictDataset({"data": X, "target": y}) + datamodule = DictModule(dataset, lengths=[0.75, 0.2, 0.05], batch_size=25) + # train model + model.optimizer_name = "SGD" + model.optimizer_kwargs.update(dict(lr=1e-2)) + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=1, logger=None, enable_checkpointing=False + ) + trainer.fit(model, datamodule) + model.eval() + # trace model + traced_model = model.to_torchscript( + file_path=None, method="trace", example_inputs=X[0] + ) + assert torch.allclose(model(X), traced_model(X)) + + # weighted loss + print("weighted loss") + w = torch.randn((100)) + dataset_weights = DictDataset({"data": X, "target": y, "weights": w}) + datamodule_weights = DictModule( + dataset_weights, lengths=[0.75, 0.2, 0.05], batch_size=25 + ) + trainer.fit(model, datamodule_weights) + + # use custom loss + print("custom loss") + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=1, logger=None, enable_checkpointing=False + ) + + model = RegressionCV(model=ff_model) + model.loss_fn = lambda y, y_ref: (y - y_ref).abs().mean() + trainer.fit(model, datamodule) + + print() + print('EXTERNAL GNN') + print() + # gnn external + from mlcolvar.core.nn.graph.schnet import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(1, 0.1, [1, 8]) + # create model + model = RegressionCV(model=gnn_model) + + datamodule = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=2) + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=1, logger=False, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + model.eval() + # trace model + traced_model = model.to_torchscript(file_path=None, method="trace") + example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=2) + assert torch.allclose(model(example_input_graph_test), traced_model(example_input_graph_test)) + + # weighted loss + print("weighted loss") + datamodule_weights = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=2, random_weights=True) + trainer.fit(model, datamodule_weights) + + # use custom loss + print("custom loss") + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=1, logger=False, enable_checkpointing=False, enable_model_summary=False + ) + + model = RegressionCV(model=gnn_model) model.loss_fn = lambda y, y_ref: (y - y_ref).abs().mean() trainer.fit(model, datamodule) From dfee8ead5c98346dfa0873af0bebf3ded5565fdd Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 14:37:43 +0100 Subject: [PATCH 172/267] Update autoencoder-based CVs --- mlcolvar/cvs/unsupervised/autoencoder.py | 11 +++++++---- mlcolvar/cvs/unsupervised/vae.py | 10 ++++++++-- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/mlcolvar/cvs/unsupervised/autoencoder.py b/mlcolvar/cvs/unsupervised/autoencoder.py index bb9839bb..ab79fec3 100644 --- a/mlcolvar/cvs/unsupervised/autoencoder.py +++ b/mlcolvar/cvs/unsupervised/autoencoder.py @@ -41,7 +41,7 @@ class AutoEncoderCV(BaseCV, lightning.LightningModule): (weighted) Mean Squared Error (MSE) loss function. """ - BLOCKS = ["norm_in", "encoder", "decoder"] + DEFAULT_BLOCKS = ["norm_in", "encoder", "decoder"] def __init__( self, @@ -67,9 +67,12 @@ def __init__( Available blocks: ['norm_in', 'encoder','decoder']. Set 'block_name' = None or False to turn off that block """ - super().__init__( - in_features=encoder_layers[0], out_features=encoder_layers[-1], **kwargs - ) + # external model not supported for autoencoder CVs (yet) + if not isinstance(encoder_layers, list): + raise NotImplementedError( + f"Encoder layer must be a list. found {type(encoder_layers)}" + ) + super().__init__(model=encoder_layers, **kwargs) # ======= LOSS ======= # Reconstruction (MSE) loss diff --git a/mlcolvar/cvs/unsupervised/vae.py b/mlcolvar/cvs/unsupervised/vae.py index 4e5580af..5946c970 100644 --- a/mlcolvar/cvs/unsupervised/vae.py +++ b/mlcolvar/cvs/unsupervised/vae.py @@ -58,7 +58,7 @@ class VariationalAutoEncoderCV(BaseCV, lightning.LightningModule): Evidence Lower BOund loss function """ - BLOCKS = ["norm_in", "encoder", "decoder"] + DEFAULT_BLOCKS = ["norm_in", "encoder", "decoder"] def __init__( self, @@ -90,7 +90,13 @@ def __init__( Set ``'block_name' = None`` or ``False`` to turn off a block. Encoder and decoder cannot be turned off. """ - super().__init__(in_features=encoder_layers[0], out_features=n_cvs, **kwargs) + if not isinstance(encoder_layers, list): + raise NotImplementedError( + f"Encoder layer must be a list. found {type(encoder_layers)}" + ) + super().__init__(model=encoder_layers, **kwargs) + # here we need to override the self.out_features attribute + self.out_features = n_cvs # ======= LOSS ======= # ELBO loss function when latent space and reconstruction distributions are Gaussians. From 1c657e8486ff690ea98089b4a823bbf7d1f5a32e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 17:02:43 +0100 Subject: [PATCH 173/267] Added more general slicing of datasets --- mlcolvar/data/dataset.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index aa93270e..a32a0ee8 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -2,6 +2,8 @@ import numpy as np from mlcolvar.core.transform.utils import Statistics from torch.utils.data import Dataset +from typing import Union, List +from operator import itemgetter __all__ = ["DictDataset"] @@ -77,14 +79,20 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = if not all([len(l)==self.length for l in it]): raise ValueError("not all arrays in dictionary have same length!") - def __getitem__(self, index): + def __getitem__(self, index : Union[str, int, slice, List, range, np.ndarray]): if isinstance(index, str): # raise TypeError(f'Index ("{index}") should be a slice, and not a string. To access the stored dictionary use .dictionary["{index}"] instead.') return self._dictionary[index] - else: + else: slice_dict = {} for key, val in self._dictionary.items(): - slice_dict[key] = val[index] + if self.metadata['data_type'] == 'graphs' and type(index) in [range, np.ndarray, list]: + slice_dict[key] = list(itemgetter(*index)(val)) + else: + if type(index) in [int, slice]: + slice_dict[key] = val[index] + elif type(index) in [range, np.ndarray, list]: + slice_dict[key] = list(itemgetter(*index)(val)) return slice_dict def __setitem__(self, index, value): From 3ad16920a97828738a381f82efcfefa3ad0cf4b0 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 17:32:22 +0100 Subject: [PATCH 174/267] Fixe dataset representation --- mlcolvar/data/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index a32a0ee8..b7e0e97b 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -59,7 +59,7 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = # convert to torch.Tensors for key, val in dictionary.items(): if not isinstance(val, torch.Tensor): - if key =="data_list": + if key in ["data_list", "data_list_lag"]: dictionary[key] = val else: dictionary[key] = torch.Tensor(val) @@ -128,7 +128,7 @@ def get_stats(self): def __repr__(self) -> str: string = "DictDataset(" for key, val in self._dictionary.items(): - if key=="data_list": + if key in ["data_list", "data_list_lag"]: string += f' "{key}": {len(val)},' else: string += f' "{key}": {list(val.shape)},' From 781015aeaac66712d0a2a8bb773810ac76b6d94c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 3 Dec 2024 17:32:43 +0100 Subject: [PATCH 175/267] First timelagged utils for graphs --- mlcolvar/utils/timelagged.py | 41 +++++++++++++++++++++++++----------- 1 file changed, 29 insertions(+), 12 deletions(-) diff --git a/mlcolvar/utils/timelagged.py b/mlcolvar/utils/timelagged.py index 3d751f0a..21ffeea3 100644 --- a/mlcolvar/utils/timelagged.py +++ b/mlcolvar/utils/timelagged.py @@ -3,6 +3,7 @@ from bisect import bisect_left from mlcolvar.data import DictDataset import warnings +from typing import Union # optional packages # pandas @@ -193,7 +194,7 @@ def progress(iter, progress_bar=progress_bar): def create_timelagged_dataset( - X: torch.Tensor, + X: Union[torch.Tensor, DictDataset], t: torch.Tensor = None, lag_time: float = 1, reweight_mode: str = None, @@ -287,13 +288,23 @@ def create_timelagged_dataset( tprime = t # find pairs of configurations separated by lag_time - x_t, x_lag, w_t, w_lag = find_timelagged_configurations( - X, - tprime, - lag_time=lag_time, - logweights=logweights if reweight_mode == "weights_t" else None, - progress_bar=progress_bar, - ) + if isinstance(X, torch.Tensor): + x_t, x_lag, w_t, w_lag = find_timelagged_configurations( + X, + tprime, + lag_time=lag_time, + logweights=logweights if reweight_mode == "weights_t" else None, + progress_bar=progress_bar, + ) + elif isinstance(X, DictDataset): + index = torch.arange(len(X), dtype=torch.long) + x_t, x_lag, w_t, w_lag = find_timelagged_configurations( + index, + tprime, + lag_time=lag_time, + logweights=logweights if reweight_mode == "weights_t" else None, + progress_bar=progress_bar, + ) # return only a slice of the data (N. Pedrani) if interval is not None: @@ -306,10 +317,16 @@ def create_timelagged_dataset( data[i] = data[i][interval[0] : interval[1]] x_t, x_lag, w_t, w_lag = data - dataset = DictDataset( - {"data": x_t, "data_lag": x_lag, "weights": w_t, "weights_lag": w_lag} - ) - + if isinstance(X, torch.Tensor): + dataset = DictDataset( + {"data": x_t, "data_lag": x_lag, "weights": w_t, "weights_lag": w_lag} + ) + elif isinstance(X, DictDataset): + dataset = DictDataset(dictionary={'data_list' : X[x_t.numpy().tolist()]['data_list'], + 'data_list_lag' : X[x_lag.numpy().tolist()]['data_list']}, + metadata={'z_table' : X.metadata['z_table'], + 'cutoff' : X.metadata['cutoff']}, + data_type='graphs') return dataset From 919aa9b9702f79e72d08182dbe41634e4593354e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 4 Dec 2024 16:00:28 +0100 Subject: [PATCH 176/267] Fixed __getitem__ --- mlcolvar/data/dataset.py | 60 ++++++++++++++++++++++++++++++---------- 1 file changed, 46 insertions(+), 14 deletions(-) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index b7e0e97b..9215b035 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -79,20 +79,17 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = if not all([len(l)==self.length for l in it]): raise ValueError("not all arrays in dictionary have same length!") - def __getitem__(self, index : Union[str, int, slice, List, range, np.ndarray]): + def __getitem__(self, index : Union[str, int, slice, List, range, np.ndarray, torch.Tensor]): if isinstance(index, str): - # raise TypeError(f'Index ("{index}") should be a slice, and not a string. To access the stored dictionary use .dictionary["{index}"] instead.') return self._dictionary[index] else: slice_dict = {} for key, val in self._dictionary.items(): - if self.metadata['data_type'] == 'graphs' and type(index) in [range, np.ndarray, list]: + if self.metadata['data_type'] == 'graphs' and type(index) in [range, np.ndarray, list, torch.Tensor]: slice_dict[key] = list(itemgetter(*index)(val)) else: - if type(index) in [int, slice]: - slice_dict[key] = val[index] - elif type(index) in [range, np.ndarray, list]: - slice_dict[key] = list(itemgetter(*index)(val)) + slice_dict[key] = val[index] + return slice_dict def __setitem__(self, index, value): @@ -187,29 +184,64 @@ def test_DictDataset(): # graphs based numbers = [8, 1, 1] positions = np.array( + [[[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]]], dtype=float ) cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) + graph_labels = np.array([[1], [0], [1]]) node_labels = np.array([[0], [1], [1]]) z_table = AtomicNumberTable.from_zs(numbers) config = [Configuration( atomic_numbers=numbers, - positions=positions, + positions=positions[i], cell=cell, pbc=[True] * 3, - node_labels=node_labels, + node_labels=node_labels[i], graph_labels=graph_labels, - )] - dataset = create_dataset_from_configurations(config, + ) for i in range(3)] + graph_dataset = create_dataset_from_configurations(config, z_table, 0.1, show_progress=False ) - print(dataset) - assert(isinstance(dataset, DictDataset)) + print(graph_dataset) + assert(isinstance(graph_dataset, DictDataset)) + + # check __getitem__ + # string + print(dataset['data']) + print(graph_dataset['data_list']) + + # int + print(dataset[0]) + print(graph_dataset[0]) + + # list + print(dataset[[0,1,2]]) + print(graph_dataset[[0,1,2]]) + + # slice + print(dataset[0:2]) + print(graph_dataset[0:2]) + + # range + print(dataset[range(0,2)]) + print(graph_dataset[range(0,2)]) + + # np.ndarray + print(dataset[np.array([0])]) + print(graph_dataset[np.array([0])]) + print(dataset[np.array([0,1,2])]) + print(graph_dataset[np.array([0,1,2])]) + + # torch.Tensor + print(dataset[torch.tensor([0], dtype=torch.long)]) + print(graph_dataset[torch.tensor([0], dtype=torch.long)]) + print(dataset[torch.tensor([0,1,2], dtype=torch.long)]) + print(graph_dataset[torch.tensor([0,1,2], dtype=torch.long)]) if __name__ == "__main__": test_DictDataset() \ No newline at end of file From 94c40ef106b1ef758902a1475031acdbfee6b1c0 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 4 Dec 2024 16:00:43 +0100 Subject: [PATCH 177/267] Added optional key --- mlcolvar/cvs/cv.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 638a20cb..c262a9b0 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -267,8 +267,8 @@ def __setattr__(self, key, value): del self.loss_fn super().__setattr__(key, value) - def _setup_graph_data(self, train_batch): - data = train_batch['data_list'] + def _setup_graph_data(self, train_batch, key : str='data_list'): + data = train_batch[key] data['positions'].requires_grad_(True) data['node_attrs'].requires_grad_(True) return data \ No newline at end of file From 0dd0d051d7207d70ce6c73c2b897491ff24e3e41 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 10:56:24 +0100 Subject: [PATCH 178/267] Make timelagged utils work with graphs --- mlcolvar/utils/timelagged.py | 31 +++++++++++++++++++++++-------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/mlcolvar/utils/timelagged.py b/mlcolvar/utils/timelagged.py index 21ffeea3..4edaac02 100644 --- a/mlcolvar/utils/timelagged.py +++ b/mlcolvar/utils/timelagged.py @@ -4,6 +4,7 @@ from mlcolvar.data import DictDataset import warnings from typing import Union +import copy # optional packages # pandas @@ -318,15 +319,22 @@ def create_timelagged_dataset( x_t, x_lag, w_t, w_lag = data if isinstance(X, torch.Tensor): - dataset = DictDataset( - {"data": x_t, "data_lag": x_lag, "weights": w_t, "weights_lag": w_lag} - ) + dataset = DictDataset({"data": x_t, + "data_lag": x_lag, + "weights": w_t, + "weights_lag": w_lag}) elif isinstance(X, DictDataset): - dataset = DictDataset(dictionary={'data_list' : X[x_t.numpy().tolist()]['data_list'], - 'data_list_lag' : X[x_lag.numpy().tolist()]['data_list']}, - metadata={'z_table' : X.metadata['z_table'], - 'cutoff' : X.metadata['cutoff']}, - data_type='graphs') + # we use deepcopy to avoid editing the original dataset + dataset = DictDataset(dictionary={"data_list" : copy.deepcopy(X[x_t.numpy().tolist()]["data_list"]), + "data_list_lag" : copy.deepcopy(X[x_lag.numpy().tolist()]["data_list"])}, + metadata={"z_table" : X.metadata["z_table"], + "cutoff" : X.metadata["cutoff"]}, + data_type="graphs") + # update weights + for i in range(len(dataset)): + dataset['data_list'][i]['weight'] = w_t[i] + dataset['data_list_lag'][i]['weight'] = w_lag[i] + return dataset @@ -352,6 +360,13 @@ def test_create_timelagged_dataset(): ) print(len(dataset)) + # graph data + from mlcolvar.data.graph.utils import create_test_graph_input + dataset = create_test_graph_input('dataset') + lagged_dataset = create_timelagged_dataset(dataset, logweights=torch.randn(len(dataset))) + print(len(dataset)) + + if __name__ == "__main__": test_create_timelagged_dataset() From 3a1c0f1be7b74b99aad90b8afe67e87390cad875 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 10:56:35 +0100 Subject: [PATCH 179/267] updated deepTICA --- mlcolvar/cvs/timelagged/deeptica.py | 118 ++++++++++++++++++++++------ 1 file changed, 93 insertions(+), 25 deletions(-) diff --git a/mlcolvar/cvs/timelagged/deeptica.py b/mlcolvar/cvs/timelagged/deeptica.py index b95c7547..8de28f2b 100644 --- a/mlcolvar/cvs/timelagged/deeptica.py +++ b/mlcolvar/cvs/timelagged/deeptica.py @@ -1,9 +1,10 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward, Normalization +from mlcolvar.core import FeedForward, BaseGNN, Normalization from mlcolvar.core.stats import TICA from mlcolvar.core.loss import ReduceEigenvaluesLoss +from typing import Union, List __all__ = ["DeepTICA"] @@ -40,9 +41,13 @@ class DeepTICA(BaseCV, lightning.LightningModule): Create dataset of time-lagged data. """ - BLOCKS = ["norm_in", "nn", "tica"] + DEFAULT_BLOCKS = ["norm_in", "nn", "tica"] + MODEL_BLOCKS = ["nn", "tica"] - def __init__(self, layers: list, n_cvs: int = None, options: dict = None, **kwargs): + def __init__(self, + model: Union[List[int], FeedForward, BaseGNN], + n_cvs: int = None, + options: dict = None, **kwargs): """ Define a Deep-TICA CV, composed of a neural network module and a TICA object. By default a module standardizing the inputs is also used. @@ -58,15 +63,13 @@ def __init__(self, layers: list, n_cvs: int = None, options: dict = None, **kwar Available blocks: ['norm_in','nn','tica']. Set 'block_name' = None or False to turn off that block """ - super().__init__( - in_features=layers[0], - out_features=n_cvs if n_cvs is not None else layers[-1], - **kwargs, - ) + super().__init__(model, **kwargs) # ======= LOSS ======= # Maximize the squared sum of all the TICA eigenvalues. self.loss_fn = ReduceEigenvaluesLoss(mode="sum2") + # here we need to override the self.out_features attribute + self.out_features = n_cvs # ======= OPTIONS ======= # parse and sanitize @@ -74,22 +77,27 @@ def __init__(self, layers: list, n_cvs: int = None, options: dict = None, **kwar # ======= BLOCKS ======= - # initialize norm_in - o = "norm_in" - if (options[o] is not False) and (options[o] is not None): - self.norm_in = Normalization(self.in_features, **options[o]) + if not self._override_model: + # initialize norm_in + o = "norm_in" + if (options[o] is not False) and (options[o] is not None): + self.norm_in = Normalization(self.in_features, **options[o]) - # initialize nn - o = "nn" - self.nn = FeedForward(layers, **options[o]) + # initialize nn + o = "nn" + self.nn = FeedForward(self.layers, **options[o]) + + elif self._override_model: + self.nn = model - # initialize lda + # initialize tica o = "tica" - self.tica = TICA(layers[-1], n_cvs, **options[o]) + self.tica = TICA(self.nn.out_features, n_cvs, **options[o]) def forward_nn(self, x: torch.Tensor) -> torch.Tensor: - if self.norm_in is not None: - x = self.norm_in(x) + if not self._override_model: + if self.norm_in is not None: + x = self.norm_in(x) x = self.nn(x) return x @@ -111,10 +119,17 @@ def training_step(self, train_batch, batch_idx): 3) Compute TICA """ # =================get data=================== - x_t = train_batch["data"] - x_lag = train_batch["data_lag"] - w_t = train_batch["weights"] - w_lag = train_batch["weights_lag"] + if isinstance(self.nn, FeedForward): + x_t = train_batch["data"] + x_lag = train_batch["data_lag"] + w_t = train_batch["weights"] + w_lag = train_batch["weights_lag"] + elif isinstance(self.nn, BaseGNN): + x_t = self._setup_graph_data(train_batch, key='data_list') + x_lag = self._setup_graph_data(train_batch, key='data_list_lag') + w_t = x_t['weight'] + w_lag = x_lag['weight'] + # =================forward==================== f_t = self.forward_nn(x_t) f_lag = self.forward_nn(x_lag) @@ -139,12 +154,15 @@ def test_deep_tica(): from mlcolvar.utils.timelagged import create_timelagged_dataset # create dataset - X = np.loadtxt("mlcolvar/tests/data/mb-mcmc.dat") - X = torch.Tensor(X) + # X = np.loadtxt("mlcolvar/tests/data/mb-mcmc.dat") + X = torch.randn((10000, 2)) dataset = create_timelagged_dataset(X, lag_time=1) datamodule = DictModule(dataset, batch_size=10000) # create cv + print() + print('NORMAL') + print() layers = [2, 10, 10, 2] model = DeepTICA(layers, n_cvs=1) @@ -163,5 +181,55 @@ def test_deep_tica(): print(X.shape, "-->", s.shape) + print() + print('EXTERNAL') + print() + ff_model = FeedForward(layers=layers) + model = DeepTICA(ff_model, n_cvs=1) + + # change loss options + model.loss_fn.mode = "sum2" + + # create trainer and fit + trainer = lightning.Trainer( + max_epochs=1, log_every_n_steps=2, logger=None, enable_checkpointing=False + ) + trainer.fit(model, datamodule) + + model.eval() + with torch.no_grad(): + s = model(X).numpy() + print(X.shape, "-->", s.shape) + + + # gnn external + print() + print('GNN') + print() + from mlcolvar.core.nn.graph.schnet import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(2, 0.1, [1, 8]) + model = DeepTICA(gnn_model, n_cvs=1) + + # change loss options + model.loss_fn.mode = "sum2" + + # create trainer and fit + trainer = lightning.Trainer( + max_epochs=1, log_every_n_steps=2, logger=False, enable_checkpointing=False, enable_model_summary=False, + ) + + dataset = create_test_graph_input(output_type='dataset', n_samples=200, n_states=2) + lagged_dataset = create_timelagged_dataset(dataset, logweights=torch.randn(len(dataset))) + + datamodule = DictModule(dataset=lagged_dataset) + trainer.fit(model, datamodule) + + model.eval() + with torch.no_grad(): + example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=2) + s = model(example_input_graph_test).numpy() + print(X.shape, "-->", s.shape) + if __name__ == "__main__": test_deep_tica() From fd2a0f903aa552242d0cc9c3caaa5a703f224313 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 10:56:44 +0100 Subject: [PATCH 180/267] Fixed formats --- mlcolvar/cvs/supervised/deeplda.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/mlcolvar/cvs/supervised/deeplda.py b/mlcolvar/cvs/supervised/deeplda.py index a1e8ffdc..e48d847d 100644 --- a/mlcolvar/cvs/supervised/deeplda.py +++ b/mlcolvar/cvs/supervised/deeplda.py @@ -73,8 +73,8 @@ def __init__(self, # ======= BLOCKS ======= - # initialize norm_in if not self._override_model: + # initialize norm_in o = "norm_in" if (options[o] is not False) and (options[o] is not None): self.norm_in = Normalization(self.in_features, **options[o]) @@ -225,10 +225,10 @@ def test_deeplda(n_states=2): # feedforward external - ff_model = FeedForward(layers=layers) print() print('EXTERNAL') print() + ff_model = FeedForward(layers=layers) model = DeepLDA(ff_model, n_states) # create trainer and fit @@ -244,12 +244,12 @@ def test_deeplda(n_states=2): # gnn external - from mlcolvar.core.nn.graph.schnet import SchNetModel - from mlcolvar.data.graph.utils import create_test_graph_input - gnn_model = SchNetModel(2, 0.1, [1, 8]) print() print('GNN') print() + from mlcolvar.core.nn.graph.schnet import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(2, 0.1, [1, 8]) model = DeepLDA(gnn_model, n_states) datamodule = create_test_graph_input(output_type='datamodule', n_samples=200, n_states=n_states) From a766446b1ef89c9aa4a62f12053bca1b32a4dc27 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 12:46:29 +0100 Subject: [PATCH 181/267] Fixed typo --- mlcolvar/cvs/multitask/multitask.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/cvs/multitask/multitask.py b/mlcolvar/cvs/multitask/multitask.py index 90712122..1e86714e 100644 --- a/mlcolvar/cvs/multitask/multitask.py +++ b/mlcolvar/cvs/multitask/multitask.py @@ -22,7 +22,7 @@ # ============================================================================= -# VARIATIONAL AUTOENCODER CV +# MULTITASK CV # ============================================================================= From 53a52ef2d80437cd322be1428b919c5adfea1740 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 12:47:04 +0100 Subject: [PATCH 182/267] Renabled original test with new signature of BaseCV --- mlcolvar/tests/test_cvs_multitask_multitask.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/mlcolvar/tests/test_cvs_multitask_multitask.py b/mlcolvar/tests/test_cvs_multitask_multitask.py index 67a23087..75106305 100644 --- a/mlcolvar/tests/test_cvs_multitask_multitask.py +++ b/mlcolvar/tests/test_cvs_multitask_multitask.py @@ -21,6 +21,7 @@ import lightning import torch +from mlcolvar.core.nn import FeedForward from mlcolvar.core.loss import TDALoss, FisherDiscriminantLoss, AutocorrelationLoss from mlcolvar.cvs.cv import BaseCV from mlcolvar.cvs.multitask.multitask import MultiTaskCV @@ -62,11 +63,13 @@ def forward(self, data, data_lag=None, **kwargs): class MockCV(BaseCV, lightning.LightningModule): """Mock CV for mock testing.""" - BLOCKS = [] + DEFAULT_BLOCKS = [] + MODEL_BLOCKS = [] def __init__(self, in_features=N_DESCRIPTORS, out_features=N_CVS): """Constructor.""" - super().__init__(in_features=in_features, out_features=out_features) + model = FeedForward(layers=[in_features, in_features]) + super().__init__(model=model) self.loss_fn = MockAuxLoss(in_features, out_features) def training_step(self, train_batch, batch_idx): @@ -129,7 +132,7 @@ def create_cv(cv_name, n_descriptors=N_DESCRIPTORS, n_cvs=N_CVS): n_cvs=n_cvs, encoder_layers=[n_descriptors, 10] ) elif cv_name == "deeptica": - returned = "time-lagged", DeepTICA(layers=[n_descriptors, 10, n_cvs]) + returned = "time-lagged", DeepTICA(model=[n_descriptors, 10, n_cvs]) else: raise ValueError("Unrecognized cv_name.") From adadd41fdfc713f37eea30d1d4687556111c0c1b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 12:57:40 +0100 Subject: [PATCH 183/267] Added not implemented error for GNN-based --- mlcolvar/cvs/multitask/multitask.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/mlcolvar/cvs/multitask/multitask.py b/mlcolvar/cvs/multitask/multitask.py index 1e86714e..8ead707b 100644 --- a/mlcolvar/cvs/multitask/multitask.py +++ b/mlcolvar/cvs/multitask/multitask.py @@ -19,6 +19,7 @@ import torch from mlcolvar.cvs.cv import BaseCV +from mlcolvar.core.nn import BaseGNN # ============================================================================= @@ -98,6 +99,10 @@ def __init__( has always coefficient 1). """ + # check if model is GNN, not implemented yet TODO + if isinstance(main_cv.nn, BaseGNN): + raise NotImplementedError('Multitask not supported (yet) for GNN-based CVs') + # This changes dynamically the class of this object to inherit both from # MultiTaskCV and main_cv.__class__ so that we can access all members of # main_cv and still be able to override some of them. From 4f89aa8f8f7124d20e5e43bf4f20f188bead6340 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 13:12:37 +0100 Subject: [PATCH 184/267] Added two model options, see also commit 4b45a9ded1f7da46cc1a0eac091afee340230929 --- mlcolvar/core/nn/graph/schnet.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index 4a08cbcb..f34f363e 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -37,6 +37,10 @@ class SchNetModel(BaseGNN): Number of filters. n_hidden_channels: int Size of hidden embeddings. + aggr: str + Type of the GNN aggr function. + w_out_after_sum: bool + If apply the readout MLP layer after the scatter sum. References ---------- .. [1] Schütt, Kristof T., et al. "Schnet–a deep learning architecture for @@ -53,7 +57,9 @@ def __init__( n_layers: int = 2, n_filters: int = 16, n_hidden_channels: int = 16, - drop_rate: int = 0 + drop_rate: int = 0, + aggr: str = 'mean', + w_out_after_sum: bool = False ) -> None: super().__init__( @@ -66,7 +72,7 @@ def __init__( self.layers = nn.ModuleList([ InteractionBlock( - n_hidden_channels, n_bases, n_filters, cutoff + n_hidden_channels, n_bases, n_filters, cutoff, aggr ) for _ in range(n_layers) ]) @@ -77,6 +83,8 @@ def __init__( nn.Linear(n_hidden_channels // 2, n_out) ]) + self._w_out_after_sum = w_out_after_sum + self.reset_parameters() def reset_parameters(self) -> None: @@ -115,8 +123,9 @@ def forward( for layer in self.layers: h_V = h_V + layer(h_V, data['edge_index'], h_E[0], h_E[1]) - for w in self.W_out: - h_V = w(h_V) + if not self._w_out_after_sum: + for w in self.W_out: + h_V = w(h_V) out = h_V if scatter_mean: @@ -128,6 +137,10 @@ def forward( # TODO check this is equivalent in torch scatter out = torch_scatter.scatter_sum(out, batch_id, dim=0) out = out / data['n_system'] + + if self._w_out_after_sum: + for w in self.W_out: + out = w(out) return out From d27fa01eb5a03c090fbde33f70791758c99c5e4c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 18:23:49 +0100 Subject: [PATCH 185/267] Added two model options --- mlcolvar/core/nn/graph/schnet.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index f34f363e..99aa8f40 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -150,7 +150,8 @@ def __init__( hidden_channels: int, num_gaussians: int, num_filters: int, - cutoff: float + cutoff: float, + aggr: str = 'mean' ) -> None: super().__init__() self.mlp = nn.Sequential( @@ -159,7 +160,12 @@ def __init__( nn.Linear(num_filters, num_filters), ) self.conv = CFConv( - hidden_channels, hidden_channels, num_filters, self.mlp, cutoff + hidden_channels, + hidden_channels, + num_filters, + self.mlp, + cutoff, + aggr ) self.act = ShiftedSoftplus() self.lin = nn.Linear(hidden_channels, hidden_channels) @@ -196,8 +202,9 @@ def __init__( num_filters: int, network: nn.Sequential, cutoff: float, + aggr: str = 'mean' ) -> None: - super().__init__(aggr='mean') + super().__init__(aggr=aggr) self.lin1 = nn.Linear(in_channels, num_filters, bias=False) self.lin2 = nn.Linear(num_filters, out_channels) self.network = network From 8797481c157973608a65e8028741e9ee4e429094 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 5 Dec 2024 18:24:02 +0100 Subject: [PATCH 186/267] Fixed model check --- mlcolvar/cvs/multitask/multitask.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/cvs/multitask/multitask.py b/mlcolvar/cvs/multitask/multitask.py index 8ead707b..1be64eaa 100644 --- a/mlcolvar/cvs/multitask/multitask.py +++ b/mlcolvar/cvs/multitask/multitask.py @@ -100,7 +100,7 @@ def __init__( """ # check if model is GNN, not implemented yet TODO - if isinstance(main_cv.nn, BaseGNN): + if hasattr(main_cv, "nn") and isinstance(main_cv.nn, BaseGNN): raise NotImplementedError('Multitask not supported (yet) for GNN-based CVs') # This changes dynamically the class of this object to inherit both from From 031aa7f14d3cb1244271fb61e2837ab12997f316 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 6 Dec 2024 18:32:19 +0100 Subject: [PATCH 187/267] First tests committor --- mlcolvar/core/loss/committor_loss.py | 141 +- mlcolvar/cvs/committor/committor.py | 111 +- test_graphs/data/biased.dcd | Bin 0 -> 31076 bytes test_graphs/data/biased.trajectory.h5 | Bin 0 -> 5026672 bytes test_graphs/data/colvar | 50001 ++++++++++++++++++++++++ test_graphs/test_committor.ipynb | 307 + 6 files changed, 50482 insertions(+), 78 deletions(-) create mode 100644 test_graphs/data/biased.dcd create mode 100644 test_graphs/data/biased.trajectory.h5 create mode 100644 test_graphs/data/colvar create mode 100644 test_graphs/test_committor.ipynb diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index 991971c8..00c467d1 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -15,7 +15,9 @@ # ============================================================================= import torch -from torch_scatter import scatter +from torch_scatter import scatter, scatter_sum +from typing import Union +import torch_geometric # ============================================================================= # LOSS FUNCTIONS @@ -26,19 +28,19 @@ class CommittorLoss(torch.nn.Module): """Compute a loss function based on Kolmogorov's variational principle for the determination of the committor function""" def __init__(self, - mass: torch.Tensor, + atomic_masses: torch.Tensor, alpha: float, - cell: float = None, - gamma: float = 10000, - delta_f: float = 0, + gamma: float = 10000.0, + delta_f: float = 0.0, separate_boundary_dataset : bool = True, - descriptors_derivatives : torch.nn.Module = None + descriptors_derivatives : torch.nn.Module = None, + log_var: bool = True ): """Compute Kolmogorov's variational principle loss and impose boundary conditions on the metastable states Parameters ---------- - mass : torch.Tensor + atomic_masses : torch.Tensor Atomic masses of the atoms in the system alpha : float Hyperparamer that scales the boundary conditions contribution to loss, i.e. alpha*(loss_bound_A + loss_bound_B) @@ -57,29 +59,33 @@ def __init__(self, """ super().__init__() - self.register_buffer("mass", mass) + self.register_buffer("atomic_masses", atomic_masses) self.alpha = alpha - self.cell = cell self.gamma = gamma self.delta_f = delta_f - self.descriptors_derivatives=descriptors_derivatives + self.descriptors_derivatives = descriptors_derivatives self.separate_boundary_dataset = separate_boundary_dataset - - def forward( - self, x: torch.Tensor, q: torch.Tensor, labels: torch.Tensor, w: torch.Tensor, create_graph: bool = True + self.log_var = log_var + + def forward(self, + x: Union[torch.Tensor, torch_geometric.data.Batch], + q: torch.Tensor, + labels: torch.Tensor, + w: torch.Tensor, + create_graph: bool = True ) -> torch.Tensor: return committor_loss(x=x, q=q, labels=labels, w=w, - mass=self.mass, + atomic_masses=self.atomic_masses, alpha=self.alpha, gamma=self.gamma, delta_f=self.delta_f, create_graph=create_graph, - cell=self.cell, separate_boundary_dataset=self.separate_boundary_dataset, - descriptors_derivatives=self.descriptors_derivatives + descriptors_derivatives=self.descriptors_derivatives, + log_var = self.log_var ) @@ -87,14 +93,14 @@ def committor_loss(x: torch.Tensor, q: torch.Tensor, labels: torch.Tensor, w: torch.Tensor, - mass: torch.Tensor, + atomic_masses: torch.Tensor, alpha: float, gamma: float = 10000, delta_f: float = 0, create_graph: bool = True, - cell: float = None, - separate_boundary_dataset : bool = True, - descriptors_derivatives : torch.nn.Module = None + separate_boundary_dataset: bool = True, + descriptors_derivatives: torch.nn.Module = None, + log_var: bool = True ): """Compute variational loss for committor optimization with boundary conditions @@ -108,7 +114,7 @@ def committor_loss(x: torch.Tensor, Labels for states, A and B states for boundary conditions w : torch.Tensor Reweighing factors to Boltzmann distribution. This should depend on the simulation in which the data were collected. - mass : torch.Tensor + atomic_masses : torch.Tensor List of masses of all the atoms we are using, for each atom we need to repeat three times for x,y,z. Can be created using `committor.utils.initialize_committor_masses` alpha : float @@ -138,63 +144,94 @@ def committor_loss(x: torch.Tensor, The boundary loss term on basin A gamma*alpha*loss_B : torch.Tensor The boundary loss term on basin B - """ + """ + # ============================== SETUP ============================== + # check if input is graph + _is_graph_data = False + if isinstance(x, torch_geometric.data.batch.Batch): + batch = torch.clone(x['batch']) + node_types = torch.where(x['node_attrs'])[1] + x = x['positions'] + _is_graph_data = True + + # inherit right device device = x.device + dtype = x.dtype - mass = mass.to(device) # Create masks to access different states data - mask_A = torch.nonzero(labels.squeeze() == 0, as_tuple=True) - mask_B = torch.nonzero(labels.squeeze() == 1, as_tuple=True) + mask_A = torch.nonzero(labels.squeeze() == 0) + mask_B = torch.nonzero(labels.squeeze() == 1) if separate_boundary_dataset: - mask_var = torch.nonzero(labels.squeeze() > 1, as_tuple=True) + if _is_graph_data: + # this needs to be on the batch index, not only the labels + mask_var = torch.nonzero(labels.squeeze() > 1) + aux = torch.where(mask_var)[0] + mask_var_batches = torch.isin(batch, aux) + mask_var_batches = (batch[mask_var_batches]) + else: + mask_var = torch.nonzero(labels.squeeze() > 1) + mask_var_batches = mask_var else: - mask_var = torch.ones(len(x), dtype=torch.bool) - - # Update weights of basin B using the information on the delta_f + mask_var = torch.ones(len(x), dtype=torch.bool) + mask_var_batches = mask_var + + # setup atomic masses + atomic_masses = atomic_masses.to(dtype).to(device) + # mass should have size [1, n_atoms*spatial_dims] + if _is_graph_data: + + atomic_masses = atomic_masses[node_types[mask_var_batches]].unsqueeze(-1) + + else: + atomic_masses = atomic_masses.unsqueeze(0) + + # Update weights for bc confs using the information on the delta_f delta_f = torch.Tensor([delta_f]) - if delta_f < 0: # B higher in energy --> A-B < 0 + # B higher in energy --> A-B < 0 + if delta_f < 0: w[mask_B] = w[mask_B] * torch.exp(delta_f.to(device)) - elif delta_f > 0: # A higher in energy --> A-B > 0 + # A higher in energy --> A-B > 0 + elif delta_f > 0: w[mask_A] = w[mask_A] * torch.exp(-delta_f.to(device)) - ###### VARIATIONAL PRINICIPLE LOSS ###### + # weights should have size [n_batch, 1] + w = w.unsqueeze(-1) + # ============================== LOSS ============================== # Each loss contribution is scaled by the number of samples - # We need the gradient of q(x) + # 1. VARIATIONAL LOSS + # Compute gradients of q(x) wrt x grad_outputs = torch.ones_like(q[mask_var]) grad = torch.autograd.grad(q[mask_var], x, grad_outputs=grad_outputs, retain_graph=True, create_graph=create_graph)[0] - grad = grad[mask_var] - - # TODO this fixes cell size issue - if cell is not None: - grad = grad / cell - + grad = grad[mask_var_batches] if descriptors_derivatives is not None: + # we use the precomputed derivatives from descriptors to pos grad_square = descriptors_derivatives(grad) else: - # we get the square of grad(q) and we multiply by the weight grad_square = torch.pow(grad, 2) - - # we sanitize the shapes of mass and weights tensors - # mass should have size [1, n_atoms*spatial_dims] - mass = mass.unsqueeze(0) - # weights should have size [n_batch, 1] - w = w.unsqueeze(-1) - grad_square = torch.sum((grad_square * (1/mass)), axis=1, keepdim=True) + grad_square = torch.sum((grad_square * (1/atomic_masses)), axis=1, keepdim=True) + + if _is_graph_data: + # we need to sum on the right batch first + grad_square = scatter_sum(grad_square, mask_var_batches, dim=0) + grad_square = grad_square * w[mask_var] # variational contribution to loss: we sum over the batch loss_var = torch.mean(grad_square) + if False: + loss_var = loss_var.log() + + + # 2. BOUNDARY LOSS + loss_A = torch.mean( torch.pow(q[mask_A], 2)) + loss_B = torch.mean( torch.pow( (q[mask_B] - 1) , 2)) - # boundary conditions - q_A = q[mask_A] - q_B = q[mask_B] - loss_A = torch.mean( torch.pow(q_A, 2)) - loss_B = torch.mean( torch.pow( (q_B - 1) , 2)) + # 3. TOTAL LOSS loss = gamma*( loss_var + alpha*(loss_A + loss_B) ) # TODO maybe there is no need to detach them for logging diff --git a/mlcolvar/cvs/committor/committor.py b/mlcolvar/cvs/committor/committor.py index 819a5a3b..692ff657 100644 --- a/mlcolvar/cvs/committor/committor.py +++ b/mlcolvar/cvs/committor/committor.py @@ -1,9 +1,10 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward +from mlcolvar.core import FeedForward, BaseGNN from mlcolvar.core.loss import CommittorLoss from mlcolvar.core.nn.utils import Custom_Sigmoid +from typing import Union, List __all__ = ["Committor"] @@ -31,18 +32,20 @@ class Committor(BaseCV, lightning.LightningModule): Utils to initialize the masses tensor for the training """ - BLOCKS = ["nn", "sigmoid"] + DEFAULT_BLOCKS = ["nn", "sigmoid"] + MODEL_BLOCKS = ["nn", "sigmoid"] def __init__( self, - layers: list, + model: Union[List[int], FeedForward, BaseGNN], mass: torch.Tensor, alpha: float, gamma: float = 10000, delta_f: float = 0, cell: float = None, - separate_boundary_dataset : bool = True, - descriptors_derivatives : torch.nn.Module = None, + separate_boundary_dataset: bool = True, + descriptors_derivatives: torch.nn.Module = None, + log_var: bool = True, options: dict = None, **kwargs, ): @@ -73,16 +76,16 @@ def __init__( Options for the building blocks of the model, by default {}. Available blocks: ['nn'] . """ - super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) + super().__init__(model, **kwargs) # ======= LOSS ======= - self.loss_fn = CommittorLoss(mass=mass, + self.loss_fn = CommittorLoss(atomic_masses=mass, alpha=alpha, gamma=gamma, delta_f=delta_f, - cell=cell, separate_boundary_dataset=separate_boundary_dataset, - descriptors_derivatives=descriptors_derivatives + descriptors_derivatives=descriptors_derivatives, + log_var=True ) # ======= OPTIONS ======= @@ -90,12 +93,18 @@ def __init__( options = self.parse_options(options) # ======= BLOCKS ======= - # initialize NN turning - o = "nn" - # set default activation to tanh - if "activation" not in options[o]: - options[o]["activation"] = "tanh" - self.nn = FeedForward(layers, **options[o]) + if not self._override_model: + # initialize NN + o = "nn" + # set default activation to tanh + if "activation" not in options[o]: + options[o]["activation"] = "tanh" + self.nn = FeedForward(self.layers, **options[o]) + elif self._override_model: + self.nn = model + + if self.nn.out_features != 1: + raise ValueError('Output of the model must be of dimension 1') # separately add sigmoid activation on last layer, this way it can be deactived o = "sigmoid" @@ -106,13 +115,18 @@ def __init__( def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" # =================get data=================== - x = train_batch["data"] - # check data are have shape (n_data, -1) - x = x.reshape((x.shape[0], -1)) - x.requires_grad = True - - labels = train_batch["labels"] - weights = train_batch["weights"] + if isinstance(self.nn, FeedForward): + x = train_batch["data"] + # check data are have shape (n_data, -1) + x = x.reshape((x.shape[0], -1)) + x.requires_grad = True + + labels = train_batch["labels"] + weights = train_batch["weights"] + elif isinstance(self.nn, BaseGNN): + x = self._setup_graph_data(train_batch) + labels = x['graph_labels'] + weights = x['weight'].clone() # =================forward==================== # we use forward and not forward_cv to also apply the preprocessing (if present) @@ -158,20 +172,65 @@ def test_committor(): datamodule = DictModule(dataset, lengths=[1]) # train model - trainer = lightning.Trainer(max_epochs=5, logger=None, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + print() + print('NORMAL') + print() + # dataset separation + model = Committor(model=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0) + trainer.fit(model, datamodule) + model(X).sum().backward() + bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) + bias_model(X) + + # naive whole dataset + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + model = Committor(model=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0, separate_boundary_dataset=False) + trainer.fit(model, datamodule) + model(X).sum().backward() + + + print() + print('EXTERNAL FEEDFORWARD') + print() # dataset separation - model = Committor(layers=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0) + ff_model = FeedForward([6, 4, 2, 1]) + model = Committor(model=ff_model, mass=atomic_masses, alpha=1e-1, delta_f=0) trainer.fit(model, datamodule) model(X).sum().backward() bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) bias_model(X) # naive whole dataset - trainer = lightning.Trainer(max_epochs=5, logger=None, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) - model = Committor(layers=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0, separate_boundary_dataset=False) + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + model = Committor(model=ff_model, mass=atomic_masses, alpha=1e-1, delta_f=0, separate_boundary_dataset=False) trainer.fit(model, datamodule) model(X).sum().backward() + + print() + print('EXTERNAL GNN') + print() + from mlcolvar.core.nn.graph import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(1, 0.1, [1, 8]) + + model = Committor(model=gnn_model, + mass=atomic_masses, + alpha=1e-1, + delta_f=0) + + datamodule = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=3, n_atoms=3) + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0, enable_model_summary=False) + trainer.fit(model, datamodule) + + example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=2) + + model(example_input_graph_test).sum().backward() + # bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) + # 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0.000000 0.000000 0.000000 diff --git a/test_graphs/test_committor.ipynb b/test_graphs/test_committor.ipynb new file mode 100644 index 00000000..5f47389e --- /dev/null +++ b/test_graphs/test_committor.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DictModule(dataset -> DictDataset( \"data_list\": 1700, \"z_table\": [6, 9], \"cutoff\": 8.0, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=1700, shuffle=False))\n" + ] + } + ], + "source": [ + "from mlcolvar.data import DictModule\n", + "from mlcolvar.utils.io import create_dataset_from_trajectories\n", + "from mlcolvar.utils.io import create_dataset_from_files\n", + "\n", + "\n", + "dataset_graph = create_dataset_from_trajectories(\n", + " trajectories=[\n", + " 'data/r.dcd',\n", + " 'data/p.dcd',\n", + " # 'data/biased.trajectory.h5',\n", + " 'data/biased.dcd',\n", + " #'data/biased.trajectory.h5',\n", + " #'data/r.dcd'\n", + " ],\n", + " top=['data/r.pdb', \n", + " 'data/p.pdb',\n", + " 'data/r.pdb',\n", + " #'data/r.pdb'\n", + " ],\n", + " cutoff=8.0, # Ang\n", + " create_labels=True,\n", + " system_selection='all and not type H',\n", + " show_progress=False\n", + ")\n", + "\n", + "\n", + "datamodule_graph = DictModule(dataset_graph, lengths=[1], shuffle=False)\n", + "print(datamodule_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from mlcolvar.utils.io import load_dataframe\n", + "\n", + "df = load_dataframe('data/colvar', stride=100)\n", + "weights = torch.exp(1*torch.tensor(df['opes.bias'].values))\n", + "weights = weights / weights.sum()\n", + "weights\n", + "\n", + "aux = torch.zeros(len(dataset_graph))\n", + "for i in range(len(weights)):\n", + " aux[-(len(weights)-i)] = weights[i]\n", + "aux\n", + "\n", + "for i in range(len(dataset_graph)):\n", + " dataset_graph['data_list'][i]['weight'] = aux[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1700" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dataset_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + ] + }, + { + "data": { + "text/plain": [ + "Committor(\n", + " (loss_fn): CommittorLoss()\n", + " (nn): SchNetModel(\n", + " (_radial_embedding): RadialEmbeddingBlock(\n", + " (bessel_fn): GAUSSIANBASIS [ \u001b[32m16\u001b[0m\u001b[36m 󰯰 \u001b[0m| \u001b[32m8.000000\u001b[0m\u001b[36m 󰳁 \u001b[0m]\n", + " )\n", + " (W_v): Linear(in_features=2, out_features=32, bias=False)\n", + " (layers): ModuleList(\n", + " (0-1): 2 x InteractionBlock(\n", + " (mlp): Sequential(\n", + " (0): Linear(in_features=16, out_features=32, bias=True)\n", + " (1): ShiftedSoftplus()\n", + " (2): Linear(in_features=32, out_features=32, bias=True)\n", + " )\n", + " (conv): CFConv()\n", + " (act): ShiftedSoftplus()\n", + " (lin): Linear(in_features=32, out_features=32, bias=True)\n", + " )\n", + " )\n", + " (W_out): ModuleList(\n", + " (0): Linear(in_features=32, out_features=16, bias=True)\n", + " (1): ShiftedSoftplus()\n", + " (2): Linear(in_features=16, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (sigmoid): Custom_Sigmoid()\n", + ")" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlcolvar.cvs.committor import Committor\n", + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "import torch\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset_graph.metadata['cutoff'],\n", + " atomic_numbers=dataset_graph.metadata['z_table'],\n", + " n_bases=16,\n", + " n_layers=2,\n", + " n_filters=32,\n", + " n_hidden_channels=32,\n", + " )\n", + "\n", + "model = Committor(model=gnn_model,\n", + " mass=torch.Tensor([12, 19]),\n", + " alpha=1)\n", + "\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "020e80012d2d46809f41270cdb5692e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# plt.hist(out_graph.detach().squeeze())\n", + "plt.plot(out_graph.detach().squeeze(), '.')\n", + "plt.ylim(0,1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "graph_mlcolvar_test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 409da5007d2b5ccb21a927008c2bac6c2b5f5d39 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 9 Dec 2024 12:39:44 +0100 Subject: [PATCH 188/267] Updated layers key to model in notebooks --- docs/notebooks/examples/ex_TPI-DeepTDA.ipynb | 4 ++-- docs/notebooks/examples/ex_committor.ipynb | 4 ++-- .../paper_experiments/paper_2_supervised.ipynb | 2 +- .../paper_experiments/paper_4_multitask.ipynb | 4 ++-- .../notebooks/tutorials/adv_preprocessing.ipynb | 2 +- docs/notebooks/tutorials/cvs_DeepTDA.ipynb | 17 ++++++----------- docs/notebooks/tutorials/cvs_committor.ipynb | 4 ++-- .../tutorials/intro_3_loss_optim.ipynb | 17 ++++++----------- 8 files changed, 22 insertions(+), 32 deletions(-) diff --git a/docs/notebooks/examples/ex_TPI-DeepTDA.ipynb b/docs/notebooks/examples/ex_TPI-DeepTDA.ipynb index 52805569..c7ab7826 100644 --- a/docs/notebooks/examples/ex_TPI-DeepTDA.ipynb +++ b/docs/notebooks/examples/ex_TPI-DeepTDA.ipynb @@ -167,7 +167,7 @@ "target_sigmas = [0.2, 0.2]\n", "nn_layers = [45,24,12,1]\n", "# MODEL\n", - "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, layers=nn_layers)" + "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, model=nn_layers)" ] }, { @@ -417,7 +417,7 @@ "target_sigmas = [0.2, 1.5, 0.2]\n", "nn_layers = [45,24,12,1]\n", "# MODEL\n", - "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, layers=nn_layers)" + "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, model=nn_layers)" ] }, { diff --git a/docs/notebooks/examples/ex_committor.ipynb b/docs/notebooks/examples/ex_committor.ipynb index 3c780f0a..6c24bf13 100644 --- a/docs/notebooks/examples/ex_committor.ipynb +++ b/docs/notebooks/examples/ex_committor.ipynb @@ -327,7 +327,7 @@ " 'nn' : {'activation' : 'tanh'}}\n", "\n", "# initialize model\n", - "model = Committor(layers=[45, 32, 32, 1],\n", + "model = Committor(model=[45, 32, 32, 1],\n", " mass=atomic_masses,\n", " alpha=1e1,\n", " options=options, \n", @@ -794,7 +794,7 @@ " 'nn' : {'activation' : 'tanh'}}\n", "\n", "# initialize model\n", - "model = Committor(layers=[45, 32, 32, 1],\n", + "model = Committor(model=[45, 32, 32, 1],\n", " mass=atomic_masses,\n", " alpha=1e1,\n", " options=options, \n", diff --git a/docs/notebooks/paper_experiments/paper_2_supervised.ipynb b/docs/notebooks/paper_experiments/paper_2_supervised.ipynb index 38842fde..e5a524c4 100644 --- a/docs/notebooks/paper_experiments/paper_2_supervised.ipynb +++ b/docs/notebooks/paper_experiments/paper_2_supervised.ipynb @@ -190,7 +190,7 @@ "options = {'nn' : {'activation' : 'shifted_softplus'} }\n", "# MODEL\n", "if run_calculations:\n", - " model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, layers=nn_layers)\n", + " model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, model=nn_layers)\n", "else:\n", " model = torch.jit.load(f'{RESULTS_FOLDER}/model_deepTDA.pt')" ] diff --git a/docs/notebooks/paper_experiments/paper_4_multitask.ipynb b/docs/notebooks/paper_experiments/paper_4_multitask.ipynb index 03ca22bd..fe17b8b6 100644 --- a/docs/notebooks/paper_experiments/paper_4_multitask.ipynb +++ b/docs/notebooks/paper_experiments/paper_4_multitask.ipynb @@ -226,7 +226,7 @@ "nn_args = {'activation': 'shifted_softplus'}\n", "options= {'encoder': nn_args, 'decoder': nn_args }\n", "if run_calculations:\n", - " main_cv = AutoEncoderCV(encoder_layers=[n_descriptors, 32, 16, n_components], options=options)\n", + " main_cv = AutoEncoderCV(encoder_model=[n_descriptors, 32, 16, n_components], options=options)\n", "\n", " aux_loss_fn = TDALoss(n_states, [-5, 0, 5], [0.2, 0.2, 0.2])\n", "\n", @@ -605,7 +605,7 @@ "if run_calculations:\n", " for i in range(9):\n", " # define model\n", - " main_cv = AutoEncoderCV(encoder_layers=[n_descriptors, 32, 16, n_components], options=options)\n", + " main_cv = AutoEncoderCV(encoder_model=[n_descriptors, 32, 16, n_components], options=options)\n", " aux_loss_fn = TDALoss(n_states, [-5, 0, 5], [0.2, 0.2, 0.2])\n", " model = MultiTaskCV(main_cv, auxiliary_loss_fns=[aux_loss_fn], loss_coefficients=[1.5])\n", " stat = Statistics()\n", diff --git a/docs/notebooks/tutorials/adv_preprocessing.ipynb b/docs/notebooks/tutorials/adv_preprocessing.ipynb index dd0b4a7d..766467b8 100644 --- a/docs/notebooks/tutorials/adv_preprocessing.ipynb +++ b/docs/notebooks/tutorials/adv_preprocessing.ipynb @@ -247,7 +247,7 @@ "source": [ "from mlcolvar.cvs import RegressionCV\n", "\n", - "model = RegressionCV(layers=[2,10,10,1], \n", + "model = RegressionCV(model=[2,10,10,1], \n", " preprocessing = pca ) \n", "\n", "# the preprocessing can also be saved later, like in:\n", diff --git a/docs/notebooks/tutorials/cvs_DeepTDA.ipynb b/docs/notebooks/tutorials/cvs_DeepTDA.ipynb index a2ee41e7..e6ea7feb 100644 --- a/docs/notebooks/tutorials/cvs_DeepTDA.ipynb +++ b/docs/notebooks/tutorials/cvs_DeepTDA.ipynb @@ -3,17 +3,17 @@ { "attachments": {}, "cell_type": "markdown", - "metadata": {}, + "metadata": {},model= "source": [ - "# Deep-TDA: Deep Targeted Discriminant Analysis\n", + "# Deep-TDA: Deep Targeted Discriminant Analysis\n",model= "Reference papers: \n", - "- *Deep-TDA*: _Trizio and Parrinello, [JPCL](https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c02317) (2021)_ [[arXiv]](https://128.84.4.34/abs/2107.05444).\n", + "- *Deep-TDA*: _Trizio and Parrinello, [JPCL](https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c0model=2021)_ [[arXiv]](https://128.84.4.34/abs/2107.05444).\n", "- *TPI-Deep-TDA*: _Ray, Trizio and Parrinello, [JCP](https://pubs.aip.org/aip/jcp/article/158/20/204102/2891484) (2023)_ [[arXiv]](https://arxiv.org/abs/2303.01629).\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/luigibonati/mlcolvar/blob/main/docs/notebooks/tutorials/cvs_DeepTDA.ipynb)" ] }, - { + {model= "attachments": {}, "cell_type": "markdown", "metadata": {}, @@ -1366,7 +1366,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pytorch", + "display_name": "mlcvs_test", "language": "python", "name": "python3" }, @@ -1382,12 +1382,7 @@ "pygments_lexer": "ipython3", "version": "3.10.8" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "1cbeac1d7079eaeba64f3210ccac5ee24400128e300a45ae35eee837885b08b3" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/notebooks/tutorials/cvs_committor.ipynb b/docs/notebooks/tutorials/cvs_committor.ipynb index 665098ae..533b1e27 100644 --- a/docs/notebooks/tutorials/cvs_committor.ipynb +++ b/docs/notebooks/tutorials/cvs_committor.ipynb @@ -13,7 +13,7 @@ ] }, { - "cell_type": "markdown", + "cell_type": "mmodel= "metadata": {}, "source": [ "## Introduction\n", @@ -130,7 +130,7 @@ "options = {'optimizer' : {'lr': 1e-3, 'weight_decay': 1e-5}, \n", " 'lr_scheduler' : { 'scheduler' : lr_scheduler, 'gamma' : 0.99999 }}\n", "\n", - "model = Committor(layers=[2, 32, 32, 1],\n", + "model = Committor(model=[2, 32, 32, 1],\n", " mass=atomic_masses,\n", " alpha=1e-1,\n", " delta_f=0,\n", diff --git a/docs/notebooks/tutorials/intro_3_loss_optim.ipynb b/docs/notebooks/tutorials/intro_3_loss_optim.ipynb index 36e27ccf..7eb10823 100644 --- a/docs/notebooks/tutorials/intro_3_loss_optim.ipynb +++ b/docs/notebooks/tutorials/intro_3_loss_optim.ipynb @@ -83,7 +83,7 @@ "from mlcolvar.cvs import RegressionCV\n", "\n", "# define example CV\n", - "cv = RegressionCV(layers=[10,5,5,1], options={})\n", + "cv = RegressionCV(model=[10,5,5,1], options={})\n", "\n", "# choose optimizer\n", "cv.optimizer_name = 'Adam' \n", @@ -123,7 +123,7 @@ "options = {'optimizer' : {'lr' : 2e-3, 'weight_decay' : 1e-4} }\n", "\n", "# define example CV\n", - "cv = RegressionCV(layers=[10,5,5,1], options=options)\n", + "cv = RegressionCV(model=[10,5,5,1], options=options)\n", "\n", "print(f'optimizer_kwargs: {cv.optimizer_kwargs}')" ] @@ -155,7 +155,7 @@ "options = {'lr_scheduler' : { 'scheduler' : lr_scheduler, 'gamma' : 0.9999} }\n", "\n", "# define example CV\n", - "cv = RegressionCV(layers=[10,5,5,1], options=options)" + "cv = RegressionCV(model=[10,5,5,1], options=options)" ] }, { @@ -251,7 +251,7 @@ "from mlcolvar.cvs import DeepTICA\n", "\n", "# define CV\n", - "cv = DeepTICA(layers=[10, 5, 5, 2], options={})\n", + "cv = DeepTICA(model=[10, 5, 5, 2], options={})\n", "\n", "# print default loss mode\n", "print(f'default mode: {cv.loss_fn.mode}')\n", @@ -554,7 +554,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pytorch", + "display_name": "mlcvs_test", "language": "python", "name": "python3" }, @@ -570,12 +570,7 @@ "pygments_lexer": "ipython3", "version": "3.10.8" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "1cbeac1d7079eaeba64f3210ccac5ee24400128e300a45ae35eee837885b08b3" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 From bb94a9a3792ac039c3c2ded0998f2933be281586 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 9 Dec 2024 12:40:06 +0100 Subject: [PATCH 189/267] Udate layers key to model in tests --- mlcolvar/core/transform/utils.py | 2 +- mlcolvar/tests/test_cvs.py | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/mlcolvar/core/transform/utils.py b/mlcolvar/core/transform/utils.py index ac663121..bc82839d 100644 --- a/mlcolvar/core/transform/utils.py +++ b/mlcolvar/core/transform/utils.py @@ -146,7 +146,7 @@ def test_sequential_transform(): import lightning masses = initialize_committor_masses(atom_types=[0,0,0,0], masses=[1.008]) - model = Committor(layers=[6,2,1], mass=masses, alpha=1) + model = Committor(model=[6,2,1], mass=masses, alpha=1) model.preprocessing = sequential pos = torch.rand((5, 4, 3)) diff --git a/mlcolvar/tests/test_cvs.py b/mlcolvar/tests/test_cvs.py index 40d1425a..46f44d97 100644 --- a/mlcolvar/tests/test_cvs.py +++ b/mlcolvar/tests/test_cvs.py @@ -64,10 +64,10 @@ def dataset(): # ============================================================================= @pytest.mark.parametrize("cv_model", [ - mlcolvar.cvs.DeepLDA(layers=LAYERS, n_states=N_STATES), - mlcolvar.cvs.DeepTDA(n_states=N_STATES, n_cvs=1, target_centers=[-1., 1.], target_sigmas=[0.1, 0.1], layers=LAYERS), - mlcolvar.cvs.RegressionCV(layers=LAYERS), - mlcolvar.cvs.DeepTICA(layers=LAYERS, n_cvs=1), + mlcolvar.cvs.DeepLDA(model=LAYERS, n_states=N_STATES), + mlcolvar.cvs.DeepTDA(n_states=N_STATES, n_cvs=1, target_centers=[-1., 1.], target_sigmas=[0.1, 0.1], model=LAYERS), + mlcolvar.cvs.RegressionCV(model=LAYERS), + mlcolvar.cvs.DeepTICA(model=LAYERS, n_cvs=1), mlcolvar.cvs.AutoEncoderCV(encoder_layers=LAYERS), mlcolvar.cvs.VariationalAutoEncoderCV(n_cvs=1, encoder_layers=LAYERS[:-1]), ]) @@ -113,7 +113,7 @@ def test_lr_scheduler(): initial_lr = 1e-3 options = {'optimizer' : {'lr' : initial_lr}, 'lr_scheduler' : { 'scheduler' : lr_scheduler, 'gamma' : 0.9999}} - model = mlcolvar.cvs.RegressionCV(layers=[2,5,1], options=options) + model = mlcolvar.cvs.RegressionCV(model=[2,5,1], options=options) # check training and lr scheduling trainer = lightning.Trainer(max_epochs=10, From 1fe47a3f1d4c89dd9ac91c4189580c43d1e8d497 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 9 Dec 2024 16:13:33 +0100 Subject: [PATCH 190/267] Fixed checkpointing AE based CVs --- mlcolvar/cvs/unsupervised/autoencoder.py | 4 ++++ mlcolvar/cvs/unsupervised/vae.py | 4 ++++ 2 files changed, 8 insertions(+) diff --git a/mlcolvar/cvs/unsupervised/autoencoder.py b/mlcolvar/cvs/unsupervised/autoencoder.py index ab79fec3..ac8a7741 100644 --- a/mlcolvar/cvs/unsupervised/autoencoder.py +++ b/mlcolvar/cvs/unsupervised/autoencoder.py @@ -73,6 +73,10 @@ def __init__( f"Encoder layer must be a list. found {type(encoder_layers)}" ) super().__init__(model=encoder_layers, **kwargs) + # this makes checkpointing safe, to avoid double model keys + self.save_hyperparameters(ignore=['model']) + self.hparams.pop('model') + # ======= LOSS ======= # Reconstruction (MSE) loss diff --git a/mlcolvar/cvs/unsupervised/vae.py b/mlcolvar/cvs/unsupervised/vae.py index 5946c970..28b34640 100644 --- a/mlcolvar/cvs/unsupervised/vae.py +++ b/mlcolvar/cvs/unsupervised/vae.py @@ -95,6 +95,10 @@ def __init__( f"Encoder layer must be a list. found {type(encoder_layers)}" ) super().__init__(model=encoder_layers, **kwargs) + # this makes checkpointing safe, to avoid double model keys + self.save_hyperparameters(ignore=['model']) + self.hparams.pop('model') + # here we need to override the self.out_features attribute self.out_features = n_cvs From f2637915e34fe212990f332aac5d75e29a0467e6 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 9 Dec 2024 17:41:06 +0100 Subject: [PATCH 191/267] Update notebook tda --- docs/notebooks/tutorials/cvs_DeepTDA.ipynb | 82 ++++++++++------------ 1 file changed, 37 insertions(+), 45 deletions(-) diff --git a/docs/notebooks/tutorials/cvs_DeepTDA.ipynb b/docs/notebooks/tutorials/cvs_DeepTDA.ipynb index e6ea7feb..47ba9b2e 100644 --- a/docs/notebooks/tutorials/cvs_DeepTDA.ipynb +++ b/docs/notebooks/tutorials/cvs_DeepTDA.ipynb @@ -3,17 +3,17 @@ { "attachments": {}, "cell_type": "markdown", - "metadata": {},model= + "metadata": {}, "source": [ - "# Deep-TDA: Deep Targeted Discriminant Analysis\n",model= + "# Deep-TDA: Deep Targeted Discriminant Analysis\n", "Reference papers: \n", - "- *Deep-TDA*: _Trizio and Parrinello, [JPCL](https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c0model=2021)_ [[arXiv]](https://128.84.4.34/abs/2107.05444).\n", + "- *Deep-TDA*: _Trizio and Parrinello, [JPCL](https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c02021)_ [[arXiv]](https://128.84.4.34/abs/2107.05444).\n", "- *TPI-Deep-TDA*: _Ray, Trizio and Parrinello, [JCP](https://pubs.aip.org/aip/jcp/article/158/20/204102/2891484) (2023)_ [[arXiv]](https://arxiv.org/abs/2303.01629).\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/luigibonati/mlcolvar/blob/main/docs/notebooks/tutorials/cvs_DeepTDA.ipynb)" ] }, - {model= + { "attachments": {}, "cell_type": "markdown", "metadata": {}, @@ -81,18 +81,10 @@ "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/etrizio@iit.local/Bin/miniconda3/envs/mlcvs_test/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -160,7 +152,7 @@ { "data": { "text/plain": [ - "DictModule(dataset -> DictDataset( \"data\": [4002, 2], \"labels\": [4002] ),\n", + "DictModule(dataset -> DictDataset( \"data\": [4002, 2], \"labels\": [4002], \"data_type\": descriptors ),\n", "\t\t train_loader -> DictLoader(length=0.8, batch_size=0, shuffle=True),\n", "\t\t valid_loader -> DictLoader(length=0.2, batch_size=0, shuffle=True))" ] @@ -200,7 +192,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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VVaxfvx673c7gwYORJIn8/Hxqamrw+XxUVFTQqVMn4uPjaWpqwuFwoCiKEFO/309sbGzU+TlbP+w0jh1JOnav3bP110sTU40zhtZpXb1ej9lsFuIRCARoamoS34/s1K2vr+fw4cMoioJeryc7OxubzUZzczM7d+4U4pucnExRUREWi4XKyko2bdpEc3MzEI5Uu3btSlFRkUjNQjhKbm5uFg5GgUAAvV6PTqcjLi5OdOFGYrVaGTt2LLt372bt2rXs2bOH1NRUCgoK0Ov1dO3ale3bt3Po0CHy8vJEajcYDOL1ekU0GggEkCRJpH3VzTQaGh0hnOY91prpj3QwpziamGqcEbRO67ZuMvJ4PKJ+qdPphENQMBiksrJSNCDFx8eLtO3u3buprKwUz1dUVERqaipNTU2sWLFCbC8yGAx0796d4uJiLBYLAI2Njezfv5/9+/dTXl7eZpF4a9LS0igsLGTQoEHiOSRJolu3bvj9fjZt2sSGDRuEJ296ejq7d+8mGAxit9tJTEzEarXicrnwer0kJCQACAGN7CjW0ND44dHEVOO0pvXsqLr+TI2+FEURjT0QbhZKTExEp9PhdDqFJZ9a20xKSsLhcLB9+3bxmMzMTAoKCpBlmTVr1rBnzx4gLMrFxcX07NkTs9mMz+dj48aNlJWVUV1dHXWcke5IVqtVbIVpaGigpqaGuro66urq2Lx5MxMnTqR79+7isT179uTAgQO0tLSwadMmke5NTk4WLkyJiYlYLBYhpklJSeJCIhQKaWKqcXwcRzcvWjevhsbpRWtf3dZpXdUhSK2fxsfHi/phXV0dhw8fBsICq0Z85eXl7N+/X3y9e/fuJCUlceDAAdavXy9WrnXq1Il+/foRFxeHw+Fg+fLlbN68WbyWTqcjLy+PLl260KVLFzFO0x5ut5v9+/ezcuVKmpqa+PDDDxk0aBBjx44VKdpBgwaxcOFCdu/eTXFxMTabjZSUFA4fPkxjYyMFBQUiolWPMbKLN/LiQkOjo2jdvB1HE1ON0xJ12XakL25kt67f76epqUmkOJOSkjCbzYRCoSiXosTERLKysggEAlH1z/T0dIqKiggEAixevJiqqioAbDYbgwcPJiMjA7vdzvz589myZYs4jpSUFEpKSujVq1dU48/RiImJoVevXnTv3p0VK1awevVqvvnmG+Li4hg0aBAQbmjKzs6mqqqKffv20a9fP5HKdbvdwvwBiBJ0IMqsQRNTjWNBmzPtOJqYapx2tK6Pms3mKN9Zt9st6qMGg4GkpCThZFReXo7f70eSJLKyskhKSqKpqYlt27aJxqCioiLS09M5ePAga9euxe/3o9Pp6N27Nz179iQYDLJkyRLWrl0rmpny8vIYNmwYnTp1ahOBqsu+AbFmrb0o1WAwMGbMGGJjY1m0aBHLli2joKCA5ORkADp37kxVVRWVlZX069dPzMyqnr6qYEa+FmgCqnH8aGLacTQx1TitCAaDUbZ9reujDodD2P5ZLBYSExORJAmHw0FFRQWyLGM0GsnLy8NisXDw4EGR1o2NjaVXr14YDAZWrFhBeXk5EI42hw0bhs1mY9OmTSxbtkzUU/Py8hg1ahS5ubniGB0OB9u2bePgwYNUVlZSVVUV1YBks9koKSmhpKSEwsLCNnOnAwcOZO/evRw8eJCFCxdyySWXAJCVlQVAc3MzHo8Hq9VKTEwMTqcTt9tNUlISQJuuXU1MNY4XLc3bcTQx1TgtaD0/2ro+Kssyzc3NQmhbj72onbcxMTHk5eUBsHXrVurr64Fwk1FRURFNTU0sX74cl8uFJEkiZdvQ0MBbb70luntTUlIYO3YsXbt2RZIkPB4P69evZ+PGjezdu/eoAma321mxYgUrVqwgMTGR6667jvz8fPF9SZKYPHkyL7zwAgcOHKC5uVk0GNlsNux2Oy0tLVitVsxmM06nU4zARJ4vDQ2Nnw5NTDVOeVrPj0buHYW2jUbqmIiiKFRXVwszhaSkJDIzM/H5fGzZskUIZlFREVlZWWzbto1NmzahKApxcXGMHDmSxMREUcdUo9rRo0fTv39/dDodhw8fZtmyZXzzzTdRvru5ubl069aN3NxccnNziY+PB8Kif/DgQTZv3kxZWRnNzc0888wzXH755fTv3188PjExkU6dOnHgwAG2bNnCyJEjgfBFgt1ux+FwkJmZ2Sa12/q8wdnrSKNx4mhp3o6jianGKU3rRiOTySQMCSCc9m1sbBQdq+r8aCgU4tChQyLlm5mZSXJyMs3NzWzdupVgMIjJZKJXr15YrVaWLl1KRUUFEO7UHTx4MHa7nddff110/RYVFTFx4kTi4+Opq6vjs88+Y8OGDeJYMjIyGDx4MH379iUlJeWI76lHjx706NGDCy+8kNdff51t27bx2muv4fV6GTZsmLhf7969OXDgAFu3bmXEiBFIkiREWZ2LPZqYtj6PoAmrxrGhpXk7jiamGqcsamONKgSqMbyK3++nsbFRuBYlJydjMBgIBAKUl5fj9XqF9V98fDzV1dXs2rULRVGIj4+nd+/eeL1e5s+fj8PhQKfTUVpaSkFBAevXr2fJkiWEQiEsFgvnnHMOxcXFOJ1O/ve//7Fy5UrRKdyjRw9Gjx5Nt27djkmsLBYLs2bN4sMPP2Tp0qXMmTOHbt26CSEuKipCkiRaWlpwuVzExcWJ8ZfW22cURYlK7UqSJC5ANAHVOF40b96Oo4mpxilJZMdu60YjCM9Squlbo9FIUlISer0en8/HwYMHCQQCGAwG8vPzsVgs7Nu3TzQUpaWlUVxcTHV1NStWrCAYDBITE8Po0aOJiYlhzpw57N27F4CuXbty7rnnEhMTw8qVK5k3b57YFlNcXMz5559PTk7Ocb9PnU7HhRdeSFVVFXv27GHJkiXMnDlTvK+EhARhRRgXFxc17hL5X71eH9XFq3b5qq+hRaYax4OW5u04mphqnHJEduy2XpsG0aMvkY5Gbreb8vJyQqEQJpOJTp06YTAY2L59u0jV5ufn07lzZ7Zv387GjRuBcHp25MiRNDY28t5772G329Hr9YwbN47+/ftTVVXFiy++yMGDBwHIzs5m+vTpFBUViWNSFIWDBw+yZ88e9uzZw4EDB5BlGbPZjMlkori4mClTprTx4FXf44QJE9izZw9r1qxh6tSpIgJVU9MNDQ3k5+e3EdPI9XKRnsSR92kttBoaHUVL83YcTUw1TilaC6nFYokSAJfLhd1uB8Jm8AkJCUiShNPpFFtdrFar6I7dvHkzzc3Nwuc2PT2d1atXs2/fPiCcSi0tLWXz5s0sXLgQWZZJTEzkwgsvJDU1lYULF7JgwQJCoRBms5kpU6YwcuRIIVg1NTUsW7aMZcuWCcFuj61bt/Lpp59y7rnnct5557UR1e7du5OcnCw8fXv06AGEx2jU9w1ta5/qyI3BYIj6f0VRoiJT9XuamGpo/DhoYqpxyhAIBITHbuvRFwg33ajORTExMdhsNiRJwm63ix2isbGx5OfnEwgEKCsrw+l0CsOFuLg4Fi9eTE1NDZIkMXDgQAoLC/niiy/YtGkTEBa1c889F7vdzjPPPMOBAwcAKCkp4aKLLiIhIQFFUdi+fTtz584Vj4Pv7AcLCgro2rUrZrMZv9+P3W5nwYIFHDx4kDlz5vDJpwv49b0P0LfXd5GtJEl07tyZxsZGKioqhJi2Nl5QLzRUtyP13+prQTg9HNmQpKV5NY4XLc3bcTQx1TjptJ4hbT360tqMQZ0hlSRJLNOGcBSXk5ODz+dj06ZNYhVZSUkJBoOBL774gubmZgwGgxh7efvtt8Xjx4wZw+DBg1m/fj3vvvsufr8fi8XCzJkzKS0tRZIktm7dyjvvvMOuXbuAsDj16dOHUaNGUVpaKtKzrRk7dizr1q3jL489DT43f37kr7z16uwo56bc3FzWr18vuorV549EFUyz2SxGhtR/q6lvtZsZaGMjqK1f0zgWtDRvx9HEVOOk0lpIW3vsthbS+Ph4kSJtbGwU21kSExPJzs7G7XazadMmIYR9+vQhEAiwYMEC3G43FouFcePGoSgKb7zxBi0tLZjNZqZNm0ZeXh7vv/8+K1euBKCgoIArr7ySpKQk6uvreeONN1i1ahUQFvyxY8dy/vnniwXiR0M1qzem9CdQ9w1GnHzzzTdRozDq86hGEtDWGlA1sVcjUTWVq26tAcRqOfU4NQMHjeNFkqRjzmacrdkPTUw1Thqtt760niFVFAW73S66Z202mzCPV1eXQbhJJzMzE6fTKTa3xMTE0LdvXxwOB4sXL8bn82Gz2Rg3bhwNDQ3MnTsXn89HYmIiF110ETqdjn/+859UVFQgSRITJ07k3HPPRVEUPvzwQz744INvu4uh1hXLfffcwpTxpcf8nl998g5eePEVli9dyMKFC6PEVH3vkSla9SJCfd9qvTg+Pl58z2q1otPpoqLUSDGNrJ2erR90GseHlubtOJqYapwUjlVIExISiImJAaLtAVNSUsjIyMDhcLBp0yZCoRBxcXH06dOHxsZGlixZQjAYJCUlhXHjxrFnzx4+++wzZFkmJyeHGTNmUF1dzSuvvILT6SQ2NparrrqK4uJiKioqeO6558SYTG5+Fz79xoU7YKK+OXrOs6OYTQYuu2Q6K5Z9ydatW6murhaeu+0ZMKjmDHFxcSiKErXEXN1wExsbSygUEtG9xWIRY0ORYqoJqcaxoqV5O44mpho/Oa2FtPXWl44KaWpqKunp6djtdjZv3kwoFCIhIYGSkhLRZSvLMpmZmYwePZqNGzfy1VdfAeEZ0alTp/LNN9/w/vvvC3GdNWsWCQkJfPLJJ7zzzjsiyr3mmmsYNWoUgz9bS32jk0vOHwKEx3QqKytZuWYTL/33C4oKuvKvv92F2Ww+4vtPTU2ld+/elJWVsWnTJiGmKqr4wXeRaFxcHC6XSzg9xcbGitpqbGysSP8ajcao7t3IZiStXqqh8eOhianGT8qJCGlDQ4MQ0rS0NNLT02lpaRFCmpiYSElJCZWVlSxfvhxFUcjLy2P48OHCXxfCW1nGjh3LvHnzhLj269ePyy+/HK/Xy9/+9jcxg9qvXz9uvPFGsQbtkvMG09jYyCcff8jChQujunkBtjeWMWnSPEpKSvjZxZcwdszodiPCnJwcysrKRAQJiE5l1TLQ5XKJ85CSkhJVH9bpdEJobTabuJ/VaiUYDAqzC9URCjQx1Th2tDRvx9HEVOMno7Vh/bEKqVojTUtLIy0tjebmZsrKyqKE9NChQ6xcuRJFUejUqRNDhw7lyy+/FKI3ZswY+vfvzxtvvCEEc8qUKUyaNIkdO3bwz3/+k6amJoxGI9dccw0TJkwQYnjgwAFeeuklvvrqq6joMSEhAfQx1DZ6MeKCoJ8NGzawfsMG0ruOYO7rf2sjqOq6tEgxjRRHgLq6OiAsniaTiYaGBiAsrIFAQKyBs9lsYnl5TExMVDOXel6BNqveNDS+D0k6jjTv2amlmphq/DR0REgdDke7QtrY2CiENDU1lbS0NOx2uxDSpKQkevfuTXl5OV9//TWKotC1a1cGDRrE/Pnz2bZtm1hrVlhYyOzZs9m/fz96vZ7LL7+cAQMGsGDBAl577TVkWSY7O5u77rpLGD9UV1fz4osvsmDBAiGiPXv2ZOLEiYwbN46MjAwURWHxqh1kpNqIM4e4+c6HaK7dxeF9Kzl8uI6MjPSo86FGn2o0CogaqCqmahSenh5+rCquqampYgzGarViMBjEeYuJiRGNSEajMapeqtVMNY4VLTLtOFreR+NHR03tHklIIdxoo3an2mw2IaTNzc0ivZmSkkJ6erro2lUj0t69e3Po0CEhpIWFhQwaNIh58+axbds2dDod06ZNo1OnTjzzzDPs378fi8XCzTffTN++fXnhhRd45ZVXkGWZkSNH8sgjj5Cfn4/f7+c///kPl19+uWha8krJ6NJHceHltzP1/AvJyMgA+Dalqqe23k5+fj6P//0RdOZkJBRef/PtNudEjSrV9wmI6FIdkVFrotnZ2fj9fhGZZmRk0NjYCIQjXI/HI5aBWywWMYsaOW+qpXg1jgdVTI/1djaiRaYaPyrfVyOFsJBGdqlGjoGohgrJyclkZGTgcrlE167abFRRUSFSuwUFBQwcOJCPPvqIPXv2oNfrueCCC0hKSuLpp5+moaEBm83GTTfdRHx8PH/605/YsWMHkiRxxRVXcP755yNJEmvXruXvf/87hw4dAmDAgAFYUkt4f+FuGutD3PHwGwzp15V3nrkVgFUb9vLzu/8NwPRz+vOrG6ZQ708lmUY+/uhD7rjtpqimpMgRFxVVTLOzs1EURbz3vLw8amtrxbab2NhYkR5OTk6OGp8JhUJCQE0mU5SjlIbGMaPj2EOus/S67Sx92xo/BR0RUrfbLVKdqrMRhAVWjcwSExPJzMzE4/GwadMmgsEg8fHxlJSUUFVVxYoVK0Rqt7S0VAipwWBgxowZ2Gw2IaQpKSn88pe/xGAw8Lvf/54dO3YQUnRcdc0vmDZtGqFQiGeffZZf/vKXHDp0iLj4BO67/3c8/fTT3Hv75aSn2sSxNzQ7xf8bDd+J1dzPN3Cgsp7iXv0JYSbg9wizBxU1TaumdB0Oh/hadnY2hw8fFg5OGRkZIjrPzMzE7Xbj8XiQJInExMSo8ZlIS0FJkrTIVEPjJ0L7C9P40QgEAlFzpK2F1OPxCAGJjY0VQup2uzl06BCKomCz2USac9OmTQQCAWJjY+nTpw91dXWia7dz586UlpbyySefiIh0xowZWK1WnnnmGex2O1lZWdxxxx3Y7XYeeughDtfW4gvq2VKbRrPXSnV1NbfccgtvvvkmAElZxex2duelj/cgSRLpqTYevOMCcfz7D9Xj9YWbfeobHYwe3A2dTiIxPoY/Pf0R5ZWN9O/XG/iuHqqiroNT17ft2bMHCAupujIOwovKJUkSFxY5OTnCISkhIUGcLwiLaaRxQ2STlCamGseFpBzf7SxES/Nq/Cj4/X7RVdrakAHCBu2qwFitVuLj45EkCa/XS3l5ObIsExsbS05ODsFgkE2bNuHz+bBarfTt25empiaWLFmCLMvk5eUxZMgQ5s2bx65du4SQmkwmnn32WVwuFzk5Odxyyy0cOHCAxx57DI/HQ15ePvqkPnQKShyu3sfPr34It8uFwWimU8+xHHbFoEi1OJwecdylJZ3F/4dCMl99vZ2X31vKN5sOiK/fed0k/vjPDwHYsK0SPd/VSCG86UUV086dw8+3e/duAAoLCwGi9qk2Njbi8XgwGAxkZmaKzuS0tDQRlVosFoxGY5SYRvrzas1HGseF9O3tWB9zFqKJqcYPTnteu62/r9b8LBaLWKMWCAQ4ePAgoVBIrFFTFIWysjLcbjcmk4k+ffrgdDpZvHgxoVCIrKwshg8fzueff87OnTvR6XRMnz4ds9nMv/71L9xuN3l5edx8883s2LGDJ554gkAgQM+ePfn1r3+N1Wrl1rv/yLvzP0cCUtJz2VaXTnmZAwinn2vq7Lz07lJmXTKatOQ4OuemcKAi3Ax031/exe70Rr0/i+W79+v1y8QSLaaHDh0iGAxitVpJT09HURR27twJhFfCuVwuUT8tKCiIilqDwaCot6akpIi1b/Hx8fj9fjFfajQahZGDVi/VOG50Svh2rI85C9FyPxo/KMFgMKpup64Ki/x+Y2MjiqJgMplITExEkiSCwSAHDx4kGAxiNpvFWMrWrVux2+0YDAb69u1LMBjkq6++IhAIkJaWxqhRo1iyZAlbtmxBkiQuuOACYmJieO6553C73eTn53PLLbewefNmHn/8cQKBAKWlpdx///0YDAb+/Oc/s2lNWEjdUjrTLrmZkBTe/KL7tivR7fbw//72Is/+6znGTJrB3nUfIDv2oMgBIaQ6HSQnxvLUw1dwzqjexFrDzUZpiWFh9QV1fPjFBgLBEFu2bAHCLkxqCrexsRGj0UhRUZEQ1szMTOLi4sRS8vz8fCGeNpsNo9EoIlObzSbEU91cE7kcXEPjuJCO83YWokWmGj8YoVBIpBkNBkObiDQUCtHY2IgsyxgMBpKSkpAkCVmWKS8vx+fzYTAYyM/PR6/Xs3PnThobG9HpdJSUlKDT6Vi4cCFer5ekpCTGjh3L6tWrWbduHRA2X0hMTOSZZ57B5XKJiHTdunU8++yzKIrC8OHDufXWW/F6vdx///2sX78eSaejWemEi0z+8txnAJiMeoryk6jev47D5etB9vHoI4vEe1E81dC4ASk2H11SP2TM3Hj5WC6cNACA/82+nRffXsKOVbtxA0+/8TWuUBn7yg/jqS0DwjtSAZG27dGjB2azme3bt4t/19XV4XK5MBgM5OTkCKMJ1Y9YURTMZjMmk0nUny0WS9S2GS3Fq3HcHE8N9CytmWqRqcYPQigUikorRu4jhXCU1NTURCgUQq/Xk5ycLJZWV1RU4PF40Ol0dOrUCZPJxIEDB4RRQ8+ePYmJieGrr77C6XQSFxfHuHHj2LJli1iXNnHiRLKzs3nuuedwOBxkZ2dz0003sWHDBiGkY8eO5fbbb6epqYlbb72V9evXYzZb+P2D/w9DQleQJGRFQZEDeGrXsWnRcxze9zXIPqyxNozxeegSe6NL7ANGGyghFOd+5Mb1ALw5d6V4v8+/9RXvf7qGhoaw0YKsD8+TSihCLEtKSlAUJcq6MHI/a3FxMfv37wfCUanP5xORaFpaWlRHcDAYRJZlJEnS6qUapyXPPvssnTt3xmKxMGTIENasWXPU+z/55JN0794dq9VKXl4ed999t/gMOhlokanGCSPLsohIdTodZrM56gNcURSam5sJBALodDqSk5NF6rG2thaHw4EkSeTn52OxWKiurhapzW7dupGcnMzixYtpamrCYrEwfvx4ysvLWbhwIQAjR46kqKiIp59+mubmZtLT07n55pspKyvjmWeeQVEUxo8fzw033EBlZSV33nknNTU1hDBSEehGQVFPBpUcYOGKbcieamhchxL4duzFlITOVow/Ng9J0omrTymhB4pzH3LDNyjecOp1wvBeuD0+YqxmehRkMw8HEpCcnMKrz9zHvvI64g0OVn0RIDU1ldzcXPbt20dDQwNms5mePXuKDxD1Q+XAgQMAdOnSJWrlnCRJQlgTEhJETVadZW29HFxD47j4iSLTd955h3vuuYfZs2czZMgQnnzySSZPnszOnTuFA1gkb731Fvfffz8vv/wyw4cPZ9euXVx77bVIksQ//vGPY379HwItMtU4IRRFwev1oiiKcOBpLaR2u12IbVJSkhiRaWhoEK4+OTk5xMbG0tjYyK5du4BwNJaVlcXq1auprq5Gr9czduxYmpqa+OSTTwDo378//fv35/nnn6euro6kpCRuueUWdu7cydNPP40sy4wdO5YbbriBgwcPcuutt1JTU0OsLZl6qYQAcXh8fr5aWUaofjVy7RLkgBNzTAK69NHos85BF9cJqZVBqSRJ6OPzAQlCHpSgh1f+t5z7/vIeADdeMZZfzAyPxZSWDqSocwaTR/dm7dpvABg6dCiSJAnx7NevH0ajkc2bNwPQt29fUUOOj48nLS1NiGl2dnaUnaDJZBJX5FarFVmWNT9ejR8G3XHeCBuTRN7Uz4D2+Mc//sEvfvELrrvuOnr27Mns2bOJiYnh5Zdfbvf+K1euZMSIEVxxxRV07tyZc845h8svv/x7o9kfE01MNY6bSCFV04utU4qRm09Uw3YImxSo4pCenk5CQgJOp5OtW7eiKAoZGRl06dKFsrIy9u3bhyRJjBo1ClmW+eCDDwiFQnTr1o0xY8bw8ssvU1lZSVxcHDfffDOHDh3iySefJBQKMWrUKG688Ub27dvHbbfdRkNDA3EJaex1dCUhMYXf3nY+l938d3wVn6M4wylVY1Ix+uxz0cVkHzVFOvuRX5CT1yl8Lvxhe7/GZpf4/q4d4Uaj0tLwEnGv18v69eGU8NChQ/H5fKJeOmjQIPbu3YvL5SImJobCwkLRxVtQUEBjYyOBQACTyURSUpLohk5KSsLv90eleNXZXi3Fq3HCnMCcaV5eHgkJCeL26KOPtvsSfr+fdevWMXHiRPE1nU7HxIkT+frrr9t9zPDhw1m3bp0Qz3379vHpp58yderUH/gEdBwtzatxXKjG9WrHqMViaWMM4PV6hbuRzWbDarWKr6smBElJSaSmpuL3+4VxfUJCAt27d+fAgQOUlYWbdQYPHkxCQgKvv/46fr+f3Nxcpk6dyttvv82ePXswm83ceOON2O12HnvsMYLBIIMHD+bmm29m7969/PKXv6SlpYXu3bvjtvZF3lxBY4uLP/39eeS6VaAEQG9BnzYcxZKOP/j95+A3j7yN/tv7dc1LIT23C088dDnPvbEIJehh69at4tgBVqxYgdfrJSMjg4KCAlauXInP5yMtLY2uXbvyzjvvAOFaalNTEw0NDeh0OgoKCtixYwcQ7vD1er34/X50Oh02m02MyqjnV03xtjbJ0NA4Zk5gzvTQoUPC4Qs44o7f+vp6QqGQ8LlWycjIEL/3rbniiiuor69n5MiRKIpCMBjk5ptv5re//e0xHuwPhxaZahwXgUBAfGi3J6SBQECYMsTExAhD90AgEGXKkJWVhSzLlJWVCVOG3r17U19fLyz4evbsSX5+Pv/73/9wOBwkJSUxY8YM5s+fz4YNG9Dr9Vx33XVIksRf/vIXfD4fJSUl3HHHHZSXl3PnnXfS0tJCz549eeqpp7BYY1EUBbllB/LhZWEhNaeiz5qMZGlbnzkSdqeXum9HVYKKmXefvY1la3bx19mf8uwLbwFhYVRnST///HMAJk2aBMCyZcuAcM23vr6e8vJyJEmif//+4kOkc+fOBINBcS6zs7NFVKrO56op3piYGC3Fq/HDIinfzZp29PZtZGqz2aJuRxLT42Hx4sU88sgj/Otf/2L9+vV88MEHzJs3j//7v//7wV7jWNEuXTWOmUhTBpPJ1OZDWx2BUWdJbTabGIE5dOiQSFfm5uYCsHPnThwOBwaDgZKSErxeL0uXLhXuRn369OHDDz+ktrYWq9XKz372M9atWycWe1922WWkpqby4IMP4nQ6KSgo4Fe/+hWHDx/mzjvvpLm5meLiYp588km2761j8aodyI0bUBzh2qwuvggpuX+buuj3oSgyhMKNPwP6FgPQOS8VvU4iJhSuBY8fPx6AXbt2cfDgQYxGI2PHjmXXrl3U1tZiNpsZNGgQS5YsAcKmDQaDQTgkde/eXXT3pqamYjAYRCSqboyBcBRqMBiijO21FK/G6UBqaip6vV6sHFSpra0VG5Ra8+CDD/Lzn/+cG264AQhftLpcLm688UZ+97vfnRT7TC0y1TgmQqFQlClD61lSRVFoampqM0uqKArV1dV4PB70ej35+flCNA4fPowkSfTq1Quj0ciSJUvw+XwkJyczfPhwli1bJvx2Z86cSXV1NR988AEAU6dOpWfPnvz1r3+lsbGR7Oxs7r//fhwOB3feeSf19fUkpmRw169/h9FkYd2WfUiNq74T0qR+6FIGHrOQAuBvBhR0eiO9ehTw349WMaBXJ95+4ucYFTs6nU6I6bx58wAYMWIEcXFxfPnll0A4BRwMBkVKuLS0lB07dqAoCunp6cTFxYkPmdzcXHGRYrVasVgsoh6tpnjVeqmW4tX4QfgJTBtMJhMDBw4UfxMQnhD48ssvGTZsWLuPcbvdbQRTvahXMzM/NdpfnEaHkWU5apa0PSFtaWkhEAggSRJJSUniF76hoUGkKnNzczGbzdTX14s5yqKiIhITE1m6dCktLS1YrVbGjBnD9u3bRZPBlClTkCSJ1157DUVRGDJkCGPHjuVvf/sb5eXlJCQkcP/996MoCnfffTdVVVXoTXHsaMzjZ7e/wMQRxXz89r9QPFWADl3qYHRxnY/7fCiecANVQmonHv3XpwDkZ6ew8qsFQDh9q3bhfvNNuIv3vPPOo7y8nN27d6PT6Rg7dixr164lFAqRm5tLWloaK1asAMLp7crKSmRZJj4+nvj4eNGUlJKSErVIICYmRqTd1Z+PhsYJ8xONxtxzzz1cc801lJaWMnjwYJ588klcLhfXXXcdAFdffTU5OTmiiWnatGn84x//oH///gwZMoQ9e/bw4IMPMm3atJP2u6+JqUaHUDt3of1ZUgh37qppx8gRGKfTKaIr1SLP6XSybds2IFwHzM7OZtOmTVRUVKDT6Rg9ejRNTU2izjhs2DDy8vJ44okn8Pl8FBYWctFFF/Hyyy9TVlaG2Wzm3nvvJSEhgbvvvpt9+/ahN1ppNpQgByV8Pt93Qirp0aWPRGfNOqFzInvCa9FaAjZ0VjAY9MRZ9Xz2WdhF6eKLLwbgs88+Q1EU+vbtS15enmj3HzBgAFarVZg2DB06lF27dhEMBklMTCQjI0PUjfPy8nA4HASDQQwGAzabLWo8RqfTiYyBwWDQUrwaPww/kdH9pZdeSl1dHQ899BA1NTX069eP+fPni6ak8vLyqEj097//PZIk8fvf/57KykrS0tKYNm0af/7zn4/9xX8gNDHV+F5aj8C0niWF8BaYyM5dtdnA7/dH7SVNTk4mEAiwdetWZFkmMTGRwsJCysvLhWftkCFDMJvNvPPOO2IEZujQoTz33HM0NTWRlpbGtddey+eff85XX32FJEn88pe/pEuXLjz88MNs3LgRs9nCIX83giEJgwF8lSsihHQUOmv7tZgOn5OAHXxhdyMpJrxG7bHfXsL6NUvwer0UFBQwYMAAmpqaWLQobEN4/vnnU1lZSVlZGZIkMWHCBNasWUMgECA9PZ3c3Fw+/DC8baZXr15UV1cTDAaxWCykpqaKtWzJycnIsiwuXGJiYlAURevi1fjh+QmN7m+//XZuv/32dr+3ePHiqH8bDAYefvhhHn744eN6rR8DrWaqcVTUBd+RIzCthTQYDIoOU6vVKjp31YYjdQtMVlY4Ety+fTsej0e4/tjtdjFPVlxcTH5+PnPmzMHlcpGWlsaUKVOYM2cO+/fvx2KxMGvWLPbs2cMbb7wBwM9//nMGDBjAH/7vL3z55ZcYDAZ6DbmAoBTu2vVV/bBCCiDbw+lWW2pXJEMsACVF2bz77rvimCRJ4sMPPyQQCFBUVETv3r1ZsCCcAu7Xrx82m03MnY4YMYJdu3bh9/ux2Wzk5uZy6NAhIGxe4XK58Pl8wkFKrZWqywTUdK9Op9NSvBoaJ4GTKqZLly5l2rRpZGeHh+Pnzp171PsvXrxYGHdH3tThf40fnmAwKD6ozWZzm6K/2nCkKAoGg0GMa6gNR16vF71eT15eHjqdjgMHDgjz+t69ww5BS5cuJRgMkpGRQb9+/fjiiy+oqanBYrEwY8YM1q5dy9dff40kSfz85z8nFArx1FNPCb/dKVOm8MAfn2ThgrAr0g033k5QnxQef2lcj+I+BOi+Te2euJAqckAYPLj1+cTHhpt/np79Mi0tLeTk5DB+/Hjq6+uF5eEll1xCRUWFiErPOeccEZVmZGTQqVMn4dnbu3dvamtr8fv9mM1mMjMzxUJwtQ6timlsbPiCQe2u1qJSjR8UbWtMhzmpf3kul4u+ffty/fXXM3PmzA4/bufOnVHDwO15N2qcOK07d1t/UKsNR8FgUDQcqVFrU1OTaDjKy8vDaDTS0NAQ5bkbFxfH0qVLcTgcxMTEMHLkSMrKyoTgXHDBBTQ3NzNnzhwg3LnbuXNnfv/73+N2u+nevTuzZs1i8+bNLFv4PwAcUi4PP78GRQG9exchR3jpti51CHmde3LtxSMZP6wHWemJ+ANBDlY2MG/RJl77YAVeX6BD50Vx7gvPphriUSwZOFweJCXE+tXhdO7Pf/5zDAYD77//PsFgkJ49e9KrVy+ef/55IGyBGBsbK6LSkSNHsmPHDhGV5uXliYalvLw8PB4PbrcbSZJISUnB4/Egy7Kwb4ycLdXEVOMHRdsa02FO6l/elClTmDJlyjE/Lj09ncTExB/+gDQE39e5C+H29PYajtxut8gWZGRkEBsbi9frFZFXTk4OmZmZbN26tU3DkdoeP3r0aFJSUnj88ccJhUL07duX8ePH88QTT1BVVUVycjITp/yMB//+NusWv4Eiy3Qu7E3PQefz4jtLkZ0HCNZvAECX1J9Jk6fwxIOXY4uziuOPsZpItMXQt0cel04bzPW/eYmDlQ1HPS+KIiO3hPeNxmeW4JEkBpZ0JjZ0iD1bPGRnZzN16lQOHTokZkcvv/xydu7cya5du9Dr9UydOpXly5cTDAbJzc0lOzubjz76CAh78tbU1ODz+TCbzWRlZYl0b2JiIgaDQVykxMbGiqXqoDUeafwIRHjtHtNjzkJOy8vYfv364fP56N27N3/4wx8YMWLEEe/r8/miDJbVgXeNI6NaBcKRO3f9fr84l/Hx8aLhKBgMcujQIRRFwWazkZKSgizLbN26VZi2FxQUUFNTE+VLGxMTw3vvvUcoFKKoqIiBAwfy/PPP09LSQnp6OpdddhkfffQR33zzDQoSI8ZdyNW/fplUeQtGXASIpcrXiZXvLCXB7KbhQHicRrIVU1I6jmf+eBVWiwmn28u/Xl/EqvV7MZuNTJvYjysuGEpBfjov/30WF8x6CpfnyIbciqscQm7QW3j+yT/g9oXo0y2dq39+JQC/+MUvMBgMvP322yiKwuDBgykoKOCxxx4DEP7Cqk3i2LFjxblJTk4mOzub1atXA9CpUye8Xi8ulwtJkkhNTcXn84lMgNZ4pPGjo0WmHea0uobIyspi9uzZ/O9//+N///sfeXl5jB07VqTL2uPRRx+NMlvOy8v7CY/49CSy4ag9IQ2FQqLhyGKxEBsbbsBRd5MGg0HMZrOohe/Zs0c4HPXq1Qufz8eKFStQFIWuXbvStWtX5s2bh91uJykpialTp7JgwQLhuXv99dezd+9e4V27vymBFZtrSZD3Y8RFCAONUjEHKptQgm4a9nwOyEgxOeiS+vLQndOxWkwEgiGuvvsF/vX6ItZvPcjX6/fw27+9zyPPhmutBfnp/OLyMUc8L4ocQm4OdxzrbN345R//y4BenXj9tVdwu93k5Xdm0qRJlJWVsW7dOnQ6HZdddpnYemO1WpkwYYJwburevTtxcXFiS06/fv2oqKggEAhgsVjIzMykri7cMZyYmIjRaBRr12JiYtDpdCIq1RqPNH4UTsDo/mzjtBLT7t27c9NNNzFw4ECGDx8udtk98cQTR3zMAw88QEtLi7ipKTON9ok0AmjPc1fdTSrLMnq9XjQcAdTV1YkoKjc3F71ez+HDh6mqqgKgR48emEwmYfiekJDAoEGDWLt2bXguVK/nwgsvZP/+/aJx59JLL8VkMvHPf/4TRVEwxmWjmDPpnhkkhsMgSeQVT8BgjkWRg4QOL4eQF4wJ6FKH0q9nPkP6dQXg3U/WsH7rwTbv+YW3l7B7f3gO9rqLR2LQt/9nodh3QNAJeitSfBHNDg8rV6/jww/DKdqNFbHsOVDLf/7zHwDOOecckpKS+PTTsKHD5MmTqamp4cCBA+j1esaMGcOGDRtQFIXs7GxSUlLE72eXLl3weDxRUanf7xfiqTYeaY5HGhqnBqeVmLbH4MGDhStMe5jN5jaGyxrt07rhqL1Ix+l04vf72zgcOZ1OEUVlZ2djsVjweDzs3BmuL+bn55OSksKWLVuora3FYDAwatQoamtrWbp0KQATJkzAZDKJkZcRI0bQt29fnn76aVpaWsjPz+fFZx/lzceuYM57rwFQ1HsUa3c58PuDyA1rwd8IOhP69FFIOiPnjOotjv29T79p930risIHC9YCkBAfw7ABhW3vE3Qht4RNJsadeyld8rO48bIxfPHp+4CChxSCukRWrVxCVVUVCQkJXHzxxXz22Wc4nU4yMjIYNmyYiEpLS0vx+XxUVFQgSRIDBgzg4MGDhEIh4uLiSEtL4/C3JvpJSUmYTKaoqFSv1xMKhcTsryamGj8KJ7DP9GzjtH/bGzduFPOLGsdPZJ30SA1HPp9PfKAnJCSI+wSDwaiVaomJiciyzLZt28RKtc6dO1NbWxu1Us1sNvPxxx8jyzLFxcWUlJTwxhtv4HK5yMnJ4cILL2TOnDls27YNs9nMXXfdRSgU4sEHH8Tv9zN8+HCIKwBA8pajuA4AErq04UjGOABK+3QBwOX2Ubaz4ojvf/WGfeL/S0s6t/m+3LgBlBCF3XqxdIuHAxX1fPjxJ2zYsAGTycRDv7+P1/9xDV9+EXY/uvzyy2lubmb58uUAzJgxgw0bNtDU1ERsbCxDhgxh3bp1ABQWFmI0GkUEX1BQIPbARkal6oVOe+MwWuORxo+ClubtMCdVTJ1OJxs3bhR2avv372fjxo1iY8YDDzzA1VdfLe7/5JNP8uGHH7Jnzx62bNnCXXfdxaJFi7jttttOxuGfMahCerQl36FQKGqlmmqsrigKlZWVhEIhMRMJ4WW9ap20R48eBAIB4TlbUFBA586dWbBgAXa7ncTERCZPnsyiRYvYu3cvJpOJa665hr179/L+++8DMGvWLLKzs3nqqac4dOgQ6enpPPjgg2zecQgl4MBfG2440iX2jpolLewUHps6WFlPKCQf8RzsKT8s/r+gc/ReRdldjeKuACTufeD36HQSOiWA3hWOuq+55hpmTB3J10sW4PP56N69OyNHjuS9994TNoJZWVmsXLkSgDFjxlBeXk5TUxMmk4k+ffqwd+9eFEUhOTmZxMREYb+YnJyM0WgU7lJWqxWDwYAsy6Ku3d6Fj4bGD4I2Z9phTqqYrl27lv79+9O/f38gbHbcv39/HnroIQCqq6uFsEK4MeZXv/oVJSUljBkzhk2bNrFw4UImTJhwUo7/TCEYDIqO0PaENLJOqvrCqjQ0NOB0OkWdVKfTUV9fLyLV4uJizGYzq1atwuPxEB8fT2lpKWVlZezcuROdTse0adOorq5m/vz5AFx00UVYrVaefvppFEVh5MiRjB49mq+++oqPP/4YSZJ4+OGHSUhIoFuXDEJ1K0EJIlnSkRJ6iGMzmwykJIUj1OrDLUc9B3aHB5c7HJlnpyd8997lIHJjOAX8i1/cwNRJo/ji9V8zsjgIsp+uXbty1VVXsW7dOr755hv0ej033HADa9as4eDBg5jNZqZPn86XX35JIBAgNzeXgoIC0cncp08f3G43DQ0NSJJEYWEhTU1N+Hw+9Ho9aWlp+Hw+EZXGxYXfjxaVavwkSBxHZHqyD/rkcFILLWPHjj3qupxXXnkl6t/33nsv99577498VGcXkXXS9naTQthcQ62TJiYmig9vt9sdZWBvsVjw+XyiTpqTk0Nqaiq7d+8W86QjR47EbreLedJRo0aRlJTEY489hizLDBgwgEGDBvH000/T0NBARkYGs2bNoq6ujr/+9a9A2BShR8/evPPJalZ+NRf8TaAzoUsdGrVKLTbmu2XE7qOMu4j7eP3ExpiJsX73OLlpEwRdoI/hplvCGZDayr3s2b4BnU7Hb3/7W/x+Py+99BIQ3gpjs9n45JNwh/DUqVOpr69n9+7dSJLEpEmT2LRpE36/n8TERAoKCkQ3ek5ODmazWTQhpaWlodPpomqlalSqXvxoUanGj4rEsYdcmphqnG20rpO218Ti9/ujDOzVD+9QKCSWVttsNpKSwvZ9O3fuJBAIEBsbS0FBAXa7XdQG+/XrR0JCAm+99RaBQID8/HwGDx7Mu+++S0NDA0lJSfzsZz9j1apVrFy5Ep1Ox+23347FYuF3v/sddrtduB5dfOu/2LBxE0rLDgB0KYOQDDFRx242ffd+/MEQ34c/8G0Xszn8HmVPNcq3Dkox2cOpqXMRYzEJUb/00kvp2bMns2fPprGxkczMTGbOnMlbb72Fx+MhNzeXQYMGiYvCQYMGoSgKe/fuFf+uqqrC7XZjNBrp1KkT9fX1BINBTCYTSUlJR41K9Xr9SVmCrKGh0RZNTM9SOlInlWVZ1EktFouokwLU1NTg9/sxGo1inrSiooLGxkYkSaJnz54ArFixglAoRGZmJsXFxaxYsYLq6mrMZjNTp05l69atrFq1CkmSuOKKK/B6vSLKmz59OkVFRcydO5dVq1aFG30eegij0cjuAzWE6tcAClJsPrrYtvPDPn9Q/L/J8P0zmCZj+M/B6wughHzI9eE6bExqT/z6VB74+/sML3BTX19Pfn4+N954Ixs2bBCe0bfccgvbt2+nrKwMnU7H5ZdfzsqVK3E4HCQkJDB06FAx8lNQUEBCQoKI4rt27YqiKDQ0hB2YMjIykCRJXMjExsai1+uRZVmMw2hRqcaPjmba0GG0y9qzlO+rk0LYLSoUCqHT6aLmSe12uxDZnJwc9Ho9LpdLRFwFBQXExsayZcsWGhsbMZlMDBs2jJqaGrEd5pxzzkGn0wkjhrFjx1JQUMCLL76I0+mkc+fOzJw5k5qaGp555hkAbrrpJv764hIGnPcw2dZaCDSH07vJA9p9j2oNFIhK3R6JGIsJCKeE5Ya1EPKAIZ5h42cAkJfo47PPPkOn0/G73/2OQCDACy+8AIStMbOzs/nf/8IewZMmTUJRFBGVn3POOezZs4eWlhbMZjP9+/dn7969hEIhbDYbmZmZ1NbWoigKMTExxMfH4/V6hdtR66hUM2nQ+EnQGpA6jCamZyGyLH9vndTj8Qjf3cTERJFODAQCYoQjJSWF2NhYZFlm+/btohs1JyeHhoYGsZ900KBBmEwm5s2bh6Io9OjRgx49evDBBx/gdDrJzMxk6tSprFixgnXr1mEwGLjtttvQ6/X87W9/w+12U1JSgsFWwPwlW2ior2H7+nCEp0segKS3tPs+ff4gjc0uALIimorawxZvFTXWqsqKbzfNSEy/7GZe/vuNfPjcLziwbTEAV111Fb179+bll1+msbGRrKwsLrnkEt577z1cLhdZWVmMGTNGmDX07t2blJQUMRY0YMAAXC6XmCMtKirC7XYLe0a1I1qNSuPi4tDpdFFRqclkOur70dD4QVD3mR7r7SxEE9OzDHXRNxy5ThoMBmlpCXe/xsXFCd/dyDEYi8UitvWUl5fjdDoxGAx0796dUCjEypUrURSFTp060blzZ5YvX05jYyOxsbFMnDiRzZs3s2HDBpEOdblcvPrqq0C4mzcxOY37//AUq1atQkHCmNIPk9EQXqvW8A0gI1kykWI7HfX97j4QbpDqlJOK/gjORgCF+d9tHtq9LWzuoEvuz+BBpQDM/teTOJ1OevXqxQ033MCyZctEXffWW29ly5YtIr175ZVXsmrVKpqamoiLi2PcuHGsXr0aWZbJysoiPz9fWAjm5OQQFxdHdXU1EJ7TtVqtuFwukRVQ7Rq1qFTjJ0eLTDuMJqZnGX6//6h1UnWtmqIoGI1GkV4EaGxsjLIL1Ol0OBwOsVatqKgIs9nMpk2bsNvtWK1WBg0aRGVlJWvWhOuPkydPRpZl3nvvPQDGjx9Pfn4+r7zyCg6Hg86dOzNt2jT+/tyHLP7iAwAcUh6b9zRzwaT+XHlOLor3cHjRd0rp946FrN0c3jsaG2OmpHvuEe83pH9X8f/ffPMNUmwnpPgi3vrwa15++WU2bdpETEwMf/jDH6ivrxeWgRdffDEpKSkivTt58uTw665dK/596NAhDh8+jMFgYPDgwZSXl+P1ejGZTHTp0oWGhgYxCpOenk4oFBIdvPHx8UiSpEWlGicHzbShw2hiehbRetF3e0LkdrvbHYPx+/1iDCYjIwOz2RyV3k1LSyM9PZ26ujp27Ah32A4ZMgS9Xs9nn4VdgXr16kVhYSEfffSRsNibPHkyGzZsEMu/b7rpJgwGAxW7V6EnSEgXy/hJ5/H47y4jGAzy1fywCEu2YuFydDQ+X7ZF/P/FUwe1ex9Jkpg5eSAAzc3NrPxma7g7WJI4sG+H6Ma9+PLryMjI4Omnn8bj8VBcXMy0adN488038Xq9dOrUiVGjRol0dq9evcjIyGDDhvAquL59+6Ioihh9KSoqQlEUYcOYkZGBwWDA6XSKZetq01dkVKp18GponHpo3bxnCYqiiDqpwWBoN00YCASi1qqpKWA1vasoCrGxsSQnJwNhxyp1rKOoqAhZllm1ahUQNmrPyclh2bJlIr07fvx4du3axZo1a5AkiUsvvZRgMCi6d6dOnUqXLl145b+fsqMsvIbsr4/8gbGjwyv2/vXcixw6VA46M7qE4g69703bD7F64z6G9OvKJecP5n+frW1jdv+Ly0ZT1CVcp3z5P6+gJA9F0hlIjAWbdy/BgIKLDP77xSHiLO+wd+9eYmNjuf322/nqq6/Yt28fZrOZq666iiVLltDc3IzNZmPChAlib2laWhrdunVj/fr14uIjLS2NQ4cOIcsyVquVxMREAoEAbrcbCI8ctReVaiYNGj8ZWjdvh9HE9Cyg9RhMe2lC1eUIwlFrTMx3M5sNDQ243W50Op0Yg7Hb7SLC6tatGyaTiQ0bNmC327FYLAwcOJDDhw+L3ZyTJk1Cr9eL9O6IESPo0qULb775JvX19aSmpnLxxRejKAovvTgbgIAhXQjpp4s28Mi38526hJ5Iuo6Phfy/p+by/nO3Y7WYeO2JX/Cv1xbx9YY9WEzf7jO9cBgAe/fu5YUPtiAZE0EJYXBsIYgPS1wqVa4uJPrq+fjjjUC4s9jlckW5NjU1NbF582YgfGFw4MABamtr0ev1DBs2jEOHDonacmFhIQ6HQ1y8ZGdnA9/t27VYLKJWrdVKNU4a2nLwDqOJ6VlAKBT63jEYp9MpxjAix2B8Pp/oOs3MzMRkMiHLspiPTE9PJy0tjcbGRrZv3w6ETeyNRiOff/45sizTrVs3unXrxmeffUZ9fT0JCQmcd955VFVViY7X6667DovFwqJFiwh56lHQMf2iK3jhv4vZsquS7RuXQtDz7fqztltdjsbW3VXc/vAbPPHg5djirNx789Q299m7dy8P/+1VMKeDx0eisg8TLmSMPPLIn7nmVy+RZgw3CZ177rn06tWLxx9/HFmW6d+/P8XFxVHmDAkJCcKLt3///uh0Og4cOACEje0NBoOoNaekpIgtO2r2ID4+HkCrlWqcXLTItMNoYnqGI8uycDk60lq1QCAQtQ1GvY+iKFRVVYn0bmJiIhDu3nW5XBiNRgoLC5FlmdWrV6MoCvn5+eTl5bF+/XqqqqowmUxMmDCBw4cPCwvBGTNmYLFYeP311wmFQvTv35+BAwfS1OwQM6W6+ALGjujPlXc9j6KEMNYtC389oSeS7tijsy9XbGPKNf/guotHMm54D7LSEgkE/Ozft5d58z7h1Tmr+eqTV/F4/Vx2/W8weetQgKuuu5X+Jd0YkO9CCSikZeRw5ZVX8tprr9HU1CQi6o8++giPx0N6ejrDhw8PXxSEQmRlZVFUVCT2liYnJ5ORkUFtbS2BQACj0Uh6ejqyLIuoNC4uTqTYVXHV6/VaVKrx03M83blnaRVCE9MzmMg6qU6na9cxR+3ehXDUarF8N7PZ1NTUJr3rdDpFRFVYWIjJZGL79u3CnKG0tBSHwyF2lI4ePZq4uDjeeOMNQqEQxcXF9OnThw0bNrBhwwb0ej0///nPefDxD/jgg/dIUGoIYeKwM5n5S8vISLVRvX8DXrf926i0a5v30FEqa5v40zMf86dnPkb21CDXLgVkpNjO6FKH8MLbS4iRmjH79qIAE879GbfOupgXX3wRJeAgPj6ehx98gOXLl7Nlyxb0ej3XXHMNmzZt4uDBgxiNRqZNmxZ1PoYOHcqhQ4fEBp3u3bvj9XqF01F2djY6nQ673S4Wrqsd1JEZBS0q1TgpHM/c6Fk6Z6qJ6RlMR1yOXC4XgUCgTXo3sns3PT0dk8mEoijs2rULRVFITU0lPT0dt9st6oT9+/fHarXy8ccf4/f7ycrKol+/fpSVlbFr1y4MBgMXXXQRoVCI119/HQinTLOzs/ly+WbilLDXb9eeI8jRZ/L+p2vxeP3oPQeQAZ2tO5J04tGZ4q1DPrwMkJFictClDqZ/r0688d4CUpUydCicf/40Hnjgbj7//HMWLVqEJEncfvvt2O12YWI/Y0bYGWnZsnDUPGHCBAKBAFu3bgXC3cyhUCgqvWs0Gtm3L7w7NSEhgbi4OAKBAC5X2FxCbTpq3TCmdfBqaJzaaH+hZyiRLkdGo7HdD+NAIBBlYh+Z3q2urkaWZWJiYkT3blVVFXa7Hb1eT1FREZIksXbtWoLBIKmpqRQUFHDw4EFRO500aRKhUIgPP/wQgHHjxpGamsqiRYuoqqoiPj6emTNnAjBlSCJ6gqSmZfD67D/y7rO3EgiGUAJ2Au46QEKK63zC50XxNRA6vBSUEJIlE13acEpLunDBuGJSlO3okPGRQHH/iWzdulUYSVxxxRV06dKFV199VWy36d+/Px9//DGKotCzZ0+6desm6qQFBQXk5OSI0aHU1FQyMjKoq6sTM6WZmZkoiiLSu5GZgVAopO0r1Tj5aKYNHUYT0zOQyG0wHU3vRprYt7S0iB2lanrX5/OJiKpLly6YzWYqKio4dOgQkiQxePBgZFkWRu79+/cnMzOTxYsX09jYSEJCAhMmTMDtdouO3osuuojY2Fg8Hg9fL/0cgEpnCq9+sBKDQU9qYhyy8wAAkjXriLaBHUX21BKq+QrkAJjT0KWPRJL0XDJ1AIvmvYYeP4o+FnPGYIryE3nyySeRZZmRI0dy7rnn8sorr2C328nIyODiiy/m008/xeFwkJSUxMSJE1m9ejUejwebzUZpaWnU6FC3bt3wer3U19cD4fSuwWCIajpS98RGRqVHuhDS0PhJ0EwbOoz2V3oGEgwGRVRzNHOG9tK7wWCQmpoaILxPUx3PUE3Z4+PjycnJIRgMCpefHj16kJSUxIYNG2hoaMBqtTJq1CgcDodoOpo2bRpms5nPPvsMh8NBdnY2AwcN49//XcyFV/6K5uZmQlho8Cbw/FuLAXjpb9cjecKp3++zDfw+ZHclcu2Sb5eIZ6DPGENJcSee//PPWTL/Tfbt20dqaiofvPsaC9+8j1f/8zxOp5OuXbty44038uGHH7J//34sFgvXX38969evZ9++fRgMBi688EIOHjwodraOGDECu90uFqR3794dg8EQtbLOZrMhy7LIDETO9QaDQbHnV4tKNU4quuO8nYVoNdMzjI5ENaFQKOpDPLJLtLa2llAohNlsJjU1FQjbCKrjMd26dUOSJLZt24bL5SImJoaSkhLcbjcrVqwAwgu/LRYLH3/8MT6fj/z8fPr374/T6RT1xosvvpgH/zGX+Ys3kaFsQw84pGyQJAKBEM++9iU2q4zsbwEkJGvWcZ8T2bHvWz9fhZjkLvjiS5EkPTdcOoqln7/Lxo0biY2N5fHHHyctLY2//vWvVFRUkJSUxK9//Ws2bNgg3tuVV16Jy+Vi+fLlwHfzs+qC7/79+xMXFycuNLKzs0lNTaWmpgafz4fBYCArK/xe1KYjg8Eg/Hcjf36aQYPGyed4Is2zMzLVxPQMo6PpXdV7N9KcweVyCeOGrKwsJEkiFAqxe3d4QXZOTg7x8fE4nU62bdsGhMXDYDCwaNEifD4f6enp9OnTh5qaGuGGdMEFF6DT6fj444/xeDzk5+dTtt/LynW7sVKHHj8hjHQp6kdtg5P6Jid///dnDCoIR9eYU5D0x97NqigKctNmFHu4hivFduL8n92MyWzi3U9W8/hjjxFylWM0GvnLX/5CYWEh//73vykrK8NsNnPvvffS0tIi0tLnnnsu+fn5oo7at29funfvzmeffYYsy+Tl5dGtWze2bt2K3+8nJiaGgoIC3G636N7NysrCYDDg8/nEVp7IzIBq0CBJUrtLCDQ0flK00ZgOc5YG5Gcmkd27R4pqvF6vENxI711ZlqM2l6iRUkVFBR6PR5iyA2zYsIFQKERGRgadOnWivr6eTZs2AWHjep1Ox6effoosy/Tu3ZuCggIcDgcLFiwA4JJLLuGJlz/H7vSSbAzXEO+8/SbmvfJrUpPjxbGuWh3e3iJZMo75XChyELlu+XdCmtATXepQPvh8A18u30J8aB8hVzmSpOMPf/gDAwcOZO7cuXz11VdIksQvf/lLEhISePnllwmFQpSUlDB27FjmzJmDx+MhIyOD8ePH8/XXX+N0OomNjWXo0KFUVlbS0NAQtSBdTfcmJCSI9K5ar46JiRFjL7IsCzHVolINjdML7dL3DKF1ere9Af8jGQNAOJWrdplmZITFy+v1ipnSgoICDAYDtbW1lJeXI0kSAwcORJIklixZgqIoFBUVkZ+fT3l5OWVlZUiSxHnnnQfA/PnzhRn8wIED+cVl9fz3gy9QmlvQ6w28saAcJ4uxOz3fHXAw7FHbEUP7qHPhbyZUtxICdkCHPnXwd53AioLRuwcjNSjAAw88wLhx41iyZIlYVH7ttdfSq1cv/vnPf+J0OsnJyeGKK67giy++oLa2FqvVyvTp09m9e7eok44aNQqv1yuatAoLC4mLi6OyslKYM6jpXYfDQSgUQq/XC6cj0AwaNE5BNAekDqOJ6RlC5Gq1IzWtOJ3ONsYAEE4tRm4uUT/I9+3bhyzL2Gw24dKj1gILCwtJSkqivLycvXv3IkkSo0ePBhAWgQMHDiQzMxO32y08bGfMmIEkSdxzw7lg38b774MuJpNte+vZ9fynBEMyRoOOYEhB+VZM0X+Xij4aiqKgOPciN24AJQR6C1k9plDnDDdRjRhYSFFKM5/P/xoAu66A0aPHsW7dOp5//nkAzj//fCZNmsRLL71EdXU18fHxzJo1i7KyMrZt24YkSVxwwQV4vV42btwo3mdCQgJr164VYzDZ2dnY7XaRNs/JyUGv1+P3+4WRfUJCgqhpdySroKHxk6N583aYs/Rtn1mEQqHv3SoSaQwQWaODcNNR5OYSCK8iU5uO1JnS/fv309zcjMlkEuvEFi9eDEC/fv1ISUlh37597Ny5E51OJ3Z7fvXVV7hcLrKzsxk8eDCBYPh41TGaSedMJj7WQo/C7G/fjxzuZlXC7wkl9L3nQAk4kQ8vR25YG54htWZRPPwaHvvjbSTGWynolEZJtoPP538UPt6h5/L03x7gvQ8X8o9/PIEsy4wePZrLL7+cDz74gG3btmEwGJg1axbNzc3ifY4fP560tDSWL1+Ooih06dKFwsJCduzYgdfrxWw20717d4LBIFVVVUDYezc2NjZqmYDVao1auq4ZNGickmijMR1G+6s9zYn8INbr9e02rUTOlEZuI4Fw05H6PbXpSFEU9uzZI74WHx9PMBgUddHevXtjNpvZvXs3NTU1GI1Ghg8fDsDnn4fnRQcPHkxqaiqyLIuo9LzzzuO5N7+i27j7uO2+J2lubiYhIYH777yGsgV/Ys7zdzBlTAnyt3+LUkx4mbfi3H/k9y8HkZu3EKr6DMVTCUjokvqRW3IBf7jnUsYMKea2q8dTv38VH304B4B7772Xf/3jIerrapn/8X8JhYLkdy7ixhtvZOnSpaxYsQJJkrjyyiuJi4vjo48+QlEUSkpK6Nu3L0uXLsXr9ZKUlMTgwYOpqqqivr4eSZLo1auXGIMJhUJYLBbS09OB79K7Op1OzJRC9CiMZhuocSohIx3X7WxEE9PTnMiZ0iN9EEfOlEZ+iKtORxBuOlKNG2pqanA6nej1etF0tGPHDjweD7GxsXTr1g1ZloWNXmlpKbGxsRw4cEBEpRMnTgRg3bp11NXVERcXx8iRI5m/uAxFgfXrwqvZxowZIy4ADAY9N1w2BoM+/Gup+9aHV3FXogRdUe9JCTgJNW4iVPExcvOWbx2N0tFnn4suoZh//ekaRpQWMe/LDbzz5ovEUosC/Obe+5g+fTqVlZXM//ANDDoFh8/ERZdeTVlZGR99FI5cL7jgArp3787//vc/fD4fOTk5TJw4kbVr19LQ0IDJZGL06NG43W727t0LhOvKNpuNhoYGXC4XkiSRm5uLTqfD7/dHZQbU6FMbhdE4lZGP83Y2otVMT2NafxC3lx5sbQwQ2djSXtNRMBgUTTSdO3fGZDLh9XqF32zfvn3R6/Vs3bqVhoYGLBYLgwYNAhBp29LSUlJSUoDvItXx48djNpu5/ZoJvPTOUpp2b8HlD38d4MV3lrJ5+yEuv2AIwVD4z1EyJYEpGfyNhKoWhLt6FT9K0AeB5u/epD4GXXI/pJg8IUZX3PEcQ/t3ZtPKD7HQDJKOmZdcx4zpF1JTU8Of/vQn3G4XWdm53D/rVuLMMs8//xYQnpMdOXIk7777roieZ8yYwb59+0R9eMSIEZjN5qg6aU5ODm63W3gaZ2VlYTab26R3I5cJRC4i0EZhNDROX7S/3tOYjnwQOxwOFEXBYDBEzZSGQiHRdJSeni5EtqKigkAggMViIScnB4CtW7cSDAZJTk6mc+fOyLLM11+Hm3gGDx6MxWKhtraWrVu3IkkSEyZMAMIRrtrVO2nSJOZ+vp7fPPoOit9OuhJeIt6vXz+qDzfzp6fDEaGa7lTRpw4hVP81+JtR3IeividZMpHiCzHZcgm2Kqv6vC7KVr6PBRcyOn71q99y8cyp1NfX86c//YmmpiZyc3N56KGHcDjCq9+CwSAlJSVceOGFLFiwgIqKCkwmEzNnzsRut7Nu3TogXB/OysqirKwMn8+HxWKhe/fuyLIsxmBsNpuoPx8pvduRWreGxslEViRk5dh+L4/1/mcKWpr3NKWjTUdq56i6jUSlrq5OOB0lJSUBYcOH8vJyIOy/q9PpcLlc7Nq1CwiLiCRJYsWYxWKhf//+AKJBp1evXqJGqFoJ9u3bF70xhrv/7y0CgRBmwjXakCGRoTP/TNXhZnoUZqPX6xhRWoTZFL4wMBp0SKYE9FnnoEsdhi6pP1NmzuLam+/HkHs+8Z0nkZBeQE5mCqMHdxPvrW+hjTSlDBMuYmLj+P2Df+LimVM5fPgwf/zjH6mvryczM5Pf//73+P1+nn/+ebxeLwUFBfz85z9n1apVbNmyRXTuWq1W0XDUuXNnevTowcGDB2lsbESn04k6aVVVlRiDifQ0PlJ6V533NRgM2iiMximJlubtOFpkehrSuumovQ/i1ttIIpuOfD4fjY2NAGRmZgqRPXDgQNQoDEBZWRmyLJORkUFmZmZUVDpo0CDMZjMOh0OMzIwbNw4Ip5fVnaYTJ04kxmoiNTmeugYHucngaIAGlxmn28WS1TuZ9/Jd+PxBynZW8NhvL+WOP7xJIPhtulfSIcWFvXm/3hEk9bATDHHExpipa3TicPmwWkzo9TpSLC4c5evR4yMnJ5fHH3+M/Px8Dh8+zP/7f/9PCOmDDz6IJEk8//zzwit41qxZ7Ny5U1gHTpo0iby8PBYsWIDP5yMlJYUhQ4bQ2Ngo1qp169aN+Ph4GhsbxfnOzc1Fr9dHmTO0Tu9qTUcapwPH01B0tjYgaWJ6GtKRpiOv19tmG4lKbW0tiqIQFxcn5k3dbrdoRiooKECSJBwOh6if9u3bF0mS2L17N42NjZjNZgYMGADAqlWrCAaD5Ofn07VruGlox44dtLS0EBsbS79+/TAYDCx8414O17fw5z/ej6MBRgzpj1tJ5JLzBqHT6fjnK1/w3BtfHfF9S4DT7cPpDkd0dY1O8b0xQ7ozc2Qir77yMl5FobS0lD/96U/YbDZRI62vrycrK4sHH3wQk8nEs88+S319PcnJydx4443U1taKzuMhQ4bQp08flixZQktLC1arldGjR+P3+4WVYnZ2NpmZmXg8HrEcICMjQ6TT7Xa7MGeI/BlErsfT0rsapzLHE2lqkanGaUFHjOwVRRFNR62djlwul/ie2nQEsH9/ePwkJSWFhIQEIFwrVRSFrKws0tLSUBRF+O0OGDAAs9lMKBQSkdzIkSPF861eHe7WHTRokHj9hHgrCfFWMb/661tmUFRUJB6zaVt0TdRsMpCXnUyz3U19oxOTyYDPHxTfz89OweHykpVqpXbXl3y8JvyaM2bM4O6778ZgMFBeXs4jjzxCc3OzEFKr1cq//vUvampqSEhI4JZbbsHr9TJ37lxkWaZHjx6MHj2a9evXU1VVhV6vZ8yYMZhMJtavX08oFMJms1FYWEgoFKKiokJcnKiNVx6PR3jvJiYmRv2ctKYjjdMFrWbacbS/5NMM9YP4aE5HLpdLNLyoHrsQFlk1gkpKShJpR4fDIZqR1FGYyKi0T58+AJSXl1NTU4PBYGDgwIFAWHBbWlqIi4sT9VNFUfjmm7Cv7pAhQ6KOLRAIiM7WtLQ07E4Pz772JZ1zU/nr/Rdz/b0vcbCyAX8ghM8f5A93Tic9xcZHCzfQr2c+9//1PZITY/m/X11E3x557Nm9kwcffJA1u2sxmUzcc889XHDBBQDs3r2bv/zlL7hcLvLz83nggQeIiYnh+eefp6Kigri4OG655RYMBgPvvPMOfr+fvLw8pkyZwp49e9ixYwcAw4cPJzk5ma1bt+J2uzGZTPTq1QtJkqiqqsLv92M0GsnJyRHLAdT0bmxsbFT2IBQKaU5HGhpnIJqYnkbIsvy9TUeyLON0htOf8fHxURGRw+HA6/Wi0+lETRQQ9b/09HSR9lWjUnWFGCDqoiUlJSKVqUagQ4YMEVFWRUUFjY2NQnQi0ev1whgiFArx6pxlYn/p20/fzBUXDmPrrko+WLCO9OR4SrrnkmCL4dc3Tgkfw8jw8wUCAV555RVee+01QqEQeXl5/PnPf6awsBCALVu28Nhjj+H1eikqKuK+++7DaDTy4osvsn//fqxWKzfffDNxcXG8+eabuFwu0tLSmDlzJnV1deJioE+fPuTn57N///4oYwaz2UxjY6MQzdzcXAwGQ9RWHoPBEOW9qzUdaZxuhL69HetjzkY0MT2NONZRGNWEAcIf5Or8Y0pKinh8S0uLWA/WuXNnIBzZqmnf3r17A9DQ0CDMCdSotKWlRURvgwcPFq+1efNmILw0vHVNV6fTERcXh8PhwOFwkGSLRQKSE+P4+/PzWbflAJIEigKZ6Ykk2Nr68u7du5f/+7//E13GEydO5L777hNR+LJly5g9ezahUIjevXvz61//Gp1OxwsvvMCePXswm83ceOONpKam8vbbb4tZ0osvvhi3283SpUtF527v3r2pq6sThv/du3cnISHhiHVSt9vd7lYe0JqONE4/lONoQFK0BiSNU5nW6cH2CAaDRxyFaW5uxu/3o9frRV0PvquVZmZmCkHYvn276OBNS0sDEMuvCwoKSE5OFl+TZZnOnTtHRboffRpuIpKNie0epyqmH3++msdf+4YYq4m5/76D/3v6YwBSkuJxOD0kxFt5+d2l9CjMZtiAQpqbm3n55ZeZM2eOqFv+5je/EXOtiqLw0Ucf8d///heAoUOHcuuttwLw4osvCiG9+eabycnJ4f3336e2tpaYmBguvvhidDodixcvJhgMkpGRwdChQ3E6nWzfHl7jlpubS2ZmJsFgkEOHDqEoCvHx8eJ8BoNB0dFrs9mi0vBa05HG6YjWgNRxNDE9DWhthH6k9KDaWNR6FEaWZdH0k5qaKh7f0tJCc3MzkiTRqVN49MTn8wlfXjVF6/P52LJlCxB2N1JRBTbya4qi0FBXhUEH32xtpPpwMy+/u4zhAwsZN6wHAMXFxVRXV/PFZ3NAycHt8ePxBnjyoctZs2k/C5dv5Y25X7Nk9U6WrN6JnhD3XVvC++++LVLYo0aN4je/+Y1IQYdCIV599VXhuHTeeedx5ZVXEggEeOmll9i9e7eISPPy8pgzZw6HDh3CZDLxs5/9jPj4eL744gvcbjc2m41Ro0YRCoXYsmULsiyTlJRE165dURRFrFUzmUyiThrpcmQymaIMMkBrOtI4PdEakDqO9ld9GhAKhcQozJGajvx+P16vFyCqTgdh28BgMIjRaBRRJSBSl5mZmSIlvHv3bkKhEElJSWRmZgKwbds2AoEASUlJ5OfnA2HTh4qKCiRJEg1KEE4HG3QyCnDDVVP56+xPmfv5ev7z/jK2LPgzFrOR2267jTVr1lBfW45NksnvNozzrn+C4oIsPnj+DqwWIx8t3ICk+JEdB4ilmldeDtdmi4qKuOOOO6IE3Ol08uSTTwrBv/rqq5k6dSper5d///vf7N+/Xwhply5d+OSTT9i3bx8Gg4GLLrqItLQ0lixZQlNTE2azmXHjxmE0Gtm4cSM+nw+r1UrPnj3R6XQcPnwYp9OJJEnk5eWJCxOHwyH8j9tL76pZBbPZrEWlGqcNWmTacTQxPcU51lEYq9UaJbihUIj6+nog3D2rPt5utwvjBlUgQ6EQO3fuBML1TjXiUrfFqA5IgPhaUVFRlHiraeMunTtz/oT+VNQ0h/+dm4bJGBae7Oxs7rvvPh566CFilQoqdi3HoMSye8dhPpm3gJqqgwzJq2Xr1m1AuMaoN8Vz/29+ybnnnktlTTMP/PU9Rg7qRv/iNP72t79RU1OD2WwWQut2u3n++ecpLy/HarVy0003kZ+fz4IFC9i+fTs6nY4LL7yQ3Nxcvv76a6qrq9Hr9YwbN47Y2Fh27tyJ3W5Hr9dTUlKC0WiM6nrOysoS3dCtXY4iMwfaejUNjbMDTUxPcdSmlaONwvj9fvGB3ToqbWpqIhQKYTKZhFcsIGwDMzIyRFR64MABvF4vMTExIu1bW1vL4cOH0ev1ohkJEFFg3759o14vUmwAbrpiLOeOKSEzLWylt2bTPl5+dxkXTSllxOhJrFj6BTFyNWpS9O9//VPU8xmsyQQtXXjo/puYNCr8+n//92d8/OVGPl+4hD55bjxuN6mpqfzmN7+hU6dO2O12nn/+eaqqqoiNjRU10oULF7J582YkSeK8886joKCAjRs3sn//fiRJYtSoUaSkpIgRIAinumNiYvD7/cJ3NykpSVgwyrIcZWIf2fQF4a5j9eenNR1pnG5oDkgdRxPTUxhFUQgEAkA4Km0vPRgZlcbExERFRbIsR0Wl6uPdbrf4el5enngeNSrt1q2biKBU0SwqKhJC4XQ6hRj37Nkz6nhUYVGFW5IkOuemiu//8akP2bqrkjWb9rH+kz+ybPlk7rzvL6AEscUY6ZqfQteuXRkwYAD9+/cXohxJ/175bPxmCTk2Bx53+HjvueceEhMTaWhoYPbs2dTX1xMfH8/NN99MVlYWixcvZsOGDQBMmTKFHj16sHPnTrENZ8iQIeTk5FBXVyfmawsLC0lOTkaWZQ4dOiQWqKvpb3UMRpblNi5H6vn/vp+fhsapzE8pps8++yx///vfqampoW/fvjz99NNRUwKtaW5u5ne/+x0ffPABjY2NdOrUiSeffJKpU6ce1+ufKJqYnsJERjVHalrxer2iVqfOiKo0NjYSCoUwGo3C1Qi+q5WmpKSIxxw+fJimpib0er2Y1QwGg8I6LzIq3b59u5hBjYx2oa2YtmbiiJ5s3VXJhOFhEa5uMdAodQMJrrtqMndcO+mo56SxsZHKnWEhhbAwXnnllRgMBqqrq5k9ezZ2u53k5GRuueUWUlJSWLZsmZgbPeecc+jduzcHDhwQc7N9+vShoKAAh8MhOnezs7PJyclBURSqqqrwer3o9Xry8vLEhYbH4xF16tYuR4AQUq3pSON0RVbCt2N9zLHyzjvvcM899zB79myGDBnCk08+yeTJk9m5c2fUpICK3+9n0qRJpKen8/7775OTk8PBgweP+LnzU6D9hZ+iREalRxqlUBRFdLfGxsZ2KCr1er2is1dN5UK48QjCDkhqJ7Ca9o2NjRUzqICYNy0uLo46no8WbuCzxZuI1XHEjuO7Z03m5ivHYbWEU54l3XMxGw0owLCBRe0+RmX16tW88MILOJ1OzGYzN910E8OHDxfH9NJLL+HxeMjMzOTmm2/GZrOxfPlyYYE4YcKE8Mq36mph1t+tWzd69+4tOpbVzt3CwkIkSYoyZsjLyxOp9sgxmPj4+DYpXG29msaZwE8Vmf7jH//gF7/4Bddddx0As2fPZt68ebz88svcf//9be7/8ssv09jYyMqVK8XfZORn1MlA64Y4RYmMao4kTF6vl2AwiCRJUbaBEB2VRl6tVVZWoigKCQkJIi3p9Xo5dCjsixvplasaMhQXF0dFXapjUutf3pfeWYrdFe5aVVPP7aEKKUCPwmySEmPxB4I89u/P2r2/0+nkueee44knnsDpdNKlSxceeeQRIaQbN25k9uzZeDweOnXqxO233y6EVBXN8ePHM3DgQBoaGli6dCmyLNOpUydKS0uRZZktW7a06dx1u91RxgzqOY50OTIajW3OfUe2+mhonA6cyAo2u90edVPNTFrj9/tZt24dEydOFF/T6XRMnDhR/P225qOPPmLYsGHcdtttZGRk0Lt3bx555BHRNX8y0MT0FKQjtbbWZvaRYifLsnA1ioxKg8EgVVVVwHe1UghHdbIsk5KSIkZnAoGAiFYjI1CPxyOclFqL6S8uGyOERY3mOkJmWkLUfyPf48qVK/nVr37FkiVLkCSJCy+8kP/7v/8Ti8uXLFnCa6+9JhZ733rrrcTExLQR0tLSUpqbm1m0aBHBYJDMzEyGDRsGhNPWDocDo9FInz59MBqNBINBYWAfacwAYYcov9/f7hgMRI8yaU1HGmcreXl5JCQkiNujjz7a7v3q6+sJhUJRizcgfAGrXsy2Zt++fbz//vuEQiE+/fRTHnzwQR5//HH+9Kc/tXv/nwItzXsK0tGoNBQKIUlSG4OA5uZmMVcaWSutra0lFAphtVqFOCiKItK2kVHpwYMHCQQCxMfHk52dLb6uinFSUlKbzuHzJ/Qj2Xop//znP9m6dSuyLEeJfHllA5f98jksZiPvPnsrqUnhx7/55E1s3V1Jv5754r41NTW8/vrrrFu3DoCcnBxuuOEGevQIGz+EQiHmzp3L8uXLARgxYgQzZ85EkiSWLFnCmjVrgPB+1dLSUpxOJ4sWLcLv95OSksLo0aPR6/Xs3btXeO727t0bq9V6RGMG9WejXsTYbLY2tdCOjDJpaJwunIid4KFDh6Ka8iKNZE4UWZZJT0/n3//+N3q9noEDB1JZWcnf//53Hn744R/sdY4FTUxPMTpiZt+6Vhr5ga0oiohKU1JSxPdUgQCixKGurg6Hw4HBYBDzpvBdXVStHaqo9dbWV5EqpaWlxMbGUldXx6ZNm8QmGYAV63ZTVdsMwLqyg0weHW5qio0xM7hveA+q0+lkzpw5zJ8/X+wCnTFjBtOnTxfC5fF4ePXVV9m5c6cYcxk/fjwAixYtEgI8YcIEBg4ciMfjYdGiRXg8HhISEoQpQ1VVlUhvFxcXiwuPIxkzqOldCH8wtB6DgWj/3SONMmlonC6ciGmDzWZr0+HeHqorm5rxUqmtrRWd863JysrCaDRGBRs9evSgpqYGv99/UjJCmpieYkRGpUeKao5WK3U4HMKDN7JW2tTUhNvtRq/XR/2CqmMg+fn54sM/MlpVO3tVVDFVPXtbYzKZGDNmDJ9++inz58+PMnqYMrYPi1Zux2IxMnpwt6jHNTY28sUXX/D5558LA4SSkhKuvvrqqJT04cOHeemllzh8+DAmk4mrrrqKkpISFEXhiy++YOPGjQBMmjSJ/v374/P5WLRoEQ6Hg9jYWMaPH4/ZbKahoUEY5Xfq1ElcHNjtdtG4lZ2dLYwZICz0aud0QkJCmwudjowyaWicToS7eY/VTvDYXsNkMjFw4EC+/PJLpk+fHn4OWebLL7/k9ttvb/cxI0aM4K233orKfu3atYusrKyTVlrRxPQU4oeISlUhSEpKirpqU6PSzMxMEeEFg0ExJtO1a1dx3/r6epxOJ0ajMUrIICzKgPDEbY9Jkybx2WefsWnTJh577DFuu+02YmJiSLTF8MJfrhP38/v9bN26lRUrVvD111+L5oHc3Fyuuuoq+vXrF/W8W7du5Y033sDr9ZKQkMCsWbPIy8tDlmU+/fRTMcZz7rnn0qdPHwKBAF999RXNzc1YrVYmTJhATEwMTqdT3DcjI0PUfv1+vzhPycnJURcjgUBAnPfWLkcqHTHY0NA4nfip7ATvuecerrnmGkpLSxk8eDBPPvkkLpdLdPdeffXV5OTkiLrrLbfcwjPPPMOdd97JHXfcwe7du3nkkUf45S9/eRyv/sOgiekpxIl28LrdbjweD5IkRTXMeL1ekfpVG3cgXP8MBoPExMREzXKphgzqjs5IPB4PQJs6bSRZWVnMmjWLN998k3Xr1nHTTTfRv39/evfujdfrxeFwUFlZyZYtW0R9EcKp1ilTplBaWtpmzGfhwoXMnz8fRVHo0qUL1157LTabjWAwyMcff8zu3bvR6XScd9559OjRg2AwyJIlS2hoaMBsNjNhwgTi4+Px+XyUlZURCoVITEyke/fuSJJ0RGMGIMrE3mKxtJvejYxKDQaDFpVqnBGEkAgdY830WO8PcOmll1JXV8dDDz1ETU0N/fr1Y/78+SJjVF5eHhU45OXlsWDBAu6++2769OlDTk4Od955J/fdd98xv/YPhSampwito9L2UBRFpEBbR6WAEMzExMQoEVSbhhITE6NEUB1x6dSpU9SHvxqtRtZQVVSTgsj0Z3tMnDiRoqIinn76aSoqKlizZo1oCookJSWF/v37M378+KjoWMVut/Pmm2+KlOyIESNE/dTv9zN37lwOHDiAXq/nwgsvpLCwkFAoxLJly6itrcVgMDBu3DgSEhIIBoOUlZWJEZhevXqh0+lQFIXq6uooY4bI8+FwOAgGg+h0uiPWgEKhkFYr1dA4AW6//fYjpnUXL17c5mvDhg0TM+SnApqYniJ0JCr1+/2iZtc6KvX5fKLLNDIqlWVZtJdHduUGAgEhspEjLoqiCA/a9sQ0sib4fXTq1Im///3vlJeXs2LFCioqKoiLiyM+Pp6kpCRKSkrIz88/YhS3d+9eXn31VRwOByaTiZkzZzJkyBAgHIX/73//o7q6GqPRyMyZM+nUqROyLLN8+XKqqqrQ6/WMHTuWlJQUFEVh+/btIn2tmtdDOHWtRp65ublR7y0QCIgLGJvNdsSfjVYr1TgT0bbGdBxNTE8BFEURUenRREr9ULdarW2iUnUDTFxcXFQLelNTE36/H6PRGFXnrK6uJhQKERcXJ0zbITxWo0Zo7dl4qRGpmu79PtRdqZFuS99HMBjk888/58svv0SWZTIzM7n22mujmoTeffddGhsbsVqt/OxnPyMrKwtZlvn666+pqKhAp9MxZswY8Zg9e/bQ0NCATqejd+/eIkL3eDziYiM9PT3KkrEj3bsQvmBR50o120CNMwmZ49hnepYa3Z/UIbilS5cybdo0srOzkSSJuXPnfu9jFi9ezIABAzCbzRQWFvLKK6/86Mf5Y9PRqFR1EGkdlYZCIRFZRUalEBZNCDfaRM18flsXbZ3SjLx/e8eivrbb7e7YmztGqqurefLJJ/niiy+QZZnS0lLuuusuIYqHDx/mzTffpLGxkfj4eK644gqysrJQFIU1a9Zw4MABsQFGNcmvqKgQjUWRIzChUIhDhw6hKApxcXFtmqo8Hk9U9+6RUC+EjtaBraFxOqLaCR7r7WzkpF5Gu1wu+vbty/XXX8/MmTO/9/779+/nvPPO4+abb+bNN9/kyy+/5IYbbiArK4vJkyf/BEf8w9PRcYrIqLR19KNuLjGZTFFCGwgERB01sqFGluV2nZAAMet1pPkuNXJTu3p/KEKhEEuWLGH+/PkEAgFiY2P52c9+FtXRe/DgQebMmYPf7yc5OZlLLrkEm82Goih888037N27F0mSGD58OLm5uUC4M3nPnj1A2HdYjbZVA/tAIIDRaIyavYXwOYp0mDqaJaDahaxFpRpnGlqat+Oc1L/+KVOmMGXKlA7ff/bs2XTp0oXHH38cCA/pLl++nCeeeOKIYurz+aI8IVVz8lMFVUglSTriB3YwGBSNP+35wKop3uTk5DYGC2rUFZm+rK+vJxAIYDab20Sy6nO1/rpKly5dWLVqFbt27eK88847lrd6RA4ePMi7774rBL64uJjLL788qtlnx44dzJs3j1AoRG5uLjNnzsRisaAoCuvWrRPWh8OGDRM1YIfDIUZgsrKyomrATU1N2O12YczQWgidTieyLGMwGNqc80gURREpXs2DV+NM40QckM42TqtL6a+//jrKDBlg8uTJ3HXXXUd8zKOPPsof//jHH/nIjo+ObIaB76JSs9ncpqbqdrvx+XzodLo264ciTdojUUUrKyurTVpSjTgj66iRdO/eHQhbhTmdzjZr346FxsZG5s+fz9q1a1EUhZiYGC688EIGDRokzoWiKHz99dfCNrBbt26cf/75GAwGFEVh48aNYg/r0KFD6dKlCxDuOi4rKxNbYIqKiqI250TWSdtb6B3ZdHS0hiI1KpUkSUvxamicxZxWYlpTU9OuGbLdbsfj8bTbIPLAAw9wzz33iH/b7fY2qc2ThVprO1pUKsuyaPZpL0JSI8nWRgJut1ukKVufM7Uu2nrxdmTDzZH2AiYkJJCTk0NlZSWrV69mwoQJR32P7aHa+y1evFicg9LSUi688MIocQ6FQsyfP18s8B44cCDjxo0T4yybNm0SkeegQYMoKCgAECMwfr+f2NhYMQID4fOpGtjHxcW1G4Gr581sNn+vn6gWlWqcyfxU+0zPBE4rMT0eOvKBeDLoaK3U7XajKAoGg6HN/GnkTk1124uKavuXlJQU9Ti/3y+iz9Z10UAgICKto5kyDB8+nPfee4958+ZhtVrFKrTvo7GxkSVLlrB69WqRei8sLOT8889v0+3r8Xj48MMPKS8vR5IkJk2aFFU/LSsrEyJbWlpKt25he0J1BMblcokRmMgU7uHDh/H5fOj1+jZ1UvX8qMfWEV9RdbZUG4fROBP5qfaZngqEQiHKysro1KnTETNzR+O0EtPMzMx2zZBtNtsRxxZOVSKH/I/UuKIoiuiajY2NbfOBrYqi1WptY6JQV1cH0Ga8pa6uTqwVay2Yal1Wp9MddURn6NChVFVVsWLFCt577z3cbjcTJkxoV1Dsdjtbtmxhy5Yt7NixQ7znzMxMzjvvPHr16tXmcbW1tcydO5eWlhaMRiPTp08X6VsIC2lZWRkQjlbV1DOEZ1PVEZiSkpI23rqRTlDtnXfVMrC9Ri8NjbONM7kB6a677qKkpIRZs2YRCoUYM2YMK1euJCYmhk8++YSxY8ce0/OdVp8Ww4YN49NPP4362hdffCH2Up5OdMR6zufziTVrrcUy0uKu9VWUy+XC5XIhSVKbcQ81Ym1vhlSNyMxm81EjLZ1Ox0UXXYTVamXhwoXMmzePefPm0a1bNzIyMpAkicbGRhoaGqipqRECCuGa57hx44SNX2t27tzJvHnzCAaDJCYmMmPGjChT/bKyMjZv3gxA//79o3atVldXC8OJ4uLiqMgyFAqJ8Zj21sdB+GeinoMTqQVraJwpnMmR6fvvv89VV10FwMcff8z+/fvZsWMHr7/+Or/73e9YsWLFMT3fSRVTp9MpxhYgPPqyceNGkpOTyc/P54EHHqCyspLXXnsNgJtvvplnnnmGe++9l+uvv55Fixbx7rvvMm/evJP1Fo6LyOXRHTFpiImJadPc4vF48Pv97VrcqWb3iYmJbZ5frbG2Z1R/LA006uqz+Ph41q9fz8GDB9m1a5ew/YskPz+fkpISSkpKjri6TXUuUu3BunTpwrRp06IuIiKFtF+/fvTs2VN8r7m5Wbx2586d21wsVFdXEwwGMZlMRxz7UbMAFoulw1Gpes7U9LiGxpnEmVwzra+vF58Fn376KRdffDHdunXj+uuv56mnnjrm5zupYrp27VrGjRsn/q02Cl1zzTW88sorVFdXC3MBCH/Azps3j7vvvpunnnqK3NxcXnzxxdNuxlSNSvV6/REFLBAICBP49hqP1Kg0Pj6+TfOLmspsvSat9a7T1qjPozYFdYTRo0czdOhQqqurqampEWn45ORkkpOTycrK+t76g8Ph4OOPPxZRZWSjkcqWLVuihLRXr17iex6Phy1btqAoCmlpaW3qrw6HQzRW5eTktHvOIxu9jlYvbo16zmRZFhtjNDQ0Tn0yMjLYtm0bWVlZzJ8/n+eeew5ArKo8Vk6qmI4dOzYqBdia9tyNxo4dy4YNG37Eo/pxkWVZRDFHi0ojo6TWP1hZloU4tBYqn88nmpJaC6a6j1Ov17fr6BO5mu1YhMFkMh2zZaBKeXk5H3/8MS6XC5PJxOTJk+nRo4f4vqIoUTXS1kKqdu4Gg0Hi4+MpLi6OOu5QKCRGgVJSUo4olB6P54iNXkdDp9MhSZJoKDtZuxQ1NH4MzuQ073XXXccll1xCVlYWkiSJscvVq1dHlY86ymlVMz0TiLSe68g4THsf/g6HA1mWMRqNbb6vNiW19uiF76LZhISEdqMz1fNXdf/pSDfr8RIKhVi+fDmrV68GwlH0hRdeGNWVrCgKmzdvZsuWLUBbIVUUhZ07d+J2uzGZTPTu3bvNOa2trRXp3fbqxCpqrdRqtR5zdGk0GsUSAp1OpzUuaZwxnMkNSH/4wx/o3bs3hw4d4uKLLxafl3q9nvvvv/+Yn0/7q/8J6aihvdfrRVEU9Hp9u5GOKoqJiYltPvjVNG7rURn4zv3pSCKp1+tJSkqioaGB+vr6H01MGxoa+OSTT0RKuE+fPowfPz7qvapzpOr4y4ABA6IiVgivkKurq0OSJHr16tXm4sHj8YiLi/YMKiJfS02pH88YlcFgECv0fD4fiqJoa9g0zgjO5MgU4Gc/+1mbr11zzTXH9VyamP6EqOMwRzNpgO9SvDExMW3EMhgMivGN1qlaWZaFeLRXE1VTw0cTybS0NBoaGqisrGx3v+iJEAqFWL16NStXrkSWZSwWC5MnT44abYGwuK1fv54dO3YA4Rpq67TL4cOHxd7Vbt26tTkX6o5SCJ+no3XnBgIB8XM5nqhSkiRMJhOKohAKhfD7/YRCIYxGo2bmoHFaIwPH2lp3Kkem//znPzt831/+8pfH9NyamP6EdGQcJhAIiPu1NzurRpcWi6VNFOV0OgkGg+j1+nYFUxXpowlLQUEBO3bsYNOmTQwdOvQHi7Bqa2v57LPPxGhO165dmTx5cpsRFdW0XvXaLS0tbSO2LpdLCG1eXl4bJycIp8I9Hg86ne6IHcQqkTXs420gkiQJs9ksfn6hUIhQKCTSvkf7mWtoaPw0PPHEEx26nyRJP42Ydu7cmeuvv55rr7223QXSGm3p6M7LozUewXdi2l4D0dHSv5HPfbRu1eLiYpYtW4bdbqesrIwBAwYc8b4dweFwsGzZMlH3tFgsTJw4kR49erQ5RlmWWbNmDXv37gXC5hCqRaBKIBBgy5YtyLJMYmJilJlD5POoKeSUlJTvvSD4oVyM1AjVYDCI6FSWZfx+vxhjMhgM6PV6JEnSxFXjlEdWjmOf6THe/6dk//79P9pzH5cz91133cUHH3xA165dmTRpEm+//XbUZhaNtnRkHOb7Go+CwWCUAXtrIsW0PdTnPppblF6vZ8iQIUC4q011RTpWPB4Py5cv54UXXhBC2qNHD66//np69uzZRkhCoRArVqyIWqPWWkhVq0CPx4PJZKJnz57tnkt1IbrBYDji9ptI1IucH8qoXqfTYbFYiImJiVpgoAqrx+PB7Xbj9Xrx+/0Eg0ExWqOhcSpxNuwz9fv97Ny585hGAtvjuMV048aNrFmzhh49enDHHXeQlZXF7bffzvr160/ogM5EfqjGo8gUb+vvK4oivt+emKoNMsD3jm+UlJRgs9lwOBzMnTv3mH7J7HY7ixYtYvbs2axcuZJgMEhOTg5XXXUV06ZNazfFHAwGWbJkCeXl5eh0OkaMGNFuxHngwAEaGxuFVWB77yNyljYtLa1DNUtVRE/0j6k1kiSJjmur1YrJZIoS7FAoJFyXVIH1eDyayGqcMsjHeTsdcLvdzJo1i5iYGHr16iU8De644w7+8pe/HPPzndCl+IABA/jnP/9JVVUVDz/8MC+++CKDBg2iX79+vPzyy9qHwLdEboc5WvQTGTm2lwJUt5m0F5V6PB6CwSA6na5dkwc1+oLv33BiMBiYOXMmRqOR8vJyXnzxRTZt2nRElx+n08mGDRv473//y+zZs1m7di2BQID09HQuuOACrrjiCrKzs9t9bCAQ4KuvvqK6uhq9Xs/YsWPbnVdtaGiIajhqzw4Qwk1WgUAAg8FwxAi9NWrtOdLo/4dG9Tu2Wq3ExMSICyKDwRD1O6HOIbcnsj6fTxNZjZ+UMzkyfeCBB9i0aROLFy+OclqbOHEi77zzzjE/3wk1IAUCAebMmcN//vMfvvjiC4YOHcqsWbOoqKjgt7/9LQsXLuStt946kZc4I1DF9GhNKGoXKLSfhg2FQiLF256QqFFpfHx8u4IdKRIdidbS09OZMWMGn376KXa7nQULFrBy5UrS0tIwm82YTCaam5upq6sTtViVvLw8hgwZQpcuXY5aF3S73Xz11Vc0NzdjNBoZO3Zsu7OgXq+X7du3A5CdnX1EO8DIqDQ5ObnDaVu9Xo/BYBCjLcfigHQ8qN3ckT8HRVHEonH1Frl4PLLmHolOp2tz02qxGj8UZ7Kd4Ny5c3nnnXcYOnRo1N9Mr169RN/GsXBcYrp+/Xr+85//8N///hedTsfVV1/NE088ETW+MGPGDAYNGnQ8T39G0dHGIzUqVaOV1rhcLhRFwWQytTsLqUatR4rYIn9ZOhrRdO7cmRtvvJGNGzeyevVqHA6HeJ3WZGRk0KNHjzYG80fCbrfz5Zdf4na7sVgsjB07tt36pizLbN26VTgcFRYWHvE5fT4fXq8XSZKOeYWSxWLB6XRit9tFzfOnRG1Ian0B0J7IRkal7Yms+jxqfV4TWA2NttTV1bV78a4uCTlWjktMBw0axKRJk3juueeYPn16u3XALl26cNlllx3P059RqFHp0RqP4Lv1Z0f6EFdFLC4urt0ftBq1HmnsRR37UBTlmKIvg8FAaWkpffv25eDBg6Km5/P5sNlspKWlkZKSckw2evX19SxevBifz0d8fDzjx48/4nHv27cPh8OBwWA4YsORijpHGxcXd8zzonFxcaLrtqmpCZvN1u6c70/N0US2dQSr/r867xqZjVBFVY2IT/b70jg9OJNNG0pLS5k3bx533HEH8F3A8eKLLx7XJrLjEtN9+/Z9rw9rbGws//nPf47n6c8YIhuPjvbhHgwGRbdve2KqKIowamgv8oz8fnv1UvhuDlIVwmNNZRqNxqNGhR1l3759rF69GlmWSU5OZty4cUe8gGhoaBDm9927dz9qF3JkA9bxODdJkkRycjItLS14PB7sdjt+v5/Y2NgTmj/9sWgvVQwIIW0vXRzZhBb5eE1cNY7EmSymjzzyCFOmTGHbtm0Eg0Geeuoptm3bxsqVK1myZMkxP99xNSAdj6H52UhkOu5odcrIFG979/P5fASDQSRJalcEA4GA+JA8mkiqYqQK70+J6rP79f9n77zj26jv//+6O+0tWbK8984ekMUmEFoo8OtglAKlFFpa6ODbAR1A6QBaSumgQPkWKF+gtKWMltIECCSskL3jON7bsq29193vD/H5RLJlW5LtOOOej4ceTqTTLUn3uvfesgU8z6OkpARr166dUEgjkQhtzFBcXDxuAs5YkqfsTOTqngqGYaDX6+n7Q6EQ7HY7RkZG4PF4EAqF0sYtjydIFyeZTEbLc5RKJeRyeUrMntzohcNhmuBEamPFxCYRAqkzzfZxInDGGWdgz549iMViWLBgAd544w3k5+djy5YtWLZsWdbrm9EOSNdffz16e3vx9ttvz+RqT1gySTwCjrp4J7K8JptrChwVY7lcPqlo5+Xlwel0YnR0FKWlpZkdxAwQjUaxdetWmo3b1NSExYsXT3hOBEHA4cOHEY1GoVKpMmprmNxbdzot/BiGgUajgUwmo7WgJPmLfA4kYYk8iAv/eG3GQFy8xDuS7AYm4kks1+R66OQGEyKnJkIO4iicIGIKJDq+PfHEEzOyrhkV06KiohkrfD/RycbFS5abyEojF/GJXLhTxVsJZrMZbW1tdHj4scDpdOK9996D1+sFwzA4/fTTp3QX9/b20nrSpqamjMSRNA2ZqRFoMpmM9tslrnFiuZHHRI1KxmbWkr8T/TvdcrMJsV7TiSv5LibHXIkrWGyJeOrBCyx4IbtrerbLzyXxeBwvv/wyrRZoamrCZZddllOP7mmLaXIrtvvuu2+6qztpIBeiqWpLiRCOLegnCIJAS0+mK6bEVTo6OopgMDhpDHIm6OzsxNatWxGPx6FSqbBmzZpJx6ABiUQr0vKrpqZm0j7CyWTSFCMXGIaBUqmk54pYb+QmiNR8JrtHJypjyWabYwU5OTOXiBv5/0wcIxFXmUxGj4fMtSXCmtwSURRWkROdgwcP4tJLL8XQ0BDt//3AAw/AYrHg3//+N+bPn5/V+nIW0z//+c/4zW9+QxuS19bW4lvf+ha+/OUv57rKk4rkLN5MXLyTxQ7j8TgYhplwmUyFRKfTwWw2Y3R0FK2trVi4cOGUx5ELoVAI27dvpx1FCgsLsWbNminHm8XjcTQ3N0MQBJjN5rQN7CciebD5bMKyLORyedpjSVfCkpxhm8m/yXrGZuNOBBFa4pJN/ptLSUxyYhIR1lgslpLURLKeOY6DVCoVS29OYk623rzJfPnLX8a8efOwY8cOWkrndDrxxS9+ETfffDM+/PDDrNaXk5jeddddeOihh3DbbbfRFOItW7bg29/+Nnp6enDvvffmstqTBnIxBCZ38SbHqCYSSmKVksHd6SDryMQqq6+vp2I6b968GR0RJggCenp6sH37doTDYTAMg/nz52P+/PkZWVDt7e0IBAKQSqWoq6vL6gJNjp3ETueCiTJsM2WimtLkBxE1YgmTcEK6mwhicUql0pS/2VizLMtS1zkR1rEWa/J2RFE9uUg0bchWTGdpZ2aYPXv2pAgpABiNRvz85z/PqUdCTmL66KOP4oknnsDVV19Nn7v00kuxcOFC3Hbbbae8mGbq4iUxN2JNpCOT5vTJVvBUlJWVYdeuXQgGg2hubs7alTERLpcLO3fuxNDQEIBEf+BVq1alHVKejpGREQwMDABITK7JNvZJlg+Hw4hGoyfkcO6Jakongohqcrwz+a8gCCkj/QjEopRKpZDJZBmLIBFWqVQ6TljJdkRr9eTiZI6Z1tXVwWazYd68eSnPDw8P51QGmJOYRqNRLF++fNzzy5Ytm3U324lAcuLGZBcUIqaTuT+nyvRNJtML4pIlS/Dhhx9i3759UCgU06of9fl8OHToENra2iAIAliWxbx587KyesPhMFpaWgAkWhFmMullLHK5HEqlEsFgEL29vaioqDjpk+FIzDTdjUOyxUpivNFoNEWAkycCkYQr8pjsuzTWFTw2dkzmuJLh6KKonricbHWmpBYdAO677z584xvfwD333IOVK1cCAD766CPce++9eOCBB7Jed05ieu211+LRRx/FQw89lPL8n/70J1xzzTW5rPKkIdnFO5mYkE5EwMRimskyuVBRUQGbzYb29nZs3boVgUAACxYsyPiiR3rgNjc3o7e3l8b6SktLsXTp0oyThsi6yPgjjUaTdlpMJjAMg+LiYnR0dCAYDGJgYADFxcWn7IWcTKwhzfUJpIk+eUQiEQiCQOOgBNK2cmx96mTbIVYxEVbi6ifu5VP1sziROdlipmNnPQuCgCuuuCKl/hoAPvWpT2U99GJaCUhvvPEGVfStW7eip6cH1113HW6//Xa63FjBPdnJtFEDiX0BE8c6iSVBuhdNxNgvwlQwDIMVK1ZAoVDg4MGD2L9/PwYGBlBVVYWSkpIJZ6k6nU709vait7c3pfFDQUEB5s2bN2ED+skYHBykZTCNjY3TsiblcjlKS0vR3d0Nt9uNUCgEo9EIvV6fU6r7yQixKEmMntz8RSIRWv6TnGTk9XrBcRzkcjmddDORKCZbq0SsiVAT17soqiJzyTvvvDNr687pCnPgwAEsXboUAGh3fbPZDLPZTAdBA5m5HU82MnXxEiuAxJemWmaydRGhGBsbmwyGYbB48WJotVrs2LEDdrsddrsd27dvh16vT2m47/F4aK0rgeM4lJWVobGxMeum8gSfz4e2tjYAiV7OE5X+ZINGo0FJSQkGBwcRDocxNDSEoaEhqNVq2gmIWFtTNYHPJiEoXfu+sZm6yY/kbSRvP10NanJJDPmbnLU7neYKyWUxKpUqpTNScm1tIBBAIBCgWeVkPmu6bTIMQ2OrxL0siuqJyckWMz377LNnbd05ielsqvuJTiYuXuCoUE6WaEPEcapkHGK15pLJWl1djcLCQnR2dqKvrw+jo6O0YXy67RQUFKCsrAyFhYXTSvKJx+M4dOgQeJ6H0WhESUlJzusai16vh1qthsvlgsfjQTAYTOlglExykwQyCCC5ROVEITmjlggZGZU3UZvKidZD3LYajYaGGkjjCp7nEQwGEQwGwbIsrcFN911IdvGmE1WyX6KoHr+cbG7edAQCAfT09Iy7fmZbOij6vmaQ5PmTMyGmyZbpZBAxJZm/2UImzc+bNw/BYBAOhyOlaF+j0UCv10Mul8/Yha+trQ2BQAAymQyNjY0zfkGVSCTUW0LclWOtrXSW4kRMZCVONE90og5IZF1jjzdZwCeyiJPLYibL3B07X5acD+KqJY9MPk9iiSoUCiqEoVCI9ikmNykSiQRqtRoKhWKcp2WsqJI4bTgcphnCM1miJTJz8AKD+EkqpiMjI7jhhhvw3//+N+3rxyxmKjIeIqRTtYQjF0RgcqHMpFYVODp2zev1guf5acUdlUoliouLc35/JgwPD2NwcBAA0NjYOGMtACdCJpONyxBON7aMQD6/Y9nibzqQMpWxiUXkkdytKdk6T+7upFQqoVarJ/2ukdi9XC6HTqejTfLJIAa32w2PxzPhupJFlewnz/MIhUK0+9LxfJ5PRU5my/Rb3/oWXC4Xtm7dinPOOQcvv/wybDYbfvazn+HXv/511usTxXQGIeI3VY0dKR8iFs5U65vqrl2j0YDjONqQPdepKccCt9tNp8GUlZXlHG+dLtNtsHA8Qay7iW5KSB9h4q5NtixJLJQgk8mgVquh0WigVqsnPD/JFmvyepLjq3K5nA4NGPteEpMnXgJiYYuu3+MLHiz4LIeLZbv8XPH222/j1VdfxfLly8GyLMrLy3HBBRdAp9Phvvvuw8UXX5zV+kQxnUEydfFm2rEoUzElo8McDgccDsdxK6aBQAD79+8Hz/PIy8tDRUXFXO/SKQHHcVCpVClZ2sRlGwwGqfgRFzgZkA4kQgAajQY6nW7CjHKWZan4RiIR+P1+Kt7hcBgymQwajWbc+1mWhUKhoGJPXL8kI/hkrxMWmVv8fj/tFW40GjEyMoK6ujosWLAAu3btynp9opjOIERMp7oIZDJNBkgdIjAVZrMZDocDg4ODKCsrO+7u7MPhMPbu3YtYLAatVoumpibxYvkxY13O5LnZdDcnu2wNBgOAxPcyEAjA7/fD5/MhEolQoR0eHoZMJoNOp4Ner0/b/jJ5ndFoFH6/n85JdTgckMlk0Gq14yxVjuOgVCqp6zcejyMYDNKsa5G542R289bX16OlpQUVFRVYtGgRHn/8cVRUVOCxxx7Lqi84QfymzhDJF8KpRCLTWGg2WK1WdHR0IBQKYXR0dMph2seSYDCIffv2IRwOQ6lUYsGCBSeFezUdyUk6ybWbyfHMdMlDmZI8tWVsW8DkRgtT1YSmQyKRQKfTQafTAUgkwPl8Pni9Xvj9fkQiEYyOjmJ0dJSKsF6vT+thkUqlMBgM0Gq18Pl8CAQCiEQisNvtNOaa/P1Pdv2SrOFwOExdv8fbzeGpwskspt/85jdp7sbdd9+Niy66CM8++yxkMhn+8pe/ZL0+UUxniEyTj4DseulmCsdxKCoqQk9PD3p7e2E2m4+LC5Db7caBAwcQjUYhl8uxcOHCWU84mm2IYBIrjpSKBINBhEKhWS2pSW6oMBXJpStqtRoqlYr+zcQrIJPJYDKZYDKZEI/H4fV64fF44PP5EA6HYbPZYLPZoNVqYTQaodFoxn3nOI6DXq+HRqOhohoOhzEyMgKVSgWtVpuyL8T1O/bGI12WsMjswyOHOtMTJGb6hS98gf572bJl6O7uxuHDh1FWVgaz2Zz1+kQxnSEytUqBzGOrZF2ZzsYsLi5GX18fPB4Pbac3VwiCgKGhIRw5cgSCIECtVmPhwoUz2hbxWECSa7xeL3w+H3w+H/x+/5Q9qImFmNzvlliRyZbl2BKb5JuxicpkkrNzSakJyeAdWxNKSleSB8KTGCcpedLpdFAoFJPefHEcB4PBAIPBgHg8DrfbDZfLhWAwCK/XC6/XS7OmDQbDuN8BEVWVSkXLlAKBAEKh0LjtEyuV4zgaSxXdvnPDyWaZJnfnm4psu/eJ38wZItN4aXIZxlTLZtvZSC6Xo7KyEu3t7Whra4NSqcx4astMEgwGceTIEZrEkpeXh8bGxhPiQhgOh2mJB7HCJrqZUSqVNLEnucRkppJnkoU1Wy8GKTkhFjMRVb/fj3g8To+PTOqRyWQwGAwwGo0wGAyTDlbgOI5arKFQCE6nEy6XC5FIBIODgxgeHqavj/3MpVIpTCYTPc/xeBwulwtyuRx6vT7lOEkslWQekxsEcdTbseNkE9Pdu3dntFwu36/j/+p2gpDs5s1kuUyWzWXgdUlJCbxeL4aHh3HgwAHMnz//mAlqNBpFX18fent7aU/hioqK4zIhihCNRuFyueB0OuF0OtM2vuA4DlqtllpzxFV6PMd9WZYdl8ELgFp5xJp0u9002Wh4eBjDw8MAElm8pOmFVqud8PNTKBQoLCxEfn4+XC4X7HY7otEoRkZGYLfbYTKZYDabx50ruVwOi8VCrX3i+tXr9SlCTkpwkmPOPM/PaAMRkVOH4643r8h4MrU2s4mtJs/ozBSGYdDQ0IBoNAqn04l9+/ahsrISJSUls3bxD4VC6O3txeDgID0+g8GAurq6tE3z5xq/3097EadrnahWq6n7U6fTQalUnjQXboZhqMharVYAoJYquanwer20xVpPTw/kcjny8/ORn5+fNi4KJG448vLyYDKZ4PF4MDIygnA4jNHRUTidTlgsFphMpnF9iLVaLRQKBdxuN72xCYVC0Ov19LdE3L4Mw9C61FAoNKVrWmT6CAKTdcxUOI4t09lEFNMZINl1m+mPO5PlyB06SWrJdN0sy2LBggVoaWmBzWZDZ2cnBgYGUFlZCavVOiMXoEgkgpGREQwPD6cIkkajQXl5+XGTAAUkPh+/34+RkRGMjIyMa7dH3OHExXm8uKOT46XJZTIzfV45joPRaITRaERlZSVisRjsdjtGR0fhcDgQDofptCCVSoXCwkJYrda0iWSk5lmn08Hr9cJmsyESiWBoaAgOhwMFBQXj6qClUiny8vKolRoKhRCNRmE0GlMyhclQCOL2DQaDYmLSLHOyzTOdTY6Pq8ZJxExe6Igri8SL0tX2TQTLsmhoaIDBYEBnZyfC4TAOHz6Mrq4uakEYDIaMrFUSf/P5fDTxZGzTeIPBQDsaHS8iGo1GMTQ0hMHBwRQBZRgGBoMBeXl5yMvLy2jw+kwgCAI9j6SWk2QAB4NBhMNhmsFK+iKnI7k8hiQ6keHoxAWt0Wig1WpzGkYgkUhgtVphtVoRj8fhcDhgs9lgt9sRCATQ3t6Ojo4OWCwWlJaWpm0SwjAMdDodtFotnE4nhoeHEYlE0NPTA51Oh4KCgpR9I1aqXC6H0+lEPB7H6OjouPhtchyVnE9RUGePky1mOpuIYjoDjO3rOlMwDAO1Wg2fzwePx5OVmJL3k3hWf38/uru7EQqF0N/fj/7+fgAJVzIZTZbcBpH0eiVdctJd2LVaLfLz82GxWLLet9kkGAyit7cXQ0NDKW71vLw8WCwW5OXlzbr1GYvF4HK5aFcqktSUy2SfsSSXx6Rrap+MUqmEXq+nyUUmkwk6nS5j8eE4DhaLBRaLBbFYDDabDYODg/D5fDTGajQaUVZWNm7wMpA47yaTCXq9nsZRyUi/goIC2jCCIJPJYDabaUKTy+Wig+PJukn5jCios8/JNoJtNhHF9BiTXO6SievWYDDA5/PRuFMuYk1mjxYVFdEkEeK+y6ZmUaVSQafT0RKJ461e1OfzoaenhybRAIn4Z1FREaxW66wKKKm7JK5kh8MxoWWpVqtp/1uSAUymuCSXzyRPnUl2+cbjccTjcXqzQ0phSLciYvWGw2GazTs0NES3TybqWCwWejOUiYdCIpGguLgYxcXF8Hq96O3txfDwME3e0uv1qKysHCeQQOI7SMSzv7+f3tT5fD4UFhaOy+I1mUy0WYTP50M8Hoder59UUE+m2PbxwrG0TB955BH86le/wtDQEBYtWoTf//73OP3006d83wsvvICrr74al112GV555ZWctj0TiGI6A2QTL01eJhMx1Wq14DgOsVgMPp9vWn13k8eSAQk3KLnYkrFYxJIjszGlUilUKtVxnT0ZjUbR2tqaIqKTWUszhdfrRXd3N3p7e+FwOMa9rlAoUmKxJJZ4rLKAw+Ewzdh1Op1wOBxwOp2IxWJ0aDpw1PosLS1FaWlpRm5v0hKysrKSJp+53W7s2bMHRqMRNTU1aYe9KxQKVFVV0ZsOt9uNQCCAkpKSlGQ14ibmOI7OpCWzb6eyUI/X76nIxPztb3/D7bffjsceewwrVqzAww8/jHXr1qGlpYX2z01HV1cXvvOd7+DMM888hnubHkY4kSYgzwAejwd6vR5ut5u2TZsuJLuQZEpOhiAIsNlsEAQBFoslI2tpaGgIdrudXojEi8VRnE4nmpubqXVtsVhQVlY2a83+o9Eouru70dHRgZGRkZTX9Ho9tfQsFgvUavVx91nxPI/m+78Pu9uNYGkt/EXVKeVADMMgPz8flZWVKCsryzjmGgqF0N3djaGhIXqTWFpaivLy8glvHgKBAPr7+xGJRMAwDIqKitJataFQCC6XC4Ig0K5MyeeVJCMBiRuD4/nGb7aY6esaWd83Xv8G5OrsGq2E/WH87pO/y2pfVqxYgdNOOw1/+MMfACQ+09LSUtx2222444470r4nHo/jrLPOwpe+9CW89957cLlcomV6KkFGf5Ekk0zE1Gw2w+l0IhQKweFwjJvNeSoiCAL6+vrQ3t4OIBEbbGxsnLEbpLG43W60tLSgs7OT1v0yDAOr1Yry8nIUFRUdl2VAY2FZFlrEwXYfgkIKVHz9O/B4POjv70dPTw/sdjttE7hjxw6UlZWhpqZmyuxshUKB+vp6lJWVoa2tDXa7nbrc6+vr047aU6lUqKqqwsDAAN2HUCg0LuOcWPgOh4M2zU8W1GQLNR6PIxKJnHCdto5XphMz9Xg8Kc+TRLmxRCIR7Ny5E3feeSd9jmVZrF27Flu2bJlwO/feey/y8/Nx44034r333stqH2cDUUznAKlUShN8MkncIdmVpLuMTqfLKUvzZEEQBLS0tFA3ZUFBAWpra2fFfepwOLBnzx7aEBsAdDodqqurUVFRcUII6FiKv/VT+A/sgLppCS1l0ev1aGpqgs/nQ1dXFzo6OuD1etHR0YGOjg6YTCY0NjairKxs0kQfpVKJ+fPnY3R0FK2trQiFQti7dy+KiopQXV097jPiOA4lJSXU7Wu32xGJRFBSUpKyHWKREkF1Op0pLl9ikZJB5SzLntK/kZliOqUxpaWlKc/ffffduOeee8YtPzo6ing8TuueCVarlc4+Hsv777+PP//5z9izZ09W+zabiGI6AyT3Us2EbNsEAokYILFOu7q6UFFRccpeLHp6eqiQ1tTUoLi4eMbdetFoFPv27UNLSwt1W5aUlKCurm7GanXnCk6lhu70s9O+ptFoMH/+fMybNw8jIyNob29HV1cXHA4HPvjgAxw4cACLFy+e9JwzDAOLxQKj0YiOjg4MDAxQ63PevHnjYrLEtSyXy9Hf3w+v14uenp5xwi2TyWA0GmnynNvtTklKkkgk4HmeJmaxLHtcd6k6EZhOAlJvb2+Kp2imvAVerxfXXnstnnjiiZwa0s8WopjOANkmFcnlcni9Xpr0k2niUmlpKbq6uhCJRE5ZQfV6vejq6gKQmEeYy9zBqRgaGsKWLVto2UlZWRmWLFkCjUYz49vKBP+h3YgOD0B/xjowx6ihBBG4/Px8LFmyBEeOHEFLSwvcbjc2b95Mn5/sYiaRSFBXVwez2Yzm5mb4fD7s3LkTTU1NaVtc6vV6SCQS9PT0wO/3o7u7G+Xl5SmCKpfLqYUaDAZpq0eCVCqlGc9k5N+JfOMz1wg5uHmFj5dPHuc3GaTdpM1mS3neZrOhoKBg3PLkBu9Tn/oUfS45cbKlpQXV1dVZ7fNMIIrpDJOJOJKyB1IvmOkdm0wmQ0VFBRXU7u5ulJWVHXclKrMFz/PUUjSbzWl/aNNd/+7du6lrSaPR4LTTTkNRUdHMbSMShuvt1yAvrYR63lLEAz74drwPVdMSSM1WCIIAp9NJM11dtiEMf7gRcYYDd7ANjMGcMjBcAgFKtQZKtRo6nY42mNfpdDMmIgqFAgsXLkRDQwMOHjyIw4cPY3h4GBs2bEBVVRWWLl066XfYZDJh+fLlOHDgALxeL/bt24fq6upxbkAgUTZUXl6O7u5uBAKBCQVVp9PRQQQSiYRau2RAOamNDofDp2RC0omETCbDsmXLsHHjRlx++eUAEr/FjRs34tZbbx23fENDA/bv35/y3I9+9CN4vV789re/Tfu9OhaIYjoDkItbclvBqZYnP/hQKJSV+4MIKulq1NbWhvz8fOTl5Z30FwyHw0EvnrW1tTN6vIIgYOvWrejo6ACQcB8vXbp0xi1/+6vPYvSfTwEMi5o/voTBPz+EvkP7YC+shn/BKthstvG9mI0fj9LzhwF/f0bbkclkKCwsRFFREYqKilBaWjrtmy6ZTIYlS5agrq4Oe/fuRWdnJzo6OjA4OIjVq1dPenMjl8uxePFitLa2YmhoCO3t7YhGo6isrBz3OapUqhRB7evrQ2lpacpyarUa8Xgcfr8fLpeLlnIBR39fJCEpFoudch6cmeJY1ZnefvvtuP7667F8+XKcfvrpePjhh+H3+3HDDTcAAK677joUFxfjvvvug0KhwPz581PeT7LAxz5/LBHFdIZgWRbxeBw8z2cUp1EqlQgGgwgEAtBoNFnFdmQyGSorK9Hf349AIACbzQa32w2r1XpclmPMFC6XC0Ci/GWmszX37t2Ljo4OMAyDNWvWoLy8fMr3CHwckcE+yApLwLCZfX6c1gABgNNYgDff/xCtvA7hhrMSL/b0ADhaD2w0GqHT6SCPRcBFQlCVVVFR4Hkezg/ehGv3R4hyMqgu+H/wRuO0lpR4Lrq7uwEkvp8lJSWorKxEXV1d2uzaTFGr1Vi9ejVqa2uxZcsWeL1ebNy4EQsWLMD8+fMnTFDiOA719fVQqVTo6OhAT08PBEFIW+5FBLWrqwterxdDQ0PjXPparRaxWAzhcBhOpxNms5lum+M4yGQy2pSEzI0VyY5jJaZXXnklRkZGcNddd2FoaAiLFy/G+vXraVJST0/Pcf/5iXWmMwQZ0Ez6pU6FIAh0XBXpo5otgiDA5XLBZrMhHo8DSLjk8vLysmoZd6KwY8cO+Hw+NDY2jsv8mw5tbW3YunUrAGDlypUZx1v6//BTeN7fAO3pZ6Pk9p9PuXw0GsWOHTuwd+cOeAJHaztlHIuy0jJU1dWhqKgIeXl5Gd1cxX0ejL7yf5CXVMJwzifp8zzPY3R0lCb+9Pb2jpuOY7Va0dDQgEWLFk2rFWQ0GsXOnTtpiVJBQQHWrFkz5Tr7+/vR2toKIDE2sLq6Ou1NoNvtRl9fH1332LIwcqzxeBwKhSKlSQdp5MDzPC2fOVlvNGerzvSGV+6ALMs604g/jKcuv3/Gr7HHO6JlOkMktwnMBNJ3lzSNz2U+JsMwMBqN0Gq1GBkZoeOr+vv7MTAwAI1GA51OB41Gc9xMQskVnufh8/kAJBJVZopQKITt27cDSLiIsklcCHcnxCDU3Tblsk6nE6+88gpt9CCTyVBfX4+mpqacx+NxGh2sX/g6ACDmsoNVa8FKE4PJSfLQ4sWLaRy2s7MTbW1t6OnpobWkH769EactXYIV563NyRUqlUqxcuVKWK1WbN26FUNDQ3jjjTdw/vnnp+2ARCDZwEeOHEFfXx8kEgkqKirGLafX6xGNRmGz2TA0NAS5XJ6SCMayLAwGA+x2Ox0YQMqVksMpPM+L7t4ciAtAPEtLM35KmWdHOS6usNn0ZHz66aepH51A4iNzSbY9d4GEFSmVShGNRuF2u3OeuCKRSFBYWAiLxUL7pEajUToAGkhcvFUqFe0FSxrbnyiMnYM5U3R0dIDneZhMJixcuDCr9xbd+mO4Nr0O/ZnrJl2utbUVr7/+OsLhMFQqFc455xzU19envbBHhvrQ+6vvQ6I3ofR7vwSrmLq1n2vzfzH46M8htRaj6sH/AytNjY2SZvMmkwnLli2D3+/Hgc0bsWv7NniVOny4dz/2tnXgzDPPRHF/C7xbNsJy5c1Qz1ua8bmorKyEyWTCO++8A6/XizfffBNr166dNAO6qKgIgiCgtbUVXV1dUCqVaT0OeXl5CIfDcLlc6O/vR1VVVcq5k8lk0Gq18Hq98Hg8kMlk9OaRZVnR3TsNxBFsmTPn3yrSk/Huu+/Grl27sGjRIqxbty6lz+pYdDodBgcH6YPEheYSlmXpj5S4XKeCFMwDiT6q070hkEgksFgsqK2tRVVVFcxmM3U5kwkcg4OD6OjoQHNzM1pbW2nNptPphN/vRzQazbhe9ljCMAy9QJIORNNFEAS0tSWsylwSmhTltSi4/ptQVjVMuMyuXbvw8ssvIxwOo7i4GNdffz3mz58/oYXk3bYZkf5uBA7tRrDtUMprjg0v4chXL4X938+nPB9qTywXtfWDD6SOxkuHWq3G6Rd+ApfkybEy5oJOo4Hf78f69evxz492Y7CvD6Mv/2XK9YxFr9fjwgsvhFarhd/vx5tvvklv5iaiuLiYZl8ePnw47bB2Mv1ILpcjFouhv79/3HdUrVZDJpPR0Efy6yR7HgAtRxMRmWnmXEwfeugh3HTTTbjhhhvQ1NSExx57DCqVCk8++eSE72EYBgUFBfQxk/Gz6UBcddlc7KVSKb17d7vdMzKii2EYepdfU1ND27yZzWao1Wq6n5FIBF6vF3a7HQMDA+jq6sKRI0fQ3NyMtrY2OhXE7XYjHA7P+UWICNBMnCMgkR3s9XohkUgySjjKFpvNhrfeegsAsHjxYlx11VVpY+OuTf/B4evOx+CfHoBu1flQVDdCs2wNlHWpmYnDz/4ecZcDw399HEe+/En0/ur7EHgejEwBRiaH4fxLIdFnlljERyKQ51mwYMVK3PSVr+Dcc8+FTCaDS23A+/VnYKhh6mkd6VCpVLjgggug0+kQCASwcePGKW8Sq6qqkJeXB0EQcPDgwbSfL8uyNKPX7/djdHQ05XVyY8owDKLRaMq8XeLuBUAn7ohkBklAyvZxKjKnYkp6Mq5du5Y+l0lPRp/Ph/LycpSWluKyyy7DwYMHJ1w2HA7D4/GkPGYLIlLxeDwr4dFoNPSumrRLm0kkEgm0Wi2sVisqKirQ0NCAuro6lJeXo7CwECaTie4DAFqf5/F4MDIygr6+PrS1tdHh4sPDw/D7/RnHh2cKIkTpJrTkAulApVarZyWW1tzcDCBRZnPBBRdMGBd1v7cBQiQM1+b/QmK2ovLnT6D0uw+AlaUmfnCahBeDlcsR93ng2/kBYm4HXBtfhRAJI9hxGEPP/B59D/0QMZd93HaEWBS+PR8h5nbCueFF2F99FkNP/BLRgW6cdtpp+OKnL0dJPACBYbG5o58mZWWLUqnE2rVrqYX63nvvTfpdYRgGjY2NUKlUiEQiaG5uTvv7kcvlNKN3eHg4pUE/kPiek4QXr9ebclOb3F5QtE4zh/TmzfZxKjKnRz1ZT8bk+YvJ1NfX48knn8Srr76KZ599FjzPY/Xq1TTjbyz33Xcf7T2q1+tntaA3OR6TjXVKEolmU1DHQixik8mEwsJClJeXo7a2Fk1NTaipqUFZWRmsVisMBgPNguR5Hn6/HyMjI9SKJTMpj8XFyWKxAABGRkZmdHuzse+CIODIkSMAgKampkldyOZPfxHK+gWwXncbXS7Yfhj2/7yQyNh99f9w5KaLYVz3aRTe8kOUfu+XUNbNh+nSayA1mmH53JchKyqD7vRz4Hz9b/Bu2wzX2/8et53+3/8Evfd/B10/vAmy0ioADDitHhJDIkM2tvt9LN77FmqGEolVmzdvxjvvvJPT+VEqlTj77LMhkUgwPDyMnTt3Trq8RCLBvHnzwLIsnE4nent70y5nMBioYPb3948TaaVSSW8K3W53yr5LpVJaDz7bv6+TBdEyzZzjIgEpG1atWoVVq1bR/69evRqNjY14/PHH8dOf/nTc8nfeeSduv/12+n+PxzOrgiqRSFLKZDKNw7EsS/uORqNROBwO6HS6Y94OjbjE5HJ5ikuSWKtk+LTf70c8HofL5aJF82R252xlDptMJrAsi1AoRFP3pwM5r7NhYZPzwrIsqqqq6POej95GsOUATJd+HlJjohWfet5SqH/yKF1GiEXRfe+tEMIhhHs64NuzBXGvG+73NqD6188BACrufYwub/rkFTB98grEA36439uAmGMY6oWpblrXptfh3boJABAP+qBdugY1f3wZrFIFTpnIftUuPxOut17BUjWL4jNWY/P7H2L79u2QyWRYs2ZN1udAr9djzZo12Lx5M44cOQKLxZI2Y5egVqtRU1ODI0eOoLOzk2aqJ0Pip36/H+FwGCMjIyk348TdOzIygkgkkja7NxQK0YlNYu/eyRGQvTgKYgLSsSfbnozpkEqlWLJkCU0kGQtpPZb8mE2IkAiCkHWiDMuyMJlM1EIlQ52PhxgPwzB0FFZpaSnq6+tRUVEBo9FIR8oNDw/jyJEjGBgYmLEkoWQ4jqODgtvb26dtUZIYm8/ng9PpnIldpCRb86SkJ+7zoP/hu+H4798TXZAmgmXBqRPfU4nOAMsVN0FWUgnzZ26Y+D1INLCv+vWzqHtyA5Q1TSmvRe1HE/qKv3EvGJaF1GSmQgoA8uJy1Pz+RVT89HGsWH0GLrzwQgDAli1bJk0InIySkhLMmzcPALB9+/ZxrtmxFBYWwmw2QxAEHD58OO2NjkQioS0eR0dH07p7iQh7PJ6U3w/HcfQ3Krp7p0Z082bOnB51ck9GAunJmGx9TkY8Hsf+/ftnpeF5LjAMQ91MufxYiaCSi0E4HMbo6Cj8fv9x9cMndbJFRUWoq6tDcXExFAoFrWlsbW3F6OjojFt9lZWVYFkWHo9nXBJKtigUCpSUlAAAbSM4UyiVSmqFkdgpq1BCak2IgKKidsL3MiyHyvufRNldv4fl6q/AeP6lqH7w/6BfvXbC99D3MgyYNKUfeZ+6GvnX3oqS7/0SmkUTJxe539uAzh/eBM+2zVi0aBFqa2vB8zz++9//5vxZLliwAEajEZFIBNu3b5/wexxzOeDdthk1ZWWQSCS02X06km+M07l7SRxcEIRxeRLk90lqT0VEZoI5v4W4/fbb8cQTT+Avf/kLmpubccstt4zryZg8NPbee+/FG2+8gY6ODuzatQtf+MIX0N3djS9/+ctzdQjjSHbv5hKbYRgGGo0GZrOZjpUiyUCBQOC4ElXgaOF8VVUVKioqoFAowPM8bDYb2tvbUzIrp4tcLk8RwOla7cQFS3odzySNjY0AgP379yMYDIKRSFH1q2dQ84d/wrj28knfK9EZEvNGWS4RR3/97xh6+mHEfbkl0LEyOfIuvgrapasnXW74+UcRam/GyF8fB8MwuOCCCyCXy2Gz2XKeHclxHFatWgWGYdDb2zthPLT7J19H/29+hNEnH0RdXR2ARBs5YtmPpbCwEBzH0RvOZJLLzkKhUEpG8dgb3mOdSHciQepMs32cisy5mF555ZV48MEHcdddd2Hx4sXYs2fPuJ6MyYOZnU4nbrrpJjQ2NuKTn/wkPB4PPvzwQzQ1NU20iWNOcip+LBbL+e5XKpXCbDbT1oDxeBxut5v24s1mHuqxgFirVVVVKCoqAsdxdFzc4ODgjF20SNP2YDBIp8jkSlFREXQ6HcLhMD766KOUdXl3vIeOO74E51uv5LTu2tpaaLVaeDwevPjiiwiHw2BlckjN2ZVyhXvaYXvmd3CufxGODf+ccnk+FMTIP/4M1zuvZb3PhvMvBSNXwnB+YryVRqPBGWecAQDYt29f1usjGI1G+hvdtWtX2psgIRb9+G8E+fn51N070WdMmpUACXfv2BIcqVRKuzC53e6U759Ye5oZYgJS5oi9eWeRcDhMhVSpVE6r84ogCDT5J/lCJJFIoFAooFAoskp4OhbE43EMDQ3RBvVSqRSlpaXjhkPngsvlopZSbW0tiouLc16Xw+HAhg0bwPM8TjvtNGoVddzxJYS7joBVaVD/5Pqc1j06Ooq//vWvCAaDKLRacfG8ahgWnjau7GUy4j4P2r9zLeIuBwpu/h4MZ10ERjJxKY/9X89h+PlEQlPlA09DUV4DIPEd4oMBcKqJ2/ylIxgM4pFHHgHP87jhhhtoVnW2xGIx/Otf/0IwGMSiRYvGTfiIDA8icGAntKefBU6TuMHZtm0b4vE4ampqqEciGUEQ0NvbC6/XC6VSOW4KjSAIGBkZQTweh1KppNNFgISbl8Rb5XL5Cd1yc7Z6837qbz+DVJVd7+ZoIIR/X/mjU64375xbpiczMpmMCuh0mx4Qq89iscBkMtFG4rFYDD6fD6OjoxgeHobL5UIgEDgukpY4jkNxcTHKy8tp28TOzk7Y7fZpWwIGg4H20W1ra5tWApHJZMKSJUsAADt37qRlWca1l4JVaWC88NM5r9tsNuOKK66AXC7HoM2Gf/z7dRx+/FdZrYPT6FDzu7/DeNFnMPSnB9D9k9smXV5WXAGAAatS07IXABh8/H4c+dI62J77Y1bbVyqV9FwfOnRoiqUnRiKR0PN84MABOnyd7nd+IQznXQJOk7gAy+XyFDd8uuYPJLuXZVkEg0HY7fZxrxMBDQaDKa58sfZ0akTLNHNEMZ1Fkt29PM8jFApN+wdL1mk0GmG1WqHX61O2EQwG4Xa7MTw8TMV1rtsEajQaVFdXQ6vVQhAEDA0NoaenZ9rJHyUlJbBYLBAEAfv375+WoNbX16OkpAQ8z+Odd95Bb28vjGsvR/2T65F/1c3T2k+r1YorrrgC0ngMbrUBrweleP/99yc9fu+O9+F+dz2Ej12TrEyOyEBiRFu4t33S7WmXrYHlczdCVlCKcM/RZf17E00Y/Hs+yvoYKisrAYA26s+ViooKmM1mxOPxjNzGxA0fj8cndPdKpVIaFhoeHh4nuqQvNZDwaCS7e5NrT4+HLl/HG4KQ2+NURBTTWYZlWerWnClBTV63SqWCyWRCQUEBTCYT1Go1dVfF43EEg0Ga+Wqz2WC32+H1euloqmMFx3EoLS1FQUEBLUdpb28fZ51kA8MwaGhogMlkAs/z0xJUhmFwxhlnoLS0FDzP47333qNjxWaCwsJCXPvZT6NUqwQPBh9++CGeeuopdHV1jVs22HoAfQ/egYE//gyeD9+izxd86XaYPnEFSr77wJTbG33lLwh1HMZIUglO4VfvhG7V+Si48X+y3n8Se/QO9KH7Z99CqPNI1usAEud56dJEA/2Ojg4aAphs+YaGBtrMITl/IhlSkyoIAvr6+sZ9t3U6HTiOA8/zKb17x7YaFLN7RXJFFNNjABFUUncYDAZn3A1LLgo6nQ4WiwVWqzWlTWBy5xdSV2mz2TAyMgK3203HVM0mDMMgLy8PVVVVkMlkiMVi6OrqwujoaM43GBzHYd68eSmCmmvJDMdxOOOMM1BdXQ1BEPDRRx9hz549M3ZezLUNuOqrt+Kyyy6DWq2G0+nE3//+d7z44ot0UDYAsAoVwHw85Fp9tGmBrKAE1uu/kdE0F/25lyQSic7+BH1Os2gFir/5E6gaFmW97+SG0O92InBgB0ZffTbrdRAsFgtKS0shCAJ279495fIqlYpaxu3t7WlrVRmGoUlv4XB4XO066TIGJEIuyevgOC7F3Stm9x5FtEwz58SNuJ9gkOHExDINhUJ0VNRsJA2xLEs7GQFHm0iQ7kyRSITeicdiMWohSqVSyGQyOh5uNvZNoVCgqqoKAwMD8Hg8sNls8Pl8KCkpySkJhAjqwYMH4XA4cODAAVRWVqKsrCzr/WdZFitWrIBcLsehQ4dw8OBBDA4OYs2aNTOSTMEwTGLwQGEB1v/hQbRxGnR0dKCjowNWqxXLli1DQ0MDqh58BnwoBGX1xBNpJqPgum/CfNm1kOblT3ufgaN9jDm5AoxUCt2Ks6e1vsWLF6Ovr48OL5+qK1lJSQlGRkbg8Xhw6NAhLFmyZFxCn0QiQXFxMXp6euBwOMYlHEmlUjqqze12QyKR0BIZqVSKeDwOnucRDodP6kHiIrODaJkeQ4iFmjy1JRwOH5M7YYZhaKmAwWCgw6MNBgNUKhUVMTJxw26305jrbMSSOI5DSUkJCgsL6SSQ6dSkchyH+fPn0844nZ2dOHToUE4eAIZhsGTJEpxxxhmQyWRwOBx4/fXXp12Gk4zQ34WGfe/gnENvo3ykCxwE2Gw2vP7663j00UexpbULIdPUJTQCH0fvA9/FkZsvgf9QwsoTBAFdd9+Ctq9/GqOv/t+M7C/pfV02byHqn3kbulXnT2t9Op2O1uHu2LFjyjIvhmHQ1NQEiUQCr9c7oQteq9XCbE60aRwYGBhnxarVanqDmdxdbGx+g9i7N4EgMDk9TkVEMT3GkB8tuSMmcc25yCbkOA5KpRJ6vR4WiwX5+fnQ6/UprfCCwSAcDgeGh4fh8XhmtLaVDK2uqqqisyq7urpgs9lyOhcsy6Kurg51dXVgGAYjIyPYtWvXhEX/U1FeXo6LL74YBQUFiMfj2LFjBzZu3DhlnC8TFDWN0Jx2FrRCDAt792Fd1zacddZZ0Ol0CAaD2LZtG5544gn89a9/xZ49eya8yYjZR+DbvQVxjwueDz/uJMbHEepONKtPnonq3f4u2m77LIb//kTW+9vf3w8gMX90piy2BQsWQK1WIxAIYP/+/VMur1Ao0NDQQPdnrCuXkJ+fD41GQ8tmkuOgJLs3XfyUeI+ARJb88VbHPReIbt7MEetM5xByB5xsPUml0llzr2YDia+GQiEEg8Fx0zdUKtWMNuHneR6Dg4NUqBQKBW1RmAtutxsHDhxANBoFwzCorKyk8zCzhUyA2b17N+LxOBiGQU1NDRYuXJjz/hGCbYfgfOMl6M+8COoFy8HzPNrb27Fnzx50dnamLFtUVITa2lpUV1cjLy+PxsFtz/weobaDKLjpe1CUJUpYvDs/gG/PFuRdfDVkBYka3O6ffgOBg7sAlkPDc5syOheej95GZ2sbNgy6AAA33ngj8vLyJn9TFvT392PTpk0AgPPOOy+jtqDt7e3o7e0FwzBYuHAhjYUmE4/H0dHRgUgkAoVCgYqKipSm9tFolJZoKRQKGAyGlK5lREgVCsUJ0Qx/tupML/y/X+RUZ/rGtT84Lq6xxxJRTOcYQRAQj8fHWaYSieS4mWpBygaCwWBK2QHJJlar1dNqSJGM2+3G4OAgFa38/HwqHNkSDofR0tJC55/qdDrU19fTzNRs8fl82LVrF22HR8aGNTQ0zErBv8fjQXNzM1paWsaNJNTpdKisrKSx4UxE3bvjPdie+T10q8+HqmkplDWN4FSaccvFAz4wnAQxlx2Hv30NNjWdg5BMhcWLF9Pm9zPJ1q1b0dbWBoVCgU9+8pNTNvUgQ8RHR0fBcRyWLFkCjWb8cYTDYXR2diIej0OlUqG8vDzlexoOh+l3Q6VSQafTpZTJkJvc6TZcORaIYjr3iGJ6nEBENRqNpsRQWZalono8/KDj8TgCgQACgUDKfqpUKmg0mhkR/2g0ioGBAeqeValUKC4upq7xbCB1rW1tbVSgKyoqUFpamvP5tNls2LVrF70QK5VKzJ8/H9XV1bN28+P1etHW1oa2tjb09PSMiwVbLWYYhnuQr5Kj8ZqboTeaJrwB6f/dPfB8+BbkFbWouj91ek2w9QC67rkVrEKJ4h/9Dq/++TH066zQKWT40le/ltNnMBWxWAwbNmyAy+WC1WrFeeedN+VnQ+pU3W43ZDIZFi9eTGtJU44nGERXVxd4nodGoxn3uQeDQeoN0Wq1VJRJkiDP83Ri0vHw+5sIUUznHlFMjzMEQQDP84hGo+MumMeTsJKLjc/nS4lJEUt1upYamT4zNDQEQRDAMAwsFgvMZnNOVmooFMKRI0dSBDDZXZrL/nV1dWHv3r00nqlUKtHY2IiamhpaajEbRKNR9PT0oLOzE11dXfSYklGpVDQOTrpmkc5ZPT//Nvz7t0NitqL2D6m9fh1vvITev/weXZYK9FQsRDCScHd+/vOfT9vOb7rwkTDCfZ0IG/Ox4Y03EYvFUFZWhjVr1kz5HY9GozSeLJVKsXDhwnHzTwHQ6TOCIECtVqO0tDTlpsfn88Hr9QJINBjRaDTUQiUhjuNdUGdLTC94JjcxffM6UUxPeo53MU2GlLPEYrFxGb8Mw4DjOPqYqxhrcu1qcgbkTFmq4XAYg4ODVLDkcjmKiorSWiGZ7KvNZqOxNCBR7F9TU5Oz6zcej6OtrQ2HDh06Wl7EsihFBIvWXQyVyZzTerPB6/WiffcOtG74F5xqAzwy9YQZ4gqFAmqlEvJIEFqLFTJtYoiCIAjwer1wOZ1wu1wg79bpdDj77LNp5m0mxFx2MHJlyqzUiSBxXP2ZF0G4/Aa8++674HkepaWlWLNmzZTfn3A4jP3798Pn86XUHI/F5/Oht7cXPM9DqVSi7OMxbwSv10s9IWq1GlqtlibhkXI2hmFmNE9gJpktMV37l9zE9K3rRTE96TmRxDQZnudpXWi6CyXLslRYWZadkx98OBweJ6pqtRoajWbaTf7dbjeGhoaotW40GpGfn5+TBRyLxdDd3Y2+vj4apy4qKkJFRUXObsx4PI7Ozk4c3L8fvo9FlRUE1DY0oL6+Pq3FlA7PR2/D/q/nYPrEFdCfuS6nfYlGo7RX8/DwMOx2OxwOR1ZZzUYOaGTDWHHjbZDqDBm/z7fnI/Q+8F2wai2qH3oOEt345KBkWr/+GcTsNijr5qPi3sfQ399PBbWoqAhnnnnmlJ9xLBbDgQMH4HK5aMck0l4wmUAgQF3kMpkM5eXlKZ93soU6kaCSjN/jTVBnTUyfvg+SLMU0FgjhrS/eecJdY6eLKKYnICS+Sh7pPsK5FNdwOAyv10szIsl8VrVaPa39iMVisNlsNMbFcRzy8/NhNBpzWm8gEEBHRwftmMSyLEpKSlBaWpqzmzYWCmLHT7+DgeJaBPVHp6uUlJSgoaEB+fn5k+5r27euQnSoDxKjGbWPvpLTPkxEJBKB2+2Gz+eD3++ncW+e5yEIArRaLfR6PWSD3XA+/AMwAKxf/BZMF302422MvvosRv76GACg8r4noaism3T5UHcrPB9tguHsT9Ks48HBQWzevBnxeBw6nQ5r1qxJa20mw/M8mpubae/goqKitDHscDiMrq4uxGIx2uIy2Svh9/vpMPHkLN/kCTPHo8t3tsT0/BzFdKMopic/J4OYjoVYreRvuo90rEt4tsWVZER6vV4aU2VZFlqtdtquMr/fj8HBQToBRC6Xo6CgIG1GZya4XC60t7dTq4Q0lCgtLc3J8o37vYg6RuCUKNHc3JzST9ZgMKChoQHl5eVp121/7a8YffEp5F36eZg//cWcjme6xDxOdP3gJsQDPpT/5I9QlFZl/N54MAD7K89AmmeF4YLLc/6ch4eH8f777yMYDIJlWSxevBgNDQ2Trk8QBHR2dqKnJzEQQKPRoKmpaVxIIBKJoLe3l2amk77WZN2BQAButxtAogzMZDKBZdlxLt/jSVBFMZ17RDE9yRAEYZzlOpZjGW8lSRw+n4/ui0QigVarhVwuz3nbJEFpeHiYrlej0cBqteZU+ykIAux2Ozo7O2l8ViKRoKSkBMXFxdNKKHK73WhpaUFHRwfdV7lcjpqaGtTV1eUU/51thI+r75ljJBZCPAb/gZ2Ql1TSFoihUAhbt26l3ZcKCgqwcuXKKePbDocDzc3NiEaj4DgOdXV14zwCPM9jYGCAiqbBYKCj3ICEBet0OiEIAjiOg8lkgkQiOW4FddbE9KkcxfQGUUxPek52MR1LJuLKcRzNEp4tYRUEAX6/Hz6fj1rOMpkMWq12WuUWsVgMIyMjKRmtBoMBFosl51KakZERdHV10YQiYqmWlJRMS1TD4TDa29vR0tJC180wDMrKytDQ0EDb4J2o2P/zApwbXoLlczeOi/fGXHa4310P9aLToSivHffe4Rceh/2V/0vMbn30FbDSxGcX7GlH26FDOOjyIx6Pg+M4NDY2oqmpadLPIhwO49ChQ1Qs8/LyUFtbm3KjRW6gSCcluVyOkpISukw0GqUtB0nnJIVCMW760/HQ2GG2xPS8J3MT07e/JIrpSc+pJqZjmSreOtvCyvM8jdkRFAoFtFrttMppyKQQ4qolrQrNZnNO6xUEAcPDw+ju7qbCx7IsioqKUFpaSvu45gLP8+jv78fhw4cxPDxMn8/Ly0NDQwPKysqOC2snW1pu/AR4vxfy0mpU/eovKa/1/vL78O36AJxWj7on/jPuvUNPPwzn+hfByOSoe+I/YOUKxP1etH71MgjRCJSf/hJajaX0fCkUCixcuBDV1dUTniue59HT00PLYliWRVVV1biWiD6fD/39/YjFYmAYhk5cYhgG8XgcTqeTxv+1Wi21jJPHGMrl8llp3JEpsyWm5/45NzF958ZTT0zFqTGnGAzD0O5KpKaVZAknCy1wtAvTTCYwsSwLnU4HtVoNr9dLuyqFQqFpldPI5XKUlZUhEAjAZrMhEAjAbrfD6XRSUc1mveSimp+fj9HRUXR3d8Pn86Gvrw/9/f0oKChAWVnZlN160sGyLEpLS1FaWgqHw4GWlhZ0dXXBbrfjgw8+wO7du9HQ0DDr9aoAEHWOovcXtwMMg7If/AYSw+SJPpNhvvxaONb/E6aLrxz3msSYsLo5ffr151/1FchLKqCsbgIr//jizTCJBwA1C6xduxZ9fX203/K2bdtw+PBhLFq0KG2rSJZlUVFRAYvFgpaWFng8HrS1tcFms6Guro5mWJPh9QMDA/B6vRgaGoLP50NxcTEkEgny8vLg8XgQCARoYh3pYU06JZFhELP9eR1rhI8f2b7nVES0TEUAIK2wEkiziNkYFxeNRuH1emkyETD9chriUrbZbDTJhGVZ5OXlIS8vLyexFgQBDocD3d3dNNsTAKxWK8rKynKuUyWQphJHjhyh50Imk6GhoQF1dXXTsoSFWBTRkSFIC0rGfX7ud9dj4I8/AwAU3XY39GsuyP0gAIz8488YfekvMH3qalg/f0vSPsQQbD0AeXlN2haGExHu70K4rwva5WeA4Y4OvW9tbcWBAwfoudLr9WhqakJFRUXa740gCBgYGEiJWxcVFaGyspIK4NhGIRzHobCwEHq9HkBqpq9EIoHBYIBEIkEkEqFJdmSs27EunZkty/ScHC3TTaegZSqKqcg4JuvCJJFIIJVKZ9wNOVE5jUqlmpaoer1eDA8P04sux3HIy8uDyWTKOc7lcrnQ3d0Np9NJnzObzSgvL8+4nnQiSL3qoUOHqMtaIpGgsbERjY2NOVk+XT/+CoKtB5F3+bXIv+orqdvzedD/u7sBhkXxN+5JGUaeC+3fvhqRwV5I8vJR+8hL01rXVEQiERw6dAhHjhyh3xu1Wo3GxkZUV1endbuGw2G0tbXREhqJRILKykoUFRVRAQyFQujr60sR6oKCAiqcTqeTthkkFmo0Gj0685XjppVclwuzJqb/e39uYvrlO065a6wopiKTQrowRaPRFGuV4zgqqjN10ZionIaI6nQyfz0eD4aHh2lDiZkQVY/Hg56eHlqnCgAmkwnl5eXUmskVEu87cOAATaKRyWSYN28e6urqMo7PCYKAli9eACEcgnrxSpTd8eCEywZbD8D2zO+hWboG5v93XU777du9BY7X/w7jhf8P2tPOymkd2RKJRNDa2orDhw9TT4RcLkd9ff2EVr3T6URbWxuN3Ws0GtTW1tLPjed5jI6OpohuUVERtFot4vE4XC4X/S6RJvnE3QskvrdyufyYxb5nS0zPfiI3Md18kyimJz2imOYGiafGYrEUa5VlWUil0hlNWEpXTjMTNaqkk9LIyMiMiirp/ZqcTGQymVBRUTHt7xiZybl3717qYtRoNDjttNPoIPQp9+/ATvh2fgDjus/Qxgjp6Hvoh/Bu2wwAqP/LW0djlxkSDofR29sLm82GYDBI5/SSBiJSqRRKpRJqtZoKkMFgoL1wp0ssFkNHRweam5tptyeO41BVVYX6+vpxNzhk7F9nZye9ebNYLKiqqqKx8EAggP7+/pT2k1arFSzLwuv1UjGWyWQwGAwAQAX9WJbOzJaYnvWn3MT03ZtFMT3pEcV0+hAX8Nihy1KpdEbjqoIgIBAIwOfz0axJjuOg0WiOS1El7eqSh5ubzWZUVlZOO6bK8zxtrE+yi8vLy7Fs2bKckqDS4dm2GYOP/QKapatR9PW7Mjq/giDg0KFD2Lt3LwYGBibsCTwZHMfBaDTSmLbZbE7b2cr9/huwPfM76M+5OCUeOxZi1R86dCjFFV9UVITGxkZYrdaU9UYiEXR2dtLmGgzDoKSkhDbW4HkeNpuNll9JpVIUFRVBo9EgFArRAeMsy8JgMEAqlR7z0pnZEtMzcxTT90QxPfkRxXTmEAQhJVYEzJ6o+v1++P3+FFHVarXT6pM6W6JKxn6R+kUgkahUUVExbeGLRqPYt28fWlpaIAgC5HI5VqxYgdLS0mmtNxe8Xi/eeOMNtLe30+f0GjUsQhTGyhpoLFbIZLKUxLZAIADH4QNwD/YiKFMhqNCk7dgFJETLarWioKAAhYWFiP/9UbDNu8BKpGh49p0p94+UNx0+fJg2fgAS1mVjY+O4+aY+nw/t7e1UgKVSKaqrq6n4+v1+9Pf30+97Xl4e8vPzwfM8nE4nvbnUarVQqVQIh8P0+yqTyWY103e2xPSMP90PiTJLMQ2G8L4opic/opjOPBPFVaVSKaRS6YyJKs/z1FIl25FIJNBoNLMiqhaLBUajMWc3nd/vR2dnJ42pMgyD0tJSlJeXT9tSsdvt2Lp1K73wNzY2YsmSJccs6aWzsxP//ve/EQqFwHEcVq5ciaamJjju/Roig71pZ6USwn2d6P3l9yExWVDy/V/BF47A4XDAbrfTOOXI8DDiaaxceSwCq16DyuWrUFJSgoKCgozOpdfrxeHDh9He3k5DByqValwJEmnk0N7eTnvx6nQ61NTU0LiozWaj510ul6O4uBgKhQJut5u+R6FQQK/XIxKJ0O3N9O8hGVFM5x5RTEVmjGMpqsRSTRbVmWhROFZUpVIpLBYLbXieCx6PBx0dHbRBv1wuR21t7bQ7HsXjcezduxfNzc0AEm7f1atXz3qMbmhoCM899xzi8TisVisuvvhieiw9930H/r0fQbviXJR8+6eTrkeIRTH66rNgpTKYLrkKDHtUFFu+/hm4/QH4SmrAn3MZBgcGMDQ4AIFJPTaSGFReXo7y8nIUFBRMevzhcBjNO7ejrbcf4Y8tSalUitraWjQ0NFDPAc/z6Ovro4PFgUQ7w6qqKshkMni9XvT391OhzM/PR15eHoLB4LjyGeLBIc/NRunMrInp4zmK6VdEMT3pEcV09iHJSpFI5JiLqlQqpS0KpyOqLpcLw8PD1HUnl8uRn59Px3Llss7R0VG0tbXRjE+z2Yza2tpp1ZACCSvxo48+As/zKCkpwRlnnDFrMbpgMIhnnnkGbrcbVVVVuPzyy+H/6G34922H+f9dB4nZinBPOxTltWCmyDhOrnEt/f6voFmyir428Ogv4N78OowXfRYFX/wWBJ5Hy62fgT0cQ2jxmfCU1KKvr49aggS5XI7y8nJUVVWhsrJyXKlSZLAX7f/zBfAA+Bu+j65QnIofy7Korq7GvHnzaIw7HA6jo6ODuuwlEgmqqqoSbud4HIODg/T9arUaxcXF4HkeLpcLPM+DZVka+032esx06cxsiemaxx7ISUw/+Or3T7lrrCimIrPGsRTVsS0KZ6LvL8/zcDgcGB0dTXENFhQU5Bz7jMfj6OrqQm9vLwDQRuzp5m9mQ/Ic0NLSUpx55pkzbv0IgoBXXnkFra2t0Ov1uP766yFjGbRcfwEg8NCtOh/F3/xJxusLdhxG14+/CgAwnHUR8q+9lTZ0EAQBca8bQiwG/56PoFm2BkI8hlB7M1TzliE6OgRZcTmcLjdtGdjT00MzaQlWqxW1tbWora2FyaDH8HOPwrX+HwCAwpvvgP7ci9Hf34+DBw+mjOKrrq5GU1MTnUTkdrvR2tpKs4R1Oh3q6+uhUqngcrkwODhIGz2UlJRAqVTC4XDQmzG9Xg+ZTJZS7zyTgjpbYro6RzH9UBTTkx9RTI89x0pU4/E4tVQJcrkcWq12Wskf8Xgco6OjsNvtdP+nM5wcSCS7HDlyhFo1hYWFqKmpmZZFOTg4iE2bNoHneSxZsgRNTU05rysdra2tePnll8GyLK4470zofU5oV52H7p/cilDbIRR86X9gvPD/pbyHDwXg2boZqvoF4HQGsApVyiSaQMt+dN+dyMw1f/ZLsHz2Synv77jjBoS7WqFsWIiKe/4I4KgrWX/WJ1D0tR8e3dbHWbcdHR3o6OhIGX0HAJp4BIXDnShyDqBi7SXI//xXqWuZJCvt37+fWqEMw6CmpgYLFiyAUqmkk2Y6Oztp8/vy8nKUlZUhGo2ir6+PirnFYkFeXh7cbjcVUJKFPhuCOltiuurR3MR0yy2nnpiKvXlFZh3SD5jjuBRRJZnAMplsRrJ/OY4b1/c3HA4jHA5DqVRCo9HkJH4cx9Hm5zabDW63G06nEx6PJ+fh5BqNBosXL0Z3dze6u7upu3DevHk5j2QrLCzEsmXLsH37duzZswdWqxV5eXk5rWsssVgMb731FgBg+aKF8P/6+/DHYzCPDKLi3kfBB/zgNOMvnENPPQz35tfByJUQwkEoa+ej/N5H6fmSF5eD0xkQ97ihqBg/TYaRJG6CGJZD74N3wr9vG5iPJ8qE+zpTlmVZFoWFhSgsLMSaNWvg9/sTU3oO7EfPwAB8kKG1sB6thfU44OMwf/sOzJs3j9a5Wq1WWK1W2Gw2Kqqtra3oaG9DQ2MTmpqaUFJSArPZjCNHjsDhcKCrqwujo6NobGxEZWUlhoaG4HQ6MTIygkAggJKSEgQCAToxied5qNXqlJ6+x7pbUlaIzXkz5sQbTSFywkJEValUpsQ0I5EIgsHguMSlXOE4jo5hI+O0gsEgRkZG4PF4cqqFBBKWdElJCSoqKiCXy2nMrLOzM6W3cKawLIvKykosXLgQUqkUfr8fu3btoolKuVBbW4uysjIIgoAtW7bkfKwEgecR7uvEwf374fV6odVqsXL5MjAfW9CMRAqG5dIKaeL1xM2LEEvEC4OtByBEjp4rTqNDze/+jppHX4Z2+Znj3l92x4Mo/tZPIcmzwLfjPQiRMFilGqZLrkbR13806b6r1WrMq6nGwjeexoW7XsMaFVDCxsAyDOweDzZv3oxHH30UL774Itrb2+m5slqtWLt2LZrad0DtGEKcF3Dw4EH861//QltbG+RyORYsWIDGxkZIJBL4fD7s2LEDAwMDKCwspFNpSDa3XC6nFhpplk/i5MlN8kVObEQxFTnmkFrUZFEVBGHGRVUikdBmACR26vf7MTIykpK0lC1qtRrV1dU0czQYDKK9vT3FDZwNJpMJy5cvh06nQywWw759+2C323PaN4ZhcNppp0Eul8PtdqOzs3PqN03C0JO/Rsd3rsX2DYmxaUuXLoXCmIfK+59Cyfd+ibxPXT3p+63XfxPF3/4ZJDojAIDV6MZ1VmIVKkiN6TObOY0OupXngpEeTdKKjQ7B/f4bENLM5k1GiMXQ+8D3wIeCkPBxVPABfP47P8Ctt92GCy+8EMXFxRAEAR0dHfjnP/+JJ554Atu2baM3RoXllaj74GXM9w1Cp9MhHA5j69atWL9+PRwOB6xWK0477TSYTCYIgoC2tjbs378farUaVVVVkEqltCEEgJQOSR6PJ0VQx4ZAjhc+nhGf9eNURIyZisw56UpqGIaBTCab0TaFoVAope+vRCKBTqebVjZtNBrFwMAATUwhGZ25xGjj8TgOHjwIh8MBhmEwb968nMtnDh06hN27d0Or1eKSSy7JuVym665bYOvpwuamc8GyLL72ta+ldUPb//08HOtfhOXKm2A46xPjXnes/ydGX3oaeZddg7yLr5pwe263G/0dbejZ+B945BpEjPkIhUII+rzwth4CA4CBAFYQoFQoYDn9TKhUKuj1eigdQ+DffQ1la85D0WdvQHigBx23fz5l/WOzhh0OB/bu3Yv9+/fTeKdMJsOiRYuwfPlyKIUYOG2ivKWlpQX79u2jc0/r6+uxcOFCSCQSDAwMUOtWLpdj3rx5UCqV6O3tRTAYBMMwtB6VdFIic3yJeEul0pwT5mYrZrrikdxiplu/furFTEUxFTluSNdRiWVZKqoztQ3iaktu96bT6XLeRrrRXcXFxTlNkOF5Hs3NzRgZGQHDMFiwYAFMpuxnjEajUbz66qsIh8NYvXo1Kisrs14HAIQHevDGSy+iORBDXV0dLr/88rTL0cHgZdWo+mViMHjc54Hr7X9D1bgIipp58G7bDD4YgPOd1yCEg1A1LkbeVV/BwebDOHjwINpaDsPl9eW0n2MxGY0wKyTQHdkNa8SH/KgfUoFH6fd/BWX9QrAyeUrpTjQaRXNzM3bs2JGS1btgwQKsWbOGZvUGg0Hs2rULXV1dABI3TytXrkRBQQF8Ph8OHjxIxZNkaff399NEs6KiIiiVStr0QaFQQKPR0LKZXDslzZaYnv6HByDJMnM9Fgxi262nnpiKCUgixw3EGpVKpVRUeZ6nXXZkMtm0GxIwDAO1Wg2lUgmv14tAIIBQKIRwOAydTpdTz1+GYWAymaBWq2lGZ09PD/Lz82E2m7NaH8uyaGxsBACMjIzg4MGDWLp0ada9faVSKRoaGrB3714cOXIkZzGVFZaij1MC8GL+/PkTLme68NOw//t5gOPAR8JgZXLYnv0D3JteByOTw/yZGzDy18fo8m5OjmZ7EK37exCMH43rMoIAPcNDF/JCzwGqoBdyPgYZy4CLRSCAgQAgzrCIKjVg6xbAcWgffJwMPk4GLydHiJPC4XTCAQDaErreknwzqrfthOGhe2BVK1D38AtgFUp6vhYuXIgFCxago6MD27ZtowMGDh06hOXLl+P000+HUqnEmjVrUFFRge3bt8Pv92Pjxo1YsGAB5s+fj6VLl+Lw4cOw2+1oaWlBOBxGWVkZOI6D0+nEwMAAioqKYDQa4XQ66XebjHFLHgxwPJCL2/bUMs+OIoqpyHEHEVWJREIb6sfjcQSDwRnrIMOyLPR6PVQqFdxuN6LRKG0Hp9frc8r6lcvlKRmdZORbYWFhVjcBRFAjkQjcbjf279+PZcuWZW2xVFdXY9++fRgdHYXH40lrJcS8bgw/+wgkJgssn7sxpWwFSIwq83q94DgO5eXlabcjxKKIuR0QYlGEO48geOQA1POXQfJxHJTT6hEZ6AEAhBgJtmuL0ayyAAwDxHno9XrUCAFYOw8gP+qDTODBSKTQn/8puDZMMhM15ABz0AXB50l5OshK4JQo4TIWYDAcx5BMCz8nQ++IHb0jdsDcBGU8ivoHf4FlZ5+H2vmLIDcmrH+GYVBdXY0ShQRD/hDe27YdAwMD2LJlC/bt24dzzz0XjY2NKC4uRn5+Pnbu3In29nbs378fw8PDWLNmDebPn4+uri50d3ejq6sLkUgENTU1YBgGDocDAwMDKC4uhsFggMvlgt/vB8dxNNudZJ8fFxm+YjZvxohiKnLcQmZCkkQO0iw9FovNWI2qVCpFXl4eLV2IRCIYHR2lzcqzXT/LsigqKoJCocDg4CCde0msk2zWM2/ePOzatQuhUAjNzc1YsGBBVvujVCpRWFiIgYEBdHV1YeHCheOWcb39L7g3vw4A0C5dBWVtqvXZ3d0NIOGenEjMh558CK63/w1IJFDVLYCyJlHfavncl6FZvAry4nLw0Sh2tnbg/bgSETZx2ak2G3H2JZehaf58OF//G0ZaPqTrFGJRuDa8BM5kQdwxMuExjhVSAFDyMSgjXhTZvGgCIDGa4bR70C/ToVeuR59chyAnxR67D3te+hcUL76ElWefizPPOQcmkwmuzf/F4KM/B6fR4TN3/wF7/vk89ggqeP1+vPbaa9i7dy/WrVsHk8mElStXwmq1Ytu2bbDZbHj99ddxzjnnoLKyElKpFG1tbRgYGEA0GkVDQwOARJy2v78f5eXl0Gg08Pl88Hg8MJlMNBnvuC+ZERmHmM0rctzDsiwUCkXKbMhoNIpgMJgyWzVXGIaBRqOB2WyGTCajw8SdTmfO6ydDwlmWRSAQQFdXV8rIukyQyWSYP38+WJalFk22EGuyp6cn7euqxiVgZHJIzFbICsvGvU4aH0w2lSbuTQwvZxgG8WAAg//7K/Tc/x24Nr2Onl98G/1/+Cne3LIVbwtaRFgJ8qIBfE4P3PrDu1DuH8Xw07+B/+CuxMpYFvKqhqPrdjvHbY/TGaBZuhrmz92I0jseRO2fXkPjC++j8YX3UXjLD8YtH3OOQhuPoCE4igtc7bjetgcXB/vQEHJAEY8ixEqw6b338LOf/QxPPfUUetqOJLbt88D5+t9g+Gg9ztz2ClYtWQSJRILe3l785S9/waFDhwAAlZWVuOiii6DX6xEKhfDWW29haGgIJSUlaGpqAsMwGBkZQVtbG6xWK52r2tvbC6lUSsu3XC4XvWEhN45zjZDjIxceeeQRVFRUQKFQYMWKFdi2bduEyz7xxBM488wzYTQaYTQasXbt2kmXPxaIlqnICQOJLcViMVpKMJPxVIlEApPJhEAgAI/Hg3A4jNHRURiNxpyyLDUaDSorK9HV1YVQKITu7m46IzObdVRVVaGtrQ3t7e0wmUxZtTIkNY9ut5vWiSajqpuPuj//FwzHpTSaJ5Ca14maP4R62gGGhWrBcgT270C4swXhzhYAQMwxAiEcwvutXdhuexMAsNzbhyWhUdT88BmEejvQ/7u7U1fI8wh3HIasqBzxcAhxu23sJlH3p9cyPv50sBBQ7BpEMYAzAPTIDThkKEEfq8S+ffuwD0DtvPNx3mlLobXkwb15PSQch5L3Xsb8W+7C+k2b0dvbi9deew29vb04//zzodfrsW7dOmzevBk2mw3vvPMOzjzzTJSUlIBlWRw4cACDg4OQy+UoKyujZWB9fX2oqKhANBpFPB6H1+uFRqOh8VOO447JcPGJOFYx07/97W+4/fbb8dhjj2HFihV4+OGHsW7dOrS0tCA/P3/c8ps2bcLVV1+N1atXQ6FQ4IEHHsCFF16IgwcPori4OPsdmAFEy1TkhILUqKpUKipKJJ46E/WpJEHJbDaD4zjwPA+73Z4y9i0bFAoFKioqwHEcFdRsrd3i4mLo9XrwPE/nmGYKadAPIGWmZzKsVJZWSAHQrFNSIzmW3l/dAe+2TQjs30GfY+QKsGotGLkC+7WF2K5LJAGdX1uGpcERmM6/FLLC0kSjB2n6sqTIYE9aIR1LdGQIvr1bp1xuIlgAFWEXPmk7gM+OHsSi6kowDINWhwePb9iElw92QH3VVyFEIwgdOQCutxVXXnklVq1KlNfs3bsXzz33HHw+H6RSKc4991yUlJSA53m8++676O3tpQMNAKCrqwvDw8MoLS2FRCJBOBzG0NAQjMZEHW44HEYsFqMCSrJ854xcakxz+Ak+9NBDuOmmm3DDDTegqakJjz32GFQqFZ588sm0yz/33HP42te+hsWLF6OhoQH/+7//C57nsXHjxukd7zQQxVTkhIRhGMjl8hTXbyQSQSgUmnbXHyARSzWbzdQK9Hq9cLlcMyKoPT09We0jwzBoaGgAy7JwuVwYGhrKavslJQkxm0hMJ4JM5QEwYYmDRG+k/zZdfh0AQAiHYLrkagx2tuMjdcJKOG/JAlzytW+j8blNKLjh2wj1dqDnvu+AnchKn+Q8j7z4JHp/+T0c+cqn0HbbZzH0v7/K6rgmwhQNYMX7f8fnJW7UBO2AIGD37t14/MM96K9cCFXTEqjnLQXLsjjzzDNxxRVXQKlUwmaz4YUXXoDP5wPHcTjzzDNRUVEBQRDwwQcfwOl0ori4mLrKjxw5gkgkQv/vdrsRCoVo+Y3X601x985EKCNnBCa3BxLlNcmPibqERSIR7Ny5E2vXrqXPsSyLtWvXYsuWLRntZiAQQDQazamMbKYQxVTkhIa4fokblud5BIPBGekoQzJ+SYwrFArBbrfndHEjgkpiqP39/Vntn1KppOUtHR0dKbW4U0HEdGRkBN7+HgjxyWNxrndeQ+cPvwzPjvfocxMlTxV94x6wOgMYhQqOV54BOAnAslBU1GGnrgQCw6A85ET160/CfygRFx147D50fu96RHrawAf9adc7GaMvPgnfrg8RdLvQCxkO4Kjbux8yvAUd3oMW26HGESjgBIdsbq/UfW04z9WBT48eQl7Uj0A4gv+E5NjedDYE+dFtVVRU4Nprr4VOp4PD4cDf/vY3+P1+sCyLVatW0TFt7777LsLhMKqqqmAymaiHQalUUvf54OAgVCoV9YaQzHUAWX3WM810YqalpaX096PX63Hfffel3QaZyjR2cpLVas34xvH73/8+ioqKUgT5WCOKqcgJT3J7QnLRj0ajCIVC076rZxgGKpWKZlpGo1HY7facLnAKhQJlZWVgGAYejwfDw8NZvb+4uBgqlQrRaJRm2WaCRqOhLe/2/Pan6L73tkmXtz33R4TaD2P0xaMuNiL8fCQM/8Fd4EOJOaLBQ3vAe1wQQgEAgNRSgNpHX4Vfb0anzAAAOM3bDwZA769/gL7f3g33pv+kWp5s5jHkEUjwX+jxCKy4D0V4EvnYCAN9fRRSfAAd3oYer8OIv8KM36EQ96EYf4YFG6HDAKSTeyL5xHfGHAvg8tFmrCxMfPYffPABHnnkEdqAAUi4v6+88kpoNBrY7Xb8/e9/RzgcBsuyWLNmDdRqNXw+Hz788EMIgoC6ujpwHAePx4P+/n5YLBZaAuZwOGhMm4gycBxYpznS29sLt9tNH3feeeesbOf+++/HCy+8gJdffpkmc80FopiKnDSQUhrSHpA0fJiJWKpcLqdx1Hg8DrvdnlM8S61Wo6ioCEDijjybpvYsy6KmpgZAYn5p8qi5qSDWqbOoGqHOI5OeD+Pay8EqVDCe+yn6HLmY9//+J+j56TfQff930PfQDzH6r2chr6wHpzdBYikEK5Vh+Pk/YldzCwSGQWnIBVM80aZP8Pvg3ZImpsVPnbUaBoMN0ONRWLENWoxCCoCBBnGU4aj7MB8RrIAXi+FHA4LIRwQcBMTAoA9yvA8dnoAVf0I+DkMxqahKLIUo/94DuPJ7P8aNN94IhVyOrq4u/PF3v0sZSm40GnHVVVdBrVZjZGSExu3kcjnOOusscBxHR7cpFApUV1cDSAx153meWmSjo6O05EsQBASDwZSbw7lgOr15dTpdymOitp3kd0VG3xFsNhsKCgom3b8HH3wQ999/P9544420pV/HElFMRU4qyGQa4jIDEjGZmZjMIZFIYDab6cXO4XDkNC3GYDDQnrsDAwMpF+apMJlMyMvLgyAIaG9vz/iYKioqAABecwlMt/1k0vrF/KtuRv3TbyDvE5+lsVIi+nFXogF/dKAb3m2bER3ogRAJI+52IDYyiHBvB9yb/4u+fQmXbnHEA0hTM6ElefmJhg0Z4gaH/0U+PoIWAhjUIYgrMYr/wQD+B4P4f4leRwCAQkRxEdy4DE5cCTtuwTB+gH7ciiFcCgfmIQAJBAxBhr/BjCeQjzakv8jHRgYx8uKTiLnsmDdvHq7gXFDFI7DZ7XjqqadSrEWTyURbLR44cICWIplMJnqRP3DgAHieR2FhIdRqNeLxOAYGBqDX6yGXy8HzPNxuN+12lezqjcfjc9II/1g0upfJZFi2bFlK8hBJJiKJXun45S9/iZ/+9KdYv349li9fnushzhiimIqclJAEJRJLJRm/03WXsSwLk8lE61EdDgdtkJ4N+fn50Gq1EAQBvb29WdUUVldX0246pGn6VGi12oQFxDAYVhoy3haJ6ZF+tcXf+AksV38VeZ+5gS6jXrQCYFhAIgGnN0JZNx+ej4OUulgYSBq5xmp0iNmHM77ijkCCJ2HBKKTQIoZrMIKrYUcDQtBkGAllAeQhhiUI4LNw4HYM4Ax4IAWPQcjwHCz4B0zwp7kchjsOo/Xrn0GotxP5Vis+4TgCKQS0trbiv//9b8qyxcXFWLx4MQDgjTfeoJ9pXV0dFAoFfD4fOjo6wDAMysoSNb19fX3geZ4mzjgcDtqsged52lQfwJy4eo/V1Jjbb78dTzzxBP7yl7+gubkZt9xyC/x+P264IfE9u+6661LcxA888AB+/OMf48knn0RFRQWGhoYwNDREB07MBaKYipy0JMdSSWeZUCg07eQkIqjEbUV6rGa7b8XFxZDJZIhGo1klJKlUKuq2bWtry/giSxKYkmd3TgURUxLflVoKYL7sC8hb9xlU/PwJVD30PAquuw3mK24EYjEwLIeyH/8OLl/CBa2VpiYu8Wk6Fk3GyzDBAwnMiOJGjKAG2XsCxqKEgPPhwTcxhJXwgoGAQ1Dh/2BG2luaeAyd370WjFKNZXc/jKs+fw0AYOPGjejt7QUA+A/ugu3//oCVjfVQq9VwOBzYt28fAIAD0PRxv2XS6IHM2o1GoxgZGYFerwfDMNSLQmJ/4XA4xTo9Wbnyyivx4IMP4q677sLixYuxZ88erF+/nrrAe3p6aAMRAHj00UcRiUTw2c9+lg6ELywsxIMPPjhXhyCKqcjJD8uyUCqVKdmR03X7MgwDo9FIL3q5CCrHcSgtLQXDMPD5fFklJJWXl0MmkyEYDNIL+lSUlZVBJpPB5/Nl/B7SQam1tZUKsG/vVnh3vA9FVQPkRQkLK+5MuH9jbgeEcAgKf6Irkjee+zmOARhCokTkGoxCj5kVEzV4rIMbN2EYKsRhgwxvJCUzjcW98V/gI2EsPe00aoG+9ec/wv7fF9H7y+/B8Z8X4H7+EZx++ukAgMOHD8N/aDdabrgQ3JP3g2UYeL1eeL1esCxL63+dTic4jqPu3UAgQG/UotFoSiLSsUYQmJweuXDrrbeiu7ubzo1dsWIFfW3Tpk14+umn6f+7urogCMK4xz333DPNI84dUUxFTgkmcvtOpyaVYRgYDIYUQc02hqpQKFISkrxeb0bvk0gkNJGlu7s7o7irVCpFfX09AODgwYMZ3UyQ9m5+vx+9vb0IHN6L3vv+B30P3gH3e29g9OW/INC8F3mXfh7mz3wJpd/7JViVGnnhxHHY1Ul1f2Nip5iicb8DEghgIAc/40KaTCGiNO66HZoJY6hAojUhAKxZswYA0Oz0o++Z30FmTdTTysuq6Tnu6+vD6J5tEKIRxPs6YNIm6kiJhUVKrkh2MKlpDgaDtM40Go1SNy8RDJHjE1FMRU4pSC9U4vYNBoPT6oFKBJVYEg6HI+ssX4PBQGNm/f39Gb8/Pz8fBkNicPWRI5Nn6BLq6+shkUjgdDozsk45jkNdXR0AYN++fWAkRwXQ8cY/MfK3J9D909vQdutnEbH1Qr1oBRiWQ3ljomH+aEFVovYUgLRgTJu3WGxSQXV+3O1UlVWVaG7UIIzlSMTbNmOCGZwsC0athX//dlRXV8Os1SDKcugrqEXZPY+g6tfPwXLVzdDpdNQ96apeBO2q82H+9BdRVJFws5OsVZLcRbp3JYspx3FpY6Uz0ZAkG45VzPRk4LgQ02waHAPAP/7xDzQ0NEChUGDBggV4/fXXj9GeipwMcBwHpVJJ3WfhcHhapQfE5TsdQbVarVAqlYjH4xl3SCIDqFmWhdPpRH9//5Tvkcvl1HLauXNnRse9aNEiAEBzczOcMg19XiANFz4+j97t76PluvPRdfctOP3zN4BlWXTbnRjgEiIR7e1MXbEgANEoFduxWBEFBwFOSLAbqin3c7qUIvGZ8ZjATcnz6H/gu+j5+bfR9cObYJEn4sGaS6+BRKWBvLicCiBpvxiTSFHyzZ/AcsWXaQ0p8V5IpdKU0pfkrkcMw9DXBEGYs+kxophmzpyLKWlwfPfdd2PXrl1YtGgR1q1bN2H86MMPP8TVV1+NG2+8Ebt378bll1+Oyy+/HAcOHDjGey5yIsMwDBQKBY2jTrd8hghqcpZvNgLNsixKS0vBcRzC4XDGCUkqlYq6e9vb2zOqPZ0/fz40Gg0CgQBNkpmMwsJCNDUlxqptev99aFaeB0auhPkzX0LJ934FzZLVUNbNh6phIYRoBMGW/dCxwNKyQgDAVn0ZhMmatU/QkcmAOM5DIva6AQa4MbsDs0c+toQLMPWNUKjjMPx9icYZgfffSPxt2QfP1k0QJrgRSnbXpnsu3etTvXZMyKX90SnInItptg2Of/vb3+Kiiy7Cd7/7XTQ2NuKnP/0pli5dij/84Q9plw+Hw+N6RIqIAEeHkBOLIBaLzYigJtehZiOoUqmUJiR5PB5ajjIVRUVFtMNRc3PzlIkqEomE1uW1tLRgZGTieaGEs846CxKJBH19fXCvvQK1j72KcGcLRl9+Gr7t7yLYegiWq74CZf1CqJeswtCfHkDD1tcg5eMYkahw0FgO/fmXZXQ8yayED6UIIwIWz8KMrknimdPBDgn2IJEAVIAMPjOWRZhLfG8Ytx3h/m503/119P/mR3Bvfp1+h5ItymzF9HiYZSrwQk6PU5E5FdNcGhxv2bJlXP/FdevWTbj8fffdl9IfcrK5jCKnHkRQiYt2uolJpGxGIpGA53k4HI6sYrJqtZp2fRkeHs6oQxLDMKivr4dUKoXP58toskxxcTHKy8shCALee++9KROYdDodVq5cCQDYsGEDWl75K+z/fh6h1oMAAFlBMfiAD8GWffDv3gL/vm1Q8TEs8yVczx/KzNh54OCUxzIWFsBlcEKFOEYhxV9gwfPIw/AMTY8UAOyCCo8jHz5wUCKOekydle2paIJNmhDfedfdAoZlaSMKgeXo7FmSZAQcbXyhUiVc1rFYjN5sSaVS+j0h7l3yHSTxffLvYwov5PY4BZlTMc2lwfHQ0FBWy995550p/SEzLQkQObWQSCQpiUnT6evLsizy8vKooNrt9qwElXQ5AhIdkjJx3crlcsybNw8Mw2B4eHjCYeDJrFixAnq9HsFgEO++++6Ux7tq1SrU1NQgHo9jo82DgEwFTqNDxS/+jMoHnkbcPz4TeYHfhvm+xG/zHdaIdsXHU2ZYFpigef5Y8hDD12DDafCBhYBWKPEorPgr8tAGeU6eRQFAN2R4Fmb8GyZEwaICIXwFw9BlkDn8oTMR91yyeDF0PgfAsqj8xf+i9M5fw13eCJ/PB7lcTkuLgKNZvORmiTQYIC0wSWmVQqGAIAj08+A47riyVkXSc9IPB0/u1SoiMhlkAk0oFKKCKpfLsxrmTSAWKrFM7XY7FdhMsFqtiEaj8Hg86OnpQUVFxZRDwQ0GA2pra3HkyBF0dnZCqVSmHaxMkEqlOOuss7B+/XqMjo7io48+wqpVqyYcRs0wDC655BI899xzGBkZwZ4zP4fPXH4ZlJbENrSnnYXCW34IPh6D/Z9PI2a3gQGwytsLprgC+90hvG2shtfTh4X+oazu5NXg8Um4sAI+tNavQKigAgDQByCkVaP24+Wi1jKEzr4UMvAgecIDmzcgCgYecLBDAgck6IUM9o+XYCHgPLixCr6p90kiRRenRo/CAJZhcFpgCAO/ewKcRofax/8FBSfBxldfBQA0NDTQzzscDsNuT9TiFhYmYskk5EQSk4iYyuVyevPFMExaV/Exg0f2luaxTTg+bphTMc2lwXFBQUFODZFFRDKBNHggc1FJDFU6RU1kOjiOg8lkomPb7HY7TCZTRusiHZJisRgCgQC6u7tRXl4+paAWFRXB7/ejv7+fdtuZTFB1Oh3OOOMMbNq0iRbCr169ekJBlclk+PSnP41nn30WdqcTz//jRXzmM5+B1WpNlAmd/QkIsShsf/41fQ+rVOFM/yCicSkOcxps05WiW2HE2e5OGGKhRCtC4WOXplJ9NEs4DXmIYf55Z8Nw9ifTvm5pmA9Lw/yU5+7avCftshwELIYfq+GFKZM6Vo6D83Nfw1vvfAAAWHnachgCA3Dh47wbIdGooaWlBcDRLGjgaF2v0WiEWq2GIAjUUiXlTaTGWKVSpWT8Jlulx1pMBZ5+NFm951RkTt28uTQ4XrVq1bhp6m+++eakDZFFRLJhJjN9OY5Lcflmk5TEsizKyspoyUx3d3dGXZZqampoKOTQoUNTJhgVFRXhjDPOSJSydHfjvffem9Tlq9frcc011yAvLw8+nw/PP/88Wltb6euMRIqSb/8MhgsuR/Vv/4bS79yPWG8bzhxuxlmuTkj5OGwyDV40z8MOTRHisqOeIyHoz9j9mylqxKFHDMWIYCH8OAduXIFRfAcDuASujIRUALC/8Uz8Y9OH4BkGlUEH1pbnw3rtbSi67W5U/uwJePx+bNiwAQCwcuXKFHfuWIEdHh6mjewLCgrg8/kQi8XAcRw0Gk2Ky5dYqbl4SKaNGDPNmDl3895+++24/vrrsXz5cpx++ul4+OGHxzU4Li4upoNlv/nNb+Lss8/Gr3/9a1x88cV44YUXsGPHDvzpT3+ay8MQOckgiUlkhmksFoMgCLQJeTYQQSVCSixU0o1pqveWl5fTLkddXV1TWqgMw6ChoQFAwmtz6NAh1NfXT+q9KSsrA8dxeO+999DX14d33nkHZ5xxxoTzIQ0GA77whS/g1VdfRVdXF15++WWsWLECq1evhlQqhXb5GdAuPwMAIDFZoF6yCsEjB9AQsKNECOE9ZSF6FQbs0hajU1uP+a5e1PE+cKNDwBSx28FHf4HBR38x1amjfCfjJQFGroQQTk3G8hRUYJumGB32hMW8KDCMsysKoFu8CqxMDv2aCxAMBvGvF19EOBxGUVER7ZAkCAJ27dpFR60VFRWB53ka0y4pKYFEIqEDC/R6PXiepzdccrmcWqkTDWifVXIRx1NUTOe8NCbbBserV6/G888/jz/96U9YtGgRXnzxRbzyyiuYP3/+RJsQEcmJmcz0JTFU4raz2+0Zj14jgkos1M7OzilLvIigWq1WCIKAw4cPTzmyrbi4GOeccw4kEglsNhv+85//TJjYByQu9J/97Gdpn9qtW7fiqaeeQmdnanMGVipDzDEK3u8FBAFln/g0/p9VjStWLIZer4fT68N7nBHPKMrxUcOZiH/2FujXfTrFQmXzUpMOZwNZYSlMl1xJ/z8k1eCdhRfiBcaCDn8ErMDjHFcHVri7oa6sA6dKZPOOjIzgmWeeweDgIORyOS655BKaNLRz50709vaCYRgsXboUDMOgu7sbfr8fHMehuLgYXq+XJiOZTCb6b1KzTM/BZLW6s4RYGpM5jHCKNXv0eDzQ6/Vwu920nZeIyFTE4/EUV2+uiUmCIKT08NVqtVCr1RlZu/F4HH19ffRia7VakZeXN+l7BUFAZ2cntYTy8vLQ2Ng46b67XC68//77cLvdYBgG8+fPx/z58ye9mB85cgRvvfUW3bfa2lqcffbZ0LECGJkcR750EYBE/JQPBgAA5s/cAMeerdhnc6JZkQen9Ki1rWN4VHmHUB10wBQLQlpYitjg7GXiS8wFKP/xb2FvPYz3n3wELSoz7B+XvjAMg/l1tTgt7oLkw/VAPA7DeZ9C4c3fR0tLC/7z6quIITEh5zPXXEtj1Pv376dNMVatWoWqqiq4XC7s2bMHANDU1IS8vDy0tbUhFoshLy8PeXl5tL7YZDLROaZSqXRST8ZMX9fI+hp/+AtwE3gnJiIeCqH55z845a6xopiKiGQISUgilqlMJoNEIsna7UuSTUjJi1KppCO4Mnnv4OAgnE4nAMBoNKKgoGBKq8Vms+Hw4cMQBAFKpRJNTU00izQdsVgMO3bsQHt7O4BEsuBpp51GewinIxwO4/3338euXbsgCAJYQUD5cAeWLlkM+XA//Pu2wfqlb2P4uUcR97pgve4bsD39cOK4ADirFqKjcRUOHDiQ0o5RHY+ghInC4h2BKRpEnlwKqc+Zsm3OZEHcMXXzibGEGA6jUjVsMg2GZFoMyjTgmcS55FgWp51+Os477zx4Hrgdkb5OyIorYDz/UrDLz8KHO3bRzmtmzwhWw4OGnz0OQRBw6NAhKprLli1DQ0MDgsEgdu3ahWg0ioKCAjQ0NGBgYABOpxNSqRTV1dVwuVyIRCJQKBTQaDT0PKhUqkm/H7Mmpnf+PDcxve+Hp9w1ds5jpiIiJwosy0KhUCASiSAWiyESiSAej2cdR2UYBjqdDhzHwePx0EbnRqNxSmuXYRgUFhZCJpPBZrPB6XQiEAigtLR00hIw0vv3wIED9KJeXl6OsrKytEIskUhoEs22bdswOjqK9evXo7q6GgsXLkwbs5XL5Tj//POxaNEivP322+jq6kKntRqd/R6UG4uw+Et3QH/mudCtPA9CJAw+HIT7gzcBAAXXfxONlXVYw0kQ9Hnx/q9/gkMjTvRJNPBzMrRAhha9mm5LoxOgDXmhjUegiYehVpsgDcQhE+KJWTMCD0YAeLUGsWAQUYZFiJUiwErh56TwSpVwsnKEuPGZ1RZEUecfQbV3CMr/7gNnlEKiMyACgM8vxn61FTuffZ4mBlXb2rAg7kHxTd9DKBTCli1baNOGBQsWoKGhAX6/H3v37kU0GoVarUZtbS0cDge9KSoqKkIwGKTiqdVqqfeCxO7nAkHIIZv3lDLPjiJapiIiWSIIAhVT4Oh4t1wSRMLhMFwuF3ieHzfSbSp8Ph/6+vpoY/TCwkIYDIZJL7yRSARHjhyhrkS1Wo36+vpJfwt+vx+7d+9Gd3eiFy3HcWhsbERTU9OEZT6CIKD57fXYuWcfBuNH90ev1WLBokWYP38+nL+7C4EDOyC1FKDm9y9i8Mlfw/3OazCcewmcb7wMAIiBwaBMC5tMA1dRNeysHG63O6PzkylGpQJFajnM9n4URr3QjfSlvC4vrYLu9vuxddPbaBmwIfZxglRRgRXVOzbAYB9A2Q9/A4+xAFu2bEEwGATLsli6dCnq6urg9Xqxb98+xGIxqNVqLFy4EKFQCH19ie1YLBbo9XqahERutOLxOL2Bm0pMZ8sybfjez8DJs7RMwyEc/uWPTrlrrCimIiI5MjaOmqvbNx6Pw+l00gxOlUoFnU6X0Xqi0Sj6+/upy1ir1aKwsHDSWlZBEDA8PIy2tja6TdJecLK43PDwMHbt2kWbD8jlcjQ2NqKmpmZSq7jjXy9gx7ub0JdXhljShBgrE0Ne72GUaJRY9PPH0PLFCyCEEyUhUksBoqO2FDMn71OfR/41X0MgEMBgXx/6t7wDd5RHQKGFZ6AX7q5WhFgJeDAQuEQpEseykMSikAhxyIU4VPEIStZ+CrG3XoIh4IY5z4SG3zyfaAcIwP6v5zD8/KOQmCyI6vIwKFVjqLQR/a6jCV8FBQW0G5QQjcDv9eDAkTZ0dHQAOFq7azQaYbPZ0NLSAp7nodVqsXDhwpSB7kajERaLBXa7nbrg1Wo1vVFLnm40GbMmpv/z09zE9Nc/PuWusaKYiohMA0EQEA6HaV0my7KQy+VZZ16OjaNyHAeDwZBR+YwgCBgdHaWTlliWRX5+Pkwm05RWant7O22CwnEcSkpKUFpaOqG7WRAE9Pb2Ys+ePbTJAMdxqKioQG1tbdptCoKAwKHdCDkd2Pn8/6I3rxR2rSVlGRXHwuQdhXG0D0a/A9bKalT88DeIOUYR6u9CZKAHxvM+BVaRviQo0LIf3fd8jYpv2Q9/A9c7/4Hnw7cgr2pAuLsViMehP+9TKLr5+/Du+hDej96B6eIroSivoedjYGAAnS3NaNuxFU6JkvbbBYDq6mqcfvrpKCkpAcMwCAQCOHz4MI4cOUI//9raWixdupTOmCU1viaTCY2NjfB4PDRDWq/Xw2q1wuFwgOd5SKVSGAyGlIYNmXz+wOyJaf23781JTFt+c9cpd40VxVREZJqMdfsCuVupoVAIbrebJjmpVCpotdqMxDkYDGJgYCClLV1hYSHUavWk73M6nWhvb6eZuBKJBKWlpSguLp5QVHmeR1dXFw4fPkzjfkDC0qqurkZ5efk4d7UQi6L/d/cgOjIE7Y3fQ5fLi+Z33oAtzoBnU13kHMvCkp8PIyuA2f42DAo5mr7zM+hNeRO606POUQQO7ASrUkO77Ax0/vAmhNqbIcnLR+XP/xcxrwuK0ioIggCPxwO73Q6Hw4Hh4WEMDQ1R6zAZoxDF/LPOQ1NTE21a73a70dzcjM7OTvo5WSwWLFmyBGazeZzVX1FRgdLSUgwODlIXtcFgQH5+PhVSiUQCg8FAv0Mcx2UVixfFdO4RxVREZIYYm+3LcRxkonpWQgAAGZdJREFUMlnWVirP8zQxiaxHp9NldHElpTfDw8PUWtLpdMjPz5/UFSsIAkZGRtDZ2Um3K5FIUFxcjKKiognfS97X2tqaMtScxHDLy8tRVFQ0YRzY89E76H3sfoQWroKroAL9A0NwcHJEJukSpVarodVqoVKpoFAoaLcqjuNoyz1BEBB1O+HrbAUshYjJFPD7/fD5fPD5fBPWCmu1WpSVlaHYqEeeawiFZ14AiVaPSCSC3t5edHR0pMxatlgsmDdvHm3j2NbWljIdpqGhAXK5HH19ffQmx2q1QqvVwul0QhCEcUKaaZw05TzOlph+M0cx/a0opic9opiKzCbprFSpVAqpVJq1lRoOh+F2u6koymQy6HS6jHr7xmIxDA8Pp1iNer0eFotlUlHleR7Dw8O04xKQEEaz2YzCwkIYjcYJjyMcDqOzsxMdHR0p2wUSbs7CwkIUFBTAbDZPmrUsCAJcLhcG+/vQ8uffwqfQwK81wS9X5zzJZyykiQaZ0FNQUIDCwkIo4lF4t78LzeKVCCvU6O/vR39/P2w2W8qNQklJCRobG2GxWOB2u9HT00NjySzLory8HMXFxXA4HBgdHYUgCNSNDoC6yKVSKfR6/bSEFJhFMb3tJ7mJ6e/vPuWusaKYiojMAmOtVNJNKdtGDzzPw+fzpYxhUyqV0Gq1GWUPh0Ih2Gw26sIFEpaq2WyetCUhsTj7+vpSui0pFApYrVZYrVY6lzMdbrcb3d3d6OvrGyesDMMgLy8PZrOZNirQaDRpY619v/4BfLs+ROFX7oD+rIsQCATg9Xrh9XoRCoUQDAYTf4f6EXaMQl5aCUikYFkWDMOAi0XgXf8PSGNRWNach6KzL6KNMpI9BiRmffjPv4E9FIPfWoawPPX4dDodKisrUVVVBYVCgdHRUfT396dkF1ssFlRWViIej2NoaIiKpEajQUFBAfx+P42Jjq0lzVVIgdkT07qv35OTmB555J5T7horiqmIyCyRzkrN1fUbi8WogBDUajXUanVGohoMBjE8PJwiqmq1ekIhS8bn82FgYAA2my3FMtRqtbBYLDCbzZMKazAYxODgIAYHBzE8PIxAIDBuGWKhGQwG6PV6qNVqaDQaqNVqSDgO7CTHyIcCaLnxk0A8RjsTEaLOUbTf9jkIsSgKbvwOtOdegmAwCJ/PB4/HA4/HA5fLlXaIO8MwsFgsKC4uRnFxMXQ6HQKBAGw2GwYHB2lMlGEYWK1WlJWVAUg0yCDHKJFIaHa12+2mMVmdTgeJRJIyEDyXvs+EWRPTW+7OTUwf/ckpd40VxVREZJYRBIE2eiBIJBJIpdKsRTUSicDr9aYItEqlSohOBlZvKBTC6OhoijUlkUhgNBqnzB6Ox+MYHR2FzWajNZEEpVIJk8lE1zNZNrDf78fw8DBGR0dp44LJ+h2TObMKhQIymQwymQxSqRQcx4FlWbAMA+eGf4IPBqCatxSy6kbE43HE43FEIhGEvB6EQyGEBaSct3TbMer1MAhRFNc3wVpWDolEAq/XC7vdjpGRkZQbAZlMhsLCQhQWFtIBBsSDwDAMPR/J1qhEIoFer0c8HqfHnGsYIJnZEtPar9yVk5i2Pn7vKXeNFcVUROQYwfM87ZpEyOVCSspxfD5fyjg3pVIJlUqVUTlFJBKBw+GAy+VK2R+VSgW9Xg+9Xj+pxRuJRDAyMoKRkZEUi4ug1WqppanVaieN08bjcXi9XrhcLrhcLng8Hvj9/hQRmkk4joNarYZOp4NOp4Ner4fJZKK1vcFgkO7LWIuVYRjawtFoNFKhTRZpg8EAs9mMUCiUIr4ajQZyuTzlM8u1x/NYRDGde8R2giIixwgSEyMWExm1FY1GsxJVMm9VLpcjEonA5/MhEokgGAwiGAxCKpVCrVZPGn+TyWQoKChAfn4+vF4vnE4n/H4/AoEAAoEAhoaGqOBotdpxF3yZTEbdn7FYDE6nE06nEw6HA6FQiMY1SZcfuVwOrVZLXbdk/1iWpTW1BoNh3H7GYrGU2Gg0GkUkEkE0GqXWXfLNAMMwYFmWZvdKpVLI5XL6UKvV9Dwn35S4XC709vbC4/GMmzdLhrybzWaYTCZEIhE4nU60trbSmwiWZalVTl5PHoqg0WgQj8fpunOtRz7mCACyHZJ0SplnRxHFVETkGEPcluTimiyq2bh/SRtDIqqBQID2+XW5XGAYhlqrE2UAsyxLLVHyPrfbTUWGxFiVSiU0Gg00Gg2USmWKSEskElgsFlgsiUYMpFaWrCsQCCAcDiMcDtM2hmT/FQoFlEolvTkgD3JzIZVK6XazQRAEen4jkQgikQj8fj/sdjvC4TCCwSACgUBa9zLpnWwwGGA0Gumwbo/Hg46OjhRLVS6XpyzjcrmoiEokEmi1WjoTl5BrDfKcIM4zzRhRTEVE5gCGYaj1lCyqsVgMsVgMHMel1E5OBYklarVaal3yPE//LZFIqGhN5FaUSqVUFElZTnLWbDAYxMjICFiWpXFaUuuZLP4kvklmEsdiMfh8Pjq3k1jAPM/T9U4GsV5pjPTjTN1keJ6nj3g8Pi6ZaCLIDYdWq6UPjUaDaDQKv98Pt9uNgYGBcXNFidiyLItgMJgSQ5ZIJNBoNGBZNmU/ck0+m0tymU96qs4zFcVURGQOSRZVYqGS5BnSwF4qlWZsyXAcRwWBWGChUIhmA3u9Xkil0pRmB+mQy+XIz89Hfn4+otEotVL9fj/i8XiK1UoEaayVSfaXNCVIduMSFysR03A4jFAohHA4TN24xJojIjnW/ZoJLMvShCW5XE6PW6FQ0BsBQRDoeXK73RgcHBxXz0qsTJ1OB5lMhlAoNK75g1wuh0qlAsuy9OYoeR9yGYQw54iWacaIYioichzAMAy1vohwxGIxmgkciUSyslaJC1WhUIDneWpdJguV1+ulJRkkUzbdeqVSKYxGI4xGIwRBQCgUSomvxuNx+u/k7ctkMuq2Tc7CJTcGZP+MRmPaYyClRcTSJDFSnufHJTwRi5VYsRKJBBKJhFqxxO1LziVxOZM4bLrzp1KpaIyX4zgq/qThAtmuUqmEUqmkngUixOQcZOpdOC7hkX3MNNvlTxJEMRUROc4gySkymYy6fYn7klyox4rFVOtTqVRQqVSIx+MIhUIIhUI0szhZCInokbjl2HUTK5Q0fCBiTyxMsm7StCJdNm6ytU0e5EYinTuXiBIRRYIgCFRYk88PGTxAzh25eZiscEEqldLjIhnR0WgU4XAYHo9nXGxVLpdDqVRCIpFQkU4+36R054QVUZGsEcVUROQ4hYiOVCpNsVYBUKEgFm2mwkrKQtRqNS3VIe5V8n+SIQwcnVxC9mOsQCQnQRE3riAIVIiI25ZYg2Ot7WMNOR5yw0AscpZlUxKV0s1MTV6eiPXYhhzZxLlPBMSYaeaIYioicgKQbK0SC4wIExFWILsLOinVIXFDYtURUUnOMiYkW5XJ1mVyUg2xJElCVDJEaMmNAXkkx4mJOzfZ8hxrVRKLlTySrVly/GTfyI0AcS+ThK9YLEazltNl9UokEnrOpVIpPedjrW2y7hMpsShjxJhpxohiKiJyAkESliQSyThhBZDiCh6bBTuZuCavV61Wp8QYifgRV2k6qzI5VpnOZZssfERoZwMivGPFmXSgmizLl5xTIp7A0fOZ3MYRODmt0HSIlmnmiGIqInKCMlZYk8tCiFWXnAVLBI/8zVRcCcQKJlYd+ZtcljJZxm2ysI6NiZIH2fZYiGVKxJI8iOWanJyUCckWK7Esk0U4Xaz3VBHQFHjkYJnOyp4c94hiKiJyEpCcDZwsrGNdpsliQ0RtsvrNsdsg4pNMsoiTv8nbJ0KYbDXPNmMze8m5SXbHJtelTpTRm2xhnzICmozYASljRDEVETkJIWIilUpTXJ9EPMa6QwljrcZMRDZ5W+kYazmmi4cmPyZirBVL9i95P8da3ck3EcRynqqpfrJ4npICKpITopiKiJzkJAsPYSKBSyewhGS3bDoX7UTCQ1zGM02y6zdZoJPd3JORToBF8RxDDjFTMQFJRETklCGdwI214sZm0mYSj5wo/jlZPHRs/Wjy/oz9m7w/mQ68Sr4BSH6IwpkBYjZvxohiKiIiAiA1RpjM2GSfdO7Zscsea8ZaysnuX1E0p4EophkjiqmIiMikpHMTJzNRpu3Y15L/jn3/RBbr2L9jrV7RNTu7CHzike17TkVEMRUREZkWk7lwRU5w4gLAZmlpxk9Ny/QkbNkhIiIiIiJybBEtUxERERGRtIgdkDJHFFMRERERkfSICUgZI4qpiIiIiEh6RDHNGFFMRURERETSIgg5ZPOemloqJiCJiIiIiIhMF9EyFRERERFJj+jmzRhRTEVERERE0iOKacaIYioiIiIikhaxNCZzxJipiIiIiEh6+BwfOfDII4+goqICCoUCK1aswLZt2yZd/h//+AcaGhqgUCiwYMECvP7667lteIYQxVREREREJC3EMs32kS1/+9vfcPvtt+Puu+/Grl27sGjRIqxbtw7Dw8Npl//www9x9dVX48Ybb8Tu3btx+eWX4/LLL8eBAweme8g5wwhzMeJhDvF4PNDr9XC73dDpdHO9OyIiIiLTZqava2R9patuAyuRZ/VePhZG75bfZ7UvK1aswGmnnYY//OEPiXXwPEpLS3HbbbfhjjvuGLf8lVdeCb/fj9dee40+t3LlSixevBiPPfZYVvs7U5xyMVNy7+DxeOZ4T0RERERmBnI9m2nbiI+Gs04o4uORlH0iyOVyyOXjhTkSiWDnzp2488476XMsy2Lt2rXYsmVL2m1s2bIFt99+e8pz69atwyuvvJLVvs4kp5yYer1eAEBpaekc74mIiIjIzOL1eqHX66e9HplMhoKCAvTv+FNO79doNOOusXfffTfuueeeccuOjo4iHo/DarWmPG+1WnH48OG06x8aGkq7/NDQUE77OxOccmJaVFSE3t5eaLXaYz4yyuPxoLS0FL29vSeFi1k8nuOXk+lYAPF4pkIQBHi9XhQVFc3A3gEKhQKdnZ2IRCI578/Y62s6q/Rk4pQTU5ZlUVJSMqf7oNPpTooLAkE8nuOXk+lYAPF4JmMmLNJkFAoFFArFjK4zHWazGRzHwWazpTxvs9lQUFCQ9j0FBQVZLX8sELN5RURERETmDJlMhmXLlmHjxo30OZ7nsXHjRqxatSrte1atWpWyPAC8+eabEy5/LDjlLFMRERERkeOL22+/Hddffz2WL1+O008/HQ8//DD8fj9uuOEGAMB1112H4uJi3HfffQCAb37zmzj77LPx61//GhdffDFeeOEF7NixA3/6U24x3plAFNNjiFwux913333SxA7E4zl+OZmOBRCP52TnyiuvxMjICO666y4MDQ1h8eLFWL9+PU0y6unpAcsedaSuXr0azz//PH70ox/hBz/4AWpra/HKK69g/vz5c3UIp16dqYiIiIiIyEwjxkxFRERERESmiSimIiIiIiIi00QUUxERERERkWkiiqmIiIiIiMg0EcV0lvn5z3+O1atXQ6VSwWAwZPSeL37xi2AYJuVx0UUXze6OZkguxyMIAu666y4UFhZCqVRi7dq1aG1tnd0dzQCHw4FrrrkGOp0OBoMBN954I3w+36TvOeecc8Z9Nl/96leP0R6ncqKPrBpLNsfz9NNPj/scjkWDgUx499138alPfQpFRUVgGCajfrGbNm3C0qVLIZfLUVNTg6effnrW91NkZhHFdJaJRCL43Oc+h1tuuSWr91100UUYHBykj7/+9a+ztIfZkcvx/PKXv8Tvfvc7PPbYY9i6dSvUajXWrVuHUCg0i3s6Nddccw0OHjyIN998E6+99hreffdd3HzzzVO+76abbkr5bH75y18eg71N5WQYWZVMtscDJLoHJX8O3d3dx3CPJ8bv92PRokV45JFHMlq+s7MTF198Mc4991zs2bMH3/rWt/DlL38ZGzZsmOU9FZlRBJFjwlNPPSXo9fqMlr3++uuFyy67bFb3Z7pkejw8zwsFBQXCr371K/qcy+US5HK58Ne//nUW93ByDh06JAAQtm/fTp/773//KzAMI/T390/4vrPPPlv45je/eQz2cHJOP/104etf/zr9fzweF4qKioT77rsv7fJXXHGFcPHFF6c8t2LFCuErX/nKrO5npmR7PNn8nuYSAMLLL7886TLf+973hHnz5qU8d+WVVwrr1q2bxT0TmWlEy/Q4ZdOmTcjPz0d9fT1uueUW2O32ud6lnOjs7MTQ0BDWrl1Ln9Pr9VixYsWE45WOBVu2bIHBYMDy5cvpc2vXrgXLsti6deuk733uuedgNpsxf/583HnnnQgEArO9uymQkVXJ5zSTkVXJywOJkVVz+RkQcjkeAPD5fCgvL0dpaSkuu+wyHDx48Fjs7oxzPH82IpkjdkA6Drnooovw6U9/GpWVlWhvb8cPfvADfOITn8CWLVvAcdxc715WkJFIx9u4pKGhIeTn56c8J5FIYDKZJt2vz3/+8ygvL0dRURH27duH73//+2hpacFLL70027tMOVlGVhFyOZ76+no8+eSTWLhwIdxuNx588EGsXr0aBw8enPNBFtky0Wfj8XgQDAahVCrnaM9EskG0THPgjjvuGJf8MPYx0UUgE6666ipceumlWLBgAS6//HK89tpr2L59OzZt2jRzB5HEbB/PsWS2j+Xmm2/GunXrsGDBAlxzzTV45pln8PLLL6O9vX0Gj0JkKlatWoXrrrsOixcvxtlnn42XXnoJFosFjz/++FzvmsgpimiZ5sD//M//4Itf/OKky1RVVc3Y9qqqqmA2m9HW1obzzz9/xtZLmM3jISORbDYbCgsL6fM2mw2LFy/OaZ2TkemxFBQUjEtuicVicDgcWY1xWrFiBQCgra0N1dXVWe9vLpwsI6sIuRzPWKRSKZYsWYK2trbZ2MVZZaLPRqfTiVbpCYQopjlgsVhgsViO2fb6+vpgt9tTxGgmmc3jqaysREFBATZu3EjF0+PxYOvWrVlnOGdCpseyatUquFwu7Ny5E8uWLQMAvP322+B5ngpkJuzZswcAZu2zSUfyyKrLL78cwNGRVbfeemva95CRVd/61rfoc3M9soqQy/GMJR6PY//+/fjkJz85i3s6O6xatWpcmdLx8tmIZMFcZ0Cd7HR3dwu7d+8WfvKTnwgajUbYvXu3sHv3bsHr9dJl6uvrhZdeekkQBEHwer3Cd77zHWHLli1CZ2en8NZbbwlLly4VamtrhVAoNFeHQcn2eARBEO6//37BYDAIr776qrBv3z7hsssuEyorK4VgMDgXh0C56KKLhCVLlghbt24V3n//faG2tla4+uqr6et9fX1CfX29sHXrVkEQBKGtrU249957hR07dgidnZ3Cq6++KlRVVQlnnXXWMd/3F154QZDL5cLTTz8tHDp0SLj55psFg8EgDA0NCYIgCNdee61wxx130OU/+OADQSKRCA8++KDQ3Nws3H333YJUKhX2799/zPc9Hdkez09+8hNhw4YNQnt7u7Bz507hqquuEhQKhXDw4MG5OgSK1+ulvwsAwkMPPSTs3r1b6O7uFgRBEO644w7h2muvpct3dHQIKpVK+O53vys0NzcLjzzyiMBxnLB+/fq5OgSRHBDFdJa5/vrrBQDjHu+88w5dBoDw1FNPCYIgCIFAQLjwwgsFi8UiSKVSoby8XLjpppvoRWWuyfZ4BCFRHvPjH/9YsFqtglwuF84//3yhpaXl2O/8GOx2u3D11VcLGo1G0Ol0wg033JByU9DZ2ZlybD09PcJZZ50lmEwmQS6XCzU1NcJ3v/tdwe12z8n+//7/t3eHIM3EYRzHf+fGVYuwMDUI02AZhqFpC4pgM1jvgigDBxPFYFC0GcTplgQRFkSwT5vWqajBJGsrCoI2TfJ/0zuQjfflfR/deF++n3bcM3gWji83tl2p5Pr7+53v+y6VSrlqtdo4l06nXRiGn+ZPTk7c4OCg833fDQ8Pu0ql0uaNf+1P3s/i4mJjNhaLuampKXd7e9uBrZtdXFy0vEZ+7h+GoUun002vSSaTzvd9NzAw8On6wb+BR7ABAGDEt3kBADAipgAAGBFTAACMiCkAAEbEFAAAI2IKAIARMQUAwIiYAgBgREwBADAipgAAGBFTAACMiCnwlzKZjHK5nHK5nLq7u9XT06O1tTW1+rtr55zGx8c1OTnZOP/y8qLe3l6tr6+3e3UAX4yYAgblclnRaFRXV1fa29vTzs6ODg4OmuY8z1O5XNb19bWKxaIkKZvNKh6PE1PgP8DDwQGDvr4+FQoFeZ6noaEh3d/fq1AoaG5urmk2Ho9rf39fQRDo6elJp6enuru7UzTKZQj867gzBQxGR0fleV7jeGxsTLVaTR8fHy3nZ2ZmND09ra2tLW1vbyuRSLRrVQDfiJgCbfT29qabmxtFIhHVarVOrwPgixBTwODy8vLTcbVaVSKRUCQSaTm/vLysrq4unZ2dqVgs6vz8vB1rAvhmxBQwqNfrWlpa0sPDg46Pj1UqlZTP5yVJq6urCoKgMVupVHR4eKijoyNNTExoZWVFYRjq9fW1U+sD+CLEFDAIgkDv7+9KpVJaWFhQPp/X/Py8JOnx8VH1el2S9Pz8rNnZWW1sbGhkZESStLm5qVgspmw227H9AXwNz7X6URyA38pkMkomk9rd3e30KgA6jDtTAACMiCkAAEZ8zAsAgBF3pgAAGBFTAACMiCkAAEbEFAAAI2IKAIARMQUAwIiYAgBgREwBADD6AZvH7+OU0Pz2AAAAAElFTkSuQmCC", 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      " ] @@ -241,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +246,7 @@ "nn_layers = [2,24,12,1]\n", "\n", "# Initialize DeepTDA model\n", - "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, layers=nn_layers)" + "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, model=nn_layers)" ] }, { @@ -267,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -354,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -393,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -426,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -480,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -522,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -564,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -583,7 +575,7 @@ " n_cvs=2,\n", " target_centers=target_centers, \n", " target_sigmas=target_sigmas,\n", - " layers=nn_layers)" + " model=nn_layers)" ] }, { @@ -596,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -683,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -722,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -756,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -809,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -851,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -890,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -899,7 +891,7 @@ "target_sigmas = [0.2, 0.2, 0.2]\n", "nn_layers = [2,24,12,1]\n", "# MODEL\n", - "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, layers=nn_layers)" + "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, model=nn_layers)" ] }, { @@ -912,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -999,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1038,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1071,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1127,7 +1119,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1176,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1187,7 +1179,7 @@ "target_sigmas = [0.2, 1.5, 0.2]\n", "nn_layers = [2,24,12,1]\n", "# MODEL\n", - "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, layers=nn_layers)" + "model = DeepTDA(n_states=n_states, n_cvs=1,target_centers=target_centers, target_sigmas=target_sigmas, model=nn_layers)" ] }, { @@ -1199,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1285,7 +1277,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1322,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1366,7 +1358,7 @@ ], "metadata": { "kernelspec": { - "display_name": "mlcvs_test", + "display_name": "graph_mlcolvar_test", "language": "python", "name": "python3" }, @@ -1380,7 +1372,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.18" }, "orig_nbformat": 4 }, From 56ad2a9c5b66e42903823ee41c56a69b8ec0d523 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 10 Dec 2024 17:09:10 +0100 Subject: [PATCH 192/267] Fixed json notebook --- docs/notebooks/tutorials/cvs_committor.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/notebooks/tutorials/cvs_committor.ipynb b/docs/notebooks/tutorials/cvs_committor.ipynb index 533b1e27..5491b533 100644 --- a/docs/notebooks/tutorials/cvs_committor.ipynb +++ b/docs/notebooks/tutorials/cvs_committor.ipynb @@ -13,7 +13,7 @@ ] }, { - "cell_type": "mmodel= + "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", @@ -449,7 +449,7 @@ ], "metadata": { "kernelspec": { - "display_name": "mlcvs_test", + "display_name": "graph_mlcolvar_test", "language": "python", "name": "python3" }, @@ -463,7 +463,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.18" } }, "nbformat": 4, From cfdc531ab7c2f569aff11a692da8a0cf231faf77 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 10 Dec 2024 17:10:24 +0100 Subject: [PATCH 193/267] Added np.ndarray to typings --- mlcolvar/utils/timelagged.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/mlcolvar/utils/timelagged.py b/mlcolvar/utils/timelagged.py index 4edaac02..59a1297e 100644 --- a/mlcolvar/utils/timelagged.py +++ b/mlcolvar/utils/timelagged.py @@ -195,7 +195,7 @@ def progress(iter, progress_bar=progress_bar): def create_timelagged_dataset( - X: Union[torch.Tensor, DictDataset], + X: Union[torch.Tensor, np.ndarray, DictDataset], t: torch.Tensor = None, lag_time: float = 1, reweight_mode: str = None, @@ -289,7 +289,7 @@ def create_timelagged_dataset( tprime = t # find pairs of configurations separated by lag_time - if isinstance(X, torch.Tensor): + if isinstance(X, torch.Tensor) or isinstance(X, np.ndarray): x_t, x_lag, w_t, w_lag = find_timelagged_configurations( X, tprime, @@ -318,11 +318,14 @@ def create_timelagged_dataset( data[i] = data[i][interval[0] : interval[1]] x_t, x_lag, w_t, w_lag = data - if isinstance(X, torch.Tensor): + if isinstance(X, torch.Tensor) or isinstance(X, np.ndarray): dataset = DictDataset({"data": x_t, "data_lag": x_lag, "weights": w_t, - "weights_lag": w_lag}) + "weights_lag": w_lag}, + data_type='descriptors') + return dataset + elif isinstance(X, DictDataset): # we use deepcopy to avoid editing the original dataset dataset = DictDataset(dictionary={"data_list" : copy.deepcopy(X[x_t.numpy().tolist()]["data_list"]), @@ -335,7 +338,7 @@ def create_timelagged_dataset( dataset['data_list'][i]['weight'] = w_t[i] dataset['data_list_lag'][i]['weight'] = w_lag[i] - return dataset + return dataset def test_create_timelagged_dataset(): From 73745d2480895ad93649626d9fc598217cbc88c8 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 10 Dec 2024 18:11:27 +0100 Subject: [PATCH 194/267] Fixed index of mask for separate datasets --- mlcolvar/core/loss/committor_loss.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index 00c467d1..7d767c55 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -159,10 +159,9 @@ def committor_loss(x: torch.Tensor, device = x.device dtype = x.dtype - # Create masks to access different states data - mask_A = torch.nonzero(labels.squeeze() == 0) - mask_B = torch.nonzero(labels.squeeze() == 1) + mask_A = torch.nonzero(labels.squeeze() == 0, as_tuple=True) + mask_B = torch.nonzero(labels.squeeze() == 1, as_tuple=True) if separate_boundary_dataset: if _is_graph_data: # this needs to be on the batch index, not only the labels @@ -171,24 +170,23 @@ def committor_loss(x: torch.Tensor, mask_var_batches = torch.isin(batch, aux) mask_var_batches = (batch[mask_var_batches]) else: - mask_var = torch.nonzero(labels.squeeze() > 1) + mask_var = torch.nonzero(labels.squeeze() > 1, as_tuple=True) mask_var_batches = mask_var else: mask_var = torch.ones(len(x), dtype=torch.bool) mask_var_batches = mask_var # setup atomic masses - atomic_masses = atomic_masses.to(dtype).to(device) + atomic_masses = atomic_masses.to(device) + # mass should have size [1, n_atoms*spatial_dims] if _is_graph_data: - atomic_masses = atomic_masses[node_types[mask_var_batches]].unsqueeze(-1) - else: atomic_masses = atomic_masses.unsqueeze(0) # Update weights for bc confs using the information on the delta_f - delta_f = torch.Tensor([delta_f]) + delta_f = torch.Tensor([delta_f]) #.to(device) # B higher in energy --> A-B < 0 if delta_f < 0: w[mask_B] = w[mask_B] * torch.exp(delta_f.to(device)) @@ -200,7 +198,7 @@ def committor_loss(x: torch.Tensor, w = w.unsqueeze(-1) # ============================== LOSS ============================== # Each loss contribution is scaled by the number of samples - + # 1. VARIATIONAL LOSS # Compute gradients of q(x) wrt x grad_outputs = torch.ones_like(q[mask_var]) @@ -219,7 +217,6 @@ def committor_loss(x: torch.Tensor, grad_square = scatter_sum(grad_square, mask_var_batches, dim=0) grad_square = grad_square * w[mask_var] - # variational contribution to loss: we sum over the batch loss_var = torch.mean(grad_square) if False: @@ -237,7 +234,6 @@ def committor_loss(x: torch.Tensor, # TODO maybe there is no need to detach them for logging return loss, gamma*loss_var.detach(), alpha*gamma*loss_A.detach(), alpha*gamma*loss_B.detach() - class SmartDerivatives(torch.nn.Module): """ Utils to compute efficently (time and memory wise) the derivatives of q wrt some input descriptors. From 72e180420a0ce916db006abdb7a1bc1c1f825a22 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 10 Dec 2024 18:16:38 +0100 Subject: [PATCH 195/267] Temporary fix with new signature of BaseCV --- .../tutorials/adv_newcv_scratch.ipynb | 49 +++++++++---------- 1 file changed, 22 insertions(+), 27 deletions(-) diff --git a/docs/notebooks/tutorials/adv_newcv_scratch.ipynb b/docs/notebooks/tutorials/adv_newcv_scratch.ipynb index 972775f1..b405b274 100644 --- a/docs/notebooks/tutorials/adv_newcv_scratch.ipynb +++ b/docs/notebooks/tutorials/adv_newcv_scratch.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -72,7 +72,7 @@ "from mlcolvar.cvs import BaseCV\n", "\n", "class AutoEncoderCV(BaseCV, lightning.LightningModule):\n", - " BLOCKS = ['norm_in','encoder','decoder'] " + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] " ] }, { @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +104,7 @@ " with the input 'data'.\n", " \"\"\"\n", " \n", - " BLOCKS = ['norm_in','encoder','decoder'] " + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] " ] }, { @@ -136,12 +136,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class AutoEncoderCV(BaseCV, lightning.LightningModule):\n", - " BLOCKS = ['norm_in','encoder','decoder'] \n", + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] \n", "\n", " def __init__(self,\n", "# ================================================ LOOK HERE 0.0 ================================================ \n", @@ -165,7 +165,7 @@ " Available blocks: ['norm_in', 'encoder','decoder'].\n", " Set 'block_name' = None or False to turn off that block\n", " \"\"\"\n", - " super().__init__(in_features=encoder_layers[0], out_features=encoder_layers[-1], **kwargs)\n", + " super().__init__(model=encoder_layers, **kwargs)\n", " \n", "# ================================================ LOOK HERE 0.0 ================================================ \n" ] @@ -185,19 +185,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class AutoEncoderCV(BaseCV, lightning.LightningModule):\n", - " BLOCKS = ['norm_in','encoder','decoder'] \n", + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] \n", " \n", " def __init__(self,\n", " encoder_layers : list, \n", " decoder_layers : list = None, \n", " options : dict = None, \n", " **kwargs):\n", - " super().__init__(in_features=encoder_layers[0], out_features=encoder_layers[-1], **kwargs)\n", + " super().__init__(model=encoder_layers, **kwargs)\n", "\n", "# ================================================ LOOK HERE 0.0 ================================================ \n", " \n", @@ -224,21 +224,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mlcolvar.core.loss import MSELoss\n", "\n", "class AutoEncoderCV(BaseCV, lightning.LightningModule):\n", - " BLOCKS = ['norm_in','encoder','decoder'] \n", + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] \n", " \n", " def __init__(self,\n", " encoder_layers : list, \n", " decoder_layers : list = None, \n", " options : dict = None, \n", " **kwargs):\n", - " super().__init__(in_features=encoder_layers[0], out_features=encoder_layers[-1], **kwargs)\n", + " super().__init__(model=encoder_layers, **kwargs)\n", "\n", " # ======= OPTIONS ======= \n", " # parse and sanitize\n", @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -291,14 +291,14 @@ "from mlcolvar.core.transform import Normalization\n", "\n", "class AutoEncoderCV(BaseCV, lightning.LightningModule):\n", - " BLOCKS = ['norm_in','encoder','decoder'] \n", + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] \n", " \n", " def __init__(self,\n", " encoder_layers : list, \n", " decoder_layers : list = None, \n", " options : dict = None, \n", " **kwargs):\n", - " super().__init__(in_features=encoder_layers[0], out_features=encoder_layers[-1], **kwargs)\n", + " super().__init__(model=encoder_layers, **kwargs)\n", "\n", " # ======= OPTIONS ======= \n", " # parse and sanitize\n", @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -442,7 +442,7 @@ " with the input 'data'.\n", " \"\"\"\n", " \n", - " BLOCKS = ['norm_in','encoder','decoder'] \n", + " DEFAULT_BLOCKS = ['norm_in','encoder','decoder'] \n", " \n", " def __init__(self,\n", " encoder_layers : list, \n", @@ -465,7 +465,7 @@ " Available blocks: ['norm_in', 'encoder','decoder'].\n", " Set 'block_name' = None or False to turn off that block\n", " \"\"\"\n", - " super().__init__(in_features=encoder_layers[0], out_features=encoder_layers[-1], **kwargs)\n", + " super().__init__(model=encoder_layers, **kwargs)\n", "\n", " # ======= OPTIONS ======= \n", " # parse and sanitize\n", @@ -625,7 +625,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pytorch", + "display_name": "graph_mlcolvar_test", "language": "python", "name": "python3" }, @@ -639,14 +639,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.18" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "1cbeac1d7079eaeba64f3210ccac5ee24400128e300a45ae35eee837885b08b3" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 From d8a1ddd1fcdbb18131a7e7e00604b4fb2dd97019 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 11:53:28 +0100 Subject: [PATCH 196/267] modified scratch notebooks --- test_graphs/test_committor.ipynb | 102 +++- test_graphs/test_graph.ipynb | 894 +++++++++++++++++++++++++++++-- 2 files changed, 933 insertions(+), 63 deletions(-) diff --git a/test_graphs/test_committor.ipynb b/test_graphs/test_committor.ipynb index 5f47389e..762d34e1 100644 --- a/test_graphs/test_committor.ipynb +++ b/test_graphs/test_committor.ipynb @@ -2,15 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DictModule(dataset -> DictDataset( \"data_list\": 1700, \"z_table\": [6, 9], \"cutoff\": 8.0, \"data_type\": graphs ),\n", - "\t\t train_loader -> DictLoader(length=1, batch_size=1700, shuffle=False))\n" + "DictModule(dataset -> DictDataset( \"data_list\": 28603, \"z_table\": [6, 7, 8], \"cutoff\": 8.0, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=28603, shuffle=False))\n" ] } ], @@ -22,16 +22,23 @@ "\n", "dataset_graph = create_dataset_from_trajectories(\n", " trajectories=[\n", - " 'data/r.dcd',\n", - " 'data/p.dcd',\n", + " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/traj_comp.xtc',\n", + " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_B/traj_comp.xtc',\n", + " '/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/biased_sims/iter_4/A/traj_comp.xtc',\n", + " # '/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/biased_sims/iter_4/B/traj_comp.xtc'\n", + " # 'data/r.dcd',\n", + " # 'data/p.dcd',\n", " # 'data/biased.trajectory.h5',\n", - " 'data/biased.dcd',\n", + " # 'data/biased.dcd',\n", " #'data/biased.trajectory.h5',\n", " #'data/r.dcd'\n", " ],\n", - " top=['data/r.pdb', \n", - " 'data/p.pdb',\n", - " 'data/r.pdb',\n", + " top=['/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/confout.gro',\n", + " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/confout.gro',\n", + " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/confout.gro'\n", + " # 'data/r.pdb', \n", + " # 'data/p.pdb',\n", + " # 'data/r.pdb',\n", " #'data/r.pdb'\n", " ],\n", " cutoff=8.0, # Ang\n", @@ -47,15 +54,20 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import torch\n", "from mlcolvar.utils.io import load_dataframe\n", "\n", - "df = load_dataframe('data/colvar', stride=100)\n", - "weights = torch.exp(1*torch.tensor(df['opes.bias'].values))\n", + "T = 300 \n", + "# Boltzmann factor in the RIGHT ENRGY UNITS!\n", + "kb = 0.0083144621\n", + "beta = 1/(kb*T)\n", + "\n", + "df = load_dataframe('/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/biased_sims/iter_4/A/COLVAR', stride=1)\n", + "weights = torch.exp(1/beta*torch.tensor((df['opes.bias'] + df['bias']).values))\n", "weights = weights / weights.sum()\n", "weights\n", "\n", @@ -76,7 +88,7 @@ { "data": { "text/plain": [ - "1700" + "28603" ] }, "execution_count": 2, @@ -90,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -109,7 +121,7 @@ " (_radial_embedding): RadialEmbeddingBlock(\n", " (bessel_fn): GAUSSIANBASIS [ \u001b[32m16\u001b[0m\u001b[36m 󰯰 \u001b[0m| \u001b[32m8.000000\u001b[0m\u001b[36m 󰳁 \u001b[0m]\n", " )\n", - " (W_v): Linear(in_features=2, out_features=32, bias=False)\n", + " (W_v): Linear(in_features=3, out_features=32, bias=False)\n", " (layers): ModuleList(\n", " (0-1): 2 x InteractionBlock(\n", " (mlp): Sequential(\n", @@ -132,7 +144,7 @@ ")" ] }, - "execution_count": 29, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -151,16 +163,22 @@ " n_hidden_channels=32,\n", " )\n", "\n", + "# model = Committor(model=gnn_model,\n", + "# mass=torch.Tensor([12, 19]),\n", + "# alpha=1)\n", + "\n", + "\n", "model = Committor(model=gnn_model,\n", - " mass=torch.Tensor([12, 19]),\n", + " mass=torch.Tensor([12, 14, 16]),\n", " alpha=1)\n", "\n", + "\n", "model" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -178,7 +196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "020e80012d2d46809f41270cdb5692e2", + "model_id": "0b16af39d1674ea68b070e8ae805baf9", "version_major": 2, "version_minor": 0 }, @@ -190,10 +208,46 @@ "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" + "ename": "OutOfMemoryError", + "evalue": "CUDA out of memory. Tried to allocate 316.00 MiB. GPU 0 has a total capacty of 7.79 GiB of which 244.31 MiB is free. Including non-PyTorch memory, this process has 6.88 GiB memory in use. Of the allocated memory 6.61 GiB is allocated by PyTorch, and 158.40 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 13\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Trainer\n\u001b[1;32m 3\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m 4\u001b[0m logger\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 5\u001b[0m enable_checkpointing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m num_sanity_val_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 11\u001b[0m )\n\u001b[0;32m---> 13\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule_graph\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 576\u001b[0m ckpt_path,\n\u001b[1;32m 577\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 578\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:989\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 986\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 992\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 994\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:1035\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1035\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:202\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 202\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:359\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:136\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:240\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 239\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 240\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 242\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:187\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 180\u001b[0m closure()\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:265\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/module.py:1291\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1254\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1257\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1258\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1259\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1260\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[1;32m 1261\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \n\u001b[1;32m 1290\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1291\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/optimizer.py:151\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 151\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:230\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:117\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:373\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 370\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 76\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/adam.py:143\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 143\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 146\u001b[0m params_with_grad \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:104\u001b[0m, in \u001b[0;36mPrecision._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 93\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 94\u001b[0m optimizer: Optimizer,\n\u001b[1;32m 95\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 96\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03m hook is called.\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 102\u001b[0m \n\u001b[1;32m 103\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:140\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:126\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39menable_grad()\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 126\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:315\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[1;32m 314\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step() \u001b[38;5;66;03m# unused hook - call anyway for backward compatibility\u001b[39;00m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_result_cls\u001b[38;5;241m.\u001b[39mfrom_training_step_output(training_step_output, trainer\u001b[38;5;241m.\u001b[39maccumulate_grad_batches)\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:382\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module:\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_redirection(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_step\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/cvs/committor/committor.py:136\u001b[0m, in \u001b[0;36mCommittor.training_step\u001b[0;34m(self, train_batch, batch_idx)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# ===================loss=====================\u001b[39;00m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m--> 136\u001b[0m loss, loss_var, loss_bound_A, loss_bound_B \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss_fn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 138\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 140\u001b[0m loss, loss_var, loss_bound_A, loss_bound_B \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_fn(\n\u001b[1;32m 141\u001b[0m x, q, labels, weights \n\u001b[1;32m 142\u001b[0m )\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/committor_loss.py:77\u001b[0m, in \u001b[0;36mCommittorLoss.forward\u001b[0;34m(self, x, q, labels, w, create_graph)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \n\u001b[1;32m 71\u001b[0m x: Union[torch\u001b[38;5;241m.\u001b[39mTensor, torch_geometric\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mBatch], \n\u001b[1;32m 72\u001b[0m q: torch\u001b[38;5;241m.\u001b[39mTensor, \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 75\u001b[0m create_graph: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 76\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m---> 77\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcommittor_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 78\u001b[0m \u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 81\u001b[0m \u001b[43m \u001b[49m\u001b[43matomic_masses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43matomic_masses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 82\u001b[0m \u001b[43m 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separate_boundary_dataset, descriptors_derivatives, log_var)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;66;03m# ============================== LOSS ==============================\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;66;03m# Each loss contribution is scaled by the number of samples\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \n\u001b[1;32m 204\u001b[0m \u001b[38;5;66;03m# 1. VARIATIONAL LOSS\u001b[39;00m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;66;03m# Compute gradients of q(x) wrt x\u001b[39;00m\n\u001b[1;32m 206\u001b[0m grad_outputs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mones_like(q[mask_var])\n\u001b[0;32m--> 207\u001b[0m grad \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask_var\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_graph\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 208\u001b[0m grad \u001b[38;5;241m=\u001b[39m grad[mask_var_batches]\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m descriptors_derivatives \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;66;03m# we use the precomputed derivatives from descriptors to pos\u001b[39;00m\n", + "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/autograd/__init__.py:394\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched, materialize_grads)\u001b[0m\n\u001b[1;32m 390\u001b[0m result \u001b[38;5;241m=\u001b[39m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(\n\u001b[1;32m 391\u001b[0m grad_outputs_\n\u001b[1;32m 392\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 394\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 395\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 396\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 397\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 398\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 399\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 400\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 402\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m materialize_grads:\n\u001b[1;32m 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m 405\u001b[0m output\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mzeros_like(\u001b[38;5;28minput\u001b[39m, requires_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m (output, \u001b[38;5;28minput\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(result, t_inputs)\n\u001b[1;32m 409\u001b[0m )\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 316.00 MiB. GPU 0 has a total capacty of 7.79 GiB of which 244.31 MiB is free. Including non-PyTorch memory, this process has 6.88 GiB memory in use. Of the allocated memory 6.61 GiB is allocated by PyTorch, and 158.40 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" ] } ], @@ -204,7 +258,7 @@ " logger=False,\n", " enable_checkpointing=False,\n", " accelerator='cuda',\n", - " max_epochs=5000,\n", + " max_epochs=500,\n", " enable_model_summary=False,\n", " limit_val_batches=0, \n", " num_sanity_val_steps=0\n", diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 908189bc..58d23a27 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -21,6 +21,7 @@ } ], "source": [ + "import torch\n", "from mlcolvar.data import DictModule\n", "from mlcolvar.utils.io import create_dataset_from_trajectories\n", "from mlcolvar.utils.io import create_dataset_from_files\n", @@ -52,7 +53,433 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b4a53ab5e07749d0aea380482e04a522", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" ] @@ -686,6 +1105,403 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'epoch': 4,\n", + " 'global_step': 5,\n", + " 'pytorch-lightning_version': '2.1.3',\n", + " 'state_dict': OrderedDict([('loss_fn.target_centers', tensor([-7., 7.])),\n", + " ('loss_fn.target_sigmas', tensor([0.2000, 0.2000])),\n", + " ('nn.nn.0.weight',\n", + " tensor([[ 0.1091, -0.1932, -0.1206, -0.0681, 0.1556, 0.1799, 0.1149, -0.2043,\n", + " -0.0908, -0.0390, -0.0684, -0.0457, 0.0788, 0.1463, -0.1411, -0.1737,\n", + " 0.1678, 0.1805, 0.2164, -0.1644, -0.0258],\n", + " [ 0.1690, 0.1007, -0.0752, 0.1875, 0.1510, 0.0228, 0.1875, 0.2199,\n", + " 0.0580, 0.1580, 0.1729, 0.1864, -0.0042, 0.1831, 0.1117, 0.0253,\n", + " 0.0455, 0.2125, -0.1677, 0.0447, -0.1825],\n", + " [ 0.0161, 0.1482, -0.0858, -0.0592, 0.0975, -0.1783, 0.0376, 0.1408,\n", + " 0.1791, 0.1822, -0.1982, 0.2098, -0.0159, -0.0619, -0.0960, -0.0942,\n", 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1.9015e-05, 6.7018e-05, 1.4956e-06],\n", + " [9.3667e-08, 1.3272e-09, 8.1737e-11, 5.5382e-06, 1.9311e-08, 8.1203e-08,\n", + " 1.4529e-07, 1.0261e-06, 2.5534e-06, 1.5420e-07, 8.6235e-08, 5.6155e-08,\n", + " 1.0224e-05, 7.8365e-07, 5.0832e-09, 2.2290e-06, 8.7714e-07, 2.7170e-06,\n", + " 8.3424e-07, 2.9562e-06, 6.6045e-08],\n", + " [3.0141e-07, 4.1560e-09, 2.4205e-10, 1.7902e-05, 6.4473e-08, 2.6192e-07,\n", + " 4.6989e-07, 3.3086e-06, 8.2681e-06, 4.9722e-07, 2.7839e-07, 1.8045e-07,\n", + " 3.3045e-05, 2.5466e-06, 1.7283e-08, 7.2004e-06, 2.8275e-06, 8.7519e-06,\n", + " 2.7154e-06, 9.5626e-06, 2.1336e-07],\n", + " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [2.1475e-07, 4.0900e-07, 4.7883e-07, 1.6241e-05, 2.0259e-06, 5.3709e-07,\n", + " 5.6838e-07, 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0.0007, -0.0014, -0.0044,\n", + " 0.0000, -0.0018, -0.0003, -0.0032, 0.0000, 0.0046, 0.0006],\n", + " [ 0.0120, 0.0495, 0.0000, 0.0000, 0.0000, -0.0091, 0.0187, 0.0585,\n", + " 0.0000, 0.0243, 0.0044, 0.0434, 0.0000, -0.0618, -0.0082],\n", + " [ 0.0207, 0.0854, 0.0000, 0.0000, 0.0000, -0.0157, 0.0323, 0.1011,\n", + " 0.0000, 0.0419, 0.0076, 0.0749, 0.0000, -0.1066, -0.0142],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 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+ " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [5.9102e-07, 1.0101e-05, 0.0000e+00, 0.0000e+00, 0.0000e+00, 3.3919e-07,\n", + " 1.4702e-06, 1.4135e-05, 0.0000e+00, 2.4240e-06, 9.0072e-08, 7.7540e-06,\n", + " 0.0000e+00, 1.5748e-05, 2.7883e-07],\n", + " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00]])},\n", + " 3: {'step': tensor(5.),\n", + " 'exp_avg': tensor([-0.0040, 0.0545, 0.0941, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0204, 0.0000]),\n", + " 'exp_avg_sq': tensor([4.7714e-07, 8.8522e-05, 2.6436e-04, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 1.2334e-05, 0.0000e+00])},\n", + " 4: {'step': tensor(5.),\n", + " 'exp_avg': tensor([[ 0.0821, -0.1237, -0.1054, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " -0.0650, 0.0000]]),\n", + " 'exp_avg_sq': tensor([[0.0002, 0.0005, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001,\n", + " 0.0000]])},\n", + " 5: {'step': tensor(5.),\n", + " 'exp_avg': tensor([0.3885]),\n", + " 'exp_avg_sq': tensor([0.0045])}},\n", + " 'param_groups': [{'lr': 0.001,\n", + " 'betas': (0.9, 0.999),\n", + " 'eps': 1e-08,\n", + " 'weight_decay': 0,\n", + " 'amsgrad': False,\n", + " 'maximize': False,\n", + " 'foreach': None,\n", + " 'capturable': False,\n", + " 'differentiable': False,\n", + " 'fused': None,\n", + " 'params': [0, 1, 2, 3, 4, 5]}]}],\n", + " 'lr_schedulers': [],\n", + " 'hparams_name': 'kwargs',\n", + " 'hyper_parameters': {'model': FeedForward(\n", + " (nn): Sequential(\n", + " (0): Linear(in_features=21, out_features=15, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=15, out_features=10, bias=True)\n", + " (3): ReLU(inplace=True)\n", + " (4): Linear(in_features=10, out_features=1, bias=True)\n", + " )\n", + " ),\n", + " 'preprocessing': None,\n", + " 'postprocessing': None,\n", + " 'n_states': 2,\n", + " 'n_cvs': 1,\n", + " 'target_centers': [-7, 7],\n", + " 'target_sigmas': [0.2, 0.2],\n", + " 'options': None}}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.load('checkpoints/epoch=4-step=5.ckpt')" + ] + }, { "cell_type": "code", "execution_count": null, From 87484a0bf38533313e6f56656c87efb35fcf18c1 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 15:08:40 +0100 Subject: [PATCH 197/267] Fixed batch elements --- mlcolvar/data/graph/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 9763d7a2..59378959 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -277,7 +277,7 @@ def create_test_graph_input(output_type: str, if output_type == 'batch': return batch example = batch['data_list'].get_example(0) - example['batch'] = torch.tensor([0], dtype=torch.int64) + example['batch'] = torch.zeros(len(example['positions']), dtype=torch.int64) if output_type == 'example': return example if output_type == 'tracing_example': From 2f95b3e3c7470caa6086ea32c5fbfc8684785ba1 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 15:09:59 +0100 Subject: [PATCH 198/267] Added GVP model --- mlcolvar/core/nn/__init__.py | 2 +- mlcolvar/core/nn/graph/__init__.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/mlcolvar/core/nn/__init__.py b/mlcolvar/core/nn/__init__.py index e8414cfc..fa34b8af 100644 --- a/mlcolvar/core/nn/__init__.py +++ b/mlcolvar/core/nn/__init__.py @@ -1,4 +1,4 @@ -__all__ = ["FeedForward", "BaseGNN", "SchNetModel"] +__all__ = ["FeedForward", "BaseGNN", "SchNetModel", "GVPModel"] from .feedforward import * from .graph import * \ No newline at end of file diff --git a/mlcolvar/core/nn/graph/__init__.py b/mlcolvar/core/nn/graph/__init__.py index ae4642f9..a8b3be6d 100644 --- a/mlcolvar/core/nn/graph/__init__.py +++ b/mlcolvar/core/nn/graph/__init__.py @@ -1,4 +1,5 @@ -__all__ = ["BaseGNN", "SchNetModel"] +__all__ = ["BaseGNN", "SchNetModel", "GVPModel"] from .gnn import BaseGNN -from .schnet import SchNetModel \ No newline at end of file +from .schnet import SchNetModel +from .gvp import GVPModel \ No newline at end of file From 29b1481cd6cc32f2dbc7d83c35fc1c5d6d4490b0 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 15:10:33 +0100 Subject: [PATCH 199/267] Added GVP model --- mlcolvar/core/nn/graph/gvp.py | 700 ++++++++++++++++++++++++++++++++++ 1 file changed, 700 insertions(+) create mode 100644 mlcolvar/core/nn/graph/gvp.py diff --git a/mlcolvar/core/nn/graph/gvp.py b/mlcolvar/core/nn/graph/gvp.py new file mode 100644 index 00000000..5a7fec82 --- /dev/null +++ b/mlcolvar/core/nn/graph/gvp.py @@ -0,0 +1,700 @@ +import functools +import math +import torch +from torch import nn +import torch_scatter +from torch_geometric.nn import MessagePassing +from typing import Tuple, Callable, Optional, List, Dict + +from mlcolvar.core.nn.graph.gnn import BaseGNN + +""" +The Geometric Vector Perceptron (GVP) layer. This module is taken from: +https://github.com/chaitjo/geometric-gnn-dojo/blob/main/models/layers/py, +and made compilable. +""" + +__all__ = ['GVPModel', 'GVPConvLayer', 'LayerNorm', 'Dropout'] + + +class GVPModel(BaseGNN): + """ + The Geometric Vector Perceptron (GVP) model [1, 2] with vector gate [2]. + + Parameters + ---------- + n_out: int + Size of the output node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a + `mlcolvar.graph.data.GraphDataSet` instance. + n_bases: int + Size of the basis set. + n_polynomials: bool + Order of the polynomials in the basis functions. + n_layers: int + Number of the graph convolution layers. + n_messages: int + Number of GVPs to use in the message functions. + n_feedforwards: int + Number of GVPs to use in the feedforward functions. + n_scalars_node: int + Size of the scalar channel of the node embedding in hidden layers. + n_vectors_node: int + Size of the vector channel of the node embedding in hidden layers. + n_scalars_edge: int + Size of the scalar channel of the edge embedding in hidden layers. + drop_rate: int + Drop probability in all dropout layers. + activation: str + Name of the activation function to use in the GVPs (case sensitive). + basis_type: str + Type of the basis function. + smooth: bool + If use the smoothed GVPConv. + + References + ---------- + .. [1] Jing, Bowen, et al. + "Learning from protein structure with geometric vector perceptrons." + International Conference on Learning Representations. 2020. + .. [2] Jing, Bowen, et al. + "Equivariant graph neural networks for 3d macromolecular structure." + arXiv preprint arXiv:2106.03843 (2021). + """ + def __init__( + self, + n_out: int, + cutoff: float, + atomic_numbers: List[int], + n_bases: int = 8, + n_polynomials: int = 6, + n_layers: int = 1, + n_messages: int = 2, + n_feedforwards: int = 2, + n_scalars_node: int = 8, + n_vectors_node: int = 8, + n_scalars_edge: int = 8, + drop_rate: int = 0.1, + activation: str = 'SiLU', + basis_type: str = 'bessel', + smooth: bool = False, + ) -> None: + super().__init__( + n_out, cutoff, atomic_numbers, n_bases, n_polynomials, basis_type + ) + + self.W_e = nn.ModuleList([ + LayerNorm((n_bases, 1)), + GVP(in_dims=(n_bases, 1), + out_dims=(n_scalars_edge, 1), + activations=(None, None), + vector_gate=True + ) + ]) + + self.W_v = nn.ModuleList([ + LayerNorm((len(atomic_numbers), 0)), + GVP(in_dims=(len(atomic_numbers), 0), + out_dims=(n_scalars_node, n_vectors_node), + activations=(None, None), + vector_gate=True + ) + ]) + + self.layers = nn.ModuleList( + GVPConvLayer(node_dims=(n_scalars_node, n_vectors_node), + edge_dims=(n_scalars_edge, 1), + n_message=n_messages, + n_feedforward=n_feedforwards, + drop_rate=drop_rate, + activations=(eval(f'torch.nn.{activation}')(), None), + vector_gate=True, + cutoff=(cutoff if smooth else -1) + ) + for _ in range(n_layers) + ) + + self.W_out = nn.ModuleList([ + LayerNorm((n_scalars_node, n_vectors_node)), + GVP(in_dims=(n_scalars_node, n_vectors_node), + out_dims=(n_out, 0), + activations=(None, None), + vector_gate=True) + ]) + + def forward( + self, data: Dict[str, torch.Tensor], scatter_mean: bool = True + ) -> torch.Tensor: + """ + The forward pass. + + Parameters + ---------- + data: Dict[str, torch.Tensor] + The data dict. Usually came from the `to_dict` method of a + `torch_geometric.data.Batch` object. + scatter_mean: bool + If perform the scatter mean to the model output. + """ + h_V = (data['node_attrs'], None) + for w in self.W_v: + h_V = w(h_V) + h_V_1, h_V_2 = h_V + assert h_V_2 is not None + h_V = (h_V_1, h_V_2) + + h_E = self.embed_edge(data) + lengths = h_E[0] + h_E = (h_E[1], h_E[2].unsqueeze(-2)) + for w in self.W_e: + h_E = w(h_E) + h_E_1, h_E_2 = h_E + assert h_E_2 is not None + h_E = (h_E_1, h_E_2) + + batch_id = data['batch'] + + for layer in self.layers: + h_V = layer(h_V, data['edge_index'], h_E, lengths) + + for w in self.W_out: + h_V = w(h_V) + out = h_V[0] + + if scatter_mean: + if 'system_masks' not in data.keys(): + out = torch_scatter.scatter_mean(out, batch_id, dim=0) + else: + out = out * data['system_masks'] + out = torch_scatter.scatter_sum(out, batch_id, dim=0) + out = out / data['n_system'] + + return out + + +class GVP(nn.Module): + """ + Geometric Vector Perceptron. See manuscript and README.md + for more details. + + :param in_dims: tuple (n_scalar, n_vector) + :param out_dims: tuple (n_scalar, n_vector) + :param h_dim: intermediate number of vector channels, optional + :param activations: tuple of functions (scalar_act, vector_act) + :param vector_gate: whether to use vector gating. + (vector_act will be used as sigma^+ in vector gating if `True`) + """ + + def __init__( + self, + in_dims: Tuple[int, Optional[int]], + out_dims: Tuple[int, Optional[int]], + h_dim: Tuple[int, Optional[int]] = None, + activations: Tuple[ + Optional[Callable], Optional[Callable] + ] = (nn.functional.relu, torch.sigmoid), + vector_gate: bool = True, + ) -> None: + super(GVP, self).__init__() + self.si, self.vi = in_dims + self.so, self.vo = out_dims + self.vector_gate = vector_gate + if self.vi: + self.h_dim = h_dim or max(self.vi, self.vo) + self.wh = nn.Linear(self.vi, self.h_dim, bias=False) + self.ws = nn.Linear(self.h_dim + self.si, self.so) + if self.vo: + self.wv = nn.Linear(self.h_dim, self.vo, bias=False) + if self.vector_gate: + self.wsv = nn.Linear(self.so, self.vo) + else: + self.wv = None + self.wsv = None + else: + self.wh = None + self.wv = None + self.wsv = None + self.ws = nn.Linear(self.si, self.so) + + self.scalar_act, self.vector_act = activations + self.dummy_param = nn.Parameter(torch.empty(0)) + + def forward( + self, + x: Tuple[torch.Tensor, Optional[torch.Tensor]] + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + :param x: tuple (s, V) of `torch.Tensor`, + or (if vectors_in is 0), a single `torch.Tensor` + :return: tuple (s, V) of `torch.Tensor`, + or (if vectors_out is 0), a single `torch.Tensor` + """ + s, v = x + if v is not None: + assert self.wh is not None + v = torch.transpose(v, -1, -2) + vh = self.wh(v) + vn = _norm_no_nan(vh, axis=-2) + s = self.ws(torch.cat([s, vn], -1)) + if self.vo: + assert self.wv is not None + v = self.wv(vh) + v = torch.transpose(v, -1, -2) + if self.vector_gate: + assert self.wsv is not None + gate = ( + self.wsv(self.vector_act(s)) + if self.vector_act is not None + else self.wsv(s) + ) + v = v * torch.sigmoid(gate).unsqueeze(-1) + elif self.vector_act is not None: + v = v * self.vector_act( + _norm_no_nan(v, axis=-1, keepdims=True) + ) + else: + s = self.ws(s) + if self.vo: + v = torch.zeros( + s.shape[0], + self.vo, + 3, + device=self.dummy_param.device, + dtype=s.dtype + ) + else: + v = None + + if self.scalar_act is not None: + s = self.scalar_act(s) + + return s, v + + +class GVPConv(MessagePassing): + """ + Graph convolution / message passing with Geometric Vector Perceptrons. + Takes in a graph with node and edge embeddings, + and returns new node embeddings. + + This does NOT do residual updates and pointwise feedforward layers + --- see `GVPConvLayer`. + + :param in_dims: input node embedding dimensions (n_scalar, n_vector) + :param out_dims: output node embedding dimensions (n_scalar, n_vector) + :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) + :param n_layers: number of GVPs in the message function + :param activations: + tuple of functions (scalar_act, vector_act) to use in GVPs + :param vector_gate: whether to use vector gating. + (vector_act will be used as sigma^+ in vector gating if `True`) + """ + propagate_type = { + 's': torch.Tensor, + 'v': torch.Tensor, + 'edge_attr_s': torch.Tensor, + 'edge_attr_v': torch.Tensor, + 'edge_lengths': torch.Tensor, + } + + def __init__( + self, + in_dims, + out_dims, + edge_dims, + n_layers=3, + aggr='mean', + activations=(nn.functional.relu, torch.sigmoid), + vector_gate=True, + cutoff: float = -1.0, + ) -> None: + super(GVPConv, self).__init__(aggr=aggr) + self.si, self.vi = in_dims + self.so, self.vo = out_dims + self.se, self.ve = edge_dims + self.cutoff = cutoff + + GVP_ = functools.partial( + GVP, activations=activations, vector_gate=vector_gate + ) + + self._module_list = torch.nn.ModuleList() + if n_layers == 1: + self._module_list.append( + GVP_(in_dims=(2 * self.si + self.se, 2 * self.vi + self.ve), + out_dims=(self.so, self.vo), + activations=(None, None)) + ) + else: + self._module_list.append( + GVP_(in_dims=(2 * self.si + self.se, 2 * self.vi + self.ve), + out_dims=out_dims) + ) + for i in range(n_layers - 2): + self._module_list.append(GVP_(out_dims, out_dims)) + self._module_list.append( + GVP_(in_dims=out_dims, + out_dims=out_dims, + activations=(None, None)) + ) + + def forward( + self, + x: Tuple[torch.Tensor, torch.Tensor], + edge_index: torch.Tensor, + edge_attr: Tuple[torch.Tensor, torch.Tensor], + edge_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + :param x: tuple (s, V) of `torch.Tensor` + :param edge_index: array of shape [2, n_edges] + :param edge_attr: tuple (s, V) of `torch.Tensor` + """ + x_s, x_v = x + assert x_v is not None + message = self.propagate( + edge_index, + s=x_s, + v=x_v.contiguous().view(x_v.shape[0], x_v.shape[1] * 3), + edge_attr_s=edge_attr[0], + edge_attr_v=edge_attr[1], + edge_lengths=edge_lengths, + ) + return _split(message, self.vo) + + def message( + self, + s_i: torch.Tensor, + v_i: torch.Tensor, + s_j: torch.Tensor, + v_j: torch.Tensor, + edge_attr_s: torch.Tensor, + edge_attr_v: torch.Tensor, + edge_lengths: torch.Tensor, + ) -> torch.Tensor: + assert edge_attr_s is not None + assert edge_attr_v is not None + v_j = v_j.view(v_j.shape[0], v_j.shape[1] // 3, 3) + v_i = v_i.view(v_i.shape[0], v_i.shape[1] // 3, 3) + message = _tuple_cat( + (s_j, v_j), (edge_attr_s, edge_attr_v), (s_i, v_i) + ) + message = self.message_func(message) + message_merged = _merge(*message) + if self.cutoff > 0: + # apply SchNet-style cutoff function + c = 0.5 * (torch.cos(edge_lengths * math.pi / self.cutoff) + 1.0) + message_merged = message_merged * c.view(-1, 1) + return message_merged + + def message_func( + self, x: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + for m in self._module_list: + x = m(x) + output_1, output_2 = x + assert output_2 is not None + return output_1, output_2 + + +class GVPConvLayer(nn.Module): + """ + Full graph convolution / message passing layer with + Geometric Vector Perceptrons. Residually updates node embeddings with + aggregated incoming messages, applies a pointwise feedforward + network to node embeddings, and returns updated node embeddings. + + To only compute the aggregated messages, see `GVPConv`. + + :param node_dims: node embedding dimensions (n_scalar, n_vector) + :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) + :param n_message: number of GVPs to use in message function + :param n_feedforward: number of GVPs to use in feedforward function + :param drop_rate: drop probability in all dropout layers + :param activations: + tuple of functions (scalar_act, vector_act) to use in GVPs + :param vector_gate: whether to use vector gating. + (vector_act will be used as sigma^+ in vector gating if `True`) + """ + + def __init__( + self, + node_dims, + edge_dims, + n_message=3, + n_feedforward=2, + drop_rate=0.1, + activations=(nn.functional.relu, torch.sigmoid), + vector_gate=True, + residual=True, + cutoff: float = -1.0, + ) -> None: + super(GVPConvLayer, self).__init__() + self.conv = GVPConv( + node_dims, + node_dims, + edge_dims, + n_message, + aggr='mean', + activations=activations, + vector_gate=vector_gate, + cutoff=cutoff, + ) + GVP_ = functools.partial( + GVP, activations=activations, vector_gate=vector_gate + ) + self.norm = nn.ModuleList([LayerNorm(node_dims) for _ in range(2)]) + self.dropout = nn.ModuleList([Dropout(drop_rate) for _ in range(2)]) + + self._module_list = nn.ModuleList() + if n_feedforward == 1: + self._module_list.append( + GVP_(in_dims=node_dims, + out_dims=node_dims, + activations=(None, None)) + ) + else: + hid_dims = 4 * node_dims[0], 2 * node_dims[1] + self._module_list.append(GVP_(node_dims, hid_dims)) + self._module_list.extend( + GVP_(in_dims=hid_dims, out_dims=hid_dims) for _ in range(n_feedforward - 2) + ) + self._module_list.append( + GVP_(in_dims=hid_dims, out_dims=node_dims, activations=(None, None)) + ) + self.residual = residual + + def forward( + self, + x: Tuple[torch.Tensor, torch.Tensor], + edge_index: torch.Tensor, + edge_attr: Tuple[torch.Tensor, torch.Tensor], + edge_lengths: torch.Tensor, + node_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + :param x: tuple (s, V) of `torch.Tensor` + :param edge_index: array of shape [2, n_edges] + :param edge_attr: tuple (s, V) of `torch.Tensor` + :param node_mask: array of type `bool` to index into the first + dim of node embeddings (s, V). If not `None`, only + these nodes will be updated. + """ + + dh = self.conv(x, edge_index, edge_attr, edge_lengths) + + x_ = x + if node_mask is not None: + x, dh = _tuple_index(x, node_mask), _tuple_index(dh, node_mask) + + if self.residual: + input_1, input_2 = self.dropout[0](dh) + assert input_2 is not None + output_1, output_2 = self.norm[0]( + _tuple_sum(x, (input_1, input_2)) + ) + assert output_2 is not None + x = (output_1, output_2) + else: + x = dh + + dh = self.ff_func(x) + if self.residual: + input_1, input_2 = self.dropout[1](dh) + assert input_2 is not None + output_1, output_2 = self.norm[1]( + _tuple_sum(x, (input_1, input_2)) + ) + assert output_2 is not None + x = (output_1, output_2) + else: + x = dh + + if node_mask is not None: + x_[0][node_mask], x_[1][node_mask] = x[0], x[1] + x = x_ + return x + + def ff_func( + self, x: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + for m in self._module_list: + x = m(x) + output_1 = x[0] + output_2 = x[1] + assert output_2 is not None + return output_1, output_2 + + +class LayerNorm(nn.Module): + """ + Combined LayerNorm for tuples (s, V). + Takes tuples (s, V) as input and as output. + """ + + def __init__(self, dims) -> None: + super(LayerNorm, self).__init__() + self.s, self.v = dims + self.scalar_norm = nn.LayerNorm(self.s) + + def forward( + self, + x: Tuple[torch.Tensor, Optional[torch.Tensor]] + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + :param x: tuple (s, V) of `torch.Tensor`, + or single `torch.Tensor` + (will be assumed to be scalar channels) + """ + s, v = x + if not self.v: + return self.scalar_norm(s), None + else: + assert v is not None + vn = _norm_no_nan(v, axis=-1, keepdims=True, sqrt=False) + vn = torch.sqrt(torch.mean(vn, dim=-2, keepdim=True)) + return self.scalar_norm(s), v / vn + + +class Dropout(nn.Module): + """ + Combined dropout for tuples (s, V). + Takes tuples (s, V) as input and as output. + """ + + def __init__(self, drop_rate) -> None: + super(Dropout, self).__init__() + self.sdropout = nn.Dropout(drop_rate) + self.vdropout = _VDropout(drop_rate) + + def forward( + self, + x: Tuple[torch.Tensor, Optional[torch.Tensor]] + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + :param x: tuple (s, V) of `torch.Tensor`, + or single `torch.Tensor` + (will be assumed to be scalar channels) + """ + s, v = x + if v is None: + return self.sdropout(s), None + else: + assert v is not None + return self.sdropout(s), self.vdropout(v) + + +class _VDropout(nn.Module): + """ + Vector channel dropout where the elements of each + vector channel are dropped together. + """ + + def __init__(self, drop_rate) -> None: + super(_VDropout, self).__init__() + self.drop_rate = drop_rate + self.dummy_param = nn.Parameter(torch.empty(0)) + + def forward(self, x) -> torch.Tensor: + """ + :param x: `torch.Tensor` corresponding to vector channels + """ + device = self.dummy_param.device + if not self.training: + return x + mask = torch.bernoulli( + (1 - self.drop_rate) * torch.ones(x.shape[:-1], device=device) + ).unsqueeze(-1) + x = mask * x / (1 - self.drop_rate) + return x + + +def _tuple_sum( + input_1: Tuple[torch.Tensor, torch.Tensor], + input_2: Tuple[torch.Tensor, torch.Tensor] +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Sums any number of tuples (s, V) elementwise. + """ + out = [i + j for i, j in zip(input_1, input_2)] + return out[0], out[1] + + +@torch.jit.script +def _tuple_cat( + input_1: Tuple[torch.Tensor, torch.Tensor], + input_2: Tuple[torch.Tensor, torch.Tensor], + input_3: Tuple[torch.Tensor, torch.Tensor], + dim: int = -1 +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Concatenates any number of tuples (s, V) elementwise. + + :param dim: dimension along which to concatenate when viewed + as the `dim` index for the scalar-channel tensors. + This means that `dim=-1` will be applied as + `dim=-2` for the vector-channel tensors. + """ + dim = int(dim % len(input_1[0].shape)) + s_args, v_args = list(zip(input_1, input_2, input_3)) + return torch.cat(s_args, dim=dim), torch.cat(v_args, dim=dim) + + +@torch.jit.script +def _tuple_index( + x: Tuple[torch.Tensor, torch.Tensor], idx: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Indexes into a tuple (s, V) along the first dimension. + + :param idx: any object which can be used to index into a `torch.Tensor` + """ + return x[0][idx], x[1][idx] + + +@torch.jit.script +def _norm_no_nan( + x: torch.Tensor, + axis: int = -1, + keepdims: bool = False, + eps: float = 1e-8, + sqrt: bool = True +) -> torch.Tensor: + """ + L2 norm of tensor clamped above a minimum value `eps`. + + :param sqrt: if `False`, returns the square of the L2 norm + """ + out = torch.clamp(torch.sum(torch.square(x), axis, keepdims), min=eps) + return torch.sqrt(out) if sqrt else out + + +@torch.jit.script +def _split(x: torch.Tensor, nv: int) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Splits a merged representation of (s, V) back into a tuple. + Should be used only with `_merge(s, V)` and only if the tuple + representation cannot be used. + + :param x: the `torch.Tensor` returned from `_merge` + :param nv: the number of vector channels in the input to `_merge` + """ + s = x[..., :-3 * nv] + v = x[..., -3 * nv:].contiguous().view(x.shape[0], nv, 3) + return s, v + + +@torch.jit.script +def _merge(s: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """ + Merges a tuple (s, V) into a single `torch.Tensor`, where the + vector channels are flattened and appended to the scalar channels. + Should be used only if the tuple representation cannot be used. + Use `_split(x, nv)` to reverse. + """ + v = v.contiguous().view(v.shape[0], v.shape[1] * 3) + return torch.cat([s, v], -1) From 9396c9a937c1a5bda427a547c1044bcdef9061a2 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 16:02:02 +0100 Subject: [PATCH 200/267] Added basic tests for gnn models --- mlcolvar/core/nn/graph/gvp.py | 33 +++++++++++++ mlcolvar/core/nn/graph/schnet.py | 53 +++++++++++++++++++- mlcolvar/core/nn/graph/utils.py | 54 +++++++++++++++++++++ mlcolvar/tests/test_core_nn_graph_gvp.py | 4 ++ mlcolvar/tests/test_core_nn_graph_schnet.py | 5 ++ 5 files changed, 148 insertions(+), 1 deletion(-) create mode 100644 mlcolvar/core/nn/graph/utils.py create mode 100644 mlcolvar/tests/test_core_nn_graph_gvp.py create mode 100644 mlcolvar/tests/test_core_nn_graph_schnet.py diff --git a/mlcolvar/core/nn/graph/gvp.py b/mlcolvar/core/nn/graph/gvp.py index 5a7fec82..68ac80d9 100644 --- a/mlcolvar/core/nn/graph/gvp.py +++ b/mlcolvar/core/nn/graph/gvp.py @@ -698,3 +698,36 @@ def _merge(s: torch.Tensor, v: torch.Tensor) -> torch.Tensor: """ v = v.contiguous().view(v.shape[0], v.shape[1] * 3) return torch.cat([s, v], -1) + + +def test_gvp() -> None: + from mlcolvar.core.nn.graph.utils import _test_get_data + torch.manual_seed(0) + torch.set_default_dtype(torch.float64) + + model = GVPModel( + n_out=2, + cutoff=0.1, + atomic_numbers=[1, 8], + n_bases=6, + n_polynomials=6, + n_layers=2, + n_messages=2, + n_feedforwards=1, + n_scalars_node=16, + n_vectors_node=8, + n_scalars_edge=16, + drop_rate=0, + activation='SiLU', + ) + + data = _test_get_data() + assert ( + torch.abs( + model(data) - + torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) + ) < 1E-12 + ).all() + +if __name__ == '__main__': + test_gvp() diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index 99aa8f40..ef77eb9f 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -242,4 +242,55 @@ def __init__(self) -> None: self.shift = torch.log(torch.tensor(2.0)).item() def forward(self, x: torch.Tensor) -> torch.Tensor: - return nn.functional.softplus(x) - self.shift \ No newline at end of file + return nn.functional.softplus(x) - self.shift + + + +from mlcolvar.core.nn.graph.utils import _test_get_data + +def test_schnet_1() -> None: + torch.manual_seed(0) + torch.set_default_dtype(torch.float64) + + model = SchNetModel( + n_out=2, + cutoff=0.1, + atomic_numbers=[1, 8], + n_bases=6, + n_layers=2, + n_filters=16, + n_hidden_channels=16 + ) + + data = _test_get_data() + assert ( + torch.abs( + model(data) - + torch.tensor([[0.40384621527953063, -0.1257513365138969]] * 6) + ) < 1E-12 + ).all() + + +def test_schnet_2() -> None: + torch.manual_seed(0) + torch.set_default_dtype(torch.float64) + + model = SchNetModel( + n_out=2, + cutoff=0.1, + atomic_numbers=[1, 8], + n_bases=6, + n_layers=2, + n_filters=16, + n_hidden_channels=16, + aggr='min', + w_out_after_sum=True + ) + + data = _test_get_data() + assert ( + torch.abs( + model(data) - + torch.tensor([[0.3654537816221449, -0.0748265132499575]] * 6) + ) < 1E-12 + ).all() \ No newline at end of file diff --git a/mlcolvar/core/nn/graph/utils.py b/mlcolvar/core/nn/graph/utils.py new file mode 100644 index 00000000..1a0e3780 --- /dev/null +++ b/mlcolvar/core/nn/graph/utils.py @@ -0,0 +1,54 @@ +import torch +import torch_geometric +import numpy as np + +from mlcolvar.data.graph import atomic, create_dataset_from_configurations +from mlcolvar.data import DictModule + + +def _test_get_data() -> torch_geometric.data.Batch: + # TODO: This is not a real test, but a helper function for other tests. + # Maybe should change its name. + torch.manual_seed(0) + torch.set_default_dtype(torch.float64) + + numbers = [8, 1, 1] + positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], + [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], + ], + dtype=np.float64 + ) + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.array([[1]]) + node_labels = np.array([[0], [1], [1]]) + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + config = [ + atomic.Configuration( + atomic_numbers=numbers, + positions=p, + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels, + ) for p in positions + ] + dataset = create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False + ) + + datamodule = DictModule( + dataset, + lengths=(1.0,), + batch_size=10, + shuffle=False, + ) + datamodule.setup() + + return next(iter(datamodule.train_dataloader()))['data_list'] \ No newline at end of file diff --git a/mlcolvar/tests/test_core_nn_graph_gvp.py b/mlcolvar/tests/test_core_nn_graph_gvp.py new file mode 100644 index 00000000..dcbc8176 --- /dev/null +++ b/mlcolvar/tests/test_core_nn_graph_gvp.py @@ -0,0 +1,4 @@ +from mlcolvar.core.nn.graph.gvp import test_gvp + +if __name__ == "__main__": + test_gvp() \ No newline at end of file diff --git a/mlcolvar/tests/test_core_nn_graph_schnet.py b/mlcolvar/tests/test_core_nn_graph_schnet.py new file mode 100644 index 00000000..2d83fdbf --- /dev/null +++ b/mlcolvar/tests/test_core_nn_graph_schnet.py @@ -0,0 +1,5 @@ +from mlcolvar.core.nn.graph.schnet import test_schnet_1, test_schnet_2 + +if __name__ == "__main__": + test_schnet_1() + test_schnet_2() \ No newline at end of file From 0fa529616b5513328c1881cd6f3176cb9b9a2083 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 16:02:18 +0100 Subject: [PATCH 201/267] Fixed imports --- mlcolvar/data/graph/__init__.py | 15 ++++----------- mlcolvar/data/graph/utils.py | 2 ++ 2 files changed, 6 insertions(+), 11 deletions(-) diff --git a/mlcolvar/data/graph/__init__.py b/mlcolvar/data/graph/__init__.py index 09234aca..5322ece6 100644 --- a/mlcolvar/data/graph/__init__.py +++ b/mlcolvar/data/graph/__init__.py @@ -1,12 +1,5 @@ -# TODO fix imports +__all__ = ["AtomicNumberTable", "Configuration", "Configurations", "get_neighborhood", "create_dataset_from_configurations", "create_test_graph_input"] -from . import atomic -from . import neighborhood -# from .dataset import ( -# GraphDataSet, -# create_dataset_from_configurations, -# save_dataset, -# save_dataset_as_exyz, -# load_dataset -# ) -# from .datamodule import GraphDataModule, GraphCombinedDataModule \ No newline at end of file +from .atomic import * +from .neighborhood import * +from .utils import * \ No newline at end of file diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 59378959..c6e08a92 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -6,6 +6,8 @@ from mlcolvar.data.graph.neighborhood import get_neighborhood from mlcolvar.utils.plot import pbar +__all__ = ["create_dataset_from_configurations", "create_test_graph_input"] + def _create_dataset_from_configuration( config: atomic.Configuration, z_table: atomic.AtomicNumberTable, From 74045e84e042bb0cda34ed7a60de092b116e6e52 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 16:14:57 +0100 Subject: [PATCH 202/267] Removed already ported code --- mlcolvar/graph/core/__init__.py | 1 - mlcolvar/graph/core/nn/__init__.py | 1 - mlcolvar/graph/core/nn/gvp_layer.py | 538 ---------------------- mlcolvar/graph/core/nn/models.py | 521 --------------------- mlcolvar/graph/core/nn/schnet.py | 112 ----- mlcolvar/graph/cvs/supervised/__init__.py | 1 - mlcolvar/graph/cvs/supervised/deeptda.py | 201 -------- mlcolvar/graph/cvs/timelagged/__init__.py | 1 - mlcolvar/graph/cvs/timelagged/deeptica.py | 227 --------- 9 files changed, 1603 deletions(-) delete mode 100644 mlcolvar/graph/core/__init__.py delete mode 100644 mlcolvar/graph/core/nn/__init__.py delete mode 100644 mlcolvar/graph/core/nn/gvp_layer.py delete mode 100644 mlcolvar/graph/core/nn/models.py delete mode 100644 mlcolvar/graph/core/nn/schnet.py delete mode 100644 mlcolvar/graph/cvs/supervised/__init__.py delete mode 100644 mlcolvar/graph/cvs/supervised/deeptda.py delete mode 100644 mlcolvar/graph/cvs/timelagged/__init__.py delete mode 100644 mlcolvar/graph/cvs/timelagged/deeptica.py diff --git a/mlcolvar/graph/core/__init__.py b/mlcolvar/graph/core/__init__.py deleted file mode 100644 index f0718b2d..00000000 --- a/mlcolvar/graph/core/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .nn import BaseModel, GVPModel, SchNetModel diff --git a/mlcolvar/graph/core/nn/__init__.py b/mlcolvar/graph/core/nn/__init__.py deleted file mode 100644 index c92aa4ec..00000000 --- a/mlcolvar/graph/core/nn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .models import BaseModel, GVPModel, SchNetModel diff --git a/mlcolvar/graph/core/nn/gvp_layer.py b/mlcolvar/graph/core/nn/gvp_layer.py deleted file mode 100644 index 5ace06a8..00000000 --- a/mlcolvar/graph/core/nn/gvp_layer.py +++ /dev/null @@ -1,538 +0,0 @@ -import functools -import torch -import math -from torch import nn -from torch_geometric.nn import MessagePassing -from typing import Tuple, Callable, Optional - -""" -The Geometric Vector Perceptron (GVP) layer. This module is taken from: -https://github.com/chaitjo/geometric-gnn-dojo/blob/main/models/layers/gvp_layer.py, -and made compilable. -""" - -__all__ = ['GVPConvLayer', 'LayerNorm', 'Dropout'] - - -class GVP(nn.Module): - """ - Geometric Vector Perceptron. See manuscript and README.md - for more details. - - :param in_dims: tuple (n_scalar, n_vector) - :param out_dims: tuple (n_scalar, n_vector) - :param h_dim: intermediate number of vector channels, optional - :param activations: tuple of functions (scalar_act, vector_act) - :param vector_gate: whether to use vector gating. - (vector_act will be used as sigma^+ in vector gating if `True`) - """ - - def __init__( - self, - in_dims: Tuple[int, Optional[int]], - out_dims: Tuple[int, Optional[int]], - h_dim: Tuple[int, Optional[int]] = None, - activations: Tuple[ - Optional[Callable], Optional[Callable] - ] = (nn.functional.relu, torch.sigmoid), - vector_gate: bool = True, - ) -> None: - super(GVP, self).__init__() - self.si, self.vi = in_dims - self.so, self.vo = out_dims - self.vector_gate = vector_gate - if self.vi: - self.h_dim = h_dim or max(self.vi, self.vo) - self.wh = nn.Linear(self.vi, self.h_dim, bias=False) - self.ws = nn.Linear(self.h_dim + self.si, self.so) - if self.vo: - self.wv = nn.Linear(self.h_dim, self.vo, bias=False) - if self.vector_gate: - self.wsv = nn.Linear(self.so, self.vo) - else: - self.wv = None - self.wsv = None - else: - self.wh = None - self.wv = None - self.wsv = None - self.ws = nn.Linear(self.si, self.so) - - self.scalar_act, self.vector_act = activations - self.dummy_param = nn.Parameter(torch.empty(0)) - - def forward( - self, - x: Tuple[torch.Tensor, Optional[torch.Tensor]] - ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - """ - :param x: tuple (s, V) of `torch.Tensor`, - or (if vectors_in is 0), a single `torch.Tensor` - :return: tuple (s, V) of `torch.Tensor`, - or (if vectors_out is 0), a single `torch.Tensor` - """ - s, v = x - if v is not None: - assert self.wh is not None - v = torch.transpose(v, -1, -2) - vh = self.wh(v) - vn = _norm_no_nan(vh, axis=-2) - s = self.ws(torch.cat([s, vn], -1)) - if self.vo: - assert self.wv is not None - v = self.wv(vh) - v = torch.transpose(v, -1, -2) - if self.vector_gate: - assert self.wsv is not None - gate = ( - self.wsv(self.vector_act(s)) - if self.vector_act is not None - else self.wsv(s) - ) - v = v * torch.sigmoid(gate).unsqueeze(-1) - elif self.vector_act is not None: - v = v * self.vector_act( - _norm_no_nan(v, axis=-1, keepdims=True) - ) - else: - s = self.ws(s) - if self.vo: - v = torch.zeros( - s.shape[0], - self.vo, - 3, - device=self.dummy_param.device, - dtype=s.dtype - ) - else: - v = None - - if self.scalar_act is not None: - s = self.scalar_act(s) - - return s, v - - -class GVPConv(MessagePassing): - """ - Graph convolution / message passing with Geometric Vector Perceptrons. - Takes in a graph with node and edge embeddings, - and returns new node embeddings. - - This does NOT do residual updates and pointwise feedforward layers - --- see `GVPConvLayer`. - - :param in_dims: input node embedding dimensions (n_scalar, n_vector) - :param out_dims: output node embedding dimensions (n_scalar, n_vector) - :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) - :param n_layers: number of GVPs in the message function - :param activations: - tuple of functions (scalar_act, vector_act) to use in GVPs - :param vector_gate: whether to use vector gating. - (vector_act will be used as sigma^+ in vector gating if `True`) - """ - propagate_type = { - 's': torch.Tensor, - 'v': torch.Tensor, - 'edge_attr_s': torch.Tensor, - 'edge_attr_v': torch.Tensor, - 'edge_lengths': torch.Tensor, - } - - def __init__( - self, - in_dims, - out_dims, - edge_dims, - n_layers=3, - aggr='mean', - activations=(nn.functional.relu, torch.sigmoid), - vector_gate=True, - cutoff: float = -1.0, - ) -> None: - super(GVPConv, self).__init__(aggr=aggr) - self.si, self.vi = in_dims - self.so, self.vo = out_dims - self.se, self.ve = edge_dims - self.cutoff = cutoff - - GVP_ = functools.partial( - GVP, activations=activations, vector_gate=vector_gate - ) - - self._module_list = torch.nn.ModuleList() - if n_layers == 1: - self._module_list.append( - GVP_( - (2 * self.si + self.se, 2 * self.vi + self.ve), - (self.so, self.vo), - activations=(None, None), - ) - ) - else: - self._module_list.append( - GVP_( - (2 * self.si + self.se, 2 * self.vi + self.ve), - out_dims, - ) - ) - for i in range(n_layers - 2): - self._module_list.append(GVP_(out_dims, out_dims)) - self._module_list.append( - GVP_(out_dims, out_dims, activations=(None, None)) - ) - - def forward( - self, - x: Tuple[torch.Tensor, torch.Tensor], - edge_index: torch.Tensor, - edge_attr: Tuple[torch.Tensor, torch.Tensor], - edge_lengths: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - :param x: tuple (s, V) of `torch.Tensor` - :param edge_index: array of shape [2, n_edges] - :param edge_attr: tuple (s, V) of `torch.Tensor` - """ - x_s, x_v = x - assert x_v is not None - message = self.propagate( - edge_index, - s=x_s, - v=x_v.contiguous().view(x_v.shape[0], x_v.shape[1] * 3), - edge_attr_s=edge_attr[0], - edge_attr_v=edge_attr[1], - edge_lengths=edge_lengths, - ) - return _split(message, self.vo) - - def message( - self, - s_i: torch.Tensor, - v_i: torch.Tensor, - s_j: torch.Tensor, - v_j: torch.Tensor, - edge_attr_s: torch.Tensor, - edge_attr_v: torch.Tensor, - edge_lengths: torch.Tensor, - ) -> torch.Tensor: - assert edge_attr_s is not None - assert edge_attr_v is not None - v_j = v_j.view(v_j.shape[0], v_j.shape[1] // 3, 3) - v_i = v_i.view(v_i.shape[0], v_i.shape[1] // 3, 3) - message = _tuple_cat( - (s_j, v_j), (edge_attr_s, edge_attr_v), (s_i, v_i) - ) - message = self.message_func(message) - message_merged = _merge(*message) - if self.cutoff > 0: - # apply SchNet-style cutoff function - c = 0.5 * (torch.cos(edge_lengths * math.pi / self.cutoff) + 1.0) - message_merged = message_merged * c.view(-1, 1) - return message_merged - - def message_func( - self, x: Tuple[torch.Tensor, torch.Tensor] - ) -> Tuple[torch.Tensor, torch.Tensor]: - for m in self._module_list: - x = m(x) - output_1, output_2 = x - assert output_2 is not None - return output_1, output_2 - - -class GVPConvLayer(nn.Module): - """ - Full graph convolution / message passing layer with - Geometric Vector Perceptrons. Residually updates node embeddings with - aggregated incoming messages, applies a pointwise feedforward - network to node embeddings, and returns updated node embeddings. - - To only compute the aggregated messages, see `GVPConv`. - - :param node_dims: node embedding dimensions (n_scalar, n_vector) - :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) - :param n_message: number of GVPs to use in message function - :param n_feedforward: number of GVPs to use in feedforward function - :param drop_rate: drop probability in all dropout layers - :param activations: - tuple of functions (scalar_act, vector_act) to use in GVPs - :param vector_gate: whether to use vector gating. - (vector_act will be used as sigma^+ in vector gating if `True`) - """ - - def __init__( - self, - node_dims, - edge_dims, - n_message=3, - n_feedforward=2, - drop_rate=0.1, - activations=(nn.functional.relu, torch.sigmoid), - vector_gate=True, - residual=True, - cutoff: float = -1.0, - ) -> None: - super(GVPConvLayer, self).__init__() - self.conv = GVPConv( - node_dims, - node_dims, - edge_dims, - n_message, - aggr='mean', - activations=activations, - vector_gate=vector_gate, - cutoff=cutoff, - ) - GVP_ = functools.partial( - GVP, activations=activations, vector_gate=vector_gate - ) - self.norm = nn.ModuleList([LayerNorm(node_dims) for _ in range(2)]) - self.dropout = nn.ModuleList([Dropout(drop_rate) for _ in range(2)]) - - self._module_list = nn.ModuleList() - if n_feedforward == 1: - self._module_list.append( - GVP_(node_dims, node_dims, activations=(None, None)) - ) - else: - hid_dims = 4 * node_dims[0], 2 * node_dims[1] - self._module_list.append(GVP_(node_dims, hid_dims)) - self._module_list.extend( - GVP_(hid_dims, hid_dims) for _ in range(n_feedforward - 2) - ) - self._module_list.append( - GVP_(hid_dims, node_dims, activations=(None, None)) - ) - self.residual = residual - - def forward( - self, - x: Tuple[torch.Tensor, torch.Tensor], - edge_index: torch.Tensor, - edge_attr: Tuple[torch.Tensor, torch.Tensor], - edge_lengths: torch.Tensor, - node_mask: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - :param x: tuple (s, V) of `torch.Tensor` - :param edge_index: array of shape [2, n_edges] - :param edge_attr: tuple (s, V) of `torch.Tensor` - :param node_mask: array of type `bool` to index into the first - dim of node embeddings (s, V). If not `None`, only - these nodes will be updated. - """ - - dh = self.conv(x, edge_index, edge_attr, edge_lengths) - - x_ = x - if node_mask is not None: - x, dh = _tuple_index(x, node_mask), _tuple_index(dh, node_mask) - - if self.residual: - input_1, input_2 = self.dropout[0](dh) - assert input_2 is not None - output_1, output_2 = self.norm[0]( - _tuple_sum(x, (input_1, input_2)) - ) - assert output_2 is not None - x = (output_1, output_2) - else: - x = dh - - dh = self.ff_func(x) - if self.residual: - input_1, input_2 = self.dropout[1](dh) - assert input_2 is not None - output_1, output_2 = self.norm[1]( - _tuple_sum(x, (input_1, input_2)) - ) - assert output_2 is not None - x = (output_1, output_2) - else: - x = dh - - if node_mask is not None: - x_[0][node_mask], x_[1][node_mask] = x[0], x[1] - x = x_ - return x - - def ff_func( - self, x: Tuple[torch.Tensor, torch.Tensor] - ) -> Tuple[torch.Tensor, torch.Tensor]: - for m in self._module_list: - x = m(x) - output_1 = x[0] - output_2 = x[1] - assert output_2 is not None - return output_1, output_2 - - -class LayerNorm(nn.Module): - """ - Combined LayerNorm for tuples (s, V). - Takes tuples (s, V) as input and as output. - """ - - def __init__(self, dims) -> None: - super(LayerNorm, self).__init__() - self.s, self.v = dims - self.scalar_norm = nn.LayerNorm(self.s) - - def forward( - self, - x: Tuple[torch.Tensor, Optional[torch.Tensor]] - ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - """ - :param x: tuple (s, V) of `torch.Tensor`, - or single `torch.Tensor` - (will be assumed to be scalar channels) - """ - s, v = x - if not self.v: - return self.scalar_norm(s), None - else: - assert v is not None - vn = _norm_no_nan(v, axis=-1, keepdims=True, sqrt=False) - vn = torch.sqrt(torch.mean(vn, dim=-2, keepdim=True)) - return self.scalar_norm(s), v / vn - - -class Dropout(nn.Module): - """ - Combined dropout for tuples (s, V). - Takes tuples (s, V) as input and as output. - """ - - def __init__(self, drop_rate) -> None: - super(Dropout, self).__init__() - self.sdropout = nn.Dropout(drop_rate) - self.vdropout = _VDropout(drop_rate) - - def forward( - self, - x: Tuple[torch.Tensor, Optional[torch.Tensor]] - ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - """ - :param x: tuple (s, V) of `torch.Tensor`, - or single `torch.Tensor` - (will be assumed to be scalar channels) - """ - s, v = x - if v is None: - return self.sdropout(s), None - else: - assert v is not None - return self.sdropout(s), self.vdropout(v) - - -class _VDropout(nn.Module): - """ - Vector channel dropout where the elements of each - vector channel are dropped together. - """ - - def __init__(self, drop_rate) -> None: - super(_VDropout, self).__init__() - self.drop_rate = drop_rate - self.dummy_param = nn.Parameter(torch.empty(0)) - - def forward(self, x) -> torch.Tensor: - """ - :param x: `torch.Tensor` corresponding to vector channels - """ - device = self.dummy_param.device - if not self.training: - return x - mask = torch.bernoulli( - (1 - self.drop_rate) * torch.ones(x.shape[:-1], device=device) - ).unsqueeze(-1) - x = mask * x / (1 - self.drop_rate) - return x - - -def _tuple_sum( - input_1: Tuple[torch.Tensor, torch.Tensor], - input_2: Tuple[torch.Tensor, torch.Tensor] -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Sums any number of tuples (s, V) elementwise. - """ - out = [i + j for i, j in zip(input_1, input_2)] - return out[0], out[1] - - -@torch.jit.script -def _tuple_cat( - input_1: Tuple[torch.Tensor, torch.Tensor], - input_2: Tuple[torch.Tensor, torch.Tensor], - input_3: Tuple[torch.Tensor, torch.Tensor], - dim: int = -1 -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Concatenates any number of tuples (s, V) elementwise. - - :param dim: dimension along which to concatenate when viewed - as the `dim` index for the scalar-channel tensors. - This means that `dim=-1` will be applied as - `dim=-2` for the vector-channel tensors. - """ - dim = int(dim % len(input_1[0].shape)) - s_args, v_args = list(zip(input_1, input_2, input_3)) - return torch.cat(s_args, dim=dim), torch.cat(v_args, dim=dim) - - -@torch.jit.script -def _tuple_index( - x: Tuple[torch.Tensor, torch.Tensor], idx: torch.Tensor -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Indexes into a tuple (s, V) along the first dimension. - - :param idx: any object which can be used to index into a `torch.Tensor` - """ - return x[0][idx], x[1][idx] - - -@torch.jit.script -def _norm_no_nan( - x: torch.Tensor, - axis: int = -1, - keepdims: bool = False, - eps: float = 1e-8, - sqrt: bool = True -) -> torch.Tensor: - """ - L2 norm of tensor clamped above a minimum value `eps`. - - :param sqrt: if `False`, returns the square of the L2 norm - """ - out = torch.clamp(torch.sum(torch.square(x), axis, keepdims), min=eps) - return torch.sqrt(out) if sqrt else out - - -@torch.jit.script -def _split(x: torch.Tensor, nv: int) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Splits a merged representation of (s, V) back into a tuple. - Should be used only with `_merge(s, V)` and only if the tuple - representation cannot be used. - - :param x: the `torch.Tensor` returned from `_merge` - :param nv: the number of vector channels in the input to `_merge` - """ - s = x[..., :-3 * nv] - v = x[..., -3 * nv:].contiguous().view(x.shape[0], nv, 3) - return s, v - - -@torch.jit.script -def _merge(s: torch.Tensor, v: torch.Tensor) -> torch.Tensor: - """ - Merges a tuple (s, V) into a single `torch.Tensor`, where the - vector channels are flattened and appended to the scalar channels. - Should be used only if the tuple representation cannot be used. - Use `_split(x, nv)` to reverse. - """ - v = v.contiguous().view(v.shape[0], v.shape[1] * 3) - return torch.cat([s, v], -1) diff --git a/mlcolvar/graph/core/nn/models.py b/mlcolvar/graph/core/nn/models.py deleted file mode 100644 index ad8b8125..00000000 --- a/mlcolvar/graph/core/nn/models.py +++ /dev/null @@ -1,521 +0,0 @@ -import torch -from torch import nn -import numpy as np -import torch_geometric as tg -from typing import List, Dict, Tuple - -from mlcolvar.graph import data as gdata -from mlcolvar.graph.core.nn import radial -from mlcolvar.graph.core.nn import schnet -from mlcolvar.graph.core.nn import gvp_layer -from mlcolvar.graph.utils import torch_tools - -""" -GNN models. -""" - -__all__ = ['BaseModel', 'GVPModel', 'SchNetModel'] - - -class BaseModel(nn.Module): - """ - The commen GNN interface for mlcolvar. - - Parameters - ---------- - n_out: int - Size of the output node features. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_bases: int - Size of the basis set. - n_polynomials: bool - Order of the polynomials in the basis functions. - basis_type: str - Type of the basis function. - """ - - def __init__( - self, - n_out: int, - cutoff: float, - atomic_numbers: List[int], - n_bases: int = 6, - n_polynomials: int = 6, - basis_type: str = 'bessel' - ) -> None: - super().__init__() - self._n_out = n_out - self._radial_embedding = radial.RadialEmbeddingBlock( - cutoff, n_bases, n_polynomials, basis_type - ) - self.register_buffer( - 'n_out', torch.tensor(n_out, dtype=torch.int64) - ) - self.register_buffer( - 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) - ) - self.register_buffer( - 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) - ) - - def embed_edge( - self, data: Dict[str, torch.Tensor], normalize: bool = True - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ - Perform the edge embedding. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - normalize: bool - If return the normalized distance vectors. - - Returns - ------- - edge_lengths: torch.Tensor (shape: [n_edges, 1]) - The edge lengths. - edge_length_embeddings: torch.Tensor (shape: [n_edges, n_bases]) - The edge length embeddings. - edge_unit_vectors: torch.Tensor (shape: [n_edges, 3]) - The normalized edge vectors. - """ - vectors, lengths = torch_tools.get_edge_vectors_and_lengths( - positions=data['positions'], - edge_index=data['edge_index'], - shifts=data['shifts'], - normalize=normalize, - ) - return lengths, self._radial_embedding(lengths), vectors - - -class GVPModel(BaseModel): - """ - The Geometric Vector Perceptron (GVP) model [1, 2] with vector gate [2]. - - Parameters - ---------- - n_out: int - Size of the output node features. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_bases: int - Size of the basis set. - n_polynomials: bool - Order of the polynomials in the basis functions. - n_layers: int - Number of the graph convolution layers. - n_messages: int - Number of GVPs to use in the message functions. - n_feedforwards: int - Number of GVPs to use in the feedforward functions. - n_scalars_node: int - Size of the scalar channel of the node embedding in hidden layers. - n_vectors_node: int - Size of the vector channel of the node embedding in hidden layers. - n_scalars_edge: int - Size of the scalar channel of the edge embedding in hidden layers. - drop_rate: int - Drop probability in all dropout layers. - activation: str - Name of the activation function to use in the GVPs (case sensitive). - basis_type: str - Type of the basis function. - smooth: bool - If use the smoothed GVPConv. - - References - ---------- - .. [1] Jing, Bowen, et al. - "Learning from protein structure with geometric vector perceptrons." - International Conference on Learning Representations. 2020. - .. [2] Jing, Bowen, et al. - "Equivariant graph neural networks for 3d macromolecular structure." - arXiv preprint arXiv:2106.03843 (2021). - """ - def __init__( - self, - n_out: int, - cutoff: float, - atomic_numbers: List[int], - n_bases: int = 8, - n_polynomials: int = 6, - n_layers: int = 1, - n_messages: int = 2, - n_feedforwards: int = 2, - n_scalars_node: int = 8, - n_vectors_node: int = 8, - n_scalars_edge: int = 8, - drop_rate: int = 0.1, - activation: str = 'SiLU', - basis_type: str = 'bessel', - smooth: bool = False, - ) -> None: - super().__init__( - n_out, cutoff, atomic_numbers, n_bases, n_polynomials, basis_type - ) - - self.W_e = nn.ModuleList([ - gvp_layer.LayerNorm((n_bases, 1)), - gvp_layer.GVP( - (n_bases, 1), - (n_scalars_edge, 1), - activations=(None, None), - vector_gate=True, - ) - ]) - - self.W_v = nn.ModuleList([ - gvp_layer.LayerNorm((len(atomic_numbers), 0)), - gvp_layer.GVP( - (len(atomic_numbers), 0), - (n_scalars_node, n_vectors_node), - activations=(None, None), - vector_gate=True, - ) - ]) - - self.layers = nn.ModuleList( - gvp_layer.GVPConvLayer( - (n_scalars_node, n_vectors_node), - (n_scalars_edge, 1), - n_message=n_messages, - n_feedforward=n_feedforwards, - drop_rate=drop_rate, - activations=(eval(f'torch.nn.{activation}')(), None), - vector_gate=True, - cutoff=(cutoff if smooth else -1), - ) - for _ in range(n_layers) - ) - - self.W_out = nn.ModuleList([ - gvp_layer.LayerNorm((n_scalars_node, n_vectors_node)), - gvp_layer.GVP( - (n_scalars_node, n_vectors_node), - (n_out, 0), - activations=(None, None), - vector_gate=True, - ), - ]) - - def forward( - self, data: Dict[str, torch.Tensor], scatter_mean: bool = True - ) -> torch.Tensor: - """ - The forward pass. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - scatter_mean: bool - If perform the scatter mean to the model output. - """ - h_V = (data['node_attrs'], None) - for w in self.W_v: - h_V = w(h_V) - h_V_1, h_V_2 = h_V - assert h_V_2 is not None - h_V = (h_V_1, h_V_2) - - h_E = self.embed_edge(data) - lengths = h_E[0] - h_E = (h_E[1], h_E[2].unsqueeze(-2)) - for w in self.W_e: - h_E = w(h_E) - h_E_1, h_E_2 = h_E - assert h_E_2 is not None - h_E = (h_E_1, h_E_2) - - batch_id = data['batch'] - - for layer in self.layers: - h_V = layer(h_V, data['edge_index'], h_E, lengths) - - for w in self.W_out: - h_V = w(h_V) - out = h_V[0] - - if scatter_mean: - if 'system_masks' not in data.keys(): - out = torch_tools.scatter_mean(out, batch_id, dim=0) - else: - out = out * data['system_masks'] - out = torch_tools.scatter_sum(out, batch_id, dim=0) - out = out / data['n_system'] - - return out - - -class SchNetModel(BaseModel): - """ - The SchNet [1] model. This implementation is taken from torch_geometric: - https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py - - Parameters - ---------- - n_out: int - Size of the output node features. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_bases: int - Size of the basis set. - n_layers: int - Number of the graph convolution layers. - n_filters: int - Number of filters. - n_hidden_channels: int - Size of hidden embeddings. - aggr: str - Type of the GNN aggr function. - w_out_after_sum: bool - If apply the readout MLP layer after the scatter sum. - - References - ---------- - .. [1] Schütt, Kristof T., et al. "Schnet–a deep learning architecture for - molecules and materials." The Journal of Chemical Physics 148.24 - (2018). - """ - - def __init__( - self, - n_out: int, - cutoff: float, - atomic_numbers: List[int], - n_bases: int = 16, - n_layers: int = 2, - n_filters: int = 16, - n_hidden_channels: int = 16, - drop_rate: int = 0, - aggr: str = 'mean', - w_out_after_sum: bool = False - ) -> None: - - super().__init__( - n_out, cutoff, atomic_numbers, n_bases, 0, 'gaussian' - ) - - self.W_v = nn.Linear( - len(atomic_numbers), n_hidden_channels, bias=False - ) - - self.layers = nn.ModuleList([ - schnet.InteractionBlock( - n_hidden_channels, n_bases, n_filters, cutoff, aggr - ) - for _ in range(n_layers) - ]) - - self.W_out = nn.ModuleList([ - nn.Linear(n_hidden_channels, n_hidden_channels // 2), - schnet.ShiftedSoftplus(), - nn.Linear(n_hidden_channels // 2, n_out) - ]) - - self._w_out_after_sum = w_out_after_sum - - self.reset_parameters() - - def reset_parameters(self) -> None: - """ - Resets all learnable parameters of the module. - """ - self.W_v.reset_parameters() - - for layer in self.layers: - layer.reset_parameters() - - nn.init.xavier_uniform_(self.W_out[0].weight) - self.W_out[0].bias.data.fill_(0) - nn.init.xavier_uniform_(self.W_out[2].weight) - self.W_out[2].bias.data.fill_(0) - - def forward( - self, data: Dict[str, torch.Tensor], scatter_mean: bool = True - ) -> torch.Tensor: - """ - The forward pass. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - scatter_mean: bool - If perform the scatter mean to the model output. - """ - - h_E = self.embed_edge(data) - h_V = self.W_v(data['node_attrs']) - - batch_id = data['batch'] - - for layer in self.layers: - h_V = h_V + layer(h_V, data['edge_index'], h_E[0], h_E[1]) - - if not self._w_out_after_sum: - for w in self.W_out: - h_V = w(h_V) - out = h_V - - if scatter_mean: - if 'system_masks' not in data.keys(): - out = torch_tools.scatter_mean(out, batch_id, dim=0) - else: - out = out * data['system_masks'] - out = torch_tools.scatter_sum(out, batch_id, dim=0) - out = out / data['n_system'] - - if self._w_out_after_sum: - for w in self.W_out: - out = w(out) - - return out - - -def test_get_data() -> tg.data.Batch: - # TODO: This is not a real test, but a helper function for other tests. - # Maybe should change its name. - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - numbers = [8, 1, 1] - positions = np.array( - [ - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], - [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], - [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], - [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], - [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], - ], - dtype=np.float64 - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = [ - gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=p, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - ) for p in positions - ] - dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False - ) - - loader = gdata.GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=10, - shuffle=False, - ) - loader.setup() - - return next(iter(loader.train_dataloader())) - - -def test_gvp() -> None: - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - model = GVPModel( - n_out=2, - cutoff=0.1, - atomic_numbers=[1, 8], - n_bases=6, - n_polynomials=6, - n_layers=2, - n_messages=2, - n_feedforwards=1, - n_scalars_node=16, - n_vectors_node=8, - n_scalars_edge=16, - drop_rate=0, - activation='SiLU', - ) - - data = test_get_data().to_dict() - assert ( - torch.abs( - model(data) - - torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) - ) < 1E-12 - ).all() - - -def test_schnet_1() -> None: - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - model = SchNetModel( - n_out=2, - cutoff=0.1, - atomic_numbers=[1, 8], - n_bases=6, - n_layers=2, - n_filters=16, - n_hidden_channels=16 - ) - - data = test_get_data().to_dict() - assert ( - torch.abs( - model(data) - - torch.tensor([[0.40384621527953063, -0.1257513365138969]] * 6) - ) < 1E-12 - ).all() - - -def test_schnet_2() -> None: - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - model = SchNetModel( - n_out=2, - cutoff=0.1, - atomic_numbers=[1, 8], - n_bases=6, - n_layers=2, - n_filters=16, - n_hidden_channels=16, - aggr='min', - w_out_after_sum=True - ) - - data = test_get_data().to_dict() - assert ( - torch.abs( - model(data) - - torch.tensor([[0.3654537816221449, -0.0748265132499575]] * 6) - ) < 1E-12 - ).all() - - -if __name__ == '__main__': - test_gvp() - test_schnet_1() - test_schnet_2() diff --git a/mlcolvar/graph/core/nn/schnet.py b/mlcolvar/graph/core/nn/schnet.py deleted file mode 100644 index 5d1ef645..00000000 --- a/mlcolvar/graph/core/nn/schnet.py +++ /dev/null @@ -1,112 +0,0 @@ -import math -import torch -from torch import nn -from torch_geometric.nn import MessagePassing - -""" -The SchNet components. This module is taken from the pgy package: -https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py -""" - -__all__ = ['InteractionBlock', 'ShiftedSoftplus'] - - -class InteractionBlock(nn.Module): - def __init__( - self, - hidden_channels: int, - num_gaussians: int, - num_filters: int, - cutoff: float, - aggr: str = 'mean' - ) -> None: - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(num_gaussians, num_filters), - ShiftedSoftplus(), - nn.Linear(num_filters, num_filters), - ) - self.conv = CFConv( - hidden_channels, - hidden_channels, - num_filters, - self.mlp, - cutoff, - aggr - ) - self.act = ShiftedSoftplus() - self.lin = nn.Linear(hidden_channels, hidden_channels) - - self.reset_parameters() - - def reset_parameters(self) -> None: - nn.init.xavier_uniform_(self.mlp[0].weight) - self.mlp[0].bias.data.fill_(0) - nn.init.xavier_uniform_(self.mlp[2].weight) - self.mlp[2].bias.data.fill_(0) - self.conv.reset_parameters() - nn.init.xavier_uniform_(self.lin.weight) - self.lin.bias.data.fill_(0) - - def forward( - self, - x: torch.Tensor, - edge_index: torch.Tensor, - edge_weight: torch.Tensor, - edge_attr: torch.Tensor, - ) -> torch.Tensor: - x = self.conv(x, edge_index, edge_weight, edge_attr) - x = self.act(x) - x = self.lin(x) - return x - - -class CFConv(MessagePassing): - def __init__( - self, - in_channels: int, - out_channels: int, - num_filters: int, - network: nn.Sequential, - cutoff: float, - aggr: str = 'mean' - ) -> None: - super().__init__(aggr=aggr) - self.lin1 = nn.Linear(in_channels, num_filters, bias=False) - self.lin2 = nn.Linear(num_filters, out_channels) - self.network = network - self.cutoff = cutoff - - self.reset_parameters() - - def reset_parameters(self): - nn.init.xavier_uniform_(self.lin1.weight) - nn.init.xavier_uniform_(self.lin2.weight) - self.lin2.bias.data.fill_(0) - - def forward( - self, - x: torch.Tensor, - edge_index: torch.Tensor, - edge_weight: torch.Tensor, - edge_attr: torch.Tensor, - ) -> torch.Tensor: - C = 0.5 * (torch.cos(edge_weight * math.pi / self.cutoff) + 1.0) - W = self.network(edge_attr) * C.view(-1, 1) - - x = self.lin1(x) - x = self.propagate(edge_index, x=x, W=W) - x = self.lin2(x) - return x - - def message(self, x_j: torch.Tensor, W: torch.Tensor) -> torch.Tensor: - return x_j * W - - -class ShiftedSoftplus(nn.Module): - def __init__(self) -> None: - super().__init__() - self.shift = torch.log(torch.tensor(2.0)).item() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return nn.functional.softplus(x) - self.shift diff --git a/mlcolvar/graph/cvs/supervised/__init__.py b/mlcolvar/graph/cvs/supervised/__init__.py deleted file mode 100644 index d1bbea46..00000000 --- a/mlcolvar/graph/cvs/supervised/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .deeptda import GraphDeepTDA diff --git a/mlcolvar/graph/cvs/supervised/deeptda.py b/mlcolvar/graph/cvs/supervised/deeptda.py deleted file mode 100644 index cade3fcf..00000000 --- a/mlcolvar/graph/cvs/supervised/deeptda.py +++ /dev/null @@ -1,201 +0,0 @@ -import torch -import torch_geometric as tg -from typing import Dict, Any, List, Union - -from mlcolvar.core.loss import TDALoss -from mlcolvar.graph.cvs import GraphBaseCV -from mlcolvar.graph.cvs.cv import test_get_data -from mlcolvar.graph.utils import torch_tools - -""" -The Deep Targeted Discriminant Analysis (Deep-TDA) CV based on Graph Neural -Networks (GNN). -""" - -__all__ = ['GraphDeepTDA'] - - -class GraphDeepTDA(GraphBaseCV): - """ - The Deep Targeted Discriminant Analysis (Deep-TDA) CV [1] based on Graph - Neural Networks (GNN). - - Parameters - ---------- - n_cvs: int - Number of components of the CV. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_cvs : int - Number of collective variables to be trained - target_centers : list - Centers of the Gaussian targets - target_sigmas : list - Standard deviations of the Gaussian targets - model_name: str - Name of the GNN model. - model_options: Dict[Any, Any] - Model options. - extra_loss_options: Dict[Any, Any] - Extra loss function options. - optimizer_options: Dict[Any, Any] - Optimizer options. - - References - ---------- - .. [1] E. Trizio and M. Parrinello, - 'From enhanced sampling to reaction profiles', - The Journal of Physical Chemistry Letters 12, 8621– 8626 (2021). - - See also - -------- - mlcolvar.core.loss.TDALoss - Distance from a simple Gaussian target distribution. - """ - - def __init__( - self, - n_cvs: int, - cutoff: float, - atomic_numbers: List[int], - target_centers: Union[List[float], List[List[float]]], - target_sigmas: Union[List[float], List[List[float]]], - model_name: str = 'GVPModel', - model_options: Dict[Any, Any] = {}, - extra_loss_options: Dict[Any, Any] = {'alpha': 1.0, 'beta': 100.0}, - optimizer_options: Dict[Any, Any] = {}, - **kwargs, - ) -> None: - if model_options.pop('n_out', None) is not None: - raise RuntimeError( - 'The `n_out` key of parameter `model_options` will be ignored!' - ) - if optimizer_options != {}: - kwargs['optimizer_options'] = optimizer_options - - super().__init__( - n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs - ) - - # check size and type of targets - if not isinstance(target_centers, torch.Tensor): - target_centers = torch.tensor( - target_centers, dtype=torch.get_default_dtype() - ) - if not isinstance(target_sigmas, torch.Tensor): - target_sigmas = torch.tensor( - target_sigmas, dtype=torch.get_default_dtype() - ) - - self._n_states = target_centers.shape[0] - if target_centers.shape != target_sigmas.shape: - raise ValueError( - 'Size of target_centers and target_sigmas should be the same!' - ) - if len(target_centers.shape) == 1: - if n_cvs != 1: - raise ValueError( - 'Size of target_centers at dimension 1 should match the ' - + f'number of cvs! Expected 1 found {n_cvs}' - ) - elif len(target_centers.shape) == 2: - if n_cvs != target_centers.shape[1]: - raise ValueError( - 'Size of target_centers at dimension 1 should match the ' - + f'number of cvs! Expected {n_cvs} found ' - + f'{target_centers.shape[1]}' - ) - elif len(target_centers.shape) > 2: - raise ValueError('Too much target_centers dimensions!') - - self.loss_fn = TDALoss( - n_states=target_centers.shape[0], - target_centers=target_centers, - target_sigmas=target_sigmas, - **extra_loss_options - ) - - def training_step( - self, train_batch: tg.data.Batch, *args, **kwargs - ) -> torch.Tensor: - """ - Compute and return the training loss and record metrics. - - Parameters - ---------- - train_batch: torch_geometric.data.Batch - The data batch. - """ - output = self.forward(train_batch.to_dict()) - - loss, loss_centers, loss_sigmas = self.loss_fn( - output, - train_batch.graph_labels.squeeze(), - return_loss_terms=True - ) - - name = 'train' if self.training else 'valid' - self.log(f'{name}_loss', loss, on_epoch=True) - self.log(f'{name}_loss_centers', loss_centers, on_epoch=True) - self.log(f'{name}_loss_sigmas', loss_sigmas, on_epoch=True) - return loss - - -def test_deep_tda(): - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - cv = GraphDeepTDA( - 2, - 0.1, - [1, 8], - [[-1, -1], [1, 1]], - [[1, 1], [1, 1]], - model_options={ - 'n_bases': 6, - 'n_polynomials': 6, - 'n_layers': 2, - 'n_messages': 2, - 'n_feedforwards': 1, - 'n_scalars_node': 16, - 'n_vectors_node': 8, - 'n_scalars_edge': 16, - 'drop_rate': 0, - 'activation': 'SiLU', - } - ) - - data = test_get_data() - - assert ( - torch.abs( - cv(data) - - torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) - ) < 1E-12 - ).all() - - assert torch.abs( - cv.training_step(data) - torch.tensor(404.8752553674548) - ) < 1E-12 - - try: - cv = GraphDeepTDA(2, 0.1, [1, 8], [-1, 1], [1, 1]) - except ValueError: - pass - else: - raise RuntimeError - - try: - cv = GraphDeepTDA(2, 0.1, [1, 8], [[-1, -1], [1, 1]], [1, 1]) - except ValueError: - pass - else: - raise RuntimeError - - -if __name__ == '__main__': - test_deep_tda() diff --git a/mlcolvar/graph/cvs/timelagged/__init__.py b/mlcolvar/graph/cvs/timelagged/__init__.py deleted file mode 100644 index 33e71a3d..00000000 --- a/mlcolvar/graph/cvs/timelagged/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .deeptica import GraphDeepTICA diff --git a/mlcolvar/graph/cvs/timelagged/deeptica.py b/mlcolvar/graph/cvs/timelagged/deeptica.py deleted file mode 100644 index be78d5b9..00000000 --- a/mlcolvar/graph/cvs/timelagged/deeptica.py +++ /dev/null @@ -1,227 +0,0 @@ -import torch -import torch_geometric as tg -from typing import Dict, Any, List - -from mlcolvar.core.stats import TICA -from mlcolvar.core.loss import ReduceEigenvaluesLoss -from mlcolvar.graph.cvs import GraphBaseCV -from mlcolvar.graph.cvs.cv import test_get_data -from mlcolvar.graph.utils import torch_tools - -""" -The Deep time-lagged independent component analysis (Deep-TICA) CV based on -Graph Neural Networks (GNN). -""" - -__all__ = ['GraphDeepTICA'] - - -class GraphDeepTICA(GraphBaseCV): - """ - Graph neural network-based time-lagged independent component analysis - (Deep-TICA). - - It is a non-linear generalization of TICA in which a feature map is learned - by a neural network optimized as to maximize the eigenvalues of the - transfer operator, approximated by TICA. The method is described in [1]_. - Note that from the point of view of the architecture DeepTICA is similar to - the SRV [2]_ method. - - Parameters - ---------- - n_cvs: int - Number of components of the CV. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_cvs : int - Number of collective variables to be trained - model_name: str - Name of the GNN model. - model_options: Dict[Any, Any] - Model options. Note that the `n_out` key of this dict is REQUIRED, - which stands for the dimension of the output of the network. - extra_loss_options: Dict[Any, Any] - Extra loss function options. - optimizer_options: Dict[Any, Any] - Optimizer options. - - References - ---------- - .. [1] L. Bonati, G. Piccini, and M. Parrinello, "Deep learning the slow - modes for rare events sampling." PNAS USA 118, e2113533118 (2021) - .. [2] W. Chen, H. Sidky, and A. L. Ferguson, "Nonlinear discovery of slow - molecular modes using state-free reversible vampnets." - JCP 150, 214114 (2019). - - See also - -------- - mlcolvar.core.stats.TICA - Time Lagged Indipendent Component Analysis - mlcolvar.core.loss.ReduceEigenvalueLoss - Eigenvalue reduction to a scalar quantity - mlcolvar.utils.timelagged.create_timelagged_dataset - Create dataset of time-lagged data. - """ - def __init__( - self, - n_cvs: int, - cutoff: float, - atomic_numbers: List[int], - model_name: str = 'GVPModel', - model_options: Dict[Any, Any] = {'n_out': 6}, - extra_loss_options: Dict[Any, Any] = {'mode': 'sum2', 'n_eig': 0}, - optimizer_options: Dict[Any, Any] = {}, - **kwargs, - ) -> None: - if 'n_out' not in model_options.keys(): - raise RuntimeError( - 'The `n_out` key of parameter `model_options` is required!' - ) - model_options['drop_rate'] = 0.0 - n_out = model_options['n_out'] - - if optimizer_options != {}: - kwargs['optimizer_options'] = optimizer_options - - super().__init__( - n_cvs, cutoff, atomic_numbers, model_name, model_options, **kwargs - ) - - self.loss_fn = ReduceEigenvaluesLoss(**extra_loss_options) - - self.tica = TICA(n_out, n_cvs) - - def forward_nn( - self, - data: Dict[str, torch.Tensor], - token: bool = False - ) -> torch.Tensor: - """ - The forward pass for the NN. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - token: bool - To be used. - """ - data['positions'].requires_grad_(True) - data['node_attrs'].requires_grad_(True) - - return self._model(data) - - def forward( - self, - data: Dict[str, torch.Tensor], - token: bool = False - ) -> torch.Tensor: - """ - The forward pass. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - token: bool - To be used. - """ - nn_outputs = self.forward_nn(data) - outputs = self.tica(nn_outputs) - - return outputs - - def training_step( - self, - train_batch: Dict[str, tg.data.Batch], - *args, - **kwargs - ) -> torch.Tensor: - """ - Compute and return the training loss and record metrics. - - Parameters - ---------- - train_batch: Tuple[Dict[str, torch_geometric.data.Batch], int, int] - The data batch. - """ - data_t = train_batch['dataset_1'].to_dict() - data_lag = train_batch['dataset_2'].to_dict() - - nn_outputs_t = self.forward_nn(data_t) - nn_outputs_lag = self.forward_nn(data_lag) - - eigvals, _ = self.tica.compute( - data=[nn_outputs_t, nn_outputs_lag], - weights=[data_t['weight'], data_lag['weight']], - save_params=True - ) - - loss = self.loss_fn(eigvals) - name = 'train' if self.training else 'valid' - loss_dict = {f'{name}_loss': loss} - eig_dict = { - f'{name}_eigval_{i+1}': eigvals[i] for i in range(len(eigvals)) - } - self.log_dict(dict(loss_dict, **eig_dict), on_step=True, on_epoch=True) - return loss - - def set_regularization(self, c0_reg=1e-6) -> None: - """ - Add identity matrix multiplied by `c0_reg` to correlation matrix C(0) - to avoid instabilities in performin Cholesky. - - Parameters - ---------- - c0_reg : float - Regularization value for C_0. - """ - self.tica.reg_C_0 = c0_reg - - -def test_deep_tica(): - torch.manual_seed(0) - torch_tools.set_default_dtype('float64') - - cv = GraphDeepTICA( - 2, - 0.1, - [1, 8], - model_options={ - 'n_out': 6, - 'n_bases': 6, - 'n_polynomials': 6, - 'n_layers': 2, - 'n_messages': 2, - 'n_feedforwards': 1, - 'n_scalars_node': 16, - 'n_vectors_node': 8, - 'n_scalars_edge': 16, - 'drop_rate': 0, - 'activation': 'Tanh', - } - ) - - data = test_get_data() - - assert ( - torch.abs( - cv(data) - - torch.tensor([[0.4301124873647384, -0.3866279366944752]] * 6) - ) < 1E-12 - ).all() - - assert torch.abs( - cv.training_step({'dataset_1': data, 'dataset_2': data}) - - torch.tensor(0) - ) < 1E-12 - - -if __name__ == '__main__': - test_deep_tica() From d9026655fc7e1d52ed27e423c4bd956ed56a477b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 18:19:58 +0100 Subject: [PATCH 203/267] Condensed variational mask defintion --- mlcolvar/core/loss/committor_loss.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index 7d767c55..f908e63a 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -163,18 +163,17 @@ def committor_loss(x: torch.Tensor, mask_A = torch.nonzero(labels.squeeze() == 0, as_tuple=True) mask_B = torch.nonzero(labels.squeeze() == 1, as_tuple=True) if separate_boundary_dataset: - if _is_graph_data: - # this needs to be on the batch index, not only the labels - mask_var = torch.nonzero(labels.squeeze() > 1) - aux = torch.where(mask_var)[0] - mask_var_batches = torch.isin(batch, aux) - mask_var_batches = (batch[mask_var_batches]) - else: - mask_var = torch.nonzero(labels.squeeze() > 1, as_tuple=True) - mask_var_batches = mask_var + mask_var = torch.nonzero(labels.squeeze() > 1, as_tuple=not(_is_graph_data)) else: - mask_var = torch.ones(len(x), dtype=torch.bool) - mask_var_batches = mask_var + mask_var = torch.ones(len(x), dtype=torch.bool) + + if _is_graph_data and separate_boundary_dataset: + # this needs to be on the batch index, not only the labels + aux = torch.where(mask_var)[0] + mask_var_batches = torch.isin(batch, aux) + mask_var_batches = (batch[mask_var_batches]) + else: + mask_var_batches = mask_var # setup atomic masses atomic_masses = atomic_masses.to(device) From d5671eab7aa9fa26ddc44b40661cd4db3b34179b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 18:20:26 +0100 Subject: [PATCH 204/267] added in_features and removed model type --- mlcolvar/core/nn/graph/gnn.py | 56 ++++------------------------------- 1 file changed, 5 insertions(+), 51 deletions(-) diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py index 8955574b..ea9b5c5f 100644 --- a/mlcolvar/core/nn/graph/gnn.py +++ b/mlcolvar/core/nn/graph/gnn.py @@ -42,7 +42,6 @@ def __init__( basis_type: str = 'bessel' ) -> None: super().__init__() - self._model_type = 'gnn' self._n_out = n_out self._radial_embedding = radial.RadialEmbeddingBlock( @@ -62,6 +61,10 @@ def __init__( def out_features(self): return self._n_out + @property + def in_features(self): + return None + def embed_edge( self, data: Dict[str, torch.Tensor], normalize: bool = True ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: @@ -124,53 +127,4 @@ def get_edge_vectors_and_lengths( if normalize: vectors = torch.nan_to_num(torch.div(vectors, lengths)) - return vectors, lengths - - -# def test_get_data() -> tg.data.Batch: -# # TODO: This is not a real test, but a helper function for other tests. -# # Maybe should change its name. -# torch.manual_seed(0) -# torch_tools.set_default_dtype('float64') - -# numbers = [8, 1, 1] -# positions = np.array( -# [ -# [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], -# [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], -# [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], -# [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], -# [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], -# [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], -# ], -# dtype=np.float64 -# ) -# cell = np.identity(3, dtype=float) * 0.2 -# graph_labels = np.array([[1]]) -# node_labels = np.array([[0], [1], [1]]) -# z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - -# config = [ -# gdata.atomic.Configuration( -# atomic_numbers=numbers, -# positions=p, -# cell=cell, -# pbc=[True] * 3, -# node_labels=node_labels, -# graph_labels=graph_labels, -# ) for p in positions -# ] -# dataset = gdata.create_dataset_from_configurations( -# config, z_table, 0.1, show_progress=False -# ) - -# loader = gdata.GraphDataModule( -# dataset, -# lengths=(1.0,), -# batch_size=10, -# shuffle=False, -# ) -# loader.setup() - -# return next(iter(loader.train_dataloader())) - + return vectors, lengths \ No newline at end of file From 8b21a151657f438088b65d790bb197c46bb02e0d Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 11 Dec 2024 18:20:49 +0100 Subject: [PATCH 205/267] Simplified model parsing --- mlcolvar/cvs/cv.py | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index c262a9b0..35a986e1 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -41,6 +41,8 @@ def __init__( # The parent class sets in_features and out_features based on their own # init arguments so we don't need to save them here (see #103). + + # TODO check if need self.save_hyperparameters(ignore=['in_features', 'out_features']) # MODEL @@ -74,7 +76,7 @@ def example_input_array(self): return create_test_graph_input(output_type='tracing_example', n_atoms=3, n_samples=1, n_states=1) - # TODO add general torch.nn.Module + # TODO add general torch.nn.Module TODO TODO TODO TODO def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): if isinstance(model, list): self.layers = model @@ -85,16 +87,16 @@ def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): elif isinstance(model, FeedForward) or isinstance(model, BaseGNN): self.BLOCKS = self.MODEL_BLOCKS self._override_model = True - if isinstance(model, FeedForward): - # self.nn = model - self.in_features = model.in_features - self.out_features = model.out_features - elif isinstance(model, BaseGNN): - # GNN models need to be scripted! - # self.nn = torch.jit.script_if_tracing(model) - # self.nn = model - self.in_features = None - self.out_features = model.out_features + self.in_features = model.in_features + self.out_features = model.out_features + # if isinstance(model, FeedForward): + # # self.nn = model + # elif isinstance(model, BaseGNN): + # # GNN models need to be scripted! + # # self.nn = torch.jit.script_if_tracing(model) + # # self.nn = model + # self.in_features = None + # self.out_features = model.out_features else: raise ValueError( f"Keyword model can either accept type list, FeedForward or BaseGNN. Found {type(model)}" From b1059899231b07a406d0e7ab09939aab68e4c55b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 15:13:00 +0100 Subject: [PATCH 206/267] Restore default dtype after test --- mlcolvar/core/nn/graph/gvp.py | 1 + mlcolvar/core/nn/graph/schnet.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/mlcolvar/core/nn/graph/gvp.py b/mlcolvar/core/nn/graph/gvp.py index 68ac80d9..c4d983b7 100644 --- a/mlcolvar/core/nn/graph/gvp.py +++ b/mlcolvar/core/nn/graph/gvp.py @@ -728,6 +728,7 @@ def test_gvp() -> None: torch.tensor([[0.6100070244145421, -0.2559670171962067]] * 6) ) < 1E-12 ).all() + torch.set_default_dtype(torch.float32) if __name__ == '__main__': test_gvp() diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index ef77eb9f..d48b95ed 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -293,4 +293,6 @@ def test_schnet_2() -> None: model(data) - torch.tensor([[0.3654537816221449, -0.0748265132499575]] * 6) ) < 1E-12 - ).all() \ No newline at end of file + ).all() + + torch.set_default_dtype(torch.float32) From 468a7464f2964109363c3f01860f29397c1a849d Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 16:27:10 +0100 Subject: [PATCH 207/267] Improved streamline and tests --- mlcolvar/data/datamodule.py | 36 +++++++------- mlcolvar/data/dataset.py | 96 +++++++++++++++++++++++++------------ 2 files changed, 85 insertions(+), 47 deletions(-) diff --git a/mlcolvar/data/datamodule.py b/mlcolvar/data/datamodule.py index 665f560e..85ffaed3 100644 --- a/mlcolvar/data/datamodule.py +++ b/mlcolvar/data/datamodule.py @@ -122,22 +122,7 @@ def __init__( """ super().__init__() self.dataset = dataset - - # check the type of data we have - if not isinstance(dataset, list): - self._dataset_type = dataset.metadata['data_type'] - else: - it = iter(list(dataset)) - self._dataset_type = next(it).metadata['data_type'] - if not all(d.metadata['data_type'] for d in it): - raise ValueError("not all the dataset are of the same type!") - - - # decide which loader to use - if self._dataset_type == 'descriptors': - self.DataLoader = DictLoader - elif self._dataset_type == 'graphs': - self.DataLoader = torch_geometric.loader.DataLoader + self.DataLoader = self._get_dataloader() self._lengths = lengths @@ -173,6 +158,25 @@ def __init__( self.valid_loader = None self.test_loader = None + @property + def _dataset_type(self): + if not isinstance(self.dataset, list): + _dataset_type = self.dataset.metadata['data_type'] + else: + it = iter(list(self.dataset)) + _dataset_type = next(it).metadata['data_type'] + if not all(d.metadata['data_type'] for d in it): + raise ValueError("not all the dataset are of the same type!") + return _dataset_type + + def _get_dataloader(self): + # decide which loader to use + if self._dataset_type == 'descriptors': + DataLoader = DictLoader + elif self._dataset_type == 'graphs': + DataLoader = torch_geometric.loader.DataLoader + return DataLoader + def setup(self, stage: Optional[str] = None): if self._dataset_split is None: if isinstance(self.dataset, DictDataset): diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index 9215b035..ce4075c2 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -54,7 +54,12 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = # initialize metadata as dict if metadata is None: metadata = {} - metadata['data_type'] = data_type + + if 'data_type' in metadata.keys(): + if not metadata['data_type'] == metadata: + raise ValueError(f"Two different data_type specified. Found {metadata['data_type']} in metadata and {data_type} as keyword") + else: + metadata['data_type'] = data_type # convert to torch.Tensors for key, val in dictionary.items(): @@ -76,20 +81,19 @@ def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = # check that all elements of dict have same length it = iter(dictionary.values()) self.length = len(next(it)) - if not all([len(l)==self.length for l in it]): + if not all([len(l) == self.length for l in it]): raise ValueError("not all arrays in dictionary have same length!") - def __getitem__(self, index : Union[str, int, slice, List, range, np.ndarray, torch.Tensor]): + def __getitem__(self, index): if isinstance(index, str): return self._dictionary[index] else: slice_dict = {} for key, val in self._dictionary.items(): - if self.metadata['data_type'] == 'graphs' and type(index) in [range, np.ndarray, list, torch.Tensor]: - slice_dict[key] = list(itemgetter(*index)(val)) - else: + try: slice_dict[key] = val[index] - + except: + slice_dict[key] = list(itemgetter(*index)(val)) return slice_dict def __setitem__(self, index, value): @@ -97,7 +101,7 @@ def __setitem__(self, index, value): # check lengths if len(value) != len(self): raise ValueError( - f"length of value ({len(value)}) != length of dataset ({len(self)})." + f"length of value ({len(value)}) != length of dataset ({len(self)})." ) self._dictionary[index] = value else: @@ -153,10 +157,13 @@ def feature_names(self, value): def test_DictDataset(): # descriptors based # from list + data = torch.Tensor([[1.0], [2.0], [0.3], [0.4]]) + labels = [0, 0, 1, 1] + weights = np.asarray([0.5, 1.5, 1.5, 0.5]) dataset_dict = { - "data": torch.Tensor([[1.0], [2.0], [0.3], [0.4]]), - "labels": [0, 0, 1, 1], - "weights": np.asarray([0.5, 1.5, 1.5, 0.5]), + "data": data, + "labels": labels, + "weights": weights, } # this to have the right signature in asserts @@ -196,7 +203,7 @@ def test_DictDataset(): config = [Configuration( atomic_numbers=numbers, - positions=positions[i], + positions=positions[i] + 0.1*i, cell=cell, pbc=[True] * 3, node_labels=node_labels[i], @@ -212,36 +219,63 @@ def test_DictDataset(): # check __getitem__ # string - print(dataset['data']) - print(graph_dataset['data_list']) - + out = dataset['data'] + assert( torch.allclose(out, data) ) + out = graph_dataset['data_list'] + assert( torch.allclose(out[1]['positions'], torch.Tensor(positions+0.1))) + # int - print(dataset[0]) - print(graph_dataset[0]) + out = dataset[1] + assert( torch.allclose(out['data'], data[1]) ) + out = graph_dataset[1] + assert( torch.allclose(out['data_list']['positions'], torch.Tensor(positions+0.1))) + # list - print(dataset[[0,1,2]]) - print(graph_dataset[[0,1,2]]) + out = dataset[[0,1,2]] + assert( torch.allclose(out['data'], data[[0,1,2]]) ) + out = graph_dataset[[0,1,2]] + for i in [0,1,2]: + assert( torch.allclose(out['data_list'][i]['positions'], torch.Tensor(positions+0.1*i))) # slice - print(dataset[0:2]) - print(graph_dataset[0:2]) + out = dataset[0:2] + assert( torch.allclose(out['data'], data[[0,1]]) ) + out = graph_dataset[0:2] + for i in [0,1]: + assert( torch.allclose(out['data_list'][i]['positions'], torch.Tensor(positions+0.1*i))) # range - print(dataset[range(0,2)]) - print(graph_dataset[range(0,2)]) + out = dataset[range(0,2)] + assert( torch.allclose(out['data'], data[[0,1]]) ) + out = graph_dataset[range(0,2)] + for i in [0,1]: + assert( torch.allclose(out['data_list'][i]['positions'], torch.Tensor(positions+0.1*i))) # np.ndarray - print(dataset[np.array([0])]) - print(graph_dataset[np.array([0])]) - print(dataset[np.array([0,1,2])]) - print(graph_dataset[np.array([0,1,2])]) + out = dataset[np.array(1)] + assert( torch.allclose(out['data'], data[1]) ) + out = graph_dataset[np.array(1)] + assert( torch.allclose(out['data_list']['positions'], torch.Tensor(positions+0.1))) + + out = dataset[np.array([0,1,2])] + assert( torch.allclose(out['data'], data[[0,1,2]]) ) + out = graph_dataset[np.array([0,1,2])] + for i in [0,1,2]: + assert( torch.allclose(out['data_list'][i]['positions'], torch.Tensor(positions+0.1*i))) # torch.Tensor - print(dataset[torch.tensor([0], dtype=torch.long)]) - print(graph_dataset[torch.tensor([0], dtype=torch.long)]) - print(dataset[torch.tensor([0,1,2], dtype=torch.long)]) - print(graph_dataset[torch.tensor([0,1,2], dtype=torch.long)]) + out = dataset[torch.tensor([1], dtype=torch.long)] + assert( torch.allclose(out['data'], data[1]) ) + out = graph_dataset[torch.tensor([1], dtype=torch.long)] + assert( torch.allclose(out['data_list']['positions'], torch.Tensor(positions+0.1))) + + out = dataset[torch.tensor([0,1,2], dtype=torch.long)] + assert( torch.allclose(out['data'], data[[0,1,2]]) ) + out = graph_dataset[torch.tensor([0,1,2], dtype=torch.long)] + for i in [0,1,2]: + assert( torch.allclose(out['data_list'][i]['positions'], torch.Tensor(positions+0.1*i))) + if __name__ == "__main__": test_DictDataset() \ No newline at end of file From de43be17e931b667f1676eaac437ae5d88c0b659 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 18:15:28 +0100 Subject: [PATCH 208/267] Added tests --- mlcolvar/core/nn/graph/gnn.py | 66 +++++++- mlcolvar/data/graph/utils.py | 10 ++ mlcolvar/tests/test_core_nn_graph.py | 4 + mlcolvar/tests/test_data_graph_utils.py | 3 +- mlcolvar/tests/test_utils_io.py | 52 ++++++- mlcolvar/utils/io.py | 198 +++++++++++++++++++++++- 6 files changed, 328 insertions(+), 5 deletions(-) create mode 100644 mlcolvar/tests/test_core_nn_graph.py diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py index ea9b5c5f..796e6f3b 100644 --- a/mlcolvar/core/nn/graph/gnn.py +++ b/mlcolvar/core/nn/graph/gnn.py @@ -127,4 +127,68 @@ def get_edge_vectors_and_lengths( if normalize: vectors = torch.nan_to_num(torch.div(vectors, lengths)) - return vectors, lengths \ No newline at end of file + return vectors, lengths + + +def test_get_edge_vectors_and_lengths() -> None: + dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.float64) + + data = dict() + data['positions'] = torch.tensor( + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + dtype=torch.float64 + ) + data['edge_index'] = torch.tensor( + [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] + ) + data['shifts'] = torch.tensor([ + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.2, 0.0], + [0.0, -0.2, 0.0], + [0.0, 0.0, 0.0], + ]) + + vectors, distances = get_edge_vectors_and_lengths(**data, normalize=False) + assert(torch.allclose(vectors, torch.tensor([[0.0700, -0.0700, 0.0000], + [0.0700, 0.0700, 0.0000], + [-0.070, -0.0700, 0.0000], + [0.0000, 0.0600, 0.0000], + [0.0000, -0.0600, 0.0000], + [-0.070, 0.0700, 0.0000]]) + ) + ) + assert(torch.allclose(distances,torch.tensor([[0.09899494936611666], + [0.09899494936611666], + [0.09899494936611666], + [0.06000000000000000], + [0.06000000000000000], + [0.09899494936611666]]) + ) + ) + + vectors, distances = get_edge_vectors_and_lengths(**data, normalize=True) + assert(torch.allclose(vectors, torch.tensor([[0.70710678118654757, -0.70710678118654757, 0.0], + [0.70710678118654757, 0.70710678118654757, 0.0], + [-0.7071067811865476, -0.70710678118654757, 0.0], + [0.00000000000000000, 1.00000000000000000, 0.0], + [0.00000000000000000, -1.00000000000000000, 0.0], + [-0.7071067811865476, 0.70710678118654757, 0.0]]) + ) + ) + + assert(torch.allclose(distances, torch.tensor([[0.09899494936611666], + [0.09899494936611666], + [0.09899494936611666], + [0.06000000000000000], + [0.06000000000000000], + [0.09899494936611666]]) + ) + ) + + torch.set_default_dtype(dtype) + +if __name__ == "__main__": + test_get_edge_vectors_and_lengths() \ No newline at end of file diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index c6e08a92..46a940a1 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -294,6 +294,15 @@ def create_test_graph_input(output_type: str, import numpy as np +def test_to_one_hot() -> None: + i = torch.tensor([[0], [2], [1]], dtype=torch.int64) + e = to_one_hot(i, 4) + assert ( + e == torch.tensor( + [[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0]], dtype=torch.int64 + ) + ).all() + def test_from_configuration() -> None: # fake atomic numbers, positions, cell, graph label, node labels numbers = [8, 1, 1] @@ -609,5 +618,6 @@ def test_from_configurations() -> None: ).all() if __name__ == '__main__': + test_to_one_hot() test_from_configuration() test_from_configurations() \ No newline at end of file diff --git a/mlcolvar/tests/test_core_nn_graph.py b/mlcolvar/tests/test_core_nn_graph.py new file mode 100644 index 00000000..3517d3fd --- /dev/null +++ b/mlcolvar/tests/test_core_nn_graph.py @@ -0,0 +1,4 @@ +from mlcolvar.core.nn.graph.gnn import test_get_edge_vectors_and_lengths + +if __name__ == "__main__": + test_get_edge_vectors_and_lengths() \ No newline at end of file diff --git a/mlcolvar/tests/test_data_graph_utils.py b/mlcolvar/tests/test_data_graph_utils.py index 98b59a5b..f765c6a9 100644 --- a/mlcolvar/tests/test_data_graph_utils.py +++ b/mlcolvar/tests/test_data_graph_utils.py @@ -1,5 +1,6 @@ -from mlcolvar.data.graph.utils import test_from_configuration, test_from_configurations +from mlcolvar.data.graph.utils import test_from_configuration, test_from_configurations, test_to_one_hot if __name__ == "main": + test_to_one_hot() test_from_configuration() test_from_configurations() \ No newline at end of file diff --git a/mlcolvar/tests/test_utils_io.py b/mlcolvar/tests/test_utils_io.py index 33b6c64e..efbe929b 100644 --- a/mlcolvar/tests/test_utils_io.py +++ b/mlcolvar/tests/test_utils_io.py @@ -2,7 +2,7 @@ import urllib from mlcolvar.utils.io import load_dataframe from mlcolvar.utils.io import test_datasetFromFile -from mlcolvar.utils.io import test_datasesetFromTrajectories +from mlcolvar.utils.io import test_datasesetFromTrajectories, test_create_dataset_from_trajectories example_files = { "str": "mlcolvar/tests/data/state_A.dat", @@ -27,3 +27,53 @@ def test_loadDataframe(file_type): if __name__ == "__main__": test_datasetFromFile() test_datasesetFromTrajectories() + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, None) + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') \ No newline at end of file diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index fdf23b76..e9d6177f 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -551,6 +551,10 @@ def _configures_from_trajectory( return configurations +# ================================================================================================= +# ============================================= TESTS ============================================= +# ================================================================================================= + def test_datasetFromFile(): # Test with unlabeled dataset torch_dataset, pd_dataframe = create_dataset_from_files( @@ -621,6 +625,196 @@ def test_datasesetFromTrajectories(): show_progress=False ) +def test_create_dataset_from_trajectories(text: str, system_selection: str) -> None: + with open('test_dataset.pdb', 'w') as fp: + print(text, file=fp) + + dataset, trajectories = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + system_selection=system_selection, + return_trajectories=True, + show_progress=False + ) + + assert len(dataset) == 6 + assert dataset.metadata["cutoff"] == 1.0 + assert dataset.metadata["z_table"] == [1, 8] + assert len(trajectories) == 2 + assert len(trajectories[0]) == 2 + assert len(trajectories[1]) == 2 + assert len(trajectories[1][0]) == 2 + assert len(trajectories[1][1]) == 2 + + assert dataset[0]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[1]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[2]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[3]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[4]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[5]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + + dataset, trajectories = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + create_labels=False, + system_selection=system_selection, + return_trajectories=True, + show_progress=False + ) + + assert dataset[0]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[1]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[2]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[3]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[4]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) + assert dataset[5]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) + + def check_data_1(data) -> None: + assert(torch.allclose(data["data_list"]['edge_index'], torch.tensor([[0, 0, 1, 1, 2, 2], + [2, 1, 0, 2, 1, 0]]) + ) + ) + assert(torch.allclose(data["data_list"]['shifts'], torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, -2.0, 0.0], + [0.0, 0.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['unit_shifts'], torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['positions'], torch.tensor([[0.0, 0.0, 0.0], + [0.7, 0.7, 0.0], + [0.7, -0.7, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['cell'], torch.tensor([[2.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, 0.0, 2.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['node_attrs'], torch.tensor([[0.0, 1.0], + [1.0, 0.0], + [1.0, 0.0]]) + ) + ) + + for i in range(6): + check_data_1(dataset[i]) + + if system_selection is not None: + + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + system_selection='type O and {:s}'.format(system_selection), + environment_selection='type H and {:s}'.format(system_selection), + show_progress=False + ) + + for i in range(6): + check_data_1(dataset[i]) + + dataset = create_dataset_from_trajectories( + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], + 1.0, + system_selection='name H1 and {:s}'.format(system_selection), + environment_selection='name H2 and {:s}'.format(system_selection), + show_progress=False + ) + + def check_data_2(data) -> None: + assert(torch.allclose(data["data_list"]['edge_index'], torch.tensor([[0, 1], [1, 0]]))) + assert(torch.allclose(data["data_list"]['shifts'], torch.tensor([[0.0, 2.0, 0.0], + [0.0, -2.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['unit_shifts'], torch.tensor([[0.0, 1.0, 0.0], + [0.0, -1.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['positions'], torch.tensor([[0.7, 0.7, 0.0], + [0.7, -0.7, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['cell'], torch.tensor([[2.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, 0.0, 2.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['node_attrs'], torch.tensor([[1.0], + [1.0]]) + ) + ) + + for i in range(6): + check_data_2(dataset[i]) + + __import__('os').remove('test_dataset.pdb') + + if __name__ == "__main__": - test_datasetFromFile() - test_datasesetFromTrajectories() \ No newline at end of file + # test_datasetFromFile() + # test_datasesetFromTrajectories() + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, None) + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') + From 7c2ea79a5cd0fe2b21503a4cc4d19fce56866322 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 18:16:03 +0100 Subject: [PATCH 209/267] fixed save dataset --- mlcolvar/data/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlcolvar/data/utils.py b/mlcolvar/data/utils.py index 4fc596e7..d5e33113 100644 --- a/mlcolvar/data/utils.py +++ b/mlcolvar/data/utils.py @@ -61,8 +61,8 @@ def save_dataset_configurations_as_extyz(dataset: DictDataset, file_name: str) - # create file fp = open(file_name, 'w') - for d in dataset: - d = d['data_list'] + for i in range(len(dataset)): + d = dataset[i]['data_list'] # print number of atoms print(len(d['positions']), file=fp) From d28d9b80ba8929e89ec092d19ef76d4f7ad4696f Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 18:18:02 +0100 Subject: [PATCH 210/267] Update testing notebook --- test_graphs/test_graph.ipynb | 1316 ++-------------------------------- 1 file changed, 40 insertions(+), 1276 deletions(-) diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 58d23a27..283c1652 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -53,433 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b4a53ab5e07749d0aea380482e04a522", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00)\n" - ] - } - ], - "source": [ - "print(model_graph(xxx))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch_geometric.data.batch.DataBatch" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(inp)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[0.3392]], grad_fn=)\n", - "tensor([[0.2707]], grad_fn=)\n", - "tensor([[0.3392]], grad_fn=)\n", - "tensor([[0.2707]], grad_fn=)\n", - "tensor([[0.3392]], grad_fn=)\n", - "tensor([[0.2707]], grad_fn=)\n" + "tensor([[1.8528]], grad_fn=)\n", + "tensor([[-2.3465]], grad_fn=)\n", + "tensor([[1.8528]], grad_fn=)\n", + "tensor([[-2.3465]], grad_fn=)\n", + "tensor([[1.8528]], grad_fn=)\n", + "tensor([[-2.3465]], grad_fn=)\n" ] } ], "source": [ + "model_graph.eval()\n", + "\n", "test = inp.get_example(100)\n", - "test.batch = torch.tensor([0], dtype = torch.long)\n", + "test.batch = torch.zeros(len(test['positions']), dtype = torch.long)\n", "\n", "print(model_graph(xxx))\n", "print(model_graph(test))\n", @@ -901,161 +216,14 @@ "loaded_model = torch.jit.load('test.pt')\n", "\n", "print(loaded_model(xxx))\n", - "print(loaded_model(test))\n", - "\n", - "\n", - "# EVVIVAAAAAA" + "print(loaded_model(test))\n" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'data_list': DataBatch(edge_index=[2, 53760], shifts=[53760, 3], unit_shifts=[53760, 3], positions=[8960, 3], cell=[3840, 3], node_attrs=[8960, 2], graph_labels=[1280, 1], n_system=[1280, 1], weight=[1280], batch=[8960], ptr=[1281])}]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datamodule_ff.setup()\n", - "\n", - "a = datamodule_ff.train_dataloader()\n", - "a.dataset['data']\n", - "\n", - "datamodule_graph.setup()\n", - "a = datamodule_graph.train_dataloader()\n", - "list(a)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6e424342ecd94f51abdd1c89a3caa7fb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" ] @@ -1100,414 +268,10 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "plt.hist(out_graph.detach().squeeze())\n", + "plt.hist(out_graph.detach().squeeze(), 100)\n", "plt.hist(out_ff.detach().squeeze())\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'epoch': 4,\n", - " 'global_step': 5,\n", - " 'pytorch-lightning_version': '2.1.3',\n", - " 'state_dict': OrderedDict([('loss_fn.target_centers', tensor([-7., 7.])),\n", - " ('loss_fn.target_sigmas', tensor([0.2000, 0.2000])),\n", - " ('nn.nn.0.weight',\n", - " tensor([[ 0.1091, -0.1932, -0.1206, -0.0681, 0.1556, 0.1799, 0.1149, -0.2043,\n", - " -0.0908, -0.0390, -0.0684, -0.0457, 0.0788, 0.1463, -0.1411, -0.1737,\n", - " 0.1678, 0.1805, 0.2164, -0.1644, -0.0258],\n", - " [ 0.1690, 0.1007, -0.0752, 0.1875, 0.1510, 0.0228, 0.1875, 0.2199,\n", - " 0.0580, 0.1580, 0.1729, 0.1864, -0.0042, 0.1831, 0.1117, 0.0253,\n", - " 0.0455, 0.2125, -0.1677, 0.0447, -0.1825],\n", - 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" 0.0204, 0.0000]),\n", - " 'exp_avg_sq': tensor([4.7714e-07, 8.8522e-05, 2.6436e-04, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.2334e-05, 0.0000e+00])},\n", - " 4: {'step': tensor(5.),\n", - " 'exp_avg': tensor([[ 0.0821, -0.1237, -0.1054, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " -0.0650, 0.0000]]),\n", - " 'exp_avg_sq': tensor([[0.0002, 0.0005, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001,\n", - " 0.0000]])},\n", - " 5: {'step': tensor(5.),\n", - " 'exp_avg': tensor([0.3885]),\n", - " 'exp_avg_sq': tensor([0.0045])}},\n", - " 'param_groups': [{'lr': 0.001,\n", - " 'betas': (0.9, 0.999),\n", - " 'eps': 1e-08,\n", - " 'weight_decay': 0,\n", - " 'amsgrad': False,\n", - " 'maximize': False,\n", - " 'foreach': None,\n", - " 'capturable': False,\n", - " 'differentiable': False,\n", - " 'fused': None,\n", - " 'params': [0, 1, 2, 3, 4, 5]}]}],\n", - " 'lr_schedulers': [],\n", - " 'hparams_name': 'kwargs',\n", - " 'hyper_parameters': {'model': FeedForward(\n", - " (nn): Sequential(\n", - " (0): Linear(in_features=21, out_features=15, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=15, out_features=10, bias=True)\n", - " (3): ReLU(inplace=True)\n", - " (4): Linear(in_features=10, out_features=1, bias=True)\n", - " )\n", - " ),\n", - " 'preprocessing': None,\n", - " 'postprocessing': None,\n", - " 'n_states': 2,\n", - " 'n_cvs': 1,\n", - " 'target_centers': [-7, 7],\n", - " 'target_sigmas': [0.2, 0.2],\n", - " 'options': None}}" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "torch.load('checkpoints/epoch=4-step=5.ckpt')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 73c100e079963e66ffcf0ce44a13e5710eb4aeb8 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 18:18:15 +0100 Subject: [PATCH 211/267] Removed already ported files --- mlcolvar/graph/utils/timelagged.py | 240 -------------------- mlcolvar/graph/utils/torch_tools.py | 339 ---------------------------- 2 files changed, 579 deletions(-) delete mode 100644 mlcolvar/graph/utils/timelagged.py delete mode 100644 mlcolvar/graph/utils/torch_tools.py diff --git a/mlcolvar/graph/utils/timelagged.py b/mlcolvar/graph/utils/timelagged.py deleted file mode 100644 index f8334b6b..00000000 --- a/mlcolvar/graph/utils/timelagged.py +++ /dev/null @@ -1,240 +0,0 @@ -import torch -import numpy as np -from typing import Tuple - -from mlcolvar.graph import data as gdata -from mlcolvar.utils.timelagged import ( - tprime_evaluation, - find_timelagged_configurations -) - -try: - import pandas as pd - - PANDAS_IS_INSTALLED = True -except ImportError: - PANDAS_IS_INSTALLED = False -try: - from tqdm import tqdm - - TQDM_IS_INSTALLED = True -except ImportError: - TQDM_IS_INSTALLED = False - -""" -Build time-lagged dataset for GNNs. -""" - -__all__ = ['create_timelagged_datasets'] - - -def create_timelagged_datasets( - dataset: gdata.GraphDataSet, - t: torch.Tensor = None, - lag_time: float = 1, - reweight_mode: str = None, - logweights: torch.Tensor = None, - tprime: torch.Tensor = None, - interval: list = None, - progress_bar: bool = False, -) -> Tuple[gdata.GraphDataSet, gdata.GraphDataSet]: - """ - Create two GraphDataSets of time-lagged configurations. - - In case of biased simulations the reweight can be performed in two - different ways (``reweight_mode``): - - 1. ``rescale_time`` : the search for time-lagged pairs is performed in the - accelerated time (dt' = dt*exp(logweights)), as described in [1]_ . - 2. ``weights_t`` : the weight of each pair of configurations (t,t+lag_time) - depends only on time t (logweights(t)), as done in [2]_ , [3]_ . - - If reweighting is None and tprime is given the `rescale_time` mode is used. - If instead only the logweights are specified the user needs to choose the - reweighting mode. - - References - ---------- - .. [1] Y. I. Yang and M. Parrinello, "Refining collective coordinates and - improving free energy representation in variational enhanced sampling." - JCTC 14, 2889–2894 (2018). - .. [2] J. McCarty and M. Parrinello, "A variational conformational dynamics - approach to the selection of collective variables in meta-dynamics." - JCP 147, 204109 (2017). - .. [3] H. Wu, et al. "Variational Koopman models: Slow collective variables - and molecular kinetics from short off-equilibrium simulations." - JCP 146.15 (2017). - - Parameters - ---------- - dataset : mlcolvar.graph.data.GraphDataSet - The reference dataset. - t : array-like, optional - Time series, by default np.arange(len(X)) - reweight_mode: str, optional - How to do the reweighting, see documentation, by default none - lag_time: float, optional - Lag between configurations, by default = 10 - logweights : array-like,optional - Logweight of each configuration (typically beta*bias) - tprime : array-like,optional - Rescaled time estimated from the simulation. If not given and - `reweighting_mode`=`rescale_time` then - `tprime_evaluation(t,logweights)` is used - interval : list or np.array or tuple, optional - Range for slicing the returned dataset. Useful to work with batches of - same sizes. Recall that with different lag_times one obtains different - datasets, with different lengths - progress_bar: bool - If display progress bar with tqdm. - - Returns - ------- - datasets: Tuple[GraphDataSet, GraphDataSet] - Datasets at time t and t+lag. - """ - - if PANDAS_IS_INSTALLED and isinstance(t, pd.core.frame.DataFrame): - t = t.values - - # check reweigthing mode if logweights are given: - # 1) if rescaled time tprime is given - if tprime is not None: - if reweight_mode is None: - reweight_mode = 'rescale_time' - elif reweight_mode != 'rescale_time': - raise ValueError( - 'The `reweighting_mode` needs to be equal to `rescale_time`, ' - + 'and not {reweight_mode} if the rescale time `tprime` is ' - + 'given.' - ) - # 2) if logweights are given - elif logweights is not None: - if reweight_mode is None: - reweight_mode = 'rescale_time' - # TODO output warning or error if mode not specified? - # warnings.warn( - # '`reweight_mode` not specified, setting it to `rescale_time`.' - # ) - - # define time if not given - if t is None: - t = torch.arange(0, len(dataset)) - else: - if len(t) != len(dataset): - message = ( - 'The length of t ({:d}) is different from the one of ' - + 'dataset ({:d}).' - ) - raise ValueError(message.format(len(t), len(dataset))) - - # define tprime if not given: - if reweight_mode == 'rescale_time': - if tprime is None: - tprime = tprime_evaluation(t, logweights) - else: - tprime = t - - # find pairs of configurations separated by lag_time - index = torch.arange(len(dataset), dtype=torch.long) - x_t, x_lag, w_t, w_lag = find_timelagged_configurations( - index, - tprime, - lag_time=lag_time, - logweights=logweights if reweight_mode == 'weights_t' else None, - progress_bar=progress_bar, - ) - - # return only a slice of the data (N. Pedrani) - if interval is not None: - # convert to a list - data = list(x_t, x_lag, w_t, w_lag) - # assert dimension of interval - assert len(interval) == 2 - # modifies the content of data by slicing - for i in range(len(data)): - data[i] = data[i][interval[0]:interval[1]] - x_t, x_lag, w_t, w_lag = data - - assert len(x_t) == len(w_t) - assert len(x_t) == len(x_lag) - assert len(w_t) == len(w_lag) - - # assign weights - dataset_t = dataset[x_t.numpy().tolist()] - dataset_lag = dataset[x_lag.numpy().tolist()] - - for i in range(len(x_t)): - dataset_t[i]['weight'] = w_t[i] - dataset_lag[i]['weight'] = w_lag[i] - - return (dataset_t, dataset_lag) - - -def test_timelagged() -> None: - from mlcolvar.utils.timelagged import create_timelagged_dataset as _ctd - - numbers = [8, 1, 1] - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - ) - dataset = gdata.create_dataset_from_configurations( - [config] * 10, z_table, 0.1, show_progress=False - ) - for i in range(len(dataset)): - dataset[i]['graph_labels'] *= i - - datasets = create_timelagged_datasets(dataset) - data_reference = _ctd(torch.arange(10)) - - assert len(datasets[0]) == len(data_reference['data']) - assert len(datasets[1]) == len(data_reference['data_lag']) - - for i in range(len(datasets[0])): - d_0 = datasets[0] - d_ref = data_reference['data'] - w_ref = data_reference['weights'] - assert d_0[i]['weight'] == w_ref[i] - assert d_0[i]['graph_labels'][0, 0] == d_ref[i] - for i in range(len(datasets[1])): - d_1 = datasets[1] - d_ref = data_reference['data_lag'] - w_ref = data_reference['weights_lag'] - assert d_1[i]['weight'] == w_ref[i] - assert d_1[i]['graph_labels'][0, 0] == d_ref[i] - - datasets = create_timelagged_datasets(dataset, lag_time=2) - data_reference = _ctd(torch.arange(10), lag_time=2) - - assert len(datasets[0]) == len(data_reference['data']) - assert len(datasets[1]) == len(data_reference['data_lag']) - - for i in range(len(datasets[0])): - d_0 = datasets[0] - d_ref = data_reference['data'] - w_ref = data_reference['weights'] - assert d_0[i]['weight'] == w_ref[i] - assert d_0[i]['graph_labels'][0, 0] == d_ref[i] - for i in range(len(datasets[1])): - d_1 = datasets[1] - d_ref = data_reference['data_lag'] - w_ref = data_reference['weights_lag'] - assert d_1[i]['weight'] == w_ref[i] - assert d_1[i]['graph_labels'][0, 0] == d_ref[i] - - -if __name__ == '__main__': - test_timelagged() diff --git a/mlcolvar/graph/utils/torch_tools.py b/mlcolvar/graph/utils/torch_tools.py deleted file mode 100644 index 397d4365..00000000 --- a/mlcolvar/graph/utils/torch_tools.py +++ /dev/null @@ -1,339 +0,0 @@ -import torch -from typing import Tuple, Optional - -""" -Helper functions for torch. These modules are taken from MACE directly: -https://github.com/ACEsuit/mace/blob/main/mace/tools/torch_tools.py -https://github.com/ACEsuit/mace/blob/main/mace/tools/scatter.py -""" - -__all__ = [ - 'to_one_hot', - 'set_default_dtype', - 'get_edge_vectors_and_lengths', - 'scatter_sum', - 'scatter_mean' -] - - -def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: - """ - Generates one-hot encoding with `n_classes` classes from `indices` - - Parameters - ---------- - indices: torch.Tensor (shape: [N, 1]) - Node incices. - n_classes: int - Number of classes. - - Returns - ------- - encoding: torch.tensor (shape: [N, n_classes]) - The one-hot encoding. - """ - shape = indices.shape[:-1] + (n_classes,) - oh = torch.zeros(shape, device=indices.device).view(shape) - - # scatter_ is the in-place version of scatter - oh.scatter_(dim=-1, index=indices, value=1) - - return oh.view(*shape) - - -def get_edge_vectors_and_lengths( - positions: torch.Tensor, - edge_index: torch.Tensor, - shifts: torch.Tensor, - normalize: bool = True, -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Calculate edge vectors and lengths by indices and shift vectors. - - Parameters - ---------- - position: torch.Tensor (shape: [n_atoms, 3]) - The position vector. - edge_index: torch.Tensor (shape: [2, n_edges]) - The edge indices. - shifts: torch.Tensor (shape: [n_edges, 3]) - The shift vector. - normalize: bool - If return the normalized distance vectors. - - Returns - ------- - vectors: torch.Tensor (shape: [n_edges, 3]) - The distance vectors. - lengths: torch.Tensor (shape: [n_edges, 1]) - The edge lengths. - """ - sender = edge_index[0] - receiver = edge_index[1] - vectors = positions[receiver] - positions[sender] + shifts # [n_edges, 3] - lengths = torch.linalg.norm(vectors, dim=-1, keepdim=True) # [n_edges, 1] - - if normalize: - vectors = torch.nan_to_num(torch.div(vectors, lengths)) - - return vectors, lengths - - -def set_default_dtype(dtype: str) -> None: - """ - Wrapper function of `torch.set_default_dtype`. - - Parameters - ---------- - dtype: str - The data type. - """ - if not isinstance(dtype, str): - raise TypeError('A string is required to set TORCH default dtype!') - dtype = dtype.lower() - if dtype in ['float', 'float32']: - torch.set_default_dtype(torch.float32) - elif dtype in ['double', 'float64']: - torch.set_default_dtype(torch.float64) - else: - raise RuntimeError( - 'Unknown/Unsupported data type: "{:s}"!'.format(dtype) - ) - - -def _broadcast(src: torch.Tensor, other: torch.Tensor, dim: int): - """ - Helper function of scatter functions. - """ - if dim < 0: - dim = other.dim() + dim - if src.dim() == 1: - for _ in range(0, dim): - src = src.unsqueeze(0) - for _ in range(src.dim(), other.dim()): - src = src.unsqueeze(-1) - src = src.expand_as(other) - return src - - -@torch.jit.script -def scatter_sum( - src: torch.Tensor, - index: torch.Tensor, - dim: int = -1, - out: Optional[torch.Tensor] = None, - dim_size: Optional[int] = None, -) -> torch.Tensor: - """ - Basic `scatter_sum` operations from `torch_scatter`. - - Parameters - ---------- - src: torch.Tensor - The source tensor. - index: torch.Tensor - The indices of elements to scatter. - dim: int - The axis along which to index. - out: Optional[torch.Tensor] - The destination tensor. - dim_size: int - If out is not given, automatically create output with size dim_size at - dimension dim. If dim_size is not given, a minimal sized output tensor - according to index.max() + 1 is returned. - """ - index = _broadcast(index, src, dim) - if out is None: - size = list(src.size()) - if dim_size is not None: - size[dim] = dim_size - elif index.numel() == 0: - size[dim] = 0 - else: - size[dim] = int(index.max()) + 1 - out = torch.zeros(size, dtype=src.dtype, device=src.device) - return out.scatter_add_(dim, index, src) - else: - return out.scatter_add_(dim, index, src) - - -@torch.jit.script -def scatter_mean( - src: torch.Tensor, - index: torch.Tensor, - dim: int = -1, - out: Optional[torch.Tensor] = None, - dim_size: Optional[int] = None, -) -> torch.Tensor: - """ - Basic `scatter_mean` operations from `torch_scatter`. - - Parameters - ---------- - src: torch.Tensor - The source tensor. - index: torch.Tensor - The indices of elements to scatter. - dim: int - The axis along which to index. - out: Optional[torch.Tensor] - The destination tensor. - dim_size: int - If out is not given, automatically create output with size dim_size at - dimension dim. If dim_size is not given, a minimal sized output tensor - according to index.max() + 1 is returned. - """ - out = scatter_sum(src, index, dim, out, dim_size) - dim_size = out.size(dim) - - index_dim = dim - if index_dim < 0: - index_dim = index_dim + src.dim() - if index.dim() <= index_dim: - index_dim = index.dim() - 1 - - ones = torch.ones(index.size(), dtype=src.dtype, device=src.device) - count = scatter_sum(ones, index, index_dim, None, dim_size) - count[count < 1] = 1 - count = _broadcast(count, out, dim) - if out.is_floating_point(): - out.true_divide_(count) - else: - out.div_(count, rounding_mode="floor") - return out - - -def test_to_one_hot() -> None: - i = torch.tensor([[0], [2], [1]], dtype=torch.int64) - e = to_one_hot(i, 4) - assert ( - e == torch.tensor( - [[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0]], dtype=torch.int64 - ) - ).all() - - -def test_get_edge_vectors_and_lengths() -> None: - dtype = torch.get_default_dtype() - torch.set_default_dtype(torch.float64) - - data = dict() - data['positions'] = torch.tensor( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - dtype=torch.float64 - ) - data['edge_index'] = torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - data['shifts'] = torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], - [0.0, 0.0, 0.0], - ]) - - vectors, distances = get_edge_vectors_and_lengths(**data, normalize=False) - assert ( - torch.abs( - vectors - - torch.tensor([ - [0.0700, -0.0700, 0.0000], - [0.0700, 0.0700, 0.0000], - [-0.070, -0.0700, 0.0000], - [0.0000, 0.0600, 0.0000], - [0.0000, -0.0600, 0.0000], - [-0.070, 0.0700, 0.0000] - ]) - ) < 1E-12 - ).all() - assert ( - torch.abs( - distances - - torch.tensor([ - [0.09899494936611666], - [0.09899494936611666], - [0.09899494936611666], - [0.06000000000000000], - [0.06000000000000000], - [0.09899494936611666], - ]) - ) < 1E-12 - ).all() - - vectors, distances = get_edge_vectors_and_lengths(**data, normalize=True) - assert ( - torch.abs( - vectors - - torch.tensor([ - [0.70710678118654757, -0.70710678118654757, 0.0], - [0.70710678118654757, 0.70710678118654757, 0.0], - [-0.7071067811865476, -0.70710678118654757, 0.0], - [0.00000000000000000, 1.00000000000000000, 0.0], - [0.00000000000000000, -1.00000000000000000, 0.0], - [-0.7071067811865476, 0.70710678118654757, 0.0], - ]) - ) < 1E-12 - ).all() - assert ( - torch.abs( - distances - - torch.tensor([ - [0.09899494936611666], - [0.09899494936611666], - [0.09899494936611666], - [0.06000000000000000], - [0.06000000000000000], - [0.09899494936611666], - ]) - ) < 1E-12 - ).all() - - torch.set_default_dtype(dtype) - - -def test_set_default_dtype() -> None: - set_default_dtype('float64') - t = torch.Tensor([1.0]) - assert t.dtype == torch.float64 - - set_default_dtype('float32') - t = torch.Tensor([1.0]) - assert t.dtype == torch.float32 - - set_default_dtype('double') - t = torch.Tensor([1.0]) - assert t.dtype == torch.float64 - - set_default_dtype('float') - t = torch.Tensor([1.0]) - assert t.dtype == torch.float32 - - -def test_scatter() -> None: - src = torch.ones((2, 6, 2), dtype=torch.long) - index = torch.tensor([0, 1, 0, 1, 2, 1], dtype=torch.long) - - out = scatter_sum(src, index, dim=1) - assert ( - out == torch.tensor( - [[[2, 2], [3, 3], [1, 1]], [[2, 2], [3, 3], [1, 1]]], - dtype=torch.long - ) - ).all() - - out = scatter_mean(src, index, dim=1) - assert ( - out == torch.tensor( - [[[1, 1], [1, 1], [1, 1]], [[1, 1], [1, 1], [1, 1]]], - dtype=torch.long - ) - ).all() - - -if __name__ == '__main__': - test_to_one_hot() - test_get_edge_vectors_and_lengths() - test_set_default_dtype() - test_scatter() From 285db63d10bfb58032d8641eccc70bc17605cc36 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Thu, 12 Dec 2024 18:32:42 +0100 Subject: [PATCH 212/267] Fixed test --- mlcolvar/tests/test_utils_io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/tests/test_utils_io.py b/mlcolvar/tests/test_utils_io.py index efbe929b..4c9b7f78 100644 --- a/mlcolvar/tests/test_utils_io.py +++ b/mlcolvar/tests/test_utils_io.py @@ -27,7 +27,7 @@ def test_loadDataframe(file_type): if __name__ == "__main__": test_datasetFromFile() test_datasesetFromTrajectories() - text = """ + text = """ CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H From a834b9ea6a18d9e67bdecb031c926c87d512b84c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 13 Dec 2024 09:50:49 +0100 Subject: [PATCH 213/267] Fixed test --- mlcolvar/tests/test_utils_io.py | 54 +-------------------------- mlcolvar/tests/test_utils_io_graph.py | 53 ++++++++++++++++++++++++++ 2 files changed, 55 insertions(+), 52 deletions(-) create mode 100644 mlcolvar/tests/test_utils_io_graph.py diff --git a/mlcolvar/tests/test_utils_io.py b/mlcolvar/tests/test_utils_io.py index 4c9b7f78..34d2fa9d 100644 --- a/mlcolvar/tests/test_utils_io.py +++ b/mlcolvar/tests/test_utils_io.py @@ -2,7 +2,7 @@ import urllib from mlcolvar.utils.io import load_dataframe from mlcolvar.utils.io import test_datasetFromFile -from mlcolvar.utils.io import test_datasesetFromTrajectories, test_create_dataset_from_trajectories +from mlcolvar.utils.io import test_datasesetFromTrajectories example_files = { "str": "mlcolvar/tests/data/state_A.dat", @@ -26,54 +26,4 @@ def test_loadDataframe(file_type): if __name__ == "__main__": test_datasetFromFile() - test_datasesetFromTrajectories() - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, None) - - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, 'not resname XXXX') - - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, 'not resname XXXX') \ No newline at end of file + test_datasesetFromTrajectories() \ No newline at end of file diff --git a/mlcolvar/tests/test_utils_io_graph.py b/mlcolvar/tests/test_utils_io_graph.py new file mode 100644 index 00000000..7da38a5c --- /dev/null +++ b/mlcolvar/tests/test_utils_io_graph.py @@ -0,0 +1,53 @@ +from mlcolvar.utils.io import test_create_dataset_from_trajectories + +if __name__ == "__main__": + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, None) + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') \ No newline at end of file From ec91fbb47f4c5aa6ab650a718b048c0413ec228f Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 13 Dec 2024 11:49:33 +0100 Subject: [PATCH 214/267] Added load_args --- mlcolvar/tests/test_utils_io.py | 53 +++++++++++++++- mlcolvar/tests/test_utils_io_graph.py | 53 ---------------- mlcolvar/utils/io.py | 89 +++++++++++++++++++++++---- 3 files changed, 128 insertions(+), 67 deletions(-) delete mode 100644 mlcolvar/tests/test_utils_io_graph.py diff --git a/mlcolvar/tests/test_utils_io.py b/mlcolvar/tests/test_utils_io.py index 34d2fa9d..20822210 100644 --- a/mlcolvar/tests/test_utils_io.py +++ b/mlcolvar/tests/test_utils_io.py @@ -3,6 +3,7 @@ from mlcolvar.utils.io import load_dataframe from mlcolvar.utils.io import test_datasetFromFile from mlcolvar.utils.io import test_datasesetFromTrajectories +from mlcolvar.utils.io import test_create_dataset_from_trajectories example_files = { "str": "mlcolvar/tests/data/state_A.dat", @@ -26,4 +27,54 @@ def test_loadDataframe(file_type): if __name__ == "__main__": test_datasetFromFile() - test_datasesetFromTrajectories() \ No newline at end of file + test_datasesetFromTrajectories() + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, None) + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') + + text = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H +ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H +ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""" + test_create_dataset_from_trajectories(text, 'not resname XXXX') \ No newline at end of file diff --git a/mlcolvar/tests/test_utils_io_graph.py b/mlcolvar/tests/test_utils_io_graph.py deleted file mode 100644 index 7da38a5c..00000000 --- a/mlcolvar/tests/test_utils_io_graph.py +++ /dev/null @@ -1,53 +0,0 @@ -from mlcolvar.utils.io import test_create_dataset_from_trajectories - -if __name__ == "__main__": - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, None) - - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, 'not resname XXXX') - - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, 'not resname XXXX') \ No newline at end of file diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index e9d6177f..0e6937e0 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -268,13 +268,14 @@ def create_dataset_from_trajectories( cutoff: float, buffer: float = 0.0, z_table: AtomicNumberTable = None, + load_args: list = None, folder: str = None, create_labels: bool = True, system_selection: str = None, environment_selection: str = None, return_trajectories: bool = False, remove_isolated_nodes: bool = True, - show_progress: bool = True + show_progress: bool = True, ) -> Union[ DictDataset, Tuple[ @@ -394,6 +395,13 @@ def create_dataset_from_trajectories( assert isinstance(trajectories[i], str) assert isinstance(top[i], str) + # check if per file args are given, otherwise set to {} + if load_args is not None: + if (not isinstance(load_args, list)) or (len(trajectories) != len(load_args)): + raise TypeError( + "load_args should be a list of dictionaries of arguments of same length as trajectories. If you want to use the same args for all file pass them directly as **kwargs." + ) + topologies = [] trajectories_in_memory = [] @@ -436,18 +444,24 @@ def create_dataset_from_trajectories( if isinstance(trajectories_in_memory[i], list): for j in range(len(trajectories_in_memory[i])): configuration = _configures_from_trajectory( - trajectories_in_memory[i][j], - i if create_labels else -1, # NOTE: all these configurations have a label `i` - system_selection, - environment_selection, + trajectory=trajectories_in_memory[i][j], + label=i if create_labels else -1, # NOTE: all these configurations have a label `i` + system_selection=system_selection, + environment_selection=environment_selection, + start=load_args[i][j]['start'] if load_args is not None else 0, + stop=load_args[i][j]['stop'] if load_args is not None else None, + stride=load_args[i][j]['stride'] if load_args is not None else 1, ) configurations.extend(configuration) else: configuration = _configures_from_trajectory( - trajectories_in_memory[i], - i if create_labels else -1, - system_selection, - environment_selection, + trajectory=trajectories_in_memory[i], + label=i if create_labels else -1, + system_selection=system_selection, + environment_selection=environment_selection, + start=load_args[i]['start'] if load_args is not None else 0, + stop=load_args[i]['stop'] if load_args is not None else None, + stride=load_args[i]['stride'] if load_args is not None else 1, ) configurations.extend(configuration) @@ -490,7 +504,9 @@ def _configures_from_trajectory( label: int = None, system_selection: str = None, environment_selection: str = None, -) -> Configurations: + start: int = 0, + stop: int = None, + stride: int = 1) -> Configurations: """ Create configurations from one trajectory. @@ -535,8 +551,12 @@ def _configures_from_trajectory( pbc = [False] * 3 cell = [None] * len(trajectory) + if stop is None: + stop = len(trajectory) + configurations = [] - for i in range(len(trajectory)): + + for i in range(start,stop,stride): configuration = Configuration( atomic_numbers=atomic_numbers, positions=trajectory.xyz[i] * 10, @@ -622,10 +642,53 @@ def test_datasesetFromTrajectories(): cutoff=8.0, # Ang create_labels=True, system_selection='all and not type H', - show_progress=False + show_progress=False, ) -def test_create_dataset_from_trajectories(text: str, system_selection: str) -> None: + dataset = create_dataset_from_trajectories( + trajectories=['r.dcd', + 'p.dcd'], + top=['r.pdb', + 'p.pdb'], + folder="mlcolvar/tests/data", + cutoff=8.0, # Ang + create_labels=True, + system_selection='all and not type H', + show_progress=False, + load_args=[{'start' : 0, 'stop' : 10, 'stride' : 1}, + {'start' : 6, 'stop' : 10, 'stride' : 2}] + ) + assert(len(dataset)==12) + + dataset = create_dataset_from_trajectories( + trajectories=[['r.dcd', 'r.dcd'], + ['p.dcd', 'p.dcd']], + top=[['r.pdb', 'r.pdb'], + ['p.pdb', 'p.pdb']], + folder="mlcolvar/tests/data", + cutoff=8.0, # Ang + create_labels=True, + system_selection='all and not type H', + show_progress=False, + load_args=[[{'start' : 0, 'stop' : 10, 'stride' : 1}, {'start' : 0, 'stop' : 10, 'stride' : 1}], + [{'start' : 6, 'stop' : 10, 'stride' : 2}, {'start' : 6, 'stop' : 10, 'stride' : 2}]] + ) + assert(len(dataset)==24) + + +def test_create_dataset_from_trajectories(text: str = """ +CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +ENDMODEL +ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O +ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H +ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H +END +""", +system_selection: str = None +) -> None: with open('test_dataset.pdb', 'w') as fp: print(text, file=fp) From 2a6bd06993c7c9900a7d682378b4ce809704ad9c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 13 Dec 2024 14:51:09 +0100 Subject: [PATCH 215/267] Updated committor weights --- mlcolvar/cvs/committor/utils.py | 22 +- test_graphs/test_committor.ipynb | 475 ++++++++++++++++++------------- 2 files changed, 292 insertions(+), 205 deletions(-) diff --git a/mlcolvar/cvs/committor/utils.py b/mlcolvar/cvs/committor/utils.py index 40dd4385..a29fe7e0 100644 --- a/mlcolvar/cvs/committor/utils.py +++ b/mlcolvar/cvs/committor/utils.py @@ -67,23 +67,30 @@ def compute_committor_weights(dataset, ------- Updated dataset with weights and updated labels """ + if len(dataset) != len(bias): + raise ValueError('Dataset and bias have different lenghts!') if bias.isnan().any(): raise(ValueError('Found Nan(s) in bias tensor. Check before proceeding! If no bias was applied replace Nan with zero!')) - n_labels = len(torch.unique(dataset['labels'])) + if dataset.metadata['data_type'] == 'descriptors': + original_labels = dataset['labels'] + else: + original_labels = torch.Tensor([dataset['data_list'][i]['graph_labels'] for i in range(len(dataset))]) + + n_labels = len(torch.unique(original_labels)) if n_labels != len(data_groups): raise(ValueError(f'The number of labels ({n_labels}) and data groups ({len(data_groups)}) do not match! Ensure you are correctly mapping the data in your training set!')) # TODO sign if not from committor bias weights = torch.exp(beta * bias) - new_labels = torch.zeros_like(dataset['labels']) + new_labels = torch.zeros_like(original_labels) data_groups = torch.Tensor(data_groups) # correct data labels according to iteration for j,index in enumerate(data_groups): - new_labels[torch.nonzero(dataset['labels'] == j, as_tuple=True)] = index + new_labels[torch.nonzero(original_labels == j, as_tuple=True)] = index for i in np.unique(data_groups): # compute average of exp(beta*V) on this simualtions @@ -93,8 +100,13 @@ def compute_committor_weights(dataset, weights[torch.nonzero(new_labels == i, as_tuple=True)] = coeff * weights[torch.nonzero(new_labels == i, as_tuple=True)] # update dataset - dataset['weights'] = weights - dataset['labels'] = new_labels + if dataset.metadata['data_type'] == 'descriptors': + dataset['weights'] = weights + dataset['labels'] = new_labels + else: + for i in range(len(dataset)): + dataset[i]['weight'] = weights[i] + dataset[i]['graph_labels'] = new_labels[i] return dataset diff --git a/test_graphs/test_committor.ipynb b/test_graphs/test_committor.ipynb index 762d34e1..fcacfc1e 100644 --- a/test_graphs/test_committor.ipynb +++ b/test_graphs/test_committor.ipynb @@ -2,252 +2,134 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DictModule(dataset -> DictDataset( \"data_list\": 28603, \"z_table\": [6, 7, 8], \"cutoff\": 8.0, \"data_type\": graphs ),\n", - "\t\t train_loader -> DictLoader(length=1, batch_size=28603, shuffle=False))\n" + "Beta: 0.4009078751268027 \n", + "1/beta: 2.4943386299999997\n", + "DictModule(dataset -> DictDataset( \"data_list\": 200, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=200, shuffle=False))\n" ] } ], "source": [ + "import torch\n", "from mlcolvar.data import DictModule\n", - "from mlcolvar.utils.io import create_dataset_from_trajectories\n", - "from mlcolvar.utils.io import create_dataset_from_files\n", + "from mlcolvar.utils.io import load_dataframe, create_dataset_from_trajectories\n", + "from mlcolvar.cvs.committor.utils import compute_committor_weights\n", + "\n", + "# number of atoms\n", + "n_atoms=10\n", + "\n", + "# temperature in Kelvin\n", + "T = 300 \n", "\n", + "# Boltzmann factor in the RIGHT ENERGY UNITS!\n", + "kb = 0.0083144621 # kJ/mol\n", + "beta = 1/(kb*T)\n", + "print(f'Beta: {beta} \\n1/beta: {1/beta}')\n", "\n", - "dataset_graph = create_dataset_from_trajectories(\n", + "load_args = [{'start' : 0, 'stop' : 1000, 'stride' : 1},\n", + " {'start' : 0, 'stop' : 1000, 'stride' : 1}]\n", + "dataset = create_dataset_from_trajectories(\n", " trajectories=[\n", - " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/traj_comp.xtc',\n", - " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_B/traj_comp.xtc',\n", - " '/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/biased_sims/iter_4/A/traj_comp.xtc',\n", - " # '/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/biased_sims/iter_4/B/traj_comp.xtc'\n", - " # 'data/r.dcd',\n", - " # 'data/p.dcd',\n", - " # 'data/biased.trajectory.h5',\n", - " # 'data/biased.dcd',\n", - " #'data/biased.trajectory.h5',\n", - " #'data/r.dcd'\n", + " \"/home/etrizio/alad/boundary/A/alad.trr\",\n", + " \"/home/etrizio/alad/boundary/B/alad.trr\"\n", " ],\n", - " top=['/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/confout.gro',\n", - " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/confout.gro',\n", - " '/home/etrizio@iit.local/notebooks/projects/kolmogorov/alanine_transform/unbiased_sims/state_A/confout.gro'\n", - " # 'data/r.pdb', \n", - " # 'data/p.pdb',\n", - " # 'data/r.pdb',\n", - " #'data/r.pdb'\n", + " top=[\n", + " \"/home/etrizio/alad/boundary/A/alad.gro\",\n", + " \"/home/etrizio/alad/boundary/B/alad.gro\"\n", " ],\n", - " cutoff=8.0, # Ang\n", + " cutoff=10.0, # Ang\n", " create_labels=True,\n", " system_selection='all and not type H',\n", - " show_progress=False\n", + " show_progress=False,\n", + " load_args=[{'start' : 0, 'stop' : 100, 'stride' : 1},\n", + " {'start' : 0, 'stop' : 100, 'stride' : 1}]\n", ")\n", "\n", + "bias = torch.zeros(len(dataset))\n", + "\n", + "dataset = compute_committor_weights(dataset=dataset, bias=bias, data_groups=[0,1], beta=beta)\n", "\n", - "datamodule_graph = DictModule(dataset_graph, lengths=[1], shuffle=False)\n", + "datamodule_graph = DictModule(dataset, lengths=[1], shuffle=False)\n", "print(datamodule_graph)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 41, "metadata": {}, "outputs": [], - "source": [ - "import torch\n", - "from mlcolvar.utils.io import load_dataframe\n", - "\n", - "T = 300 \n", - "# Boltzmann factor in the RIGHT ENRGY UNITS!\n", - "kb = 0.0083144621\n", - "beta = 1/(kb*T)\n", - "\n", - "df = load_dataframe('/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/biased_sims/iter_4/A/COLVAR', stride=1)\n", - "weights = torch.exp(1/beta*torch.tensor((df['opes.bias'] + df['bias']).values))\n", - "weights = weights / weights.sum()\n", - "weights\n", - "\n", - "aux = torch.zeros(len(dataset_graph))\n", - "for i in range(len(weights)):\n", - " aux[-(len(weights)-i)] = weights[i]\n", - "aux\n", - "\n", - "for i in range(len(dataset_graph)):\n", - " dataset_graph['data_list'][i]['weight'] = aux[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28603" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dataset_graph)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" - ] - }, - { - "data": { - "text/plain": [ - "Committor(\n", - " (loss_fn): CommittorLoss()\n", - " (nn): SchNetModel(\n", - " (_radial_embedding): RadialEmbeddingBlock(\n", - " (bessel_fn): GAUSSIANBASIS [ \u001b[32m16\u001b[0m\u001b[36m 󰯰 \u001b[0m| \u001b[32m8.000000\u001b[0m\u001b[36m 󰳁 \u001b[0m]\n", - " )\n", - " (W_v): Linear(in_features=3, out_features=32, bias=False)\n", - " (layers): ModuleList(\n", - " (0-1): 2 x InteractionBlock(\n", - " (mlp): Sequential(\n", - " (0): Linear(in_features=16, out_features=32, bias=True)\n", - " (1): ShiftedSoftplus()\n", - " (2): Linear(in_features=32, out_features=32, bias=True)\n", - " )\n", - " (conv): CFConv()\n", - " (act): ShiftedSoftplus()\n", - " (lin): Linear(in_features=32, out_features=32, bias=True)\n", - " )\n", - " )\n", - " (W_out): ModuleList(\n", - " (0): Linear(in_features=32, out_features=16, bias=True)\n", - " (1): ShiftedSoftplus()\n", - " (2): Linear(in_features=16, out_features=1, bias=True)\n", - " )\n", - " )\n", - " (sigmoid): Custom_Sigmoid()\n", - ")" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "from mlcolvar.cvs.committor import Committor\n", "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", "import torch\n", "\n", "gnn_model = SchNetModel(n_out=1,\n", - " cutoff=dataset_graph.metadata['cutoff'],\n", - " atomic_numbers=dataset_graph.metadata['z_table'],\n", + " cutoff=dataset.metadata['cutoff'],\n", + " atomic_numbers=dataset.metadata['z_table'],\n", " n_bases=16,\n", - " n_layers=2,\n", - " n_filters=32,\n", - " n_hidden_channels=32,\n", + " n_layers=3,\n", + " n_filters=20,\n", + " n_hidden_channels=18,\n", + " w_out_after_sum=True,\n", + " aggr='mean'\n", " )\n", "\n", "# model = Committor(model=gnn_model,\n", "# mass=torch.Tensor([12, 19]),\n", "# alpha=1)\n", "\n", + "options = {'optimizer' : {'lr' : 5e-4},\n", + " 'lr_scheduler': {\n", + " 'scheduler': torch.optim.lr_scheduler.ExponentialLR,\n", + " 'gamma': 0.99995\n", + " }}\n", "\n", "model = Committor(model=gnn_model,\n", " mass=torch.Tensor([12, 14, 16]),\n", - " alpha=1)\n", - "\n", - "\n", - "model" + " alpha=1,\n", + " options=options)\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: True\n", + "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + "HPU available: False, using: 0 HPUs\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0b16af39d1674ea68b070e8ae805baf9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00 13\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule_graph\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 576\u001b[0m ckpt_path,\n\u001b[1;32m 577\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 578\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:989\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 986\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 992\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 994\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py:1035\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1035\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:202\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 202\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:359\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:136\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/training_epoch_loop.py:240\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 239\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 240\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 242\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:187\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 180\u001b[0m closure()\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:265\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/module.py:1291\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1254\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1257\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1258\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1259\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1260\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[1;32m 1261\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \n\u001b[1;32m 1290\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1291\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/core/optimizer.py:151\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 151\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:230\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:117\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:373\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 370\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 76\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/optim/adam.py:143\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 143\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 146\u001b[0m params_with_grad \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/plugins/precision/precision.py:104\u001b[0m, in \u001b[0;36mPrecision._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 93\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 94\u001b[0m optimizer: Optimizer,\n\u001b[1;32m 95\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 96\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03m hook is called.\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 102\u001b[0m \n\u001b[1;32m 103\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:140\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:126\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39menable_grad()\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 126\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/loops/optimization/automatic.py:315\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[1;32m 314\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step() \u001b[38;5;66;03m# unused hook - call anyway for backward compatibility\u001b[39;00m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_result_cls\u001b[38;5;241m.\u001b[39mfrom_training_step_output(training_step_output, trainer\u001b[38;5;241m.\u001b[39maccumulate_grad_batches)\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/strategies/strategy.py:382\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module:\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_redirection(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_step\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/cvs/committor/committor.py:136\u001b[0m, in \u001b[0;36mCommittor.training_step\u001b[0;34m(self, train_batch, batch_idx)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# ===================loss=====================\u001b[39;00m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m--> 136\u001b[0m loss, loss_var, loss_bound_A, loss_bound_B \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss_fn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 138\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 140\u001b[0m loss, loss_var, loss_bound_A, loss_bound_B \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_fn(\n\u001b[1;32m 141\u001b[0m x, q, labels, weights \n\u001b[1;32m 142\u001b[0m )\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/committor_loss.py:77\u001b[0m, in \u001b[0;36mCommittorLoss.forward\u001b[0;34m(self, x, q, labels, w, create_graph)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \n\u001b[1;32m 71\u001b[0m x: Union[torch\u001b[38;5;241m.\u001b[39mTensor, torch_geometric\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mBatch], \n\u001b[1;32m 72\u001b[0m q: torch\u001b[38;5;241m.\u001b[39mTensor, \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 75\u001b[0m create_graph: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 76\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m---> 77\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcommittor_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 78\u001b[0m \u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 81\u001b[0m \u001b[43m \u001b[49m\u001b[43matomic_masses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43matomic_masses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 82\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[43m \u001b[49m\u001b[43mgamma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgamma\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelta_f\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelta_f\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 85\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[43m \u001b[49m\u001b[43mseparate_boundary_dataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseparate_boundary_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 87\u001b[0m \u001b[43m \u001b[49m\u001b[43mdescriptors_derivatives\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdescriptors_derivatives\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_var\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_var\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Bin/dev/mlcolvar/mlcolvar/core/loss/committor_loss.py:207\u001b[0m, in \u001b[0;36mcommittor_loss\u001b[0;34m(x, q, labels, w, atomic_masses, alpha, gamma, delta_f, create_graph, separate_boundary_dataset, descriptors_derivatives, log_var)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;66;03m# ============================== LOSS ==============================\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;66;03m# Each loss contribution is scaled by the number of samples\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \n\u001b[1;32m 204\u001b[0m \u001b[38;5;66;03m# 1. VARIATIONAL LOSS\u001b[39;00m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;66;03m# Compute gradients of q(x) wrt x\u001b[39;00m\n\u001b[1;32m 206\u001b[0m grad_outputs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mones_like(q[mask_var])\n\u001b[0;32m--> 207\u001b[0m grad \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask_var\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_graph\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 208\u001b[0m grad \u001b[38;5;241m=\u001b[39m grad[mask_var_batches]\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m descriptors_derivatives \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;66;03m# we use the precomputed derivatives from descriptors to pos\u001b[39;00m\n", - "File \u001b[0;32m~/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/torch/autograd/__init__.py:394\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched, materialize_grads)\u001b[0m\n\u001b[1;32m 390\u001b[0m result \u001b[38;5;241m=\u001b[39m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(\n\u001b[1;32m 391\u001b[0m grad_outputs_\n\u001b[1;32m 392\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 394\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 395\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 396\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 397\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 398\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 399\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 400\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 402\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m materialize_grads:\n\u001b[1;32m 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m 405\u001b[0m output\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mzeros_like(\u001b[38;5;28minput\u001b[39m, requires_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m (output, \u001b[38;5;28minput\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(result, t_inputs)\n\u001b[1;32m 409\u001b[0m )\n", - "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 316.00 MiB. GPU 0 has a total capacty of 7.79 GiB of which 244.31 MiB is free. Including non-PyTorch memory, this process has 6.88 GiB memory in use. Of the allocated memory 6.61 GiB is allocated by PyTorch, and 158.40 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 7.33it/s]\n" ] } ], @@ -257,8 +139,8 @@ "trainer = Trainer(\n", " logger=False,\n", " enable_checkpointing=False,\n", - " accelerator='cuda',\n", - " max_epochs=500,\n", + " accelerator='cpu',\n", + " max_epochs=5000,\n", " enable_model_summary=False,\n", " limit_val_batches=0, \n", " num_sanity_val_steps=0\n", @@ -269,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -280,22 +162,215 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[1.1561e-08],\n", - " [1.6096e-09],\n", + "tensor([[9.9999e-01],\n", + " [9.9908e-01],\n", + " [1.4645e-10],\n", + " [1.0000e+00],\n", + " [1.0000e+00],\n", + " [1.0000e+00],\n", + " [3.3151e-05],\n", + 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[1.0000e+00],\n", + " [1.6868e-09],\n", + " [1.0000e+00],\n", + " [2.1633e-05],\n", + " [3.7342e-11],\n", + " [8.9162e-07],\n", + " [1.7096e-11],\n", " [1.0000e+00],\n", - " ...,\n", " [1.0000e+00],\n", - " [1.3562e-09],\n", - " [1.0000e+00]], grad_fn=)" + " [4.2786e-12],\n", + " [5.5302e-10],\n", + " [1.5385e-11],\n", + " [9.9999e-01]], grad_fn=)" ] }, - "execution_count": 36, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -306,12 +381,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -324,7 +399,7 @@ "import matplotlib.pyplot as plt\n", "\n", "# plt.hist(out_graph.detach().squeeze())\n", - "plt.plot(out_graph.detach().squeeze(), '.')\n", + "plt.scatter(torch.arange(len(dataset)).numpy(), out_graph.detach().squeeze(), c=[dataset['data_list'][i]['graph_labels'] for i in range(len(dataset))])\n", "plt.ylim(0,1)\n", "plt.show()" ] @@ -353,7 +428,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.14" } }, "nbformat": 4, From 16392d8566b9378c2e6d1e1c22a2eefb3643d733 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 13 Dec 2024 15:04:09 +0100 Subject: [PATCH 216/267] Removed ported files --- mlcolvar/graph/cvs/committor/utils.py | 184 ---------- mlcolvar/graph/utils/io.py | 511 -------------------------- 2 files changed, 695 deletions(-) delete mode 100644 mlcolvar/graph/utils/io.py diff --git a/mlcolvar/graph/cvs/committor/utils.py b/mlcolvar/graph/cvs/committor/utils.py index 2253c883..a4535a4d 100644 --- a/mlcolvar/graph/cvs/committor/utils.py +++ b/mlcolvar/graph/cvs/committor/utils.py @@ -17,190 +17,6 @@ ] -class GraphCommittorLoss(torch.nn.Module): - """ - Compute Kolmogorov's variational principle loss and impose boundary - conditions on the metastable states. Modified for Graph Neural Networks - (GNNs). - - Parameters - ---------- - atomic_masses : torch.Tensor - Atomic masses of the atoms in the system. - alpha : float - Hyperparamer that scales the boundary conditions contribution to loss, - i.e. alpha*(loss_bound_A + loss_bound_B) - gamma : float - Hyperparamer that scales the whole loss to avoid too small numbers, - i.e. gamma*(loss_var + loss_bound), by default 10000 - delta_f : float - Delta free energy between A (label 0) and B (label 1), units is kBT, - by default 0. State B is supposed to be higher in energy. - - See Also - -------- - mlcolvar.core.loss.committor.CommittorLoss - The original `CommittorLoss` module. - """ - - def __init__( - self, - atomic_masses: torch.Tensor, - alpha: float, - gamma: float = 10000.0, - delta_f: float = 0.0 - ) -> None: - super().__init__() - atomic_masses = torch.tensor( - atomic_masses, dtype=torch.get_default_dtype() - ) - self.register_buffer('atomic_masses', atomic_masses) - self.alpha = alpha - self.gamma = gamma - self.delta_f = delta_f - - def forward( - self, - data: Dict[str, torch.Tensor], - q: torch.Tensor, - create_graph: bool = True - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """ - The forward pass. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data batch. - q : torch.Tensor - Committor quess q(x), it is the output of NN - """ - return graph_committor_loss( - data=data, - q=q, - atomic_masses=self.atomic_masses, - alpha=self.alpha, - gamma=self.gamma, - delta_f=self.delta_f, - create_graph=create_graph - ) - - -def graph_committor_loss( - data: Dict[str, torch.Tensor], - q: torch.Tensor, - atomic_masses: torch.Tensor, - alpha: float, - gamma: float = 10000.0, - delta_f: float = 0.0, - create_graph: bool = True, -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """ - Compute variational loss for committor optimization with boundary - conditions.Modified for Graph Neural Networks (GNNs). - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data batch. - q : torch.Tensor - Committor quess q(x), it is the output of NN - atomic_masses : torch.Tensor - List of masses of all the atoms we are using. - alpha : float - Hyperparamer that scales the boundary conditions contribution to loss, - i.e. alpha*(loss_bound_A + loss_bound_B) - gamma : float - Hyperparamer that scales the whole loss to avoid too small numbers, - i.e. gamma*(loss_var + loss_bound) - delta_f : float - Delta free energy between A (label 0) and B (label 1), units is kBT. - create_graph : bool - Make loss backwardable, deactivate for validation to save memory. - - Returns - ------- - loss : torch.Tensor - Loss value. - gamma*loss_var : torch.Tensor - The variational loss term - gamma*alpha*loss_a : torch.Tensor - The boundary loss term on basin A - gamma*alpha*loss_b : torch.Tensor - The boundary loss term on basin B - - See Also - -------- - mlcolvar.core.loss.committor.committor_loss - The original `committor_loss` function. - """ - # inherit right device and dtpye - dtype = data['positions'].dtype - device = data['positions'].device - - atomic_masses = atomic_masses.to(dtype).to(device) - - # Create masks to access different states data - labels = data['graph_labels'].long().squeeze() - mask_a = labels == 0 - mask_b = labels == 1 - - # Update weights of basin B using the information on the delta_f - weights = data['weight'].clone() - if delta_f < 0: # B higher in energy --> A-B < 0 - factor = torch.exp( - torch.tensor([delta_f], dtype=dtype, device=device) - ) - weights[mask_b] = weights[mask_b] * factor - if delta_f > 0: # A higher in energy --> A-B > 0 - factor = torch.exp( - torch.tensor([delta_f], dtype=dtype, device=device) * -1 - ) - weights[mask_a] = weights[mask_a] * factor - - # Each loss contribution is scaled by the number of samples - - # We need the gradient of q(x) - grad_outputs: Optional[List[Optional[torch.Tensor]]] = [ - torch.ones_like(q, device=device) - ] - gradients = torch.autograd.grad( - [q], - [data['positions']], - grad_outputs=grad_outputs, - retain_graph=True, - create_graph=create_graph - )[0] # [n_nodes, 3] - assert gradients is not None - - # we sanitize the shapes of mass and weights tensors - node_types = torch.where(data['node_attrs'])[1] # [n_graphs, 1] - atomic_masses = atomic_masses[node_types].unsqueeze(-1) # [n_nodes, 1] - weights = weights.unsqueeze(-1) # [n_graphs, 1] - - # square, do the mass-weight, and sum over Cartesian dims - gradients_atomic = torch.pow(gradients, 2) / atomic_masses # [n_nodes, 3] - gradients_atomic = torch.sum( - gradients_atomic, dim=1, keepdim=True - ) # [n_nodes, 1] - # sum over batchs - gradients_batch = gutils.torch_tools.scatter_sum( - gradients_atomic, data['batch'], dim=0 - ) # [n_graphs, 1] - # ensemble avg. - loss_v = torch.mean((gradients_batch * weights)) # [,] - - # boundary conditions - loss_a = torch.mean(torch.pow(q[mask_a], 2)) - loss_b = torch.mean(torch.pow((q[mask_b] - 1.0), 2)) - - loss = loss_v.log() + gamma * (alpha * (loss_a + loss_b)) - - return ( - loss, loss_v.log(), alpha * gamma * loss_a, alpha * gamma * loss_b - ) - - def get_dataset_kolmogorov_bias( model: GraphBaseCV, dataset: gdata.GraphDataSet, diff --git a/mlcolvar/graph/utils/io.py b/mlcolvar/graph/utils/io.py deleted file mode 100644 index c0b5116b..00000000 --- a/mlcolvar/graph/utils/io.py +++ /dev/null @@ -1,511 +0,0 @@ -import torch -import numpy as np -import mdtraj as md -from typing import Union, List, Tuple - -from mlcolvar.graph import data as gdata - -""" -Some I/O things. -""" - -__all__ = ['create_dataset_from_trajectories'] - - -def create_dataset_from_trajectories( - trajectories: Union[List[List[str]], List[str], str], - top: Union[List[List[str]], List[str], str], - cutoff: float, - buffer: float = 0.0, - z_table: gdata.atomic.AtomicNumberTable = None, - folder: str = None, - create_labels: bool = True, - system_selection: str = None, - environment_selection: str = None, - return_trajectories: bool = False, - remove_isolated_nodes: bool = True, - show_progress: bool = True -) -> Union[ - gdata.GraphDataSet, - Tuple[ - gdata.GraphDataSet, - Union[List[List[md.Trajectory]], List[md.Trajectory]] - ] -]: - """ - Create a dataset from a set of trajectory files. - - Parameters - ---------- - trajectories: Union[List[List[str]], List[str], str] - Names of trajectories files. - top: Union[List[List[str]], List[str], str] - Names of topology files. - cutoff: float (units: Ang) - The graph cutoff radius. - buffer: float - Buffer size used in finding active environment atoms. - z_table: mlcolvar.graph.data.atomic.AtomicNumberTable - The atomic number table used to build the node attributes. If not - given, it will be created from the given trajectories. - folder: str - Common path for the files to be imported. If set, filenames become - `folder/file_name`. - create_labels: bool - Assign a label to each file according to the total number of files. - If False, labels of all files will be `-1`. - system_selection: str - MDTraj style atom selections [1] of the system atoms. If given, only - selected atoms will be loaded from the trajectories. This option may - increase the speed of building graphs. - environment_selection: str - MDTraj style atom selections [1] of the environment atoms. If given, - only the system atoms and [the environment atoms within the cutoff - radius of the system atoms] will be kept in the graph. - return_trajectories: bool - If also return the loaded trajectory objects. - remove_isolated_nodes: bool - If remove isolated nodes from the dataset. - show_progress: bool - If show the progress bar. - - Returns - ------- - dataset: mlcolvar.graph.data.GraphDataSet - The graph dataset. - trajectories: Union[List[List[mdtraj.Trajectory]], List[mdtraj.Trajectory]] - The loaded trajectory objects. - - Notes - ----- - The login behind this method is like the follows: - 1. If only `system_selection` is given, the method will only load atoms - selected by this selection, from the trajectories. - 2. If both `system_selection` and `environment_selection` are given, - the method will load the atoms select by both selections, but will - build graphs using [the system atoms] and [the environment atoms within - the cutoff radius of the system atoms]. - - References - ---------- - .. [1] https://www.mdtraj.org/1.9.8.dev0/atom_selection.html - """ - - if environment_selection is not None: - assert system_selection is not None, ( - 'the `environment_selection` argument requires the' - + '`system_selection` argument to be defined!' - ) - selection = '({:s}) or ({:s})'.format( - system_selection, environment_selection - ) - elif system_selection is not None: - selection = system_selection - else: - selection = None - - if environment_selection is None: - assert buffer == 0, ( - 'Not `environment_selection` given! Cannot define buffer size!' - ) - - # fmt: off - assert type(trajectories) is type(top), ( - 'The `trajectories` and `top` parameters should have the same type!' - ) - if isinstance(trajectories, str): - trajectories = [trajectories] - top = [top] - assert len(trajectories) == len(top), ( - 'Numbers of trajectories and topology files should be the same!' - ) - # fmt: on - - for i in range(len(trajectories)): - assert type(trajectories[i]) is type(top[i]), ( - 'Each element of `trajectories` and `top` parameters ' - + 'should have the same type!' - ) - if isinstance(trajectories[i], list): - assert len(trajectories[i]) == len(top[i]), ( - 'Numbers of trajectories and topology files should be ' - + 'the same!' - ) - for j in range(len(trajectories[i])): - if folder is not None: - trajectories[i][j] = folder + '/' + trajectories[i][j] - top[i][j] = folder + '/' + top[i][j] - assert isinstance(trajectories[i][j], str) - assert isinstance(top[i][j], str) - else: - if folder is not None: - trajectories[i] = folder + '/' + trajectories[i] - top[i] = folder + '/' + top[i] - assert isinstance(trajectories[i], str) - assert isinstance(top[i], str) - - topologies = [] - trajectories_in_memory = [] - - for i in range(len(trajectories)): - if isinstance(trajectories[i], list): - traj = [ - md.load(trajectories[i][j], top=top[i][j]) - for j in range(len(trajectories[i])) - ] - for t in traj: - t.top = md.core.trajectory.load_topology(top[i][j]) - if selection is not None: - for j in range(len(traj)): - subset = traj[j].top.select(selection) - assert len(subset) > 0, ( - 'No atoms will be selected with selection string ' - + '"{:s}"!'.format(selection) - ) - traj[j] = traj[j].atom_slice(subset) - trajectories_in_memory.append(traj) - topologies.extend([t.top for t in traj]) - else: - traj = md.load(trajectories[i], top=top[i]) - traj.top = md.core.trajectory.load_topology(top[i]) - if selection is not None: - subset = traj.top.select(selection) - assert len(subset) > 0, ( - 'No atoms will be selected with selection string ' - + '"{:s}"!'.format(selection) - ) - traj = traj.atom_slice(subset) - trajectories_in_memory.append(traj) - topologies.append(traj.top) - - if z_table is None: - z_table = _z_table_from_top(topologies) - - configurations = [] - for i in range(len(trajectories_in_memory)): - if isinstance(trajectories_in_memory[i], list): - for j in range(len(trajectories_in_memory[i])): - configuration = _configures_from_trajectory( - trajectories_in_memory[i][j], - i if create_labels else -1, # NOTE: all these configurations have a label `i` - system_selection, - environment_selection, - ) - configurations.extend(configuration) - else: - configuration = _configures_from_trajectory( - trajectories_in_memory[i], - i if create_labels else -1, - system_selection, - environment_selection, - ) - configurations.extend(configuration) - - dataset = gdata.create_dataset_from_configurations( - configurations, - z_table, - cutoff, - buffer, - remove_isolated_nodes, - show_progress - ) - - if return_trajectories: - return dataset, trajectories_in_memory - else: - return dataset - - -def _z_table_from_top( - top: List[md.Topology] -) -> gdata.atomic.AtomicNumberTable: - """ - Create an atomic number table from the topologies. - - Parameters - ---------- - top: List[mdtraj.Topology] - The topology objects. - """ - atomic_numbers = [] - for t in top: - atomic_numbers.extend([a.element.number for a in t.atoms]) - atomic_numbers = np.array(atomic_numbers, dtype=int) - z_table = gdata.atomic.AtomicNumberTable.from_zs(atomic_numbers) - return z_table - - -def _configures_from_trajectory( - trajectory: md.Trajectory, - label: int = None, - system_selection: str = None, - environment_selection: str = None, -) -> gdata.atomic.Configurations: - """ - Create configurations from one trajectory. - - Parameters - ---------- - trajectory: mdtraj.Trajectory - The MDTraj Trajectory object. - label: int - The graph label. - system_selection: str - MDTraj style atom selections of the system atoms. If given, only - selected atoms will be loaded from the trajectories. This option may - increase the speed of building graphs. - environment_selection: str - MDTraj style atom selections of the environment atoms. If given, - only the system atoms and [the environment atoms within the cutoff - radius of the system atoms] will be kept in the graph. - """ - if label is not None: - label = np.array([[label]]) - - if system_selection is not None and environment_selection is not None: - system_atoms = trajectory.top.select(system_selection) - assert len(system_atoms) > 0, ( - 'No atoms will be selected with `system_selection`: ' - + '"{:s}"!'.format(system_selection) - ) - environment_atoms = trajectory.top.select(environment_selection) - assert len(environment_atoms) > 0, ( - 'No atoms will be selected with `environment_selection`: ' - + '"{:s}"!'.format(environment_selection) - ) - else: - system_atoms = None - environment_atoms = None - - atomic_numbers = [a.element.number for a in trajectory.top.atoms] - if trajectory.unitcell_vectors is not None: - pbc = [True] * 3 - cell = trajectory.unitcell_vectors - else: - pbc = [False] * 3 - cell = [None] * len(trajectory) - - configurations = [] - for i in range(len(trajectory)): - configuration = gdata.atomic.Configuration( - atomic_numbers=atomic_numbers, - positions=trajectory.xyz[i] * 10, - cell=cell[i] * 10, - pbc=pbc, - graph_labels=label, - node_labels=None, # TODO: Add supports for per-node labels. - system=system_atoms, - environment=environment_atoms - ) - configurations.append(configuration) - - return configurations - - -def test_create_dataset_from_trajectories( - text: str, system_selection: str -) -> None: - with open('test_dataset.pdb', 'w') as fp: - print(text, file=fp) - - dataset, trajectories = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection=system_selection, - return_trajectories=True, - show_progress=False - ) - - assert len(dataset) == 6 - assert dataset.cutoff == 1.0 - assert dataset.atomic_numbers == [1, 8] - assert len(trajectories) == 2 - assert len(trajectories[0]) == 2 - assert len(trajectories[1]) == 2 - assert len(trajectories[1][0]) == 2 - assert len(trajectories[1][1]) == 2 - - assert dataset[0]['graph_labels'] == torch.tensor([[0.0]]) - assert dataset[1]['graph_labels'] == torch.tensor([[0.0]]) - assert dataset[2]['graph_labels'] == torch.tensor([[1.0]]) - assert dataset[3]['graph_labels'] == torch.tensor([[1.0]]) - assert dataset[4]['graph_labels'] == torch.tensor([[1.0]]) - assert dataset[5]['graph_labels'] == torch.tensor([[1.0]]) - - dataset, trajectories = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - create_labels=False, - system_selection=system_selection, - return_trajectories=True, - show_progress=False - ) - - assert dataset[0]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[1]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[2]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[3]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[4]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[5]['graph_labels'] == torch.tensor([[-1.0]]) - - def check_data_1(data) -> None: - assert ( - data['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - assert ( - data['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 2.0, 0.0], - [0.0, -2.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data['positions'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.7, 0.7, 0.0], - [0.7, -0.7, 0.0], - ]) - ).all() - assert ( - data['cell'] == torch.tensor([ - [2.0, 0.0, 0.0], - [0.0, 2.0, 0.0], - [0.0, 0.0, 2.0], - ]) - ).all() - assert ( - data['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - - for i in range(6): - check_data_1(dataset[i]) - - if system_selection is not None: - - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection='type O and {:s}'.format(system_selection), - environment_selection='type H and {:s}'.format(system_selection), - show_progress=False - ) - - for i in range(6): - check_data_1(dataset[i]) - - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection='name H1 and {:s}'.format(system_selection), - environment_selection='name H2 and {:s}'.format(system_selection), - show_progress=False - ) - - def check_data_2(data) -> None: - assert (data['edge_index'] == torch.tensor([[0, 1], [1, 0]])).all() - assert ( - data['shifts'] == torch.tensor([ - [0.0, 2.0, 0.0], [0.0, -2.0, 0.0] - ]) - ).all() - assert ( - data['unit_shifts'] == torch.tensor([ - [0.0, 1.0, 0.0], [0.0, -1.0, 0.0], - ]) - ).all() - assert ( - data['positions'] == torch.tensor([ - [0.7, 0.7, 0.0], [0.7, -0.7, 0.0], - ]) - ).all() - assert ( - data['cell'] == torch.tensor([ - [2.0, 0.0, 0.0], - [0.0, 2.0, 0.0], - [0.0, 0.0, 2.0], - ]) - ).all() - assert ( - data['node_attrs'] == torch.tensor([ - [1.0], [1.0] - ]) - ).all() - - for i in range(6): - check_data_2(dataset[i]) - - __import__('os').remove('test_dataset.pdb') - - -if __name__ == '__main__': - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, None) - - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 TIP3W 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 H1 TIP3W 1 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 3 H2 TIP3W 1 0.700 -0.700 0.000 1.00 0.00 WT1 H -ATOM 4 OH2 XXXXW 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 5 H1 XXXXW 2 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 XXXXW 2 0.300 -0.300 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, 'not resname XXXX') - - text = """ -CRYST1 2.000 2.000 2.000 90.00 90.00 90.00 P 1 1 -ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H -ENDMODEL -ATOM 1 OH2 XXXXW 1 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 2 OH2 TIP3W 2 0.000 0.000 0.000 1.00 0.00 WT1 O -ATOM 3 H1 XXXXW 1 0.300 0.300 0.000 1.00 0.00 WT1 H -ATOM 4 H1 TIP3W 2 0.700 0.700 0.000 1.00 0.00 WT1 H -ATOM 5 H2 XXXXW 1 0.300 -0.300 0.000 1.00 0.00 WT1 H -ATOM 6 H2 TIP3W 2 0.700 -0.700 0.000 1.00 0.00 WT1 H -END -""" - test_create_dataset_from_trajectories(text, 'not resname XXXX') From 1971070243e0946e63332ae1fd8985466aee85d9 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 13 Dec 2024 19:30:58 +0100 Subject: [PATCH 217/267] Added log var --- mlcolvar/core/loss/committor_loss.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index f908e63a..ed9478ba 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -218,20 +218,22 @@ def committor_loss(x: torch.Tensor, grad_square = grad_square * w[mask_var] # variational contribution to loss: we sum over the batch loss_var = torch.mean(grad_square) - if False: + if log_var: loss_var = loss_var.log() + else: + loss_var = gamma*loss_var # 2. BOUNDARY LOSS - loss_A = torch.mean( torch.pow(q[mask_A], 2)) - loss_B = torch.mean( torch.pow( (q[mask_B] - 1) , 2)) + loss_A = gamma * torch.mean( torch.pow(q[mask_A], 2)) + loss_B = gamma * torch.mean( torch.pow( (q[mask_B] - 1) , 2)) # 3. TOTAL LOSS - loss = gamma*( loss_var + alpha*(loss_A + loss_B) ) + loss = loss_var + alpha*(loss_A + loss_B) # TODO maybe there is no need to detach them for logging - return loss, gamma*loss_var.detach(), alpha*gamma*loss_A.detach(), alpha*gamma*loss_B.detach() + return loss, loss_var.detach(), alpha*loss_A.detach(), alpha*loss_B.detach() class SmartDerivatives(torch.nn.Module): """ From 2bd81f69d919284190e5f1459ea2e3743d0d6253 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 13 Dec 2024 19:31:17 +0100 Subject: [PATCH 218/267] Improved test coverage --- mlcolvar/core/loss/tda_loss.py | 17 +- mlcolvar/cvs/committor/utils.py | 30 +- mlcolvar/data/graph/datamodule.py | 879 ------------------------------ 3 files changed, 44 insertions(+), 882 deletions(-) delete mode 100644 mlcolvar/data/graph/datamodule.py diff --git a/mlcolvar/core/loss/tda_loss.py b/mlcolvar/core/loss/tda_loss.py index f3126784..4a5aa90b 100644 --- a/mlcolvar/core/loss/tda_loss.py +++ b/mlcolvar/core/loss/tda_loss.py @@ -149,10 +149,8 @@ def tda_loss( """ if not isinstance(target_centers, torch.Tensor): target_centers = torch.tensor(target_centers, dtype=H.dtype) - target_centers.requires_grad_(True) if not isinstance(target_sigmas, torch.Tensor): target_sigmas = torch.tensor(target_sigmas, dtype=H.dtype) - target_sigmas.requires_grad_(True) device = H.device target_centers = target_centers.to(device) @@ -191,3 +189,18 @@ def tda_loss( if return_loss_terms: return loss, loss_centers, loss_sigmas return loss + +def test_tda_loss(): + H = torch.randn(100) + H.requires_grad = True + labels = torch.zeros_like(H) + labels[-50:] = 1 + + Loss = TDALoss(n_states=2, target_centers=[-1, 1], target_sigmas=[0.1, 0.1]) + + loss = Loss(H=H, labels=labels, return_loss_terms=True) + + loss[0].backward() + +if __name__ == '__main__': + test_tda_loss() \ No newline at end of file diff --git a/mlcolvar/cvs/committor/utils.py b/mlcolvar/cvs/committor/utils.py index a29fe7e0..c378a533 100644 --- a/mlcolvar/cvs/committor/utils.py +++ b/mlcolvar/cvs/committor/utils.py @@ -186,4 +186,32 @@ def get_descriptors_and_derivatives(dataset, 'labels': torch.clone(dataset['labels']), 'weights' : torch.clone(dataset['weights'])}) - return smart_dataset, smart_derivatives \ No newline at end of file + return smart_dataset, smart_derivatives + +def test_Kolmogorov_bias(): + from mlcolvar.core.nn import FeedForward + model = FeedForward(layers=[4,2,1], activation='tanh') + inp = torch.randn((10, 4)) + model_bias = KolmogorovBias(input_model=model, beta=1.0) + model_bias(inp) + +def test_compute_committor_weights(): + # descriptors + # create dataset + samples = 50 + X = torch.randn((3*samples, 6)) + + # create labels, bias and weights + y = torch.zeros(X.shape[0]) + y[samples:] += 1 + y[int(2*samples):] += 1 + bias = torch.ones_like(X) + w = torch.zeros(X.shape[0]) + + # create and edit dataset + dataset = DictDataset({"data": X, "labels": y, "weights": w}) + dataset = compute_committor_weights(dataset=dataset, bias=bias, data_groups=[0,1,2], beta=1.0) + +if __name__ == '__main__': + test_Kolmogorov_bias() + test_compute_committor_weights() \ No newline at end of file diff --git a/mlcolvar/data/graph/datamodule.py b/mlcolvar/data/graph/datamodule.py deleted file mode 100644 index 4de8576d..00000000 --- a/mlcolvar/data/graph/datamodule.py +++ /dev/null @@ -1,879 +0,0 @@ -import torch -import lightning -import torch_geometric as tg -import numpy as np - -from typing import Sequence, Union, Optional, Tuple -from lightning.pytorch.utilities import combined_loader - -from mlcolvar.data.graph import atomic -from mlcolvar.data.graph.dataset import create_dataset_from_configurations - -""" -The data module for lightning. -""" - -__all__ = ['GraphDataModule', 'GraphCombinedDataModule'] - - -class GraphDataModule(lightning.LightningDataModule): - """ - Lightning DataModule constructed for `torch_geometric.data.Data`. This data - module automatically splits the input graphs into training, validation, - and (optionally) test sets. - - Parameters - ---------- - dataset: List[torch_geometric.data.Data] - List of graph data. - lengths: List[int] - Lengths of the training, validation, and (optionally) test datasets. - This must be a list of (float) fractions summing to 1. - batch_size : Union[int, List[int]] - The batch size. - random_split: bool - Whether to randomly split train/valid/test or sequentially. - shuffle: Union[bool, List[bool]] - Whether to shuffle the batches in the ``DataLoader``. - seed: int - The random seed used to split the dataset. - """ - - def __init__( - self, - dataset: Sequence[tg.data.Data], - lengths: Sequence = (0.8, 0.2), - batch_size: Union[int, Sequence] = None, - random_split: bool = True, - shuffle: Union[bool, Sequence] = True, - seed: Optional[int] = None, - ) -> None: - super().__init__() - - self._dataset = dataset - self._lengths = lengths - self._n_total = len(dataset) - - assert self._n_total > 0 - assert len(lengths) in [1, 2, 3] - assert np.abs(1 - sum(lengths)) < 1e-12 - - if self._n_total == 1: - self._n_train = 1 - else: - self._n_train = int(lengths[0] * self._n_total) - if len(lengths) == 3: - self._n_validation = int(lengths[1] * self._n_total) - self._n_test = self._n_total - self._n_train - self._n_validation - elif len(lengths) == 2: - self._n_validation = self._n_total - self._n_train - self._n_test = 0 - - indices = list(range(self._n_total)) - if random_split: - rng = np.random.default_rng(seed) - rng.shuffle(indices) - indices_train = indices[0:self._n_train] - if len(lengths) == 3: - indices_validation = indices[ - self._n_train:(self._n_train + self._n_validation) - ] - indices_test = indices[ - (self._n_train + self._n_validation):self._n_total - ] - elif len(lengths) == 2: - indices_validation = indices[ - self._n_train:(self._n_train + self._n_validation) - ] - indices_test = [] - else: - indices_validation = [] - indices_test = [] - self._dataset_indices = [ - indices_train, - indices_validation, - indices_test, - ] - - # Make sure batch_size and shuffle are lists. - if batch_size is None: - batch_size = len(dataset) - if isinstance(batch_size, int): - self.batch_size = [batch_size for _ in lengths] - else: - self.batch_size = batch_size - if isinstance(shuffle, bool): - self.shuffle = [shuffle for _ in lengths] - else: - self.shuffle = shuffle - - # This is initialized in setup(). - self._dataset_split = None - - # dataloaders - self.train_loader = None - self.valid_loader = None - self.test_loader = None - - def setup(self, stage: Optional[str] = None) -> None: - """ - Set up the datasets. - """ - if self._dataset_split is None: - dataset_split = [ - [self._dataset[j] for j in i] for i in self._dataset_indices - ] - self._dataset_split = dataset_split - - def train_dataloader(self) -> tg.loader.DataLoader: - """ - Return training dataloader. - """ - self._check_setup() - if self.train_loader is None: - self.train_loader = tg.loader.DataLoader( - self._dataset_split[0], - batch_size=self.batch_size[0], - shuffle=self.shuffle[0], - ) - return self.train_loader - - def val_dataloader(self) -> tg.loader.DataLoader: - """ - Return validation dataloader. - """ - self._check_setup() - if len(self._dataset_indices[1]) == 0: - raise NotImplementedError( - 'Validation dataset not available, you need to pass two ' - + 'lengths to datamodule.' - ) - if self.valid_loader is None: - self.valid_loader = tg.loader.DataLoader( - self._dataset_split[1], - batch_size=self.batch_size[1], - shuffle=self.shuffle[1], - ) - return self.valid_loader - - def test_dataloader(self) -> tg.loader.DataLoader: - """ - Return test dataloader. - """ - self._check_setup() - if len(self._dataset_indices[2]) == 0: - raise NotImplementedError( - 'Test dataset not available, you need to pass three ' - + 'lengths to datamodule.' - ) - if self.test_loader is None: - self.test_loader = tg.loader.DataLoader( - self._dataset_split[2], - batch_size=self.batch_size[2], - shuffle=self.shuffle[2], - ) - return self.test_loader - - def predict_dataloader(self) -> tg.loader.DataLoader: - """ - Return predict dataloader. - """ - raise NotImplementedError() - - def teardown(self, stage: str) -> None: - pass - - # def __repr__(self) -> str: - # result = '' - # n_digits = len(str(self._n_total)) - # data_string_1 = '[ \033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' - # data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' - # shuffle_string_1 = '|\033[36m  \033[0m ]' - # shuffle_string_2 = '|\033[36m  \033[0m ]' - - # prefix = '\033[1m\033[34m BASEDATA \033[0m: ' - # result += ( - # prefix + self._dataset.__repr__().split('GRAPHDATASET ')[1] + '\n' - # ) - # prefix = '\033[1m\033[34m TRAINING \033[0m: ' - # string = prefix + data_string_1.format(n_digits) - # result += string.format( - # self._n_train, self._n_train / self._n_total * 100 - # ) - # string = data_string_2.format(n_digits) - # result += string.format(self.batch_size[0]) - # if self.shuffle[0]: - # result += shuffle_string_1 - # else: - # result += shuffle_string_2 - - # if self._n_validation > 0: - # result += '\n' - # prefix = '\033[1m\033[34m VALIDATION \033[0m: ' - # string = prefix + data_string_1.format(n_digits) - # result += string.format( - # self._n_validation, self._n_validation / self._n_total * 100 - # ) - # string = data_string_2.format(n_digits) - # result += string.format(self.batch_size[1]) - # if self.shuffle[1]: - # result += shuffle_string_1 - # else: - # result += shuffle_string_2 - - # if self._n_test > 0: - # result += '\n' - # prefix = '\033[1m\033[34m TEST \033[0m: ' - # string = prefix + data_string_1.format(n_digits) - # result += string.format( - # self._n_test, self._n_test / self._n_total * 100 - # ) - # string = data_string_2.format(n_digits) - # result += string.format(self.batch_size[2]) - # if self.shuffle[2]: - # result += shuffle_string_1 - # else: - # result += shuffle_string_2 - # return result - - def __repr__(self) -> str: - string = f"DictModule(dataset -> {self._dataset.__repr__()}" - string += f",\n\t\t train_loader -> DictLoader(length={self._lengths[0]}, batch_size={self.batch_size[0]}, shuffle={self.shuffle[0]})" - if len(self._lengths) >= 2: - string += f",\n\t\t valid_loader -> DictLoader(length={self._lengths[1]}, batch_size={self.batch_size[1]}, shuffle={self.shuffle[1]})" - if len(self._lengths) >= 3: - string += f",\n\t\t\ttest_loader =DictLoader(length={self._lengths[2]}, batch_size={self.batch_size[2]}, shuffle={self.shuffle[2]})" - string += f")" - return string - - def _check_setup(self) -> None: - """ - Raise an error if setup() has not been called. - """ - if self._dataset_split is None: - raise AttributeError( - 'The datamodule has not been set up yet. To get the ' - + 'dataloaders outside a Lightning trainer please call ' - + '.setup() first.' - ) - - -class GraphCombinedDataModule(lightning.LightningDataModule): - """ - Lightning DataModule constructed for `torch_geometric.data.Data`. This data - module automatically splits the input graphs into training, validation, - and (optionally) test sets. - Being differnet from `GraphDataModule`, this class takes two different - datasets as input, and evaluates them at the same time during the training. - - Parameters - ---------- - datasets: Tuple[ - List[torch_geometric.data.Data], List[torch_geometric.data.Data] - ] - Lists of graph data. - lengths: List[int] - Lengths of the training, validation, and (optionally) test datasets. - This must be a list of (float) fractions summing to 1. - batch_size : Union[int, List[int]] - The batch size. - random_split: bool - Whether to randomly split train/valid/test or sequentially. - seed: int - The random seed used to split the dataset. - """ - - def __init__( - self, - datasets: Tuple[Sequence[tg.data.Data], Sequence[tg.data.Data]], - lengths: Sequence = (0.8, 0.2), - batch_size: Union[int, Sequence] = None, - random_split: bool = True, - seed: Optional[int] = None, - ) -> None: - super().__init__() - - self._datasets = datasets - self._lengths = lengths - self._n_total = len(datasets[0]) - - assert self._n_total > 0 - assert self._n_total == len(datasets[1]) - assert len(lengths) in [1, 2, 3] - assert np.abs(1 - sum(lengths)) < 1e-12 - - if self._n_total == 1: - self._n_train = 1 - else: - self._n_train = int(lengths[0] * self._n_total) - if len(lengths) == 3: - self._n_validation = int(lengths[1] * self._n_total) - self._n_test = self._n_total - self._n_train - self._n_validation - elif len(lengths) == 2: - self._n_validation = self._n_total - self._n_train - self._n_test = 0 - - indices = list(range(self._n_total)) - if random_split: - rng = np.random.default_rng(seed) - rng.shuffle(indices) - indices_train = indices[0:self._n_train] - if len(lengths) == 3: - indices_validation = indices[ - self._n_train:(self._n_train + self._n_validation) - ] - indices_test = indices[ - (self._n_train + self._n_validation):self._n_total - ] - elif len(lengths) == 2: - indices_validation = indices[ - self._n_train:(self._n_train + self._n_validation) - ] - indices_test = [] - else: - indices_validation = [] - indices_test = [] - self._dataset_indices = [ - indices_train, - indices_validation, - indices_test, - ] - - # Make sure batch_size and shuffle are lists. - if batch_size is None: - batch_size = self._n_total - if isinstance(batch_size, int): - self.batch_size = [batch_size for _ in lengths] - else: - self.batch_size = batch_size - - # This is initialized in setup(). - self._dataset_splits = None - - # dataloaders - self.train_loader = None - self.valid_loader = None - self.test_loader = None - - def setup(self, stage: Optional[str] = None) -> None: - """ - Set up the datasets. - """ - if self._dataset_splits is None: - dataset_splits = [None] * 2 - dataset_splits[0] = [ - [self._datasets[0][j] for j in i] - for i in self._dataset_indices - ] - dataset_splits[1] = [ - [self._datasets[1][j] for j in i] - for i in self._dataset_indices - ] - self._dataset_splits = dataset_splits - - def train_dataloader(self) -> tg.loader.DataLoader: - """ - Return training dataloader. - """ - self._check_setup() - if self.train_loader is None: - train_loader_1 = tg.loader.DataLoader( - self._dataset_splits[0][0], - batch_size=self.batch_size[0], - shuffle=False, - ) - train_loader_2 = tg.loader.DataLoader( - self._dataset_splits[1][0], - batch_size=self.batch_size[0], - shuffle=False, - ) - self.train_loader = combined_loader.CombinedLoader( - {'dataset_1': train_loader_1, 'dataset_2': train_loader_2}, - mode='min_size' - ) - return self.train_loader - - def val_dataloader(self) -> tg.loader.DataLoader: - """ - Return validation dataloader. - """ - self._check_setup() - if len(self._dataset_indices[1]) == 0: - raise NotImplementedError( - 'Validation dataset not available, you need to pass two ' - + 'lengths to datamodule.' - ) - if self.valid_loader is None: - valid_loader_1 = tg.loader.DataLoader( - self._dataset_splits[0][1], - batch_size=self.batch_size[1], - shuffle=False, - ) - valid_loader_2 = tg.loader.DataLoader( - self._dataset_splits[1][1], - batch_size=self.batch_size[1], - shuffle=False, - ) - self.valid_loader = combined_loader.CombinedLoader( - {'dataset_1': valid_loader_1, 'dataset_2': valid_loader_2}, - mode='min_size' - ) - return self.valid_loader - - def test_dataloader(self) -> tg.loader.DataLoader: - """ - Return test dataloader. - """ - self._check_setup() - if len(self._dataset_indices[2]) == 0: - raise NotImplementedError( - 'Test dataset not available, you need to pass three ' - + 'lengths to datamodule.' - ) - if self.test_loader is None: - test_loader_1 = tg.loader.DataLoader( - self._dataset_splits[0][2], - batch_size=self.batch_size[2], - shuffle=False, - ) - test_loader_2 = tg.loader.DataLoader( - self._dataset_splits[1][2], - batch_size=self.batch_size[2], - shuffle=False, - ) - self.test_loader = combined_loader.CombinedLoader( - {'dataset_1': test_loader_1, 'dataset_2': test_loader_2}, - mode='min_size' - ) - return self.test_loader - - def predict_dataloader(self) -> tg.loader.DataLoader: - """ - Return predict dataloader. - """ - raise NotImplementedError() - - def teardown(self, stage: str) -> None: - pass - - def __repr__(self) -> str: - result = '' - n_digits = len(str(self._n_total)) - data_string_1 = '[ \033[32m{{:{:d}d}}\033[0m\033[36m 󰡷 \033[0m' - data_string_2 = '| \033[32m{{:{:d}d}}\033[0m\033[36m  \033[0m' - shuffle_string = '|\033[36m  \033[0m ]' - - prefix = '\033[1m\033[34m BASEDATA \033[0m: ' - result += ( - prefix + self._datasets[0].__repr__().split('GRAPHDATASET ')[1] - + '\n' - + prefix + self._datasets[1].__repr__().split('GRAPHDATASET ')[1] - + '\n' - ) - prefix = '\033[1m\033[34m TRAINING \033[0m: ' - string = prefix + data_string_1.format(n_digits) - result += string.format( - self._n_train * 2, self._n_train / self._n_total * 100 - ) - string = data_string_2.format(n_digits) - result += string.format(self.batch_size[0]) - result += shuffle_string - - if self._n_validation > 0: - result += '\n' - prefix = '\033[1m\033[34m VALIDATION \033[0m: ' - string = prefix + data_string_1.format(n_digits) - result += string.format( - self._n_validation * 2, - self._n_validation / self._n_total * 100 - ) - string = data_string_2.format(n_digits) - result += string.format(self.batch_size[1]) - result += shuffle_string - - if self._n_test > 0: - result += '\n' - prefix = '\033[1m\033[34m TEST \033[0m: ' - string = prefix + data_string_1.format(n_digits) - result += string.format( - self._n_test * 2, self._n_test / self._n_total * 100 - ) - string = data_string_2.format(n_digits) - result += string.format(self.batch_size[2]) - result += shuffle_string - return result - - def _check_setup(self) -> None: - """ - Raise an error if setup() has not been called. - """ - if self._dataset_splits is None: - raise AttributeError( - 'The datamodule has not been set up yet. To get the ' - + 'dataloaders outside a Lightning trainer please call ' - + '.setup() first.' - ) - - -def test_datamodule() -> None: - numbers = [8, 1, 1] - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = atomic.AtomicNumberTable.from_zs(numbers) - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - ) - dataset = create_dataset_from_configurations( - [config] * 10, z_table, 0.1, show_progress=False - ) - for i, d in enumerate(dataset): - d['graph_labels'][0][0] = i - - loader = GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=10, - shuffle=True, - seed=1 - ) - loader.setup() - assert len(loader._dataset_indices[0]) == 10 - assert len(loader._dataset_indices[1]) == 0 - assert len(loader._dataset_indices[2]) == 0 - - loader = GraphDataModule( - dataset, - batch_size=10, - shuffle=True, - seed=1 - ) - loader.setup() - assert len(loader._dataset_indices[0]) == 8 - assert len(loader._dataset_indices[1]) == 2 - assert len(loader._dataset_indices[2]) == 0 - - loader = GraphDataModule( - dataset, - lengths=(0.6, 0.3, 0.1), - batch_size=10, - shuffle=False, - seed=1 - ) - loader.setup() - assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] - - data_dict = next(iter(loader.train_dataloader())).to_dict() - assert data_dict['edge_index'].shape == (2, 36) - assert ( - data_dict['graph_labels'] == torch.tensor( - [[8], [4], [7], [0], [1], [2]] - ) - ).all() - - data_dict = next(iter(loader.val_dataloader())).to_dict() - assert ( - data_dict['edge_index'] == torch.tensor([ - [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], - [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] - ]) - ).all() - assert ( - data_dict['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) - ).all() - assert (data_dict['graph_labels'] == torch.tensor([[5], [9], [6]])).all() - assert (data_dict['ptr'] == torch.tensor([0, 3, 6, 9])).all() - - data_dict = next(iter(loader.test_dataloader())).to_dict() - assert (data_dict['graph_labels'] == torch.tensor([[3]])).all() - assert ( - data_dict['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - assert ( - data_dict['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data_dict['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data_dict['positions'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.07, 0.07, 0.0], - [0.07, -0.07, 0.0], - ]) - ).all() - assert ( - data_dict['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], - ]) - ).all() - assert ( - data_dict['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - system=[0], - environment=[1, 2] - ) - dataset = create_dataset_from_configurations( - [config] * 10, z_table, 0.1, show_progress=False - ) - - loader = GraphDataModule( - dataset, - lengths=(0.6, 0.3, 0.1), - batch_size=10, - shuffle=False, - seed=1 - ) - - loader.setup() - assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] - - data_dict = next(iter(loader.val_dataloader())).to_dict() - assert ( - data_dict['edge_index'] == torch.tensor([ - [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], - [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] - ]) - ).all() - - data_dict = next(iter(loader.test_dataloader())).to_dict() - assert ( - data_dict['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - - -def test_combined_datamodule() -> None: - numbers = [8, 1, 1] - positions = np.array( - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], dtype=float - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[1]]) - node_labels = np.array([[0], [1], [1]]) - z_table = atomic.AtomicNumberTable.from_zs(numbers) - - config = atomic.Configuration( - atomic_numbers=numbers, - positions=positions, - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels, - ) - dataset = create_dataset_from_configurations( - [config] * 10, z_table, 0.1, show_progress=False - ) - for i, d in enumerate(dataset): - d['graph_labels'][0][0] = i - - loader = GraphCombinedDataModule( - (dataset, dataset), - lengths=(1.0,), - batch_size=10, - seed=1 - ) - loader.setup() - assert len(loader._dataset_indices[0]) == 10 - assert len(loader._dataset_indices[1]) == 0 - assert len(loader._dataset_indices[2]) == 0 - - loader = GraphCombinedDataModule( - (dataset, dataset), - lengths=(0.8, 0.2), - batch_size=10, - seed=1 - ) - loader.setup() - assert len(loader._dataset_indices[0]) == 8 - assert len(loader._dataset_indices[1]) == 2 - assert len(loader._dataset_indices[2]) == 0 - - loader = GraphCombinedDataModule( - (dataset, dataset), - lengths=(0.6, 0.3, 0.1), - batch_size=10, - seed=1 - ) - loader.setup() - assert loader._dataset_indices == [[8, 4, 7, 0, 1, 2], [5, 9, 6], [3]] - - batch = next(iter(loader.train_dataloader()))[0] - data_dict_1 = batch['dataset_1'].to_dict() - data_dict_2 = batch['dataset_2'].to_dict() - - assert data_dict_1['edge_index'].shape == (2, 36) - assert data_dict_2['edge_index'].shape == (2, 36) - assert ( - data_dict_1['graph_labels'] == torch.tensor( - [[8], [4], [7], [0], [1], [2]] - ) - ).all() - assert ( - data_dict_2['graph_labels'] == torch.tensor( - [[8], [4], [7], [0], [1], [2]] - ) - ).all() - - batch = next(iter(loader.val_dataloader()))[0] - data_dict_1 = batch['dataset_1'].to_dict() - data_dict_2 = batch['dataset_2'].to_dict() - assert ( - data_dict_1['edge_index'] == torch.tensor([ - [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], - [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] - ]) - ).all() - assert ( - data_dict_2['edge_index'] == torch.tensor([ - [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8], - [2, 1, 0, 2, 1, 0, 5, 4, 3, 5, 4, 3, 8, 7, 6, 8, 7, 6] - ]) - ).all() - assert ( - data_dict_1['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) - ).all() - assert ( - data_dict_2['batch'] == torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2]) - ).all() - assert (data_dict_1['graph_labels'] == torch.tensor([[5], [9], [6]])).all() - assert (data_dict_2['graph_labels'] == torch.tensor([[5], [9], [6]])).all() - assert (data_dict_1['ptr'] == torch.tensor([0, 3, 6, 9])).all() - assert (data_dict_2['ptr'] == torch.tensor([0, 3, 6, 9])).all() - - batch = next(iter(loader.test_dataloader()))[0] - data_dict_1 = batch['dataset_1'].to_dict() - data_dict_2 = batch['dataset_2'].to_dict() - assert (data_dict_1['graph_labels'] == torch.tensor([[3]])).all() - assert (data_dict_2['graph_labels'] == torch.tensor([[3]])).all() - assert ( - data_dict_1['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - assert ( - data_dict_2['edge_index'] == torch.tensor( - [[0, 0, 1, 1, 2, 2], [2, 1, 0, 2, 1, 0]] - ) - ).all() - assert ( - data_dict_1['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data_dict_2['shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, -0.2, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data_dict_1['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data_dict_2['unit_shifts'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0], - ]) - ).all() - assert ( - data_dict_1['positions'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.07, 0.07, 0.0], - [0.07, -0.07, 0.0], - ]) - ).all() - assert ( - data_dict_2['positions'] == torch.tensor([ - [0.0, 0.0, 0.0], - [0.07, 0.07, 0.0], - [0.07, -0.07, 0.0], - ]) - ).all() - assert ( - data_dict_1['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], - ]) - ).all() - assert ( - data_dict_2['cell'] == torch.tensor([ - [0.2, 0.0, 0.0], - [0.0, 0.2, 0.0], - [0.0, 0.0, 0.2], - ]) - ).all() - assert ( - data_dict_1['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - assert ( - data_dict_2['node_attrs'] == torch.tensor([ - [0.0, 1.0], [1.0, 0.0], [1.0, 0.0] - ]) - ).all() - - -if __name__ == '__main__': - test_datamodule() - test_combined_datamodule() \ No newline at end of file From 3311f3f6d9fae274eb37d1000f8e0213c1ff6dc0 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 16 Dec 2024 11:49:16 +0100 Subject: [PATCH 219/267] Improved tests --- mlcolvar/tests/test_cvs_committor.py | 6 +++++- mlcolvar/tests/test_cvs_supervised_tda.py | 2 ++ mlcolvar/tests/test_data_graph.py | 6 ++++++ 3 files changed, 13 insertions(+), 1 deletion(-) create mode 100644 mlcolvar/tests/test_data_graph.py diff --git a/mlcolvar/tests/test_cvs_committor.py b/mlcolvar/tests/test_cvs_committor.py index 02dc61a2..ac0a8d66 100644 --- a/mlcolvar/tests/test_cvs_committor.py +++ b/mlcolvar/tests/test_cvs_committor.py @@ -1,6 +1,10 @@ from mlcolvar.cvs.committor.committor import test_committor +from mlcolvar.cvs.committor.utils import test_compute_committor_weights, test_Kolmogorov_bias from mlcolvar.core.loss.committor_loss import test_smart_derivatives + if __name__ == "__main__": test_committor() - test_smart_derivatives() \ No newline at end of file + test_Kolmogorov_bias() + test_smart_derivatives() + test_compute_committor_weights() \ No newline at end of file diff --git a/mlcolvar/tests/test_cvs_supervised_tda.py b/mlcolvar/tests/test_cvs_supervised_tda.py index c1ec6bac..b3300b7f 100644 --- a/mlcolvar/tests/test_cvs_supervised_tda.py +++ b/mlcolvar/tests/test_cvs_supervised_tda.py @@ -1,4 +1,6 @@ from mlcolvar.cvs.supervised.deeptda import test_deeptda_cv +from mlcolvar.core.loss.tda_loss import test_tda_loss if __name__ == "__main__": test_deeptda_cv() + test_tda_loss() diff --git a/mlcolvar/tests/test_data_graph.py b/mlcolvar/tests/test_data_graph.py new file mode 100644 index 00000000..9c18f8c7 --- /dev/null +++ b/mlcolvar/tests/test_data_graph.py @@ -0,0 +1,6 @@ +from mlcolvar.data.graph.atomic import test_atomic_number_table +from mlcolvar.data.graph.neighborhood import test_get_neighborhood + +if __name__ == '__main__': + test_atomic_number_table() + test_get_neighborhood() \ No newline at end of file From e9dca7dc4197fe5a99633832c7dca7d53d7e54f8 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 16 Dec 2024 12:11:13 +0100 Subject: [PATCH 220/267] Removed already ported files --- mlcolvar/graph/README.md | 15 - mlcolvar/graph/__init__.py | 11 - mlcolvar/graph/cvs/__init__.py | 4 - mlcolvar/graph/cvs/committor/__init__.py | 3 - mlcolvar/graph/cvs/committor/committor.py | 176 ----------- mlcolvar/graph/cvs/cv.py | 347 ---------------------- mlcolvar/graph/utils/__init__.py | 4 - mlcolvar/graph/utils/progress.py | 66 ---- 8 files changed, 626 deletions(-) delete mode 100644 mlcolvar/graph/README.md delete mode 100644 mlcolvar/graph/__init__.py delete mode 100644 mlcolvar/graph/cvs/__init__.py delete mode 100644 mlcolvar/graph/cvs/committor/__init__.py delete mode 100644 mlcolvar/graph/cvs/committor/committor.py delete mode 100644 mlcolvar/graph/cvs/cv.py delete mode 100644 mlcolvar/graph/utils/__init__.py delete mode 100644 mlcolvar/graph/utils/progress.py diff --git a/mlcolvar/graph/README.md b/mlcolvar/graph/README.md deleted file mode 100644 index 4d40c7e0..00000000 --- a/mlcolvar/graph/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# The Graph Neural Network (GNN) Module in MLCOLVAR. - -

      - -

      - -## INTRODUCTION - -WIP - -## DEPENDENCIES - -- `pytorch_geometric` >= 2.5 -- `matscipy` -- `mdtraj` diff --git a/mlcolvar/graph/__init__.py b/mlcolvar/graph/__init__.py deleted file mode 100644 index 1e9f9239..00000000 --- a/mlcolvar/graph/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""The graph neural network module.""" - -from . import utils -from . import core -from . import data -from . import cvs -from . import explain - -utils.torch_tools.set_default_dtype('float32') -# torch_scatter will compile problems. -__import__('torch_geometric').typing.WITH_TORCH_SCATTER = False diff --git a/mlcolvar/graph/cvs/__init__.py b/mlcolvar/graph/cvs/__init__.py deleted file mode 100644 index 7833d8aa..00000000 --- a/mlcolvar/graph/cvs/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .cv import GraphBaseCV -from .supervised import GraphDeepTDA -from .timelagged import GraphDeepTICA -from .committor import GraphCommittor diff --git a/mlcolvar/graph/cvs/committor/__init__.py b/mlcolvar/graph/cvs/committor/__init__.py deleted file mode 100644 index 3af5089f..00000000 --- a/mlcolvar/graph/cvs/committor/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import utils -from .utils import get_dataset_kolmogorov_bias, compute_committor_weights -from .committor import GraphCommittor diff --git a/mlcolvar/graph/cvs/committor/committor.py b/mlcolvar/graph/cvs/committor/committor.py deleted file mode 100644 index 7478c326..00000000 --- a/mlcolvar/graph/cvs/committor/committor.py +++ /dev/null @@ -1,176 +0,0 @@ - -import torch -import torch_geometric as tg -from typing import Dict, Any, List - -from mlcolvar.core.nn.utils import Custom_Sigmoid - -from mlcolvar.graph.cvs import GraphBaseCV -from mlcolvar.graph.cvs.cv import test_get_data -from mlcolvar.graph.cvs.committor.utils import GraphCommittorLoss -from mlcolvar.graph.utils import torch_tools - -""" -Data-driven learning of committor function, based on Graph Neural Networks -(GNNs). -""" - -__all__ = ['GraphCommittor'] - - -class GraphCommittor(GraphBaseCV): - """ - Data-driven learning of committor function, based on GNNs. - - The committor function q is expressed as the output of a neural network - optimized with a self-consistent approach based on the Kolmogorov's - variational principle for the committor and on the imposition of its - boundary conditions. - - Parameters - ---------- - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - atomic_masses : List[float] - List of masses of all the atoms we are using. - model_name: str - Name of the GNN model. - model_options: Dict[Any, Any] - Model options. - extra_loss_options: Dict[Any, Any] - Extra loss function options. - optimizer_options: Dict[Any, Any] - Optimizer options. - - References - ---------- - .. [*] P. Kang, E. Trizio, and M. Parrinello, "Computing the committor - using the committor to study the transition state ensemble", - Nat. Comput. Sci., 2024, DOI: 10.1038/s43588-024-00645-0 - - See also - -------- - mlcolvar.cvs.committor.Committor - The feedforward NN based ML committor module. - mlcolvar.graph.cvs.committor.utils.GraphCommittorLoss - Kolmogorov's variational optimization of committor and imposition of - boundary conditions. - mlcolvar.graph.cvs.committor.utils.compute_committor_weights - Utils to compute the appropriate weights for the training set - """ - def __init__( - self, - cutoff: float, - atomic_numbers: List[int], - atomic_masses: List[float], - model_name: str = 'GVPModel', - model_options: Dict[Any, Any] = {}, - extra_loss_options: Dict[Any, Any] = { - 'alpha': 1.0, 'gamma': 100.0, 'delta_f': 0.0, 'sigmoid_p': 3.0 - }, - optimizer_options: Dict[Any, Any] = {}, - **kwargs, - ) -> None: - if model_options.pop('n_out', None) is not None: - raise RuntimeError( - 'The `n_out` key of parameter `model_options` will be ignored!' - ) - model_options['n_out'] = 1 - if optimizer_options != {}: - kwargs['optimizer_options'] = optimizer_options - - super().__init__( - 2, cutoff, atomic_numbers, model_name, model_options, **kwargs - ) - - atomic_masses = torch.tensor( - atomic_masses, dtype=torch.get_default_dtype() - ) - self.register_buffer('atomic_masses', atomic_masses) - self.register_buffer('is_committor', torch.tensor(1, dtype=int)) - - self.sigmoid = Custom_Sigmoid(extra_loss_options.get('sigmoid_p', 3.0)) - - self.loss_fn = GraphCommittorLoss( - atomic_masses, - alpha=float(extra_loss_options.get('alpha', 1.0)), - gamma=float(extra_loss_options.get('gamma', 10000.0)), - delta_f=float(extra_loss_options.get('delta_f', 0.0)), - ) - - def forward_nn( - self, - data: Dict[str, torch.Tensor], - token: bool = False - ) -> torch.Tensor: - """ - The forward pass for the NN. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - token: bool - To be used. - """ - data['positions'].requires_grad_(True) - data['node_attrs'].requires_grad_(True) - - return self._model(data) - - def forward( - self, - data: Dict[str, torch.Tensor], - token: bool = False - ) -> torch.Tensor: - """ - The forward pass. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - token: bool - To be used. - """ - z = self.forward_nn(data) - q = self.sigmoid(z) - - return torch.hstack([z, q]) - - def training_step( - self, train_batch: tg.data.Batch, *args, **kwargs - ) -> torch.Tensor: - """ - Compute and return the training loss and record metrics. - - Parameters - ---------- - train_batch: torch_geometric.data.Batch - The data batch. - """ - torch.set_grad_enabled(True) - - batch_dict = train_batch.to_dict() - z = self.forward_nn(batch_dict) - q = self.sigmoid(z) - - loss, loss_var, loss_bound_A, loss_bound_B = self.loss_fn( - batch_dict, q - ) - loss_z_diff = (z.max().abs() - z.min().abs()).pow(2) - loss = loss + loss_z_diff - - name = 'train' if self.training else 'valid' - self.log(f'{name}_loss', loss, on_epoch=True) - self.log(f'{name}_loss_variational', loss_var, on_epoch=True) - self.log(f'{name}_loss_boundary_A', loss_bound_A, on_epoch=True) - self.log(f'{name}_loss_boundary_B', loss_bound_B, on_epoch=True) - self.log(f'{name}_loss_z_diff', loss_z_diff, on_epoch=True) - return loss diff --git a/mlcolvar/graph/cvs/cv.py b/mlcolvar/graph/cvs/cv.py deleted file mode 100644 index beeef02a..00000000 --- a/mlcolvar/graph/cvs/cv.py +++ /dev/null @@ -1,347 +0,0 @@ -import torch -import lightning -import numpy as np -import torch_geometric as tg -from typing import Dict, Any, List, Union, Tuple - -from mlcolvar.graph import data as gdata -from mlcolvar.graph.core.nn import models - -""" -Base collective variable class for Graph Neural Networks. -""" - -__all__ = ['GraphBaseCV'] - - -class GraphBaseCV(lightning.LightningModule): - """ - Base collective variable class for Graph Neural Networks. - - Parameters - ---------- - n_cvs: int - Number of components of the CV. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - model_name: str - Name of the GNN model. - model_options: Dict[Any, Any] - Model options. - optimizer_options: Dict[Any, Any] - Optimizer options. - """ - - def __init__( - self, - n_cvs: int, - cutoff: float, - atomic_numbers: List[int], - model_name: str, - model_options: Dict[Any, Any] = {}, - optimizer_options: Dict[Any, Any] = { - 'optimizer': {'lr': 1E-3, 'weight_decay': 1E-4}, - 'lr_scheduler': { - 'scheduler': torch.optim.lr_scheduler.ExponentialLR, - 'gamma': 0.9997 - } - }, - *args, - **kwargs, - ) -> None: - """ - Base CV class options. - """ - super().__init__(*args, **kwargs) - - self.register_buffer( - 'n_cvs', torch.tensor(n_cvs, dtype=torch.int64) - ) - self.register_buffer( - 'cutoff', torch.tensor(cutoff, dtype=torch.get_default_dtype()) - ) - self.register_buffer( - 'atomic_numbers', torch.tensor(atomic_numbers, dtype=torch.int64) - ) - - for key in ['cutoff', 'atomic_numbers']: - model_options.pop(key, None) - # For DeepTICA - n_out = model_options.pop('n_out', n_cvs) - - if not hasattr(models, model_name): - raise RuntimeError(f'Unknown model: {model_name}') - self._model = eval(f'models.{model_name}')( - n_out=n_out, - cutoff=cutoff, - atomic_numbers=atomic_numbers, - **model_options - ) - - self._optimizer_name = 'Adam' - self.optimizer_kwargs = {} - self.lr_scheduler_kwargs = {} - self._parse_optimizer(optimizer_options) - - self.save_hyperparameters(ignore=['n_cvs', 'cutoff', 'atomic_numbers']) - - def __setattr__(self, key, value) -> None: - # PyTorch overrides __setattr__ to raise a TypeError when you try to - # assignan attribute that is a Module to avoid substituting the model's - # component by mistake. This means we can't simply assign to loss_fn a - # lambda function after it's been assigned a Module, but we need to - # delete the Module first. - # https://github.com/pytorch/pytorch/issues/51896 - # https://stackoverflow.com/questions/61116433 - try: - super().__setattr__(key, value) - except TypeError as e: - # We make an exception only for loss_fn. - if (key == 'loss_fn') and ('cannot assign' in str(e)): - del self.loss_fn - super().__setattr__(key, value) - - def _parse_optimizer(self, options: dict) -> None: - """ - Parse optimizer options. - - Parameters - ---------- - options: Dict[Any, Any] - The options - """ - optimizer_kwargs = options.get('optimizer') - if optimizer_kwargs is not None: - self.optimizer_kwargs.update(optimizer_kwargs) - - lr_scheduler_kwargs = options.get('lr_scheduler') - if lr_scheduler_kwargs is not None: - self.lr_scheduler_kwargs.update(lr_scheduler_kwargs) - - def forward( - self, - data: Dict[str, torch.Tensor], - token: bool = False - ) -> torch.Tensor: - """ - The forward pass. - - Parameters - ---------- - data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a - `torch_geometric.data.Batch` object. - token: bool - To be used. - """ - data['positions'].requires_grad_(True) - data['node_attrs'].requires_grad_(True) - - return self._model(data) - - def validation_step(self, *args, **kwargs) -> torch.Tensor: - """ - Equal to training step if not overridden. Different behaviors for - train/valid step can be enforced in `training_step` based on the - `self.training` variable. - """ - return self.training_step(*args, **kwargs) - - def test_step(self, *args, **kwargs) -> torch.Tensor: - """ - Equal to training step if not overridden. Different behaviors for - train/valid step can be enforced in `training_step` based on the - `self.training` variable. - """ - return self.training_step(*args, **kwargs) - - @property - def optimizer_name(self) -> str: - """ - Optimizer name. Options can be set using optimizer_kwargs. Actual - optimizer will be return during training from configure_optimizer - function. - """ - return self._optimizer_name - - @optimizer_name.setter - def optimizer_name(self, optimizer_name: str) -> None: - self._optimizer_name = optimizer_name - - def configure_optimizers(self) -> Union[ - torch.optim.Optimizer, - Tuple[ - List[torch.optim.Optimizer], - List[torch.optim.lr_scheduler.LRScheduler] - ] - ]: - """ - Initialize the optimizer based on `self._optimizer_name` and - `self.optimizer_kwargs`. - """ - - optimizer = getattr(torch.optim, self._optimizer_name)( - self.parameters(), **self.optimizer_kwargs - ) - - if self.lr_scheduler_kwargs: - scheduler_cls = self.lr_scheduler_kwargs['scheduler'] - scheduler_kwargs = { - k: v for k, v in self.lr_scheduler_kwargs.items() - if k != 'scheduler' - } - lr_scheduler = scheduler_cls(optimizer, **scheduler_kwargs) - return [optimizer], [lr_scheduler] - else: - return optimizer - - @property - def example_input_array(self) -> Dict[str, torch.Tensor]: - """ - Example data. - """ - numbers = self._model.atomic_numbers.cpu().numpy().tolist() - positions = np.random.randn(2, len(numbers), 3) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[[0]], [[1]]]) - node_labels = np.array([[0]] * len(numbers)) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = [ - gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=positions[i], - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels[i], - ) for i in range(2) - ] - dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False - ) - - loader = gdata.GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=10, - shuffle=False, - ) - loader.setup() - - return next(iter(loader.train_dataloader())) - - -def test_get_data() -> tg.data.Batch: - # TODO: This is not a real test, but a helper function for other tests. - # Maybe should change its name. - - numbers = [8, 1, 1] - positions = np.array( - [ - [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], - [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], - [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], - [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], - [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], - [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], - ], - dtype=np.float64 - ) - cell = np.identity(3, dtype=float) * 0.2 - graph_labels = np.array([[[0]], [[1]]] * 3) - node_labels = np.array([[0], [1], [1]]) - z_table = gdata.atomic.AtomicNumberTable.from_zs(numbers) - - config = [ - gdata.atomic.Configuration( - atomic_numbers=numbers, - positions=positions[i], - cell=cell, - pbc=[True] * 3, - node_labels=node_labels, - graph_labels=graph_labels[i], - ) for i in range(0, 6) - ] - dataset = gdata.create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False - ) - - loader = gdata.GraphDataModule( - dataset, - lengths=(1.0,), - batch_size=10, - shuffle=False, - ) - loader.setup() - - return next(iter(loader.train_dataloader())) - - -def test_base_cv() -> None: - dtype = torch.get_default_dtype() - torch.set_default_dtype(torch.float64) - - cv = GraphBaseCV(2, 0.1, [1, 2, 3], 'GVPModel') - - assert cv.n_cvs == 2 - assert (cv.cutoff - 0.1) < 1E-12 - assert (cv._model.atomic_numbers == torch.tensor([1, 2, 3])).all() - - assert cv.optimizer_name == 'Adam' - objects = cv.configure_optimizers() - assert isinstance(objects[0][0], torch.optim.Adam) - assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) - assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 - assert objects[0][0].param_groups[0]['lr'] == 1E-3 - assert objects[1][0].gamma == 0.9997 - - cv.optimizer_name = 'SGD' - cv.optimizer_kwargs = {'lr': 2E-3, 'weight_decay': 1E-4} - objects = cv.configure_optimizers() - assert isinstance(objects[0][0], torch.optim.SGD) - assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 - assert objects[0][0].param_groups[0]['lr'] == 2E-3 - - cv.lr_scheduler_kwargs = { - 'scheduler': torch.optim.lr_scheduler.StepLR, - 'gamma': 0.999, - 'step_size': 1 - } - objects = cv.configure_optimizers() - assert isinstance(objects[0][0], torch.optim.SGD) - assert isinstance(objects[1][0], torch.optim.lr_scheduler.StepLR) - assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 - assert objects[0][0].param_groups[0]['lr'] == 2E-3 - assert objects[1][0].gamma == 0.999 - - cv = GraphBaseCV( - 2, - 0.1, - [1, 2, 3], - 'GVPModel', - optimizer_options={ - 'optimizer': {'lr': 2E-3, 'weight_decay': 1E-4}, - 'lr_scheduler': { - 'scheduler': torch.optim.lr_scheduler.ExponentialLR, - 'gamma': 0.9999 - } - } - ) - - objects = cv.configure_optimizers() - assert isinstance(objects[0][0], torch.optim.Adam) - assert isinstance(objects[1][0], torch.optim.lr_scheduler.ExponentialLR) - assert objects[0][0].param_groups[0]['weight_decay'] == 1E-4 - assert objects[0][0].param_groups[0]['lr'] == 2E-3 - assert objects[1][0].gamma == 0.9999 - - torch.set_default_dtype(dtype) - - -if __name__ == '__main__': - test_base_cv() diff --git a/mlcolvar/graph/utils/__init__.py b/mlcolvar/graph/utils/__init__.py deleted file mode 100644 index c6fe1591..00000000 --- a/mlcolvar/graph/utils/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import io -from . import progress -from . import torch_tools -from . import timelagged diff --git a/mlcolvar/graph/utils/progress.py b/mlcolvar/graph/utils/progress.py deleted file mode 100644 index f71dd249..00000000 --- a/mlcolvar/graph/utils/progress.py +++ /dev/null @@ -1,66 +0,0 @@ -import sys -import time -import typing - -""" -A simple progress bar. -""" - -__all__ = ['pbar'] - - -def pbar( - item: typing.List[int], - prefix: str = '', - size: int = 25, - frequency: int = 0.05, - use_unicode: bool = True, - file: typing.TextIO = sys.stdout -): - """ - A simple progress bar. Taken from stackoverflow: - https://stackoverflow.com/questions/3160699 - - Parameters - ---------- - it : List[int] - The looped item. - prefix : str - Prefix of the bar. - size : int - Size of the bar. - frequency : float - Flush frequency of the bar. - use_unicode : bool - If use unicode char to draw the bar. - file : TextIO - The output file. - """ - if (use_unicode): - c_1 = '' - c_2 = '█' - c_3 = '━' - c_4 = '' - else: - c_1 = '|' - c_2 = '|' - c_3 = '-' - c_4 = '|' - count = len(item) - start = time.time() - interval = max(int(count * frequency), 1) - - def show(j) -> None: - x = int(size * j / count) - remaining = ((time.time() - start) / j) * (count - j) - mins, sec = divmod(remaining, 60) - time_string = f'{int(mins):02}:{sec:02.1f}' - output = f' {prefix} {c_1}{c_2 * (x - 1) + c_4}{c_3 * (size - x)} ' + \ - f'{j}/{count} Est. {time_string}' - print('\x1b[1A\x1b[2K' + output, file=file, flush=True) - - for i, it in enumerate(item): - yield it - if ((i % interval) == 0 or i in [0, (count - 1)]): - show(i + 1) - print(flush=True, file=file) From 290034fd31b1846dca45bb0cc3383d8d36a10550 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 16 Dec 2024 14:47:42 +0100 Subject: [PATCH 221/267] Imporved tests --- mlcolvar/core/nn/graph/radial.py | 5 +++++ mlcolvar/tests/test_core_nn_graph.py | 7 ++++++- mlcolvar/utils/plot.py | 7 +++++++ 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/mlcolvar/core/nn/graph/radial.py b/mlcolvar/core/nn/graph/radial.py index e43193fd..b8e21788 100644 --- a/mlcolvar/core/nn/graph/radial.py +++ b/mlcolvar/core/nn/graph/radial.py @@ -267,6 +267,8 @@ def test_bessel_basis() -> None: assert (torch.abs(data - data_new) < 1E-12).all() torch.set_default_dtype(dtype) + + rbf def test_gaussian_basis() -> None: @@ -296,6 +298,8 @@ def test_gaussian_basis() -> None: torch.set_default_dtype(dtype) + rbf + def test_polynomial_cutoff() -> None: dtype = torch.get_default_dtype() @@ -324,6 +328,7 @@ def test_polynomial_cutoff() -> None: torch.set_default_dtype(dtype) + cutoff_function def test_radial_embedding_block(): dtype = torch.get_default_dtype() diff --git a/mlcolvar/tests/test_core_nn_graph.py b/mlcolvar/tests/test_core_nn_graph.py index 3517d3fd..821acc8d 100644 --- a/mlcolvar/tests/test_core_nn_graph.py +++ b/mlcolvar/tests/test_core_nn_graph.py @@ -1,4 +1,9 @@ from mlcolvar.core.nn.graph.gnn import test_get_edge_vectors_and_lengths +from mlcolvar.core.nn.graph.radial import test_bessel_basis, test_gaussian_basis, test_polynomial_cutoff, test_radial_embedding_block if __name__ == "__main__": - test_get_edge_vectors_and_lengths() \ No newline at end of file + test_get_edge_vectors_and_lengths() + test_bessel_basis() + test_gaussian_basis() + test_polynomial_cutoff() + test_radial_embedding_block() \ No newline at end of file diff --git a/mlcolvar/utils/plot.py b/mlcolvar/utils/plot.py index fa941368..41b86d5f 100644 --- a/mlcolvar/utils/plot.py +++ b/mlcolvar/utils/plot.py @@ -410,3 +410,10 @@ def test_utils_plot(): cmap = matplotlib.colors.Colormap("fessa_r", 2) cmap = matplotlib.colors.Colormap("cortina80", 2) cmap = matplotlib.colors.Colormap("cortina80_r", 2) + + import time + for i in pbar(range(15), "Computing: ", 40): + time.sleep(0.1) + + for i in pbar(range(15), "Computing: ", 40, use_unicode=False): + time.sleep(0.1) \ No newline at end of file From a08d9a7f210679128c26fd5ff7682acf2b6c82a7 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 16 Dec 2024 16:19:56 +0100 Subject: [PATCH 222/267] Fixed typo --- docs/notebooks/paper_experiments/paper_4_multitask.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/notebooks/paper_experiments/paper_4_multitask.ipynb b/docs/notebooks/paper_experiments/paper_4_multitask.ipynb index fe17b8b6..ea4deac4 100644 --- a/docs/notebooks/paper_experiments/paper_4_multitask.ipynb +++ b/docs/notebooks/paper_experiments/paper_4_multitask.ipynb @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +226,7 @@ "nn_args = {'activation': 'shifted_softplus'}\n", "options= {'encoder': nn_args, 'decoder': nn_args }\n", "if run_calculations:\n", - " main_cv = AutoEncoderCV(encoder_model=[n_descriptors, 32, 16, n_components], options=options)\n", + " main_cv = AutoEncoderCV(encoder_layers=[n_descriptors, 32, 16, n_components], options=options)\n", "\n", " aux_loss_fn = TDALoss(n_states, [-5, 0, 5], [0.2, 0.2, 0.2])\n", "\n", @@ -598,14 +598,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if run_calculations:\n", " for i in range(9):\n", " # define model\n", - " main_cv = AutoEncoderCV(encoder_model=[n_descriptors, 32, 16, n_components], options=options)\n", + " main_cv = AutoEncoderCV(encoder_layers=[n_descriptors, 32, 16, n_components], options=options)\n", " aux_loss_fn = TDALoss(n_states, [-5, 0, 5], [0.2, 0.2, 0.2])\n", " model = MultiTaskCV(main_cv, auxiliary_loss_fns=[aux_loss_fn], loss_coefficients=[1.5])\n", " stat = Statistics()\n", From 06167b1cc99841e9d28eed8719a5dbf269c85baa Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 20 Dec 2024 18:08:03 +0100 Subject: [PATCH 223/267] Added atom names and indexing to dataset --- mlcolvar/data/dataset.py | 8 ++++-- mlcolvar/data/graph/utils.py | 47 +++++++++++++++++++++++++++++++++--- mlcolvar/utils/io.py | 19 +++++++++++++++ 3 files changed, 68 insertions(+), 6 deletions(-) diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index ce4075c2..8d3a02a7 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -2,7 +2,6 @@ import numpy as np from mlcolvar.core.transform.utils import Statistics from torch.utils.data import Dataset -from typing import Union, List from operator import itemgetter __all__ = ["DictDataset"] @@ -16,7 +15,12 @@ class DictDataset(Dataset): 'weights' : np.asarray([0.5,1.5,1.5,0.5]) } """ - def __init__(self, dictionary: dict=None, feature_names = None, metadata: dict = None, data_type : str = 'descriptors', **kwargs): + def __init__(self, + dictionary: dict=None, + feature_names = None, + metadata: dict = None, + data_type : str = 'descriptors', + **kwargs): """Create a Dataset from a dictionary or from a list of kwargs. Parameters diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 46a940a1..e161a58e 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -6,13 +6,15 @@ from mlcolvar.data.graph.neighborhood import get_neighborhood from mlcolvar.utils.plot import pbar +from typing import List + __all__ = ["create_dataset_from_configurations", "create_test_graph_input"] def _create_dataset_from_configuration( config: atomic.Configuration, z_table: atomic.AtomicNumberTable, cutoff: float, - buffer: float = 0.0 + buffer: float = 0.0, ) -> torch_geometric.data.Data: """ Build the graph data object from a configuration. @@ -114,6 +116,7 @@ def create_dataset_from_configurations( z_table: atomic.AtomicNumberTable, cutoff: float, buffer: float = 0.0, + atom_names: List = None, remove_isolated_nodes: bool = False, show_progress: bool = True ) -> DictDataset: @@ -139,13 +142,23 @@ def create_dataset_from_configurations( items = pbar(config, frequency=0.0001, prefix='Making graphs') else: items = config - + data_list = [ _create_dataset_from_configuration( - c, z_table, cutoff, buffer + config=c, + z_table=z_table, + cutoff=cutoff, + buffer=buffer, ) for c in items ] + if atom_names is None: + atom_names = [f"X{i}" for i in range(data_list[0]['n_system'].to(torch.int64).item())] + + print(atom_names) + + # this is only to check what isolated nodes have been removed + _aux_pos = torch.Tensor((np.array([d['positions'].numpy() for d in data_list]))) if remove_isolated_nodes: # TODO: not the worst way to fake the `is_node_attr` method of # `torch_geometric.data.storage.GlobalStorage` ... @@ -157,12 +170,38 @@ def create_dataset_from_configurations( cell_list = [d.cell.clone() for d in data_list] transform = torch_geometric.transforms.remove_isolated_nodes.RemoveIsolatedNodes() data_list = [transform(d) for d in data_list] + + # check what have been removed and restore cell + unique_idx = [] # store the indeces of the atoms that have been used at least once for i in range(len(data_list)): data_list[i].cell = cell_list[i] + # get and save the original index before removing isolated nodes for each entry + original_idx = torch.unique( torch.where(torch.isin(_aux_pos[i], data_list[i]['positions']))[0] ) + data_list[i]['names_idx'] = original_idx.to(torch.int64) + + # update if needed the overall list + check = np.isin(original_idx.numpy(), unique_idx, invert=True) + if check.any(): + aux = np.where(check)[0] + unique_idx.extend(original_idx[aux].tolist()) + + unique_idx.sort() + unique_idx = torch.Tensor(unique_idx).to(torch.int64) + # here we simply have to take all the atoms + else: + unique_idx = torch.arange(data_list[0]['n_system'].item()).to(torch.int64) + for i in range(len(data_list)): + data_list[i]['names_idx'] = unique_idx + + # we also save the names of the atoms that have been actually used + unique_names = np.array(atom_names)[unique_idx] + unique_names = unique_names.tolist() dataset = DictDataset(dictionary={'data_list' : data_list}, metadata={'z_table' : z_table.zs, - 'cutoff' : cutoff}, + 'cutoff' : cutoff, + 'used_idx' : unique_idx, + 'used_names' : unique_names}, data_type='graphs') return dataset diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index 0e6937e0..d209949d 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -276,6 +276,7 @@ def create_dataset_from_trajectories( return_trajectories: bool = False, remove_isolated_nodes: bool = True, show_progress: bool = True, + save_names=True ) -> Union[ DictDataset, Tuple[ @@ -439,6 +440,12 @@ def create_dataset_from_trajectories( if z_table is None: z_table = _z_table_from_top(topologies) + if save_names: + atom_names = _names_from_top(topologies) + else: + atom_names = None + + configurations = [] for i in range(len(trajectories_in_memory)): if isinstance(trajectories_in_memory[i], list): @@ -470,6 +477,7 @@ def create_dataset_from_trajectories( z_table, cutoff, buffer, + atom_names, remove_isolated_nodes, show_progress ) @@ -480,6 +488,17 @@ def create_dataset_from_trajectories( return dataset +def _names_from_top(top: List[mdtraj.Topology] ): + it = iter(top) + atom_names = list(next(it).atoms) + if not all([atom_names == list(n.atoms) for n in it]): + raise ValueError( + "The atoms names or their order are different in the topology files. Check or deactivate save_names" + ) + + return atom_names + + def _z_table_from_top( top: List[mdtraj.Topology] ) -> AtomicNumberTable: From f4569a617511b169e4bb96b22725b66120a9fa65 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 20 Dec 2024 18:08:50 +0100 Subject: [PATCH 224/267] Added graph node sensitivity --- mlcolvar/explain/graph_sensitivity.py | 201 + test_graphs/test_explain.ipynb | 7477 +++++++++++++++++++++++++ 2 files changed, 7678 insertions(+) create mode 100644 mlcolvar/explain/graph_sensitivity.py create mode 100644 test_graphs/test_explain.ipynb diff --git a/mlcolvar/explain/graph_sensitivity.py b/mlcolvar/explain/graph_sensitivity.py new file mode 100644 index 00000000..777a69a8 --- /dev/null +++ b/mlcolvar/explain/graph_sensitivity.py @@ -0,0 +1,201 @@ +import numpy as np +from typing import Dict +import torch + +from mlcolvar.data import DictModule +from mlcolvar.utils.plot import pbar + +""" +Sensitivity analysis. +""" + +__all__ = ['graph_node_sensitivity'] + + +def graph_node_sensitivity( + model, + dataset, + component: int = 0, + device: str = 'cpu', + batch_size: int = None, + show_progress: bool = True +) -> Dict[str, np.ndarray]: + """ + Perform a sensitivity analysis by calculating CV gradient w.r.t. nodes' + positions. This allows us to measure which atom is most important to the + CV. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + device: str + Name of the device. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + + Returns + ------- + results: dictionary + Results of the sensitivity analysis, containing 'node_indices', + 'sensitivities', and 'sensitivities_components', ordered according to + the node indices. + + See also + -------- + mlcolvar.utils.explain.sensitivity_analysis + Perform the sensitivity analysis of a feedforward model. + """ + model = model.to(device) + + gradients = get_dataset_cv_gradients( + model, + dataset, + component, + batch_size, + show_progress, + 'Getting gradients' + ) + sensitivities_components = np.linalg.norm(gradients, axis=-1) + + results = {} + results['atoms_list'] = np.array(dataset.metadata['used_names']) + results['node_labels'] = [str(a) for a in results['atoms_list']] + results['node_labels_components'] = np.array([np.array(dataset.metadata['used_names'])[dataset[i]['data_list']['names_idx']] for i in range(len(dataset))]) + results['sensitivities'] = sensitivities_components.mean(axis=0) + results['sensitivities_components'] = sensitivities_components + + return results + +def get_dataset_cv_values( + model, + dataset, + batch_size: int = None, + show_progress: bool = True, + progress_prefix: str = 'Calculating CV values' +) -> np.ndarray: + """ + Get CV values of a given dataset. The calculation will run on the device + where the model is on. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + """ + datamodule = DictModule( + dataset, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule.setup() + + cv_values = [] + device = next(model.parameters()).device + + if show_progress: + items = pbar( + datamodule.train_dataloader(), + frequency=0.001, + prefix=progress_prefix + ) + else: + items = datamodule.train_dataloader() + + with torch.no_grad(): + for batchs in items: + outputs = model(batchs.to(device).to_dict()) + outputs = outputs.cpu().numpy() + cv_values.append(outputs) + + return np.concatenate(cv_values) + + +def get_dataset_cv_gradients( + model, + dataset, + component: int = 0, + batch_size: int = None, + show_progress: bool = True, + progress_prefix: str = 'Calculating CV gradients' +) -> np.ndarray: + """ + Get gradients of the CV w.r.t. node positions in a given dataset. The + calculation will run on the device where the model is on. + + Parameters + ---------- + model: mlcolvar.graph.cvs.GraphBaseCV + Collective variable model. + dataset: mlcovar.graph.data.GraphDataSet + Dataset on which to compute the sensitivity analysis. + component: int + Component of the CV to analysis. + batch_size: + Batch size used for evaluating the CV. + show_progress: bool + If show the progress bar. + """ + datamodule = DictModule( + dataset, + lengths=(1.0,), + batch_size=batch_size, + random_split=False, + shuffle=False + ) + datamodule.setup() + + cv_value_gradients = [] + device = next(model.parameters()).device + + if show_progress: + items = pbar( + datamodule.train_dataloader(), + frequency=0.001, + prefix=progress_prefix + ) + else: + items = datamodule.train_dataloader() + + for batchs in items: + batch_dict = batchs['data_list'].to(device) + batch_dict['positions'].requires_grad_(True) + cv_values = model(batch_dict) + cv_values = cv_values[:, component] + grad_outputs = [torch.ones_like(cv_values, device=device)] + gradients = torch.autograd.grad( + outputs=[cv_values], + inputs=[batch_dict['positions']], + grad_outputs=grad_outputs, + retain_graph=False, + create_graph=False, + ) + graph_sizes = batch_dict['ptr'][1:] - batch_dict['ptr'][:-1] + + # if we used the removed isolated atoms this will give an inhomogenous tensor! + gradients = torch.split( + gradients[0].detach(), graph_sizes.cpu().numpy().tolist() + ) + + # here we ensure that all the gradients have the correct shape + # and that each entry is at the correct index accordingly + max_used_atoms = len(dataset.metadata['used_idx']) + for i,g in enumerate(gradients): + aux = torch.zeros((max_used_atoms, 3)) + # this populates the right entries according to the orignal indexing + aux[batch_dict[i]['names_idx'], :] = g + cv_value_gradients.extend(aux.unsqueeze(0).cpu().numpy()) + + return np.array(cv_value_gradients) \ No newline at end of file diff --git a/test_graphs/test_explain.ipynb b/test_graphs/test_explain.ipynb new file mode 100644 index 00000000..b090dfd9 --- /dev/null +++ b/test_graphs/test_explain.ipynb @@ -0,0 +1,7477 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9']\n", + "DictModule(dataset -> DictDataset( \"data_list\": 2000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"used_idx\": tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \"used_names\": ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9'], \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=0.8, batch_size=2000, shuffle=1),\n", + "\t\t valid_loader -> DictLoader(length=0.2, batch_size=2000, shuffle=0))\n" + ] + } + ], + "source": [ + "import torch\n", + "from mlcolvar.data import DictModule\n", + "from mlcolvar.utils.io import create_dataset_from_trajectories\n", + "from mlcolvar.utils.io import create_dataset_from_files\n", + "\n", + "# load_args = [{'start' : 0, 'stop' : 100, 'stride' : 1},\n", + "# {'start' : 0, 'stop' : 100, 'stride' : 1}]\n", + "# dataset_graph = create_dataset_from_trajectories(\n", + "# trajectories=[\n", + "# 'data/r.dcd',\n", + "# 'data/p.dcd'\n", + "# ],\n", + "# top=['data/r.pdb', \n", + "# 'data/p.pdb'],\n", + "# cutoff=8, # Ang\n", + "# create_labels=True,\n", + "# system_selection='all and not type H',\n", + "# show_progress=False,\n", + "# remove_isolated_nodes=False,\n", + "# load_args=load_args\n", + "# )\n", + "\n", + "\n", + "load_args = [{'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 0, 'stop' : 10000, 'stride' : 10}]\n", + "dataset_graph = create_dataset_from_trajectories(\n", + " trajectories=[\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.trr\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.trr\",\n", + " ],\n", + " top=[\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.gro\",\n", + " ],\n", + " cutoff=10.0, # Ang\n", + " create_labels=True,\n", + " system_selection='all and not type H',\n", + " show_progress=False,\n", + " load_args=load_args,\n", + " save_names=False\n", + " )\n", + "\n", + "datamodule_graph = DictModule(dataset_graph, lengths=[0.8, 0.2], shuffle=[1, 0])\n", + "print(datamodule_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset_graph.metadata['cutoff'],\n", + " atomic_numbers=dataset_graph.metadata['z_table'],\n", + " n_bases=6,\n", + " n_layers=2,\n", + " n_filters=32,\n", + " n_hidden_channels=32\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test_2.5/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + ] + } + ], + "source": [ + "from mlcolvar.cvs.supervised.deeptda import DeepTDA\n", + "\n", + "model_graph = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " model=gnn_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "31b618dd7728447e9348e7a49c3335ed", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(out.detach().squeeze(), 100)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/data/datamodule.py:136: UserWarning: A torch.generator was provided but it is not used with random_split=False\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1A\u001b[2K Getting gradients ████████████████████████ 1/1 Est. 00:0.0\n", + "\n" + ] + } + ], + "source": [ + "from mlcolvar.explain.graph_sensitivity import graph_node_sensitivity\n", + "\n", + "test = graph_node_sensitivity(model=model_graph,\n", + " dataset=dataset_graph,\n", + " batch_size=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2000, 10)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test['sensitivities_components'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[str(l) for l in test['node_labels']]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "plt.plot(test['sensitivities'])\n", + "plt.xticks(np.arange(len(test['sensitivities'])), labels=test['node_labels'], rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (5, 3))\n", + "ax = plt.axes(projection =\"3d\")\n", + "idx = 10\n", + "edge_src = dataset_graph['data_list'][idx]['edge_index'][0, :]\n", + "edge_dst = dataset_graph['data_list'][idx]['edge_index'][1, :]\n", + "for q in range(edge_src.shape[0]):\n", + " aux = dataset_graph['data_list'][idx]['positions']\n", + " i = edge_src[q]\n", + " j = edge_dst[q]\n", + " plt.plot([aux[i,0], aux[j,0]], [aux[i,1], aux[j,1]], [aux[i,2], aux[j,2]], 'mediumblue', alpha=0.1, lw=1)\n", + "ax.scatter3D(aux[:,0], aux[:,1], aux[:, 2], c= torch.sum(dataset_graph['data_list'][idx]['node_attrs']*torch.tensor(dataset_graph.metadata['z_table']), axis=1), s=100, alpha =0.5, cmap='fessa')\n", + "plt.tick_params(length=0, labelsize=0); plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=[1, 1], n_system=[1, 1], weight=1.0, names_idx=[10])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_graph['data_list'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import mdtraj\n", + "\n", + "# traj = mdtraj.load('data/r.dcd', top='data/r.pdb')\n", + "\n", + "traj = mdtraj.load(\"/home/etrizio@iit.local/alad/boundary/A/alad.trr\",\n", + " top=\"/home/etrizio@iit.local/alad/boundary/A/alad.gro\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=[1, 1], n_system=[1, 1], weight=1.0, names_idx=[10])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_graph['data_list'][0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['A', 'B', 'C', 'D', 'E'], dtype=' 1\u001b[0m \u001b[43mdataset_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43matom_names\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mserial\n", + "\u001b[0;31mKeyError\u001b[0m: 'atom_names'" + ] + } + ], + "source": [ + "dataset_graph.metadata['atom_names'][0].serial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "graph_mlcolvar_test_2.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1306f613be9cc21af424cb527a302c983cd1ebe7 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 20 Dec 2024 18:09:09 +0100 Subject: [PATCH 225/267] Fixed typo --- mlcolvar/core/nn/graph/gnn.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py index 796e6f3b..5181bd20 100644 --- a/mlcolvar/core/nn/graph/gnn.py +++ b/mlcolvar/core/nn/graph/gnn.py @@ -43,7 +43,6 @@ def __init__( ) -> None: super().__init__() - self._n_out = n_out self._radial_embedding = radial.RadialEmbeddingBlock( cutoff, n_bases, n_polynomials, basis_type ) @@ -59,7 +58,7 @@ def __init__( @property def out_features(self): - return self._n_out + return self.n_out @property def in_features(self): From 55d07b500a6ed7fc5ccc81b447a2dccc27e4ebfe Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 20 Dec 2024 18:34:21 +0100 Subject: [PATCH 226/267] Added z and z_reg to commmittor loss --- mlcolvar/core/loss/committor_loss.py | 83 ++-- mlcolvar/cvs/committor/committor.py | 27 +- test_graphs/test_committor.ipynb | 362 ++++++----------- test_graphs/test_committor_checkpoint.ipynb | 413 ++++++++++++++++++++ test_graphs/test_graph.ipynb | 2 +- 5 files changed, 597 insertions(+), 290 deletions(-) create mode 100644 test_graphs/test_committor_checkpoint.ipynb diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index ed9478ba..b5d0e4da 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -18,7 +18,7 @@ from torch_scatter import scatter, scatter_sum from typing import Union import torch_geometric - +import warnings # ============================================================================= # LOSS FUNCTIONS # ============================================================================= @@ -34,7 +34,8 @@ def __init__(self, delta_f: float = 0.0, separate_boundary_dataset : bool = True, descriptors_derivatives : torch.nn.Module = None, - log_var: bool = True + log_var: bool = True, + z_regularization: float = 0.0 ): """Compute Kolmogorov's variational principle loss and impose boundary conditions on the metastable states @@ -66,41 +67,47 @@ def __init__(self, self.descriptors_derivatives = descriptors_derivatives self.separate_boundary_dataset = separate_boundary_dataset self.log_var = log_var + self.z_regularization = z_regularization def forward(self, x: Union[torch.Tensor, torch_geometric.data.Batch], + z: torch.Tensor, q: torch.Tensor, labels: torch.Tensor, w: torch.Tensor, create_graph: bool = True ) -> torch.Tensor: return committor_loss(x=x, - q=q, - labels=labels, - w=w, - atomic_masses=self.atomic_masses, - alpha=self.alpha, - gamma=self.gamma, - delta_f=self.delta_f, - create_graph=create_graph, - separate_boundary_dataset=self.separate_boundary_dataset, - descriptors_derivatives=self.descriptors_derivatives, - log_var = self.log_var + z=z, + q=q, + labels=labels, + w=w, + atomic_masses=self.atomic_masses, + alpha=self.alpha, + gamma=self.gamma, + delta_f=self.delta_f, + create_graph=create_graph, + separate_boundary_dataset=self.separate_boundary_dataset, + descriptors_derivatives=self.descriptors_derivatives, + log_var=self.log_var, + z_regularization=self.z_regularization ) def committor_loss(x: torch.Tensor, - q: torch.Tensor, - labels: torch.Tensor, - w: torch.Tensor, - atomic_masses: torch.Tensor, - alpha: float, - gamma: float = 10000, - delta_f: float = 0, - create_graph: bool = True, - separate_boundary_dataset: bool = True, - descriptors_derivatives: torch.nn.Module = None, - log_var: bool = True + z: torch.Tensor, + q: torch.Tensor, + labels: torch.Tensor, + w: torch.Tensor, + atomic_masses: torch.Tensor, + alpha: float, + gamma: float = 10000, + delta_f: float = 0, + create_graph: bool = True, + separate_boundary_dataset: bool = True, + descriptors_derivatives: torch.nn.Module = None, + log_var: bool = True, + z_regularization: float = 0.0 ): """Compute variational loss for committor optimization with boundary conditions @@ -153,8 +160,14 @@ def committor_loss(x: torch.Tensor, node_types = torch.where(x['node_attrs'])[1] x = x['positions'] _is_graph_data = True - + # checks and warnings + if _is_graph_data and descriptors_derivatives is not None: + raise ValueError("The descriptors_derivatives key cannot be used with GNN-based models!") + + if _is_graph_data and separate_boundary_dataset: + warnings.warn("Using GNN-based models it may be better to set separate_boundary_dataset to False") + # inherit right device device = x.device dtype = x.dtype @@ -165,13 +178,13 @@ def committor_loss(x: torch.Tensor, if separate_boundary_dataset: mask_var = torch.nonzero(labels.squeeze() > 1, as_tuple=not(_is_graph_data)) else: - mask_var = torch.ones(len(x), dtype=torch.bool) + mask_var = torch.ones(len(labels), dtype=torch.bool) - if _is_graph_data and separate_boundary_dataset: + if _is_graph_data: # this needs to be on the batch index, not only the labels - aux = torch.where(mask_var)[0] + aux = torch.where(mask_var)[0].to(device) mask_var_batches = torch.isin(batch, aux) - mask_var_batches = (batch[mask_var_batches]) + mask_var_batches = batch[mask_var_batches] else: mask_var_batches = mask_var @@ -219,7 +232,7 @@ def committor_loss(x: torch.Tensor, # variational contribution to loss: we sum over the batch loss_var = torch.mean(grad_square) if log_var: - loss_var = loss_var.log() + loss_var = torch.log(loss_var + 1) else: loss_var = gamma*loss_var @@ -229,8 +242,14 @@ def committor_loss(x: torch.Tensor, loss_B = gamma * torch.mean( torch.pow( (q[mask_B] - 1) , 2)) - # 3. TOTAL LOSS - loss = loss_var + alpha*(loss_A + loss_B) + # 3. OPTIONAL regularization on z + if z_regularization != 0.0: + loss_z_diff = z_regularization * (z.mean().abs() - z.mean().abs()).pow(2) + else: + loss_z_diff = 0 + + # 4. TOTAL LOSS + loss = loss_var + alpha*(loss_A + loss_B) + loss_z_diff # TODO maybe there is no need to detach them for logging return loss, loss_var.detach(), alpha*loss_A.detach(), alpha*loss_B.detach() diff --git a/mlcolvar/cvs/committor/committor.py b/mlcolvar/cvs/committor/committor.py index 692ff657..44ee3338 100644 --- a/mlcolvar/cvs/committor/committor.py +++ b/mlcolvar/cvs/committor/committor.py @@ -42,10 +42,10 @@ def __init__( alpha: float, gamma: float = 10000, delta_f: float = 0, - cell: float = None, separate_boundary_dataset: bool = True, descriptors_derivatives: torch.nn.Module = None, log_var: bool = True, + z_regularization: float = 0.0, options: dict = None, **kwargs, ): @@ -77,6 +77,8 @@ def __init__( Available blocks: ['nn'] . """ super().__init__(model, **kwargs) + + self.register_buffer('is_committor', torch.tensor(1, dtype=int)) # ======= LOSS ======= self.loss_fn = CommittorLoss(atomic_masses=mass, @@ -85,7 +87,8 @@ def __init__( delta_f=delta_f, separate_boundary_dataset=separate_boundary_dataset, descriptors_derivatives=descriptors_derivatives, - log_var=True + log_var=log_var, + z_regularization=z_regularization ) # ======= OPTIONS ======= @@ -111,6 +114,13 @@ def __init__( if (options[o] is not False) and (options[o] is not None): self.sigmoid = Custom_Sigmoid(**options[o]) + def forward(self, x): + if self.preprocessing is not None: + x = self.preprocessing(x) + z = self.nn(x) + q = self.sigmoid(z) + + return torch.hstack([z, q]) def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" @@ -129,16 +139,18 @@ def training_step(self, train_batch, batch_idx): weights = x['weight'].clone() # =================forward==================== - # we use forward and not forward_cv to also apply the preprocessing (if present) - q = self.forward(x) + out = self.forward(x) + z = out[:, 0] + q = out[:, 1] + # ===================loss===================== if self.training: loss, loss_var, loss_bound_A, loss_bound_B = self.loss_fn( - x, q, labels, weights + x, z, q, labels, weights ) else: loss, loss_var, loss_bound_A, loss_bound_B = self.loss_fn( - x, q, labels, weights + x, z, q, labels, weights ) # ====================log=====================+ name = "train" if self.training else "valid" @@ -150,6 +162,7 @@ def training_step(self, train_batch, batch_idx): def test_committor(): + import os from mlcolvar.data import DictDataset, DictModule from mlcolvar.cvs.committor.utils import initialize_committor_masses, KolmogorovBias @@ -228,8 +241,6 @@ def test_committor(): example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=2) model(example_input_graph_test).sum().backward() - # bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) - # bias_model(X) if __name__ == "__main__": diff --git a/test_graphs/test_committor.ipynb b/test_graphs/test_committor.ipynb index fcacfc1e..e4bbef74 100644 --- a/test_graphs/test_committor.ipynb +++ b/test_graphs/test_committor.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 40, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -11,8 +11,8 @@ "text": [ "Beta: 0.4009078751268027 \n", "1/beta: 2.4943386299999997\n", - "DictModule(dataset -> DictDataset( \"data_list\": 200, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"data_type\": graphs ),\n", - "\t\t train_loader -> DictLoader(length=1, batch_size=200, shuffle=False))\n" + "DictModule(dataset -> DictDataset( \"data_list\": 12000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=12000, shuffle=False))\n" ] } ], @@ -33,28 +33,31 @@ "beta = 1/(kb*T)\n", "print(f'Beta: {beta} \\n1/beta: {1/beta}')\n", "\n", - "load_args = [{'start' : 0, 'stop' : 1000, 'stride' : 1},\n", - " {'start' : 0, 'stop' : 1000, 'stride' : 1}]\n", + "load_args = [{'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 0, 'stop' : 20000, 'stride' : 2}]\n", "dataset = create_dataset_from_trajectories(\n", " trajectories=[\n", - " \"/home/etrizio/alad/boundary/A/alad.trr\",\n", - " \"/home/etrizio/alad/boundary/B/alad.trr\"\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.trr\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.trr\",\n", + " \"/home/etrizio@iit.local/alad/iteration_1/md/alad.trr\"\n", " ],\n", " top=[\n", - " \"/home/etrizio/alad/boundary/A/alad.gro\",\n", - " \"/home/etrizio/alad/boundary/B/alad.gro\"\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.gro\",\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\"\n", " ],\n", " cutoff=10.0, # Ang\n", " create_labels=True,\n", " system_selection='all and not type H',\n", " show_progress=False,\n", - " load_args=[{'start' : 0, 'stop' : 100, 'stride' : 1},\n", - " {'start' : 0, 'stop' : 100, 'stride' : 1}]\n", - ")\n", + " load_args=load_args\n", + " )\n", "\n", "bias = torch.zeros(len(dataset))\n", + "bias[-10000:] = torch.Tensor(load_dataframe(\"/home/etrizio@iit.local/alad/iteration_1/md/colvar\", stop=20000, stride=2)['opes.bias'])\n", "\n", - "dataset = compute_committor_weights(dataset=dataset, bias=bias, data_groups=[0,1], beta=beta)\n", + "dataset = compute_committor_weights(dataset=dataset, bias=bias, data_groups=[0,1,2], beta=beta)\n", "\n", "datamodule_graph = DictModule(dataset, lengths=[1], shuffle=False)\n", "print(datamodule_graph)" @@ -62,9 +65,17 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + ] + } + ], "source": [ "from mlcolvar.cvs.committor import Committor\n", "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", @@ -73,12 +84,12 @@ "gnn_model = SchNetModel(n_out=1,\n", " cutoff=dataset.metadata['cutoff'],\n", " atomic_numbers=dataset.metadata['z_table'],\n", - " n_bases=16,\n", - " n_layers=3,\n", - " n_filters=20,\n", - " n_hidden_channels=18,\n", + " n_bases=32,\n", + " n_layers=2,\n", + " n_filters=16,\n", + " n_hidden_channels=16,\n", " w_out_after_sum=True,\n", - " aggr='mean'\n", + " aggr='min'\n", " )\n", "\n", "# model = Committor(model=gnn_model,\n", @@ -93,43 +104,50 @@ "\n", "model = Committor(model=gnn_model,\n", " mass=torch.Tensor([12, 14, 16]),\n", - " alpha=1,\n", + " alpha=10,\n", + " gamma=1000,\n", + " separate_boundary_dataset=False,\n", + " z_regularization=True,\n", " options=options)\n" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n" + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 7.44it/s]" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3398b676c4e749fbb904dc327cc412fc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00)" + "tensor([nan, nan, nan, ..., nan, nan, nan], grad_fn=)" ] }, - "execution_count": 44, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "out_graph" + "out_graph[:, 1]" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
      " + "
      " ] }, "metadata": {}, @@ -399,8 +261,10 @@ "import matplotlib.pyplot as plt\n", "\n", "# plt.hist(out_graph.detach().squeeze())\n", - "plt.scatter(torch.arange(len(dataset)).numpy(), out_graph.detach().squeeze(), c=[dataset['data_list'][i]['graph_labels'] for i in range(len(dataset))])\n", - "plt.ylim(0,1)\n", + "# plt.scatter(torch.arange(len(dataset)).numpy(), out_graph.detach().squeeze(), c=[dataset['data_list'][i]['graph_labels'] for i in range(len(dataset))])\n", + "cp = plt.scatter(colvar['phi'], colvar['psi'], c=out_graph[:, 0].detach().squeeze().numpy(), s=2)\n", + "plt.colorbar(cp)\n", + "# plt.ylim(0,1)\n", "plt.show()" ] }, @@ -428,7 +292,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/test_graphs/test_committor_checkpoint.ipynb b/test_graphs/test_committor_checkpoint.ipynb new file mode 100644 index 00000000..cbb9ecf0 --- /dev/null +++ b/test_graphs/test_committor_checkpoint.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta: 0.4009078751268027 \n", + "1/beta: 2.4943386299999997\n", + "\n", + "DictModule(dataset -> DictDataset( \"data_list\": 12000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"atom_names\": [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=12000, shuffle=False))\n" + ] + } + ], + "source": [ + "import torch\n", + "torch.set_default_dtype(torch.float64)\n", + "\n", + "from mlcolvar.data import DictModule\n", + "from mlcolvar.utils.io import load_dataframe, create_dataset_from_trajectories\n", + "from mlcolvar.cvs.committor.utils import compute_committor_weights\n", + "\n", + "# number of atoms\n", + "n_atoms=10\n", + "\n", + "# temperature in Kelvin\n", + "T = 300 \n", + "\n", + "# Boltzmann factor in the RIGHT ENERGY UNITS!\n", + "kb = 0.0083144621 # kJ/mol\n", + "beta = 1/(kb*T)\n", + "print(f'Beta: {beta} \\n1/beta: {1/beta}')\n", + "\n", + "load_args = [{'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 10000, 'stop' : 20000, 'stride' : 1}]\n", + "dataset = create_dataset_from_trajectories(\n", + " trajectories=[\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.trr\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.trr\",\n", + " \"/home/etrizio@iit.local/alad/iteration_1/md/alad.trr\"\n", + " ],\n", + " top=[\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.gro\",\n", + " \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\"\n", + " ],\n", + " cutoff=10.0, # Ang\n", + " create_labels=True,\n", + " system_selection='all and not type H',\n", + " show_progress=False,\n", + " load_args=load_args\n", + " )\n", + "\n", + "bias = torch.zeros(len(dataset))\n", + "bias[-10000:] = torch.Tensor(load_dataframe(\"/home/etrizio@iit.local/alad/iteration_1/md/colvar\", start=10000, stop=20000, stride=1)['opes.bias'])\n", + "\n", + "dataset = compute_committor_weights(dataset=dataset, bias=bias, data_groups=[0,1,2], beta=beta)\n", + "\n", + "datamodule_graph = DictModule(dataset, lengths=[1], shuffle=False)\n", + "print(datamodule_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ACE1-CH3,\n", + " ACE1-C,\n", + " ACE1-O,\n", + " ALA2-N,\n", + " ALA2-CA,\n", + " ALA2-CB,\n", + " ALA2-C,\n", + " ALA2-O,\n", + " NME3-N,\n", + " NME3-C]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(dataset.metadata['atom_names'])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test_2.5/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + ] + } + ], + "source": [ + "from mlcolvar.cvs.committor import Committor\n", + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "import torch\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset.metadata['cutoff'],\n", + " atomic_numbers=dataset.metadata['z_table'],\n", + " n_bases=16,\n", + " n_layers=3,\n", + " n_filters=16,\n", + " n_hidden_channels=16,\n", + " w_out_after_sum=False,\n", + " aggr='min'\n", + " )\n", + "\n", + "# model = Committor(model=gnn_model,\n", + "# mass=torch.Tensor([12, 19]),\n", + "# alpha=1)\n", + "\n", + "options = {'optimizer' : {'lr' : 1e-3},\n", + " 'lr_scheduler': {\n", + " 'scheduler': torch.optim.lr_scheduler.ExponentialLR,\n", + " 'gamma': 0.99995\n", + " }}\n", + "torch.manual_seed(42)\n", + "\n", + "model = Committor(model=gnn_model,\n", + " mass=torch.Tensor([12, 14, 16]),\n", + " alpha=10,\n", + " gamma=1000,\n", + " separate_boundary_dataset=False,\n", + " z_regularization=True,\n", + " options=options)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test_2.5/lib/python3.9/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory /home/etrizio@iit.local/Bin/dev/mlcolvar/test_graphs/modelsave exists and is not empty.\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test_2.5/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1802449e503841e08fc4249a445f60b1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from lightning import Trainer\n", + "from mlcolvar.utils.trainer import MetricsCallback\n", + "from lightning.pytorch.callbacks import ModelCheckpoint\n", + "from mlcolvar.utils.plot import plot_metrics\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# define callbacks\n", + "metrics = MetricsCallback()\n", + "checkpoint_callback = ModelCheckpoint(dirpath=\"./modelsave/\", save_top_k=10, monitor=\"train_loss_epoch\", every_n_epochs=50)\n", + "\n", + "trainer = Trainer(\n", + " callbacks=[metrics, checkpoint_callback],\n", + " logger=False,\n", + " enable_checkpointing=True,\n", + " accelerator='cuda',\n", + " max_epochs=2000,\n", + " enable_model_summary=False,\n", + " limit_val_batches=0, \n", + " num_sanity_val_steps=0\n", + ")\n", + "\n", + "trainer.fit(model, datamodule_graph)\n", + "\n", + "fig, ax = plt.subplots(1,1,figsize=(4,3))\n", + "plot_metrics(metrics.metrics,\n", + " keys=['train_loss', 'train_loss_var', 'train_loss_bound_A', 'train_loss_bound_B'],\n", + " colors=['fessa1', 'fessa2', 'fessa3', 'fessa4'],\n", + " yscale='linear',\n", + " ax = ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1,figsize=(4,3))\n", + "ax.set_ylim(0,1)\n", + "plot_metrics(metrics.metrics,\n", + " keys=['train_loss', 'train_loss_var', 'train_loss_bound_A', 'train_loss_bound_B'],\n", + " colors=['fessa1', 'fessa2', 'fessa3', 'fessa4'],\n", + " yscale='linear',\n", + " ax = ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/data/datamodule.py:136: UserWarning: A torch.generator was provided but it is not used with random_split=False\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "load_args = [\n", + " #{'start' : 0, 'stop' : 1000, 'stride' : 1},\n", + " # {'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 0, 'stop' : 20000, 'stride' : 1}\n", + " ]\n", + "dataset_test = create_dataset_from_trajectories(\n", + " trajectories=[\n", + " # \"/home/etrizio@iit.local/alad/boundary/A/alad.trr\",\n", + " # \"/home/etrizio@iit.local/alad/boundary/B/alad.trr\",\n", + " \"/home/etrizio@iit.local/alad/iteration_1/md/alad.trr\"\n", + " ],\n", + " top=[\n", + " # \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\",\n", + " \"/home/etrizio@iit.local/alad/boundary/B/alad.gro\",\n", + " # \"/home/etrizio@iit.local/alad/boundary/A/alad.gro\"\n", + " ],\n", + " cutoff=10.0, # Ang\n", + " create_labels=True,\n", + " system_selection='all and not type H',\n", + " show_progress=False,\n", + " load_args=load_args\n", + " )\n", + "\n", + "colvar = load_dataframe(\"/home/etrizio@iit.local/alad/iteration_1/md/colvar\", stop=20000)\n", + "\n", + "datamodule_test = DictModule(dataset_test, lengths=[1], shuffle=False, random_split=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datamodule_test.setup()\n", + "loader = datamodule_test\n", + "test = next(iter(loader.train_dataloader()))['data_list']\n", + "out_graph = model(test)\n", + "plt.plot(test['graph_labels'].numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1.1122e-03, 5.6951e-06, 3.0094e-06, ..., 9.9869e-01, 1.0000e+00,\n", + " 3.4672e-05], grad_fn=)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_graph[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "ref = np.load('/home/etrizio@iit.local/notebooks/projects/kolmogorov_opes/alanine_long_train/refs/alanine_fes.npz')\n", + "plt.contour(ref['bins1'], ref['bins2'], ref['fes'], 15, colors='k', linewidths=0.5)\n", + "# plt.hist(out_graph.detach().squeeze())\n", + "# plt.scatter(torch.arange(len(dataset)).numpy(), out_graph.detach().squeeze(), c=[dataset['data_list'][i]['graph_labels'] for i in range(len(dataset))])\n", + "cp = plt.scatter(colvar['phi'], colvar['psi'], c=out_graph[:, 1].detach().squeeze().numpy(), cmap='fessa', s=2)\n", + "plt.colorbar(cp)\n", + "# plt.ylim(0,1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "graph_mlcolvar_test_2.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/test_graphs/test_graph.ipynb b/test_graphs/test_graph.ipynb index 283c1652..ab153278 100644 --- a/test_graphs/test_graph.ipynb +++ b/test_graphs/test_graph.ipynb @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, "outputs": [ { From e59d038985228f8cdbd9b89630fc753a52104fd0 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 23 Dec 2024 14:37:20 +0100 Subject: [PATCH 227/267] FIxed default atom names with env --- mlcolvar/data/graph/utils.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index e161a58e..45e67913 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -90,6 +90,12 @@ def _create_dataset_from_configuration( ) ) + n_env = ( + torch.tensor( + [[one_hot.shape[0] - n_system.to(torch.int).item()]], dtype=torch.get_default_dtype() + ) + ) + if config.system is not None: system_masks = torch.zeros((one_hot.shape[0], 1), dtype=torch.bool) system_masks[config.system, 0] = 1 @@ -106,6 +112,7 @@ def _create_dataset_from_configuration( node_labels=node_labels, graph_labels=graph_labels, n_system=n_system, + n_env=n_env, system_masks=system_masks, weight=weight, ) @@ -153,8 +160,10 @@ def create_dataset_from_configurations( ] if atom_names is None: - atom_names = [f"X{i}" for i in range(data_list[0]['n_system'].to(torch.int64).item())] - + atom_names_system = [f"X{i}" for i in range(data_list[0]['n_system'].to(torch.int64).item())] + atom_names_env = [f"Y{i}" for i in range(data_list[0]['n_env'].to(torch.int64).item())] + atom_names = atom_names_system + atom_names_env + print(atom_names) # this is only to check what isolated nodes have been removed From e6d90f838db26888cdcc8ef186960a5d9497f667 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 23 Dec 2024 16:13:39 +0100 Subject: [PATCH 228/267] Fixed committor forward --- mlcolvar/cvs/committor/committor.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/mlcolvar/cvs/committor/committor.py b/mlcolvar/cvs/committor/committor.py index 44ee3338..1d80c58a 100644 --- a/mlcolvar/cvs/committor/committor.py +++ b/mlcolvar/cvs/committor/committor.py @@ -114,13 +114,12 @@ def __init__( if (options[o] is not False) and (options[o] is not None): self.sigmoid = Custom_Sigmoid(**options[o]) - def forward(self, x): + def forward_nn(self, x): if self.preprocessing is not None: x = self.preprocessing(x) z = self.nn(x) - q = self.sigmoid(z) - return torch.hstack([z, q]) + return z def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" @@ -139,9 +138,11 @@ def training_step(self, train_batch, batch_idx): weights = x['weight'].clone() # =================forward==================== - out = self.forward(x) - z = out[:, 0] - q = out[:, 1] + z = self.forward_nn(x) + if self.sigmoid is not None: + q = self.sigmoid(z) + else: + q = z # ===================loss===================== if self.training: From 7c41ee9cf8c39ceb9cee96ed897d50e145b4b53e Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 7 Jan 2025 16:10:34 +0100 Subject: [PATCH 229/267] Updated docstrings --- mlcolvar/core/loss/tda_loss.py | 2 +- mlcolvar/core/nn/graph/gnn.py | 68 +++--- mlcolvar/core/nn/graph/gvp.py | 381 ++++++++++++++++++++++--------- mlcolvar/core/nn/graph/schnet.py | 71 +++++- 4 files changed, 381 insertions(+), 141 deletions(-) diff --git a/mlcolvar/core/loss/tda_loss.py b/mlcolvar/core/loss/tda_loss.py index 4a5aa90b..50234ed4 100644 --- a/mlcolvar/core/loss/tda_loss.py +++ b/mlcolvar/core/loss/tda_loss.py @@ -173,7 +173,7 @@ def tda_loss( warn( f"There is only one sample for state {i} in this batch! Std is set to 0, this may affect the training! Either use bigger batch_size or a more equilibrated dataset composition!" ) - sigma = torch.tensor(0) + sigma = torch.Tensor(0) else: sigma = torch.std(H_red, 0) diff --git a/mlcolvar/core/nn/graph/gnn.py b/mlcolvar/core/nn/graph/gnn.py index 5181bd20..560a6e4c 100644 --- a/mlcolvar/core/nn/graph/gnn.py +++ b/mlcolvar/core/nn/graph/gnn.py @@ -13,23 +13,7 @@ class BaseGNN(nn.Module): """ - The commen GNN interface for mlcolvar. - Parameters - ---------- - n_out: int - Size of the output node features. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_bases: int - Size of the basis set. - n_polynomials: bool - Order of the polynomials in the basis functions. - basis_type: str - Type of the basis function. + Base class for Graph Neural Network (GNN) models """ def __init__( @@ -41,11 +25,31 @@ def __init__( n_polynomials: int = 6, basis_type: str = 'bessel' ) -> None: + """Initializes the core of a GNN model, taking care of edge embeddings. + + Parameters + ---------- + n_out : int + Number of the output scalar node features. + cutoff : float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers : List[int] + The atomic numbers mapping. + n_bases : int, optional + Size of the basis set used for the embedding, by default 6 + n_polynomials : int, optional + Order of the polynomials in the basis functions, by default 6 + basis_type : str, optional + Type of the basis function, by default 'bessel' + """ super().__init__() - self._radial_embedding = radial.RadialEmbeddingBlock( - cutoff, n_bases, n_polynomials, basis_type - ) + self._radial_embedding = radial.RadialEmbeddingBlock(cutoff=cutoff, + n_bases=n_bases, + n_polynomials=n_polynomials, + basis_type=basis_type + ) self.register_buffer( 'n_out', torch.tensor(n_out, dtype=torch.int64) ) @@ -68,14 +72,16 @@ def embed_edge( self, data: Dict[str, torch.Tensor], normalize: bool = True ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ - Perform the edge embedding. + Performs the model edge embedding form `torch_geometric.data.Batch` object. + Parameters ---------- data: Dict[str, torch.Tensor] - The data dict. Usually came from the `to_dict` method of a + The data dict. Usually from the `to_dict` method of a `torch_geometric.data.Batch` object. normalize: bool - If return the normalized distance vectors. + If to return the normalized distance vectors, by default True. + Returns ------- edge_lengths: torch.Tensor (shape: [n_edges, 1]) @@ -100,23 +106,25 @@ def get_edge_vectors_and_lengths( normalize: bool = True, ) -> Tuple[torch.Tensor, torch.Tensor]: """ - Calculate edge vectors and lengths by indices and shift vectors. + Calculates edge vectors and lengths by indices and shift vectors. + Parameters ---------- - position: torch.Tensor (shape: [n_atoms, 3]) - The position vector. + positions: torch.Tensor (shape: [n_atoms, 3]) + The positions tensor. edge_index: torch.Tensor (shape: [2, n_edges]) The edge indices. shifts: torch.Tensor (shape: [n_edges, 3]) - The shift vector. + The shifts vector. normalize: bool - If return the normalized distance vectors. + If to return the normalized distance vectors, by default True. + Returns ------- vectors: torch.Tensor (shape: [n_edges, 3]) - The distance vectors. + The distances vectors. lengths: torch.Tensor (shape: [n_edges, 1]) - The edge lengths. + The edges lengths. """ sender = edge_index[0] receiver = edge_index[1] diff --git a/mlcolvar/core/nn/graph/gvp.py b/mlcolvar/core/nn/graph/gvp.py index c4d983b7..ed3b0cfa 100644 --- a/mlcolvar/core/nn/graph/gvp.py +++ b/mlcolvar/core/nn/graph/gvp.py @@ -21,41 +21,6 @@ class GVPModel(BaseGNN): """ The Geometric Vector Perceptron (GVP) model [1, 2] with vector gate [2]. - Parameters - ---------- - n_out: int - Size of the output node features. - cutoff: float - Cutoff radius of the basis functions. Should be the same as the cutoff - radius used to build the graphs. - atomic_numbers: List[int] - The atomic numbers mapping, e.g. the `atomic_numbers` attribute of a - `mlcolvar.graph.data.GraphDataSet` instance. - n_bases: int - Size of the basis set. - n_polynomials: bool - Order of the polynomials in the basis functions. - n_layers: int - Number of the graph convolution layers. - n_messages: int - Number of GVPs to use in the message functions. - n_feedforwards: int - Number of GVPs to use in the feedforward functions. - n_scalars_node: int - Size of the scalar channel of the node embedding in hidden layers. - n_vectors_node: int - Size of the vector channel of the node embedding in hidden layers. - n_scalars_edge: int - Size of the scalar channel of the edge embedding in hidden layers. - drop_rate: int - Drop probability in all dropout layers. - activation: str - Name of the activation function to use in the GVPs (case sensitive). - basis_type: str - Type of the basis function. - smooth: bool - If use the smoothed GVPConv. - References ---------- .. [1] Jing, Bowen, et al. @@ -83,6 +48,42 @@ def __init__( basis_type: str = 'bessel', smooth: bool = False, ) -> None: + """Initializes a Geometric Vector Perceptron (GVP) model. + + Parameters + ---------- + n_out: int + Number of the output scalar node features. + cutoff: float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers: List[int] + The atomic numbers mapping + n_bases: int + Size of the basis set used for the embedding, by default 8. + n_polynomials: bool + Order of the polynomials in the basis functions, by default 6. + n_layers: int + Number of the graph convolution layers, by default 1. + n_messages: int + Number of GVP layers to be used in the message functions, by default 2. + n_feedforwards: int + Number of GVP layers to be used in the feedforward functions, by default 2. + n_scalars_node: int + Size of the scalar channel of the node embedding in hidden layers, by default 8. + n_vectors_node: int + Size of the vector channel of the node embedding in hidden layers, by default 8. + n_scalars_edge: int + Size of the scalar channel of the edge embedding in hidden layers, by default 8. + drop_rate: int + Drop probability in all dropout layers, by default 0.1. + activation: str + Name of the activation function to be used in the GVPs (case sensitive), by default SiLU. + basis_type: str + Type of the basis function, by default bessel. + smooth: bool + If use the smoothed GVPConv, by default False. + """ super().__init__( n_out, cutoff, atomic_numbers, n_bases, n_polynomials, basis_type ) @@ -129,8 +130,7 @@ def __init__( def forward( self, data: Dict[str, torch.Tensor], scatter_mean: bool = True ) -> torch.Tensor: - """ - The forward pass. + """The forward pass. Parameters ---------- @@ -138,7 +138,7 @@ def forward( The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. scatter_mean: bool - If perform the scatter mean to the model output. + If perform the scatter mean to the model output, by default True. """ h_V = (data['node_attrs'], None) for w in self.W_v: @@ -178,15 +178,16 @@ def forward( class GVP(nn.Module): """ - Geometric Vector Perceptron. See manuscript and README.md - for more details. + Geometric Vector Perceptron (GVP) layer from [1, 2] with vector gate [2]. - :param in_dims: tuple (n_scalar, n_vector) - :param out_dims: tuple (n_scalar, n_vector) - :param h_dim: intermediate number of vector channels, optional - :param activations: tuple of functions (scalar_act, vector_act) - :param vector_gate: whether to use vector gating. - (vector_act will be used as sigma^+ in vector gating if `True`) + References + ---------- + .. [1] Jing, Bowen, et al. + "Learning from protein structure with geometric vector perceptrons." + International Conference on Learning Representations. 2020. + .. [2] Jing, Bowen, et al. + "Equivariant graph neural networks for 3d macromolecular structure." + arXiv preprint arXiv:2106.03843 (2021). """ def __init__( @@ -199,6 +200,27 @@ def __init__( ] = (nn.functional.relu, torch.sigmoid), vector_gate: bool = True, ) -> None: + r"""Geometric Vector Perceptron layer. + + Updates the scalar node feature s as: + .. math:: bm{s}^n \leftarrow \sigma \left(\bm{s}'\right) \quad\text{with}\quad \bm{s}' \coloneq \bm{W}_m \left[{\|\bm{W}_h\vec{\bm{v}}^o\|_2 \atop \bm{s}^o}\right] + \bm{b} + + And the vector nore features as: + .. math:: \vec{\bm{v}}^n \leftarrow \sigma_g \left(\bm{W}_g\left(\sigma^+ \left(\bm{s}'\right)\right) + \bm{b}_g \right) \odot \bm{W}_\mu\bm{W}_h\vec{\bm{v}}^o + + Parameters + ---------- + in_dims : Tuple[int, Optional[int]] + Dimension of inputs + out_dims : Tuple[int, Optional[int]] + Dimension of outputs + h_dim : Tuple[int, Optional[int]], optional + Intermidiate number of vector channels, by default None + activations : Tuple[ Optional[Callable], Optional[Callable] ], optional + Scalar and vector activation functions (scalar_act, vector_act), by default (nn.functional.relu, torch.sigmoid) + vector_gate : bool, optional + Whether to use vector gating, by default True. The vector activation will be used as sigma^+ in vector gating if `True` + """ super(GVP, self).__init__() self.si, self.vi = in_dims self.so, self.vo = out_dims @@ -227,12 +249,19 @@ def forward( self, x: Tuple[torch.Tensor, Optional[torch.Tensor]] ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Forward pass of GVP + + Parameters + ---------- + x : Tuple[torch.Tensor, Optional[torch.Tensor]] + Input scalar and vector node embeddings + + Returns + ------- + Tuple[torch.Tensor, Optional[torch.Tensor]] + Input scalar and vector node embeddings """ - :param x: tuple (s, V) of `torch.Tensor`, - or (if vectors_in is 0), a single `torch.Tensor` - :return: tuple (s, V) of `torch.Tensor`, - or (if vectors_out is 0), a single `torch.Tensor` - """ + s, v = x if v is not None: assert self.wh is not None @@ -278,20 +307,6 @@ def forward( class GVPConv(MessagePassing): """ Graph convolution / message passing with Geometric Vector Perceptrons. - Takes in a graph with node and edge embeddings, - and returns new node embeddings. - - This does NOT do residual updates and pointwise feedforward layers - --- see `GVPConvLayer`. - - :param in_dims: input node embedding dimensions (n_scalar, n_vector) - :param out_dims: output node embedding dimensions (n_scalar, n_vector) - :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) - :param n_layers: number of GVPs in the message function - :param activations: - tuple of functions (scalar_act, vector_act) to use in GVPs - :param vector_gate: whether to use vector gating. - (vector_act will be used as sigma^+ in vector gating if `True`) """ propagate_type = { 's': torch.Tensor, @@ -312,6 +327,32 @@ def __init__( vector_gate=True, cutoff: float = -1.0, ) -> None: + """Graph convolution / message passing with Geometric Vector Perceptrons. + Takes in a graph with node and edge embeddings, + and returns new node embeddings. + + This does NOT do residual updates and pointwise feedforward layers + --- see `GVPConvLayer` instead. + + Parameters + ---------- + in_dims : + input node embedding dimensions (n_scalar, n_vector) + out_dims : + output node embedding dimensions (n_scalar, n_vector) + edge_dims : + input edge embedding dimensions (n_scalar, n_vector) + n_layers : int, optional + number of GVPs in the message function, by default 3 + aggr : str, optional + Type of message aggregate function, by default 'mean' + activations : tuple, optional + activation functions (scalar_act, vector_act) to be used use in GVPs, by default (nn.functional.relu, torch.sigmoid) + vector_gate : bool, optional + Whether to use vector gating, by default True. The vector activation will be used as sigma^+ in vector gating if `True` + cutoff : float, optional + Radial cutoff, by default -1.0 + """ super(GVPConv, self).__init__(aggr=aggr) self.si, self.vi = in_dims self.so, self.vo = out_dims @@ -349,10 +390,23 @@ def forward( edge_attr: Tuple[torch.Tensor, torch.Tensor], edge_lengths: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - :param x: tuple (s, V) of `torch.Tensor` - :param edge_index: array of shape [2, n_edges] - :param edge_attr: tuple (s, V) of `torch.Tensor` + """Forward pass of GVPConv + + Parameters + ---------- + x : Tuple[torch.Tensor, torch.Tensor] + Input scalar and vector node embeddings + edge_index : torch.Tensor + Index of edge sources and destinations + edge_attr : Tuple[torch.Tensor, torch.Tensor] + Edge attributes + edge_lengths : torch.Tensor + Edge lengths + + Returns + ------- + Tuple[torch.Tensor, torch.Tensor] + Output scalar and vector node embeddings """ x_s, x_v = x assert x_v is not None @@ -404,7 +458,8 @@ def message_func( class GVPConvLayer(nn.Module): """ Full graph convolution / message passing layer with - Geometric Vector Perceptrons. Residually updates node embeddings with + Geometric Vector Perceptrons. + Residually updates node embeddings with aggregated incoming messages, applies a pointwise feedforward network to node embeddings, and returns updated node embeddings. @@ -433,6 +488,35 @@ def __init__( residual=True, cutoff: float = -1.0, ) -> None: + """Full graph convolution / message passing layer with + Geometric Vector Perceptrons. + Residually updates node embeddings with + aggregated incoming messages, applies a pointwise feedforward + network to node embeddings, and returns updated node embeddings. + + To only compute the aggregated messages see `GVPConv` instead. + + Parameters + ---------- + node_dims : + node embedding dimensions (n_scalar, n_vector) + edge_dims : + input edge embedding dimensions (n_scalar, n_vector) + n_message : int, optional + number of GVP layers to be used in message function, by default 3 + n_feedforward : int, optional + number of GVPs to be used use in feedforward function, by default 2 + drop_rate : float, optional + drop probability in all dropout layers, by default 0.1 + activations : tuple, optional + activation functions (scalar_act, vector_act) to be used use in GVPs, by default (nn.functional.relu, torch.sigmoid) + vector_gate : bool, optional + whether to use vector gating, by default True. The vector activation will be used as sigma^+ in vector gating if `True` + residual : bool, optional + whether to perform the update residually, by default True + cutoff : float, optional + radial cutoff, by default -1.0 + """ super(GVPConvLayer, self).__init__() self.conv = GVPConv( node_dims, @@ -476,13 +560,27 @@ def forward( edge_lengths: torch.Tensor, node_mask: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - :param x: tuple (s, V) of `torch.Tensor` - :param edge_index: array of shape [2, n_edges] - :param edge_attr: tuple (s, V) of `torch.Tensor` - :param node_mask: array of type `bool` to index into the first - dim of node embeddings (s, V). If not `None`, only - these nodes will be updated. + """Forward pass of GVPConvLayer + + Parameters + ---------- + x : Tuple[torch.Tensor, torch.Tensor] + Input scalar and vector node embeddings + edge_index : torch.Tensor + Index of edge sources and destinations + edge_attr : Tuple[torch.Tensor, torch.Tensor] + Edge attributes + edge_lengths : torch.Tensor + Edge lengths + node_mask : Optional[torch.Tensor], optional + Mask to restrict the node update to a subset. + It should be a tensor of type `bool` to index the first dim of node embeddings (s, V), by default None. + If not `None`, only the selected nodes will be updated. + + Returns + ------- + Tuple[torch.Tensor, torch.Tensor] + Output scalar and vector node embeddings """ dh = self.conv(x, edge_index, edge_attr, edge_lengths) @@ -545,11 +643,19 @@ def forward( self, x: Tuple[torch.Tensor, Optional[torch.Tensor]] ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Forward pass of LayerNorm + + Parameters + ---------- + x : Tuple[torch.Tensor, Optional[torch.Tensor]] + Input channels, if a single tensor is provided it assumes it to be the scalar channel + + Returns + ------- + Tuple[torch.Tensor, Optional[torch.Tensor]] + Normalized channels """ - :param x: tuple (s, V) of `torch.Tensor`, - or single `torch.Tensor` - (will be assumed to be scalar channels) - """ + s, v = x if not self.v: return self.scalar_norm(s), None @@ -575,10 +681,17 @@ def forward( self, x: Tuple[torch.Tensor, Optional[torch.Tensor]] ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - """ - :param x: tuple (s, V) of `torch.Tensor`, - or single `torch.Tensor` - (will be assumed to be scalar channels) + """Forward pass of Dropout + + Parameters + ---------- + x : Tuple[torch.Tensor, Optional[torch.Tensor]] + Input channels, if a single tensor is provided it assumes it to be the scalar channel + + Returns + ------- + Tuple[torch.Tensor, Optional[torch.Tensor]] + Dropped out channels """ s, v = x if v is None: @@ -599,9 +712,18 @@ def __init__(self, drop_rate) -> None: self.drop_rate = drop_rate self.dummy_param = nn.Parameter(torch.empty(0)) - def forward(self, x) -> torch.Tensor: - """ - :param x: `torch.Tensor` corresponding to vector channels + def forward(self, x : torch.Tensor) -> torch.Tensor: + """Forward pass of _VDropout + + Parameters + ---------- + x : torch.Tensor + Vector channel + + Returns + ------- + torch.Tensor + Dropped out vector channel """ device = self.dummy_param.device if not self.training: @@ -631,14 +753,28 @@ def _tuple_cat( input_3: Tuple[torch.Tensor, torch.Tensor], dim: int = -1 ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Concatenates any number of tuples (s, V) elementwise. + """Concatenates any number of tuples (s, V) elementwise. - :param dim: dimension along which to concatenate when viewed - as the `dim` index for the scalar-channel tensors. - This means that `dim=-1` will be applied as - `dim=-2` for the vector-channel tensors. + Parameters + ---------- + input_1 : Tuple[torch.Tensor, torch.Tensor] + First input to concatenate + input_2 : Tuple[torch.Tensor, torch.Tensor] + Second input to concatenate + input_3 : Tuple[torch.Tensor, torch.Tensor] + Third input to concatenate + dim : int, optional + dimension along which to concatenate when viewed + as the `dim` index for the scalar-channel tensors, by default -1. + This means that `dim=-1` will be applied as + `dim=-2` for the vector-channel tensors. + + Returns + ------- + Tuple[torch.Tensor, torch.Tensor] + Concatenated tuple """ + dim = int(dim % len(input_1[0].shape)) s_args, v_args = list(zip(input_1, input_2, input_3)) return torch.cat(s_args, dim=dim), torch.cat(v_args, dim=dim) @@ -648,10 +784,19 @@ def _tuple_cat( def _tuple_index( x: Tuple[torch.Tensor, torch.Tensor], idx: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Indexes into a tuple (s, V) along the first dimension. + """Indexes a tuple (s, V) along the first dimension at a given index. - :param idx: any object which can be used to index into a `torch.Tensor` + Parameters + ---------- + x : Tuple[torch.Tensor, torch.Tensor] + Tuple to be indexed + idx : torch.Tensor + any object which can be used to index a `torch.Tensor` + + Returns + ------- + Tuple[torch.Tensor, torch.Tensor] + Tuple with the element at the given index """ return x[0][idx], x[1][idx] @@ -664,10 +809,26 @@ def _norm_no_nan( eps: float = 1e-8, sqrt: bool = True ) -> torch.Tensor: - """ - L2 norm of tensor clamped above a minimum value `eps`. + """L2 norm of tensor clamped above a minimum value `eps`. - :param sqrt: if `False`, returns the square of the L2 norm + Parameters + ---------- + x : torch.Tensor + Input tensor + axis : int, optional + Axis along which to compute the norm, by default -1 + keepdims : bool, optional + Whether to keep the original dimensions, by default False + eps : float, optional + Lowest threshold for clamping the norm, by default 1e-8 + sqrt : bool, optional + Compute the sqaure root in L2 norm, by default True. + If `False`, returns the square of the L2 norm + + Returns + ------- + torch.Tensor + Normed tensor """ out = torch.clamp(torch.sum(torch.square(x), axis, keepdims), min=eps) return torch.sqrt(out) if sqrt else out @@ -675,13 +836,22 @@ def _norm_no_nan( @torch.jit.script def _split(x: torch.Tensor, nv: int) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Splits a merged representation of (s, V) back into a tuple. + """Splits a merged representation of (s, V) back into a tuple. Should be used only with `_merge(s, V)` and only if the tuple representation cannot be used. - :param x: the `torch.Tensor` returned from `_merge` - :param nv: the number of vector channels in the input to `_merge` + + Parameters + ---------- + x : torch.Tensor + the `torch.Tensor` returned from `_merge` + nv : int + the number of vector channels in the input to `_merge` + + Returns + ------- + Tuple[torch.Tensor, torch.Tensor] + split representation """ s = x[..., :-3 * nv] v = x[..., -3 * nv:].contiguous().view(x.shape[0], nv, 3) @@ -690,8 +860,7 @@ def _split(x: torch.Tensor, nv: int) -> Tuple[torch.Tensor, torch.Tensor]: @torch.jit.script def _merge(s: torch.Tensor, v: torch.Tensor) -> torch.Tensor: - """ - Merges a tuple (s, V) into a single `torch.Tensor`, where the + """Merges a tuple (s, V) into a single `torch.Tensor`, where the vector channels are flattened and appended to the scalar channels. Should be used only if the tuple representation cannot be used. Use `_split(x, nv)` to reverse. diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index d48b95ed..0bdef0f3 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -19,6 +19,7 @@ class SchNetModel(BaseGNN): """ The SchNet [1] model. This implementation is taken from torch_geometric: https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py + Parameters ---------- n_out: int @@ -57,17 +58,43 @@ def __init__( n_layers: int = 2, n_filters: int = 16, n_hidden_channels: int = 16, - drop_rate: int = 0, aggr: str = 'mean', w_out_after_sum: bool = False ) -> None: + """The SchNet model. This implementation is taken from torch_geometric: + https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py + + Parameters + ---------- + n_out : int + Size of the output node features. + cutoff : float + Cutoff radius of the basis functions. Should be the same as the cutoff + radius used to build the graphs. + atomic_numbers : List[int] + The atomic numbers mapping. + n_bases : int, optional + Size of the basis set used for the embedding, by default 16 + n_layers : int, optional + Number of the graph convolution layers, by default 2 + n_filters : int, optional + Number of filters, by default 16 + n_hidden_channels : int, optional + Size of hidden embeddings, by default 16 + aggr : str, optional + Type of the GNN aggregation function, by default 'mean' + w_out_after_sum : bool, optional + Whether to apply the last linear transformation after the sum, by default False + """ super().__init__( - n_out, cutoff, atomic_numbers, n_bases, 0, 'gaussian' + n_out=n_out, cutoff=cutoff, atomic_numbers=atomic_numbers, n_bases=n_bases, n_polynomials=0, basis_type='gaussian' ) self.W_v = nn.Linear( - len(atomic_numbers), n_hidden_channels, bias=False + in_features=len(atomic_numbers), + out_features=n_hidden_channels, + bias=False ) self.layers = nn.ModuleList([ @@ -112,7 +139,7 @@ def forward( The data dict. Usually came from the `to_dict` method of a `torch_geometric.data.Batch` object. scatter_mean: bool - If perform the scatter mean to the model output. + If to perform the scatter mean to the model output. """ h_E = self.embed_edge(data) @@ -153,6 +180,21 @@ def __init__( cutoff: float, aggr: str = 'mean' ) -> None: + """SchNet interaction block + + Parameters + ---------- + hidden_channels : int + Size of hidden embeddings + num_gaussians : int + Number of Gaussians for the embedding + num_filters : int + Number of filters + cutoff : float + Radial cutoff + aggr : str, optional + Aggregation function, by default 'mean' + """ super().__init__() self.mlp = nn.Sequential( nn.Linear(num_gaussians, num_filters), @@ -173,6 +215,9 @@ def __init__( self.reset_parameters() def reset_parameters(self) -> None: + """ + Resets all learnable parameters of the module. + """ nn.init.xavier_uniform_(self.mlp[0].weight) self.mlp[0].bias.data.fill_(0) nn.init.xavier_uniform_(self.mlp[2].weight) @@ -195,6 +240,7 @@ def forward( class CFConv(MessagePassing): + """Continuos-filter convolution from SchNet""" def __init__( self, in_channels: int, @@ -204,6 +250,23 @@ def __init__( cutoff: float, aggr: str = 'mean' ) -> None: + """_summary_ + + Parameters + ---------- + in_channels : int + Number of input channels + out_channels : int + Number of output channels + num_filters : int + Number of filters + network : nn.Sequential + Neural network + cutoff : float + Radial cutoff + aggr : str, optional + Aggregation function, by default 'mean' + """ super().__init__(aggr=aggr) self.lin1 = nn.Linear(in_channels, num_filters, bias=False) self.lin2 = nn.Linear(num_filters, out_channels) From ff0162644b76b64f108891a648d9eaaad33c89ad Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Tue, 14 Jan 2025 17:35:53 +0100 Subject: [PATCH 230/267] Updated docstrings --- mlcolvar/core/loss/committor_loss.py | 19 +++-- mlcolvar/core/nn/graph/gvp.py | 10 --- mlcolvar/core/nn/graph/radial.py | 100 ++++++++++++++----------- mlcolvar/core/transform/tools/utils.py | 2 +- mlcolvar/cvs/committor/committor.py | 15 +++- mlcolvar/cvs/cv.py | 13 ---- mlcolvar/cvs/supervised/deeplda.py | 13 +++- mlcolvar/cvs/supervised/deeptda.py | 14 +++- mlcolvar/cvs/supervised/regression.py | 15 +++- mlcolvar/cvs/timelagged/deeptica.py | 19 +++-- mlcolvar/data/dataset.py | 10 +++ mlcolvar/data/graph/atomic.py | 56 +++++++------- mlcolvar/data/graph/neighborhood.py | 28 ++++--- mlcolvar/data/graph/utils.py | 43 +++++------ mlcolvar/data/utils.py | 9 +-- mlcolvar/explain/graph_sensitivity.py | 83 ++++++++++---------- 16 files changed, 251 insertions(+), 198 deletions(-) diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index b5d0e4da..3e8b34fc 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -55,9 +55,13 @@ def __init__(self, separate_boundary_dataset : bool, optional Switch to exculde boundary condition labeled data from the variational loss, by default True descriptors_derivatives : torch.nn.Module, optional - `SmartDerivatives` object to save memory and time when using descriptors. + `SmartDerivatives` object to save memory and time when using descriptors, by default None. Cannot be used with GNN models. See also mlcolvar.core.loss.committor_loss.SmartDerivatives - + log_var : bool, optional + Switch to minimize the log of the variational functional, by default True. + z_regularization : float, optional + Introduces a regularization on the learned z space avoiding too large absolute values. + The magnitude of the regularization is scaled by the given number, by default 0.0 """ super().__init__() self.register_buffer("atomic_masses", atomic_masses) @@ -138,9 +142,14 @@ def committor_loss(x: torch.Tensor, separate_boundary_dataset : bool, optional Switch to exculde boundary condition labeled data from the variational loss, by default True descriptors_derivatives : torch.nn.Module, optional - `SmartDerivatives` object to save memory and time when using descriptors. - See also mlcolvar.core.loss.committor_loss.SmartDerivatives - + `SmartDerivatives` object to save memory and time when using descriptors. Cannot be used with GNN models. + See also mlcolvar.core.loss.committor_loss.SmartDerivatives + log_var : bool, optional + Switch to minimize the log of the variational functional, by default True. + z_regularization : float, optional + Introduces a regularization on the learned z space avoiding too large absolute values. + The magnitude of the regularization is scaled by the given number, by default 0.0 + Returns ------- loss : torch.Tensor diff --git a/mlcolvar/core/nn/graph/gvp.py b/mlcolvar/core/nn/graph/gvp.py index ed3b0cfa..eeb4e36d 100644 --- a/mlcolvar/core/nn/graph/gvp.py +++ b/mlcolvar/core/nn/graph/gvp.py @@ -464,16 +464,6 @@ class GVPConvLayer(nn.Module): network to node embeddings, and returns updated node embeddings. To only compute the aggregated messages, see `GVPConv`. - - :param node_dims: node embedding dimensions (n_scalar, n_vector) - :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) - :param n_message: number of GVPs to use in message function - :param n_feedforward: number of GVPs to use in feedforward function - :param drop_rate: drop probability in all dropout layers - :param activations: - tuple of functions (scalar_act, vector_act) to use in GVPs - :param vector_gate: whether to use vector gating. - (vector_act will be used as sigma^+ in vector gating if `True`) """ def __init__( diff --git a/mlcolvar/core/nn/graph/radial.py b/mlcolvar/core/nn/graph/radial.py index b8e21788..5a7b05ed 100644 --- a/mlcolvar/core/nn/graph/radial.py +++ b/mlcolvar/core/nn/graph/radial.py @@ -11,16 +11,18 @@ class GaussianBasis(torch.nn.Module): """ - The Gaussian basis functions. - - Parameters - ---------- - cutoff: float - The cutoff radius. - n_bases: int - Size of the basis set. + Gaussian basis functions. """ def __init__(self, cutoff: float, n_bases=32) -> None: + """Initialize a Gaussian basis function + + Parameters + ---------- + cutoff : float + Cutoff radius of the basis set + n_bases : int, optional + Size of the basis set, by default 32 + """ super().__init__() offset = torch.linspace( @@ -56,25 +58,29 @@ def __repr__(self) -> str: class BesselBasis(torch.nn.Module): - """ - The Bessel radial basis functions (equation (7) in [1]). + r""" + Bessel radial basis functions (equation (7) in [1]) - Parameters - ---------- - cutoff: float - The cutoff radius. - n_bases: int - Size of the basis set. - trainable: bool - If use trainable basis set parameters. + .. math:: RBF_n(d) = \sqrt{\frac{2}{c}\frac{sin(\frac{n\pi}{c}d)}{d}} References ---------- - .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + .. [1] Gasteiger, J.; Groß, J.; Günnemann, S. Directional Message Passing for Molecular Graphs; ICLR 2020. """ def __init__(self, cutoff: float, n_bases=8, trainable=False) -> None: + """Initializes Bessel radial basis function + + Parameters + ---------- + cutoff: float + Cutoff radius of the basis set + n_bases: int + Size of the basis set, by default 8 + trainable: bool + If to use trainable basis set parameters + """ super().__init__() bessel_weights = ( @@ -122,25 +128,28 @@ def __repr__(self) -> str: class PolynomialCutoff(torch.nn.Module): - """ - The Continuous cutoff function (equation (8) in [1]). + r"""Continuous cutoff function (equation (8) in [1]) - Parameters - ---------- - cutoff: float - The cutoff radius. - p: int - Order of the polynomial. + .. math:: u(d) = 1 - \frac{(p+1)(p+2)}{2}d^p + p(p+2)d^{p+1} - \frac{p(p+1)}{2}d^{p+2} References ---------- - .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + .. [1] Gasteiger, J.; Groß, J.; Günnemann, S. Directional Message Passing for Molecular Graphs; ICLR 2020. """ p: torch.Tensor cutoff: torch.Tensor def __init__(self, cutoff: float, p: int = 6) -> None: + """initilalizes a polynomial cutoff function. + + Parameters + ---------- + cutoff: float + The cutoff radius. + p: int + Order of the polynomial, by default 6 + """ super().__init__() self.register_buffer( 'p', torch.tensor(p, dtype=torch.get_default_dtype()) @@ -180,22 +189,11 @@ def __repr__(self) -> str: class RadialEmbeddingBlock(torch.nn.Module): """ - The radial embedding block [1]. - - Parameters - ---------- - cutoff: float - The cutoff radius. - n_bases: int - Size of the basis set. - n_polynomials: bool - Order of the polynomial. - basis_type: str - Type of the basis function. + Radial embedding block [1] References ---------- - .. [1] Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing + .. [1] Gasteiger, J.; Groß, J.; Günnemann, S. Directional Message Passing for Molecular Graphs; ICLR 2020. """ @@ -206,6 +204,24 @@ def __init__( n_polynomials: int = 6, basis_type: str = 'bessel', ) -> None: + """Initializes a radial embedding block + + Parameters + ---------- + cutoff : float + Cutoff radius. + n_bases : int, optional + Size of the basis set, by default 8 + n_polynomials : int, optional + Order of the polynomial for the polynomial cutoff, by default 6 + basis_type : str, optional + Type fo the basis function, by default 'bessel' + + Raises + ------ + RuntimeError + _description_ + """ super().__init__() self.n_out = n_bases if basis_type == 'bessel': @@ -221,7 +237,7 @@ def __init__( def forward(self, edge_lengths: torch.Tensor) -> torch.Tensor: """ - The forward pass. + The forward pass of RadialEmbeddingBlock Parameters ---------- diff --git a/mlcolvar/core/transform/tools/utils.py b/mlcolvar/core/transform/tools/utils.py index c8d9a51c..3a73e589 100644 --- a/mlcolvar/core/transform/tools/utils.py +++ b/mlcolvar/core/transform/tools/utils.py @@ -90,4 +90,4 @@ def easy_KDE(x : torch.Tensor, if return_bins: return out, bins else: - return out + return out \ No newline at end of file diff --git a/mlcolvar/cvs/committor/committor.py b/mlcolvar/cvs/committor/committor.py index 1d80c58a..ce186ec5 100644 --- a/mlcolvar/cvs/committor/committor.py +++ b/mlcolvar/cvs/committor/committor.py @@ -14,8 +14,10 @@ class Committor(BaseCV, lightning.LightningModule): The committor function q is expressed as the output of a neural network optimized with a self-consistent approach based on the Kolmogorov's variational principle for the committor and on the imposition of its boundary conditions. - **Data**: for training it requires a DictDataset with the keys 'data', 'labels' and 'weights' - + **Data**: for training it requires a DictDataset containing: + - If using descriptors as input, the keys 'data', 'labels' and 'weights'. + - If using graphs as input, `torch_geometric.data` with 'graph_labels' and 'weight' in their 'data_list'. + **Loss**: Minimize Kolmogorov's variational functional of q and impose boundary condition on the metastable states (CommittorLoss) References @@ -70,11 +72,16 @@ def __init__( separate_boundary_dataset : bool, optional Switch to exculde boundary condition labeled data from the variational loss, by default True descriptors_derivatives : torch.nn.Module, optional - `SmartDerivatives` object to save memory and time when using descriptors. + `SmartDerivatives` object to save memory and time when using descriptors. Cannot be used with GNN models. See also mlcolvar.core.loss.committor_loss.SmartDerivatives + log_var : bool, optional + Switch to minimize the log of the variational functional, by default True. + z_regularization : float, optional + Introduces a regularization on the learned z space avoiding too large absolute values. + The magnitude of the regularization is scaled by the given number, by default 0.0 options : dict[str, Any], optional Options for the building blocks of the model, by default {}. - Available blocks: ['nn'] . + Available blocks: ['nn']. """ super().__init__(model, **kwargs) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 35a986e1..cab47eea 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -27,10 +27,6 @@ def __init__( Parameters ---------- - in_features : int - Number of inputs of the CV model - out_features : int - Number of outputs of the CV model, should be the number of CVs preprocessing : torch.nn.Module, optional Preprocessing module, default None postprocessing : torch.nn.Module, optional @@ -89,14 +85,6 @@ def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): self._override_model = True self.in_features = model.in_features self.out_features = model.out_features - # if isinstance(model, FeedForward): - # # self.nn = model - # elif isinstance(model, BaseGNN): - # # GNN models need to be scripted! - # # self.nn = torch.jit.script_if_tracing(model) - # # self.nn = model - # self.in_features = None - # self.out_features = model.out_features else: raise ValueError( f"Keyword model can either accept type list, FeedForward or BaseGNN. Found {type(model)}" @@ -142,7 +130,6 @@ def initialize_blocks(self): Initialize the blocks as attributes of the CV class. """ for b in self.BLOCKS: - #if not self._override_model and b!='nn': self.__setattr__(b, None) def setup(self, stage=None): diff --git a/mlcolvar/cvs/supervised/deeplda.py b/mlcolvar/cvs/supervised/deeplda.py index e48d847d..30cf5f8b 100644 --- a/mlcolvar/cvs/supervised/deeplda.py +++ b/mlcolvar/cvs/supervised/deeplda.py @@ -16,7 +16,9 @@ class DeepLDA(BaseCV, lightning.LightningModule): Non-linear generalization of LDA in which a feature map is learned by a neural network optimized as to maximize the classes separation. The method is described in [1]_. - **Data**: for training it requires a DictDataset with the keys 'data' and 'labels'. + **Data**: for training it requires a DictDataset containing: + - If using descriptors as input, the keys 'data' and 'labels' + - If using graphs as input, `torch_geometric.data` with 'graph_labels' in their 'data_list'. **Loss**: maximize LDA eigenvalues (ReduceEigenvaluesLoss) @@ -48,8 +50,13 @@ def __init__(self, Parameters ---------- - layers : list - Number of neurons per layer + model : list or FeedForward or BaseGNN + Determines the underlying machine-learning model. One can pass: + 1. A list of integers corresponding to the number of neurons per layer of a feed-forward NN. + The model Will be automatically intialized using a `mlcolvar.core.nn.feedforward.FeedForward` object. + The CV class will be initialized according to the DEFAULT_BLOCKS. + 2. An externally intialized model (either `mlcolvar.core.nn.feedforward.FeedForward` or `mlcolvar.core.nn.graph.BaseGNN` object). + The CV class will be initialized according to the MODEL_BLOCKS. n_states : int Number of states for the training options : dict[str, Any], optional diff --git a/mlcolvar/cvs/supervised/deeptda.py b/mlcolvar/cvs/supervised/deeptda.py index 549ec648..2e8e5257 100644 --- a/mlcolvar/cvs/supervised/deeptda.py +++ b/mlcolvar/cvs/supervised/deeptda.py @@ -13,7 +13,10 @@ class DeepTDA(BaseCV, lightning.LightningModule): Deep Targeted Discriminant Analysis (Deep-TDA) CV. Combine the inputs with a neural-network and optimize it in a way such that the data are distributed accordingly to a mixture of Gaussians. The method is described in [1]_. - **Data**: for training it requires a DictDataset with the keys 'data' and 'labels'. + **Data**: for training it requires a DictDataset containing: + - If using descriptors as input, the keys 'data' and 'labels'. + - If using graphs as input, `torch_geometric.data` with 'graph_labels' in their 'data_list'. + **Loss**: distance of the samples of each class from a set of Gaussians (TDALoss) References ---------- @@ -53,8 +56,13 @@ def __init__( Centers of the Gaussian targets target_sigmas : list Standard deviations of the Gaussian targets - layers : list - Number of neurons per layer + model : list or FeedForward or BaseGNN + Determines the underlying machine-learning model. One can pass: + 1. A list of integers corresponding to the number of neurons per layer of a feed-forward NN. + The model Will be automatically intialized using a `mlcolvar.core.nn.feedforward.FeedForward` object. + The CV class will be initialized according to the DEFAULT_BLOCKS. + 2. An externally intialized model (either `mlcolvar.core.nn.feedforward.FeedForward` or `mlcolvar.core.nn.graph.BaseGNN` object). + The CV class will be initialized according to the MODEL_BLOCKS. options : dict[str, Any], optional Options for the building blocks of the model, by default {}. Available blocks: ['norm_in', 'nn']. diff --git a/mlcolvar/cvs/supervised/regression.py b/mlcolvar/cvs/supervised/regression.py index c180947b..558db234 100644 --- a/mlcolvar/cvs/supervised/regression.py +++ b/mlcolvar/cvs/supervised/regression.py @@ -14,8 +14,10 @@ class RegressionCV(BaseCV, lightning.LightningModule): Example of collective variable obtained with a regression task. Combine the inputs with a neural-network and optimize it to match a target function. - **Data**: for training it requires a DictDataset with the keys 'data' and 'target' and optionally 'weights'. - + **Data**: for training it requires a DictDataset containing: + - If using descriptors as input, the keys 'data', 'target' and optionally 'weights'. + - If using graphs as input, `torch_geometric.data` with 'graph_labels' with the target values and 'weight' in their 'data_list'. + **Loss**: least squares (MSELoss). See also @@ -37,8 +39,13 @@ def __init__(self, Parameters ---------- - layers : list - Number of neurons per layer + model : list or FeedForward or BaseGNN + Determines the underlying machine-learning model. One can pass: + 1. A list of integers corresponding to the number of neurons per layer of a feed-forward NN. + The model Will be automatically intialized using a `mlcolvar.core.nn.feedforward.FeedForward` object. + The CV class will be initialized according to the DEFAULT_BLOCKS. + 2. An externally intialized model (either `mlcolvar.core.nn.feedforward.FeedForward` or `mlcolvar.core.nn.graph.BaseGNN` object). + The CV class will be initialized according to the MODEL_BLOCKS. options : dict[str, Any], optional Options for the building blocks of the model, by default None. Available blocks: ['norm_in', 'nn']. diff --git a/mlcolvar/cvs/timelagged/deeptica.py b/mlcolvar/cvs/timelagged/deeptica.py index 8de28f2b..428c7b41 100644 --- a/mlcolvar/cvs/timelagged/deeptica.py +++ b/mlcolvar/cvs/timelagged/deeptica.py @@ -17,10 +17,12 @@ class DeepTICA(BaseCV, lightning.LightningModule): approximated by TICA. The method is described in [1]_. Note that from the point of view of the architecture DeepTICA is similar to the SRV [2] method. - **Data**: for training it requires a DictDataset with the keys 'data' (input at time t) - and 'data_lag' (input at time t+lag), as well as the corresponding 'weights' and - 'weights_lag' which will be used to weight the time correlation functions. - This can be created with the helper function `create_timelagged_dataset`. + **Data**: for training it requires a DictDataset containing: + - If using descriptors as input, the keys 'data' (input at time t) + and 'data_lag' (input at time t+lag), as well as the corresponding 'weights' and + 'weights_lag' which will be used to weight the time correlation functions. + - If using graphs as input, the keys 'data_list' and 'data_list_lag', each containing the respective 'weight' + This can be created in both cases with the helper function `create_timelagged_dataset`. **Loss**: maximize TICA eigenvalues (ReduceEigenvaluesLoss) @@ -54,8 +56,13 @@ def __init__(self, Parameters ---------- - layers : list - Number of neurons per layer + model : list or FeedForward or BaseGNN + Determines the underlying machine-learning model. One can pass: + 1. A list of integers corresponding to the number of neurons per layer of a feed-forward NN. + The model Will be automatically intialized using a `mlcolvar.core.nn.feedforward.FeedForward` object. + The CV class will be initialized according to the DEFAULT_BLOCKS. + 2. An externally intialized model (either `mlcolvar.core.nn.feedforward.FeedForward` or `mlcolvar.core.nn.graph.BaseGNN` object). + The CV class will be initialized according to the MODEL_BLOCKS. n_cvs : int, optional Number of cvs to optimize, default None (= last layer) options : dict[str, Any], optional diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index 8d3a02a7..59f7bb95 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -29,6 +29,12 @@ def __init__(self, Dictionary with names and tensors feature_names : array-like List or numpy array with feature names + metadata : dict + Dictionary with metadata quantities shared across the whole dataset. + data_type : str + Type of data stored in the dataset, either 'descriptors' or 'graphs', by default 'descriptors'. + This will be stored in the dataset.metadata dictionary. + """ # assert type dict @@ -125,6 +131,10 @@ def get_stats(self): stats dictionary of dictionaries with statistics """ + if self.metadata is 'graph': + raise ValueError ( + "Method get_stats not supported for graph-based dataset!" + ) stats = {} for k in self.keys: stats[k] = Statistics(self._dictionary[k]).to_dict() diff --git a/mlcolvar/data/graph/atomic.py b/mlcolvar/data/graph/atomic.py index 567490b6..22b0f98a 100644 --- a/mlcolvar/data/graph/atomic.py +++ b/mlcolvar/data/graph/atomic.py @@ -14,16 +14,18 @@ class AtomicNumberTable: - """ - The atomic number table. Used to map between one hot encodings and a given - set of actual atomic numbers. - Parameters - ---------- - zs: List[int] - The atomic numbers in this table. + """The atomic number table. + Used to map between one hot encodings and a given set of actual atomic numbers. """ def __init__(self, zs: List[int]) -> None: + """Initializes an atomi number table object + + Parameters + ---------- + zs: List[int] + The atomic numbers in this table + """ self.zs = zs self.masses = [1.0] * len(zs) for i in range(len(zs)): @@ -36,63 +38,61 @@ def __init__(self, zs: List[int]) -> None: ) def __len__(self) -> int: - """ - Number of elements in this table. - """ + """Number of elements in the table""" return len(self.zs) def __str__(self) -> str: return f'AtomicNumberTable: {tuple(s for s in self.zs)}' def index_to_z(self, index: int) -> int: - """ - Map the encoding to the actual atomic number. + """Maps the encoding to the actual atomic number + Parameters ---------- index: int - The encoding. + Index of the encoding to be mapped """ return self.zs[index] def index_to_symbol(self, index: int) -> str: - """ - Map the encoding to the atomic symbol. + """Map the encoding to the atomic symbol + Parameters ---------- index: int - The encoding. + Index of the encoding to be mapped """ return md.element.Element.getByAtomicNumber(self.zs[index]).symbol def z_to_index(self, atomic_number: int) -> int: - """ - Map an atomic number to the encoding. + """Maps an atomic number to the encoding. + Parameters ---------- atomic_number: int - The atomic number. + The atomic number to be mapped """ return self.zs.index(atomic_number) def zs_to_indices(self, atomic_numbers: np.ndarray) -> np.ndarray: - """ - Map an array of atomic number to the encodings. + """Maps an array of atomic number to the encodings. + Parameters ---------- atomic_numbers: numpy.ndarray - The atomic numbers. + The atomic numbers to be mapped """ to_index_fn = np.vectorize(self.z_to_index) return to_index_fn(atomic_numbers) @classmethod def from_zs(cls, atomic_numbers: Iterable[int]) -> 'AtomicNumberTable': - """ - Build the table from an array atomic numbers. + """Build the table from an array atomic numbers. + Parameters ---------- atomic_numbers: Iterable[int] - The atomic numbers. + The atomic numbers to be used for building the table """ z_set = set() for z in atomic_numbers: @@ -101,12 +101,12 @@ def from_zs(cls, atomic_numbers: Iterable[int]) -> 'AtomicNumberTable': def get_masses(atomic_numbers: Iterable[int]) -> List[float]: - """ - Get atomic masses from atomic numbers. + """Get atomic masses from atomic numbers. + Parameters ---------- atomic_numbers: Iterable[int] - The atomic numbers. + The atomic numbers for which to return the atomic masses """ return AtomicNumberTable.from_zs(atomic_numbers).masses.copy() diff --git a/mlcolvar/data/graph/neighborhood.py b/mlcolvar/data/graph/neighborhood.py index 00dd64f8..09791f34 100644 --- a/mlcolvar/data/graph/neighborhood.py +++ b/mlcolvar/data/graph/neighborhood.py @@ -20,8 +20,8 @@ def get_neighborhood( environment_indices: Optional[List[int]] = None, buffer: float = 0.0 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: - """ - Get the neighbor list of a given set atoms. + """Get the neighbor list of a given set atoms. + Parameters ---------- positions: numpy.ndarray (shape: [N, 3]) @@ -29,32 +29,38 @@ def get_neighborhood( cutoff: float The cutoff radius. pbc: Tuple[bool, bool, bool] (shape: [3]) - If enable PBC in the directions of the three lattice vectors. + If to enable PBC in the directions of the three lattice vectors. cell: numpy.ndarray (shape: [3, 3]) The lattice vectors. true_self_interaction: bool - If keep self-edges that don't cross periodic boundaries. + If to keep self-edges that don't cross periodic boundaries. system_indices: List[int] - Indices of the system atoms. + Indices of the atoms to be considered as the 'system' if + restricting the neighborhood to a subsystem (i.e., system + environment), see also Notes section. environment_indices: List[int] - Indices of the environment atoms. + Indices of the atoms to be considered as the 'environment' if + restricting the neighborhood to a subsystem (i.e., system + environment), see also Notes section. + Only atoms within the cutoff will be included as active enviroment atoms buffer: float - Buffer size used in finding active environment atoms. + Buffer size used in finding active environment atoms, if + restricting the neighborhood to a subsystem (i.e., system + environment), see also Notes section. + Returns ------- edge_index: numpy.ndarray (shape: [2, n_edges]) - The edge indices in the graph. + The edge indices (i.e., source and destination) in the graph. shifts: numpy.ndarray (shape: [n_edges, 3]) The shift vectors (unit_shifts * cell_lengths). unit_shifts: numpy.ndarray (shape: [n_edges, 3]) - The unit shift vectors (number of PBC croessed by the edges). + The unit shift vectors (number of PBC crossed by the edges). + Notes ----- - Arguments `system_indices` and `environment_indices` must presnet at the + Arguments `system_indices` and `environment_indices` must present at the same time. When these arguments are given, only edges in the [subsystem] formed by [the systems atoms] and [the environment atoms within the cutoff radius of the systems atoms] will be kept. - Besides, these two lists could not contain common atoms. + These two lists could not contain common atoms. """ if system_indices is not None or environment_indices is not None: diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 45e67913..44e89f43 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -16,19 +16,20 @@ def _create_dataset_from_configuration( cutoff: float, buffer: float = 0.0, ) -> torch_geometric.data.Data: - """ - Build the graph data object from a configuration. + """Build the torch_geometric graph data object from a configuration. Parameters ---------- - config: mlcolvar.graph.utils.atomic.Configuration - The configuration. - z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable - The atomic number table used to build the node attributes. + config: mlcolvar.data.graph.atomic.Configuration + The configuration from which to generate the graph data + z_table: mlcolvar.data.graph.atomic.AtomicNumberTable + The atomic number table used to build the node attributes cutoff: float - The graph cutoff radius. + The graph cutoff radius buffer: float - Buffer size used in finding active environment atoms. + Buffer size used in finding active environment atoms if + restricting the neighborhood to a subsystem (i.e., system + environment), + `see also mlcolvar.data.grap.neighborhood.get_neighborhood` """ assert config.graph_labels is None or len(config.graph_labels.shape) == 2 @@ -127,23 +128,24 @@ def create_dataset_from_configurations( remove_isolated_nodes: bool = False, show_progress: bool = True ) -> DictDataset: - """ - Build graph data objects from configurations. + """Build DictDataset object containing torch_geometric graph data objects from configurations. Parameters ---------- config: mlcolvar.graph.utils.atomic.Configurations - The configurations. + The configurations from whihc to generate the dataset z_table: mlcolvar.graph.utils.atomic.AtomicNumberTable - The atomic number table used to build the node attributes. + The atomic number table used to build the node attributes cutoff: float - The graph cutoff radius. + The graph cutoff radius buffer: float - Buffer size used in finding active environment atoms. + Buffer size used in finding active environment atoms if + restricting the neighborhood to a subsystem (i.e., system + environment), + `see also mlcolvar.data.grap.neighborhood.get_neighborhood` remove_isolated_nodes: bool - If remove isolated nodes from the dataset. + If to remove isolated nodes from the dataset show_progress: bool - If show the progress bar. + If to show the progress bar """ if show_progress: items = pbar(config, frequency=0.0001, prefix='Making graphs') @@ -216,20 +218,19 @@ def create_dataset_from_configurations( return dataset def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: - """ - Generates one-hot encoding with `n_classes` classes from `indices` + """Generates one-hot encoding with `n_classes` classes from `indices` Parameters ---------- indices: torch.Tensor (shape: [N, 1]) - Node incices. + Node indices n_classes: int - Number of classes. + Number of classes Returns ------- encoding: torch.tensor (shape: [N, n_classes]) - The one-hot encoding. + The one-hot encoding """ shape = indices.shape[:-1] + (n_classes,) oh = torch.zeros(shape, device=indices.device).view(shape) diff --git a/mlcolvar/data/utils.py b/mlcolvar/data/utils.py index d5e33113..fa4fee84 100644 --- a/mlcolvar/data/utils.py +++ b/mlcolvar/data/utils.py @@ -7,8 +7,7 @@ __all__ = ["save_dataset", "load_dataset", "save_dataset_configurations_as_extyz"] def save_dataset(dataset: DictDataset, file_name: str) -> None: - """ - Save a dataset to disk. + """Save a dataset to disk. Parameters ---------- @@ -23,8 +22,7 @@ def save_dataset(dataset: DictDataset, file_name: str) -> None: def load_dataset(file_name: str) -> DictDataset: - """ - Load a dataset from disk. + """Load a dataset from disk. Parameters ---------- @@ -39,8 +37,7 @@ def load_dataset(file_name: str) -> DictDataset: def save_dataset_configurations_as_extyz(dataset: DictDataset, file_name: str) -> None: - """ - Save a dataset to disk in the extxyz format. + """Save a dataset to disk in the extxyz format. Parameters ---------- diff --git a/mlcolvar/explain/graph_sensitivity.py b/mlcolvar/explain/graph_sensitivity.py index 777a69a8..8e2d1dc0 100644 --- a/mlcolvar/explain/graph_sensitivity.py +++ b/mlcolvar/explain/graph_sensitivity.py @@ -4,10 +4,8 @@ from mlcolvar.data import DictModule from mlcolvar.utils.plot import pbar +from mlcolvar.core.nn import BaseGNN -""" -Sensitivity analysis. -""" __all__ = ['graph_node_sensitivity'] @@ -20,23 +18,22 @@ def graph_node_sensitivity( batch_size: int = None, show_progress: bool = True ) -> Dict[str, np.ndarray]: - """ - Perform a sensitivity analysis by calculating CV gradient w.r.t. nodes' - positions. This allows us to measure which atom is most important to the - CV. + """Performs a sensitivity analysis on a GNN-based CV model by calculating + the CV gradient w.r.t. nodes' positions. + This allows us to measure which atom is most important to the CV model. Parameters ---------- - model: mlcolvar.graph.cvs.GraphBaseCV - Collective variable model. - dataset: mlcovar.graph.data.GraphDataSet - Dataset on which to compute the sensitivity analysis. + model: mlcolvar.cvs.BaseCV + Collective variable model based on GNN + dataset: mlcovar.data.DictDataset + Graph-based dataset on which to compute the sensitivity analysis device: str - Name of the device. + Name of the device on which to perform the computation batch_size: - Batch size used for evaluating the CV. + Batch size used for evaluating the CV show_progress: bool - If show the progress bar. + If to show the progress bar Returns ------- @@ -50,15 +47,21 @@ def graph_node_sensitivity( mlcolvar.utils.explain.sensitivity_analysis Perform the sensitivity analysis of a feedforward model. """ + # check model is GNN-based + if not isinstance(model.nn, BaseGNN): + raise ValueError ( + "The CV model is not based on GNN! Maybe you should use the feedforward sensitivity_analysis from mlcolvar.utils.explain.sensitivity!" + ) + model = model.to(device) gradients = get_dataset_cv_gradients( - model, - dataset, - component, - batch_size, - show_progress, - 'Getting gradients' + model=model, + dataset=dataset, + component=component, + batch_size=batch_size, + show_progress=show_progress, + progress_prefix='Getting gradients' ) sensitivities_components = np.linalg.norm(gradients, axis=-1) @@ -78,23 +81,22 @@ def get_dataset_cv_values( show_progress: bool = True, progress_prefix: str = 'Calculating CV values' ) -> np.ndarray: - """ - Get CV values of a given dataset. The calculation will run on the device - where the model is on. + """Gets the values of a CV model on a given dataset. + The calculation will run on the device where the model is on. Parameters ---------- - model: mlcolvar.graph.cvs.GraphBaseCV - Collective variable model. - dataset: mlcovar.graph.data.GraphDataSet - Dataset on which to compute the sensitivity analysis. + model: mlcolvar.cvs.BaseCV + Collective variable model + dataset: mlcovar.data.DictDataset + Dataset on which to compute the sensitivity analysis batch_size: - Batch size used for evaluating the CV. + Batch size used for evaluating the CV show_progress: bool - If show the progress bar. + If to show the progress bar """ datamodule = DictModule( - dataset, + dataset=dataset, lengths=(1.0,), batch_size=batch_size, random_split=False, @@ -131,25 +133,24 @@ def get_dataset_cv_gradients( show_progress: bool = True, progress_prefix: str = 'Calculating CV gradients' ) -> np.ndarray: - """ - Get gradients of the CV w.r.t. node positions in a given dataset. The - calculation will run on the device where the model is on. + """Get gradients of a GNN-based CV w.r.t. node positions in a given dataset. + The calculation will run on the device where the model is on. Parameters ---------- - model: mlcolvar.graph.cvs.GraphBaseCV - Collective variable model. - dataset: mlcovar.graph.data.GraphDataSet - Dataset on which to compute the sensitivity analysis. + model: mlcolvar.cvs.BaseCV + Collective variable model based on GNN + dataset: mlcovar.data.DictDataset + Graph-based dataset on which to compute the sensitivity analysis component: int - Component of the CV to analysis. + Component of the CV to analyse batch_size: - Batch size used for evaluating the CV. + Batch size used for evaluating the CV show_progress: bool - If show the progress bar. + If to show the progress bar """ datamodule = DictModule( - dataset, + dataset=dataset, lengths=(1.0,), batch_size=batch_size, random_split=False, From fe17939466ff3cde0340055e54514ee6ffe4839b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 17 Jan 2025 13:10:48 +0100 Subject: [PATCH 231/267] Updated docs requirements file --- docs/requirements.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/requirements.yaml b/docs/requirements.yaml index 1982bea0..7f31bc60 100644 --- a/docs/requirements.yaml +++ b/docs/requirements.yaml @@ -27,9 +27,12 @@ dependencies: - scikit-learn - scipy - torch-scatter + - pyg # Pip-only installs - pip: - sphinx-copybutton - furo - KDEpy + - mdtraj + - matscipy From 6d21ce0eebab3dddf6231811ddf95e81d292749c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 17 Jan 2025 13:11:08 +0100 Subject: [PATCH 232/267] Updated doc structure to include graphs code --- docs/api_core.rst | 66 +++++++++++++++++-- docs/api_data.rst | 20 +++++- docs/api_explain.rst | 20 +++++- docs/api_utils.rst | 32 +++++---- .../mlcolvar.core.nn.FeedForward.rst | 19 +----- .../mlcolvar.core.nn.graph.BaseGNN.rst | 41 ++++++++++++ .../mlcolvar.core.nn.graph.GVPModel.rst | 41 ++++++++++++ .../mlcolvar.core.nn.graph.SchNetModel.rst | 42 ++++++++++++ ...e.nn.graph.radial.RadialEmbeddingBlock.rst | 39 +++++++++++ .../mlcolvar.cvs.AutoEncoderCV.rst | 3 +- docs/autosummary/mlcolvar.cvs.BaseCV.rst | 3 + docs/autosummary/mlcolvar.cvs.Committor.rst | 4 +- docs/autosummary/mlcolvar.cvs.DeepLDA.rst | 3 +- docs/autosummary/mlcolvar.cvs.DeepTDA.rst | 3 +- docs/autosummary/mlcolvar.cvs.DeepTICA.rst | 3 +- .../autosummary/mlcolvar.cvs.RegressionCV.rst | 3 +- .../mlcolvar.cvs.VariationalAutoEncoderCV.rst | 3 +- .../mlcolvar.data.graph.AtomicNumberTable.rst | 34 ++++++++++ .../mlcolvar.data.graph.Configuration.rst | 43 ++++++++++++ ...aph.create_dataset_from_configurations.rst | 23 +++++++ .../mlcolvar.data.graph.get_neighborhood.rst | 23 +++++++ ...aph_sensitivity.graph_node_sensitivity.rst | 23 +++++++ mlcolvar/explain/__init__.py | 2 + mlcolvar/explain/graph_sensitivity.py | 4 +- 24 files changed, 448 insertions(+), 49 deletions(-) create mode 100644 docs/autosummary/mlcolvar.core.nn.graph.BaseGNN.rst create mode 100644 docs/autosummary/mlcolvar.core.nn.graph.GVPModel.rst create mode 100644 docs/autosummary/mlcolvar.core.nn.graph.SchNetModel.rst create mode 100644 docs/autosummary/mlcolvar.core.nn.graph.radial.RadialEmbeddingBlock.rst create mode 100644 docs/autosummary/mlcolvar.data.graph.AtomicNumberTable.rst create mode 100644 docs/autosummary/mlcolvar.data.graph.Configuration.rst create mode 100644 docs/autosummary/mlcolvar.data.graph.create_dataset_from_configurations.rst create mode 100644 docs/autosummary/mlcolvar.data.graph.get_neighborhood.rst create mode 100644 docs/autosummary/mlcolvar.explain.graph_sensitivity.graph_node_sensitivity.rst diff --git a/docs/api_core.rst b/docs/api_core.rst index 4a68cb05..0bfb326f 100644 --- a/docs/api_core.rst +++ b/docs/api_core.rst @@ -1,10 +1,14 @@ Core modules ------------- +============ These are the building blocks which are used to construct the CVs. -.. rubric:: NN +NN +-- +This module implements the architectures with learnable weights that can be used to build CV models. +Descriptors-based +^^^^^^^^^^^^^^^^ .. currentmodule:: mlcolvar.core.nn .. autosummary:: @@ -13,7 +17,31 @@ These are the building blocks which are used to construct the CVs. FeedForward -.. rubric:: Loss +Graphs-based +^^^^^^^^^^^^ +.. currentmodule:: mlcolvar.core.nn.graph + +Base class +"""""""""" +.. autosummary:: + :toctree: autosummary + :template: custom-class-template.rst + + BaseGNN + +Architectures +""""""""""""" +.. autosummary:: + :toctree: autosummary + :template: custom-class-template.rst + + SchNetModel + GVPModel + + +Loss +---- +This module implements the loss fucntions that can be used to optimize CV models. .. currentmodule:: mlcolvar.core.loss @@ -28,8 +56,13 @@ These are the building blocks which are used to construct the CVs. ReduceEigenvaluesLoss TDALoss -.. rubric:: Stats +Stats +----- +This module implements statistical methods with learnable weights that can be used in CV models. + +Base class +^^^^^^^^^^ .. currentmodule:: mlcolvar.core.stats .. autosummary:: @@ -37,11 +70,26 @@ These are the building blocks which are used to construct the CVs. :template: custom-class-template.rst Stats + +Linear methods +^^^^^^^^^^^^^^ +.. currentmodule:: mlcolvar.core.stats + +.. autosummary:: + :toctree: autosummary + :template: custom-class-template.rst + PCA LDA TICA -.. rubric:: Transform + +Transform +--------- +This module implements **non-learnable** pre/postprocessing tools + +Base class +^^^^^^^^^^ .. currentmodule:: mlcolvar.core.transform @@ -52,7 +100,9 @@ These are the building blocks which are used to construct the CVs. Transform -.. rubric:: Transform.descriptors +Descriptors +^^^^^^^^^^^ +This submodule implements several descriptors that can be computed starting from atomic positions. .. currentmodule:: mlcolvar.core.transform.descriptors @@ -66,7 +116,9 @@ These are the building blocks which are used to construct the CVs. EigsAdjMat MultipleDescriptors -.. rubric:: Transform.tools +Tools +^^^^^ +This submodule implements pre/postporcessing tools. .. currentmodule:: mlcolvar.core.transform.tools diff --git a/docs/api_data.rst b/docs/api_data.rst index 671cff12..a3e6be5d 100644 --- a/docs/api_data.rst +++ b/docs/api_data.rst @@ -1,6 +1,9 @@ Data ---- +General: dataset, module and loader +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + .. currentmodule:: mlcolvar.data This module contains the classes used for handling datasets and for feeding them to the Lightning trainer. @@ -11,4 +14,19 @@ This module contains the classes used for handling datasets and for feeding them DictDataset DictLoader - DictModule \ No newline at end of file + DictModule + +Graph specific tools +^^^^^^^^^^^^^^^^^^^^ +.. currentmodule:: mlcolvar.data.graph + +This module contains the classes used for handling and creating graphs. + +.. autosummary:: + :toctree: autosummary + :template: custom-class-template.rst + + AtomicNumberTable + Configuration + get_neighborhood + create_dataset_from_configurations \ No newline at end of file diff --git a/docs/api_explain.rst b/docs/api_explain.rst index 91352802..23f514f0 100644 --- a/docs/api_explain.rst +++ b/docs/api_explain.rst @@ -1,12 +1,16 @@ Explain ------- -.. rubric:: Sensitivity analysis +Sensitivity analysis +^^^^^^^^^^^^^^^^^^^^ Perform sensitivity analysis to identify feature relevances .. currentmodule:: mlcolvar.explain.sensitivity +Descriptors-based +"""""""""""""""""" + .. autosummary:: :toctree: autosummary :template: custom-class-template.rst @@ -14,7 +18,19 @@ Perform sensitivity analysis to identify feature relevances sensitivity_analysis plot_sensitivity -.. rubric:: Sparse linear model +Graph-based +""""""""""" +.. currentmodule:: mlcolvar.explain.graph_sensitivity + +.. autosummary:: + :toctree: autosummary + :template: custom-class-template.rst + + graph_node_sensitivity + + +Sparse linear models +^^^^^^^^^^^^^^^^^^^^ Use sparse models to approximate classification/regression tasks diff --git a/docs/api_utils.rst b/docs/api_utils.rst index 1fe87a1c..d9e5c5e9 100644 --- a/docs/api_utils.rst +++ b/docs/api_utils.rst @@ -1,7 +1,9 @@ Utils ----- -.. rubric:: Input/Output + +Input/Output +^^^^^^^^^^^^ Helper functions for loading dataframes (incl. PLUMED files) and directly creating datasets from them. @@ -14,7 +16,9 @@ Helper functions for loading dataframes (incl. PLUMED files) and directly creati load_dataframe create_dataset_from_files -.. rubric:: Time-lagged datasets + +Time-lagged datasets +^^^^^^^^^^^^^^^^^^^^ Create a dataset of pairs of time-lagged configurations. @@ -26,31 +30,35 @@ Create a dataset of pairs of time-lagged configurations. create_timelagged_dataset -.. rubric:: FES - -.. rubric:: Trainer -Functions used in conjunction with the lightning Trainer (e.g. logging, metrics...). +FES +^^^ +Compute (and plot) the free energy surface along the CVs. -.. currentmodule:: mlcolvar.utils.trainer +.. currentmodule:: mlcolvar.utils.fes .. autosummary:: :toctree: autosummary :template: custom-class-template.rst - MetricsCallback + compute_fes -Compute (and plot) the free energy surface along the CVs. -.. currentmodule:: mlcolvar.utils.fes +Trainer +^^^^^^^ +Functions used in conjunction with the lightning Trainer (e.g. logging, metrics...). + +.. currentmodule:: mlcolvar.utils.trainer .. autosummary:: :toctree: autosummary :template: custom-class-template.rst - compute_fes + MetricsCallback + -Plotting utils +Plot +^^^^ .. currentmodule:: mlcolvar.utils.plot diff --git a/docs/autosummary/mlcolvar.core.nn.FeedForward.rst b/docs/autosummary/mlcolvar.core.nn.FeedForward.rst index 1078460e..5ed3efa2 100644 --- a/docs/autosummary/mlcolvar.core.nn.FeedForward.rst +++ b/docs/autosummary/mlcolvar.core.nn.FeedForward.rst @@ -17,6 +17,7 @@ .. autosummary:: ~FeedForward.__init__ + ~FeedForward.backward ~FeedForward.forward @@ -29,27 +30,9 @@ .. autosummary:: - ~FeedForward.CHECKPOINT_HYPER_PARAMS_KEY - ~FeedForward.CHECKPOINT_HYPER_PARAMS_NAME - ~FeedForward.CHECKPOINT_HYPER_PARAMS_TYPE ~FeedForward.T_destination - ~FeedForward.automatic_optimization ~FeedForward.call_super_init - ~FeedForward.current_epoch - ~FeedForward.device - ~FeedForward.dtype ~FeedForward.dump_patches - ~FeedForward.example_input_array - ~FeedForward.fabric - ~FeedForward.global_rank - ~FeedForward.global_step - ~FeedForward.hparams - ~FeedForward.hparams_initial - ~FeedForward.local_rank - ~FeedForward.logger - ~FeedForward.loggers - ~FeedForward.on_gpu - ~FeedForward.trainer ~FeedForward.training diff --git a/docs/autosummary/mlcolvar.core.nn.graph.BaseGNN.rst b/docs/autosummary/mlcolvar.core.nn.graph.BaseGNN.rst new file mode 100644 index 00000000..0d3f69da --- /dev/null +++ b/docs/autosummary/mlcolvar.core.nn.graph.BaseGNN.rst @@ -0,0 +1,41 @@ +mlcolvar.core.nn.graph.BaseGNN +============================== + +.. currentmodule:: mlcolvar.core.nn.graph + +.. autoclass:: BaseGNN + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~BaseGNN.__init__ + ~BaseGNN.embed_edge + + + + +.. + + + .. rubric:: Attributes + + .. autosummary:: + + ~BaseGNN.T_destination + ~BaseGNN.call_super_init + ~BaseGNN.dump_patches + ~BaseGNN.in_features + ~BaseGNN.out_features + ~BaseGNN.training + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.core.nn.graph.GVPModel.rst b/docs/autosummary/mlcolvar.core.nn.graph.GVPModel.rst new file mode 100644 index 00000000..68ba5df3 --- /dev/null +++ b/docs/autosummary/mlcolvar.core.nn.graph.GVPModel.rst @@ -0,0 +1,41 @@ +mlcolvar.core.nn.graph.GVPModel +=============================== + +.. currentmodule:: mlcolvar.core.nn.graph + +.. autoclass:: GVPModel + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~GVPModel.__init__ + ~GVPModel.forward + + + + +.. + + + .. rubric:: Attributes + + .. autosummary:: + + ~GVPModel.T_destination + ~GVPModel.call_super_init + ~GVPModel.dump_patches + ~GVPModel.in_features + ~GVPModel.out_features + ~GVPModel.training + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.core.nn.graph.SchNetModel.rst b/docs/autosummary/mlcolvar.core.nn.graph.SchNetModel.rst new file mode 100644 index 00000000..05a5e532 --- /dev/null +++ b/docs/autosummary/mlcolvar.core.nn.graph.SchNetModel.rst @@ -0,0 +1,42 @@ +mlcolvar.core.nn.graph.SchNetModel +================================== + +.. currentmodule:: mlcolvar.core.nn.graph + +.. autoclass:: SchNetModel + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~SchNetModel.__init__ + ~SchNetModel.forward + ~SchNetModel.reset_parameters + + + + +.. + + + .. rubric:: Attributes + + .. autosummary:: + + ~SchNetModel.T_destination + ~SchNetModel.call_super_init + ~SchNetModel.dump_patches + ~SchNetModel.in_features + ~SchNetModel.out_features + ~SchNetModel.training + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.core.nn.graph.radial.RadialEmbeddingBlock.rst b/docs/autosummary/mlcolvar.core.nn.graph.radial.RadialEmbeddingBlock.rst new file mode 100644 index 00000000..962513c3 --- /dev/null +++ b/docs/autosummary/mlcolvar.core.nn.graph.radial.RadialEmbeddingBlock.rst @@ -0,0 +1,39 @@ +mlcolvar.core.nn.graph.radial.RadialEmbeddingBlock +================================================== + +.. currentmodule:: mlcolvar.core.nn.graph.radial + +.. autoclass:: RadialEmbeddingBlock + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~RadialEmbeddingBlock.__init__ + ~RadialEmbeddingBlock.forward + + + + +.. + + + .. rubric:: Attributes + + .. autosummary:: + + ~RadialEmbeddingBlock.T_destination + ~RadialEmbeddingBlock.call_super_init + ~RadialEmbeddingBlock.dump_patches + ~RadialEmbeddingBlock.training + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.cvs.AutoEncoderCV.rst b/docs/autosummary/mlcolvar.cvs.AutoEncoderCV.rst index 13d32fab..93425727 100644 --- a/docs/autosummary/mlcolvar.cvs.AutoEncoderCV.rst +++ b/docs/autosummary/mlcolvar.cvs.AutoEncoderCV.rst @@ -32,10 +32,11 @@ .. autosummary:: - ~AutoEncoderCV.BLOCKS ~AutoEncoderCV.CHECKPOINT_HYPER_PARAMS_KEY ~AutoEncoderCV.CHECKPOINT_HYPER_PARAMS_NAME ~AutoEncoderCV.CHECKPOINT_HYPER_PARAMS_TYPE + ~AutoEncoderCV.DEFAULT_BLOCKS + ~AutoEncoderCV.MODEL_BLOCKS ~AutoEncoderCV.T_destination ~AutoEncoderCV.automatic_optimization ~AutoEncoderCV.call_super_init diff --git a/docs/autosummary/mlcolvar.cvs.BaseCV.rst b/docs/autosummary/mlcolvar.cvs.BaseCV.rst index 33fa73c0..4d368e28 100644 --- a/docs/autosummary/mlcolvar.cvs.BaseCV.rst +++ b/docs/autosummary/mlcolvar.cvs.BaseCV.rst @@ -22,6 +22,7 @@ ~BaseCV.forward_cv ~BaseCV.initialize_blocks ~BaseCV.initialize_transforms + ~BaseCV.parse_model ~BaseCV.parse_options ~BaseCV.setup ~BaseCV.test_step @@ -37,6 +38,8 @@ .. autosummary:: + ~BaseCV.DEFAULT_BLOCKS + ~BaseCV.MODEL_BLOCKS ~BaseCV.example_input_array ~BaseCV.n_cvs ~BaseCV.optimizer_name diff --git a/docs/autosummary/mlcolvar.cvs.Committor.rst b/docs/autosummary/mlcolvar.cvs.Committor.rst index 3aa14710..8b91866a 100644 --- a/docs/autosummary/mlcolvar.cvs.Committor.rst +++ b/docs/autosummary/mlcolvar.cvs.Committor.rst @@ -17,6 +17,7 @@ .. autosummary:: ~Committor.__init__ + ~Committor.forward_nn ~Committor.training_step @@ -29,10 +30,11 @@ .. autosummary:: - ~Committor.BLOCKS ~Committor.CHECKPOINT_HYPER_PARAMS_KEY ~Committor.CHECKPOINT_HYPER_PARAMS_NAME ~Committor.CHECKPOINT_HYPER_PARAMS_TYPE + ~Committor.DEFAULT_BLOCKS + ~Committor.MODEL_BLOCKS ~Committor.T_destination ~Committor.automatic_optimization ~Committor.call_super_init diff --git a/docs/autosummary/mlcolvar.cvs.DeepLDA.rst b/docs/autosummary/mlcolvar.cvs.DeepLDA.rst index 2d1eeb29..d4113105 100644 --- a/docs/autosummary/mlcolvar.cvs.DeepLDA.rst +++ b/docs/autosummary/mlcolvar.cvs.DeepLDA.rst @@ -32,10 +32,11 @@ .. autosummary:: - ~DeepLDA.BLOCKS ~DeepLDA.CHECKPOINT_HYPER_PARAMS_KEY ~DeepLDA.CHECKPOINT_HYPER_PARAMS_NAME ~DeepLDA.CHECKPOINT_HYPER_PARAMS_TYPE + ~DeepLDA.DEFAULT_BLOCKS + ~DeepLDA.MODEL_BLOCKS ~DeepLDA.T_destination ~DeepLDA.automatic_optimization ~DeepLDA.call_super_init diff --git a/docs/autosummary/mlcolvar.cvs.DeepTDA.rst b/docs/autosummary/mlcolvar.cvs.DeepTDA.rst index 9ebe1355..011f71b2 100644 --- a/docs/autosummary/mlcolvar.cvs.DeepTDA.rst +++ b/docs/autosummary/mlcolvar.cvs.DeepTDA.rst @@ -29,10 +29,11 @@ .. autosummary:: - ~DeepTDA.BLOCKS ~DeepTDA.CHECKPOINT_HYPER_PARAMS_KEY ~DeepTDA.CHECKPOINT_HYPER_PARAMS_NAME ~DeepTDA.CHECKPOINT_HYPER_PARAMS_TYPE + ~DeepTDA.DEFAULT_BLOCKS + ~DeepTDA.MODEL_BLOCKS ~DeepTDA.T_destination ~DeepTDA.automatic_optimization ~DeepTDA.call_super_init diff --git a/docs/autosummary/mlcolvar.cvs.DeepTICA.rst b/docs/autosummary/mlcolvar.cvs.DeepTICA.rst index 8dc7e0a7..6e8b0929 100644 --- a/docs/autosummary/mlcolvar.cvs.DeepTICA.rst +++ b/docs/autosummary/mlcolvar.cvs.DeepTICA.rst @@ -31,10 +31,11 @@ .. autosummary:: - ~DeepTICA.BLOCKS ~DeepTICA.CHECKPOINT_HYPER_PARAMS_KEY ~DeepTICA.CHECKPOINT_HYPER_PARAMS_NAME ~DeepTICA.CHECKPOINT_HYPER_PARAMS_TYPE + ~DeepTICA.DEFAULT_BLOCKS + ~DeepTICA.MODEL_BLOCKS ~DeepTICA.T_destination ~DeepTICA.automatic_optimization ~DeepTICA.call_super_init diff --git a/docs/autosummary/mlcolvar.cvs.RegressionCV.rst b/docs/autosummary/mlcolvar.cvs.RegressionCV.rst index 54edee56..e9e0422c 100644 --- a/docs/autosummary/mlcolvar.cvs.RegressionCV.rst +++ b/docs/autosummary/mlcolvar.cvs.RegressionCV.rst @@ -29,10 +29,11 @@ .. autosummary:: - ~RegressionCV.BLOCKS ~RegressionCV.CHECKPOINT_HYPER_PARAMS_KEY ~RegressionCV.CHECKPOINT_HYPER_PARAMS_NAME ~RegressionCV.CHECKPOINT_HYPER_PARAMS_TYPE + ~RegressionCV.DEFAULT_BLOCKS + ~RegressionCV.MODEL_BLOCKS ~RegressionCV.T_destination ~RegressionCV.automatic_optimization ~RegressionCV.call_super_init diff --git a/docs/autosummary/mlcolvar.cvs.VariationalAutoEncoderCV.rst b/docs/autosummary/mlcolvar.cvs.VariationalAutoEncoderCV.rst index dbfa2c47..5ac5a041 100644 --- a/docs/autosummary/mlcolvar.cvs.VariationalAutoEncoderCV.rst +++ b/docs/autosummary/mlcolvar.cvs.VariationalAutoEncoderCV.rst @@ -32,10 +32,11 @@ .. autosummary:: - ~VariationalAutoEncoderCV.BLOCKS ~VariationalAutoEncoderCV.CHECKPOINT_HYPER_PARAMS_KEY ~VariationalAutoEncoderCV.CHECKPOINT_HYPER_PARAMS_NAME ~VariationalAutoEncoderCV.CHECKPOINT_HYPER_PARAMS_TYPE + ~VariationalAutoEncoderCV.DEFAULT_BLOCKS + ~VariationalAutoEncoderCV.MODEL_BLOCKS ~VariationalAutoEncoderCV.T_destination ~VariationalAutoEncoderCV.automatic_optimization ~VariationalAutoEncoderCV.call_super_init diff --git a/docs/autosummary/mlcolvar.data.graph.AtomicNumberTable.rst b/docs/autosummary/mlcolvar.data.graph.AtomicNumberTable.rst new file mode 100644 index 00000000..881eff0e --- /dev/null +++ b/docs/autosummary/mlcolvar.data.graph.AtomicNumberTable.rst @@ -0,0 +1,34 @@ +mlcolvar.data.graph.AtomicNumberTable +===================================== + +.. currentmodule:: mlcolvar.data.graph + +.. autoclass:: AtomicNumberTable + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~AtomicNumberTable.__init__ + ~AtomicNumberTable.from_zs + ~AtomicNumberTable.index_to_symbol + ~AtomicNumberTable.index_to_z + ~AtomicNumberTable.z_to_index + ~AtomicNumberTable.zs_to_indices + + + + +.. + + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.data.graph.Configuration.rst b/docs/autosummary/mlcolvar.data.graph.Configuration.rst new file mode 100644 index 00000000..41e2a5fd --- /dev/null +++ b/docs/autosummary/mlcolvar.data.graph.Configuration.rst @@ -0,0 +1,43 @@ +mlcolvar.data.graph.Configuration +================================= + +.. currentmodule:: mlcolvar.data.graph + +.. autoclass:: Configuration + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Configuration.__init__ + + + + +.. + + + .. rubric:: Attributes + + .. autosummary:: + + ~Configuration.environment + ~Configuration.system + ~Configuration.weight + ~Configuration.atomic_numbers + ~Configuration.positions + ~Configuration.cell + ~Configuration.pbc + ~Configuration.node_labels + ~Configuration.graph_labels + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.data.graph.create_dataset_from_configurations.rst b/docs/autosummary/mlcolvar.data.graph.create_dataset_from_configurations.rst new file mode 100644 index 00000000..d07941ab --- /dev/null +++ b/docs/autosummary/mlcolvar.data.graph.create_dataset_from_configurations.rst @@ -0,0 +1,23 @@ +mlcolvar.data.graph.create\_dataset\_from\_configurations +========================================================= + +.. currentmodule:: mlcolvar.data.graph + +.. autoclass:: create_dataset_from_configurations + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + + + +.. + + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.data.graph.get_neighborhood.rst b/docs/autosummary/mlcolvar.data.graph.get_neighborhood.rst new file mode 100644 index 00000000..0602c951 --- /dev/null +++ b/docs/autosummary/mlcolvar.data.graph.get_neighborhood.rst @@ -0,0 +1,23 @@ +mlcolvar.data.graph.get\_neighborhood +===================================== + +.. currentmodule:: mlcolvar.data.graph + +.. autoclass:: get_neighborhood + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + + + +.. + + + + + \ No newline at end of file diff --git a/docs/autosummary/mlcolvar.explain.graph_sensitivity.graph_node_sensitivity.rst b/docs/autosummary/mlcolvar.explain.graph_sensitivity.graph_node_sensitivity.rst new file mode 100644 index 00000000..1da846f6 --- /dev/null +++ b/docs/autosummary/mlcolvar.explain.graph_sensitivity.graph_node_sensitivity.rst @@ -0,0 +1,23 @@ +mlcolvar.explain.graph\_sensitivity.graph\_node\_sensitivity +============================================================ + +.. currentmodule:: mlcolvar.explain.graph_sensitivity + +.. autoclass:: graph_node_sensitivity + :members: + :show-inheritance: + :inherited-members: Module,LightningModule + + + .. automethod:: __init__ + + + + + +.. + + + + + \ No newline at end of file diff --git a/mlcolvar/explain/__init__.py b/mlcolvar/explain/__init__.py index 7cfef572..40afa6c4 100644 --- a/mlcolvar/explain/__init__.py +++ b/mlcolvar/explain/__init__.py @@ -1,7 +1,9 @@ __all__ = [ "sensitivity_analysis", "plot_sensitivity", + "graph_node_sensitivity" ] from .sensitivity import * +from .graph_sensitivity import * # from .lasso import * # lasso requires additional dependencies diff --git a/mlcolvar/explain/graph_sensitivity.py b/mlcolvar/explain/graph_sensitivity.py index 8e2d1dc0..34116b9c 100644 --- a/mlcolvar/explain/graph_sensitivity.py +++ b/mlcolvar/explain/graph_sensitivity.py @@ -18,8 +18,8 @@ def graph_node_sensitivity( batch_size: int = None, show_progress: bool = True ) -> Dict[str, np.ndarray]: - """Performs a sensitivity analysis on a GNN-based CV model by calculating - the CV gradient w.r.t. nodes' positions. + """Performs a sensitivity analysis on a GNN-based CV model using + partial derivatives w.r.t. nodes' positions. This allows us to measure which atom is most important to the CV model. Parameters From eafb9cbc4d7ec2ea7866085e9ee215028a235d16 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 22 Jan 2025 10:43:00 +0100 Subject: [PATCH 233/267] Explicity added remove isolated nodes function to fix pyg bug --- mlcolvar/data/graph/utils.py | 68 +++++++++++++++++++++++++++++++++++- 1 file changed, 67 insertions(+), 1 deletion(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 44e89f43..49d95f43 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -1,5 +1,11 @@ +import copy +from collections import defaultdict +from typing import Union + import torch import torch_geometric +from torch_geometric.data import Data, HeteroData +from torch_geometric.transforms import BaseTransform from mlcolvar.data import DictDataset, DictModule from mlcolvar.data.graph import atomic @@ -179,7 +185,7 @@ def create_dataset_from_configurations( # pyg regarding the cell vectors as some kind of node features. # So here we first remove the isolated nodes, then set the cell back. cell_list = [d.cell.clone() for d in data_list] - transform = torch_geometric.transforms.remove_isolated_nodes.RemoveIsolatedNodes() + transform = _RemoveIsolatedNodes() data_list = [transform(d) for d in data_list] # check what have been removed and restore cell @@ -240,6 +246,66 @@ def to_one_hot(indices: torch.Tensor, n_classes: int) -> torch.Tensor: return oh.view(*shape) + +class _RemoveIsolatedNodes(BaseTransform): + r"""Removes isolated nodes from the graph + This is taken from pytorch_geometric with a small modification to avoid the bug when n_nodes==n_edges + """ + def forward( + self, + data: Union[Data, HeteroData], + ) -> Union[Data, HeteroData]: + # Gather all nodes that occur in at least one edge (across all types): + n_ids_dict = defaultdict(list) + for edge_store in data.edge_stores: + if 'edge_index' not in edge_store: + continue + + if edge_store._key is None: + src = dst = None + else: + src, _, dst = edge_store._key + + n_ids_dict[src].append(edge_store.edge_index[0]) + n_ids_dict[dst].append(edge_store.edge_index[1]) + + n_id_dict = {k: torch.cat(v).unique() for k, v in n_ids_dict.items()} + + n_map_dict = {} + for node_store in data.node_stores: + if node_store._key not in n_id_dict: + n_id_dict[node_store._key] = torch.empty(0, dtype=torch.long) + + idx = n_id_dict[node_store._key] + assert data.num_nodes is not None + mapping = idx.new_zeros(data.num_nodes) + mapping[idx] = torch.arange(idx.numel(), device=mapping.device) + n_map_dict[node_store._key] = mapping + + for edge_store in data.edge_stores: + if 'edge_index' not in edge_store: + continue + + if edge_store._key is None: + src = dst = None + else: + src, _, dst = edge_store._key + + row = n_map_dict[src][edge_store.edge_index[0]] + col = n_map_dict[dst][edge_store.edge_index[1]] + edge_store.edge_index = torch.stack([row, col], dim=0) + + old_data = copy.copy(data) + for out, node_store in zip(data.node_stores, old_data.node_stores): + for key, value in node_store.items(): + if key == 'num_nodes': + out.num_nodes = n_id_dict[node_store._key].numel() + elif node_store.is_node_attr(key) and key not in ['shifts', 'unit_shifts']: + out[key] = value[n_id_dict[node_store._key]] + + return data + + def create_test_graph_input(output_type: str, n_atoms: int = 3, n_samples: int = 60, From 8ca8eece01719a32b988e154ad8265a38b86376c Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 24 Jan 2025 16:27:41 +0100 Subject: [PATCH 234/267] Fixed multiple files bug in io function --- mlcolvar/utils/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index d209949d..fbc7b698 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -412,7 +412,7 @@ def create_dataset_from_trajectories( mdtraj.load(trajectories[i][j], top=top[i][j]) for j in range(len(trajectories[i])) ] - for t in traj: + for j,t in enumerate(traj): t.top = mdtraj.core.trajectory.load_topology(top[i][j]) if selection is not None: for j in range(len(traj)): From bd9981baa73d51f1af7b2c7e09a5c81ff43aad13 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 7 Feb 2025 13:01:57 +0100 Subject: [PATCH 235/267] Fixed bug on committor weights --- mlcolvar/cvs/committor/utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlcolvar/cvs/committor/utils.py b/mlcolvar/cvs/committor/utils.py index c378a533..99798fe9 100644 --- a/mlcolvar/cvs/committor/utils.py +++ b/mlcolvar/cvs/committor/utils.py @@ -98,15 +98,15 @@ def compute_committor_weights(dataset, # update the weights weights[torch.nonzero(new_labels == i, as_tuple=True)] = coeff * weights[torch.nonzero(new_labels == i, as_tuple=True)] - + print(weights) # update dataset if dataset.metadata['data_type'] == 'descriptors': dataset['weights'] = weights dataset['labels'] = new_labels else: for i in range(len(dataset)): - dataset[i]['weight'] = weights[i] - dataset[i]['graph_labels'] = new_labels[i] + dataset['data_list'][i]['weight'] = weights[i] + dataset['data_list'][i]['graph_labels'] = new_labels[i] return dataset From 1f709b67f79c8e7336c59118d9d20a4398620c4b Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 7 Feb 2025 15:33:18 +0100 Subject: [PATCH 236/267] test notebook update --- test_graphs/test_committor_checkpoint.ipynb | 111 ++++++++++++-------- 1 file changed, 69 insertions(+), 42 deletions(-) diff --git a/test_graphs/test_committor_checkpoint.ipynb b/test_graphs/test_committor_checkpoint.ipynb index cbb9ecf0..2a4006c4 100644 --- a/test_graphs/test_committor_checkpoint.ipynb +++ b/test_graphs/test_committor_checkpoint.ipynb @@ -11,8 +11,10 @@ "text": [ "Beta: 0.4009078751268027 \n", "1/beta: 2.4943386299999997\n", - "\n", - "DictModule(dataset -> DictDataset( \"data_list\": 12000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"atom_names\": [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], \"data_type\": graphs ),\n", + "[ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C]\n", + "tensor([1.0000e+00, 1.0000e+00, 1.0000e+00, ..., 3.4676e-04, 2.0101e-05,\n", + " 3.9714e+00])\n", + "DictModule(dataset -> DictDataset( \"data_list\": 12000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"used_idx\": tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \"used_names\": [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], \"data_type\": graphs ),\n", "\t\t train_loader -> DictLoader(length=1, batch_size=12000, shuffle=False))\n" ] } @@ -68,36 +70,36 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[ACE1-CH3,\n", - " ACE1-C,\n", - " ACE1-O,\n", - " ALA2-N,\n", - " ALA2-CA,\n", - " ALA2-CB,\n", - " ALA2-C,\n", - " ALA2-O,\n", - " NME3-N,\n", - " NME3-C]" + "[Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=2.544331344620953, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=0.5654417280661564, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=2.4702787408611155, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=4.25943931484535, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=3.95554628253752, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=3.878284225996348, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=1.9742215740918263, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=0.0003467624248340245, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=2.0100564569410774e-05, names_idx=[10]),\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=2.0, n_system=[1, 1], n_env=[1, 1], weight=3.971418695848552, names_idx=[10])]" ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "list(dataset.metadata['atom_names'])" + "dataset['data_list'][-10:]" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -117,11 +119,11 @@ " cutoff=dataset.metadata['cutoff'],\n", " atomic_numbers=dataset.metadata['z_table'],\n", " n_bases=16,\n", - " n_layers=3,\n", + " n_layers=2,\n", " n_filters=16,\n", " n_hidden_channels=16,\n", - " w_out_after_sum=False,\n", - " aggr='min'\n", + " w_out_after_sum=True,\n", + " aggr='sum'\n", " )\n", "\n", "# model = Committor(model=gnn_model,\n", @@ -131,22 +133,22 @@ "options = {'optimizer' : {'lr' : 1e-3},\n", " 'lr_scheduler': {\n", " 'scheduler': torch.optim.lr_scheduler.ExponentialLR,\n", - " 'gamma': 0.99995\n", + " 'gamma': 0.99999\n", " }}\n", "torch.manual_seed(42)\n", "\n", "model = Committor(model=gnn_model,\n", " mass=torch.Tensor([12, 14, 16]),\n", - " alpha=10,\n", + " alpha=0.1,\n", " gamma=1000,\n", - " separate_boundary_dataset=False,\n", + " separate_boundary_dataset=True,\n", " z_regularization=True,\n", " options=options)\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -165,7 +167,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1802449e503841e08fc4249a445f60b1", + "model_id": "ec6b050763f948178f4f7f56111634b7", "version_major": 2, "version_minor": 0 }, @@ -180,13 +182,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test_2.5/lib/python3.9/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 2. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n", - "`Trainer.fit` stopped: `max_epochs=2000` reached.\n" + "/home/etrizio@iit.local/Bin/dev/mlcolvar/mlcolvar/core/loss/committor_loss.py:178: UserWarning: Using GNN-based models it may be better to set separate_boundary_dataset to False\n", + " warnings.warn(\"Using GNN-based models it may be better to set separate_boundary_dataset to False\")\n", + "`Trainer.fit` stopped: `max_epochs=500` reached.\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -211,7 +214,7 @@ " logger=False,\n", " enable_checkpointing=True,\n", " accelerator='cuda',\n", - " max_epochs=2000,\n", + " max_epochs=500,\n", " enable_model_summary=False,\n", " limit_val_batches=0, \n", " num_sanity_val_steps=0\n", @@ -229,12 +232,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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4IqmpkwlAqnc6xYAdEwAuDRIAQP8EdzPQusOZVDkcmkx1KbY3fvx4fvzxR15//XUURUFRFObOnYuiKCxdupTk5GTMZjM//fQTBw8e5NprryU6OpqAgAD69OnDypUrvd7vz01AiqLw3nvvMWrUKKxWKx06dODrr78+5/P7xRdf0LVrV8xmM0lJSbzyyitey9966y06dOiAxWIhOjqaG2+80bNs0aJFdO/eHT8/P8LDwxk8eDAVFb7vMSbJJiCpgTgVK1CO6jyuyf77t07k47RtrM/Mov/sdzSJYf2999R6OIrXX3+dffv20a1bN55++mkAfvvtNwCeeOIJXn75Zdq2bUtoaChZWVkMHz6cZ599FrPZzEcffcTIkSPZu3cviYmJp93HjBkzePHFF3nppZeYNWsWY8eO5fDhw4SFhdXpuDZv3szNN9/M9OnTGT16NOvWreP+++8nPDyc8ePH8+uvv/LQQw/x8ccfc8kll1BYWMjatWsB9/heY8aM4cUXX2TUqFGUlZWxdu3aOiVLqf7IBCA1CPfTwHmgUQLoHReHSa8nr4l8swwODsZkMmG1WomJcXej3bNnDwBPP/00V111lWfdsLAwevbs6Xn9zDPPsGTJEr7++msmTJhw2n2MHz+eMWPGAPDcc8/xn//8h40bNzJ06NA6xfrqq69y5ZVXMmXKFAA6duzIrl27eOmllxg/fjyZmZn4+/tz9dVXExgYSOvWrT0DPmZnZ+N0Orn++utp3bo1AN27d6/T/qX6IxOA1CAcughQD2FwadMGbzEY6B0Xxy9ZWTx0SX/G9PD9h4zFUD9/XhdddJHX6/LycqZPn863337r+UCtqqryGjn3VHr06OH52d/fn6CgIPLy8uocz+7du7n22mu95g0YMIDXXnsNl8vFVVddRevWrWnbti1Dhw5l6NChnqannj17cuWVV9K9e3dSUlIYMmQIN954I6GhoXWOQzp/8h6A1CCcBvfTwEa1VLMYLjnRHXTz0aP4GY0+n+panel0/tyb57HHHmPJkiU899xzrF27lrS0NLp3747dbj/j+xj/1BylKAqqqtZLjH8UGBjIli1bmDdvHrGxsUydOpWePXtSXFyMXq9nxYoVLF26lC5dujBr1iw6depEenp6vcchnZ1MAFKDMBiiANDhBNWmSQz9T7SH/3rkKFUOhyYx1IXJZMJVi+Erfv75Z8aPH8+oUaPo3r07MTExZGRkNHyAJ1xwwQX8/PPPNWLq2LEjer0eAIPBwODBg3nxxRfZvn07GRkZ/PDDD4A78QwYMIAZM2awdetWTCYTS5Ys8Vn80u9kE5DUIEz6SFQBOgVQy0Dn+xq97cPDiA8K4mhpKT8dPsxV7dv7PIa6SEpKYsOGDWRkZBAQEHDab+cdOnRg8eLFjBw5EkVRmDJlSoN8kz+dRx99lD59+vDMM88wevRo1q9fzxtvvMFbb70FwDfffMOhQ4e47LLLCA0N5bvvvkNVVTp16sSGDRtITU1lyJAhREVFsWHDBo4fP84FF1zgs/il38krAKlBmI0h2DnRBKJRM5CiKAxu3w6A1AOHNImhLh577DH0ej1dunQhMjLytG36r776KqGhoVxyySWMHDmSlJQUevfu7bM4e/fuzcKFC5k/fz7dunVj6tSpPP3004wfPx6AkJAQFi9ezF/+8hcuuOACZs+ezbx58+jatStBQUGsWbOG4cOH07FjR5566ileeeUVhg0b5rP4pd8pooX1vyotLSU4OJiSkhKCgoK0DqfZyq/ahzHnXoJ1Loh8DqwDNIljR04Ot33+BVajkVV3/Q1zPd2YlaTG5Fw/1+QVgNQg/AyhFKnum44u+0HN4ugaHU10QACVDgfrM5vPU8GSVB9kApAahNUQgQ33GDwOxxHN4tApCn9p1xaAlQe1S0SN2b333ktAQMApp3vvvVfr8KQGJK+HpQahKApCFwgUozoLNY1lcLt2zNu2nR/TM3C4XBhP9FSR3J5++mkee+yxUy6TzaTNm0wAUoMRuhP91zUoDflHvWJjCLf6UVBZxaYjR7mk9emHS2iJoqKiiIqK0joMSQOyCUhqMO4rAFDUMk3j0Ot0/KWtbAaSpD+TCUBqMKrOXRzepBaCxp3Nrmzn7g666uAhnD7sMy9JjZlMAFKD0ZnaoArQYwNV2/sAyfFxBFvMFFVXs/XYMU1jkaTGQiYAqcEEW9r9XhrSpW0CMOr1XHGyGagJPBQmSb4gE4DUYKzGcKrFyQTg+9rAf3blie6gPxw6iNqynn+UpFOSCUBqMH6GEE9pSNWZr3E00C8hgQCTieMVlWzL1qZSma/IovBSbcgEIDUYkz7IcwXgcmRrHA2Y9Houa5MEQGoj7A00aNAgHnnkkXp5r02bNnHPPffUy3tJzZdMAFKD0Sk6qnA/C6Da92ocjdvgE72BUg8eanJlCGVR+Pp3thoKzZ1MAFKDcujdDxgJpzZXAEIIqp0Oz9Q7PharSU9OeSlp2dley+p7kkXh668o/PLly7FYLDWaoR5++GH+8pe/AFBQUMCYMWOIj4/HarXSvXt35s2b57X+oEGDmDBhAo888ggRERGkpKSc8zloDuSTwFKD0hliwbUbnVqsyf5tLidjls7ymhcd5/736c3zG3Tf84Y9iMUgi8LXR1H4K6+8kpCQEL744gvuvPNOAFwuFwsWLODZZ58FoLq6muTkZB5//HGCgoL49ttvue2222jXrh19+/b1vNeHH37IfffdV6OoTUskE4DUoPSGWHCBQZS7HwarpzKJzY0sCn9mer2eW265hc8++8yTAFJTUykuLuaGG24AID4+3mtMowcffJDvv/+ehQsXeiWADh068OKLL9bpmJsrmQCkBmU0JYANdKjuMYH0IT7dv1lvYN6wB73mVTudXP3Rx1TY7bwxciTJ8XENtu/6IIvCu40dO5aLL76YY8eOERcXx6effsqIESMICQkB3FcEzz33HAsXLuTo0aPY7XZsNluNeyHJycl1PubmSt4DkBqU1RhNtTjxrd913Of7VxQFi8HoNYVY/LiqXQeEUPh+/4Eay+trkkXh67cofJ8+fWjXrh3z58+nqqqKJUuWMHbsWM/yl156iddff53HH3+cVatWkZaWRkpKSo3z8ufz2ZJpngDefPNNkpKSsFgs9OvXj40bN55x/ddee41OnTrh5+dHQkICf//736murvZRtFJd+RnDqBInhl9uBM8CnHTNBZ0AWHngYKMpGC+Lwp/d2LFj+fTTT/nf//6HTqdjxIgRXjFce+21/PWvf6Vnz560bduWffv21dNRN0+aJoAFCxYwceJEpk2bxpYtW+jZsycpKSmnvSz97LPPeOKJJ5g2bRq7d+9mzpw5LFiwgH/+858+jlyqLashnEr15MNgjWcMnl6xsSQEB1HpcJB6sHEMDfHHovD5+flnLQqflpbGtm3buPXWW31eFD41NZVnnnmGffv28eGHH/LGG2942t+/+eYb/vOf/5CWlsbhw4f56KOPvIrCP/fcc/z6669kZmayePHiOhWFHzt2LFu2bOHZZ5/lxhtvxGw2e5Z16NCBFStWsG7dOnbv3s3//d//kZub2yDnoLnQNAG8+uqr3H333dxxxx106dKF2bNnY7Vaef/990+5/rp16xgwYAC33norSUlJDBkyhDFjxpz1qkHSjkkfQCUmAJz2DG2D+QNFUbi6c2cA/rd7j8bRuMmi8GfXvn17+vbty/bt272afwCeeuopevfuTUpKCoMGDSImJobrrruuno++edGsKLzdbsdqtbJo0SKv/6Rx48ZRXFzMV199VWObzz77jPvvv5/ly5fTt29fDh06xIgRI7jttttOexVgs9mw2Wye16WlpSQkJMii8D7026ExdNUfo9rUG0vsv7UOx+NoaSkjPvwYBVg6/nZiAgO1DkmSzkmTKwqfn5+Py+UiOjraa350dDQ5Oacep+XWW2/l6aef5tJLL8VoNNKuXTsGDRp0xiagmTNnEhwc7JkSEhLq9Tiks3PpwwFQXI1r/J34oCCS4+MQwDd7GseTypLkS5rfBK6L1atX89xzz/HWW2+xZcsWFi9ezLfffsszzzxz2m0mT55MSUmJZ8rKyvJhxBKAMMQCYHAVaBxJTSNPNgPt2dvkhoaoL42lKPzpYggICPA8SCbVL82eA4iIiECv19e4SZObm+t5EObPpkyZwm233cZdd90FuB8gqaio4J577uHJJ59Ep6uZz8xms9eNIsn39IZEcJ4sDFMOugCtQ/K4qn07nv9xDYeLi9mek0vP2FP/7jVnjaUofFpa2mmXxcfH+yyOlkSzBGAymUhOTiY1NdVzD0BVVVJTU0/7NGNlZWWND/mT3c5a6re3psBiiqG6SsGiCHBmg6mD1iF5+JtMDG7Xjm/27uXznTtbZAJoLEXh27dvr3UILY6mTUATJ07k3Xff5cMPP2T37t3cd999VFRUcMcddwBw++23M3nyZM/6I0eO5O2332b+/Pmkp6ezYsUKpkyZwsiRIz2JQGp8rMZwKtSTzwJoPyz0n43u0Q2A7/ftp/QPHQYkqbnTdCiI0aNHc/z4caZOnUpOTg69evVi2bJlnhvDmZmZXt/4n3rqKRRF4amnnuLo0aNERkYycuRIz2BQUuMUYIymSOgJx4lwHKOxjQbUPSaGhOBgskpK2Hz0qKd0pCQ1d5p1A9XKuXaXks6dS7Wz99B1dDFW4PQfjiHica1DquHFNWv5bNt2LklM5K1rR2odjiTVSZPrBiq1HHqdCbvi/qVUHY2zF9atPXugUxTWZWayL7/xDFkhSQ1JJgDJJ4Q+0v2Ds3E9C3BSq+BgT9H4j7akaRuMJPmITACSbxjd3fgMaqG7LkAjNK63e8z6Zfv3k1NWpnE056c5FoUfP358ox7aoT5rOvuKTACSTxiMrVEF6HCBq1DrcE6pW3Q0yfFxOFWVz7Zt9/n+ZVH4lmfevHno9XoeeOABTfYvE4DkEwGmWKrEiV+3RtgV9KTxJ64Cvtj5G2WNrEuoLArf/MyZM4d//OMfzJs3T5Nh7WUCkHzC3xRNhWi8zwKcdGnr1rQLC6PC4WDRzt98tl9ZFP7MReH/aMaMGURGRhIUFMS9997rVfDFZrPx0EMPERUVhcVi4dJLL2XTpk2e5XPnzvVUEDvpyy+/9CreM336dHr16sXHH39MUlISwcHB3HLLLZT9oVmwoqKC22+/nYCAAGJjY2scf22kp6ezbt06nnjiCTp27MjixYvr/B7nSyYAyScCjNGUn0gAqvOoz/YrhMCpVtd6cgkb43p3xahz8VnaJkqrS+u0/R+nuvSwfv311+nfvz9333032dnZZGdnewYufOKJJ3j++efZvXs3PXr0oLy8nOHDh5OamsrWrVsZOnQoI0eOPGtJyBkzZnDzzTezfft2hg8fztixYyksrHtz3Mmi8Lfccgs7duxg+vTpTJkyhblz5wJ4isI//fTT7N27l2XLlnHZZZcBvxeF/9vf/sbu3btZvXo1119/fa3PVWpqqme7efPmsXjxYmbMmOFZ/o9//IMvvviCDz/8kC1bttC+fXtSUlLqfJwHDx7kyy+/5JtvvuGbb77hxx9/5Pnnn/csnzRpEj/++CNfffUVy5cvZ/Xq1WzZsqVO+/jggw8YMWIEwcHB/PWvf2XOnDl12r4+yJrAkk/4GUKoFCagGpc9w2ffPFzCxqK9t9d5u0f/4v73u/TUc973jZ0+wqBYarWuLApfOyaTiffffx+r1UrXrl15+umnmTRpEs888wxVVVW8/fbbzJ0711Nf4N1332XFihXMmTOHSZMm1Xo/qqoyd+5cAk8MEX7bbbeRmprKs88+S3l5OXPmzOGTTz7hyiuvBODDDz+kVatWdX7/WbNmAXDLLbfw6KOPkp6eTps2bWr9PudLXgFIPqEoOpw6d+Fv1XlE42iallMVhX/ssce44IILCAkJISAggN27d/u0KPyAAQO85g0YMID9+/fXKAp/22238emnn1JZWQngVRT+pptu4t1336WoqKjW++7Zs6fXvY3+/ftTXl5OVlYWBw8exOFweMVmNBrp27cvu3fvrtMxJiUleT78AWJjYz3n6uDBg9jtdvr16+dZHhYWRqdOnWr9/itWrKCiooLhw4cD7sExr7rqqtMWw2oo8gpA8hmhjwEOo3PW/UPnXOkVMzd2+qjO2zlVlVsXfE5WcQn/1/cixifXveKWXqmfUWhPVRR+xYoVvPzyy7Rv3x4/Pz9uvPHGRlcUfvXq1SxfvpypU6cyffp0Nm3aREhIiKds4/Lly5k1axZPPvkkGzZs8Mk3X51OV6O5yXGKmtANfa7mzJlDYWEhfn5+nnmqqrJ9+3ZmzJhxypGNG4K8ApB8RmdKBMAgSkHUrjfL+VIUBYPOUufJYrBy10X9cah6Pty6i0pH3d/njzcWa0MWhT+7bdu2UVVV5Xn9yy+/EBAQQEJCAu3atcNkMnnF5nA42LRpE126dAHcvaPKysq8bjqfaRjqU2nXrh1Go5ENGzZ45hUVFdW6AH1BQQFfffUV8+fPJy0tzTNt3bqVoqIili9fXqd4zoe8ApB8xs/UBqcNDIoAZ67n4bDGKqVDe9779VcOFRbx8dY0Hri439k3Og9/LAofEBBw1qLwI0eORFEUpkyZ4vOi8H369OGZZ55h9OjRrF+/njfeeIO33noLcBeFP3ToEJdddhmhoaF89913XkXhU1NTGTJkCFFRUWzYsKFOReHtdjt33nknTz31FBkZGUybNo0JEyag0+nw9/fnvvvuY9KkSYSFhZGYmMiLL75IZWUld955JwD9+vXDarXyz3/+k4ceeogNGzZ4bl7XVkBAAHfeeSeTJk0iPDycqKio09YjOZWPP/6Y8PBwbr755hpfEoYPH86cOXPqfF/mXMkrAMlnAsxxTaIr6El6nY4JF18MwCdpaRSeaMduKLIo/NldeeWVdOjQgcsuu4zRo0dzzTXXMH36dM/y559/nhtuuIHbbruN3r17c+DAAb7//ntCQ933n8LCwvjkk0/47rvv6N69O/PmzfPavrZeeuklBg4cyMiRIxk8eDCXXnopycnJtdr2/fffZ9SoUae8Qrzhhhv4+uuvyffReFRyNFDJZ8rsOZQduY04vR0R9ihK4DVah3RWQgjGLlzErrw8xvTozuOXX6Z1SJJUgxwNVGr0/I2RVKjuVkeH/ZDG0dSOoig8fIn7KuDznb9xpKRE44gkqf7IBCD5jE7R49CFAKDaz9xlsTHpl5DAxQkJOFWVtzZs1DqceieLwtevtWvXnvFYGhPZBCT51G+HH6YraVTr4rAkzNM6nFrblZfHrQs+RwHm3XIznSMjtQ6p3uTl5VFaWnrKZUFBQT6rF3zgwIHTLouPj/fqMtmYVVVVcfTo6Z92b4jax+f6uSZ7AUk+pTO2BkcaBjXfPSx0HbtKaqVLVBQpHdrz/f4D/Pvndcy+9po6d/NsrGRR+Prl5+fXZI5FNgFJPmUwd8IlwIAdnMe0DqdOHux/MUadjg1ZR3jv181ahyNJ500mAMmnAszxnkHhmloCaBUczC093OPWvPnLBrKbeNEYSZIJQPIpf2OU51kA0UjLQ57J3X37eH5+7ed1GkYiSedPJgDJp/yNEZ6uoE57usbR1F2Q2cyCW0ajUxS+33+ATUfkwHZS0yUTgORTep0Juy4MAJdtl8bRnJtOkRHc1K0bADNXr8FRi/F7JKkxkglA8jmXqSMABkeGtoGchwcu7kuonx+HitzjBDU2sii878mi8JJUCyaze9x0A1WgNuz4Og0lyGLh7wMuAWD2xk2kF9Z+TPvTkUXhW45BgwZ5Sn8qikJ0dDQ33XQThw8f9mkcMgFIPudvTsImTvSh92F5yPo2snMnBrROxO5yMS01FVcDj8gpi8I3LyfLfx47doyvvvqKrKws/vrXv/o0BpkAJJ8LNMVRduJGsHA03QSgKApTrhiEv9HI9pxcPk3bds7vJYvCt7yi8CfLf8bGxnLxxRczYcKEOtcVPl8yAUg+F2CKoexEV1BX9c6G3ZkQoFY12BTjb2DSpRdh0TuZs+lnMgtzfl8ui8LLovC1VFhYyMKFC73KTPqEaGFKSkoEIEpKSrQOpUVLO3iLEBmXiepj9zfsjlyVQmRcps3kqqxTqJdffrl4+OGHPa9XrVolAPHll1+edduuXbuKWbNmeV63bt1a/Pvf//a8BsRTTz3leV1eXi4AsXTp0rO+98k4ioqKhBBC3HrrreKqq67yWmfSpEmiS5cuQgghvvjiCxEUFCRKS0trvNfmzZsFIDIyMs663z8bN26cCAsLExUVFZ55b7/9tggICBAul0uUl5cLo9EoPv30U89yu90u4uLixIsvviiEEOKDDz4QwcHBXu+7ZMkS8cePwmnTpgmr1eoV/6RJk0S/fv2EEEKUlZUJk8kkFi5c6FleUFAg/Pz8vP7/zuTyyy8XRqNR+Pv7C6vVKgDRsWNHkZ6eXtvT4eVcP9fkFYCkCVXvrgamc2RpHEnjJ4vC/665FIUHGDt2LGlpaWzbto2ffvqJ9u3bM2TIEK+mpoYmB4OTNKGY2oJ9E0ZR4u4JpGugG5aKBRKWNcx7/8majMP8Y9n36BSFWVePIFmx1Mv7yqLw9aexFIUHCA4O9gwa1759e+bMmUNsbCwLFizgrrvuqtd9nY68ApA0YTW3oerkBWhDPg+gKKDz88l0WdvODO3UnUqnnseXr6HgD8XLa0MWhT+75lAU/nROnruqOv7enA95BSBpIticSKlqwE9vdycAcxetQ6oXj182kB05uRwsLOSfy1fw1jUj0deyWLgsCn92zaEo/EmVlZXk5LjHw8rNzeWZZ57BYrEwZMiQOr3P+ZBXAJImQi1tKFFP9ASy7dc4mvrjZzTy4rAULAYDG7KOMHvjprNvdIIsCn92zaEo/EnvvvsusbGxxMbGcsUVV5Cfn893331X53sJ50NWBJM0k3ZwNL0MOdiMXTDHva11OPXq2717eXK5u2/+6O7dmTxIFpOXGk6TLQr/5ptvkpSUhMVioV+/fmzceOaaq8XFxTzwwAPExsZiNpvp2LEj3333nY+ileqTztQOAMXZdOoD19aITp0Yc6J2wIIdO1h6nu3DktQQNE0ACxYsYOLEiUybNo0tW7bQs2dPUlJSTts1zW63c9VVV5GRkcGiRYvYu3cv7777LvHx8T6OXKoPZr9eAJhEOai1fxK0qZh46QCiTvTgmZG6in35+RpHdGqyKHz9kkXha6lfv3706dOHN954AwBVVUlISODBBx/kiSeeqLH+7Nmzeemll9izZ0+Nblq1JZuAGo/s8jRCjv8dP50K0bPA0uPsGzUxLlVlwv++YX1mFq2Cgvh09E0EW+qne2h9kUXh61dTKgqvWQKw2+1YrVYWLVrkNcTruHHjKC4u5quvvqqxzfDhwwkLC8NqtfLVV18RGRnJrbfeyuOPP+7pQvVnNpsNm83meV1aWkpCQoJMAI1ApaOQwsxbaGWwoQbfgy5krNYhNYjiqmpuXbiQY6VlXJKYyKyRI2rdM0iSaqPJ3QPIz8/H5XIRHR3tNT86OtrTNerPDh06xKJFi3C5XHz33XdMmTKFV155hX/961+n3c/MmTMJDg72TCfHV5G052cIpRz3tzqHvfn0BPqzED8Lrw4fhsVgYF1mJm9tOPN9LknylSb1NURVVaKionjnnXdITk5m9OjRPPnkk8yePfu020yePJmSkhLPlJUlhx5oLBRFQTW4vwCo9gxtg2lgnSMjmfqXKwCY8+tmfjh4SOOIJEnDBBAREYFeryc3N9drfm5uLjExMafcJjY21utpQ3A/lZiTk3Pax+DNZjNBQUFek9R4KEZ3TyCzKxNE8y6tOLxTR8b26gnAUytWcugcRuKUpPqkWQIwmUwkJyeTmprqmaeqKqmpqfTv3/+U2wwYMIADBw54PfW4b98+YmNjMZlMDR6zVP+Mll44BehwgTNb63Aa3COX9Cc5Po5Kh4OJ3y6l/Czj90hSQ9K0CWjixIm8++67fPjhh+zevZv77ruPiooK7rjjDgBuv/12Jk+e7Fn/vvvuo7CwkIcffph9+/bx7bff8txzz/HAAw9odQjSeQqxJFEqToxI4mj+zSJGvZ6XhqYQHRBARnExTy1fidqynsWUGhFNE8Do0aN5+eWXmTp1Kr169SItLY1ly5Z5bgxnZmaSnf37t8KEhAS+//57Nm3aRI8ePXjooYd4+OGHT9llVGoags0JFJ+oDuas3qFxNL4RZrXy8rChGHU6VqenM+fXzVqHJLVQcigISXPbDo6hp+EYNkNHzPHvah2Ozyz5bRczfliFAswaeTWXJrXWOiSpiWpy3UAl6STF3BkAneuIxpH41qiuXbixW1cE8NA335KW3fzvgUiNi0wAkubMlm4IAUZRCc7jWofjU/+4bCA9YmJQhWDC19+wP79A65CkFkQmAElzwX4dKDp5I9j2m7bB+JhJr+f1q4fTMTyccrud+7/+mqOnGZZBkuqbTACS5sL9OlKkusd2clbv1Dga3wv18+O966+jXVgYxysqmfz9ihplCyWpIcgEIGnOqLNQoYQA4LLt0jYYjQRZLPxn5AgMOh3bc3J49aefZRKQGtw5JYCsrCyOHPn9ht3GjRt55JFHeOedd+otMKllcRmSANA706GFfvDFBwXxz0GXA/Bx2jbe/GXDWbaQpPNzTgng1ltvZdWqVQDk5ORw1VVXsXHjRp588kmefvrpeg1QahnMfr1xCTCIyhbxRPDpXN+1C5Mvd1cPe+/XzXy9e4/GEUnN2TklgJ07d9K3b18AFi5cSLdu3Vi3bh2ffvppnQssSxJAmPUCzwNh2FtmM9BJo3t056ZuXQGYnvoD3+7Zq3FEUnN1TgnA4XBgNpsBWLlyJddccw0AnTt39npyV5JqK8zShgLPjeDtGkejvcmDLueGrl1QheCpFSv5RiYBqQGcUwLo2rUrs2fPZu3ataxYsYKhQ4cCcOzYMcLDw+s1QKllMOkDqFAiAVCrt2kcjfZ0isKTVwzyPCg2ZcVKvtjZsrrISg3vnBLACy+8wH//+18GDRrEmDFj6NnTPcTt119/7WkakqS6EieeCDY4M0HIUTJ1isI/B13OTd27IYBnVq3mvU2/yt5BUr0xnMtGgwYNIj8/n9LSUkJDQz3z77nnHqxWa70FJ7UsgX69sZWtwKyoYD8I5gu0DklzOkXhn5dfRpDZzJxfN/PGLxsorKrisYGXolMUrcOTmrhzugKoqqrCZrN5PvwPHz7Ma6+9xt69e31WQFpqfsKtnTz3AUQLeyL4TBRF4cH+FzNp4KUAfLZtO08uX4HD1bwL6EgN75wSwLXXXstHH30EQHFxMf369eOVV17huuuu4+23367XAKWWI8SSSKFqAcBRJYdI/rOxvXry7JDBGHQ6lu7bz2NLl8nmIOm8nFMC2LJlCwMHDgRg0aJFREdHc/jwYT766CP+85//1GuAUsuhUwy4jO3dL1roE8FnM6JTJ16/ejgGnY4f0zOYnvoDdnklIJ2jc0oAlZWVBAYGArB8+XKuv/56dDodF198MYcPH67XAKWWRW/pBYBJFIMz94zrtlQDWrdm8uWXoVMUvtq9h8veeY+dufJcSXV3Tgmgffv2fPnll2RlZfH9998zZMgQAPLy8mSRFem8hFq7Uuhy901QK37UOJrG64ZuXXlj5NUEmExUO52MX7SYz7Ztk01CUp2cUwKYOnUqjz32GElJSfTt29dTxH358uVceOGF9Rqg1LJEWbuSrbofMnTIZqAzuqR1Ip+Nvol2YWE4VZUX1/zEpKXfU2azaR2a1EScc0nInJwcsrOz6dmzJzqdO49s3LiRoKAgOnfuXK9B1idZErLx23X4XrqwG5suGnOrBSC7O56REIJ527fz6k/rcKoqrYKCeHFYCl1kj7wWw+clIWNiYrjwwgs5duyYZ2TQvn37NuoPf6lpEJZkXALMai44j2odTqOnKAq39uzJBzdcT2xgIEdKS7n98y9YsH2HbBKSzuicEoCqqjz99NMEBwfTunVrWrduTUhICM888wyqqtZ3jFILExHQn8ITzwNgk+MC1Vb3mGgW3HIzV7Zri1NVmfnjGh74+htyysq0Dk1qpM4pATz55JO88cYbPP/882zdupWtW7fy3HPPMWvWLKZMmVLfMUotTKilLcdPJABX1a8aR9O0BFksvDxsKBMHXIJJr2ddZiY3fjafr3fvkVcDUg3ndA8gLi6O2bNne0YBPemrr77i/vvv5+jRxnvZLu8BNA0bD91JX/0BHLpQjAlfah1Ok5RRVMTUlalsz3F3ER2QmMhjAy+lTVjoWbaUmhqf3gMoLCw8ZVt/586dKSwsPJe3lCQvZuslqAKMahE4c7QOp0lKCg3l/Ruu56H+F2PQ6fg5M5Ob5s3n5bU/yZ5CEnCOCaBnz5688cYbNea/8cYb9OjR47yDkqQI/16/3weoTtM0lqbMoNPxt4uS+WLsGC5vk4RTVfkkbRujPvmMpfv2yWahFu6cmoB+/PFHRowYQWJioucZgPXr15OVlcV3333nGSaiMZJNQE2D3VXBgfSb6GKswOl3JYaoqVqH1Cysy8zkhR/Xcri4GIC+reKZfPnlslmoifNpE9Dll1/Ovn37GDVqFMXFxRQXF3P99dfz22+/8fHHH5/LW0qSF5Penyp9AgDCtrnFFoqvb5ckJvL5rbfwwMX9MOv1bDxylJvmzec/69ZT5XBoHZ7kY+f8INipbNu2jd69e+NqxINTySuApmNH7lwuqPoAgwLEvAPmTlqH1KwcKSnh+R/X8tOJ8btiAgJ48orLGZiUpG1gUp35/EEwSWpoMYEXk+NyDwuhVm3QOJrmp1VwMLNGjuDfI4YRGxhITnk5D/7vW574fjkFlZVahyf5gEwAUqMV7teB48I96qxLDgzXIBRF4Yq2bVk8dgy39eqJTlFYtm8/13/6Gd/u2StvEjdzMgFIjZZO0aNakgEwOA+CWq5xRM2Xn9HIowMv5ZObb6RzZAQl1TaeXLGS/6z7RevQpAZUp5rA119//RmXF5/oWSBJ9SUsYAAlRSsJ1rmgegtYL9M6pGatS1QUH990I9NSf+C7vfv4YMsWKhx2Hri4H8EWi9bhSfWsTgkgODj4rMtvv/328wpIkv4o3K8D2flmgnWVOCvWYpAJoMEZ9Xr+OehyqhwOVh1KZ+GOnSzff4AJ/S9mVJcL0Otkw0FzUa+9gJoC2QuoaRFCsCXjXpJ1e3AogRgT/ieHh/ahTUeO8PyPazl44gn/LlGRPH7ZZfSMjdE4MumPZC8gqVlSFIXAoGE4BRhFGdj3aR1Si9KnVSvm33IzkwZeSoDJxK6844xb9AXPrf6Rcrtd6/Ck89QoEsCbb75JUlISFouFfv36sXHjxlptN3/+fBRF4brrrmvYACVNxQVe4ukO6ixfqnE0LY9Rr2dsr558ddtYRp4YA2zhjp3c8Ok8fsnM0jg66XxongAWLFjAxIkTmTZtGlu2bKFnz56kpKSQl5d3xu0yMjJ47LHHGvWwE1L9CDBFka9LBECtXKtxNC1XuNXKM1ddyX+vu4ZWQUHklpdz71df02vWm3yweYt8krgJ0jwBvPrqq9x9993ccccddOnShdmzZ2O1Wnn//fdPu43L5WLs2LHMmDGDtm3b+jBaSSvmgL+gCjCp+eA4onU4LVq/hAQ+v/UWbuzW1TPv9XXr6T/7He5e8iU7cuTorU2FpgnAbrezefNmBg8e7Jmn0+kYPHgw69evP+12Tz/9NFFRUdx5551n3YfNZqO0tNRrkpqeuKCBvxeJqZBXAVrzMxp56opBLBwzmuT4OM/8TUeOctvnX/DYd8vIKCrSLkCpVurUDbS+5efn43K5iI6O9pofHR3Nnj17TrnNTz/9xJw5c0hLS6vVPmbOnMmMGTPON1RJY0GmVhwlkmiO4ahYgT5kjNYhSUDHiAjmXD8Kh8vFqkPp/Jieznd797Hy4EFWHjxIcnwcTw++knjZ465R0rwJqC7Kysq47bbbePfdd4mIiKjVNpMnT6akpMQzZWXJm1ZNkaIoYL0UALPzILgKNI5I+iOjXs+QDu15dshVfH7rLVzeJgmAzUePcf0nnzEj9QdPV1Kp8dD0CiAiIgK9Xk9ubq7X/NzcXGJiavYzPnjwIBkZGYwcOdIz72QReoPBwN69e2nXrp3XNmazGbPZ3ADRS74WHZxCQfZiwvVOXOXL0QfLq4DGqH14OK9fPYIdOTk8t3oNu48fZ8mu3Xy5azdXtW/PXX2S6VjLL3BSw9L0CsBkMpGcnExqaqpnnqqqpKamegrN/FHnzp3ZsWMHaWlpnumaa67hiiuuIC0tjYSEBF+GL/lYmKUdx4gCwFn2P42jkc6me0wMn42+iTnXj+LKdm0RwPIDB7h53gL+78uv+PnwYdSW9Rxqo6PpFQDAxIkTGTduHBdddBF9+/bltddeo6KigjvuuAOA22+/nfj4eGbOnInFYqFbt25e24eEhADUmC81P4qioPO/CrXqQ8yuo+DIAqNM+o2Zoigkx8eRHB/Hztxc3v91C6vT09mQdYQNWe7eXD1iormxWzeGdGiPxaD5R1KLovnZHj16NMePH2fq1Knk5OTQq1cvli1b5rkxnJmZiU6OPSKd0CpkCDkV84jT23GWfYMh7D6tQ5JqqVt0NK+OGMbR0lLmbdvOkt92UeFwsD0nl+05uby09ieuuaAzN3TtQtuwMK3DbRHkWEBSk7M9/U566A7gUEIwJnwpxwZqospsNrYey2Z/QQFf/PYbx0rLPMsGJrVmyhVXEBXgr2GETce5fq7JBCA1OXvzv6Bt+SyMioDo18ByodYhSefJpaqsz8xi0c7fWJ2e7pk/5YpBDO/UET+jUbvgmgCZAGpJJoCmr8pRxLHDt9LOWIndMgBT9HNnXL/cXk1+dRkO1YXrRK8xRQEFhSCTHya9AYveiJ/B5O5uerb9O+0UVJW770kooFN0KCjoFAU/gxGrwex5nwqHjfyqUgKMFgJMFsx6+UF2NvO3bef5Nb8/7Bfq58ffB1zCyM6davX/0xLJBFBLMgE0D9uynqSn+hMu9Oy1vs2xKgfFtgoAVCE4Ul5AXmUp+VVlFFaXU9tf8sGJ3Si2VeJwOal02qlw2HAJFQXQ6/Q4VRe5lSVnfA+9okOv6NApCtUu7/FxTDo9gSY/AowWAk0WT2IINPkRaLQQavanV1QSIWbrOZyV5iMtO5tP0rbxW24u2WXuSnDdoqO4pUcPBrdvJ28W/4lMALUkE0DjJ4SgoLqcA8U57CvK5nhVGcFmP3IqSnCoLgqry9ErmcxovZpgnYt3jnZnacGZx4QKMvlh1hvR/eEbpCpUimyVqKqKWusU8TurwYwqVAQCIQSqEDiFesp1dYpSpy6PrQLCsLucXNW6OxGWQKKsQUT6BRFmCWhRBVmqHA7e2fgrH6el4Txx9eZvNJLSsQNje/WknbxZDMgEUGsyATROuZUlpGbu5FBJHgeKcymxV55lC8HjHdZwsV8xx+yB/Cd3LPEBYQghcKguEgMjiA8IJcIvkBhrCEFmv9O/kxDsKMhi2/HDKLgTRIRfIGGWAPyNZvSKDoHAeaIJqcrl4IKwOELMNW9Q2lwOyuzVqELFJVRsLifR1mAseiNVTjtljmrK7dWU2avcPzuqKbO75+VWlrAx9+AZj1qv6IgPCCXWP4SEwAgGtboAp6oS6ReI1dh8H3g8XlHBl7t288XO38gp/7029OVtkri5ezf6JSRgaEGJ8c9kAqglmQC0J4QgozSfDTn7WbDvF0LN/hSdaL45SacotA6MwKg3EGKyUmSroEt4PImBEURYAgm1+FNSsZQLqt9FrwAx74C5kzYHVI8qHTaOlBeytyiboupyim2VHK8q43hVKQVVZae9wgAIMVuJ9Xcnhzj/UOL8Q4gNCCXWGoLZ0DzuPThcLn49eozPd+xk1aFDnuu2CKuVYR07MKJzJzpFRLS4ewUyAdSSTAC+JYSgzFHNgeIcCqrK+fnYPjLL8mt84AO0DYrikriOdA1vRZvgyLPeMK12lpJ7+GZaG6qotlyGJfqZhjqMRsElVAqryjlcls936VspslWQXVGMzeU867bhlgDC/QLx0xtJDIwgxj+EhMAw2gRFEWBqmsXeDxUWsnDHTpbt209xdbVn/t+Se/PQJTVHEmjOZAKoJZkAGtaB4hyWZmwj7XgGhdUV6BUdrlN8azXpDHSLaIVe0dM7KokBcR0JNJ2+meZ0dh6ZQjfXGlzo0bdaAvrg+jiMJkMI4Wlqyqko5lhFEccqisguLyb7xM/lDtsZ3yPU7E9CYDiJgeG0C46mXUg0cQGh6JWm0aTicLn4+XAms9b/4hlwbmiHDgzv1JH+iQkY9XqNI2x4MgHUkkwA9U8Iwaoju1hxeAd7io6dcd1IvyCubnMhKa171EuzRF7FLox5DxKqc+IMGo8h9I7zfs/mptReRXZFETkVJVQ6bRwtLyK3soTM0nzyqk5dHyPAaKZ7RCK3dhpAq8CmcaO1wm7nji8Wsy/fe6TY5Lg4RnTuxJAO7QkwmTSKrmHJBFBLMgHUHyEEPx7ZzetpyzzzdCj0jGxNpDUIk05Pr8gk2gZHEWrxx6m6MOjq99uYEILt6ePpqc/AqQRgSPgKFNlFsLZO3nPIKisgo/Q4B0vyOFSS69Ws1DOiNd0jEugYGkP7kBj8DI33Q1QIwc7cPL7bt4/v9+2nsKrKs8ys19MlKoor2rbhsjZJJIWGahhp/ZIJoJZkAqgfWWUFzN6+kl2FRz3zRrXvw4ikXoT7Bfo0lv0F/6NV2av4KSpq+JPoAob4dP/NjUtVSc3aydvbV9ZYpkOhdVAEXcJbcUlsBzqFxTXapiKnqvJLZhabjh7lx0PpZBQX11hnYFJrLm+TRP/ExCZdtEYmgFqSCeD8FFaXM3/ven48sgu76kKv6IjwC2Rqv+uJC9DmG5VTrWZf+hi6GAqx6RMwx38sxweqB5UOG9kVxewqPMruwqPsL8ohv7rMax2z3oDN5STQaKHMUU2XsHi6hreiW0QC0dZggkx+jeKKQQjBxiNHWHHgIIeLi9l6LNvzXMFJveNiubpzZ1I6tMe/iTUVyQRQSzIBnLv0kuM8veELim3uPvqdQ+N4NHkEET7+xn8qv+XOoXPVR+gVENGzUCw9tA6pWSqoKmNvUTbrs/ezJS+DSueZbzADXN3mQtoGR9M5LI5Y/5CGD7IWKux2fsnM4mBhIT9nZrI9O8fTpTTUz4+bunWlf2ICHSMimkQykAmglmQCqDv1RFv/uzt/oMppx6w3cE/3KxnUqovXk7VasrsqOJpxM20M5diMnTHH/VfrkJo9u8vJoZI89hVlY9DpKXNUYdIZyCg9zq6CozWuFsD9rEKcfyhtg6OIDwgjr6qUWGsIScGRtAoI0+xqIbe8nO/27mPhjh2eoScATHo9fVrFc0liIkM6tCfSv3GOTioTQC3JBFA3WWUFPLT6Q8/r9iHRTEoeSZS18Z27ncf+TRf7l+gUEDGzUcwXaB1Si1bhqObHI3vYX5xNbmUp+4qyT9kl+CQFiPMPpV1INB1DYxmc0M3nD7BVO52sOnSI5fsPkHYsm6I/PF8AEBsYyBVt23BLj+4knihG1RjIBFBLMgHUjsPl5L3fVrP88HbPvJs69OPmjhfXe0+e+lLhyCc/8zZaGyqpNF2INfY1rUOS/qDKaSej9Dg5FSWkl+SRXprH0fIiQszuJ71PNi3+0eDEbvSISOTimPYY9b7t3SWEYF9+ASsPHuTH9HSv7qU6RWFgUmsGtXH3KAq3ajt4n0wAtSQTwNkVVpfzwq//Y19RNuAef+YfF42kb0w7jSM7u9+OzqSrcxmqUFDi5qKYkrQOSaqlYlsFB4vzmLd3HQdLcr2WWQ0m+sW0p32I+0G1pKCzPyle345XVLAjJ5c5v27mt7w8z3wFd/3jK9q24fI2SbQJDfX5UBQyAdSSTACn51BdvL1tBauO7ALA32jm0d4juDAqSdvA6qDSXkDJkbHE6quoNPXAGjtL65CkOnKpKluOZ5BRcpyC6jJ+Pra3xtPMOkUhMTCCAXEd6RLWirbBUVh81FwkhGB/QQGrD6WzOj2DXX9IBgCJIcEMatOGQW3a0DM2xiejt8oEUEsyAZyazeng1S3feUajTAgMZ3Kfa4j1b3oPy+zLeZMO1QtRFBAx76KYO2odknQenKqLLXkZHCjO4VBJHgdLcms0FylAtDWYNsFRjGrfhw4hMT6LL7e8nDXpGaw+lM7GI0dw/KF7aaifHwOTWvOXtm25rE1Sg3WakAmglmQCqCmvspQXfv2aQyXubzJdwuJ5qt+oRtF/+1zYnGXkZt5Mor6SKn0Sfq0+PPtGUpMhhKCwupytxzPYlHOI/cU5NQYXfLBXCl3C4omyBvu0p1qF3c66zExWH0pnbcZhSm2/X7kkBAfRPzGRfq1acVGreIIt9TcIn0wAtSQTwO8qHDae3fglu088zRtk8mNS8tV0i0jQOLLzty/vA9pVzkWvgCN8BsaAQVqHJDWgYlslv2Tv5787Ur3mm3R6ov1DSAwMp2t4K/wNZtoERxHrH9LgnRkcLhdp2Tn8cPAgi3/bhc3l8ixTgM6RkfRpFc+FcbEEms0cLS2lU0QEbcPCMNVxADuZAGpJJgB3/+2F+37hiwMbPfOSgiKY3OdaoqzNYzRNh6uKrIybaGsow6aEYk5YJMcIagH2FB5jXfY+duZnkVVeiFN1nXZdk05Ph9BY2gVHE+sfQtfwVrQKCGuQG7ilNhu/HjnKxiNH2HjkCIcKi067rl5ReP3qEVya1Lr27y8TQO209ARQaq/imV8Wc+APvSyubZfMbRcMbLRjupyrQ4X/I670FSyKwB50F6bQ27QOSfIhl6qSV1VKdkURB4tz2VGQxYHiXBwu52kL64RbAugf24EBcZ2IDwjF32hpkCak4xUVbDxyhC1Hj7HxyFGyStx1pgNMJsrtdhaPHUPbOpS7lAmgllpyAnCoLmb88gW/FRzBajBxVWJ3rmmXTJglQOvQGoQqVPZk3EkX3SEcGDG2WgJ67YetkLQlhCCvqpQDxblUOKo5UlbI4bJ8dhcexfGnKwarwUSn0FgSgyK4IDSe2IAQWgWE13tSyCkrI9Bsxmo0kldRQYTVWqfeQzIB1FJLTQA2p4N/b/2ODTkH8TOYmDngFloHRWgdVoMrqjqALuf/CNY5qfS7EmvUVK1Dkhopm8vBr7np/HR0j7sk5ymq1gEEGi0MSujCpXGdaBsc1SgejJQJoJZaWgIQQrAuez8L9q4nq7wAg07Pk32upVcT6tt/vn7LmkxXdR2qABE7F725jdYhSU1AldNOdkUxW/MyyCzL50BxLvlVpdj/cJVg1hvcA92FxtIpLI5OoXGEmH3/VLBMALXUkhJAqb2K2dtXsj57PwABRguPJY+gZ2Ttby41B3ZXBYWZNxGjq6BM14rAVp/I4aKlc+JQXaw9uod1x/axryibMkd1jXVi/UPoFBpLh5AYOoXG0SY4qsG7osoEUEstIQFUOKr5cNda1mXvo8JhQ4fC9R36MqLNhZp8O2kMjhX9j6iSlzEoUBb4NwLDxmkdktTEqUJwtLyQ/cU57Ck8xp6iY2SVFdRYL8jkR1JQJP1jO3iGsTDWc7ORTAC11BwTgEuoLD+8nf8d2oIqBLmVJZ5lCQHh/K3bIHq1sG/9p3I46wFaqzuxCz26+HkYjNFahyQ1M+X2avYVZ7On8BgHS3LZVXCUapfDax2dohBtDSbOP5S4gFDCLYHsLMjC4XIS4x9CuCWQy1p1JroOXbJlAqil5pQAhBB8f3g78/eup8RecyTF2y8YyDXtkptd985zZXcWU5V1M8E6G0W6REJbfSSbgqQG5XA5SS89zi85B0gvyWN/cQ4VjrMX0Xn2kpvpEt6q1vs51881+WRME/b1oc3M3bXGa959PQbTISSWpKAIn49I2NiZDCGUhUzAVfIKoWomxQWzCYm4T+uwpGbMqDfQMTSWjqGxwO/DWByrKOJoeRHHyosoqC4nvTSPElsVI9r0orC6nBgfVU6TCaAJ+vnYXl7e/K3ndXJUG/7eezj+RrOGUTUN4aHXcLTiB+JdWwkoX4gtMAWzua3WYUkthKIohPsFEu4XSPeIRK3DQbYNNDE787O8Pvyva3cRT/a9Tn7410F07EyKhBWDomLLeQRVdZx9I0lqhmQCaEIqHDZe2fL7h//MAbcwrstlsqmnjgx6P4iYhl0oBFHC8WOPaB2SJGlCJoAmotxezfRfFlFsqyTU7M9nwybQOSxO67CarNCAiymyjgIg2rWTgoKPNI5IknxPJoAmYvaOlRwoziXQaGnSY/U3JtGRD3Fc5y4WE1D2AWWVOzWOSJJ8q1EkgDfffJOkpCQsFgv9+vVj48aNp1333XffZeDAgYSGhhIaGsrgwYPPuH5zsCUvnZ+P7QPgqX6jaBscpXFEzYSiEB7/OqUiALOiouY9hsN+ROuoJMlnNE8ACxYsYOLEiUybNo0tW7bQs2dPUlJSyPtTnc2TVq9ezZgxY1i1ahXr168nISGBIUOGcPToUR9H7hu7Co7wzIYlAIxo08vTnUyqHzqdFXPMy9iEnmClisrsBxGumo/3S1JzpPmDYP369aNPnz688cYbAKiqSkJCAg8++CBPPPHEWbd3uVyEhobyxhtvcPvtt591/ab0IFhhdTn/t/I9nEIl2hrMa5ff7rPC1y1NQelKggv/hUERFOjaERb/HooPinlLUn041881TX/D7XY7mzdvZvDgwZ55Op2OwYMHs379+lq9R2VlJQ6Hg7DTFE+w2WyUlpZ6TU1Bqa2KO1e84ylc8WjyCPnh34DCgwZzPOB2VAHh6kHysh/VOiRJanCaJoD8/HxcLhfR0d5jskRHR5OTk1Or93j88ceJi4vzSiJ/NHPmTIKDgz1TQkLTqHf76d6fPT+/ecUddAiJ0TCaliE24m8U+A0HINq5hdzsKRpHJEkNq0lf4z7//PPMnz+fJUuWYLFYTrnO5MmTKSkp8UxZWVk+jrJuHC4nr2z+luWHtwPwyIXDiAsI1TiqliMy+nHyTAMAiLavISfnXxpHJEkNR9OhICIiItDr9eTm5nrNz83NJSbmzN94X375ZZ5//nlWrlxJjx49True2WzGbG4aT8kWVVfw3KYvOVDsPh/DknpyeasLNI6q5YmKeZa8YxOJcm4hxraCI9kK8TH/lA/cSc2OplcAJpOJ5ORkUlNTPfNUVSU1NZX+/fufdrsXX3yRZ555hmXLlnHRRRf5ItQGl5aXwd9W/Nfz4X9zx4u5u9tfNI6qhVIUouJeJV/fHYA423Iyj02hhQ2cK7UAmjcBTZw4kXfffZcPP/yQ3bt3c99991FRUcEdd9wBwO23387kyZM967/wwgtMmTKF999/n6SkJHJycsjJyaG8vFyrQzhvr21ZyowNiz2vZ1x8I2M6XSK/cWpJUYiI/w/Fhh7oFEh0rCX9yETUEzflJak50DwBjB49mpdffpmpU6fSq1cv0tLSWLZsmefGcGZmJtnZ2Z713377bex2OzfeeCOxsbGe6eWXX9bqEM7L8sPb+fHobs/r8V0uo0ek9qMESoCiIyTuPxSb+qIo0FbdQnrm3ThV+ZyA1Dxo/hyArzWm5wAqHNX8ddlbntefj3gYQz2XipPqgRCU5s0gqHoVABkinqi417GaIjUOTJLcmuRzAC3dH4d1fnfw3fLDv7FSFIKip1Ph7x48Lkk5SsGR2zlSukHjwCTp/MgEoJHDpcdJO34YgOvb9yHCL1DjiKSz8Y94hKqQB1EFJOgrMec/yW+5c+XNYanJkglAI69tXer5eUynSzSMRKoLv+AbIfwJVBQi9Q7aVc1lQ+ZkbM4yrUOTpDqTCUADuwqOklGaD8BfO18qm36aGF3gMJTo/6BiwKIIksUvbMu4j4Kq/VqHJkl1IhOAjxXbKnly3QLP61Ht+2gYjXSuFEsPdLHv4DQkYFQEfY1Z5B99mF3HF6IKp9bhSVKtyATgQ3aXkzuWz/a8vrf7YHSyr3/TZWqHIW4uroBrAehkrCCm/L9sOPwYRdUZ2sYmSbUgE4APTVu/yOt1StLph7CQmgjFgD58IiLiX7gUP8J0TvqylSNZD7A1Zy4O+cyA1IjJBOAj1U4He4qOeV4vGP6QhtFI9U3xH4g+7lOclj7oFehuKqd11cf8dOg+0kvWyGYhqVGSCcBHnt/01e8/X3oLJr2m4/BJDcEQjiHqJYh4ClWxEqZzcpnhEOV5z7Mi/XGyy9Nkl1GpUZEJwEe25Wd6fu4UGqdhJFKDUhTwvwpd/CeofpecuBqooK+SxrYjM1iT9TyltmNnfx9J8gGZAHwgsyzf8/PLA8dqGInkM/pwdJHPQcRUhC6QUJ2TIZZCwu1rWX5oImm5H2N3VWodpdTCyQTgA7sLfv/G1zY4SsNIJJ9SFPC/EiXuI7Behk6BbsYKrrLkcbz4C749+CD7i5bL+wOSZmQC8IGdBe4qZFe3uVAO8dwS6cMg4mmImIHQhRKsczHYXEQ33VF25LzDdwcnklGyRg41LfmcTAANTBWCvSd6/1wYlaRtMJJ2FAX8B7mvBvyHoSjQwVjFCL8CosUhNhybxbJDj5JRsgaXatc6WqmFkAmgge0vzuZ4VRkWvZEuYa20DkfSmj4IIp6A6NfA2AazotLHVMZwSxHBroP8cmwWSw89ysHiH3DJpiGpgcm+iA1sZ/4RAHpGtsZiMGocjdRoWC6E2Peg7Eso+YhAShhgLqFENbHNYWNT9ttsyp5NfEAfOoWNINJ6gWw+lOqdTAANbEe+u/2/e0SCxpFIjY5igKAbIWAYlC6C0gUE6yq4zGynSLWwzW7haPkmjpZvIsTcmnYhg2kdPACTPkDryKVmQiaABmR3OdldeBSAbuEyAUinofOHkHEQOApK50PZF4TqqhlkqaZAiWNzpZ1C22E2584hLe9jWgX2JSGoPzH+PTHoTFpHLzVhMgE0oL1Fx7CrTkLN/iQGhmsdjtTY6YMg9B4IugmK34PybwgXxxjiB5W6OPY6LeyrLudw6U8cLv0Jg85CXEBvWgX2I8a/Bya9v9ZHIDUxMgE0oF0nv/1HtJLtt1Lt6UMhfBIEDHdfEVSuxaoe40Id9AwIJkfXlt8qyylwlpJZuo7M0nXoFCOR1s7EBSQTZe1CsDkRnSL7eEhnJhNAA9pflANAh5BYjSORmiRzV4h8Bpy5UPYVlH+HTi0iTt1KrEnBEdCFYyKa3VX5lNhzya3YQW7FDgCMOisRfp2ItHYm0noBYZZ26HWyE4LkTSaABuISqucGsGz/l86LIdrdNBRyB1SshvJvUWxbMdl/I4nfaG30xxGYTI6IJL26hOPV+3GolWRXbCW7YisAOsVImKWdJyFE+HXCpLdqe1yS5mQCaCB5laXYVScmnZ7EINn+L9UDxQgBV7knx1GoWAblS1FcxzFVrSERSNQFIyIuo0zfkRwX5FXt53jlHmyuEvKr9pBftYfdBV8CCiHmRML9OhBmaUeYXzuCzQnoFFmetCWRCaCBnOz9Ex8Qhl62xUr1zRgPIXdC8Hiw7YCKH6ByNaglKOX/IwgIUix0tFyICBpGub4Nxx1FHK/cw/HK3ZQ7cim2HabYdpiDrARAr5gItbQl3K894X7tCbO0x98YKe9fNWMyATSQkyOAXhAWr3EkUrOm6MHSyz2FPQTVW6FyFVT9Aq4CqFqPUrWeQCDQkEhbv2QIHkWVPol8ex6F1QcprDpEYfUBHGqV5yrhJJ1iINyvA5F+FxBiSSTEnESAKUbeYG4mZAJoIMfKiwH3FYAk+YRiAL8+7kkIcByEqg3uybYTnJlQlgllS/ADEgytSDB3g7DeCPNtlKkGCqoPuKeqAxRVp6MKJ8crd3O8crdnN3rFSJC5FcHmRELMCQSbEwkyt8JqCJdXC02MTAANJLuiCIC4gFCNI5FaJEUBU3v3FDwW1HKo2gy2bVCdBo5D4DziniqWoQBBumCCzN1o49cNgm/EpoRS6Cim0llMXuUuyuzZlNgycQk7RdXpFFWne+3SoJjxN0UTZIojwBSDRR+ESR+AvzGKQFMMfkb5ZaixkQmgAbiESk5FMQBx/jIBSI2ALgD8L3dPAGoZ2H6D6h3uqwP7blBLoOpn9wSYgVjFAqYLaGftCMFXoJraUyEMlNiyKbZlUmLLpMSWRZk9B6ewnXidecoQTPpAAk0xBJpiCTDGEGCKIcgUR6ApFoPOjCKblXxOJoAGcLyyFKdQMer0RPgFah2OJNWkCwS/i90TgHCAfb/7hrJtJ9gzwHkURDXYtron3MMHB2Ig0JhAK2MbCEiC0GRUYwLlqpFy53FKbUepcBzH5irD5iylxH6EamcxdlcZBVVlFFTtrxGOUeePvzECf2OU+19TNMHmBILNCVj0wbJpqYHIBNAAjpYXAhDjH4JO/uJKTYFiBHMX98Ro9zzhBNsecBwARwbY9oIjHUSV+1/H701AOiAII0HGBOKMSWBJAtOFYEwCQxx21U6FI48yezbl9hzK7DmU23MotmXiUCtwqBUU2yooth2uGRp6LIZgAozR+BnDsBrCsBrDsRoisBiCMRuCMOkCMOn95VVEHckE0AAyywoA5Pg/UtOmGMDSzT2dJFRw5YLjMNjT3YnBke5+Lard9xYch/70RkZMxgRMxiRCja3BEgv+/cAQjtCFYRdQ6SygyllIhf04FY7jFFYfpKDqAC5hQ+CiyllIlbMQqs4QLnoMOgtOtQo/Qyh+xjAs+mAMOgtmfSAmfeCJZGHFrA/CpPfHoLNg0Plh1FsxKOYWd6UhE0AD+D0BRGgciSTVM0UHhlj3dLL5CNyJwZn7h4SQcWI6U2IABTArfpgNcYQaokAfDeZwsF4E+sE4hYpDF0yVy06Zs5IqVymVznwqHe6EUe0sxeYqwalWI3DhUCsAd0KpdBbU7dDQYdRbMer8Men9Mer8MOn90Ssm9IoJqzHixPITSUPnh15nxqjzw6izerZpSlchMgE0gMOlxwFIkFcAUkuh6MAY657o//t8T2JI/z0huPLAme9+TkFUnGhSOuie/sRwYvIDwtCBPgT0EWAMBnMg6NuDPgxVF4RdGKjGgssQg0O1Y1ersbnKcKk29/2IE/ckHGolNlcpDlclTrUah1qFQEWgYneVY3eVU+E4x9OAglFnxah3N0mZ9P6YdP6YTr7W+f9pWQAmfQBmQ5AmVyAyAdSzcns1h0vdD4F1CpWDwEktnFdiuKTmcrUKnDknkkIOOI+7E4OrANRiEDb3PFEJqOAqdE9/ogMsJybPHMUEuiDQB4MSAHp/MAaALhR0rUDxB50/QvHHpRhwCh0OBA5Vxa46cAgXNuHAqao4hY1KRwEOtQqXsOFQq3C4TvzsqsKhVuASDgQCu1qBXa2ocxLRK0aMOit6nZlL4h8m3K9D3d7gHMgEUM92FmShIoj3DyXMIis3SdIZ6fzA1AZoc+b1hMvdTdWV7756UMtALQVXkTtRuArBVeJ+rkEtBVR305Or2p1czkDh9ysNy2nXMoJiBp3J/fS1wQRGA6B330BXghHoUVHckwAX4BLuawunUHEKFZdw4VRd2IULh3BgVx3YVTsu4cJFNSplqC4FRS2vy1k8Z40iAbz55pu89NJL5OTk0LNnT2bNmkXfvn1Pu/7nn3/OlClTyMjIoEOHDrzwwgsMHz7chxGf3s/H9gHQO+osv9CSJNWeogd9mHsydTz9ekK4k4NwuJuW1DJ34lDLQa04MZW7ryg88ypPvK76w792wPmHN3a439N1hhAB/Ynp7MdzYjrN7QKn7gw7qkeaJ4AFCxYwceJEZs+eTb9+/XjttddISUlh7969REVF1Vh/3bp1jBkzhpkzZ3L11Vfz2Wefcd1117Flyxa6det2ij34ztHyIn7JOQDAZa06axqLJLVIiuKurFYfhMudCITtxL/VJ35WTyQIh3sdXCcSjsPddZYT/57ytePEa/ufphPLcYJwYtD75qlpRQghfLKn0+jXrx99+vThjTfeAEBVVRISEnjwwQd54oknaqw/evRoKioq+OabbzzzLr74Ynr16sXs2bPPur/S0lKCg4MpKSkhKKj2vyh7i46RX1WOKlRcQkUVApd64l+hUlhdzg9Zv1Fkq6BXZGum9ru+xXUpkyRJG+f6uabpFYDdbmfz5s1MnjzZM0+n0zF48GDWr19/ym3Wr1/PxIkTvealpKTw5ZdfnnJ9m82GzWbzvC4pKQHcJ6wu5qetYVNezW5sfxZjDeb2NhdTVlZWp/eXJEk6Vyc/z+r6fV7TBJCfn4/L5SI6OtprfnR0NHv27DnlNjk5OadcPycn55Trz5w5kxkzZtSYn5DQcFW65jChwd5bkiTpdMrKyggODq71+prfA2hokydP9rpiUFWVwsJCwsPrNnRtaWkpCQkJZGVl1ekSqyWQ5+bU5Hk5PXluTu1cz4sQgrKyMuLi4uq0P00TQEREBHq9ntzcXK/5ubm5xMTEnHKbmJiYOq1vNpsxm81e80JCQs455qCgIPkLexry3JyaPC+nJ8/NqZ3LeanLN/+TNH1m2WQykZycTGpqqmeeqqqkpqbSv3//U27Tv39/r/UBVqxYcdr1JUmSpFPTvAlo4sSJjBs3josuuoi+ffvy2muvUVFRwR133AHA7bffTnx8PDNnzgTg4Ycf5vLLL+eVV15hxIgRzJ8/n19//ZV33nlHy8OQJElqcjRPAKNHj+b48eNMnTqVnJwcevXqxbJlyzw3ejMzM9Hpfr9QueSSS/jss8946qmn+Oc//0mHDh348ssvG/wZALPZzLRp02o0J0ny3JyOPC+nJ8/Nqfn6vGj+HIAkSZKkjaYzbqkkSZJUr2QCkCRJaqFkApAkSWqhZAKQJElqoWQCqKU333yTpKQkLBYL/fr1Y+PGjVqH1KCmT5+OoiheU+fOv49wWl1dzQMPPEB4eDgBAQHccMMNNR7Qy8zMZMSIEVitVqKiopg0aRJOp/PPu2rU1qxZw8iRI4mLi0NRlBpjTgkhmDp1KrGxsfj5+TF48GD279/vtU5hYSFjx44lKCiIkJAQ7rzzTsrLvcd73759OwMHDsRisZCQkMCLL77Y0Id23s52bsaPH1/jd2jo0KFe6zTHczNz5kz69OlDYGAgUVFRXHfddezdu9drnfr6+1m9ejW9e/fGbDbTvn175s6dW7dghXRW8+fPFyaTSbz//vvit99+E3fffbcICQkRubm5WofWYKZNmya6du0qsrOzPdPx48c9y++9916RkJAgUlNTxa+//iouvvhicckll3iWO51O0a1bNzF48GCxdetW8d1334mIiAgxefJkLQ7nnH333XfiySefFIsXLxaAWLJkidfy559/XgQHB4svv/xSbNu2TVxzzTWiTZs2oqqqyrPO0KFDRc+ePcUvv/wi1q5dK9q3by/GjBnjWV5SUiKio6PF2LFjxc6dO8W8efOEn5+f+O9//+urwzwnZzs348aNE0OHDvX6HSosLPRapzmem5SUFPHBBx+InTt3irS0NDF8+HCRmJgoysvLPevUx9/PoUOHhNVqFRMnThS7du0Ss2bNEnq9XixbtqzWscoEUAt9+/YVDzzwgOe1y+UScXFxYubMmRpG1bCmTZsmevbsecplxcXFwmg0is8//9wzb/fu3QIQ69evF0K4Pxx0Op3IycnxrPP222+LoKAgYbPZGjT2hvLnDzlVVUVMTIx46aWXPPOKi4uF2WwW8+bNE0IIsWvXLgGITZs2edZZunSpUBRFHD16VAghxFtvvSVCQ0O9zsvjjz8uOnXq1MBHVH9OlwCuvfba027TUs5NXl6eAMSPP/4ohKi/v59//OMfomvXrl77Gj16tEhJSal1bLIJ6CxODlk9ePBgz7yzDVndXOzfv5+4uDjatm3L2LFjyczMBGDz5s04HA6vc9K5c2cSExM952T9+vV0797da+TWlJQUSktL+e2333x7IA0kPT2dnJwcr/MQHBxMv379vM5DSEgIF110kWedwYMHo9Pp2LBhg2edyy67DJPJ5FnnZFGkoqIiHx1Nw1i9ejVRUVF06tSJ++67j4KCAs+ylnJuTg5BHxbmLvJSX38/69ev93qPk+vU5XNJJoCzONOQ1acbgro56NevH3PnzmXZsmW8/fbbpKenM3DgQMrKysjJycFkMtUYVO+P5+R0w3afXNYcnDyOM/1u5OTk1KhsZzAYCAsLa/bnaujQoXz00Uekpqbywgsv8OOPPzJs2DBcLne5w5ZwblRV5ZFHHmHAgAGe0Qrq6+/ndOuUlpZSVVVVq/g0HwpCapyGDRvm+blHjx7069eP1q1bs3DhQvz8/DSMTGoqbrnlFs/P3bt3p0ePHrRr147Vq1dz5ZVXahiZ7zzwwAPs3LmTn376SetQTkleAZzFuQxZ3RyFhITQsWNHDhw4QExMDHa7neLiYq91/nhOTjds98llzcHJ4zjT70ZMTAx5eXley51OJ4WFhS3qXAG0bduWiIgIDhxw181u7udmwoQJfPPNN6xatYpWrVp55tfX38/p1gkKCqr1lzSZAM7iXIasbo7Ky8s5ePAgsbGxJCcnYzQavc7J3r17yczM9JyT/v37s2PHDq8/8BUrVhAUFESXLl18Hn9DaNOmDTExMV7nobS0lA0bNnidh+LiYjZv3uxZ54cffkBVVfr16+dZZ82aNTgcDs86K1asoFOnToSGhvroaBrekSNHKCgoIDY2Fmi+50YIwYQJE1iyZAk//PADbdq08VpeX38/9TI0/rne2W5J5s+fL8xms5g7d67YtWuXuOeee0RISIjXHfrm5tFHHxWrV68W6enp4ueffxaDBw8WERERIi8vTwjh7saWmJgofvjhB/Hrr7+K/v37i/79+3u2P9mNbciQISItLU0sW7ZMREZGNrluoGVlZWLr1q1i69atAhCvvvqq2Lp1qzh8+LAQwt0NNCQkRHz11Vdi+/bt4tprrz1lN9ALL7xQbNiwQfz000+iQ4cOXl0di4uLRXR0tLjtttvEzp07xfz584XVam3UXR2FOPO5KSsrE4899phYv369SE9PFytXrhS9e/cWHTp0ENXV1Z73aI7n5r777hPBwcFi9erVXl1gKysrPevUx9/PyW6gkyZNErt37xZvvvmm7AbaUGbNmiUSExOFyWQSffv2Fb/88ovWITWo0aNHi9jYWGEymUR8fLwYPXq0OHDggGd5VVWVuP/++0VoaKiwWq1i1KhRIjs72+s9MjIyxLBhw4Sfn5+IiIgQjz76qHA4HL4+lPOyatUqAdSYxo0bJ4RwdwWdMmWKiI6OFmazWVx55ZVi7969Xu9RUFAgxowZIwICAkRQUJC44447RFlZmdc627ZtE5deeqkwm80iPj5ePP/88746xHN2pnNTWVkphgwZIiIjI4XRaBStW7cWd999d40vTc3x3JzqnADigw8+8KxTX38/q1atEr169RImk0m0bdvWax+1IYeDliRJaqHkPQBJkqQWSiYASZKkFkomAEmSpBZKJgBJkqQWSiYASZKkFkomAEmSpBZKJgBJkqQWSiYASZKkFkomAElqZE5VXlGSGoJMAJL0B6eqY3uqWraS1BzIegCS9CdDhw7lgw8+8JpnNps1ikaSGo68ApCkPzGbzcTExHhNJ4ceVhSFt99+m2HDhuHn50fbtm1ZtGiR1/Y7duzgL3/5C35+foSHh3PPPfdQXl7utc77779P165dMZvNxMbGMmHCBK/l+fn5jBo1CqvVSocOHfj6668b9qClFkkmAEmqoylTpnDDDTewbds2xo4dyy233MLu3bsBqKioICUlhdDQUDZt2sTnn3/OypUrvT7g3377bR544AHuueceduzYwddff0379u299jFjxgxuvvlmtm/fzvDhwxk7diyFhYU+PU6pBTi3AU8lqXkaN26c0Ov1wt/f32t69tlnhRDuoX7vvfder2369esn7rvvPiGEEO+8844IDQ0V5eXlnuXffvut0Ol0nqGQ4+LixJNPPnnaGADx1FNPeV6Xl5cLQCxdurTejlOShBBC3gOQpD+54oorePvtt73mhYWFeX7+c8Wl/v37k5aWBsDu3bvp2bMn/v7+nuUDBgxAVVX27t2LoigcO3bsrDVxe/To4fnZ39+foKCgGuUTJel8yQQgSX/i7+9fo0mmvtS2VqvRaPR6rSgKqqo2REhSCybvAUhSHf3yyy81Xl9wwQUAXHDBBWzbto2KigrP8p9//hmdTkenTp0IDAwkKSmpRi1XSdKCvAKQpD+x2Wzk5OR4zTMYDERERADw+eefc9FFF3HppZfy6aefsnHjRubMmQPA2LFjmTZtGuPGjWP69OkcP36cBx98kNtuu43o6GgApk+fzr333ktUVBTDhg2jrKyMn3/+mQcffNC3Byq1eDIBSNKfLFu2jNjYWK95nTp1Ys+ePYC7h878+fO5//77iY2NZd68eXTp0gUAq9XK999/z8MPP0yfPn2wWq3ccMMNvPrqq573GjduHNXV1fz73//mscceIyIightvvNF3ByhJJ8iawJJUB4qisGTJEq677jqtQ5Gk8ybvAUiSJLVQMgFIkiS1UPIegCTVgWwxlZoTeQUgSZLUQskEIEmS1ELJBCBJktRCyQQgSZLUQskEIEmS1ELJBCBJktRCyQQgSZLUQskEIEmS1EL9P0is+azKOx/xAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
      " ] @@ -255,9 +258,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C]\n" + ] + }, { "name": "stderr", "output_type": "stream", @@ -298,16 +308,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "sigmoid = copy.deepcopy(model.sigmoid)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 7, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -326,39 +346,46 @@ "datamodule_test.setup()\n", "loader = datamodule_test\n", "test = next(iter(loader.train_dataloader()))['data_list']\n", + "\n", + "model.sigmoid = None\n", "out_graph = model(test)\n", "plt.plot(test['graph_labels'].numpy())" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([1.1122e-03, 5.6951e-06, 3.0094e-06, ..., 9.9869e-01, 1.0000e+00,\n", - " 3.4672e-05], grad_fn=)" + "tensor([[-2.1484],\n", + " [-2.6768],\n", + " [-2.7178],\n", + " ...,\n", + " [-0.3951],\n", + " [ 1.9461],\n", + " [-2.2958]], grad_fn=)" ] }, - "execution_count": 8, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "out_graph[:, 1]" + "out_graph" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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q0qVLKVSoULKgRJkyZVI8XL8Fp06dok7tmlw7MI5V0zowb4wvXuXyYWdjSWBIRPI4QYCNm/9EYvf7SULnzvrJshYE2LeiL1KJ9l+dlUzKnFVHiIlTr/ZUKnB1caBYwazI7NxwyVUHx0zlSYx5jyroFK1bt2b79u20rFeMawfGM3d0S6MqIKQSMZvmdePhqWnMHNHi29zoF+TO7Ma5FYO5tn8le3dspkOHDilW2enS2Ous5AC1sueqVavo3LkzkZGRWsckeQ7Gjh3Lu3ea+RnFihWjatWqzJo1K8X2YcOGMWvWrBQxWIlEwrp169i/f7/GC75o0aJMmTIFHx8fIiIiks89f/58/vnnH7Zt25ZivJWVFVu3buXEiRNa49m+vr5MnjyZFi1acOnSJZ2fwZfoMxTKly+fwmASBIGyZctq9cq0bt0aPz+/ZMPnc7p3767ViADo0qUL27dvTxGKAejVthquLg5k8XChbeNyDB06lB07t5MQqx536eYzg/emUqk4/e8DTp2/lyr3/p07d5BIJOTJk8ekef/F0MOHDx8MGgq6ElcBLl26RKlSpbTuk8vliES6q7r2799Pw4YNTbvgn0BS7svX/PwufFdDIWPGjEybNo2rV69y5coVqlatSsOGDbl79+43O8fx48epXr06UqkEr/L5sbK0QIWKqOi4FONUKjh7+jgyR+1W7K/Mq3eh1KxYkCql8/Dnkl6EhEYiV6R82GTL6IJP/ZJIJGKu3XmZvL14wSzsPHQZezsrhnSro85jqFqKBXOn4FmpA2efOLB5zxk6dOhA48aNWbFihdYX5ueEhkcTFaP+fGVfkfX7ObcfvWbmqsNcu/syxXY7a0vc0tiTI0cO9u/fz4oVK9i3bx9vQ8PpsmoHXVfvJCAsQsdRIUuWLIwbN45OnTpprIyTz2Fnx8KFC+nUqZPW5KuePXty8+bNFC9RW1tb2rRpo/FClEgkrF27Fj8/P7Zv355iX/HixRk3bhw+Pj7JCY4ikYjly5fj5+en4YmQSCQsX76cwMBARo0apfGCKViwILt372bmzJksWbJEq0v/S9KlS6eh1JZEqVKlNHIydIUfRCIRQ4cOZdq0aRr7MmXKREJCAgEBARr7JBIJw4cPZ9KkSSm2Fy+UlX/3jObUlqFky5QWiUTCimVLiA84h4VUTN8ONQze2/pd/9B+0Co6DlnD8i2nDY7/ktR4EyB1hsKPCD18b4+CUqnUWXnx/PlzsmbNqnXfvXv3yJdPM1wI6oReJyenVFeEmPk+fFdDoX79+tSpU4ecOXOSK1cuJk+ejK2trcbD6Gv4/Av57/WndBuxjpP/3NdIAFQp5SjlsUhkv1/YISY2gaNnbvM+LIrT/z5g+rJDGi+NN0FhbPO7RORnBpJUIuL6vZcMnrKdBp3n06VlJY5tHESxAllYuO4Etx68Qmxhw63XVqzbsJn169fj7OzMgAEDaN68OadOnUKlUhEXn8juI1e5cO0Ju49cpWi9cRSvP57rX7zUTUGlUrHz2BUmrzzI/WcBtOi3lKVb/sJnwDJCQrVXIlhbW7Nx40Y2b95M/+nz+PeJPxcev2TZyQt6z1W+fHlq166tUa3wOfny5aNVq1YaLzBQrxyWLl3K6NGjk70BAG3btsXPz48PH1LmdCQZCwcOHNAwFkqXLs3w4cPx8fFJdlcneSKOHj2q0WdeEAQmTZpE+vTpadOmjYaL29HRMTknoGbNmqxbt05v7oJEItFpUNja2hIdHZ3iu5UnTx6ePn2q1YDy8vLi4cOHKeq2k+jatSsrV67Uep5q1arh7+/P48ePdV4nQNnSxZg8ph91PONpVLOo3rGgDl+IPq7U7j7SzMPQx5UrV0iTJo3Ol5s+TDUU4OtDD8YYCm/fvsXd3T1V54iIiEh1yV5cXBwymUznPerLT/Dz86N+/fpa9ykUym+aNP21mJMZvwMKhYJt27YRHR2tUz4yPj6eiIiIFD/6iIqKSmH1hnzQ7l4GSIgOxsLWLXUX/4tw68FrFq4/yc37mmVen+diJJEoVxL7scPZu+Bw3gaGcf7qE2auOJwiLGMhlXDs7B11zbNNFopWaUMWzzoMGz+f+g0a0WfUCgZM2krLP5axfMtfqFQq4hMS8Tt5I9X3cvrKI4Yt2M26/efpO2MbsXGJqIDERAXRMfGEhEby6Nk7DYPIwsKCjRs3cu/MCaJePUepUuFiZ7jWukOHDsTHx7Nv3z6dY3x9fXnx4gUXL2pWv9jb2zN69OgU2fAikYiJEyfSq1cvjetMMhYOHz6sEZOvUKECAwYMoGXLlskvGKlUysqVK3n16hUTJ07UOF7Pnj3p0KEDTZs21XDbi0QievXqxcGDB4mJiaFBgwY68zL++ecfvVoGhQsX5vLlyym2eXt7s2HDBq3jJ0yYwNixYzW2V61alTNnzug0WiZPnsyYMWN0XkcSXbt2JXfu3HqNvCQ6Nq9ABjcn3NM50LVlZYPjk1CpVIwdO1ZvuZ4+QkJCdLrgtREUFGS0RoM2wsPDjdZGSG2YQ6VSGRR803XswMBAvQaKv7+/ToPs6tWrlC1bNsW2uPhEhk/fQa6qw8hXfQSXbz43cPU/BnOOwjfk9u3b2NraIpPJ6N69O3v27NHpdpo6dSoODg7JP4ZaTIeFhaUo4alZqSC+DUvjaK/ptlIkROGURru4zv8Lfcdv0mpMJSTKGThpGz1Hb2Dg5G0sWn+SHYfvEKLMRZx1PnZtmk9k4G1UKhWZMqirS0QiEV7l8hESGsnQaTsYNWsX4ZHGr6qiY9XaCypUxCfKmdy/MYVyZ2RUj3pEx8RTvukUarSdxcwVmjktMpmMvw/5Yfv4Gm0LZqFbVeN0y6dNm8aCBQtSVB98ybx58xg+fLjW5LSqVasiFos5evRo8rZSpUpRrlw5ZsyYoTFeIpGwZs0abt68qZHjULVqVfr06UPLli2Ji1N7gZISHKVSKYMHD9Z4EFerVo1x48bRrFkzQkJCNM4nk8no2bMnixYtonv37mzZsiXF/pcvXzJ27FiWLFmi8/5bt27Nxo0bU2zr0qULW7du1fCcgDr8oVKpePQoZYmqIAjUq1ePAwcOaD1Pjhw5cHR05MqVKzqvJYkePXogCIJOD0USeXOk5+zOEZzfNcqoCocktmzZQqVKlUifPr3Rcz7n2LFjVK9e3ejx//77L6VLl07VuUAthqSr7PBz8uXLl6KM91uiUCh0hh0cHR31LvIsLS21VsyAWkzqS5GlwVO2s9XvEgqFkvgEOfPXHUv9hZtJFd/dUMidOzc3btzg4sWL9OjRg3bt2un88g4fPpzw8PDkn8+z3LURFhaWwpKXSsRMGdyUKmXyaoyVJ0Qhx/LrbuYXwcpSikgkYCEVUyjvJ2PKwkDL0xv3XjF92UGd+w+fvp1ygwAWVi44ZK+PSp5A2BM/iuRy4uCafkwd3JR/bzxj7Ny97DioVrOcs+qI0fdQq1x+ujSuQMWiuZg7qAW+9Uuzb+kfdGpWkXOXHxH3UdgqSfvhS5wdHTnht5eja5YSHKg/pyIJmUzGxIkTGTJkiM4xTk5ODB48WKP8MYlp06Yxffr0FC/NXr168eDBA41yRlAbVPPmzSMqKkpj5e3l5UXPnj1TGAugTpTMnj073bt31wgTlCxZklmzZtGiRQutOQAA2bJlw8/PjwcPHtC+fXsiIiKIioqiU6dOrFy5Uu9qNH/+/Dx69CjFg1wikTBu3Did8fsBAwZoLTFt27atXtGe0aNHaw31aGPy5MmcPn2a48ePGzXeWCIiIlizZg39+vVL9TH0ZfBr4+HDh+TOnTtV50pS9TSGwoULa+hgfCuCg4N1qpra2dnpNRT0hWpiY2M18hPuPU4ZRsqdLXXhlG+NOfTwDbGwsCBHjhwUK1aMqVOn4unpqfWhAuoHeVKFhDGtN8PDw7W6Ubv5VsHGOqX0qCI+CsV/xFCIjUtEqVSRkKhA+lmFQkKi4WS2t0HhBscA5M7mRrM6JahRMT8qBOwzFMPOoyKDBg6kbuMO9B+7nIXrjvP3xQeoVGpRqN1HrpKQqDs+vnzbafLWHEnrgSuQy5UM61ibNePbUzRvphTjqlfIj6Od+mHRqqFub4GrqyvLly+nXbt2WrPvtVG2bFmsra05ceKEzjG1a9cmNjaWv/76S2OftbU106ZNo2/fvsnbBEFg8eLFzJ49W6tSnSAIjBs3DkDDs1CjRg26d++Or68v8fGfFC579OhBhQoVaNeuncbqq1ChQixdupRWrVrx8qX2PBGpVMqECRPo1KkTDRs2pHnz5owZM8aoGHytWrU4ciSl0Ve+fHliY2O5evWqxvhChQrx+vVrDb0JZ2dnHB0def5cu6s4ffr05M6dm1OnThm8JpFIxKpVq5g9e/Y3TYaeMGECw4cPN2qFro1nz56RNWtWo3MO4uPjkUqlJvVx+Zzr169TtKjhfA1QGwqfi2gZi0KhMHh9+vIfRCKR3pCHpaVlCsP4c+Lj4zVko4f1rIe1lQVisYhqZfMxqrf2HIYfjSB8Zejh97ETfryOglKpTPFATA0JiXImLtzP+Hk7iYnX/EJOXuSnISusSIhC/BvrJ+ji6h3tLwqxWKSzhNIQNlYWLJrQhunDmtPKuyxZMqq19yWWjqTN602syp6It1d5d+dP3t4/TmyYPyqlgsjoON7pMERUKhUzVx4hLj6Rf64+4dxV3Yls9rZWrJrRicv7xtDVt7Lea82ZMydTpkyhdevWRn+vJk+ezJQpU4iOjtY5ZubMmUycOFHryqhkyZJkzZo1RUmjtbU1O3fuZM6cOTrd7ePGjeP169caLvSaNWvStWtXWrRokeKaWrduTfPmzbXKIufKlYs1a9bQoUMHDbf/51SoUIE9e/YwbNgwKlY0TplQV6XD1KlTGTlypNbqkR49emgtiezSpQsrVqzQea6kygljKjasrKzYsGEDvXv3NliZYwxJ4RQvL69UH+PYsWMmNdi5fv06RYoUSfX5rl27ZrShkD17dq1y4IYwRpUxICAg1aEaKysrnYYCaCZ6Zk6fhpjYBJRKFRdvPDWoZPujMHsUvhHDhw/nzJkzvHjxgtu3bzN8+HBOnz5Nq1atvuq4u49cZfX2M9x9GsaJy0HJ26Ni4hg8ZTtnL396cCZ955TyOESS/4ZHwRgUCiWJ8tT9QUXHJlCn/RzeBoaS1tkO7xqfHkyCINCnezv6Dx5DjSb9qORVj4SoAIIf7CPyxVFOHtuvsbJMmlcgl7qm20IqIWdm7aprL16HULH5FJr2WMT05Ye0jvmSkiVL0qtXLzp27KizBPJzbG1tGTZsmN7kNTs7O8aNG8egQYO07h89ejRr1qxJoUlgY2PD9u3b2bp1K5s3b9aYIwgCc+bM4d9//9WohqhVqxYDBgygadOmKbwjDRo0oGfPnjRv3lzDaMmSJQubN2+mR48e3Lp1S+e9ODo6Gm0kALi5uREfH6/hpXF1daVu3bqsXbtWY07NmjU5ffq0hrFWpkwZrl+/rjMvxNnZmYYNG+qV2/6cdOnSsWTJEtq1a2eyyFESKpWKiRMncuHCBb1GjDGcPHmSqlWrGj3+woULOpO5jeHmzZsUKlTIqLFJK3tTExqNacwVEBCQ6ooKS0tLk6pEEj6qcqpUKhLlihTqu2Z+DN/VUAgKCqJt27bkzp2batWqcfnyZY4ePWpS4o82kkR2ZLZpcc+Yjaf+QfSfuIUuQ9fy5+ErfG6nff438jsJXPxsEuUKjp1VNw8qkj9z8vbMGdLQon4pxvb1ZvfyPyhepDDuOSuQq3Rrtmxci4WFBR06dGDAgAHJL9Frd17SeegaShfKyvzRvhxe05/MH5Miv+TSzWdEf2yedfys8S7m2rVr4+XlZbRGf40aNYiMjOTCBd2llRUrVsTOzo6DBzXzOpIUEr+seJDJZGzYsIGzZ89qFUpK0k3Ys2cPhw6lNIQqVqzIpEmTaNasWQqtg2rVqjF69GiaNWumYYS5u7uzfft2hgwZ8k3FzJo1a6ah6wBqz4G2xEZBELT2eRAEgcGDBzN9+nSd5+rWrRtbt27VqObQRd68eRk6dKhebQxdxMfH07FjRxwdHVmwYIFRZYa6SExMJCYmxqSKh4sXL+oUIjIGbcl++sicObPW8lV9/AhDQZ9H4UsK5MrAhP6N8Cqfj6mD6nHmb82Q4M/A7FH4RqxevZoXL14QHx9PUFAQJ06c+GojAaB+NU/G9fOmee3ClCucgc5D1rD36DUuXH+qfmj/Pp//L4WlLOVDs1iBLKza/jf7jl9DKlEnSr58857qrWdSt+NcmvdewsrtZ4iJTSAsIoZth27iVbMejdoMpkjJivTp04chQ4bQddgKTvxzj2WbT7Nkw0kmL/Qj6L16dRwXn8jJf+7x8upF3i/pRaW3f5LbTS3H3a5peZOuv0OHDkRHR3PsmO6s6BcB79l27DJvg8OYOXMmo0aN0huymDRpErNnz9bqGs+XLx/lypXT8B6IxWKWLl3Ks2fPtLrwJRIJ69evZ+nSpRrNn4oVK8aCBQto27ZtitLCUqVKMWPGDHx9fTVWYy4uLsnS0V/qMKSWhg0bau3iKJFIGD16tNYqj1atWml4SkBt6Ny/f19n8mWSCNOXTbL0UbVqVWrVqkWXLl148eKFUavmM2fOUK9ePZo2bUqfPn2MPpcuzp49S7ly5Ywen9RG3BTD4nPu379vstJiiRIl9PY70YZCodDZ9TEJQ8aElZWVzs6stra2OpVSdf0e2zYpx6ppHXGyStTrPfuhiL7Bz2/Cb3SpnxCJRLRvWp7YqA+MmziV569DkgWW0qdzxNFOs++DGf00rV2cET0/JQlJJSLqd57PpIV+7D5ylcQvmvI8eBrA1dsvUmyLj0+kea8ljJ+/jzGLzzF87CzehFtx99wmIgNuolIpefg0gL/+vc/STepVQY9R6+k0dA33188gMeAZvLrNju45uH5wPAM6GR/7TWL69OlMmzZN6+o0LDKGhgOWMGrJPhoNWoq1jS09e/bU+4KysrKiVq1aOrPt+/Tpw5o1azQSDgVBYMaMGaxevVprWaFMJmP9+vUMGjRIo9Qxb968+Pj4aLz0PT09GTBgQHLDpM+xtLRk9erVvHz5kilTpny1TLC1tTXW1tZar71ixYpcvXpVw3iytLQkQ4YMWhMsR4wYkaKN9ZdUq1aNx48fm7T6bdeuHb6+vkyfPp06derQokUL5s2bx+XLl1O86J49e0bLli3Zs2cPO3bsoG7dukafQx9JIRBj2bRpEy1apF7ufMyYMSZ1tQS1wWdqt9Fy5cpp7X3yOfqEu0BdPaOtnTmAh4eH3oo2fd9dBwcHg/o6Zr49v6WhkERYnIyEmJQP2bdBYeTL+SnJRmJE3wIzcPbyI+pW80z2HJia3+DkYMPQ7nXwf6t+sahU0LTnYv65G026/E1AEIh+foCYsBcolark9tZXbr0A4HW0FLlSBSoVFs5uODkY7179HBsbG8aPH8/gwYM19gV9iEzWb3gfHk1EdBxNmjTh4cOHOh9qoC7z0yU4JJPJaNu2rdYa/6TKA10Pd2dnZ+bOnUvHjh01hInatWvH0aNHNVbhNWvWJEuWLFpj64IgMH36dFQqFcOGDftqY6FOnToa4ZGk83h5eWmtHGncuDF79uzR2F6qVCnevn2r1xAYP358cnWIsVSrVo2lS5dy+PBhli9fTp48edi/fz9NmjShfv36tG/fnqFDhzJ+/Hjmzp37VUJHn3PlyhXc3NyMXuErlUq2bt2Kr69vqs73119/kSVLFpOVI52dnZFKpQQFBRke/BGJRIKjo6PWXKMkZDKZXk+cp6enzoqLTJky6azWcXZ21lvBlCVLFpO8ON8Tc+jhN6G+VxFEIilK+afVnEgk8M/VT5m+DbwKk9b5v1ft8K2JjIrlxasQDc+BsYSGR3PlzgvsbTUTRgVBhEeu0pz7+xhFsklxF27RyCs/AP061UAsEpj2xIMKfxdgm1V9EiTO3Lv7nLj4RAJDwhk+fSdTlxxI1lYwRIUKFbCxseHgoUMEhkYkvzBzZkpHq9olcbKzplezyqR1UusJzJkzh0GDBulcIbm5uZGYmKhV5AigTZs27N69W2sVRdmyZRGJRDpXaJ6enrRs2VLDmBCJREydOpVhw4ZpzBkxYgQnTpzQqiApCAIjR47Ew8ODnj17GlVNoIu6detqzc8AdUXGpk2bNLZXq1ZNZ+npyJEj9XpvChYsiCAIqSrpA3XSZq1atZg4cSL79+9nz549jBs3jp07d+pteZwapk2bpvV3ows/Pz9q1aqVqjJMpVLJ1KlTjVKn1IaPj49G0zFDeHl56S1blclkOkWTQP291hUisLS01GlkuLu762xaBmpDokYNw30/fgRmZcbfhAZeRciWMz/xkZ8EOZJWxEnsPnqN4A/aY2VmPhETl0iTHou+6hgSsSiFNycJR3trTm4eQp6cWTiyfytLFkynXds27Ny5k07NK+JVPj9yhQoVsPrIYwKm9efp2O7UaTeLkbN2sf3gJVZsPc3qHWqNAqUqkcDIfwmOesDT0GAUWhLaxo4bT/veAyjfdzo9Fm1PbsQzvlsDLm8cQf9Wn0ri0qdPT9OmTVmwYIHOe2vdurXWSgZQ5yT06tVLa/IiwJQpUxg3bpzOB2vLli1RKpX4+fml2O7p6Ymzs7OGnoNIJGLlypUMHz5c50qxd+/elClThvbt2xuMN+vCxcWFyMhIrQ/1DBkyEBkZqRHikclkODo6ai1fLFKkCOHh4Tx7prsT5Lhx40z2KuhCIpGQJUuWb3Ksz0nyJpjScGn58uV07do1VefbvHkzdevWTbU3RJdnSB/Vq1fXm+tjYWGh16OQOXNmnV4Dfejrbmrm5/FbGwoWUgmLZgwiLvxTvEtbzwMzP4Zdh69o1DhbSMXMGN6MNE6fvDpFihTh4MGD3L17F19fX0TKTxnQRSzDAMhpEUPQ20Cio6I/lnjBlVvPUSqV3AlYxM2307n2ejiTzi1m1InNyMNDibx6DkWUOknK/0MkVvkrEHzxKKdvPeFDpP5Suk6dOnHq1CmdsdM6depw8OBBne78Ro0a8ffffxMcHKyxz8nJiU6dOumN0U+cOJF58+ZpGBPjx49n0qRJGtsdHR2ZP38+nTp10tlPoW3btjRp0iRFTwlTqVy5MqdPn9a6T1dlhLe3t86eGqNGjWLy5Mk6z+fh4UGePHm+uQLjt8RUb8I///xDoUKFUtVkKTY2lrVr19KjRw+T5yZhYWFB9uzZuX//vtFzMmXKxKtXr3R+3w2FHpIqzHTNt7a21uqBS58+vc6k118Nc+jhNyBRrmDyIj8GzTyWIk+hQomcP/Gqfm0ypf828dkkJBIRDnafQg23HrzmyhcNWxISFfQctZEHT1P+8VtYWKhbLrfuxPoV04gOeUhaR2uGFlO7Zo9HuZA5oy3Ty/yNi40SAfjrwgP2HrtOeJxaJ0MQwNU6ittB73g2pDVvZg/nxchOqOSJ5EifltwFPBFZyEiX+B47Kxlvg8N0PrgEQWDs2LHMnDlT636pVEqRIkU0GiZ9Pn/cuHE6Gx35+Pjg7++fok/E51hbW9OxY0cWLUrp1bG3t6dz587MnTtXY07BggXx9fXVm+Dm7e1N9+7dadCggVF9Fb5EV/UDqI0jbfkItWrV0lmqmT9/fuRyOQ8fPtR5zmHDhjFjxgyTSx9/BKnxJsyfP58//vgjVedLKsFNrXJkEm3bttXo4WGI3Llz6xTzsrCw0Bt6AHU+ga7mZLo8DrpCD0nVUa/e6s6b+NGYDYXfAL8TN1i57W+CP0QhEklQKj66VwUBmYW22mizSIf/W93NkFKDXK4kPDIOFydbXF3UqyUVmuEfuUJJ056LyV11GJ61R3Pyn0+9PuKUdqTN04jEmPc8ub6H9ENnkHP1UZqsXMv+Ns9wtVWSoFQl//bi4hPJ4eKLWLAiNMaa+6HpqBnxGmWkOhM6MTgAeXgo1jIL9o/rxpjZk7n172HKTJ1Hhc4z6Tpxo05joXjx4rx8+VKnO79Tp046wwugTtiLi4vTKHkEtSGxaNEiZs+erdO12rJlS44eParhzvfx8eH8+fNaXfYtW7ZELpfz559/6rwuLy8vNm3axLJly+jdu7deRcovUSgUXL9+Xes+a2tr3NzcNB74tra2WFhYaK2YAMNeBQcHB+rVq6czgfRnMmXKFJO8CU+fPsXOzi5VKoZPnz7l7NmzNG7c2OS5X1KyZEkuX75skiqul5eX1v4loK4GMvQ9KlCggM4k4QwZMvDmjWYr8AwZMmj16rUftIpOQ9dQvc0sXr7Rnitk5vvx2xoK1lafLGyxhS1KeTQF82Tk7KVHWsMPIokV8gRzrsL3IDI6jrF9venQrDwSiUhrQmRUdBzxCXLCI2NTdISsVi4f2TK54pS5HF27dqNx48a8DHhHeldHxOnLcuSpHZGCDCQC1vYymtYpjrt9Barl2kIjh57Mi3tC3thwLknToQQcKtVB4qxuVmNpIeHPp49wKFOOdzf+RZLOklOXHhCmp8vlwIEDmT17ttZ9uXLlwtXVVW+r6hkzZjBkyBCtD2QrKytmzpypMylNJBLRqVMnjVwIQRCYO3cuffv21brKnjFjRrLIky5cXV1ZtWoV3t7e1K9f36B34d27d/Ts2ZNJkybpXYlWrFhRq2hV/fr1NXIuksiZMycikUiv9HTPnj3ZsmXLN5Fq/lacOHGCnDlzmuRNOHLkCN7e3iafKzQ0NFn++lsIxQmCQPv27fV2Dv2SHDly6PQIFC1alEuXLumdL5FIdBrl7969w9VVU501U6ZMWnuDXL2jvo64+ETuP/k1QhPmZMbfAL8Tn1Y5Iqk1ioRYwsN1x6GtnLISG/pr9DH/rxGfIKfn6A2s3XkO+RdllcUKZqFa2ZTdPJ+8DKR+p3l8CIti5orDvHz7HhsrC05cCSF9/npUrFoP7w4TeZmmBRnKd0cQC4itJeTLm5EEuYJ/bj0lPCoWi2zluJSpO70f5WCKZRG6uHojNO2V/GCddvJvIiLjsM9dkIg7t5GHx1GyQNbkhlPaqFixIvfu3dNZ4TB58mQWLlyo8wWWNm1a+vTpozME4enpia2trU4RHG9vb/bu3avxgM2WLRs1a9bU2k9BKpWyZcsWpk+frrUp1ed4eXmxc+dOZs+ezdSpUzWqIqKiohg3bhxdunShQ4cObNq0iezZs+s8XrFixbQ2itJnKAAMGTJEq2jT5/c0c+ZM+vXr99Vlnt8ChULB9OnTTa48OHPmDBUqVDD5fLNmzWLkyJEmGSWGaNmyJUeOHNHp6fmSdOnSpVAI/Rx95Y9JREdHY22tXdPm8ePH5MypGSYWiUTY2NhoeNX6dayBWCyiSP7MVCjxbStYUosgfGXo4TdSCv4tDYWIqFgO/vWp9EYstUKRGIN/wAckOhohWTllIS70xQ+6QjMA2xb1YNfS3qya3pElE9vQq001HO2tkcuV3H74mq4j1rHN7yJKpYqomHjeh0Zx7vpbRK6VOLpnFYOGTqF49jSsm9SB5tWL075eGRoPXUr7ieuoO3AR0bHxVC6SnUr5s6NKUBD8LoKeM7ckn//oA3XjKUEkol7jprQpZM+6YU34sG8dEWd1Jybq8yrIZDLmzp2rId38OQ0bNiQkJESnMTBhwgTGjx+vtXTRwsICT09PrSv+nj17cujQIa0rLltbW7Zv386MGTMMGgtp0qRhy5YtuLm50bBhQ16+fEliYiLLli3D29ubEiVKsH//fkqUKKH3OAB58uThwYMHGtsdHR1RKpU6xXHy5ctHVFSU3sx4T09P8uXLZ9Iq+Huxbt06GjVqZJKqYtL9a+twq4/g4GBu3bplUg8JYxCJRIwaNYqJEycaNd7JyUlnj46kF7ouhUVQGwq65Kbj4uI02kknUaVKFY0E2l5tqvHk9HT2LO+j0Rn4ZyEIX//zu/BbGgp2NpaU9FQLj4jFIsRSa5SJanfylyvaJEQSS1RKOSqluSriW/N5RcPnTF9ykJptZzFl8QFqVSrI4G61KVM0R/L+JLGlzxEEEEltSJunHlfP/MnF5T15d/86W3ddoPfoTTx5ol7hBH6IYM7F47Q/spbQDOGQoAC5kuDQyOSEpxZF1M1znK2tmD54IGf/Osa7lZMI9VtP0NoZRF3UXu9fuXJlbt++rVNwpmDBgpQrV07r6j6JOXPmMHr0aK0yts7OzjRu3JhVq1Zpndu5c2etAk4ikYgFCxboDEEkNaWaOXMmf//9t85rA/VqqEOHDsyfP5/u3btTs2ZNLCwsOHr0KHXr1jV6tSORSFAqlVqNJn06DKD2KuhKHk1i1KhRPH78WGsy548iKiqKLVu2mFzeeOfOHQoUKGDy+ZLCV99jxVmhQgXevXvH48e6u7cmYajVdIUKFfSGu3T1pVAoFIjFYo3tD58FsGbHWQp4ltCqxfE7rcD/a/yWhoIgCGye350uPpVQKJSIPnoUDGHp4JGilNLMt+F9aBQymYRJAxtTu3LB5O3X7/nz8Nk7Vm77m+L1x1PZZxp92nsx5o8GKeancbKhed0S9OtYne2LetK5RUU6l1Qxs2N5xu28y+6dOwD1793D2RGVUkVmB0cuBD/FVp6AU0wQ1avlIXdGV4KffKBC86nMW3OMHmWKMrR6BTK5OXD2tT/NmjRmz4VPiYaKSN1NiAYMGMCcOXN07u/Xrx+HDx/Wmb3v4ODAyJEjGTp0qNb9Xbp04c8//9TqBs6dOzdv377VulrLli0bNWrUYPny5VqPm2QszJo1S2dZ4+dkz56dAwcOcOTIETp27Kj1AW6IrFmzavVyNGzYUG8+R9GiRQkICNBbDicSiZg7dy7BwcFMmjTpp4QhZsyYwcCBA01uIHX69GmqVKli0pykl3hqwhXGMnHiRJ2hMVOoUqWKXlEmXaGHFy9eaOhbBH+IpGGXBUxYsI9+Uw7w8KHu/JVfBXWewdeEH372HRjPb3SpKZFKxNy4p5aDFUussbWEvNndqVBSd/zKyjk7MR+e/qhL/L8iPl5OywalWTBWewvxD+HRvHgdQo9R67l2J6W72VIq5Zl/ML4NyhAVHceq7WdYd92KgSczIMvRlJMXb9AgzQ1a5Yli2djWtKpgTYVCN3B6FcTsJ2eY+OQ8o6N20bG4LFm9UXi3j9DzzZh74Rx3g4K4t289Fa7uwO/mQ2TZ8mFXtib2FXVr/lepUoUbN27ojOeKRCKWLFlCnz59dAoaVatWDbFYrFW4RiwWM3bsWJ0P7JYtW2ptKAXqEMTBgwd1JppZW1uzfft25syZo/dB/vm1fE35XdGiRbVWRri4uBAbG6s3O37gwIF6cxVAbSBOmTIFQRAYMWLEDzUWXr16xa1bt6hdu7bJc8+cOUP58qY1Nps6darJ/RxMJUeOHAiCoFXz40sEQdBZppo/f37u3tXd4VVX6OHhw4fkzp07xbag9xHJf7sfwqNxc3Pn9evXBq/vp/K1pZHm8sgfQ4v6JREEAWtbewrnTsfh9QNZP6szWTKqu5p96amSWjkhjw39JZKj/ous23WWOau16wQk8fL1ew6cSpkE9SYojCu3XzCy9wTe+m3HRiRHqVL/8u4EW3GgR2Fev7pPYdE1pEv+oMXlQzRJE8CIHNeRRCcQG5JA3ONQyr7bzfZKz8lmG0/niu+QCXJkIgUC0CjoIdYSEeksBAIz5sW18whEMt1JjYIgMGDAAObNm6dzTMaMGenYsaPeUr9p06bpbFJVvnx5oqKitCaFNWnShD///FPrd1UkEjF//nz69OmjU2zJ2tqabdu2MW/ePA1lx29NkSJFtJaEgrr995EjR3TOLVu2LK9fv+bpU8MG/MiRI0mXLh0DBw5M9bWayujRo5k4caLJbu+YmBgSEhJMEll6/fo1b968+ao21MZSr149Dhw4YHCck5OTXmPZwcFB5/6YmBiteQgPHz7USGTMlyM9HZtVwD2dI8N61KVOHd2N2Mz8eH5rQ6Fp7RJcOzCO64enkRCndtOKRCJObxvG49PTeXJ6hkaGu8wuPfERmvW7Zr6eiQv8WLYpdS+l/LJIBkkuUuLVKSalf5b8YFZIBZxtJaxsW5Rlp56RGBCIoADRqXjIDImCWmNBpYQYmZycdvFs75AOG9dSWIsVTI86S93X9xFJ1AlQvgUysv6MccJDVatW5eLFi3oNSx8fH609F5Kwtramd+/erF27Vuv+SZMmMWHCBI3tlpaWVKtWTadXIXv27LRt25auXbvqXPElGQszZsxIdf8EY0ibNq3OpLfatWsbfOBPnDjRaNnm/v37Y2dnx/r16029TJNQqVTMnTuXtGnTUrBgQcMTvmD8+PF069bNpDnTp0/XGar61tSvX5/du3cbHFeyZEm9eQiNGjXS2loc1DoK2kooCxQooJHoKwgCY/o25MLuUXRvVUVn07FfCXN55G+Ek4MNtjaWZM+enXXbDlOpxVS6DF9LZFQsbwND6dEmZYzQxjU/UUG6OwWa+TpM9dWIPhoE6aVqGWeVClyJQqVSIXKyIL2tnPU3rbn0wQ6loxUfEj6GFrLYkyHbYCwrlEcAEECZVYSAioNXAwly7IIq12RcrgXR8sUtrINCcGjYhVpL9/E0INioVrWCIFC4cGFu3Lihd5yrq6vOMjKABg0a4Ofnp/WFnjFjRjJnzqy1adSAAQNYs2aNzpdws2bNKF++PH379tUrlbtp0yYGDx783VZoSX00tJE5c2aePn2q19jKkycP1tbWRitHjhkzhp07d5okSWwKERERtG7dGqVSaTAsoo1r164REBBgUjvrgIAA3rx5Y1SlybfAzs4Od3d3vVoWoDYE9BkU3t7e7NmzR+vvt1WrVlr7o3h5eXH+/Hm9ISkXFxdCQ0M1KoNevgnh0s1nv4Rqp1mZ8Tekbt26jJuykJdv3nP87F2K1R9PheZTWbA2pVUqtXTE2kKVouOkPnSVW5rRjlRqWjKcChUX94wiInNhzsc4EiiXsfhDFgDSKuNYkeY6HWTPsQmKIXPNLJzOoeB1A1celOlPrbbn+OOIPfJW+RBaO+Lv78aYG+5MuiBj+8GrCM5Fsc5XBACRjR32ZWsidXGnffv2Rq9Ik3QN9FGmTBmtokNJSCQSqlSponOFNHz4cKZNm6bxsJVKpYwdO5ZRo0bpPHbHjh3JkSOH3vr+NGnSsG/fPjZv3sysWbO+eehNqVTqzZAvXry4QSNg7NixTJgwwahrE4vFrFixgt69e6e6h4Uu7ty5g7e3Nz169GDgwIEmhxwSExMZNmyYzvJaXcyaNYtBgwaZNOdr6dq1q9Z25Z/j7u5OaGioTkVHmUxG4cKFtUqbe3h4EBoaqlH5IwgCnTp1YvXq1XrPXaJEiRTHvX73JVV9Z9C81xLGzdurd66Zb8t/5i0YEmtLSMCn9tJJD5zo2E8Ggb2tJSsmt8PJPR/R73VrzX+OrnJLM9pJTDTc1jiDm2Pyv12c7Rgzdy8X775hfFBu2r0pzD8xzgB4yCOxEqk//wzKMMaXtebOtbcUeRlNibNzyWTxBkW6F6yJhA/ZBnI4rC5+rx2RqwTy5HAHIOPQWWQavZCs09chkqgf+o0aNWLv3r1GrUpKlSrFv//+q3eMIUMB1FUOusoh06ZNS9GiRbX2gahQoQIJCQl6wxt9+/ZFJpPpzaewsrJi7dq1iEQi2rdv/01fsPo8CgDNmzfX2jzqc9KnT0+hQoX0diz8cvyQIUPo37+/Sdeqj02bNjF8+HC2bt1qchJiErNnz6Zt27akTZvW6DnBwcE8fvyYsmXLpuqcqaVYsWLcunXLoKyzoTBAly5dtJbzAjRu3FirR6JJkybs3btXb2fTWrVqpchvuXzzOYqPTef+vmjc8/t7YvYo/OKoVCqu3HrO81fqrF25XMHERQdRKlUoFZqegrw53MmSIQ2Z0jvTdeR6YsXpiXlvuI7YzPchJubT76h4wSyc0fJH72orobN7MHFySFQKbAh2J42DiHfvEpl5y5VB1zNy6Y0NV45IOTRVhP+xLUz4oy7zx7Zi28IeVC+fHwD/iGgGn7nHqJnTCVnQlujjy0g4MIWKHjKO+O01eK0ikYgsWbLobY2cP39+nZr2yffj6opMJsPf31/r/kGDBjF79mytIkxTp05l5MiRepvwjB07lgcPHuiMF8OnBM02bdrQsGFDnddiKoY8CoULF+b69esGvQVDhgxh1qxZRruVa9asSb58+ahevTqjRo0y6hzaiIuLo2fPnty/f589e/ZolRY2hsePH3Pp0iVatdJe+aOLOXPmMGDAgFSd82sQBIHGjRtrbez1OY0bN2bXrl069+fMmZOgoCCtCbtNmjTROlcikdC0aVO939eSJUumyHGoU7UQ7ukcEYtEdG9lWtnp98Cco/CLM23pQZr2XEy1VjP49/pTxGIR6dM5YuWYmYRwf0b3aUDepBWluxM7FvcijZMtdx6pm/GIxFIkFnYkxvw6ncj+n4iKicfJ3pq0znY88w8m9mNZVEY3JwZ3rY1vw9JsHVOf7HzgTayMmg+KsT44AxUn5uJxpCubHkv594MtIGDrIBAhsyTrgZs871ST4vf3UqpwtuRzdV+wlYMbr7H9rMDoJ5lJuHEYxYurtM2uYNnM8cTtGUb88ZnExYZzJ+QV0Ymaqyt9bZNB7QqXSCQGu+l169ZNp6vX3t6eRo0aaQ2JuLi40K9fPzp06KDVkIBPTaf+/PNPg1UOXl5eLF26lA4dOhhUcTQGQx4FQRA03MjasLe3p379+lrj2rr4448/OHr0KPXq1WPLli3UqFGDQYMGcf78eaMMjhcvXtCgQQPq1KnD5MmTTdZKSEKpVDJgwADmzp1rUrjiw4cP3Lp1i0qVKqXqvF+Lr6+vwc87U6ZMvHv3Tu/qv1WrVmzZskVju52dHfb29lobQHXo0IF169bpNO7EYjGOjo7JwmcZ3Zz5588R3DsxhZYNSuu9ZjPfltT9VfxkktxOSqWKizeeUrpIdnYs7sWGHTm4cGo3HZqVZ9Iitc7864BQVm39m6tf1O7buhYgKuguTlkqptguCOqEOjPfj0S5gtAItUBW8IdPokJtGpWl28eVgkoh5+3FMmS5dZGuBWD1XRXvoyTEyTwQIt8htXSgX9bX1Fa94bXMjlN5cpDmQSQXn4ciDBpAgSxFaN+1Je9ff1rlPAqRkKCwQRwbRhpbC5wlcTy8eYXsbnYMS7TkVWIiLlZ2LKraDivJJ12BqlWrsnjxYr1u7sKFC3Pz5k29yWjlypVjwoQJJCQkaNUt6NKlC7Vq1aJFixYa9ef16tUjPDyc7t2762wUJJFIWL9+PU2aNMHZ2RlPT0+d15I9e3b27dtH586duXPnDj179tQ51hBKpdLgy7F58+Zs3ryZkiVL6h3XvXt3ateuTbNmzbC0tNQ7NgmRSETp0qUpXbo0KpWKW7dusWvXLsaNG0emTJmwsLAgLi6O2NhYYmNjk42tpGtevnw5WbNmNepculi1ahU1atQgc+bMJs1LUtr8WaqD9vb2yUmNuXLp1qBJklWuXr261v1Jzca6d++ucS++vr5s2bKFwYMHp9huZWVF5cqVOXz4MHXq1NF63Bo1anDs2DFatmwJqH/XMotfY337teEDc+jhO9O1ZWXEYhFp09jRsHpRANzSOjCkV3NiotQ6CRU/Ci852Fmx66hm05qMmXOSEB2EUh6XYrvZSPg5SCUiypfMhUqlUq9QxRIy9J9O9uVHuBcQieLj78XSPiNx4f645REIaOvBZp/i2FkkoggVE2xhQ/+oWwxOvIXVw0MM7DUOVegHxGK1AdgovZgPN/wJvhdBnK0n1bNn4vitQMJFUl59XC2FxEYSEBWW4tpkMhkSiURvLLdq1ap69QJA/WKqWbOmzhW/VCqla9euOkspW7VqRebMmfW2lE6qcujfv79WV/Dn2NrasmXLFp4/f86kSZNSnUn+5s0b0qVLp3eMp6enXnGeJCwsLGjTpo1JXoXPEQQBT09PJkyYwLFjxxg/fjzDhw9nxowZrFq1il27duHn54efnx/79+9n//79X20k3LhxgwMHDphsbF27do1bt27pfPn+KHr37s306dP1jmnZsiULFy7Uufq3sLAga9asWsNZ1atX1+m56tWrF4sXL9Z53rp167Jt2za91/azMIcefnEa1yrGveNTuLhndLK4UhIu7tlp13sS/TvVZNey3hxaO4BM6Z1TjBEEqFImLw4eZfjwXL8mvpkfQ3o3J4ZP30mRumMpVGs0F2+oBXgEsZQYLNQlkKgoki8zTpbhFK+pIlEqJspWxu087qSLiyafPBSbdDKktmKq2YXy9PV7Vud8wIoiTxAE8Ai6l2wIvjl3gekPnJlxz5nLVnUpm14tAFPQxYPM9i4a15crVy6ePHmisT2JChUqcObMGYMx8mrVqukNDTRu3Fhr98gkBgwYwLJly3SGIEBd5TBp0iS9jauSEIlEzJgxg3Tp0tG4cWO9ZZ66OH78OF5eXnrHCIKAlZUVcXFxeseBWpti+/bt36Q6I0OGDHh4eODi4oKNjU2qJKr1ERQUxIABA1i7dq1Jx46IiGDQoEGsWrXqp/cwKFRI3RNFXxlwxowZKV++vNbwQhJFihTRqtchkUgQBEGrOJiTkxP58+fXWh4M6kTf7Nmzc/78eQN38eMxd4/8DZBZSDQSqG49eMXpW3Hs37eXjkNWE/I+kko+U7l881MimkgkMHFgY5rVKYmDSybEUiti3ut+AZj5Mbx8/Z5bD14TFhFDVEwc6/9UC7IIYjFTxncli52CNBZyFMFvaZ7VCodr90EQUCHw6IWUv8MceZ42Q/LxBFQ8j7PG2ULOoyhrlAjsVaUnXiQiXGzN0Le5+KC0JlGeyPJzIQwtUZ+tdXozsWxTxFoS8/Lly8e9e/d0Xr9IJKJo0aIGywALFSqkV/xIKpVSvnx5nX0arK2tady4scEVd9myZcmTJw/r1q3TOw7UD7yuXbsyadIkfH19jdZbCAsLY9SoUdy7d48iRYoYHF+wYEFu375tcJylpSVFihTRW+nxKxAfH0/79u1ZuHAhadKkMWnuqFGjGD16NM7OzoYH/wAmTJjAmDFj9Bpn/fv3Z+3atTp1PfS1ntb3u+/fv7/epl+GWpKb+f78toaCNuLj5UitnUmMDSUuLoEt+/9FLleS+FmJo5WlBYf/us2MFYeIT1DgmKkskQHXEZTfth7bjGk4FbLGvaoDFo5iVCp4/jqEuPhEVHERyKxjCM/rToSLPY0zfKBBHhfenbxL6DNLrvu7ctYpO/08/WlV4CnRYUoSohXEyxMY0uItuxTOZLGOp52TP+1yvuVEYQ98XnnyKMEWlVyCUp5AKU91R0trqYVOKz9v3rwGBX7atGnDxo0b9Y4RiUTY29sTFhamc0yXLl301rd37tyZdevWGUyeHD58OLt27dLZuOpLChQogJ+fH7t27WLYsGE6k9eio6OZNm0aPj4+VKpUid27dxu1mi5atKhOqecv6d69u97unL8C/fr1o3v37uTPn9+keRcvXiQmJsbkhlHfkwwZMlCkSBEOHTqkc4xUKmXcuHGMHDlS6359xkDp0qV1lhm7u7vj6uqq06Ph5uZGlixZDJYp/2jM5ZG/KcULZaFX26rkzutJz2YF8CqXT2NMdEw856894e07tVUsiCQ4ZqlI8JPvq4dvRjdiGxEZvJxwLmRD5gbqldn9J29ZvvMEbUZPo/m6U5DNCvvKrtwVnMntYsu9D3E8F9kjCAKyrDYkhkiJugkJYjGDYwvg1k5C+6pB9GkXwLa3DvhYvcHNP4raN/2RCQpARXYngexOYsoVyqH/AjHOUChQoAAPHz7Umx0OUKlSJb3VBhkyqD0jb9++1bpfJpPRunVr1qxZo/c8YrGYZcuW0bt3b4O18klYW1uzbNkyihUrRv369VN0hYyPj2fRokU0bNiQPHnycPjwYapXr260C9UUQyFr1qxERUURFBRk1PgfzYYNG3BxcaFBgwaGB39GYmIio0aNMpgTYIg1a9bQrVs31q9fz5MnT75JmGbw4MHMmTNHZ/8QUPcnkcvlWl/aNjY2OtUWDemRDBo0iFmzZunc/yt6Fcw5Cr8ps1ceYfGGU8RJ0nP14mnaNC7HsQ2DaF63BCJBSG4SpVKpcLC3Tv6/zNYViaUjGWy0u9QAShfJhvg3sgB/dcRiAQuJGKlEjNRGvRpVocLCUl2IIwjw55XbXA2zIjJYgSJM/fL1++BM7/d5icubm4hn6koWq/h4rgZaERBvwbFwZx5GWiOVKhEEEItALhaScxNUIhhYLpj+nu9ZXiUISUwgXUeuo/byZfQ+tJuI+E8x9KTESlBnh2tr+/wlNWvW1Cqc9DlVq1Y12NXRkGpe27Zt2bFjh0HhpIwZM9K7d2+TVf+aNWvG0qVL6dGjB9u2bWPdunXUqVMHR0dHjh49ire3t8kx1gwZMpjUEbBTp04GjaGfwe3bt5OrKkxlzpw5tG/f3uRQRRJyuZy+ffvy8uVLRowYgUwmY/78+dStW5dmzZoxa9Yszp8/b7Rh+Dm2tra0bNlSp3hSEpMnT2bKlCla99nZ2WmVR0+XLp3ebpXZsmVDLBbrlJROnz49GTNm1No7wsz35z9lKGzzU8c045Q2XLl2E6VSSa5sbozq04C2TcrhU78UObKos7PvPX6bosIhp2d1Yt7fQ5Gg3SK+/zgAhdJcEvGt6OpTGYVSSaJcQVxQIs93BXN/QQAh+wJp2zSIqSNe4ujw6fNWxMhxex5Aj2wBOARHktE9A2+3Xybx3xge/PmeKS+y0PJefla9ciXig5Kq4zwp+EcRGk/JzT9vHJn4PheHotIx+G1e3gRZ0CVvKJnsVbyPUaFSwfvIaK6/e8PRp2o3/dXbLyhSZwxF64371M5cLNa72gL97aGTyJ07Nw8ePNA7pnLlypw7d06nd0IikdC1a1ej3PMNGzbExcWF+fPnGxz7OVmzZsXPz49Hjx4RGxvL4cOHad26daoTAgVBQCKRGPS4JFGzZk1OnDihN3HzRxMREUG/fv1YuXKlyZ/D06dPuXDhAr6+vqk6d1hYGE2bNqVs2bKMHz+ezJkz4+Pjw8KFCzl06BBr166lSJEinDx5Ei8vL4Nqodro0KEDu3bt0tsLJW3atOTKlUtrgmGhQoV0hh/0daIEtddg5syZOvcPHTr0qz0x3xJz6OE3pWENdamkrY2MalUrJ3cos7e1Ylw/bwrm9uDJC+2uTJc09ijsi/L+6QlUKhUSiQhLmTR5f3iUOYfhW5ItU1qUnxle0S/jUSkhMFCBbbyKfDljmdw2HXWL5kP+IQ7lrRC2lnxA12zvGCB6wL27boQHvuX1zfcoE9THkSMiUilFolLxKkBKdLyY6y/s8E0XwoU4ZxaFZUNuKeN9jMD7UAWbgwsRb+FIsVo5kTqrdQ2yODgBsO7Pc4RHxRIWEcOGXervUd68eQ0m47m7uxMZGal3pS8IAunSpSMgIEDvmEaNGulVxGvevDmHDx9OFqTRx5gxY7h3757JpWZSqZQxY8bQo0cPrdoPppItWzZevHhh1FiRSETt2rUNKgf+KFQqFT169GDChAkGy0G/RKFQ8McffzB79uxUZbu/efOGpk2bMnLkSFq0aKF1jK2tLdWqVWP06NHs2bOHESNGmFzFIhaLGTZsmN7W6QADBw7UaqSWKFFCozNkEuXKlePvv3VXmeXPn5+QkBCd+TsZMmQgTZo0equPfiTm0MNvyqje9Tm5ZQj//DmKzh3bajxkLSy0rwDsba24/ySAiAQrrJyyEvHmEgq5kvh441Y+Zkxn/IJ9KTpNOknVK3WRoKJwaCIujt7kz+rDrB5NGOMRSgvnIOwslMQGJSII4C5NQBBEqFRKUCiTwwQisYrskmhUwsejC/AqgzvVyuclX870KF3saaJ4QeStUAr63yKf9D0LE04yKWc2FtdpTIkMmQB1votKpX45FCuYBVC35tWn0JhE+fLlDZZz1atXj4MHD+od0759e9auXasz/iwSiZgwYYLehlBJCILA4sWL2bdvn96Ete+NVCo1yUPQo0cPlixZ8s2bP6WGpUuXUqRIEcqVK2fy3IkTJ9KiRQuyZ89u8tynT5/Spk0bli1bZnR3SRcXF5YsWYKvr6/JxoKXlxc3b97U+3tyd3fX6h2oUqUKx48f1/qdbdKkicGeH7Vr19bbV6J27dqcPHlS7zHMfHt+a0PhzZs3KcpxBEEge6Z0ONhZkT9/fu7evZviC9uoRjGG96xLi3op1eEiPvMW2LoWRJUQTmz4K50tk/PnysDUoU2/6b38vxEVnTKGaiFSMaPIK3aUf4aFOJG7dzMhEtTKfL6jR9OybEaCgm2xyeFCgkjMJJf7ZLMRMcL2GntdL9LS5hWCWKBGlhhGZ32hPujHhVugpxNXw1+Qxs2eoIA4Or7xpPM7T4gMR5UYjyryA3mOr8V1SV+iL6nzC9o1Kc+OxT35c0kvWnmXAYxr/gTG5SDUrFnToECTjY0NJUqU0LsKK126NGKxWGcd+udIJBLWrVvHypUrv4l084/A2tqavn37Mm3atJ96HdeuXePUqVOp6slw+vRp/P39adu2rclzb9++TdeuXdmwYQM5chhOuv2cvHnzsmjRolQZC+XLlzfqO/WlQSCRSChUqJDWpFVXV1fi4uL0CoHVrl2bw4cP69zv5eVlULPjRyEIwlf//C781oaCSqXSGXcVBIGKFSsyd9EaVm49TdD7CMRiEd18qzBtaLPkpkEZ3ZySkxqlEjHrZnfm7pUTuAhPcbLR/oucOqQJCQlyShcxfXVgRjuB8RaMu5Wev2NtyNwqikIeMzn511SmLzvElrOPsA64Dv7BRD/6wL4cTen8rjDZnWxZH+7AkMhCVC4cyaXhdxha+i2r02SBRBUqhQqnfJZYukhQxKo4d+ERUbFq7YW3Cisu2+VNsiVQRoejjA4nfP/y5Gsq6ZmN4oU+qfYlNYh6+vSp3nspUqSIwex+BwcHEhMTiYmJ0TuuT58+LFy4UO+YyZMnM2bMGI12vtqQyWRs2rSJadOmcfWqpmLpr0iDBg24c+fOT3M5h4eHM2jQIJYvX663+ZU2goODmTBhAgsWLDD5vBcvXmTgwIFs27aNjBkzmjwfUm8s6Or6+DkuLi5aw15dunTRmTvTtGlTveE0Dw8PXr9+rdOLZmdnlyqvzPfAnKPwm5AxY0bevn2rU3q2ep2mjBwzkUmL/GjT/1MGuSAIrJjanjPbh3Nq61B2Lu7FoC61OL5pMFVK5+Xmw3fIHYrz8NKf5M7kiHtah+S5GVwdefD0HWPn7uXf6/pfGGZMI0YhJshZjLWl+vc5dlEgyzafYsys/dwKs0r28By/8Ag5Is4nePAoNJ4niTbsDHfH3jqGt4+iib6TgIAKQaFE9jKSR0sDiHz6sZrhM6OwaC5HLFzAvuinh5LUPYv2a4uNJz5BblS3PbFYjI2NjcEqierVq+t1s4J6FZYmTRq9Yk9OTk6MGTOGrl27GlUmZ2Njw5YtWxg2bJje4/4qCILAjBkzGDp06DcpAzQFlUpFz549mTx5ssmVCkqlkm7dujF37lxsbW1Nmnv16lXGjx/Pzp07TWpZrY28efOycOFCWrVqZXS5ab58+bh//77ezztHjhxajbc8efIQGBioVZipYcOG7N27V++5TcljMfNj+K0NBVD3VNe1MgqPVmDlmJWYkIe8fpfySysIApkypMFCKqF4oaz0budFlowuJCTK+WPcJuJUtjhmqcDfh1bg7/+plvxNYBh//Xuf38hr9Fvxzw17IqLVuSSRMbLkypRl8QVpeiU3fd6UJSFevVFq6UBiTBjKOCVvg6V8OCPH8VUMLfHH3kKBjYWKgFAxcdGAClRKFQ7uAuOaRnFw8CNEjo+QiMVIs8H74pk56VCR2/lbsWnv+RT97g+cvEHBWqMpVn8s6TLkMtidEaBixYqcPXtW75j69euzf/9+g8cypFwH6iqJcuXK6Sxb+xJHR0c2bdpE7969U2gl/Kpkz56dggULGvV5fUuWLl1K4cKFKVOmjMlzZ8+ejZeXl97mXNoIDg5m0KBBbNq0CQcHB8MTjCBfvnwsWLCAli1bGm0sGOr4mTNnTh4/fqx1X8eOHbWqgtrY2JAmTRq9hkClSpX0htt+FczJjL8RdevW1ZkUVrVsXtq0bY887AGTBnobdTyRIGBtJQPAwiYdLjnr8OHZKeLCXyWPOfzXbcoWy0HJwtmoXbkgGdycvvo+/h8Qi1NaVzILicb/XwfJqN83L1dfjKCVd2UypXemQ7PyRIRZ8iZSysUHEeSWRWItyJFYOiKPCwPggb8lRx84IwC5LaJZ3ug182qFfDyySu1JUCmJDVaQy+0980RFWJipJNclGak6PBsNFjsy5lg4HUdvZczsXbQbuJK/LqgFljbuOY9CoSQ6Jp6Df93G2dmZd+/e6b1XY/IUMmfOzOvXrw0m9+XNm5fw8HCDGgQ9e/bk5cuXHDhwQO+4JFxdXVm3bh2dOnXS2gb4V2Po0KEsWLDAYLjmW3H48GFOnjzJwIEDTZ7r5+fH/fv36dGjh0nz5HI5nTp1Yt68ed9c3jlfvnwsXLjQaGPBUPhBl0cBPiXravP2durUSa9Ww+9jKJhDD78NpUqV4tRff/PidYjGPqlEzPzx7ZgzbSQBT3Vbxp8jkYjZtrAHhfOps98lMltc8zXEOvE50e9uJLviHj0PZOWU9iyd1I5zO0dwYfcoWtQ1LiP5/xWFIqUbMz5BjoX0UyVKUjlqXIKYfSdesHTTKfzffiA0PAZnx09tl+NVYpa53uKPNG9AoZYxrpxeRSaxHJmtBNuChVmdkI+rEhs6VYpU/0EKAggiEhIF1txwJVSlNgat0ljxKu6zF7UKmrq8I7s0Ovk7Va2sWuFTEAQqlMxN3bp19SZcAcnJtIYoWbKk3lVbEsOHDzdYsiYIAgsXLmTx4sUGVSSTyJQpE8uXL6djx46sWbPmu7v2P3z4gFQqNTxQC1ZWVvTv39/g5/C1KBQKBgwYwJEjR9i4caPJeQl37txh+fLlLFu2zKSENZVKxYABA2jRooXJXghjSTIWfH19DTboKlq0KJcvX9ape5EzZ06dvR0kEglVq1bV+ndSrlw5Ll26pNPgc3d35/Xr10brbfwsPj5SUv/z+9gJv7+hcPzcPW4+iaRM3X7sPqI9BNG2bVv27NmjN9v2bWAY5689IVGuIF/O9IzoVS+5OVC2TOkpUL4lCqWS4Af7kMdHEvw+ko5D1nDu8iMEQSCNky2Xbr34Hrf4n8bSUl2bLxIJeOb1SN5+9Iz6JSsI8PD5Ox4LUYhcLLGwkHAxzonlkdm5JnZBJRaBRKBwnvTktIoDBxm7sxTn+J4Ylu62Yc0ZW6QSgU9/kwJnrsggRF11oUiwIjH6Y2wCsESOhzyKhrbvqFNQ3UWyq29lDq0dwF9bh1KpVG5sbW0N9lkQBAFLS0uDCnk1a9bk2LFjBj+nIkWKEBgYSEiIpkH8OTKZjDVr1tCrVy+94jafkzNnTg4dOsSHDx9o3Lix1lbB34LLly8THx//Vclo9erV4+nTp9y5c+cbXtknoqOj8fHxoXjx4syfPx9ra2uT5sfHx9O/f39Wr15tku6ESqVi2LBhZMmShVatWpl62SaRL18+evbsabCSRBAE2rRpw5IlS7Tut7e3J0uWLDo9Z7169WL+/PkaXgVBEOjTp49e8aRWrVrpPK+ZH89/wFC4i527J5HvbnD8nPaHh1gsZsiQIUydOlXr/uevgqnqOx3fP5bRa7S6qc/UJQdQfPyCP3sVzK0Hb7BPXxSnzBV4/+Qo0cH3uXrnBa37r6BgzVEsWHuc568+SZQ62Fl94zv9bxIRGUsGV0cK5fGgUJ5MKfblze5OzsyuxKMg9l00knSW5C6XhRG96tOmiAP3lfagUIFcxZwT74huMoaJBWqy49GnMJGAwLi+TWhRryQuTrYgEkhIEHFnawIXZ4UQ9kSKIiGSpCzHBETMDs3JnNDsrN/1SQshX870ZM6gNhyio6ONSk4rUKCAwRdayZIljZalNSTrnIS7uzszZ86kQ4cOBpUkkxCLxQwaNIhp06bRrVs3li9f/k29C7GxsQwbNox58+Z99bFmz57N4MGDdSYxp5bAwEAaN25M7969U62eOGrUKPr06YOrq6vRc1QqVXInydSUX6aGRo0acfv2bZ2SyUm0bduWQ4cO6ZRfnjhxIpMnT9bqnXBwcKBOnTpaRb7q16/PzZs3efnypd7zGjKMfybm0MNvROOaxbC2SwvKBLxK616pVK9encePH2sk0fi/ec+khX7EfRRX+ueqOjknTw53rceRWqehYr2eJMaGEvLoEIrEGCKj41i88STZPNTZyVKpmPBI/QIxYvFv/9F/M94EhnHjnj+LNqSsAGjfvDw5srry/EkQ8uA4FCFxNKtSjI51C5P5xb9Mkd345NoVwJ80vElMxCG3JVauUgQxKFUqpizYQ2hYNEO71VYPFQQc7KxJ62CH2MIOefynskIlIkCFALyVy7Reb3R0NDY2Nlr3fY4xTZCkUimWlpZG9ZGoUaMGp06dMujNAHWSr6+vr8nx9dy5c3PgwAFiY2Np2LDhN8s+Hz16NP379091j4PPyZAhAw0aNGD58uWGBxvJgwcPaNmyJfPmzaNSpUqpOsapU6eIjIw0uVHUhAkTsLS0ZOjQoak6b2oQBIFZs2YxePBgvQZhkqjXmDFjtO63s7OjT58+OhdhPXr0YNWqVRqeNUEQmDp1qk6xMJFIxNixY1PVT+NHYTYUfiPKFc/JtYPjWL9iJo/v6BcImThxosYXvt2glZw8/6lErGfrqgCM/cObJrWLaT3Ow+dBOGYqi136YoQ8PEjMe3VCz7OPHoXERP3JaTkyp0Oh+Larof8iJ8/d45l/UPKDzMPBAUVsIiJbexSZ8pDJIR5P+zi8XGOYO7QRJQplJeJyPIp4FYWrWSKSqv8Qo+LlHDt7l8Pn7tK3Yw0aVMzLtMq27O1VmEyZPD56FD7h4Z6GfDkz0Lt9Da3XFRUV9c0MBTDcTTIJkUhE06ZN+fPPPw2OBWjRogUODg4Gm/x8iVgspl+/fsyZM4fevXuzePHir+q3cPbsWcLCwqhXr16qj/El3bp1w8/P75skYZ45c4Y//viDLVu2kDdv3lQdIzQ0lEmTJuntgKiNKVOmoFQqGTVqVKrO+zVkzZqV0qVLG5T1LlWqFPHx8Vy/fl3rfm9vb+7fv6+1f4lMJqNjx45ajbq8efOSLl06Tp8+rfW4ZcuWJSIi4ruFmcwYz29vKADYWlvSqGF9gw1k8uXLh62tLRcvXkzeFhbxKaFm6aS29GpbDVAn1tWqWDDFfJnsiyx9W1c8CjdDHvOO4EdHUMr1Jwcl8eTlr9k692chFou0Vo4cO3uXB08/VRe8Cg9j1oaDXJg1jjMvwogTQUZLBeMyPaJGhkQc7a3ZPak33bMXZVqpqrS1fk02qbrJl8hRyj+Br1n69wW8os7jenkX7zfMpmvNAigSPnkUurasxIqp7anvVVjn9RobesiUKZNO1+rnVK9enePHjxscB9CmTRs2bNhgdFhg3LhxnDx5MlVKjDly5GD//v2IxWJq1qzJmDFjePbsmdHzQ0NDWblyJSNHjmT27Nkmn18fIpGImTNnMnDgwK8KkWzbto0FCxawe/du3NzcUnUMlUrFH3/8wdSpU03SS5g1axZRUVE/ddU8cOBAVq1apbO/QhKTJ09m1KhROj/r2bNnM2jQIK37fX198fPz09poauzYsUyaNElniGzy5MmMHDnyh+tnGIXoG/z8JvxGl6ofkUhEnTp1DJaGjRs3jvHjxyd/8eaPbUWJQlnp4lOJGhUKpBibO5s7FlK1ceCe1oHNc7tp5B6IxBLsPcpj61qQoAf7kcoDyZ3V8APHxurrG+z8VxAJAvtX9qF0kWw6RqiwEJSkEyfQSPYW96dnqen4nuiIBCRyuB1ixUN5GmIe3ubd1iEo3y7lsGIzwSWq8izRFgQBC2e1HDQCXIgQOBqZlkavSjDlz9s42lvi6GyDhb0FK7b+TZ3Oc5m++jANu8wnJlYzGdHY0IMgCEZ1nDSmm2QSNjY2eHp68u+//xo1XiQSsWrVKiZOnGj0Ob6c3717d44fP0716tWZNGkS9evXZ9OmTVr7L8TGxrJjxw6aNWtGly5dsLOz4/Dhw99MD+Bz8ufPT5UqVahVqxarVq0iOlp759cvCQsLY/ny5dSuXZubN2+ybds2kwWRPmfLli3kzJmTUqVKGT3n6NGjPHnyhMmTJ/9UKV8LCwvGjh1r0KPh7u5OpUqV2L59u9b9Hh4eeHl5sXbtWo19IpGIgQMHavW2ODo60rx5c51eLw8PD4oUKYKfn58Rd/ODEVRf//ObIKh+SVNNTUREBA4ODoSHh2Nvb29wfGhoKB06dDCo/DVt2jSyZ89Os2bNDB7zmX8wdx+/oXLpPNjZWBIdE0/g+3BmLT/MnUdveBUQmmx0lCmSCUnETfYeu4pT5gqIJNpj3AB92nmRIJezdd9FXJxtqVwqD2cvP+Sxju6W/2U882bk5n3dGgGOogTGuzxEnj0vrtk8yHDjICqViichUWy995YW+fPRM8iT+envk0MUjiAI3MOWQf75QBAxuFttnoR+4MCN+6iUKrrmS8+yk09QIqBSqgi8shOvfkO5deyzxC5LEYIKLu4YRbo0Kb97/fr1o0+fPkZl7w8ZMoS2bdtSoEABveM6duzIpEmTSJ8+vcFj+vv7M3z4cDZv3mxwbBLv3r3D19eXzZs34+6uPf/GWCIiItixYwe7d+8mc+bMdO7cmdevX7Nnzx5CQkKoX78+TZs2/Sb5CMYQFxfHn3/+mfzCHjVqlFY1w1u3bjFjxozkyoaGDRtiaWn5Ved+9eoVXbt2xc/PD4lEYngC8P79e1q0aIGfnx9WVr9G0nOnTp3o2rWrXmMnPj6eOnXq4Ofnp7UaRC6XU6dOHbZs2YKLi0uKfSqVigYNGrBq1SqNRE+FQkGtWrXYuXMnjo6OGseNiYmhfv36HDlyxGBpranvjNSQfI71DbG3Tl2pL0BETCIO7fZ912v9VvxnPAqglrK1sbExKCbSr18/li5dalRSWLZMaalfrTB2NuoHio21jGwe6VgyqR0b536SzBWAgnmykqtYQ2xcchP0YB+hL04T8eYK0SEPSGMZhUgRiVKhTpp88jKQDbv+ISIqlmf+wazZeZbalQsh/o0SXL4Vtx5oxpmtLT95XMKVUrJKY6hWx4uSjX2wKVMLm6KViFeIkIgEDkenQ6mC+Ay2yauzfESRWRqLUqXCPZ0js/s0ZV3fVmwf3J7qDWqi5FMSJIKAyFoCEvU2wUqMIAhk80inYSSAWgvAWDGc7NmzGxV+qFixotEiM5kyZUIulxutsAfg5ubGkiVLaNeundErb13Y29vTuXNnDh06RJ8+fdi6dStv375lwoQJHDhwgG7duv0wIwHA0tKS1q1bc+jQIXx9ffHx8UnR5VOlUrF8+XLGjh3LtGnT2LNnDy1atPhqIwHUiYgzZsww2kgAWLhwIcOHD/9ljASAmTNnMnz4cL0l5DKZjC5dumj1GoBaO6Fv375a9wuCQKdOnbT2eRCLxTRr1kxnmaW1tTU1a9Y0ShH1hyJ8g5/fhP+UoQDqBjKGZF4tLS1p166dwazpd8HhtB+0ijb9V/DqrWbzk8wZXJg+rFmyEbFi699cuf0cSwcPXPM1oWzlOqT3yA4qFc+e3Cfk5RXePz5C0L09bFg6gVe39hEZcJ34qEBUSgXLt5xGofxlHTzfDW1OLRtrC0oXyY5YLKJz1exkHTodC49sPB3QivdH9iM4uuMxchpvPBw4q0hHhcyx1C6pIFRQGxhRSglySxtKF8mGV3m1YFKZQtkoki8ThXJlpIN3OdI42ODqZIdbOgcWtm9M0+4VqdmyBB2bVaBlnVJsnNFZ81oTEwh5+hDpk+tGxU3Tpk1r1AvdGCXHz2nXrh3r1683ejyoNfhHjBhBhw4dvio58XPy5cvHrFmz6NGjB5kyZTI84TtTqlQpDh48yD///EPnzp158+YNbdu2JTQ0lF27dqW6uZI2nj59SkxMDAULFjQ8+COxsbGcP3+eqlWrfrPr+BY4OzszZcoUunfvrvd73bRpU/bs2aMznFazZk2OHj2q9Rj6cnHKlSvHP//8o/O8zZo1MzqJ94chUn39z2/Cf85QMNSmNInWrVtz4MABveIyi9af4Mylh/xz9TFzVh/VOqZFvVLkyOKaXOpSpXRe7G0tkUql5MyZmyilEzZp8+KQsQTO2aqQNk99XPM3Im3eRjhlrYxE5kDs+ydEPj/C65s7CX3+N9Ehj5DHRaBS/f9WRgR/iGLOqJY8OT2dkRN6Yle0LDG3r4BC/YCKvHQGsZULT0IzsKxJCBubBWJtKXAkXu3WtBHJ2VJNzLaFPbG1tkSZEE/gpsW8WTSBxMC3jO5ej3+3jGDp6NZkyZgGmaWEWT71Wd6rBaO71mNK/8akT+eocV0fdi0lyP8llUf5UbPVBN4Eaja++Zx06dIZZShkypQJf39/o5O29D2Q9VG5cmXq1avH4MGDTZr3O2FpacmMGTNo164dHTp0oGvXrgwbNsxkhUV9qFQqRo4caXK1wsaNG2nTps13z0tITUS5dOnSlChRgkWLFukcI5FI8Pb21tkBUiKRULJkSa05NDY2NgiCoLUUOG/evHrVRLNmzYq/v/8vr9b4X+W7GgpTp06lRIkS2NnZkS5dOry9vXn48KHhiV+Bvb09SqVSZ136U/8gFm88yb3HASxZsoTOnTtrTcoCcHG2Q6lUoVKpcHGy03nOeWNa0qxOCQZ3rU0r7zJERMUhVyjZf/w6NtaaeQpJf8NiqTVWztlwzFwO++x1yVuuNTauBVAp4gl/c4ngB/sJureHoHt7CH7gx4fnp4l4e5XokEckxhinuve78GXIJW8Od+xsZRw/d5e3gWEA2JWqjNjWHhBwqtWE92FRvA+LpsvxDAw568alNF2pXix38ucbe/86RN6Dh1OIOzuZDwe2EvHPCQJWqhXhhszYSctpa7geFMCApTO5OmsEEedP6r3OxOC3vE6w5INCytOgGLb4nUcVqdsQMNZQAHWYwtiqArFYTNmyZTl3Tn9JsDbatm2Lvb293hfCf4EKFSpw7NgxKlSo8M2PvXXrVgoWLGhSOaVSqWT79u20aNHiq84dHx9P165dqVevHg0aNND6U758eXbu3Gnysfv378/Zs2f1yoq3a9eOTZs26dzv4+Oj89w1atTQ6lUQiURYWlrq7eNRpUqVX6sHxP9R6MH4wFoq+Pvvv+nVqxclSpRALpczYsQIatSowb1794zKGk8ttWvX5siRIxrJinHxiTTptpCwyFjmrznOuZ0jGDRoED179mTNmjUaVn7PNlVxsLNCoVDSulFZnedThyCa8/JNCK/ffcDBzorwyFhkFhJG9KzLyFkpG6tk9XDh+StNxbHwiDgsrNNgYZ0GW9eU7kylIhFFfCTyhEgU8ZFEBFxDHh+BjUturNPkQiROfVLNj8Qzrwc377/S2P55yCV3Nje2zu+OT59l3H30BhsrC05tHYpr+kzkWLoXVUI85+68pXXPqchjFSjkFoQ5lqJwqcrEJsby7rZauEkQAQ8mgjwca2uw9hAT81qF8DGefOrCfWQFrRBuqhhw428sFXLeXDmDLFM2ZBmzar1+h3odsJuxEwUCKiBnwH4Stq9ClL8+0jIdNcabYih4eXlx8uRJoyWOO3bsyPjx41P1Ihw7diwdO3bkwIED31Tf4P+BgIAA1q9fr7MZnS4OHjxIjRo1TJJ2/pKgoCDatWtHr1699P7eEhMTadiwIZ6enuTKlcvo4wuCwPLly2nWrBlHjx5FLBZrjLGzsyN9+vQ8fPiQ3Llza+wvWLAgt2/fRqVSaTxT69Wrx5gxY2jcuLHGvKS+J7oEr5o2bcqMGTPw8vIy+n6+K19bufAbVT18V4/CkSNHaN++Pfnz58fT05N169bh7++vsy30t6Jhw4Za8xTi4hMJj1J7DxIS5XwIj6ZGjRrkz5+f+fPna4y3kEro0KwCnX0qJTcs0sWZSw+p0nI6jbotpG5VT9zS2mNva8mpC+qW1EnNj7JkdGH7op7076gp5pMo1x03FomlSK2dsXLMjK1rAdJk9yJt7vqgUhH80I8PT0+SEP3rV0xoMxI+Z9nktmxd0INuI9dz95E6yTE6NoHHzwMBEEktENvYcej0LZQKJQICJXJlYsXoNsgspNiX8cImf2Ekdrak9e0Ngpgk092hYj0cvRrg3nUYAJ2aVSDuRQyoVIg/C/OoFHJCYqM4+PQWryJSem6i7VwoW60ii8a0YlO/4tR2V3/mysfaPREODg4Ga9STqFKlikl5ClmyZCE0NNTo43+OIAgsW7aMZcuWGSUKZUZNkmbC7NmzTUpgBFixYgVdu3ZN9blv376Nj48PM2fONGjcSaVSVqxYQc+ePU3utunk5IS3tzdbtmzROUafnLggCBQrVkzrcz5jxowEBARozZExlKeQlBhsrCz5d+cnlUcuXryYLFmyYGlpSalSpQxKwM+bN4/cuXNjZWWFh4cH/fv3N9gQ7Et+aI5CUkatrozx+Ph4IiIiUvykBldXVz58+KBR1eBob83Yvt5k9UhL77bVyJ1NrXcwcOBArl27xsmT+t3OAC/fhFCv41y8Ws/k7uNP2frnLj9KjguevvCAwJBIgj9EcfKf+6hUkJCooGbF/Cwc14rQ8GhiYhOw1RKWMAWRWIqtawFc8zXG1s2TqMA7BN7dRWTgbZRywxUdvyIHT91i894LXLr5yQVftmgOCrtKCdq+gqgb/6JQKLl+50Xy/nrVCif/W2RljcfIheRYfhjHKvUh73hIVx2y/YFj/cG4dxqExMmF8PMnaJsdLi8bRkmPzLj1m4RdiQq4dRqExCMbfU9sZeG1U/Q+sYXQuE9VAoGBgbi5ulKnYiHKVKoEMnX9vSiH9lWQKbFoJycnwsPDTeph4Ovry9atW40e/zkymYwNGzYwaNCg79YI6r/Gxo0bKVasmMFy1y+5evUq2bJlw8kpdS3pDxw4wLBhw9i+fbvR586YMSNDhw7ljz/+MPl8Xbt2Ze3atTorw4oVK8adO3d0vnCaNm2qM4+hRIkSXLlyxejtn/O7tKD+Xmzfvp0BAwYwduxYrl27hqenJzVr1tTptdyyZQvDhg1j7Nix3L9/n9WrV7N9+3ad0tm6+GGGglKppF+/fpQrV07nF33q1Kk4ODgk/3h4eGgdZwxeXl4cPaqZgNi+aXn+2jqUQV1rJz/Ek1ZXU6ZMMbg6W7XtDPeevOXpyyAWrvvUm6BhjaLY2VgiFouoWamA1mSio2fu4vPHUhp3X8TyradJSJRTvkROg/fi7GhDWmfdORIAFjYuOGerSto8DRAEMSGPDxP88ADRIY+SSzJ/BUQGyj/9Tt5g/a5zfP7xDe5Wm4BpA3i3exPXJo3k+fUbPHoemBzjy58zAwBRNy/wclRH3q2ajkr+8Z5tspOQUJTo64+RB6h1Et7v28jbBeN4s2AsCad2IU14j2vYQjKWikcZ/Z53J/fz/qNxEP/Ru5BEbGxscg25YJcOixbLkTZbhKRMF533ZEpiWa5cuUxSP2zYsCF//vlnqqsYnJ2dWb16NZ07d9b5YDfzic2bNzNo0CCT582ZM4d+/fql+pzbt29n9+7dWvUh9FG9enU8PDxYs2aNSfMsLCzw9vbWmxju7e2ts8KsWLFiXLlyRet3PykR90ssLS1JSEjQ+1328fH5ZhU7X81PUGacM2cOXbp0oUOHDuTLl49ly5ZhbW2t8/d7/vx5ypUrh6+vL1myZKFGjRq0bNnS6EZ0n9/qD6FXr17cuXNHr654Uh1v0s+rV/rd1Prw8fExqGH+OdbW1kycONFgNni2TGmTExyzZ0qXvD1/zgxc8RvH7SMTGdvXmw1zujB3dEua1imORPLpY46NSyQ6Rq32l5Co4NzlxxrnkH42PqO7E39vH06xApmNug+RWIptunyky9sQ52xVUSbGEvLwACGPDhHz4Skq5c912ymNKP8MCY1K8f+/Fi0kNuwDVxMc6PjOk4XbzlMkf2YEQYyTgxU37/tz4txdAldOI97/CeF/H+D13BGE7FlD6MLWRO+bTsKNo0RsHMKHwzuJf/PiYzN5gfAXT7FKfAfKRISoZ8Rf2UrU2jk0t3LCQWZF7WwFyO746fdsZ2eXIlFWsLBG5JBBp+cgPj7epJh0zpw5efr0qdHjZTIZ9evX/6rSsaxZs3LgwAGuXbtGhw4d9NbS/z/z+vVrMmbMaHLI4eHDh1hYWJA1q/a8F32sW7eOU6dOsW7dOmSy1HkgR48ejZ+fHzdu3DBpXsOGDfUqIrZo0UKnUqMgCHh6enLr1i2Nffo6phYqVEjrnCSyZs1KjRrae7D8cL5R6OFLL7qu1vQJCQlcvXo1RY6GSCTCy8uLCxcuaJ1TtmxZrl69mvx5P3v2jEOHDlGnTh2TbvWHGAq9e/fmwIED/PXXX3rrmGUyGfb29il+9PHs2TOtlimoJUejo6NNit+WLVsWGxsbvbr77ZuWZ8nENswd3ZL+nVJ+YS2kEqyt1H/MFUvmplHNYswa4cOT0zMY2asepQpnY/YIH0b/0YAcmdORJ7umQl6V0nmwtpJhYyWjSuk8bF3QAzsbSyqVzmP0fSQhllpj5+5JunyNcMxcAXlcGEH39/H+8VEUCabFLX8eKmpEXkYmUlLWJoyiVuHsv/iCrfO7MmFgU9I4WjF02k46D1tLpNgalUqFMlFF1NULBG9fQ8zLdx+PogJ5AoHr5yN1cUOWMQuyjFlJLFkNt49KhSolxIcqQBBokAjbG3SjbzEvRJ8ZAV8aCoZ4/vy5SS+I7Nmz8+TJE6PHg9pNvHLlyq9aaVlYWDB58mS6dOlCo0aNTMqV+H/h5MmTqUqkmzlzJkOGDDF53ooVK7h48SIrV67UmlRoLCKRiJUrVzJgwACTnoeZM2fm1atXOkNhzs7OWFhYEBgYqHW/rgZmUqkUCwsLrcJf/4+hBQ8PjxSedF2dOENCQlAoFBrKlq6urrx7907rHF9fXyZMmED58uWRSqVkz56dypUr/1qhB5VKRe/evdmzZw+nTp1KlUWtj3Tp0rF48WKd+7t3725yM5pJkyYxdepUoqKitO4XBIE6VTxpVLMYEonuP95n/sGcv/o4uUtkjYoFePYqmEFTt2Nva0WnFhV58DQgebxUIqZKmbxcuf2C8MhYomPjsbGW4eHujEqlIqO7MwM61zSYVKkLicwO+/TFcM3fBFv3woQ8PvzTvQvasLJMeX8zhrUgRFDnAShUECC3pEaFAoyavYex8/bx+Jn6MxQJAqezN8KmSMoKAEWi2mpPSBTz4UUMCAKCWEK2mRvJNnMDYYIU1/y1ocgwEnIOQexWELtSVXCqoZmVDaYbCk+fPjW6igHUjZhMNRSsra1p0KCB3uQzYylbtiz79+9n586dDBgwQGfp8P8jJ0+eNFko6eXLl0RHR5vclfL48eNcunSJJUuWfBP9BxcXF6ZNm0bXrl1NyoEpXry43uTzJFlwbZQsWZKLFy9qDT9UqFBBa2lvakt+fwrfqDzy1atXKTzpw4cP/2aXePr0aaZMmcKSJUu4du0au3fv5uDBg0ycONGk43xXQ6FXr15s2rSJLVu2YGdnx7t373j37t03e/jY2tri4OCgs9VszZo1uXHjhk5rS9cxR4wYwciRI1N1TU/9g2jcbSFVfafj23c5o2ar475+J24Q/D4ShULJii2nCY1IuaK3kIr568J9IqM/JQc52luz89BlitYdS5v+K5iz6qhWSWFAw4AQ6Umik9m6Yp++GO+fHP9lurJJP1aFxMZ9yqeoXbkgzeuVRNVuDEvDstEvID8JDq6M+aMBR/6+DYIIpVJBGkcbCubJSAufGtiXr5U8X7C0xqFBN1aGFKXpyYz8G2SDPG8pnOs0Tx7z7t073NJnBNfSyHKWJdPoBWT4YzxiG+05ITY2NiZJIJtqKGTOnJkXL14YPT6Jbt26sXbtWp1uS1OwtbVl6dKlVKtWjXr16v0yVREJCQncvXuXnTt3Mm7cOFq0aEHVqlVNdqmnBpVKRVBQkMZqzhCzZs0yOachISGBadOmMXfu3G8qzFSyZEkqV67MzJkzjZ5Tr149vY32atWqxZEjR7TuE4lE5MuXj3v37mnsq1atmtbkcTs7O+Lj438PA1X4SlXGj6GHL73oukJMLi4uiMViDQ9OYGCgzs6no0ePpk2bNnTu3JmCBQvSqFEjpkyZwtSpU00yGL+robB06VLCw8OpXLky7u7uyT+64lqpQV8ugiAIjBkzxmTrycvLi9jYWL2lOroYP28v1+990vb/54o6B6FU4ezJokKVS+fh6ctPv+xc2VyJjk2ZXexgZ8WNe/4MnrI9hVHh6qLdUIiLT5mw2KJ+yeSSTG1YOWVBZudO+KvzRt7Z90MsFpBrKQ09c/EhCoWS7HlzcN0mD48SbAkMiWD/iRu0bVwOQRBjJRNzeP1A9q3si3s6RxKD3352BBVvVy6g8qtzxColjArJy/2izVMYAYGBgSY9/EUikUnG1bNnz0wyFCwsLFKlPieTyejYsaNBWXJTqFu3Ltu2bWPOnDm0bduWI0eO/NDStKioKJYuXUrz5s2pV68eLVq0YMOGDcTExFC3bl1Wr17Nli1bGDp06HdPcLt7967JlQ6BgYG8fv2aYsWKmTRv3rx5dO7cGTs7/QnMqaFHjx7cu3eP06dPGzVeXz4BqMMI+fPn5+bNm1r36wo/FChQgNu3b2udo8/4+H/GwsKCYsWKpTCwlEolJ0+epEyZMlrnxMTEaHikksJYpjzHvnvoQdtP+/btv9k5atSooTNPAdQlN6GhoTx+rJk0qI8ZM2YwevRok7wRAA521iT5lEQigZ5tqqmvwzMrJ7cMZdfS3ozoVY+AIHWXQ5EgUMozO/a26n4RH3sUER4Zy51HKT0lljIpl289T7FN/PFL8GXb6jOXHpKQqP/haedeGKUikaggTYv/R6JQqND2nU1IVNC89xIqtpjKyzefem145svEkO51OLtjJNXK5knhZbEvUw2pawYEiRRVotr4shMSKZ9OTqVSualb1TPFOd69e2fyKtEUXr58SebMxiWiJiESiUyy9pPw9fXFz8/PpNCIIdKmTcumTZuYMmUKt2/fpk6dOvTr109nRvu34NGjR/Tt25cWLVrg5OTE2rVrOXDgAHv27GH69Om0a9eOEiVKYGtri5ubG97e3t/UQNLGiRMnTM5PmDt3LgMGDDBpzuvXrzlz5gw+Pj4mzTMWQRBYsmQJEyZMMEpfQSwW4+bmxuvXuru7tm3bVmffkbJly3L+vOZiRCQS4ezszPv3mj10mjRp8ntU4PwEZcYBAwawcuVK1q9fz/379+nRowfR0dF06NABUP8uPg9d1K9fn6VLl7Jt2zaeP3/O8ePHGT16NPXr1zcp7+W37/WQlKChzxAYN24cU6ZMMem4jo6OLF68mA4dOpi0ipo0sDF92lVjzB8NuHdsMj71P7VtzZLRhWIFsyAIAiN61qNArgyUL5GLfh1rMKRbHQTUxkUaR9sUx7S3taRZ3RK4a+k9oFAqsbKUIrNIGXp48y7MqOt1ylKR6OB7KOVf77I2hc8rQbSRxsmWRLmCq7dfpNherEBm8ufMwD9XHuPgaEdsbMqHndQ5LdnnbSP3hpM4eXmrz+WcltmrJ7N+dhcNSW1TPQqASS9xuVxusDXul6RPn563b98aHvgFIpGIwYMHpzpspo+MGTMyePBgjh07RseOHdm5cyc1atRg3Lhx3Llzh4iICKNX9QkJCQQGBvLgwQPOnz+fHEpo1qwZ9erVY9asWbRv356DBw/i4+NjUMW1W7duHDly5LsmwZ0/f57y5csbPV6lUnH16lWTVTOnTJnC5MmTv2svCBsbG/r06WN0/lajRo30eoELFy7M7du3tf5diEQicuTIoVW6X1cJu5ubG1FRUYSG6u+j8tP5CYJLLVq0YNasWYwZM4bChQtz48YNjhw5kvwM8/f3JyDgU+7bqFGjGDhwIKNGjSJfvnx06tSJmjVrmmxYf1cJ5x9F/fr1OXDgAP3799e6P1euXERGRhIUFES6dOm0jtFG3rx5adWqFZMmTWLcuHFGzXGwt2ZA51oGxxXInRG/1f24/+Qtkxbt59zlx6hQSxkXyuvBqfPqBin2tlbMGeVD3hzpmbvmKM9fBWscKzYuMUVs3xQEQYSVY1biowKwcsySqmOkBrlc/8s2QzpH3odqJpRmdHemVrvZBASFkdUjLTItMXnhY+mjW4f+SF0zELx1GS8n9CHz6PlIHFO2Pw4KCsTVxVLdgMOIh7NSqfzuDX0yZ86Mv79/qjod1qhRgxMnTrBt27bvtiotVKgQhQoVQqlUcv78edasWUNgYCDR0dEpXhZfehwEQUClUmFhYYGTkxNOTk44Ozvj7u6e3KgqNdLuIpGIrVu30qZNG8LCwmjYsOFX3+OXxMbGYmtra3jgR968eUO2bNlMPseLFy/w9PQ0PPgr8fb2ZvPmzTplmD+nbt261KxZk969e2uNnwuCQJkyZbh48aJWF3ijRo3Yu3cvQ4cOTbG9YcOGdO/eHV9fX4053bt3Z9GiRYwePdrEO/vv07t3b3r37q1135chJYlEwtixYxk7duxXnfM/YShUqVKFZcuW6TQU4FMJmamrrdatW9O2bVvOnz9P2bLa+z34v3nPpr3nyZczA941ipp0/O4j1+P/9kPyQ1UsEsiT3Z2/LjxApVIhEkHnYWuxlEmoVbEQzeqUYP+J68QnyMmROR1P/YO0uu11kfSw/hyZfXpiQ5//UEPBEMU9s3L70WtUKhAJHxtpCRAdk0BAUBgAz18FUzidDaGhoToV70IP70SVmEDCmxdEXDiFc+3P+n+oEogPv4osuBlYVwKX8QaNBX2JQ9pIjVHh4eHxVRoiU6ZMwdvbm0KFCpEvX75UH8cQIpGI8uXLm7TS/l7Y2Niwbds2OnfuzIcPH5JdsT+LO3fukD9/fpPm7Nu377sYOdoQBIE5c+bQs2dP9u/fr7eyQiwW4+vry6ZNm+jUqZPWMQ0bNmTnzp1aDYVKlSoxZ84cDUPB2dkZhUJBeHg4Dg4OKfbVrFmTOXPmEB0d/V37An0VX9vY6TdqCvXbhx4ArKyskEgkemOzXl5e/PXXX6lKxlq4cCGjRo3SKUTTcchqVm47Q78JWzh/1bRcCEEQkr8vI3rWY/HENizddAqVSkXe7O6ER6qrIOLi5ew7cY2dhy4z5o8G7F3Rh0mDmphkJID2BBYLm3S/VJ8IiURErzbVGNKtDoXzZaJa+fzqF7gK/rrwKZ/Cyd6aYkWLpEikin/rz+NBbXk0sA3xb15glbuQeocgwjLbF1oUCU9B9fE7E/M3KA0LDfn7+5MpU6avvkd9JLWcTi0SiYRVq1bRp0+fb5qv8KtjYWHBunXruHHjhkmZ/d+D1CQ/7tix46s7S5pCpkyZqFGjBqtXrzY4tnXr1mzdulVneKlo0aI6q2OkUikuLi5aw2ne3t7s3btXY7sgCHTp0oWVK1cavLafxtdUPCT9/Cb8JwwFUEuV6hNKEolENGzYUOuX0hAODg5MmDCBvn37at0fERWX3Ds6Isq0ZhvLJrfDu0ZR+rTzolalAkTFxCe//IM/RNKrTdXkBMek7SNn7Wbz3n+Jik55Lg93Z51VEdrInSUt1pYWCCIxqFSoVKYn0H1rBAEqFM/FH+M2M33ZIW7c8+fi9afkye6eHJpJIjQiBqXYKUV53P3VS0h49YzE18+5tmQ+6bsPJ+OQGWSdsQ7r3Ck7ciLNDMJHV6pFbhAZ/uz8/f1NSk5MTcLf1xoKoI7zjhs3ju7du/8yJbA/ApFIxLx584iLi2Pw4ME/7d5N9Si8ffsWa2trHB0dv99FaaFXr17s2rUrRVxbGxYWFtSrV4/du3dr3S8IAjlz5tSaiwC6G/U1bNiQffv2aZ3TuHFjDhw4oLPfxE/n/6jN9H/GUKhbty6HDh3SO6Zdu3Y6s3MNUb58eTJlyqRV1GbBuFaUKZaDbr6VqV4+P6/ffWDtzrPcf2I4IS1PdnfqexVm8caTVGwxjbi4RCqUyEXaNHaM/qMhCqUKFWojwS3tJ/fc4dO36DVmY4pjdfGpxMW9YzixeQg9WlXGxsoCFycbrK21SwiHRcWT1UOtHS+1TkNijGYG8o9GpQKRWMQ/n3lmIqLitH6WYrEIZM6Mm7EG764LCI+M5V6YCpGgDlfcCkpEkEiwK1oWSw/NeHFMHFg7lQG3xeC2EHVfav38CI9ChgwZ9GaZG0uFChUoVqwYCxYs+AZX9fsgCAKjR48mW7Zs9OrV66uPFxMTk9zfw1hMzYfatGkTrVu3NvXSvhqxWMzMmTMZOHCgwbFdunRh1apVOo0vb29vnS/9WrVqaU1ctLe3RyKRaK1++DzkYebn8p8xFDJlyqRXbhTUX8rMmTPr1RLXx+jRo9mwYYOGIE7pItnZMr87w3vWQ6lS4d11IePn78O76wKCQgx3wDx29m7yH9/J8/fYOLcrl/eNpWH1Ijx48hZBUAsoVSqVJ9lYKF4oC/EJ6jBKGkcbTmweQtsm5dTJYhIxR8/cJTo2gZDQaEp7ZtNaZRAYEpHcAVNml574SP2rih/FyX80yzWTvCqO9tbUqVKQjs3KAyrW7blCQlwUN+75c/Tv22T26cjM0JzM+JCDtI3a6T1PYGAgrm4ZQFbgk2fBAD/CUJBKpanSUtBG//79uXTpUqo0QX53evToQWJioslKl1/y4cMHnR1vtZGU8GpsfopKpeLEiRNUr149tZf4VRQsWJCsWbPqbPCUhI2NDeXKlePYsWNa9+uTX7axsUEsFmsN3zZp0kSnp6J169Zs2bLl12kE9Tk/qc30z+A/YyiAOk52/fp1vWN69erFsmXLUnV8qVTK4sWL6dmzp85ch5jYBEI+qOPC8QlyAnUYCmcvP2Lign006rqAXYevIBJECAIEhUQwZOr25MZRvdt7kcHViexZ0tGzTRXO7hzB9YPjmTqkWbLRIJVKGD1rF0f+vs2gKdup2GIqzz6rjjh14YHBKgMLW1cSoozXjLCxlmFtmTo56dSgQr1SDIuI4dBft/n70sPP9BcERIIKt7QOlCicnZnbFjJty3wa1ymh95jGlkaGxT7g3rtlBEVe5PXr12TIkMGoa5bL5anW6P9WlRVJnVEnT56sc7X3X6ZDhw5GxeD1ERYWppFsp48XL16QJUsWo8ffunWLQoUKfVU/h69l1KhRzJs3z6Cbv3fv3qxYsULrvqRqFl1hjFq1amk1MurVq8fBgwd1HrNu3bp61SF/Gj+he+TP4je6VMNUrVrVYC113rx5ef78eaoV5rJnz07z5s2ZPn261v0OdlaM6FkP93SOtG9SjgK5NV8qL9+E0HbAClbvOMv1e/4kJMpBgKL5M3P/SQA7D11hw271CrBYgSyc+3MkxzcOJnMGF6QSMXHxifQavZGg9xEIgsC74HAuXH9K95Hr2XVYfz93Xcjjw5HITMlvcMUtrWOqzpVakjpPCgJkck/z8d8CObKmp17VQrQduJJKzaeiVKlwdTH8YDfGPaxQJnD11Thehx/jxttpxMZFGN3FLyAgAHd3zcZfxiCTyb6JHDOoZXH37t3LyZMnGTJkyA9VV/zZlClThuvXr+vs3WIMjo6OJjVTunHjBoULFzZ6/JUrV3RWVP0orKysaNSokcEXsrOzM3FxcTrDD/p6P3h6enL//n2N7TY2Nsjlcp1eg4YNG+r0Ypj5MfynDIUyZcrobLf5ORUqVODs2bOpPk+7du24e/cuFy9e1Lq/q29lLuwexbj+jbSuDAOCwzWqFfLnyoCLs1qyVaVSYfdRqVEb/SZs4drdl8ntrlNL0qVJJSISIt9hYWf8S+3aPf8UXouvxdHeKvnfGdwccXa0/qRW+cVHWDCPBwvGt+bAmv6c2jKEArk9OPa3Wg42IDg8WTbbEMYYCiqUKFVy9b+UKpMSkF69epXqMIWbm5vBBDNTsLCwYMGCBRQtWhRvb+9veuxfGUEQ6Ny5M6tWrUr1MVxdXXV2SNTGjRs3KFKkiNHj7969a3Ip5ffA19fXqMZibm5uBAVpr5JKUsrV9lzKli0bz5490zovT548OhMhs2bNqnPeT8Ucevg9sbW11RB90YYu/XFjEQSBxYsXM3r06FQ1zCmcxwOZheTjsWDiwMZsmd+NqUOa0aN1Vcb2bUjL+qV1zg+P0myYItXT1+FzPu/OmPS3LFeoiI96h8zWeH2Ab01YhPqeLGUSZo3w4arfeM7vHs2lvWOYP7ZVirHWlhbY2VhSIFcG0rs6IRaLqVJWXfpob2tJiULGdSk1xlCQiCzxTD8YF5tiOCQ2ImvmXEbf09fkM6RPn/67vMx9fHyYOXMmrVu3/r9p59uoUSP8/PxSnfchlUpN8sKYWvHw5MkTk/qBfC/SpEmDVCo1KFuvq9ETqEtzixYtypUrmp5NZ2dnQkJCtM4rUaKEzp4SgiCQJk0anXN/GmZD4fclX758Wt1bn5MrVy6ePHnyVQkyTk5ObN++neHDh+ttmqINS0sL9q/qyx/tvdixqBdtGpXF1toSZ0cbBnerTYdmFdQZ/ToY3qNuiv8LApQp+qlfRLWyefFtUBpbG1ny/tzZ3BD41J1RKhEnr9RVKhVKRQIiiXEu9e9JXLycNv1X0KDzfArWHEmPUeuZtHA/EvGnpfz70CjW7zpHnmrDyF11GMfP3aNF3RIsn9KesztGkMFNu/jSlxibmZ7OrhRFM44iOigDuXKZZih4eHgYPf5z3N3dUyXjbAx58+Zl3759rF69mhkzZvznyyfFYjHNmzfX2TzuWxMfH4+lpW6P4JcoFAokkl9D+65NmzZs3LhR75j8+fNz9+5dnfubNm2qUxtBFyVLluTy5cs691esWPGrvMBmvo7/nKFQsWJFzpw5Y3BcuXLltDYrMQUnJyd27NjB2LFjTT5W7mzuDOhcixKe/2PvrMOkqv4w/rl3aru76e7uLunuEhEVpQRFsBAFpQRFQTAIFVAJRVqRNkBSupfYZbt3J+/vj9lk504sIfjjfZ7hWeace+6dmXvP+Z5vvG/B7jf6ViKdRsyneb/ZHDt9XfbYE2cLWPtUSgWlIwLY9+cFMrK0zJrSm+Xvj2TWK33o91R9wOw58PVyLeLD1xuMhAWZM7mNukwUKsfKvx4kDEYTp86bWRn//uc6cYnpGIwFi9m1mwm8t3gzOVrzLi8zS8fQiUsZM20FW36zv6LF0RK2CxcuOGQo3Evo4UF5FPLg5ubGypUrcXV1pX///o/ebu0+Y9iwYaxatarERpG9Ql3x8fH4+fnZPW5KSspD506whvbt27Nz506r35M1jwJAnTp1LHoUwJx7k5NTnGsmKiqKq1evyo7ZvHnzR88D9iSZ8fFF06ZNOXDggM1+90uhzNPTk++++4733nvPLgPlbqSmZfH89JX0G/sJC77YwfnLsdyISeSjr+TJo5w0BeEDvcGIc+7/JUni+q3EfDrWaWO78NHbg+nQvCp/HL9CgK87gX4eqFUKBnVryI2YJAC06TFo3EqWdGcv1Cr7b7WWDSsgivK7j1aNKlGxTMH1CoWkn385KL/TuRvp6ekO8fdfuHCBcuXK2d3fkQqJuxEcHPzA8wgEQWDs2LFMnjyZsWPH0q1bNxYuXPhoxoPvEc7OzrRq1com14oc7HV9Hzt27LHMT8iDUqmkTp06Vr2k4eHhVgnBRFGkVKlSXL58uVhbqVKlipWXg/letJbAW6FCBdkchn8NT0IPjy98fHzsUh2rXLkyp0+fvi/16u7u7nz33XfMnTvX4Xr1FesPsH3fP/x14ip7/zyHSTKX/JUvLZ8vMLBbg/xcA083J4b1boJCIeLu6lREa0KpVNCqUSV27PsHk0niTkIaT7WqjigK/HKoYEegzYhF7R5U6Lj7f1vo9PazPu7543x+hUMeypUy7/xVSpGyUQF88cEo5rzWjw+m9qV5g0po1CJKhUj/Lg0sDSkLR8oQHS170+v1DitH5uFBhh7uRv369Vm3bh3fffcd5cuX54MPPqBDhw5Mnz6dQ4cOPZo17CXA888/z6efflqiY+013ByteHjUDAUwl5RaI6bL04ux5nXo1q0bmzdvLva+tYTGGjVqFGFZvfucnp6eDlWfPMH9w6MRGLvPCA0NtVmaJggCXbp04eeff6Znz573fE5XV1e++eYb+vTpw44dO+xegPy83fMfuOTULAQBXn2uM8/0by57jLurMztXTWb/4Yv4eLqSmJLBge+n4evthlpl/kmNRhN/HLuMv4877m5OpOdSS6/4/gASkKMt4Hcw6bPzQw8uTmpytGbjSa1Smks3HwFcvBZHaJA3t+8ks+Tr3YiCwORnOwHwz5/lmN65K9WqV8fNxf7YsCNGgslkQqvV2h1L1uv1VoV2bCEtLc2h2v37AScnJ5566imeeuopTCYTf//9N1u3buW9995DqVRSpkwZqlSpQpUqVahUqdJDv757hbe3N9WqVWPPnj20bNnSoWODgoLsqnzIyMhwKJSQlpZGxYoVbXd8iKhQoYJNCvGoqCiuXbtGqVKWE4fDwsIshmOt0ZM3a9aMffv20aCBZWO/bt26HDt2jFatWtn4BA8J/0eiUP9JQ6FWrVocPXqUzp07W+03bNgwnn766ftiKICZ+bFFixb8/PPPdO3a1a5jBnZrgCAKvDF/A0ajCUkyl0oqldarGMJDfKleMZuuzyxCkiRqVYlg42fj8tvfXriJ1RsPIYoCgX4e+YaCxT1AoQVTEAVMuYbLo2IkACBBbG5ZqSBAcmpmflN4eDjxcbG4udR/YKc/c+aMQzu/kogCFca5c+f+1QVEFEXq1atHvXpm0iqDwcDly5c5c+YMv/zyCx999BFpaWkoFApKly5NYGAgvr6++Pj4FHu5uLg8cGluezF58mRGjBhBixYtHLqmgIAA2ZLAwnB2diYrK8vuce3Nffg3IEmS7HeUF+KVMxQyMjIsqj6GhYXxxx9/WDymcePGfPzxx0yZMsVie40aNThx4sQjZijcQ/jg0Xgk7MJ/0lCoXbs2u3btsmkoeHt74+XlxdWrV2VveEfx0ksv0bdvX7p06WLXRCSKorlCwUXD4pW/0rBWaRrXLmvXua7eTMj3Rly+XpTTIE/F0mSSbBodeXB2UpGdc39Ifh4EjEbzhCpJ0Kx+hfz3w8PDLboz0zNz0OkM+Hrbn4cgh71799K8ubyX524cOXKEunXrlvh8586do06dOiU+/n5DqVRSoUIFKlSoUMSwNhgMXL16lbi4OJKSkkhKSiI6Ojr/76SkJDIzM4uNJ4oiHh4eeHh44O3tjb+/PwEBAUVevr6+952t0M/Pjxo1arB7927atGlj93EBAQE2WV8BXFxcyM4uXr4sh0fVUMhLpg0JCbHY3rRpU959912GDh1qsT0jI8Ni/k9YWBi3bt2yeIyzs3O+584SqVmNGjVkyZz+FQjcW/D+iaHw76J69erMnz/frr55UqazZs26L+f28PCgadOmbNu2jaeeesru47q1rUW3tpaToC5dj2Pj9r9pUKs0zQstkO2aVqFdsyqcOneTV58req4XhrZh6gff4+HmhNKGCzzQz5PSZYO5FpvNozZnCVj2gkSG+nInIRVRFAkPDy+WEX3s9HUGjluKVqdnzmv96WuDztkW9u/fz5IlS+zuf/jwYaZPn17i8507d47Bgwfb7vgvQ6lUUq5cOYeSPMFcEpienk5aWhpJSUnEx8cTFxfHyZMniYuLIy4ujoSEBAwGA6GhocyePdshvQVrePnllxkyZAitW7e226vgiEfh3zYUbty4Qd++fXn33Xdp27ZticYoX74858+flzUUwsPDuXHjhsU2kDcUfH19LQpA5SGPNM9SaCggIID4+PtH8vYE9uM/aSi4uLjY7f5r1KgRb7/99j0lnt2Nl156iQEDBtCpU6d7drmaTCYGvPgpiSkZfPr1bnatnkzZKLM+gZNGxfLZIy0e17tTXTzcnBj92goSU4rv6PKgVIiEBHpy5tJthyicHxRUKgV6fUHynCUjoUPzqmzde5LFq39FIYosnN6n2KS1ZfcJtDo9kgTf/fyXRUNBoVBgMBhs5h1IkkRKSgre3vbxM4B5si4phwJAbGysXToUjysUCgVeXl54eXnZLCH9/fff6devHyNHjmTQoEH3/Ez5+PhQt25ddu3aRfv27e065nEJPRw9epTJkyfz2WefMWnSJJo1a2Y35XhhVKhQgQsXLlh18/v4+JCYmIivr2+xNjlDwdZv16ZNGzZv3iybQ5InmHa/5up7wr1WLjypevj34e3tbdVyzYMgCHTr1u2+CuZ4eXnRtGlTh+RRM7Jy+HzdPn7cebRINvH+wxdISs1EknIXrDT7J6HVG+XprD3dnXF10eDn7c7tO6kyyQsPFy7OaoviVYIA1SqGEhLgiUat5Og/V/n4q1+QDBIGnZH3PtlRLBu6bdMqKHI9KV3b1LR8PjvdxBcuXKBChQo2++UhJycHjUZzzwvaoxLX/7fRqFEjtm3bxo0bN+jZs6fFsjtHMXHiRD788EO7eRVs7YTz8G+GHjZv3sxbb73F+vXrqVGjBmPGjGHevHklGivPo2AN1rhoMjMzZUuPrVVM1KlTh7///lv2nBUrVuTcuXNWr+uhQbgPr8cE/1lDoXbt2rKlNndjyJAh913zfOrUqfz00092k4RMm7Oe9z7+ifHvfMuG7eYH5eyl24yY/Dkmk4RapeCl4W2pUy3K7muoWaVgp+br7Ya7a0FFQGp6NpnZWmITUnPFpewe9oGha5uaFicQSYJT525xOy4Vrc5AfFJRD0lMXEoxr0nDWmU4tOF19q17jWG9m1g8n727v71799KiRQu7P8fJkyepUaOG3f3vRkJCgsVd2v8zVCoVU6dOZf78+bz88svMnj37nkqbvb29adiwIdu2bbOrv1iIq8Ma/q3Qw0cffcSmTZtYv359vuerb9++HD582CqRkRyslTHmwRpnjZxHAawbXQqFAjc3N4ty1AA1a9a0e15/gvuH/6yhYIs9rDC8vLxwdna2a8dgL5RKJStWrOCdd94hPT3dZv9bd5JBMO8iY+JSAIhLTM/XYxBFkUnPdCiyy/z7n2us+/lPEpMzuBmbVGwimzCyHUvfG87TfZtRvWIYzw9plV8+CeR7EYz6HASF+p4+771CEGDjDvmdhDUoFSLubs7F6v0DfD2ICJVfcLVaLWq17c99/PhxhxITDx06RMOG8lodYN5VJW7+llsfvUX25aI7pIMHD95TIuR/GWXKlGHjxo1ERETw1FNPcfKk/Uycd2P8+PEsX77coWMssQoWRlhYmEOEVR4eHvfMDbBjxw7OnTvH559/XuR+FgSB119/3aHcmjyoVCq0Wq1VDo0qVarIzrHOzs6yip21a9eWFdQDaNiwoSydc8OGDXFxeURYZEXp3l+PCR5rQ+HixYuyBEfh4eHcvHnT7rFatWp13ylCXV1dmTZtGm+++abNvm+O60bV8mG0blSJIT3NkrNN65ZjeO8mVCwTzLxp/YsYCcdOX6fP84t59f3vadz7XZr2mcWkd9cUGVMURSqVDebL7/fz2+/nmLtsm8WSR5NRi6i0n3/gQUCSQKe3n9hHqRSpWiGUWVP68POXEwkLDWPR8k2cvmg5o9oSkpOT7co7uH37tmxSlyXs37+fpk2bWu2TefwP4r75lLTfd3NzwbQibevXr6dXr152n+//DYIgMHjwYFavXs3bb7/NihUrSjSOl5cXTk5OduUegDl+/ssvv1jtU7VqVU6dOmX3NZQtW/aeQilZWVl88MEHzJkzx2Koqk6dOiXegTdo0EC2lBGse1mqV68ua8Q99dRTbNmyRXbcSpUqyYYXoqKi6N27t5Wrfoh4Enp4PKBUKmVDBiUxFH777bf7dWn5aNOmDUlJSTZLq2pUimDzFxP4Ys7TeHmYLWaFQmTGxJ5sX/kyXe6Ks1+Ojsv3Nmh15sV/446j+SWECcnpxCWkoVIqEUUBQQDBwp1p1GejyDUSIkN9adukcn6bm8u/62UAZEMiBoOJf87fYtWGg2jUSn47cpM5n3xHj2c/4mZskp1j2/ekOiLaYzAYyMnJsU0NXagSRRAK/tZqtSQmJjpkmPy/IigoiO+//55Tp07x1ltvlUjHoV+/fnz//fd29e3ZsycbNmyw2kcURTw9Pe1ihwWzoXDp0iW7+lrCO++8w8svvyx7vwmCQGRkpEXaZFvo3r27zdytvITGu2HNUMgzjuR+r0qVKtkU9nuCh4vH2lDIYwezBA8PD9LS0iy2WULZsmW5ePHifbqyopgzZw6vvfbafaXC7dSiOs3rl8fVtSCjOTjAE4VCZPehMzTo/g4Nes5k/+ELNK5dlshQX6a91KWYqaDLiEWdKy99/VYivxw8g6AQEJwVjOhnP2/A/YYoCDRvUJ4e7eswql9TVIW4IFydCwyY81diWPjFTrINagw5qej1Rm7fSbE5fmZmJs7Ozvf9uo8ePWoX/4Fr9foEjpiAZ/NOhE2Znf/+L7/8Qrt27e77df1XoVAomD9/Pv7+/owaNQqdTufQ8Z06dbJb/yEiIoLbt2/blJxu0aKF3bovPj4+JCXZZ9jejZMnT3Ljxg2bfDGdO3cukcaFPfkAcmq9Xl5eVkMq1hQorbE3PlJ4ovXweOB+8n8LgkBAQIBdNK2OIjAwkB49ejgcD7UGVxcNqxY8y5iBLfPfy8jMof3QuazaeChXM0Li09W/cujoJaJvJfHxil+LFTdoM2LRuBXVlZCMEhgllv64n2YNy+Hpfv8X1HzIbOpNksQ7E3uh1xv48vuD6A0FRpanu3O+N0CSIDzEhzFDu6ISsuj7VD3qVI2yedqrV69SunRpm/3kGObksHv3blq3bm2znyAI+HTsQ8jz03CKLOAgeBJ2KBlefPFFevToQe/evR2aD5ycnAgMDLR7x22PEdC6dWt2795t9zWUxBNiMpl49dVX7apqsCdkYgmCINisfrCWC+bk5CSb2Nm5c2fZ8IO9iaP/Op4YCo8PGjVqJBtHEwTBoYziVq1asWfPnvt0ZUXx7LPPsmnTpvtuiAzr1YR2Tavg5e5MeqaWC1fvcPjEVURBMD/opYLMpZVIpFoordRlxKFy9S/2vgSYJBP7/7hIarr9WdwOw8qzMnTiMn7efaLIpKEQBWLj0xCAWlUieXdyL0b0bcquP66TnpJAZrbWqvJkHq5cuUKZMmVs9nOUD+H333+3mcgoB51OR0xMTImlqa3BkJrMzfnTiJ79Mro79udxPE7o1q0bb775Jr179+b6dXmZ9rsxcOBA1q5da1ffXr162Qw/2FNaWBgeHh6yWf5y2LhxI+3bt7eqZ5MHd3d3jEajQ9UYeejevTs//fSTbLs1Q6Fq1aqyXoNmzZqxf/9+2XFL8p08wYPDY28oNGnSRDah0d/f3yEmr1atWjm0E3AEoigye/ZsXnnllfs6rpeHC8vfH0nd6gUU1AqFyMENr9O9XS2u3oynXbMq+HoVxDBLhfvh6+WaW89sRHRSF70TBBDUIqbsf1frobCeA0C5qECG9GyMSZIwSRJ1qkYwqFtDjp+OJiYxG5NBy9bfTvLim6ttjn358mW7PAqOGAparRZJknByKlli6G+//eYQrbAjSNy0mvQjB8g8+Rdxaz97IOd4FFCvXj2++OILnn76aav1+IXhyHNfoUIFLl68aHUD4qh3skyZMg4nNP79998O3SslzcFq0aKF1c1TqVKlrKpBnjhxwmKbWq3G1dVVNuxiLaHxkYF4H16PCR6jS7WMmjVryibNVKxYkX/++cfusaKiohxKgHQUtWrVwmQyyXKd3wvmvNaPymVDCAnwYum7wzh9/habdh7l0rU49v15noTkglKlT94ZSnJaFkZdBkonN7P3v3Bin5T7jxVnTOXezjSd4k54wwfHkJaZVaA7oRAF2jerQtO65XHSmBMLP1+3n/4vLqFahTA83AoW5y2/nSwSqrAEew0Aa3z3d+PYsWMlLmuUJIklS5bQt2/fEh1vCwoPT5DMP6jS3euBnONRQVRUFD/88APvvPMOq1atstlfqVRSvXp1uyXiGzZsKMsfkIfWrVvz66+/2jWeox4IgLi4OIcorbt3784XX3zhsEs/r9xSzjBSKBSyuVe1a9eWLXMEaNmypex3fq9Jng8FT0IPjw+USqXsTdyyZUuHrej7LUJzN/z8/GzWYpcEPl5ubF0xiUMbXqdxnXIE+nvmr/0hAV6UjjCHF8pFBVI2KhBBENBnJ6Jy8TMbCYXuBMFZgZRjBL3lG1npArp0E0ongTJtnR8YWVPhsxtNEp+s3s3o174iR1vg6Th88iqp6dls/WoSarXZgOjYolqR5EdLyMrKsl2ZgDnp0d3d3a7rdVRhsjBWrVpF8+bNiYyMLNHxtuDbdTCBw8bh3/9ZAgY9/0DO8SjB29ubDRs2sHfvXrvi81OnTmXmzJl2hSpHjRrF559/brWPI0mS1nbecmjXrp3NEEhhlCpVigYNGvDFF184dB4we0OtSabLeU+sJZuDWXhKzuvi6+trd+XIv4b/o/LI/6TWQx5q167NW2+95dAxgiBgNBofmMFwPxJ1YuJSWPLNb8TGpTLl2Y6UK1WQjLh+2xGWrdlDiwYVWL3gWU6eu0nPDrXx8nDh3OUYKpUNQSGKRIX6cuzGEVTOPkgGU5FVuUyoH5cv3SmaPpB3Y5vAkAWXdmrRZUr4lVfxsPOO3Fw0ZOR6G0qF+xMR4sOv+4/Sqmktpr81ltS0LNIzc4owUd6N7Oxsu0IEmZmZdhO8nDt3jhEjRtjVtzBMJhOrV68uUWa6vRCUSnye6vfAxi+MnJwcoqOjuXbtGtevX+fOnTu4ubnh6emJl5cXnp6eRf729vZ+IM+bQqHgo48+olu3btSsWRM/Pz/Zvn5+frRr1461a9cyaNAgq+NGRESQmZlJfHw8/v7F83vAnMCcmJhol5ZISWiJ+/TpQ8eOHRk6dKjdGiQvv/wy3bt3p2XLlpQta59CrT2oWbMmJ06csKibUbp0aS5fvmwxH8jf3182v8FW1cQTPFz8JwwFjUZDTk5OsYlfoVDg6upKWloaHh72CR75+vqSlJQkOwHcKxxNsLwbUz/4nrWbzaxmAnAnMZWalSJIScti/Mj2TJ61FkmC81diad+sKi8MLcjAr1Ulkk07jzJ51jqMRiP67CRcfMsWCzG4qtXFcwwlivmfog/qiD7kWDkaAAoBjCW3Lvp3bcBTraoz5b116PQGJr23lg0/bkObeotLk78gPTOH0hH+7Fw1WVZiOzs7267yyKysLLsNhUuXLtmVIHk3tm/fTps2bexiiXyQMKQmk33xH1wq1kDhZvt5kSSJ48eP8+OPP3Lu3Ll8Jj4nJyciIiKIiooiMjKSatWqkZWVRUpKCikpKVy7do3U1FRSUlJITU0lPj4elUpFkyZNaN26NTVq1LhvhoOrqyvz5s3jhRdeYN26dVa5M1588UU6depEz549bd4bI0eOZMWKFUyZMkW2T54Wgi15crVa7TAdtUKh4I033mDGjBksXLjQ7mM++eQTxowZw88//2w3N4gt1KxZkz///NOiodC+fXt27tzJ888X92L5+fmRkJBgcUwvL69H36Nwr+yKjxEz43/CUMjTTi9VqlSxtrxypi5dutg1Vp5K3IMyFO6F212nN+QbCQAIkJScycr15jjfrdjkIrv7z9fuLZLkCPDhFzsw5Mbvjdp0FOribvUTZ2TkYy1ddknu9RIaCe5uTgzq1pBxI9pSu8vb+URTMXEpGHLSUDp5kJ5pDutciY4nJT0LP2/LYQOtVmu3R8He8kidTlcipb7PPvuMr776yuHj7ieM2VlceWU4xtQkVH5BlFm4BkFZPP9EkiQOHz7M+vXrOXr0KDVr1qRnz568/PLLdodoLCErK4tDhw7xww8/8Prrr+Pq6kqLFi1o3bo1lSpVuieBrFq1atGgQQM+++wznnvuOdl+Go2GF198kQ8//JBp06bJ9gNzaOHDDz/k5ZdflnXLd+vWLT+kZAuBgYHExsYSFBRks28emjdvztKlSzl79iyVKlWy65jIyEgGDx7MrFmz7GKMtcf7WaNGDT77zHKCbKtWrXjmmWcsGgrWks29vb0ffY/CvYYPHqPQw2OfowBmQ0EuQbBNmzZ2JxWB+YF9EFwKebgXQ0GtUtKkjtll6OKsZmC3hkQW0jK4eK3ode87fKHYGN6eRRc9hybg+20AO/ig9GhXm9de6MKCz3fkGwkAfj7uYEjDLyCY+jXNlQw9O9TmzQUbGTz+My5dL07RK0mSXZ/dXo+CPS5mSzh+/DgREREOJaY9COgTYjGmJuX/bUgpyEY3mUwcOnSISZMm0b59ezZt2kT//v3ZuXMnc+fOpXHjxvdkJIBZdbFt27bMmjWLrVu38sUXX1CqVCmWL19Op06d7pk1deLEiWzfvt2m/kvPnj05dOgQsbGxVvspFAqbOVDVqlXj5MmTdi22ee57RzFr1iymT5/u0DFDhgzhwIEDdhHApaam4uXlZbWPl5eXbCmjp6cn2dnZFj0m1sIL94sf5wnuD/4zhsLt27cttlWsWNEhOlB7dedLinsNPayYP5qNn73E7+tfZ9aUPjzVsnp+W7UKYXRuXfD/+jWKl/+9PaEHggAmQYeocUJwVvx7lq2DhsfqjYdISslk294CLv1SYX5s/nw8XZqXYe/6WXy3+AUu7fmA0EBvtu85xe9HL/HeYvk6cFuw11C4efMmYWFhDo+/cOFCxo8fX5JLu6/QhEbh3rAVCAKeLZ9C6RtATk4OS5cupV27duzYsYORI0eyc+dOZs2aRe3atR+oDLaHhwedO3fmww8/ZM2aNWzfvp1evXqVWARKFEWWLFnC+PHjrSYTC4LAjBkz7NptDxkyhG+//dbqWBUrVrSroqGkqohRUVFUrlzZofwWQRCoXr06Fy4U30jcjcTERLuUTK0psTZo0IC//vrL4nXIoSThmIeOJ1UPjxeCgoKIiYmx2CYIAj4+PnbHux60oaBWq0tEfJIHlVJBrSqReObqQQzo1oCZk3rSsUU1rt1MAATmTevPsJ6NmTetf/5xl67HceN2IhmZOXRsUR2DNgWVizeCIKBQio/FnSCKAkmpmfTvUh8ADzcnvpw7igA/TxISEvLDRUqlAh8vNzM7JRK+3rarG+SQnp5uV3XEtWvXiIqKcmjspKQkMjIy7mtiWUkhiCJhE2ZS8dt9hDw3jR9//JHOnTvj6urK9u3bmTFjBtWqVXugxoEcvL29+eCDD1i0aBEfffQRI0aMsGuRuxvBwcGMGzfOphFQp04dDAaDzYW7VKlSxMTEWDU8unXrxqZNm2xeW0kqH/IwdepUFi1a5NAx9p4vNTXVrvvfmpJkmzZtZHkqrHlbHnl2xv8jHoX/RI6CtRJJMLuxsrKy7MoOvhfudXvQtGlTfvvtN+rVq2dX/+jbiaiUCoIDvCy2i6LI0F5N+PCLnSSlZnIzNpmtv51AkuDUhVusWjCap1/5gsMnrpqrIHNDH6KowWTIwdfLlY9fG8iAl5bex0/5YGAySTw37StuxCQTFe7Hynmj80MvKpWqiHjT8N5NUClF0jJyGN6nSYnPaW91xNmzZ6lYsaJDY1+/fp3KlSvb7vgQIQgCH3/8MSdOnGD79u2oVA+OJ8NRhIeH8/nnn3Pu3Dnee+89BEHgtddeo0KFCnaP0bVrV9asWWMzrv/uu+8yevRofv75Z6vGUZcuXdiyZYusomHLli2ZM2cOU6dOtXpdnp6eJWYidHNzQ61W2x1OA/KTvG2hUqVKdqlhhoaGyoZrIiMjZT2+oiiWOGz3BA8Pj5FNIw+TyXTfsqQftKHQokULu+WsZyz6keb9ZtOo17ssXmWuBT9+Jprla/ZwK7aohyQipCDGnWeIX7gay2ff/MbhE1fz3zeZTEgSKERXyoe4sXXpOEZOcby2WhYPeMN56Xo8Wp2BazcS2PNHQUjp7sQohcJsQI0d1gY3l5IxJWq1WrsrEU6fPu0wh8KdO3cIDAwsyaU9EJhMJiZPnkxiYiLLly9/pIyEwqhYsSIrV65k6tSpTJ8+nS+//NKh499//32mTp1qdccaEhJC8+bNWbdundWx+vXrZ7WPUqmkcuXKdoVM1Gq1w6JWefDz87Oo4igHe3frzs7OiKIoG1YofH65CgZrc2peIrolqNVqtFqtxbZHA/cadnjEPSaF8J8xFKwRgjiCB20oqNVqvL297UqYXL/tSP7f32z6nWs3Euj13Me898nP9HlhcREvypdzn+HNcd1ZMe8ZWjeuhK+3G2+N646TpmCyV4giU57tRPWKYYgKJafP32DgS5+RnXNXLPAuOme7oRAe2r0vCFCtQgGz4oMIGZ07d87ubPLr1687TJbkaJb7g0ROTg5Dhw6lUqVKvP322/9KiMFRVKxYke+++46//vqLZcuW2X1cREQEdevWZePGjVb7TZgwgWXLllldrPz8/DCZTFbnjKFDh/L111/bvK4KFSqUKKQC1vO05GDvb2zP5saaoeDs7Cwbbo2MjJTV5QgNDXX4Mz1U/B8RLv0nDIX7SZDkqDx1SdClSxd+/vlnm/3aNStwS9epGkXnUR9iMplX4riEdPSGAkPBx8uVp/s1o2XDinw5ZxR/b36bfl3q88yAFowd1oaBXRuw77upVCobwvlrsflehys3cnfhhW9cCVAV+tver/Yh3vhqlZLjZ8xStDFxKRz+J4bPVm+1Sd0M5jJGe3bLJ0+epHr16jb7gXmH5qix+rAMhZQ9W7m1+B2yzh633J6SQu/evRk6dCgjhwxGF3vr0Y8P50IURT799FNOnDjBkiVL7D5uypQpLF682OpOWaPRMHLkSJYutR6W69evH99//71se+3atTl27JjNKoOqVava5ea3BEcNBUd+3w4dOrB9+3arfawZCtZgzVAICwt7oJT6T2A//hOGwv30KAiC8MAnyU6dOrFt2zab/eZNG8C6xc/zwydjcXJSkZVtdku6OKmZ+XIvNGrbcb3zV2L46vv9rNvyF9PnrmfklC/Q5phLCwWFigbVwxEKqy0KmD1qChFBJeJU3Run6j7m6ghrUIlgeHDfm0atRKUquAatzsA7H/3I8TPRvPL+dxw7H8d3P+1nzY+WlUQLw14CLnsNhfj4eKusf3J4GKGHnGsXiVk6i7SDu7jxwRQkQ3Ghr1dffZWpU6fStlEDLo/vz+UJ/YlZOvuBXtf9hCiKLF68mHPnzvHxxx/bdYyTkxPjx49nzpw5VvsNHjyYzZs3W80f6Nq1q1WFRUEQaNmypc1dedWqVR3SpimMB+lRsIc58v/SUHhS9fB44X7mKDwMXL2VyvVb8dyKse4qFwSBBjXLULdGKerXKJ1vwEx9vjODustLGUuSxJzPttKo17vMWPQj2Tk6TCaJ/XfxKig1HqhFLZJJMvvyhYLQgZRjQhXlhsLfCYWPBk1ZGwvrA/EmFDxIWp0Bvb74jkytUmI0mFAoXTDpszAYTaRlZLP70BkSktMtjpqWloanp6fNs1+4cIFy5crZ7FeS/AR4yKEHCYtU23/+aSbwatasGVlnj2NIMce5Uw/seDjXdZ8gCAILFy7k6tWrfPjhh3Yd061bN44fP251MRJFkcmTJzN37lzZPs7OzgQFBXH16lXZPoMGDbIZfigJlXMeHqRHQRAEqws62A7Zyp0vMjKS6Ohoi22PvKHwf1T18BhdqjyMRqNNj8Kj4ko9ee4GPcd8zLUkd7oPsr9+vl/n+vy4fBzrl77IsN7Ws/iv3kjg09W7iYlL4eg/1/N3Dnd/R9WrVuS3/bl5ECbJ/CoESWfKP1bSmVAGOKGKdLVsFBhKzg1RHPb9Vm2aVKZyuRBmv9qHti3qULWsD/261KP76I94+pUv6TB0PmkZxWOjqampdnkUCldRWENJDYWMjAy7Ss/uBU5R5Qh+dioejVsT/uochLs+z1tvvcV7770HgEulmii8zFUknk2L0/E+6hAEgfnz5xMTE2N1YS/cf+bMmbz++utW+3Xo0IGTJ0/KJt2B2fPwzTffyLaXKlWKO3fuWA11ODk5lbh0OiQkxOFF1ZE8lA4dOrBjh7zxqFQqMVjwVuVBpVJZTNS0VtoeFhbGjRsyLLGPAp54FB4v2CLFsZfXvzDuhRTJGi5fjzMbLYJAfGKmQ8fWqBRBnapRNvv5eLnkJzF6uDmxa/Vkvv/khXzq5jxcjckkKyMl//+CSgRlwS2hj85Aez4V3eV0jOk6XBsHmPtYur8fyNdlfSI7+s81hk1azrBJy4lLziYkwJ3sHD1Xc/MuElMyiL5VPBP87NmzlC9f3urYlrRD5HDjxg2HExkNBsMDu8fuhlfrLoSOm4Fr5VpF3s/TNMkLmyg9vSm7aB1lFq4l+DnrFMaPKgRB4IMPPiA9PZ3XX3/d5gahWrVqqNVqjh07ZnXMKVOm8NFHH8n2yWNptJb42Lt3b6u5DADlypUrUZ5CaGgoly9ftntDpFAorC7sd6NevXpWvyNbkDO6FQqF7HMQGhr6aHsU/o/wnzAUbMWcHRGFAnNNc3q6Zbf1vaJD86o0ql0GXVo0QZGVOHPx/mb15mj1LPxyJzlacyVD26ZVKBMZiK+3e7H13SRoMOpzyWJEc9hByPsbQAL9jUz01zIQXZRIkoQ+2jHjxiEIIChFLOpWCxS7W5NTs9j313mu30rkzMU7/PH3Bfx93OnfpT6CAK0bV6JimeBiQ/355580aNDA6qXcuXPH7rBAXFwcAQEBdvXNw19//WX1GiSTkTurF3Pt7bFknjrs0Nj24uLFi8VCK6LGCXVQ2GNR9SAHQRB455138PX1ZcKECTYXz7feeou3337bar+mTZty/Phx2XlBFEWGDh3KypUrZcfo378/a9eutXotQ4cOZfXq1Vb7yJ3fEdImjUbjUOmhrTCArQRha3lkcveaRqMpcbnow4AJ4Z5fjwseqKGwb98+unbtSkhICIIg2MVQVhLYijkbDAaHasIfZImkk0aFj5cb+pw04lJMzPzYnAR1+04KA15aQq/nPraoTWAPLlyJpX73Gaz44WD+e9v2nOSV97/jhTdWERV2FxWrqMFksODqtDBh6m5mYkjQoggsGSeBXbD23Ejkey06tqjKsvdHFj1UFJFMZo/JB1P7cWnPHL6cM8qieuTFixdtsiE6kj+QmJjosFbDrl27aNeunWx7xtFDJG1ZS/a5E9z6eIZDY9uLS5cu2ZWD8SAhSZLNxeDYuWheWfADG3496tDYEydOJCgoiE8//dRqv9DQUKpVq8bOnTtl+wiCwKhRo6xyNgwePJh169bJ7tRdXV0pW7asVU6FOnXq2FUhYQn9+vXju+++s6uvo4aCKIpWDamkpKQS65U8KmFhR2G6D6/HBQ/UUMjMzKRGjRp88sknD/I0dsec7cWDNBQWfrWLH7cdRFS5IkkSQf5mA2fpN7v568QVjp+O5sMvSpZItuW3E6RlFKWT9fRw4buf/+Lc5Riu3cx1wwuAQkDh7IzRkG3eybsrEH01iF4qy6EFrYms3+Mw3LBOvHJPMIFkNFlNUfD1dmP73n8Y89pXDOvVGHfXXMNFUFD40VMoLN/aOp0OtVptc8fsiKFQkmTaw4cPW2XnVLjm3s+CYJfkc0lw8eJFSoUGk7zjBzJPFefidxQpe7ZwfeZ4Un6zXPqr1+s5efIkK1euZMKECXTu3JkuXbrQv39/unTpQu/evZk2bRrffPMNx44dIzs7G6PRxMg3vmLDr38zZf73/HPJsvibHF599VX27NmTn7Qph1deeYV58+ZZXaB79uzJTz/9JGsIqFQqevToYTW8MGbMGFmlRTAbJK1atSqRCFa9evU4fPiwXQuvo4YCmIW7MjMtexStaUKYTCarz5uLi4ts7sa9MFY+wf3DA+XN7NSpE506dbK7v1arLXLz2stn4GhowRYepKFw9uJttGk3cfIIo3bVSN59uRcAIYHe+RwJoYFeJRq7Sd1yLPpqV5H30tMteQwAEUSlMyZjDoJChEwTpmwdklzcPO9BFyTz8QKgFkFfqH/hQ3PLLB2GhWOUChEJ8PZ0oVxkAL8nZyBJ8PWm33lzXDfeXvgjgqjAw9U2i+KJEyeoUaOGzX4xMTGEh4fb7FcSpKam4uzsbDVR0qVSDUInvEPO1Qt4te76QK7j0qVLuBpuE3fNzHAZPm0RLpVro4+PRTLoUQfb//n1yQnELH0fkMg6/Teu1euj9PHnjz/+YOXKlURHR6NUKqlUqRK1atXi+eefp2zZskUMrJycHM6fP8+ZM2dYv349586dIyMjg5gbOtwi6oEgYjQ6tg8TRZHly5fTq1cvvvvuO9kyVg8PD7p168Y333zDsGHDLPZRKBT07NmTH374gQEDBljsM2rUKLp3786AAQMsLo5Vq1bl2rVrVuXLBw8ezFtvvUXbtm3t/JRmCIJA7dq1OXr0KHXq1LHatySGQvny5bl48SI1a9Ys1mbNUEhKSrIqLJXHKmkpzyyv2sJePpOHCZMkYJJKHj64l2MfNh4pgu3Zs2czY4bjbtbHyaPwwtDW/Pz9Z0RVbsHcaf1xddEAMHpAC3w8XdHqDfTrXL9EY4cGFtWyEETQaFSUCvfn1p0UUtOz8o0RDJKZP8FoQjKYUCjNk7AomDDlLfICIHczS4C20KStEszETHlcCvfRm2jIXRwSkjIY1qMxvx+7Api1H3YfOsvXHz5LVo6Oj+dctDnWn3/+Sf36tr/f2NhYu/Q4HOHXz8OePXto1aqVzX4eDVvj0bC1Q2M7gpSUFFzSs8nbH+vjY0j/+yA3500FSSLo2VfxttNIEZQqBKUSyWhAJwl8s34Da9ZvpEaNGkyePJkyZcrY/J6cnJyoUaNGEUNOkiTefHceq7/+hnFT3qRGBceNNy8vLxYsWMDo0aP54YcfZL0/Y8aMoVOnTvTr1082kXXkyJH06tWL/v37W/w8Li4utGrViq1bt9K5c2eLYwwYMIC1a9cyatQoi+2RkZEkJCRYNSbkkJcHYctQcHJyKpGhcP78eYcNBVt8IXkcDJYM86ioqEfXUODewgdPQg8lxGuvvUZqamr+y97SmIftUbgRk8Skd9cw+9Of0erszxwGqFUlkhrlvDj00/uUiShIgFMoRPp1qc/Qno3tIlKyhJBAL2pUMj9saicliCLJGdmcvniL1PQsdqyaXNBZAgp5WY0GExglwkobEFQKBJVIeFm92QCwB3rJbHyoH6wS5YIvi8aRvTxcaFqvPO2bVbVrwf7rr7/sNhTsCT1kZmY6XOJoKz/hYSJwxCRUQeG41W2Oe4PWZBwtyG9JP7zf7nGU7p44PfcGn2e5MDFah15QsHHjRubMmUPZsmVLnBwpCAIz35jC7m0b2LV+eYkS/cAs49ytWzdmzpwp20etVvPcc89ZrW5wdXWlfv367NmzR7bPCy+8wCeffCIbAujTp4/N6oeePXvapJi2hFq1anHs2DGb4YeSeBSsUUxbMxRsPUu+vr6yZE2RkZFcu3bNoet8gvuPR8pQ0Gg0eHh4FHnZe5y1+mNHy9CsxcwAXvvgezbtPMqyNXv4etMhh8YGc82xIxOnyWTivcWb6Th8Ppt2yid0HT8TzYmzZuNKl2PInywkzLvvV9+3nOhUeFJJT1eQ5w6Ii1EBEgofNapwV1AK1u8YCSStqYgB8qCgUSupXDaEublS2nq93q48gdTUVLuSruxNzjIajQ4vgpcvX/7XpaWzs7NxcXHBuUJNAoZOIOiZVxGdnPFs2gFBqQJRgVcryzviuyFJEt9++y3PvvM+XSZM5deDvzN69GirJcuOwJiVidPOtXzaohqXTp1gxIgRJaJZHzlyJDdv3rS6yPfp04cDBw5w+vRp2T4vvfQS8+bNk12Mvby8qF27Nr/88ovFdmdnZ0qXLs2lS5dkz9G3b19++OEH2XY5CIJA5cqVuXjRunfNy8vLYa9pVFSU7KLt4+PDrVuW80ecnJzIyMiQHdcaq2NJGCcfFp5UPTxmqFChAufPn5dtVyqV6PV62fa7YSs5LS9RzqzC6PhX6OjCcvjkVZav3cu5yzFMnrVOVs/g+t2cAZJEiL8XjWqVQRQFjv5TnFlNoXHHqCso+crJFJBy0xC0GQLoAYWAlGUA3cMxAmzB092Z41vfYeuKSWjU5mqWa9euERUVZfNYa2p1hWEvj4Kj2iB5JEv/dvlhSkoKXp6eXJ02ihuzJ3HlleGYcrJxKl2B8KnzKPvpBjzqt7BrrLFjx3L58mW2bNlCq1atHP5s2ptXyTjxJ5LRsncuadt3pO7dRs6JPxjlA8888ww9evTg8GHHy0YXLlzIzJkzZRdJQRBYtmwZ48aNk13cAgICaNasmdWFfPLkyVaNic6dO7NlyxbZ4/M2SnKshdZQs2ZNm2WSJRFR8/X1lVWo7NSpE1u3brXYVrduXY4cOWKxDYorvxaGt7c3KSkpDl3nw8KTqofHDJUrV+bMmTMlbr8btrQj3n+1LwO6NmD8yHZWqZStwZGSoNiEgoXIYDDSoPs7/Pb72SJ9UtOymDa36MQlSLDojYHMnz6wIDfhLrh7BeIsFmQyZ6cJ5jCC3txfqZTo0/kWUeEPkD/BQbRtUhlnp6KJi6u/287pK2kcOy1PMwvWd0WFYW/ugaOL4tGjR6lbt65DxzwIpKSk4OGsQR9rro03JMWjvXWNK6+OIHrmOG7MmiS7cBfG4cOHEQSB6VNfLZEsddaFf7gyZTg3Zr9MzHLLuguik0tuya6EwsWNpk2bsn79ehYsWMDcuXMdepbc3NyYM2cOL7zwguxxQUFBvP7667z00kuyfSZMmMCSJUvIycmx2O7l5UWDBg1k9R1atWrF7t27rV7r8OHDWbVqldU+lmCPoVASTRulUilbFeLs7Iyvr69FrgWNRoPRaJTdrPn5+Vk1FJKTkx26zie4/3ighkJGRgbHjx/n+PHjAFy9epXjx4+XyEq2hkqVKnH27FnZ9jp16vD333/bPZ4tSujgAC9mTenDxFEdUKsczydwdBdqvIseOSk1s1gJZVxiWr5oFMDsV/qwZ+1U6lYvRUigF03qWqiXFyCqdBkS4+Vde7UaZFK/aSbPjrlJpWoPsDTSATg7a4r8//TFWyz+YiMnL6cxbNJyq5nx9hoKjkAQBLvDW/YmUz5opKSk4BMUgkfjNgC41moECmW+4aCNvowhybbIz8w33mBg2iXOD29Hyj7bQmd3I/viaZDM313WmeMW+/h06E3A4Bfw7TaEoKcnAeYF5Ntvv0WlUjFx4kSHFr06depQp04dli9fLtunVatWlC1blq+++spiu5OTE88++6xVjoYxY8bIyl+7uLigVqutLoKtWrViz549Di/o9m6M1Gq1w3kK1tC/f3/WrVtnsS2vGsMS/P39ZUMP1koy/23kVT3cy+txwQM1FI4cOUKtWrWoVctMHTtp0iRq1arFm2++eV/Pk5eNKwdHDYUHLTIVHBxMbGys3f07taxGu2ZV8PEqyICuXrEgQ/j7rYdZvfEQPTvUwdfLlZeGt8XHy411P//F9VvmB/C15++KNysFRFcVNzJAn23BDasEFAJpKebvQRQhouxdxDj/0n1+/WbR3YfBYMSQk4LCyROD0WR1Yn0QhkJgYKDdbtwjR47YzEh/GEhJScHLy4vQcTOosHo3Ea/OxSm8FM4VzNnlrjXqo/Szrmy5b98+wjXgq88Ek5Gkzd86fB0ejdqgCgpDUKrw6zWiSJs+4Q66mBsISiW+XQcRMHBMEU4JQRCYMGECUVFRTJs2zaEF9eWXX2bLli1WNxhTp07lhx9+kBVD6tevH9u2bZM1+kNDQ5EkSTZ237VrV6ty86Io0rhxYw4cOGDlkxSHvYmKeRUF9wvt2rVj165dFttatGjBvn37LLZZ8yj82yE6azDeh9fjggdqKLRs2RJJkoq9VqxYcV/P4+zsLOsCBMczZ++nbLUlWBNCsQRnJzXLZ4/k6M8z+OHTsSx9bzivv9iVn389zpKvdzNl1jpWb/ydo/9c4++fZ9C6cSXGTFvBkq93M3SieUdTsUwwdapF5Y8pKM1MawqNOyaDBYvdCCBx/pwL898JZsHMYHb85G2md877av4lQrUGtYomApaLCqRMqCud2jRi+ewRFtkY8/AgDIXw8HC7K3TS09Pva4VOSZFnKACIKnMYR1AoiXxrMeWW/kT41PkgScSvX8GNua+Sdb4om6AkSXzwwQe88sorZisScK1hnRbbElQ+fpRduJYKq37Fq+VT+e9nnPiTSy/15fLEgSTv2mR1jAkTJuDu7m61ouFuiKLIkiVLGDdunCwzpEKhYNGiRYwbN86ix0gURSZMmGBVrfLZZ5+V9VzYkqcGGDFiRInmS19fX1kDJQ9lypTh8uXLDo2r0Whk51qVSkVUVJTFRMqGDRvy+++/WzyuJKWajwKke0xklJ4kMz58qNVq2QdeEASHEhrtUaO8FzjqUSiMutVL0bFFNT78cicvvvU1HyzNSyCSyMrWse+v89yIMXsIJMmshzBj0SaqPvUGf5+5nu8FkHKJkgRBQBQEenaoXfREuZTJglrg9k0Nt2+qCxkG/+4NvuTrX6ncbhrth87l8zV7qdrhda5E32Ha2G40rWdd7KkkSVy2EB4eblc47aHIShuy4coGuLEDJMt7FpM2m+iNX6DfsIiMg0V3tIIoovTyQRAEMk/8ScL3n5Px9yFuflhUYXHXrl3Url2byCatKTP/GyLf/oSAwWNLfNnCXc9bxt8Hybvh0v+yHOcvjGnTpqHX6+1SjcxDSEgIY8aM4Z133pHtU65cOdq2bSvLpvjUU0/x559/yrrOW7ZsycGDBy3OPd7e3uj1eqsVAaVLl+bOnTtW+1jClClTePHFF62GxEqXLs2VK1ccGjcgIEB29w8FHBF3w8XFBa1WWyJq6if49/GfMRTCw8OtipZERkbavetztErCUdwPQqfTF28jCOYlu1blCFo2qIhGrWTYpOXMWPQjPTvUJjLUl8E9GvHVDwfQ6gwIgmBmYQQwSkiZBqRMA4gurN9sgTJWAik7b6K5j8bBPWqxZ2bpyMrWceHqHWYv3YLRaEKvN7L5V7O6XY5Wz4HDF4hPKi7g8yBcmS4uLnZN5KdPn6ZatWr3/fxFcO5zuLgaziyBaMs5A9mnfifl5jVcJQPJP1qOoQMI6txcEAFEVdG8kCVLlvDyyy8DoA4Ox6Vijfvy3Zr0ZnZQjybtEFRqEES8WnWx69h33nmHO3fuWOVBuBt9+vTh0qVLXL16VbbP2LFj+emnnywamIIg8OqrrzJv3jyLxwqCQPfu3WU9B127dmXDhg1Wr3HgwIGyuRJyqFatGu3bt7f6XURERDgs4xwWFmY1XNGsWTP27t1r0UCpXr06//zzj8XjHke9hydVD48hBEGwOlE5IgzliCu5JNBqtXZLGMthwsh2BAd4Ub50EIveGsy86QO4GWtOjEpMzmDrbye4fiuR9duPmDdmuQ9i49pleHFYa9SqAve8q38l0mIsP8BoRERXJahtcCgUgYWHvvCx1p4S0bHFxmg0YTJqUaicaFy7HJIkMWj8UoZMXEbbwXMsGgv3GwcPHqRRo0Y2++Xk5Nw3bgEA3aGVZH3Wl5xts/MFsdDmJccJoEuxeJwqOBK9JKESBdRh8qJQrlVqEzxmKl5tuxP+akFFQlZWFpIk5Ycu7heSd2/m/PC2XHqxN0pvP8ov20z55T/nJ1zagiAIzJ07l8uXL7NkyRK7zzt27FirC7EoikyfPp05cyxXZTRv3pwTJ07I5ioMHDiQNWvWWGwbNGgQq1evtrpQDhgwgJ9++slhzYPnnnuO3bt3y3oN3NzcHE4UbNy4MQcPHpRtVygUNGvWjP37i5N11atXT7ZMUqVSPdDN2YPAk2TGxxAGg8FqAmIewYw9eNBsYFlZWfe8YNStXopD619nx6rJRIT64uvtRt+nzJTDNSqFo9WZF474xHQ+eWcIz/ZvwWfvDuOTt4eyeNVudPoCF6DGIxRt+m0kyUIcViGi9NGg9NIgqEQEjWg2GqzCQru9GwaZMk5r8HNX0rtLM+rVKIVOb8zni0hNz+bcZftzQQpDFEW7KxksyTVbgl6vL1EJoSVI2gwMxzeCUY/p6p+Y4nLJeyqMBO8qEFAfIrtZPFYdWganhk/h1/0Z/Me8a/U8Xq26EDxqMpqwUvnv7d+/n+bNm9+Xz5GH7Iv/cOerD8FkwpAUT/ofuxGdXBwWxBIEgYULF3Lq1Ck+//xzu45p2rQphw4dsuoWb9q0KZcuXbKYWyQIgtUKB19fX1QqlcVwo7OzM82aNZNNAgTz4vvqq68ye/ZsOz5N0etauHChrNS2LWI5S2jQoIFNga1u3bpZ5FSwxqcQGBjInTt3LLYpFIonIYt/Gf8ZQ8FoNFoV2cnKysLZ2dmusTw8PByOCToCR67FFm7cTiQ905xcNHdaf87vfp8NS1+id8c6eLo7M3FUezq3rslrL3ShfdOquLs6ERZk1oTQaFSgNE8oTp4R5KTeFWcXwZRlwJiuQzJJ+V4bUaVAcHawKiRPO+IBIOZOLCbMCXkatZKRfZsCULNyBPWql7J2qCyskcsURnx8PL6+vna53XU63X0zFFA5I3gGm/9WuyB6mnMfDC7BnCw1lJjyz4C66CJryski89QRjBlpSC4euFdvjKhx/D7cuXMnHTp0KPa+MSvDLu4FAGN2FjcXTOfKqyPIPH2UmM/nIelzc4wEAZdKRYW7ss8fI2HdYnIuy3i+CkEQBBYvXszhw4ftSgQUBIH27dtblZkGcxXE+++/b7GtW7dubNu2TTZPaujQoXz99dcW21544QWbUtht27blwoULDlcplC5dmtatW1s0mpydna0y2lqCs7OzzVyDGjVq5JfEF4a1XJ7g4GDZBO9H1dvwJPTwGMJoNFr1KNjLtJeHBxkzu1ePQh4z47xl22jWbzaNes3kSrQ5wUijVnL20m2G9mrCiW0zeW5wa5597StqPvUGi1f9ikIhsnHZOOZM7cfWryYiSuZEB9fAymTGnTEv5gJmumalefEzJukwxuUgIaGKcEUZ7IzookRwkbl9Cq+ZhX+SXNXK+wa1CE4iCnRUrVxQCfHW+B6c+3U2Gz97CSdN8YXZHrIZaxNXYezbt48WLexjMNTr9ajVthUu7YEgKnDqPQd1+yk49V+E4GyWK//qnw18emINM3//lOtpBVnvktHA1emjiX5vAlemDEObnVVio+X06dNUrly5yHsJG1dx4emOXHqpL3or/AuSJJF6YCc3P3yd9L/2or1+idgvF6D08c/vEzhsHM7lqub/35CWRMzCyaT9up6YBZMwZtkOJ+VVNezYsYM//vjDZv9hw4axcuVKq30aNmzIzZs3LeZCiaLIwIED+eabbywe2759e3bs2GHxvvPz8yM0NNTi4loYM2fOLFFp+UsvvcSGDRuKXbezs7PDHgUwK2DK5RqA+bsICgoqRr0sCAIqlcpihYO1502pVMpKe/+beELh/BjCVuhBkiSHKhmslQHZgzMXb/PORz/yy4HinPGOhEEKQ6sz0P/FTynX8lXeXriJH7aZ3XgZmVr2/nkOgPXbjtBl1EJ6PPsRq9Yf5Lffz7Jz/2lS0rKZt2wbHYfP5+2Fm+jQohplIgLo2romAEonD0xGPUaDtkBS2lh0UpOyjICE7kIaxkQtUpZlm1jtq0TIC0/cvfG4n2a0zkTDJuUZP6wZlSuULtLkpFHJ7vKdnJxs/rb2Ggr2KkHC/Q09AAhO7ijLNkF0L1hkL6WYd2wmJK6nFUzUhtQUdLfMu1FDcgK61JQSXcvt27cJDg4u9t0mbTXriBiS4sk4Jq9/knHsd24vfoesk3/lv6f09iV07Bv4DxhDyItv4t2xT5FjJJ0WjOZ7z6TVYUi3L1afZyxMmzbNZnw/KCgIk8lksyJm2rRpsiGAoUOH8s0331gMWSmVSmrVqiXL5zJx4kSrZZYAVapUwdnZ2WHqaoVCwYIFC4qFIBQKhcM6OGBOWJTjRMhDhw4dLHpo5BIarVWCPaoehf8n/GcMBVuhB0fhSJXE3TAYjAwav5Svvt/P6Ne+4uLVog9ASUMPx05f58/j5sSkFT8coEd7M5GVu6sTLRtWBOD3Y5fMIQLgj2OXCQ8uKmx07nIMW3afYPUGc0LSorcGM3/aACpEBeIaUJGs+HPmxEeDZF7k8zwMudBesx2SEdUiku7BeWQ0zrkLnFJgRNcm6HIy8ff3t35QIdgTm7XXULh48SJlypSx67z3NfQggy5lWqEUFYS4BVAroGDXr/T2xaNxWwBcq9XD5Oxi9XmJW7uEi892IvbLonoFO7b8TMuqFTFpixpabnWbAOZKCZeKRcMGhWG0sMgbkhNQuHng12Monk3bFzNCVH7B+A2eiOAeiC7DwNVXR5Jz4wpZ508S/f5k4tYtR5JZ8Ly8vJg5cybjxo2z6UUaNmyYTcrkOnXqkJCQYDEEoNFoaNeunayGw/Dhw2WTJsuWLYtOp7M557z99tvMmDHDYY9npUqVqFWrFt9+W5QUqySeU3sMBTnypbp161o0dB7P0MMTj8JjCWvWsaMPRERERImppk2SmdNAksxrbmZ20bil0WiUjWVaQ7moQDzdzQZGlfKhvPpcZ/Z/P43fN75OqXDzQjmoWyPcXDS4OGsY0qsxVcqHMrJvUxR3VRN8t/Uw3/z4O2cv32byrLWcvxqLi08ZspIKKdopBZT+ToheBe5ya7F40Rk0TpBz28Zns/F8BPtbT2DTZut5/pm2bFn1MrWjQrl4+ToBAQFWjykMtVpt06MQFBRkk+sijzzJ3rJAg8FwX41ZS2gWWoePW73Omw1fwF1tZvKUTCbS9/6IS0QYZRf/QPi0Beh08mGQnKtnSP75W0xZGaTu/hHdrWsAmHRafl44m3J/bCT67WeRCrmDg0e/StS7yyj70XdoQiNlr8+zSVu82nRHHRqV/54u9pbV53PDxcNM0SWQkmO+r6ScbDL+PsithW+RefxPEjeuLCKPfTeaNGlCmTJlbEpUd+zYka1bt9p8NqdNm8a771pOAn3uuedkOReqVKnC5cuXZccfP348CxcutHruoKAg6tevbzOfwhJeeeUVVqxYkX9fZ2Vllcil7+npSWZmptXfzN/f32KOT61atSyGWKzxmzyyhoJ076+S4JNPPiEqKgonJycaNGjAX3/9ZbV/SkoKY8eOJTg4GI1GQ/ny5WUFvOTwYGeth4gqVapw6tQp2XixKIo28xgK417YwtQqJR/PGMKKH/bTrF4FalQKL9I+dOhQ3n33XRo2dExQytfbjV1fT+HMxdvUr1EKQRCKeQxqV43k+FYzgYxCIXLq3E2+32oW7lGIYMy9O6NvJzJ9/gZG92ueVzmJICpRqt3RZ6egcvJCUIoY4swLquhp3gmbsixPLH71nIlq445KZ+TvL5LRpRYy2vLW0byERhsPSHKa9d2+Rq3k6W5NuHYjgcZDFxBzei8NW/WlQkw2qzYconn9Cjzdr5ns8QkJCfj5+Vk9R17SljW4uLg4FJ5ypJLiXnC34ZLx506S1n0EgoD25iWCXnyf2NhYAgMtUzSnfDcXQTRLMAgaZ5Te5u9Kf+cmyWlp+DiFobt1DUNKIqpcmmdBFHEuW9nieEWuTakiePQUJIOeW4tncv3on2xX+iO+/TZ6vb7YKyM7i39csoloU589of50TIxHUGtwq9WI1L1b8+8nUWM9/2jatGl07dqVZs2aUaqU5QRXpVLJc889xwcffMAbb7whO1aNGjUwmUwcP36cmjVrFmnz9PSkVKlSnDhxgho1intW2rdvz6+//kqnTp2KtTVs2JCZM2eSmpqKp6en7PmHDBnCnDlzLCaUWoNKpWLBggW8+OKLfP/997zyyitMnDjRoTHy4OvrS2pqqtUSWUtzrZOTk0VDyVrJeEmSLh8G7tUrUJJj161bx6RJk1i6dCkNGjRg4cKFdOjQgfPnz1vcLOl0Otq1a0dAQAA//PADoaGhXL9+3eHS5v+MR6FBgwZWk5YcZUO810m9Q/OqrPnoeV4Y2rrYxF2hQgW8vb3ZvHmzw+MG+HrQsmFFXO4SRioMhULMl8Jev/0ImVk6DEYTnh6F8iIE84LboUVB0hgSuARUICvBrJsh6QoSDEQXJaKrEoWfxiLXQXAdFwRBwKBR4lPhrgc+L4lRiV1lkjla67uc7Stfxt/Hnd2/n8VoMiGZjGzbd5bnpq9izx/neOejHzl1Tp58S6fTodHIf39gH+mWozFelUr1ryRlmbIyiv1tSc9En3DHXLmQnY6znwaNp5LgES+icHUHQB0SiejmgSgIuNZqgtLXfi/O3RCUKjSDX+LNWwaaDRhGl3ZtaKvKpquLnheeHc0LrRoxrm4FZk9/DTdPd/587wu+vBCL06vzCR79CgpXd8KmfIB3h94EP/cartXqIUkSJp1l406pVPLRx/N5ZkxvbiV8j0my/Nv27duXkydPWtWAAHNi4RtvvGFxV22tiqF3795W5alHjx5tVawKzJUMV69eLVHYoFq1ajRs2JDhw4ejUCho3769w2OAmXjJGsFdcnIy3t7exd6X26wVphS/G66uro+sMNTDxoIFCxg9ejQjR46kcuXKLF26FBcXF7788kuL/b/88kuSkpLYtGkTTZo0ISoqihYtWlg0Yq3hP2Mo2NI8d5RE6UHv/ubNm8dnn31mUw72XtGsEKWxqpAGQoPqpVn70fNMm7O+SP+2rVuRk3rDPAnlfXwlYJQwpRmQ0o0W/VApl8wTtMkokX7DgmvVSEGS5D1AIQqs2nCQbs8sxMvdOV9uekDX+mjUyvxTOGnuzVlmb6a1IzXe/1b2tnuzLrg16YxzpTr4tm5F1l/forrryU/86WuuTOjD5Zd64dpmOE5lquLVvi9uTQrExASFEueyVSmzZAshE2bdExNjUlISgwYNYs6rL1PHkIrXD58SfP4w/uePIC6bifjDUhS/bUJa+wnfvbGAJd+tZlz7Pgzv1YtXn3uGwy/0waTTYszKIOPoIbS3r3P9zTFcGNGWOysXWTyn2mMvTdtIzJr9JrHJltkQBUHgww8/5OWXX7b6/IeEhFCnTh1++eWXYm2VKlUiJiaGlJSUYm2RkZHcvn1b1gjt2rWr1TLLPFSpUsUuhUhLmDhxIpGRkbKlnvYgNDTUqpbE6dOnqVKlSrH35XLJrHlRSsL38DBwv8oj09LSirzkPJk6nY6///6btm3b5r8niiJt27aV1dH46aefaNSoEWPHjiUwMJCqVasya9Ysh3kp/jOGgi2WsUfNUFCr1axYsYKJEyc6JBDlKNo0qcyv37zClq8m8uEbAykV7kedalGMHdqGGhXDuHS9gOREFAUuX09E7RZUwKkggMdT4aAsuFUEUSyWZ3DjlwzOrEzi1NIEMmMsLIYCoL+3BEdRFOjQoipffX+Ak+duMv/zHRz8/jVaNqrMoO6N+Hrhswzu3oil7w2nXCnLmgpyCYVJKZlcuBKbv0uzd1F35L560NTgchDVTvgNeRm/7n3h+Ndc3vkVEWI8JlNB2CR1nzlmKWmz0Sen4D92IS71u2JILM7rr3B1t2gkWNvR50Gnv83ZC9Pp2bEur3VrictXs4lbtQhDbMHuVB9f8DwYE28hLJxM20tnGdKsGUvrR9EwwJPXD55hTJf2nN++mfTD+4lZPpecy2YvQPKOHzDpiy+0BmM6XXqGcel8GseOypf3hYWF0aVLF9lcgzyMHj1atqTSmphTnny0JSgUCvr27ct3331n9dxdu3Zl06ZNVvvIQaFQMHPmzHvicrHlUZAzFOSq06wZCo+qR+F+iUKFh4fj6emZ/5KrqklISMBoNBYLGQYGBsp6y69cucIPP/yA0Whk69atvPHGG8yfP182x0YO/xlDAcxWrtzN+6gZCmCun/70008ZMWLEA43Bublq+P3vS+ZysXeHc+rcTYa9vJzX521gSA8z9bBKqWB4ryakpmXhEVKH9Nt/mxdNUUDQKFBHueWPJxlNFkMImbf16NJkvjNHbAQRBJVQrOLCZJLY+tup/P/r9AY2bT9EeFgIADUqRfDu5N50bCGvpxATE0NISEiR9y5ciaVJn3dpP2wesz4xiyTZm0BVunRpuxX4/q3QQz6yUgC4FJeObyUd/1zpzbWYWUiShGdzc8xc0DjjVqsxKb/9zJWXB3F5Qj/S/y5IFJTzIpi0OVx7/VnOD2vDndWLZS/hytVFjBnwGS96exB8aFs+tXgR5O52FB6eKIxp6GOukrp1Fafff4VvhDJoAyvwSbNKdA724J0jF9l1Ix6Vjz9ibojEqVxVBGVxYzDMbzhervV474NRzJ+1z+ou9bnnnmPz5s1WF8PQ0FAyMzMteg66devG5s2bLc4htsIPeZwO1kIL9lQePEiU1FAoSejhUfUo3C/cuHGD1NTU/Ndrr71238Y2mUwEBASwbNky6tSpQ//+/Zk+fTpLly51aJz/lKFgjV70UTQUACpWrMgrr7zCM88888DON2TiMt5dvJkBLy3hhTdWodObF6tfDp7hnUm9+GvTm/z989usWH+QbK0ehcoZlWsAOak3wCiR/XciSj8NznV8UIW53B8hdStea9FViehszomwZWC8Ne8bzken2f3d3bx5k7CwsCLvHThykewcs1Gw+dfjgP0ehTJlytitwPdveRTyIJZtjliuFee1aoIqmkM2aZkH0Rvi8e02lNILv6fMR+vRhJch/UgBV3/higK5xSvr7PH8HX3SlrX5FRGZp48S88U8Mk8dJiM9nbGD1/J8lTCq+ZkXdfIWdFGBW/0WuNVvAYIIgoiocQEJ9NlGshJ1xCWms4lw5glVOYoPl0vXonHXXnwem8M/pWpTet43hE/7kIjpCy0aNBpVIOVC36RulXeYOnU6U6ZMwWi0fN+Iosj8+fNl6Y/zMGDAANatW1fsfaVSSevWrS2GJsqUKcP169dl7y8XFxeaNGnC7t27Zc+rVCopVaqURUnnhwFrmzKAq1evWkwaLUno4VH1KNyv0IOHh0eRl1z+lJ+fHwqFohjV9Z07d2RVaYODgylfvnwR46xSpUrExsY6VHn3f2MohIWFOWwoPCx+8TZt2tCyZUuHudztxa2YAqXKPAZHgOG9zbXvAX4euDipKbwqm70KR5AkCd2VdPSx2Ug5JhSeatR+KtwDCk3ECooyMNqCSkT0UuNW2gn3Cs54VCmUZCkW2rXaEQLPyUzln0tJnDovP2kVxs2bNwkNDS3yXtsmlfH2NJcSDu3ZGLB/US9TpoxDHoV/01AQlBquVOvFr+mehEeZ6Z81qlBUSnPljMovKD9x0atlZxAVCEoVnk3NCW8xiakkpmagMxjR3rrO9ZnjuLlgOoa0FBAlxFwWTEGtwZiVTvaV80S/N5GUX37kyuxJ9KlfgbEV/Gng4gVqFSoffyKmzSfqvc/xaNoOQ8Id3Ou3wK1OE9xqNUJwciE7VY8hV8G0FBlUxyx6dalCBN/6VeUnv4oIfcew6NMlHPz5e7L2ryFj3w82E/3KVarF+u1Hiajdn217TlrsU6lSJWrXrm1RNjkP3bt358cff7TY9swzz8gmJjZv3py9e+Xls8eOHWtTBdOeEMWDgi25dpPJZJHgzmAwWHw/NTUVd3d3i2PZq876sGEucbwXUSjHzqdWq6lTpw6//vprwTWYTPz666+yonRNmjTh0qVLRTZSFy5cIDg42CGW2P+UoRAUFCTLz28PG9/dYz3I3IG7MXr0aJv1sCXB1Rvx1KlWCj9vNzq1rIazk3kyjwjx4fkhrbgRk0RicgZKpYJ6NUojOot4NvbBu0kIGs8QsnN5FUypuTXsBhOVWwkElC986wggCqj9lIgqAfe7qx4Kw0tpFpeSQBIEfGq54xKkKbgTTWDSmZBMEpK24OYWBAjwdcfVRYObszr/tAZtOhoXLwL95MvJCkOtVhezpCNCffl9w+sc2fwWY4eZlQrtLclydXW12y3q6elp0U39sJCl1/LeX2s5d/0qa9Nr4+/3BmXDFyEIxXd4kl6P0i8It/ot0USW48gn82j13Jucjk3juXeXcufrT8g6c4z0w/uI+2oecYtfQ6WRUGhEJJ2Wq9Oe4dq0UWAygiSx+PR1elfyp3GkL07eatRBUZT9ZCOulWujj79N2r7t5Fw5R9zqxYRNeAdTdia66EtFmDxNgAYTT4m36NvjEsPKXaC9cwrv9+/Gt19/zYw3X+fAvn2k71iB7rr1RL8N2/9GE1ifxOt/snjlDtl+kydP5ssvv5Q1PJycnIiIiLC4sw8MDMTd3Z3z588Xaxs5ciSffPKJ7Hn9/f2JiIiwSuvcsmVLdu/eXeIy7nuBrYofue/L3d3d4qJfqVIlTp60bLCVLVuWCxculOxC/2OYNGkSy5cvZ+XKlZw9e5bnn3+ezMxMRo4cCZjDVoVDF88//zxJSUmMHz+eCxcusGXLFmbNmsXYsWMdOu9/ylCwBUcy1MuXL2/xAX/cMHrqV+w/fJ6E5Az2/H6OVg0rMee1/mxaNp6V6w/SrO8sGvaayd+nrrF4xhBCmgfjUtYN10ruhHRsQ1rMMSSTAe2FNHJOJ5N9Igk3LyjfUknVLkp8y4qgyBWW8ldTcWwIYR190PgVWnyEQjkH2SakLAPGFB2Zl7O5vjaO2F+TiywIUo4RU4YBSVfwpiSBi7OGQD8PMrV6c4mmIGDUpfPprOcI8rdsKHy/9TBPv/IFW38zV5eEhoYW46AHM+Wzn3fBjsbNzc2uXYwjjKDWFPIeBgwmIwaTEZPBiE7UoFBXRCFaTmi7vex99LE3Sdu/g7hvl3Ds0O8kXP4Hj9LVOXwtDl3sDdyCNbj4qTDlmLUXJEBUAIKAIaHgc/6h8CIjx0CXCuYkLCNOBD/9cn670tM39y8BpZcP2ZfPkXX2eJHrET19EIFX+YcXxPOoYo10+/Usz57ZS42D6/H08mJZ/8Z8+tc1DlxPQnSyTpFep1oUokKFR3BtcuKOyfZTq9U0a9asyC7ubgwaNKgY42EeJk2axIIFC4q9HxwcTIUKFdixQ95IGTduHIsWWa7gAPN8NmjQIFmxqX8T3t7eJCUlFXtfzhPRqVMnWRKgSpUq2SxX/Tfwb4hC9e/fn3nz5vHmm29Ss2ZNjh8/zvbt2/MTHKOjo4tscMPDw9mxYweHDx+mevXqjBs3jvHjxzN16lSHzvufMxSsuRxLlSrF1atX7RonLCysxMyMjxKycnT5+WLZWj1b95zko692otXp+XGXeYI0GIzsOnCanfv/ISOzwOvi6uqCe1AN0m7/DRI8270poiRwarOOlNsSSrVAxaAUprx4k2YNk0k7n83FlXe4/G082oSC+Gt+zoGbErQleTzMuHYzgSvR8UgmKT8JzsPJSLtWDYr027HvHwa8tIT3Fm9myqx17D50lpfe+pqUtCxCQkKslnXlQalU2mVU2tIYKYzAwECbWgL3AyZjJonxi4i/8wEGQ4FAk4fGhedrdMZN5cyoqh0IcPFCkiRyoi9jzEgrMoZzgCdepV1wD3NCqTtNTUMCUuxlnAMj6G24iqDPILyRD5HN/fBtXCv/NzFoJTQeCkS12XiSoiqw6nYmr7WqT2p0Num3tQS89DHO5QoS3dRBIbhUrIpTRBQhz09HExqJwr3A8NOUqoAptdCio3bFxdAwP1cm8+RfCKJI2MgZLBnSiRVnU9lx6Gh+99R927k+czzJvxSECFo2rMi2FS+z8eu5eKizrc4LY8aMsVoB0bRpUw4cOGBx7qlatSoJCQkWs9KnTZvGnDlzZMNR5cqVIyMjw6pnc+jQoXz77bf/igyzWq2W9WbUqVPHoq6FJY8emL1ter3eYi5C3vP1qElNGxHu+VUSvPjii1y/fh2tVsuff/5JgwYF89+ePXuKVds0atSIP/74g5ycHC5fvsy0adPsnrPy8J8yFGzVdVeqVMnu2mNRFFEoFI8kdaglGI0m5i3bxphpK/inULz+4xlD8mmf83DrTjJfb/ydvk/VA8xMkk3qlKVMRABph5PJPJ+O7nwmmhtGXHzLo027hUGbwdJv92Aymki9LfH7lzou/ZpNmaom4rJdGTE0Hlc3E2KAK4oID1TBzua7SyEgiGa1RklvclxqWqZ/xdJB/LD4BQJ8XFm29gAJyeZdrU5v4MU3V/PHscssX7sXwex4QKFQIIqCXdTMjsARtk9PT0+b4kT3A6kp68hI20ZWxm8kJxaVF24WWpXy3qG0jagJQOwX87j6ynAuvdQXXWyBAeXsloOoFFC7KnFS3iEtPIcBpSQ2OR9jgOI2/rUKaJrVxKPLktClG5GMEpIJlJ4ehE2dy+6AKnQRUlCkJIDGFVVUVXQxRXOFEjd8ge76OQzxN0jbtxmFmwel531N2Ktz8R/yAoJSzHVV5MIk4VtzJKqAEBAEfLoOAiB5zw4Ml67zQZVglr0/k29mvkb8tx9xe8l7xJ86zoz53/HG7G9IyWX+rFgmmCZ1yvH+++9b3WHlhRAuXbpksV0URerUqSPL4zJ+/HiL+Qbu7u4MHz7cagjihRdeYMmSJbLtarWaLl26sHHjRtk+DwohISGyRkzdunVlBbDkICckBeak73Pnzjl8jU9wf/CfMhTAukehcuXKDrmwqlWrxqlTp2x3fASw5bcTLF71Kzv3n+altwtckSEBXjg7qREE8PM2lzhKEpQvFcjgHo1YvWA0zk5qhk5azr4/z1M5Mpi0P5NJ/CORG7eSEAQBr4impEQfKFaBYPRxZ92v4az9JZwNe0IwuDmh9NEgqhWoItzyfWsmnQlTjhEpx3JZZREIgEIoYH+UkaaOvp1EfFI6Zy/dZtFXuxj3tlne12AwYSi085gyphMDujRg5fxnyMnRM2PRT1y8GoNWZ/Z4JCSnc+JstGz2uy04EnqwR976fkAQ1Bb/BvPzUdigTvvdnFlvys4k80zBLlxQKPL7pd1QsebAbbqGBeAiZBDQazAe3V4AjScoRBRxf1FuUC08m3fEs2VHdOkGdPFJ3FwwlTULZtHOz2yoSlkZZJ89zs0F0zFmZaCPj+XSxEEkHdiFZDJiMhpI3v0zF1/oSfT7k8k48QeJ65agv34eMKIOL51/rbFfzENdpjKS0UTmycNIRgOCSgVIqBUCH/Vpw3crv2TN55+hdFLwbUYYP2YG8832E8xdvq3Id1KhQgWioqKs6ie8+OKLfPzxx7LtgwcPlpWYbtasGUePHiU9vbg89pAhQ9i+fTvx8cX5KsCch/DHH39YzZcZPXo0y5Yteyj3VmGEhIRYDOOBWdPh6NGjFtvkNnTdunWTTQytX7++w6qZDxr/Rujh38J/ylCwx6PgiKFgrYriUYNGnZtxLhT8DbB97yli41ORJFCpFCx8cxAr5j1D47rlGDNtBcMnf27eYQnw+bp9uLoUlOaIbkpETxVqjwAEQYE2PW/3YJ6QjIVKHX49EgDuTvmTlZSTu1gbJSSt0T6yJYECA0EoeAkIqNRFd+3hwT4cOXEZBBFJkjj09yWWr9lD9O3EIqX5LRpUZParfWlUuyzT563nmx9/5+qNBFatP0D0rURa9H+f7qM/4uX35DPbrcERj8LDgodXXzy9B+Lh2Qtv32eKtKWlpeHhUSC65d2+JwAKTy/cauS6MCUJzz5TUfr4kZWiIPFCAueuZ1DW1QVJkkjZvgb8K0OTySCZf2el9jbe3fqQefp4/th7r8XTwNcNlUV5d4Ebc19FHxMN2VqMWgmTDkw5OgxJ8Wivnidl+3pMegmTwYSoEHCtXDP/6OxzJ8k4uAsEc+ghcctavDv2w6fLQHy7Dsajck3mNK/IlitxXFd7oo4sZ54fBDMfx914/fXX+eCDD2RLxmrXrs2lS5dIS0uz2F6tWjXOnj1rseRREASeffZZixUQoigyY8YMWW0JQRCsGiFgzqdp3LixxVLMBwlrhoJc0iKYE0AtJQCHhoZy584di99hvXr1HkFD4V4qHp6oRz6y8PPzIyEhwXbHXNSvX/+xMRTaN6vCOxN75jMTpmVk8+OuYwQFeOVTN7dqWIke7WvTsmFFPvxiBzv3ny6YNAUBvdGYL2Pt5K1B9FCZ9R281HhFNiEl+lCuIWC+wXUxmUgGyewxwMzWaIjNxpiUjZCZYy6ZdBIpxhdsAYJaROmvQRAFs0y2UNijIKA3GEGAMpH+iAKcvxLL0tVb8PYpkJf+8Iud9H3xUxBAqRQZ1b8ZlcsWkCsZ8rwGgoBWq+PIqWtkZpljrL8cOF2i792RHAV4sGW3KTlZbLl4mui0TLx8huPt9ywKRVElzsTExCKCWAFd2lDuWV/KDjGi0h2C+KPw60DUt5fhM2oWkmswv8em0DjQK98AE109MWWmgFcUeEQAoPWvyNHlE9DHF4R1vrt8h76lLQhPmYwYM1LR3iou1WwJkgncG7bFf9ALeDTrUBCGKCQwFv/tUq5NexpNeBmknGRSf/4SjY8f7w3rxfxLaUz9YApDezZhULeGvPJscUEmd3d3nn76aasliSNHjpSViQZo3bq1LPdB9+7d2bJli0VDpEGDBuj1eo4ds5xUOWDAANauXWvVY/DSSy/ZLKe837BmKIBZOMpSFVpQUJBsUm/z5s3Zv39/sfejoqLszi97WHgiM/2YwmQy2fQqWKrhlUNQUNBDST7Lg0KhKHECpSAIDOvdhJkv9yIqzI8hEz5j/IxvGPf213y98Fm+/2Qs707uld/fzeUuUg9Jyp90BQG8Pd0K8RkIKFQuuPiUIT2m0GSWI6G7mkGFtHgMiVqM6Xp01zLIOZ+OMc0ACjF3DNveBNFdafYmqAUko6k4+6PJPMzl6/H59ceGnFSyDQWfw9lZTXpmjjnr3iQxcVSHIvfDuy/3okf72lSuUJrOLSvSvEF5QoPMwjUj+xZXm7RHjjo8PJzTp+03MqpUqfJAdkYmSWLitjV89/dGntn8DXcyiru5AeLi4goMBV06nFuJQjSQdkFP4uZvkK6sB2MOki4F6cZOQsa+zllnPzoMGIz/kAm4NmiPRxCkLBpM0qoZRO+JIcnYhGNlymNUFPxgGQYDzhoFXk7FGRIBtDeu4FzBMoNmklHFZYNLvmHiVLoiSv9Qrr8xBkPCHZzKVjI33HVbmbIyub34HZJ2/YxRa0LKSKLqiHG079KNzT9uYMaEHrw7qVc+Z8bdGDRoEDt37pR18/fo0YOffvpJNm9p4MCBrFmzxmKbQqGgR48e/Pzzzxbb33rrLdkKB41GQ7169axq2fj6+lK7dm2+//572T73G7GxsVZVLkuVKmVxPgsODpY1MFq0aGFRt0AQhAcu0f4E8vhPGQr2qAI6iodJkvPuu+/eF/pOSZI4fcGcmKbXG8nI1FKvRqkiRtKEp9szbkRbxo9sR9UKofmTrpuLhlpVIlnwcl8ifbyQso2YUvW4OKtxC6pBTuoNdJlFvTKJOQqMCVoMtwrcidoEHehMSNlGc85B8ass8j9jhh5DXI45e76wbSHkUkYXQvncXaohOwmVS4HMdudWNahWPgxRFHh2QAvcXIryOYQGebPg9YG0blqTnOxM/Lzd2bt2Kie2vsPLozsWu8LIyEiuX7e+6w0KCkKj0djNzjhixAiru9KSQp8Tz0ehG1hZ9gDzK+zlRprlmHcRFre/50LMP6QcMxGzK4e43TGkX0kGCQQ9xGzZxPqLW4gODiCic3fca9bBvWJZ0Jl/Z2P0MYyJMSTv2oTP+UyiW/qQEaJGcnPhsiGbGiEeOHubDQWPxm1RhRcw9d1eNoeAoWMRcvUGlP7BOFevj97JDR+FngSjhhVZZm9FzvWLJG5YgTb6spkB8pJ1w0wymtkcddkGjJ7JjB07lhUrVuS7wo0pdzClF/csCoLAwIEDLTItgrkSZsCAAbJhgIiICOLj42WNS2uGRN6xclUEPXr0kI3f52H69OksW7bsoZXgrl+/np49e8q2y+XkBAcHyyZBRkZGPjbVZk9yFB5T6HQ6m2xTjireVahQ4aGRfVSuXJmgoCCrNdv2QBAE6laLyv+/s4VdnYuzhomjOjBxVAd8Cu2wOrSoxlMtqzNj4SYunbqNMVmHZDSRla1DEAR8y7Ql+epvSKbcOKIAVWsVKmkSQVALRZ+CnOKPhKegw0uR54aVQJurVpltKv4EFZprBAEu3IhDUIvos5NQOvswrHcTXhnzFKs2HOTUuRu0blWFjh3kZVS9vLxITjYz/CmViqLy24VgbzntuHHjWLxYXt+gMMqXL8/NmzfvO3e9KvMU7qI5pFFFzMDf1XJ8+M6dOwWiMtlmY8KQZczNBxFI/EdHylkD5CgJiXJGmXWGszEXOH9oAdp14yDlej7tsiHLiFFvQpduwLjsa+quSsH9tg4hI4vjt3Mo6+REdpIeFErSDv2C/kbBd2lKTSJu2XsoBB1KZwU+7boQ0HsEqhzzdTfQJHNK5Y3S1x9BKnRDCCAIEoICmxU0JrVEln43arWayZMn8/7776M99QtpS58m9dOR6C8X36EPGDCANWvWyLr5hw0bxurVq2XDR0899ZQsH0CeJ0cu/NmuXTvZhEp7YvRqtZr58+czbty4B57YePv2bdzc3Irku9wNubnWWrXEwya6uxfcL1GoxwH/d4aCo3jYlQ9vvfUWs2bNumcvRmJqweJ97LR1C3380+0JDfSmXKlAKpQK4t3Fmzl3uVAJYW5aQodyGSjULrgF1SAl2uwe9HA18OzAeJyC1PmeA0l31yRlYc5KlTSkGPN+q0IPzF1CUHcfKwkFE5BBl07zxrV4Z2LBrkbp58TBuFsMXrCaHw4dt/h5vb297WJIjIqK4tq1azb71a1bl/Pnz8smut2N3r17s379etsd7cRfJ6+w/ncj2ZgX8CuiOwEuUcX66U0Grt68lm8oZJeujN7JhGsDP1xrNsA5MgKFPgGlypwgagJCjFp0mTloXDQYdSZS/9iPstlY1G3GoazdA6NUYIRKhcIdZ+OSqejuikEBktGypoFkAkEQEQQQNU5oIspiyPV6ndR50KliHMYUc4xbyE1yLbz2WFqHAp6eVOgE4ORcGzAT+pw9e5YL+zbntknoLhwqdryzszM1a9aUzU3SaDR0795d1sXft29fq+5/ax6LPn36yIpFiaJIqVKlbNKFV69enZo1az5wEqZvv/2WQYMG2eznqEfBFuPjE/w7eGIo2EDVqlUfqqHg4eHB8OHDi+xQtToDo1/7ilqd32L1hoNWji7AswNbohBFAnw96NqmptW+dapGcXD9dHatnoK7mwz9sgSLusfTv3oa3gFlMeozyUm7hU4ncOmOGzlxOjBKxb0BCgHRU4XoroK8EKOtu060ktcgFZp8JIk/L0TTdMQH+Pi5US4qEJWn+fcXBDh2xTKxUmGPgjU4QtD19NNPy8oK3w1bi4kjOHbmOgMmfcari37lmW/bElf6TSKqfYmzquhOL0OXyTu/L+TnEzv5M+MkidmJXNX+SkLpTFJLx+M/tgeiNgmy08m4nYNJL6HNMXE1TcAzS0fdpGTunEwl5WIStz9+h5sfvUvKb1vzih5QqAUUGiHfyEvRGfDWqEisFcnt8pbj2PrUNJSh5XCq3gRB444hKR4l5gKZMqoM2l+PMeermCQUXv6gUBWpaJEkswEhiEK+lyNh7bL8doXSC3fPLvn/nzVrFu9uPW0WnVIoUFduYfG6nn/+eT799FPZ7/yZZ57h888/t7ig+fv7o9VqZY3Gzp07y+YphIWF3XP4AWDKlCl8++23Vumf7xU7d+6kffv2VvvIhR6seRQeJ5i1Hu7t9bjgiaFgA5UqVXroRB9DhgzhwIED+RnU+/86z679p0lOzeSdj3+y6Vb8fO1e5ny2jW7tanLg+2lEhPpa7V8YvTvVZcyglnRoXpXypc2xbLVKycgxzWj4WSTrTnqiM4n4lGpJSvRBsrKNLFvpa3YFW4DoojQT5ijAXI6AuRLCEhQgOCtQuuX2tdSuNgtKCZ4ColqNoBSJSUhl8brf2PX1FN5/phsYzZUYztmWvydXN3eib9qeqCIjI+3yKECBOJA9niB3d3f8/PzszmuwhuuFBL8uRKcS4NMYJ1VxcZ2LKVdJ1qaSnZzJef15Pjj8ASvulOdMpjegRKmOQpFLpazLMJETMQBtkhNd0kVCAyuC0gej3oSgEBByfz5jWgqSNsccDsitVlFqRJI6qNE4iYg+XlxvFMbJ9qHoVbm/Z6E8GUmXQ/aFM6T/sY+YpbO48spwnCvWQKUQcBVNSCbybwNDUhwY9WZD0ZhblSkBCKhDo/K9TKasDNzrt8Cjbj1Chz+LVChsUaFCBcIq1uBYzefwfOkbVJGWw1NRUVFkZ2fLxvpdXFxo164dP/30k8X2nj17yhIgaTQaoqKiZOcUa6RDrVq1sqoomQelUsnXX3/NW2+9JZsTcS/Yt28flStXtplcKGco+Pj4yGrywL+vsmovnlQ9PKbQ6/V2GQqOxO+cnJwemujKH8cu8+2Pf5CZrWP16tV8+OGHHDhwgFIR/igV5p+qXFQgfxy7zJRZ69i+t7inIztHx3uf/ExCUjobdxzlxFn7FTPBbBS89kIXxo9sz4Ur5vCDTm/gm68OkZJecLuISic8wxqQfG0fV284IelzkxBFCtZ4JeR5BkyZBjBIYAC0JnPppVvBRCNoRNTeGkQXJZLCwgSkAFGlQFCLqP2cMUppaPwLSiNTUrPoPmYRr769jux/ksk5k8Kvu4snvd2KTeaV9zfxwaebmPvZtmLtheHm5ma3vK1SqaR///52u3xHjhxptwfCGjo2rUr7JlUIC/Jmxks9ZPuV9ozETeWCUW8kyFVP3u9yU2qEf9hyVOpwAsfNx7PrKAJenItr0754PP8V2UIpsi+dwYgznhHO+FVwQ9PVFeOrbhj7OSOJuUOpzEnEpiAXzrtoierii/BybZqHViBA70RMv+a4tGgPubtwpV8g+RZHHowGss8cy99qWYpxC7n3lxEQnUUkkwnvdr3wbNYBANHJBbfypfBQXSbtp8XEL3klX/Ia4I033uC9uQv4I+EI55PkkyLleA/y8Pzzz7NkyRKLc0mPHj3YtGmT7LF5eQ6W0Lt3b9nwg0ajwcPDQ5acqTB8fX1Zv349R48eZfLkyXZJptuDI0eOMGvWLGbMmGGzr1yOgq08sYCAAIuf0dH8sie4f/hPGQoqlcpmkpi7u7tdbufC8PHxua+0v5bwx7HLDHhpCdPm/sDYN1bh4uLCmjVrmDVrFomx1/jp8wnMea0fy2ePZMTkz/lh22Gef30l0beKWuZqlZKQQK/8v/PK/ywhR6snNj6VT1b9yuKVv5CdU1DjHRrohUduGEIhCuh0xZO3nL0iEQQF2clXQBAQNQqzMmQeb4IBTBkGjClas5GQC0GV20+jQHBSgBLUpd0pVzkUY5IOY7rlSc1kMCHpTEiShDYxBo1vMMYMPaYsA5mp2Zy4dIscXcFOpEvrGsQlFHUB7/vrPGnZRiSjjm9/Kl6GZQn2TrLDhw+3O/egadOmdpVJJmZlcis9RbbdSaNi6Yxh7Pt6Kk+1qC7bz1PjzluNJlLWTUlP36sokFAIAg3COiFmGDEc+w7RlIFHuwEYfP8k6dYYEncvJmX3ZgwZ6aSciUHlpiQjQUvWjxkIX2dBeSVCWRUezTsS+NxUTK4e5PhpOXsylYpVPbly7CymFd9Sf8VxItfsxXjlDGKuZ0FUa/Bs8VTRi7wrN8WsH1GoOVdwSlAIzPGszTknPzQeStQRZVCHRaH0DcCUk0XGb2tJjc4i5WoWifv+4PrMguQ+Ly8vIpoE896imXxz7nNZY6Ft27bs2bNHdmfr4eFBvXr1OHSoeJ6Du7s7rq6usiWADRs2lGUtDAsLIzk52aKgEpi9FfZ6CZRKJXPnzqV27doMGTLknnfp+/bt4/XXX2ft2rVWyyLzkJSUhJubm8PnkQsNPmzmSVt4UvXwmKJatWqcOHHCap9GjRpx8KB9cf489OrVS9bKv1+4El1gQV++buZucHNzY82aNbz55pvkpMfSr3N9PNydMRpNZtoDCYx3xUkVCpGNS1/i3cm9+OHTsUyZvY7yrV7lhTdWsXrjIXK05sni/JUY6nWbQcOeM5m7bBvzP9/Oh1/s4PCJqzz72ld8vel3tnw1CXdXJ4xWgmlekU1JvXUEk7FQSdjdyYh5LzCHIFR5iWmC+W8D6O5kcv6fm+YPZYnF0YiZ5VFnQn8rk4yrF1BKQWCQkAwmXCu7mQWncg99b3Jvvt96mPo93mHmxwUu4sZ1yuLqogYkurWtJf+D5KJBgwYW67otwWg04uJiXbUwD/bUhR+PvUn/jV8w5MeVbDwvf18bM35BHzMBY5r1e9RZ6YSzUykqBvfjjWr1eavhW1Rwj0K/+TWyflvN5SlPc2lsV9LPfYdWe4WjaQdJyNHj56xG4aJGwkRWrBbBBEK0ES7oEZNNGG6fJ33t+zgpsnA+ZeTMt3do8LcSn1+SMV7RgiChUItkJ95EoRFQuyvRxUSTvOsu97w960BuGkSfzMuUTY5Dm2Yg+t1xxK3+GEOi+bnJStRj0BXchNnnT3JxycR+oQABAABJREFUzXLeWbSSiYu+x6WaH2f3XCI7LYdkrWUXuCAIVnf3AP369ZMNMYwcOZIvvvhCdmyVSiVbOfHSSy/Jcir07NmTTZs2yTJIWsKgQYPo168fI0aMKLFnYf369SxatIj169fj5eVl1zGnT5+mYsWKDp8rOzvb7ufo38ST0MNjCrVajSRJVi3nFi1asHfvXofG7dixI9u2WXdT3yu6tatJ8/oVCA304s3xPfLf9/T0ZM2aNUydOpVTp07h7urE0veG06F5Vea81o9S4f7Fxgrw82BIj8YkpmRw8MhFdHojW387yRvzN/DeJ+as762/nTSTE+VCEATSMnJ47vWV7Np/mrnLtnElOp6IEF+L2eUCoFYpGDSwFl4RTUi+vBcBc2mjpL/LVhYLBhBVAh4eRvwD9Lg45SZAAlKKAUOSLn+x8Al0R/RSW+RgMKRmY8rWIhrNCWwKDxUKl4JF18VVQ0ZmDrHxZgGmb38sWOgjQ/34duFz9Otcn7cn9MAWOnXqxAcLl/Py++s4fdG66mR8fDwBAQE2x8yDu7u7Rf7/PBy8eQVT7i5qz3XLJbqSKQNjwmwk7QmMSZ8g6SzzPuT8s5eUb9/GlJ6MwmswLt79cFK5QMZtMm+nEHc6DaPWhDE1neyDRrbFhTI9oQXzvEohlC5FWL0cfKs6o/bITUYRwfmwCVJMGBNuIAGGbHPiQLreiI9WiUeGlrwUAUkCMQv0GSZ0mQbz76w3L3aiUsCeOVMygV4S+MkpKq8QxwyDoYiRoY6qgt/zcwoYHEV4d8sxvjl8hS1HTnPoL3+qdG3MvqXHqelfT/Z8Q4YMkZWPBvPG5OTJkxZ3uq1atWLv3r2yxkB4eDg3b9602Na2bVv++usvi5U5eVwO1q7LEnr16kXXrl0ZPXq0w8ygn3zyCdu2bWPt2rW4ulomq7obFy5coFy5chbDBVqtFpXKMhEXQGZmpt3neYKHg/+UoQBmMRI5KlQwcxXYqyCZBycnJwICAu4rEcjvRy/xy4HT+ZnTbi5OrFowmoPrX6dD86pF+np7e/Ptt98yceJEzp49S9umVfhs1gj6da5v9RxlowLRqAvlAQgCN2PMLr0WDSrmUzuXiwqkXdMqTBzV3qzymNv/0NGLfDn3acYOa0P9GqWKjC1hDiW/0KcrUaUrIyo16O5cNJMj5dkJIrm8CmL+nWbSQdoduHMR/CMwEzJBMT9chmQAlWAOTYjQOjSD2v7ZeKhNuApJDB7am0M/vU6D2mVAApWfBufSrii9VWTr9CxYsSt/rJqVI4qM7e7mjJeHS5FJ7GpKIoM3raD/hi85m1AQZsqS3Pl1z3427jrKM6+vsPp9x8XFOWQohIWFyS4WAK0iy6FSKBGATmWqyPRSgqAmP0FELF61YspIIe2H90k/fQgh7gqGpFjz4nb7TzLXjefOiTT0Wbm/gyThVq4r1297Ikkix7JNnK5QHUEhkHQuG88oZzwjXfAr746niwuugRpEpYBR7YrOScOllCzKeboUmVmyq7tiLCwvfrcdqbJvZyWI4BYo0UO8TllTanHbQhRwqVqboGFjca1UgzILviFw2DgEQaSakEJpwWyUSRolsU51ccv25coF+aoWd3d3/P39ZZNOBUGgatWqFpk5RVGkdevW/PbbbxaPLVu2rKwapSAIjBs3TpaSefjw4Va5HOQwYMAAWrduzXPPPWdXCaIkSUyfPp2YmBiWL19udXG/G5s3b6Zr164W22JiYggODpY99nExFJ5UPTzGaNasmUWu8DyIooibm5vdNe956N+/P9999929Xh4AG3f8zcBxS3lm6lcs+HyHXcf4+fnx7bff8uKLL3Lx4kW7jgkP9qFK+VAARFGgQukgpuTy3FcuF8LuNa+ya/Vkdn09hc9mjSDQz5OGtcrkH3/+ciwebs58sW4ff50omFB7tK9Fu2ZVKFM2kJYj5nInNZ3JDUuhizmMSVeI/lYQEEQR7uZVyJ3ipVxa5mIQQJeuxZSoMxsSEvQpl8rqdrdY1fYmzpnnCCrjh0KZzFcfPI27sxNJe+KR9BKmbHP4QVcoV6F0ZNHF29IuZ/2548RkpBGflc66M4XlcQWUajcM2jRzzNwKHDUUwsPDuXFDPtm0sn8wG/uMZmOfZ+lYprLFPoLohDLoQ0SP3igDP0BQWtBWEEUQRNK0BtyUSm6/PZSYmcPJOfIDiedykzVz0wFCJ7+Pa6U2PPXbGaJIQn8nhgHKFG7tTSXpdBZp13LQuCtRqEUkkxGNuxK3ACf8h7/O3h4zWK7yo3Y9PxSiCYWrG6KHD97O9UFlIcyiVCGqRCSkYnmNedgWHc8Hx66y/UYC6aE6Ip9WEjVKiVJlLotUuRYcKABZZ44RPeN54pY/x/7s74hv6IfKy4Meylt8qDhCmQA9LmHO1CoXzhefLbaZlPf0009bZdK0lrhoTcypbNmyVp/jjh07cujQIYteBY1GQ7du3Vi5cqXVa7eEoUOH0rBhQ0aNGsX27ds5e/asxbwuvV7P6NGjCQkJ4d1333U4kXDPnj20atXKYtutW7cIDQ2VPTYrKwvnXMbOwhAE4ZHiWHgSeniM0ahRI/744w+rfZo0aeJwnkKbNm3umzrbqfM3zcJHwIlz9lclBAQEsHr1asaMGWNXaZ3JZMonWzKZJKY824nK5ULYvvcU1Tq8TqcR88nRFsQsP1+3jz1/nMsPNfTqWIe/T10jO6dg0V301mAWvjmYp/s352K0OSYsqESqeeiY1Syc5Ov7EJxEBBdF0cklr9TNJOHhLxFYXuDm3Y6dPPVIQUBwLrSwCAJzTwRgNIGPs4FEbQwRzU/z0+W3SdSfZ/WsUSj1AtqYHNSigoggbyRjwaLesoHtOGlZbz8kzN6Ust4F4ZxmdcsxeEBvyvsbWfbucKtjFGE9tAO2PAoALio1nk7FJ83CEDUVUfqMRXSua7nd2Q3vPsPQhUXhplKCZMIQf4uEg6dQuxbUtbrVbIh77Sak7tmCV3IOsw7totKVswzRxqHPME/QiXolP95w53y0ifSYAqNw57FzZK77lJtHz1JK64kxA/Sp6egTE8nct9scHrAICZNWyudjANCUroBz9XosO3OTownp9CodSJbByPxNN2jX+wQD3/yHLy7e4I/YZFJS7orVSxImg4QYc4OUm39x6OYXmLTmhVABjGtWkf41avFe+6eIiopCo9FYLYFt0qQJv//+u+zuvXHjxrJzSWRkJAkJCRYrZ6x5FMC8KE6cOJEPP/zQYvvYsWPZuHGjQzojeRg1ahSjRo3i0qVLfPnll4wcOZKuXbvStWtX+vTpw6RJk+jVqxcdO3Zk7NixDo+flJSEi4uLLJ2+LUNBkiSLmjyOVCE9DPw/JTP+51Q28m4mSZJkreAWLVqwdu1aOnUqriInB5VKRWRkJBcvXqRcuXL3dI1DejRm96GzZGTlMHZoG4eODQkJYeXKlTz99NNs3brVqjtQFEWe6d+c5Wv3UrFMMA1qlQbg642H0BuMGIwmfth2mEvX75CUksmsTzbnq0kuenMQN24n8dMvx9GolWh1Bpw0Kto3Ne9sy0cF4unuTGp6Nt4BLnxVvR51/7mEQnWGjNjTuIdXw5QXoM7biYvmWHSWpMa5dgA+9QRS9iWQczV3RyMBkoTgokBK0hXJbbiapqbd9tKEekUTVdMvn3jn8+3r6VB2CHq9eSLPydFz/XZSEb2I46ejaddUznVvRvfy1Qlx98JgMtIo1BxmMZpM6A1GZr85nueee47qFcKtjhEXF0eFChWs9imM8PBwTp48aXf/EuPaKhR3vif5cgoeeRwWCgUmlT/auGgUGhHX+m0JGTMNwMyGKAgENHHC5ZIS73Z+pF0tRdzlK0ymEnEKF1SSkY8zD6COdCXNSUP57V9RUaflx5x0InKnwPyfoJAjRlQLGHW55LUGvcXJMvPSOWYfu0KoqxPTapVCEATKebrQp3wQCifwHVmGn988xomEVJafuUWv0gE8FeGfrxOidlOQk6wj7Kd4Ltb25WCpptSNPoJ3ldp07DKATsqCZ+aFF17g008/5f333yf6TjKh/gVqq2BesNu1a8cvv/xChw4dil2rUqkkJCSEGzduEB5e/P7I49e4m8WwdOnSNo399u3bs2jRIpKSkvDx8SnSplQq+fzzzxk4cCA//vgj7u7FuTOsoWnTpjRt2rTY+zk5Ody4cQNRFClTpoyFI21jy5YtVufWW7duUb26fIWOHNzd3UlLS3P4sz7BveM/51EAKFeunFWq05LSMg8YMECWftURlI7wZ8/aqRz56e0irn5Jkrh2MyG/MkEO4eHhjBw5ktdee81mydD0F7tyZtd7bFsxKV8kqWWjSnlnRJIkJs5cw8yPf8Ld1dyuUinI0emZu2wbvx48jYebEyvnP8M3i8bQ/7XPWLnqNTxvT2XH7Mq0bFCetGwt1xOUfHHeD8+wxuhSE7jz90a0qTFglIgKyZ3kjOYFwrmUa36Soku54uVTnsbcXaJJMpuyovnYuGwVu/ffQGeqgEEvkJ6jZudxOHH2BiGeWrxcDCjE3O+jkJF4d4moJalnQRCoHxJJ47DSCILAjfhkWkxaSK3n3mfXyat2hapSU1MdKgeLjIx8KDoippQjSBKkZOnx9dKg9lDgN2wqIa98hG+fsQS9soD5FcPo+eNiPj+xC/+BY3GpUhpFhIinmxJv3xx+On6VvqejiBPN3g29oOBc+RCOVPcmzhUUeh1X07Ip7emCqJBQeMNabRh/Ggp994KZwdGaw9VgknjtzwvU9vPg6YqhBcZ+7kylz5CQMrpR18+dURXDWNq8Mr/cTOS3W0n52Y36HAMZsVo8bmip+WMMn14V6GNqzJYUJRl/7ylyvgYNGnDixAmGTF9O2+c/pPukT9Hqij5/w4YNY9WqVbLXnGcMWELfvn3ZsGFDsfc1Gk3+hkYOgiAwYcIEWZbIwMBAZsyYwaRJkyy2lwROTk6UK1euxEZCTk4OX331Fb1795btEx0dbdGoyoNceCHPUHhU8CT08JjDy8vLaja5QqEoUbxLTiv9fmHSu2toOeB9Wg54n6QU6y62QYMGER4ezrhx42x+DhdnTRHvyjP9m7Plq4n8+s2r+Ul9giBQvlQQC14fyNYvJxIenLeDEdCoVbRoUJEX3llN3KXLDK96FGXORZSxX/HbntPo72hJ2W8WuhFEJd6RzfCOaEFm3Bnir/xEQsqV/AlRlATKO/uYJR0EgZxrReOjQa4GXm+ciMZfRWjfQEI6+uHnZkRSCaTHHsWoyyImNoK5U/xZubUcibcFGtcpS8e6TjQvn0rh9d/HTcWbrb3oWaGo48zHx8cml8a2v84Qn5qBSZL4YtvvdiVyNW3aVDZ5zRL8/f3R6XSy9fb3A7qsv8hwOo+gkbgu5hDh4YFRJ5H83VzQZeHVvh8/xf7BP/FpZOnhu7P/oAgrhXH0NO5kS3i4KNFnS8RnKBEk0MdkIUoSnaqWpmy/FqSmKbnj5oLBWUN0hpZSfl6ISjAmQ2tVPNUjJPyruOPsrcIlwoV4hby6q0mSePvIJdqE+dI58q5qHhOYcstmE9Z/jio4HATwDnfmy6frsu1WAlui41FqxKLcC0iIkgkkiezrF4n5dCY5FwtKTQVBoHW7Duz+xcyGeCH6Dheii0rLBwcHk5GRIev2btu2Lbt27bLY5unpiV6vt6go2aRJE5v3S+vWrWV1J8Cck2UwGDh79qzVcR4Wxo8fzyuvvGJ113/u3DlZQ+TOnTvFvCd5sFUl9LBhIrdqu4Svxyn08J80FJRKpc164TJlytgUWLkbCoWCqKgoh4+zB5Ik8eMuc7VGbHwqh0/KZ2PnYfz48dStW5eRI0c6TKZSpVwopSP8Gd67KS0aVKB6xTDemtCDXh3rUK5UEI1ql2X2K30Y0LUBX8x5GgCjJJGtE8jRC0gSXIlzQZJyDRAT9B/UGHdX80KgdPIgtG8fop4dTpwhmuw7G1BxAYPByNHfLnPnh9vEbbhF1oVCKociLOt4h+7lsmjV1QlRLZJySYvJU8D58g+gNeBTOpdfXhTIOJyE7lIG128m8vr0Wey/4JdfjlGpUgClw3XkxJ8n4bNpGFML6uW9vb1lCW3yUL9iJIrcOGmLGmXt+k5LksdiLQ59P2DUX0PvJJEcquNUjo5yXrn5DkYD+tjrfHZsPaUO7M5fTJ0xoRBFfrz+Bwuyy3BJ4cmmrKr0LC1SPSqHAD8tzdum8s7Izlw66sc330ew8vsoLmY7oXtqKBHhkUi5a2KAqCVEo8MtSIN7qBN6f3dmN2yLZw0VgkpA7VFgwEmSxNzj16jh606nSD/5ckkJjKlp6GNugATZSTqc3JyZXbccR+LT+PrcbXRKFXqFAoVGgVDam0Y+6fTX3CAyRM8XnRtxKn02ObEfszt6E79e38DgIf1QpV8DoEyYP+XCiyekdu7cWVYV0sXFBbVaTWpqqsX2pk2bcuDAgWLvP/PMM1bZH8E8lwmCYPX5njZtGrNmzbI6zsPAihUrCAwMpGPH4pLtebh16xZBQUEoFJY533ft2iWrIeHh4fFIGQr/T/hPGgoqlcqmoVCzZk2b5EyWcL+V//IgCEI+AVCgnwf1qpeycYQZw4cPp2fPngwcOJDs7GzbB9wFX283Vsx7hh+Xj6dq+YIEI0EQGNitIbNf6UOF0uZSprIh/qRmKRnwUWl+j61NjXYzCAnyyj/m4O6zpGcV0F0rfZxQurkT3KkLlQf0xi/gDjm31uHlfASF1lCcgdEEr+3149AtJ5wNkH5FS0b0Dc79ugFd6ea4BdZBQDATL0kSptzj9/55HkFUEBgQaI44KASu6LI5ZfBgUU457hiUHD5+gX8umXkQRFEs5vKNjU/l+JnofO9MzTJh7Hh/LGunj+S1AdbFb/Lg5OSEh4eHrEaAJTRu3JgzZ844zBZ6N64kb+OXKxM4G7+uyGfTuLVHqamMoPAlNsGXSv3HIqid0ZSrTVJIGfbeOIsPObxy7g963zrPrArVEQUBf2cfkvUi8Uo3NFdvUPG5Hnw74zxbXjnO8Ip6XLPWszP3+zQgcCKkGme3bMA3ORaFWsQz3BmNh5LU6Cyu/5FC3D/paI/cwT8tHZ2TisAqHgRUcUftbjYWPjtzE18nFf3KB4EAokKwa3Yy6kwkXUxG7efLW3VLczJboP1ZJQO92nE2LIyIYIGZEVd4MSAaf0nHbXcPlsVX5mDcQf6M+ZW/Yn/jdM4hmtQsx9dvDuCnD8fipCnuPerZs6fFEEIeOnXqxPbt2y22dejQgR07ilc3hYSEIAgCt25Z5+eoVauWVYGncuXKoVarS5TYeL9w8uRJNm7cyFtvvWW1388//0yXLl1k262JTT1yHgVJuOfX44L/pKFgj6hIjRo1SmQotGrVyiH3siP48I2B/LbmVfasnYqPl/11xD169OD555+nb9+++TE8SZI4fyWG1DTrlNaOwNPVGVEQOBHtwrGktqg8qxShDb4dl2Ke5N2UiK5Kci6nIRkljGk6bh0RSMhuiV+N3lSrmUXalQ0kXzuAQZv74Ocu8KcTNAzfEsSG1WnknPyb5LOHCG7WD4/Qort6jbpgMm/Z0FzV8Mk7Q2ndsTp+bcJQhbggKEWUgsQ/QQ0Y/MFPdB+3mF//LO6iPXc5huYD3qfHcx/z5sJN+e+H+3tTq2y4Q6VhPXr0kBULksOLL77IJ5984tAxhaE1pnPizhek625yLvF7Eq4X0AqLCi88Qz7CO3wtouiFW4NOhLz3E76d+uAevQuVzpnVNSqRE6igff2GVKnVDoA+ZdrS9OotKienUO1KDCnff4X+qJa0TTpW71bRetUdygSaK2raBGbyYsgpoqOvEuqmJrSeN/6VPQhr6IvSSWHW+cA82Yy7cJrsP7OIPZ5K2u1sdOkG1lyMIcNg5JlKYUU+l1/Fu/I98nRECr0kI+hSDRiTkhAQKduwFS10t6iz+zMu3Mn93STQpumpFSpRw5gGWomPLtTlz1sRZOsVaBMvMWTIEA7t2YnGUhkn5jBRVlaWbPihc+fObN682WJbHjGTJdjSlABziMKSR6Iwpk+fznvvvWe1z4NCamoqEyZM4PPPP5f1FORhx44dsoaAyWQiPj5etnLokTMUnuQoPN6wx6NQuXLlElngKpWK4ODg+0q+lAdBECgV7o+zk+MKmG3atOGNN96gT58+xMfHM/WD7+kwbD7N+s3iRox1N7s9SM/M4fSFW0gaEY2PM4JGweGTV/niu335fUwmEw0Dsmjkn4mgAHeVmrTNN8jYHYs+S8JohOxsJ1Q+Neg0qhXOXlEkX9tL/IUt6Ax38nfCJmMOiee3knw9jeDmfVEHe2JyKXqrBvl5sHz2CH7+ciJrdx+h1sCZHD53DcKcyTEZEVQiVav406bUTdacTyYvcL1s3V4MBnMiQ975/jx+BZ3efL/8csAxMq670alTJ1kXtRw6duzI3r17beqUyEEpqFGJriCBYJCIffc1krYW5fyIuXQWt5izxLzek7SNH3Lo8lx+VG2hd+QR2oQ1o9awpZRqPiK/v2HfaqqmJBBu0CGYQHsjh+SdCvZcDGJ/xv/YO+s4qcr2jX/PmZ7Z7t5laRZYukNpBAGREAtREANRMVBEfQ3kNbETRDFBEWmVEKW7G7a7e/qc3x+zyc7sDqXye70+n/mwnPOcmnie+7mf676ucPJNWrYU+LHCbydvtTpL8akyKmx2ojp4o/ZxDBaCACpdzcAhqgXU+TVS5cZ8K0fzSzmcX8pj7aOBSmdIyfHZFCVe8H7IDr8HUek821AuKknsEkPeY9PJbd6M3Vs2kbq7AFQyYV190Qdo6J2ZgZgrYZYUmG1K/E7D9cf2M7hTjNNZf22MHDmStWvXOt0XHBxMYWGh0wmKIAiEhIQ4tVbu378/27dvb3Bi06NHj0ZLvmNjY/Hw8PhrqmhqQZZl7rvvPubNm0dgYGCDbcvKypBl2SV/4ciRI8THO3f0hH9eoPC/hP+XgYI7GQWtVuuUYOQOxo4d22Aa8q9EYXE5uw6ex2S20r17d9566y1uueUWfl7nIF2WlJnYfcg9TkVOfglL1+zmXHJOvX2HTqSQllOIqFdilSXe/HoDEx/6ALkOcQz8dXYW9ktjakwuBecqAxSNiOihomrh2cNg49A+T7TeEQS2HIl/TD/ISyTn6I+UZOwn5+QqvCK6oA/pCBY7/h098OjqgVglHyxASnoBM178hv2nk9l1NJGSchOvfvErcqaJij25mFPKGNLShqHMzhNDM9CrJYI8Lezef54VGw7g6elJWZmDHzG4TxzBAV4IAtwzoW+d587MK+aGB95m64GzbNl7utH30NPTE4VCQdovS0mbM47cT59Dtjasyy8IAg8++OAlrzMrRA39oucRmRZF1OIClGUSZQcdWQVJsnAm801+W/YwLSrT/KUHtpAQ6Hgvi/XQLGsdoR4Okx9Jlikor8B4Zh/l2FErajmGagRaBNgcfAYgSirHX2XFWmBG7alE568koL0elS/YJTtGqxcV+TXPrlCJNcJKCgXWMjtfn81kRtuoulmbSitpm/ECupcMsq2S1FhpMy3USgCc9w+gqFLRzzpxNN2CvXh6+xlS/iigIt+MRdYSdjiDloV5IAjIgkCr0iKkXDOyUkNcXFyDKf7Glh/69evHn3/+6XTfkCFDnNpHC4LQqNtk1dp8YxVOfwdXYcGCBfTo0YOePXs22nbDhg0MHjzY5f6Gsg3wzwsU/pd0FP5fBgoqlcotcl9UVFSDYiuuMHjwYJee8X8lCorKGXjba9zy0EcMvfMNWg96miff/I3X33wHa9YfWE3FBPp7olGpyMlvuKzIbpe4afp7zP7vD4y8e0E918X2rSIJCfCu7qwUolDlGIyHXkOLJiF0iFDyYLxjrV2lBEEhOmIDu4yoVyAaFDT3MXP9nlxmGpIxiI6Z/avP3MVDjz5FUOvRKFQGAluPQuPl4EVYss2UHCymeE8BweF2mnW0OFwEAYvVzurfDmKvsGIvteCr17F5zWGkEiuWMyV0Ce9EaoGKQa2LOfLsYeaPSapmxAcGBpKT4wiIwoJ92Lb0afa9M57b2nvW6ZB//G0fZ5KzsVhtLPjKvc98xIgR/PTOK0ilhRiPbsd4Yk+jx4wZM4b09HS3DaguhJcmgrjYaRjylaBQ4jP4JgDyyraRV/oHxwszaRbkSOVr2/YhyOggNWosMj6SFwAWm43bFn3LhLffpMCYRpnJRlmQCkEFXhFaNF4qmshlvFu2nfvyD/FywU4spXaKEozIOlCpa7qTgvNlqAbNRqwlFmWtkuvGYQeda7RgtktEeNSXnXYbtUwfYgoL8KwM/uPzMhkVHUiMt47NWYVkHgNFhw6oA+DmpJM8f3IbL6buppsxlwJ9MNqg5kyePJnPP//c5aUCAgIwmUwuB6tRo0a5XHYaPHiwy8qIO+64w6XtdBWaNGnSqO5CTEwMvr6+Lp0przS2bt3K/v37mTlzplvtV6xY4VLWGWDbtm307t3b5X4PD49/WKDw79LDNQ2bzdaoMx84fliXUpqm0WgICwtrUFntr8Cp8xnVZZTJ6fkYTVZOnM3geEIxWzauoYnuNL4GmYf+8zWDbnut3uBfG0azhfQsxyBvMltJy6pLrvP21PH7t7P5aPYkHp40gGenjKiuCujbrSVfvjmVnxY9SVR8D35ODWLxKV/HgCsKIMlIZjuCUmSGIYG2ujL6exUx2s8xUG/YfoLbx/Ykonc0gb260yG+BbJdcgjoSDKlJ0oxJpvITlKQck5L5342RFGgf7eWnEvIBpMEVpmi7LrPt35rPr37DOW2j5ow45fm3P1ZUzz9VPToEMvxs4kk5JSQmlGA3S5R/uca8hY8Ttp/H6HotxrHwNaxoQ5DI6WG5hE+bn0uvXv35nhZlciUiDIoouEDKvHGG2/w8ssvu2ena8yDsrrfXV3T1jT/bC0tF63Hq1t/ADTKAABS8irwv7s9gQ+/j9/4xxjY6RMGCSMYnNYWqSwA0+n9nMzK4VhGNhO0SXiV2DEWWwmSRKwVdqxxjkACoLm9GEkrYJBrgvETBcVECRrMRXaKEkwUp9mQLKa6zyJD1aguVZTye2YBI2IaTlc7hU9lBys4ZuQKtYCgFPC0mXl283qe3rmRu3c61BLvbhPhqIQwm7ApemPJdWhVh2UX0MxUgqgQ8Lc6KmLi4+NJTk6ut0RwMimLIY+8w/DH3qPP9YNdOkq2atWK06edZ52CgoLIz893+tl6eXk1SoIdPHhwo0sj4OAqvPjii422u1wkJibywgsv8PHHH7vF4dm9ezcKhcKlfsK5c+cICgpyqeYIcPDgQdq0cS5l/i+uLv6SQOGDDz4gJiYGrVZL9+7d2bOn8RnW5cBsNjf4hauCTqe7pEoBgGnTprm0gv2r0KltDO1bOQahqEpRI1EUiGsRQVRUFJ9//jl/rvscm7mUkjITJ8/XXyOtgodeyxP3DsfHS8/NwzoT3zqS33eepPPI5xk2+U2ycovRadUM7dOOmZMGcsfonnzw4h0A/PLHEabOXoxg8Mdj8CzGzn2f6bcPw9Ogdcz+RQGp0Iq3RUlIlGMt+mC5JxuK/AHYvOMk23f9TpHSjhCiJdHLBAJoBAfXQKgMSASNAkmrZuiA1pzf/CpfvHYPA3u2qH6GYH9vfL1r7GlX/X6IPmGraNdF4vc9BmQJyopsPPH2cnYcOsm9z35P31vmM/6hDzlp2sSRJ4M4M9mP8qQaMa5BPdqw9PXpDLuuG3cM6+DW59KkSROyfKLwmzSLkMc/QB0aU2e/LDusmaWyutwRf39/OnXq1HiJZc4h2HA/bHoIEusy7UWVus4s3lvfnlZhz1JWEEy/vq+ijmqJIIooRRVB0TdS/sc2yvduIu/TOcTqlIR4eXLM7IsA5FdY8PRSo/ZQYgxQY1TYKM00UppuIlQux6+nhMofPNsInFaW0kqlJ3FtAZk7SsBiI23eTHTNanXsVWOkAAqVwLbMInrXqppxF8KNGrhVgxCjQAoVHJwGmwwS6Gw2wnILEXFwHbQaDeObh/DVoXN8uuALjLLjuyQLVC+DCEJNEnju3Lm8/PLLda73ycqtJGUVkJCeR7YQ4LLqSRAEQkNDXVYxNGnSxGUGsyH+A7ifxYyIiKBNmzYusxdXAkVFRUybNo1Fixa5pZJoNpuZM2cOb7zxhss2H3zwQaNy0WvXruWGG2646Pu9WvjXFOoKYunSpcyaNYvnn3+eAwcOEB8fz9ChQ6vTvlcDFxMoXCqBrHv37pSUlPxlaT5n0GpUrPzsYY7/No8NXz/JJ6/cxdrPH6VTW8dg3KJ5M+5/+Bnyzq6nVYx3PQfIC/HgnQM5tO5F3pw7CYVC5M3PfiG/qJzT5zNZtrYmuCssLmfWS9/x6beO6g9ZhrzCmpSgRq1i5p2D0OvUDs8Fm+MX8UALidyUHBblhjM7uRm5NkflQtNwX/wOLEeWZGRZQrLYET1VxHqYEPUO50m1QUQd64EqysDR03rsxjJW/fo7q/edQOcpMqKLLx/Pm8xH9/egpUcFnQKN6CwFjF0YxfenIvH2rhR8QqawpAAkGdniGCAOHEvmVFk2ggSaHCumcEcqXpZMlGc8Qgv9vXSLs5Kfl1u53Y7x+E7MiXXJsLLdhizLqFQq7DLo43thO7MF047vke015NqKX96j7OvHKPl0Kvb8ul4fs2bN4q233mo4q5C1n2pySEYNyc149iiF67/DWlD3t+Xn0RVRDsDT4wI2ud3ueCGDZMdm3MnC8dlMu20Q6lueIk8PTUUVej814iaJE4buSBYZWRaJbdqe7E1WAvqLeLUT2XOimDH/8SF4RE2XIlklKo7X+n0IlQRSGcosdhBkPHSuM39n/QOwCWI93zD5WxN8b0ZOtEOqhGRz8V4JYDdZGBbuz/bMIr7I1TDT2JFPzbFs8g/CGiUjegoIQTVeIF27diUjM5sjx2pIrc0jAh3luLJM69gIwsLCXBo6DRw40GVVVJ8+fVwKtg0fPrxBK3uDwYBCoXAr9T579mxef/31i3aXdAfJycmMGzeOV199lejoaLeOmTdvHg888AD+/v5O95eVlXH69Gk6derk8hwVFRVYLBZ8fHwu5bavCv5deriCeOutt5g2bRpTpkyhTZs2fPzxx+j1eqdrgWazmZKSkjqvS4G7gYJer7/kjALAa6+9xpw5c67KD9JdCIKAQa9Bo1YytF9bWjcLq7N/wQvT2bt1PdbMLZQUX1ytfrvKbIUMtKl13veXbOLnDQc4eCIFP28DGrWSLu1i6g1uHz5/B36ehsr7hINHzvNcQhDf5oZgr651A1NZIfl5EqYd2dh35SDabHgPDye9WRiRvmaaRdjwjVZUyjLLbD6dxnMvzmHX8p+w28FsFzCZCvA+9hsha9/gkyan8DSbMCitxAilNB2oJ3RcEEN6Wfgw4BC++VkEqBQM1ufgIVhpqiqn+5psopcWELGymNSfN3Huz3XYjPuxm48Cdvw9DlcvUxWvWUjB58+R9/4jZG8eh9V8juI/f+H0XYM5//AErHnZaDQa8le/jeXAGkzbvsZ8sGa2aDtfGXTZLNhS6wYbvr6+9OzZk/Xr12OTXFTuRPQBRSU5NNrhFWLJTCFt/kPkLf2ItFcecmv5QuHlh9/tT6Fp1RXFpFtJMX5KhXEHSusHeEY3pdxoJFDn+B3pBSvzDwgcmXQXgWNvx+fwQcy5EucWVrD7VwUFFgth0Rr8eirQuPLFkqnOKmxPK6RXkE+12iKAstbk1KxQkOXphUJ20p1eKGvn5FEFRc31FKLIfW0jsR7ZRJLkwSpbGG37WSjvKFM8NALDxFerj1v8w1b2JHnSb/BETp5zfN7339SPdx+dyAeP3cKUEb2YMmUKX3zxhdNHHDBgAJs3b3a6z5XwEjgUHC0WS4MTF1eESGfnuueee7j77rsvWoitIezdu5e7776bhQsX0rlzZ7eOOXz4MGfPnm1Q0nnJkiVMntyw4VpjRMi/A/8GClcIFouF/fv3M2jQoJoLiiKDBg1yStqaP38+3t7e1a+G9MAbwl+RUQBHSdTo0aMbrYP+u9GuXRzvvfcet912G3l5eW4f9+KssXz40h388MGDDKplqmTQqZFlGVmGguJyzBYba38/wtHTdZ0QO7eN4f0X78Df14OoIE9aaUrJs9f/XNKL4ekjEUiygNkmYM61Ykkoo2R/EUkJIi3bFXLrzSkYPO2AgDWtlG+2qvn+pBp7iRXJLLHppMje1aurx6IpXUpYOzmTX29JobOUj0phRxHvRRlqdGWnGe9nYpbPeZaEHeSjuOP4d4YASceWmGa8dP0wpqee5WCBDgQ19jIZ35Ig0s+cAsCaXKPFYM8spqJ4OfmrvgK7DVteFiU7NtKiRQsS0rOoZtvZatj/mi5jABA8A1A17Vrv/Xho5kM8/vJsHt3yPF+f+LH+oO/XEoZ9DsO/gAhHlYatMJcqdqmtMBeTxUxOuWP2WVRU5NKDQt95IGUTB7HDaz3n7DpKJaVD1ErrTz6hRDb3Ik+t5P3m7Qi8OYi1piQKbcuwxTm8FazlMnJiGT4GR2bAbgFVqIt15Cr9A2BzegHXhfnVGeRttSbLZlFB36SEi+5KZUBSCkh20IaH4NG6JbrmcXSJCCbWE54LPMUfQ5PolCIQtleHQhLrrLF/v3o3Kr0/dlng828c/g2iKDK8RxxDurVBEAR69OjBnj17nE4QwsLCyMjIcBqoRUVFNWgrPmjQIDZt2uRy/8iRI11qNVyIiRMnMnr0aG677bZLru6qjRUrVvDSSy+xfPlyYmJi3DrGZrPx+OOPN6g8KssyP/30E2PHjm3wXKtWrWL06NEXc8v/4griqgYKeXl52O32egIawcHBZGVl1Wv/9NNPU1xcXP1q6EfVEEwm01XnKFTh3nvvZcWKFRelxvd3oE2bNixYsIBbbrmFgoICDh5P5oV3VrJtr2tTIpVSwQ3Xx9P1giWLB+4YyCN3D0Gnratgl5CSy4Xo1akZ+1f/h8fGtMfgYWC0r2NdfkgzNZsnpnJP+2LqUNcByWzHllfVuckknVfj52Wl53UVtLemY85wDLqSLKBRKau9KhYmemC2Q5msxCtMVZ2djzUV4K8thwBQPtCeWEMGER6OTIdBZafpPQqChyuIvkPmdJPI6rT+nuxSDKGfkfepB5odBZxevgRrQS6eg29D0GrAG8Q2oFQ3RR9XOcMSRHQt2tGqVSuS/LugatkXdaeRaDrVsL21Pcbj/fAyvKYvQvSsn44tUxjxiQsgcccZ9mQfosRSVq8NSh2oawZ/XasOeF03ClVwONpbZnDLz0uYsHwxn+zfxurVq+s5H5aak6mwZGEvLSLl/E/VA7ZN9CEi7D+Isprc5ELKTwvQUctOMQybqCDT5k9+Gz30UyFO0PJds06cziqihcmL/I1wZjF4dRmMsUsgpaoL9EAq7STtkkyeyUKI3vVv1LOypPRil3EFQLTLqLQi9qJcLOnnUft703LxBt787mdWp0p4WhzfLdEuEEBdl8MxQxzp78DYPhza4XxQFgShQX+Hhiyk/f39XQbrN954Y4NiXeHh4WRnZ7udwRw7dixTp05lwoQJl1wtIMsyb775JuvXr2f58uUXlfp/4403uOOOOwgJCXHZZuPGjfTv379BLxW73U56ejpRUVEXc+tXHf8qM/5N0Gg01Qzgqtel4K9aegCH/8P8+fOZPXv2ZZ3nr0C7du144403GD9+AhPuX8AXP25l8mOfkZlTdFHn0WpUPDxlCOOG150Nr9/iXOwlO68Y/1Xv0tWewn2GM6x8fjgvT/Al0ttOnwgjtYMEQSnQN6icwJJiBBHUapnrBhrYfagd+5baiPU10ryFkfunZ3Lj8BJeeHQMoiCjDVRyIjiIHSXe6G0W9GlF2GWw22WSKlTVGZC9FXYWHlXwhtCbb8zRiGFqFJXVeaJoYsyAQYiiiEahZGBMSwTZH1tuIYE6NTllRqw5GWhbdSX05dUEPP4yvi1fRu99M8GTHyHymbdp8toS9K3a06pVK86kZmEY9ST6QdMR1HVLAAWNHkF0rmIXqPOnz4TrOPLzfgI0fnio9E7b1TmfqCD4rseJefU7Dke3oNjsGAzXnjvBihUruOmmm6rbJhWuZXvSo/yZ+CAZi57C+4/zIMuIEsTKmZwpsVB6cCfWgmyseYUEmsvxEBwpbE/M6MvMSF8ZUWy3cnvOEfZnlhDv7UXRPhmf5gM5H9qZ23a04T9lceRQEyzkR+rIivNkm8JMvH/NOoOzYEC44N+Lhd0qYbdIDp7EqSMkPTudCLVAREQEW40Ou3U8Q1BF9q9z3P23D2DL90+xd+1rtG/bus5SgSzLfLBuKze+9CmGZvEuHSUHDhzY4PLD9u3bne6LiYkhJSWlQZO3nj17smPHDpf7L8SQIUN46qmnGDduHPn5+Y0fUAt2u50ZM2ZgtVr55JNP3DJGq8KpU6fYs2cPd9xxR4PtPv30U+69994G2+zYscMtnYa/GvJlBgnyv4GCAwEBASgUinqz7ezs7AajzMtFbm6uSwey2igvL0ervYwa7kp06tQJjUbzl6uiXQo6dOjAf154ibQjP2EqzsAuyZRXmNl3JLG6PNJdvPDoGB6fVmMA06dL8+q/zRYbRpNjVqhRq9AINStyXmoFH60+iyhAv0gTE1o5uCjRob4cvTePV4NP8lXkYX6YcYxVn54kMjSHvSUGcjIVeERpeHpWGj26lTF+fDY9eyiJG2cg6MZgPPsEITRxBJeqrAqKsvM5vyKXHvvPoTHbsNtBm15OfrEJpcaTH8wRSLk28rbZsZmU/JjRjkd+OcaomC78NO5eWgeEIGp1+AwYgUGlpAIRVZCDqyEIAhp9dzT67o6MhihiiOuMJtxB8GrdujXHjh27pM9Iq9Tw/HVPMHbYGDoUNkfhIqBwhc6hkXioHIFybImFoKAgvL29q/fnlu2t/ttanIPveQsdPiuk/+k8BEnBmsTlvJK/nTPBAfzeN5bsrUpeUW3jQfUhnonaif4Pf6QzdqxpdrwClCQUlRGjURMy+0NCRg9i44bV9CKbNzUHCcKCIDo4A8YANcHHS0k4lU/baIf9tF2ANC9vRJWAUn9p3ZGgAqFW8kKqFGySrDKSTcZSUIrx7HGyvvmQOU8/xTvrT8Kw/yK1v8uRmbkAMREB+Pt6MG3atDoCSwnZ+by/divnsvL4YPMBCouKnU40BgwY4LKUsW/fvi5FmcDh69CQtPzYsWNdlme6Qq9evXjttdeYMGECaWlpjR+AIyh68MEH6dGjB0899dRFyZiDIzu8YMGCBo/LyclBqVS6lGyuwtdff83EiRMv6vr/4sriqgYKarWazp0711l3kySJTZs2XdUIsby83OWabG2cOXOGli1bXpFrzp49m1dffbXxhv8A9O3Tk0WLv0JjPkfXmDI+/fZ3xj3wAddN+m81gcsdiKLIjMmD+Py1e3hy+nBGDXakbQ8eT6bTiOdpP+xZftt6DB8vPYo7Z5Pp14yKfuN5ac05Fh/1ZMQPoczcGMgPpxyzS9FWiDkzn5JUI1mFAq8viWTOghiCpCKUBhmlt4oj+d5olTJC5Tz0mcUryVd6Y7WKyLLAV00cErBFOi2iLyBBSH4Zty8/QPtTeSgFJYJsRVAoaaEtZ7MikoKjGk69ZWfrDi3DihP44cABxIQDyLLM0iNHea5E4khQOIJso3Sbe/LMl+J0J0sSsuRIKxtUeh6eNotHZ7/EzY9+yKFT7kuGh3p68/3Nd/HZsPEc+e4n5s+fX2d/pM8QBBQoBA1eE6ejiY1D26Y9uyLD+aKwGUazL+sPWLAagvhB155CRQDSsnyaLz1H+LkCVLaabJ1KL2KSZLw9NCiKDyIcns8Qvx20UxQ7Bmy5SpIZFGYJBMg2WgjTakCAA+FRRClNSFYZW4V0ScGCbAW5kgJSdxGL6ooIQQTziT0UvXgvgUqJrf+5m8Iv51K07L+OhrYSR3RRC02bNiXx3EkoPQOyjJdOi1rpCNo8dRquu+46/vjjj3r34+3tjUqlcrrE0Lp16wYtoRuzsm/bti0nT55sVKL+QsTHx/Ppp59y5513utR6qEJpaSlz5swhLi6u0YyAM/z222+0bt260aqIVatWMWbMmAbbFBUVkZmZSatWrRps93dAksXLfl0raFyV6DIxa9YsJk+eTJcuXejWrRtvv/025eXlTJky5apcz2g0up0lOHPmDOPGjbsi142NjUWj0XDy5Elat259Rc55NXHrTf2ZNGY3Cxcu5MnnXsAj8jrAwN7DifUqJxpCZk4RDzy7BJPZysoNB/nly8dYtnYPFSYzsgzf/LyTIX3b0nHkDTDyBv7cc5rN334GCJwq0JAj+SLjIJQm5sKBAj0xFPF6XlMOWrwRTsFb3zchoJUdQ34eAzUltDWbyVAp0RsFKjKsVITJ6HwdQ4SkEVkwuB8au5n7/9iOLEGOpOFMbB+W+3ihkOz4+8o8FJnJu6eCOJoTyo4AA8/5nuLxnH0AdDdlUfL9WhIiejO/zBfUXhyM7Y0nm1CFulcSJlWU4KmQKczNwjew8eyZKfksKS8/gmQxE/Hoy3i0bcGWP94lp8jEvoNHeO4DO6vee6jecWa7BZWoRBTqdjoeag1fvf8RDz74IL6+vnX2hXj2IqB5R0ewIKqhlYO/YD+7kyYfv8FBg43l+aCPCEOyKwk1GKjyBLXkeeMVFYxQmITWV4HWV4UggFWnJGXVV/h62gkqKeOmtgLlZ1XIkkSuVSRQtuKdYqQ0REumxUqEoAAZOmamodAIVA178mUWEHn1GUz57o01JZOV/wgKR/hgL8rnJlMZn+3IZOHz7ahIPgSZH0H+clCHQdMPQOEIXFUVp7DmH4Ajj0LELQRG38HXs+5g64kEhnRoibUoj08//dSprfL48eP58ccfue++++psF0URg8FAaWmpUw2C3r1788UXXzSodjhw4EA2bdpUj3fSGJo2bco333zDHXfcwbx58wgKCuLUqVOcPn2a06dPk5aWhizLeHh4MHjwYO65556LOj84yhj/+9//snLlykbbrlu3ji+//LLBNl988QV33XXXRd/HX4HL5RlcSxyFqx4oTJw4kdzcXJ577jmysrLo0KEDv/zyS6PppkvFxQzUaWlphIeHN97QTcyePZv58+e7LJ36K3E2MYvNO09yfY/WtIh1PlAJgsC0adPILFYx/+XnadL2eob0a3tR10lIycVkdqxfn0nMJr+glF/+OFZNJLy+Z93PIirMH6VCxGaXaBoZwKJmCUzfLnLa4sgALSqMwDOqCQcSrFBZ4uaXWMHj8jYUPrkU71eh6qGilcHKm39GUZhiIj7jDJ1jdQT17s3K9ds45OfLf8/uo6ry7qOyWA6dsBASCY/s/ZO3Sgux5VSljGWSy9RoesRhSzmFpcRGXFEqtmgd9tP7IGwQgiCgUirRxbbGs+t1dZ5HMpuwZCajCW+CUEnek4ylFHw4nVbmRNa8cAutnnmZrqF96hxny03GlngIVfNuKHxDKdq8BntZCcgyBet/wCOqFQHaBLyjO1OcvJ/g6+pn4NYl/smq85sJ1PnyZNepeKprHEePHj3KmVNHeHGO84BcKdZNucuyjLDlFxR5eXTMyyMszYqqdWumS7uwns9EVAjIMpTtK0Tf9gj+bb3RBtgRENEGKCmXJbRnysgDND5KrGV2wgd4cPBMCH6JZxEE0JfYoNSO1WxDVzkzV9oltN4ayiocEYJkvXQFfK2fGq0tEZNaROevJDTem8LEcvLPlCNLjuUPWZaJ9dKTW2ihVGchoJ8f5FcOapYMKDsI3v0c/y8+jEohYLFKqAv3QPQdtIsOo120I5CWQwJcZgdGjBjB+PHj6wUK4FgK2Llzp1NfA29vb0pKShyfh4u0/aRJk3juuecuOlAACA0N5ccff+Sxxx5Dr9fTsmVL4uLiGDt2LBEREYji5c1yn332WZ588slGhZiKiooQRbHBdpIksXr1arcUKf/F1cVfkvuYMWMGycnJmM1mdu/eTffu3a/atY4ePUq7du3cbn+5P4zaaNmyJXa7nfPn3TNhulooLTcxZvp7zP9wLWOmv0txacOEzecev4u0hMP0aaPm7TfnX1Ttddf4JlzfszVajYpH7xnCkdNpFBbXWPHecVOvOu1jIgJYufBhXnliHF881Btz4knmBZ/iDr90brqpC9rItmzc5VDZwy4jygIvvvEoimxHRYW3ZOXuT1rQ78OOfLw7DKHMymyOMShxHx2WL+SWYG8KjpjqyAt7i1YseVYqjhTRtDAPZJmB+hx6+pQQrrPiHRPCtuYDsZQ50uSSVaY03UxAbhGPnt3BcLXExzePRelVd2YuWcyk/uce0l68l7RXHqxeNrDnpmArKqCNn4HEs1lsOLOUcmtN5YJUXkzJ4kep2PgpJV/MQrZZ0LeOr6620LfpCMogRnbO44OZpYR7FPGf6fUV6TYkO0hxucZCjuXVCADZ7XYef/Re3nqwABKnQv4PjX6Oh7M/JUG3F2QZBTKGhLO8VnCS+BMZYKua5svIdsg9UUz6nwWYCyQqTDb0WiXawpryT5VOgcZHRcHBClqoktGohMqjgUpSaW0CY1mmufpv+RLjBIVWJLSDFwZtIWGdfSnPMpN/rhxRWanEaJfB4ItgcJRk3tophM9+TkPpGwSelf2RaAB9rcA2oD9NI31IyDBB2Jh61xQEgejoaKdqizqdDn9/f6cqjQ2ZR0HDUtDgKLPMy8u75NJuHx8fFi1axHvvvceMGTMYPHgwUVFRl90X7t69m8LCQqcZlguxdu1aRo4c2WCbjRs3cv3117slx/93wKGueDmExr/7CdzHtbNI4ibcDRRsNtsVDRKq8OSTT/5tXIXC4nJW/LqfY6fTKK9wdL4VRgslpY13KP5+Pnz66ad07NiRUaNGua2cqVYpWfz6PZzaNJ+Zdw2mY1w0IYEO4twN17dHoaj/Hsc1D+fW0T0IjYtDNHjiqbAztbMPb82ayMmjGQiigKBVoPBUIYtg0niT2dJBTs3QehDtZWU4mSzwPsLUtpXr5bKMUZYoHz2SefMmEDH5PkSdDkkUOWP1QJZlcg+VY6sciPTYebtTMi92zuTmTmfIC1qHWZRq6t8FBQqDJz3L07g3YSPRJ/dhPHuctOVLWLXzKCk5BZgzEzHlp6H2FbGkn8FWmMeO9EX8lvAWZZkmmio07E4tAa0elVjDGJfKC8Hm+HxkYwmyuQKvHgNoMv9zol/4EP9Rt4HnjQhBcxg97FaemT2X1SvrygbLp9fxRGEy3Y2FqEQVsd41fhLvvvsu42+IJSyokuFX1jhLPq1kJyVN1VR4i6BSUW6z46FWomqvQKGvHOgr3zvJKmMts5G+tYTMAhN+SqVDMtmgxeatRe2pRBBAVAlYiiX0wWoEpUChVo9FkhEFgTPe/rX1l9yGjHPXPYVKIHVHAUlb8sk5XuKQrjDZ8YjwRhMRg2gwYC8pxl5cgDoqlu46H9atyaeM0RD1HMS+Dy2WgKqW94Q+khZ9H+aM/kEIGuj0foYOHepyxjt27FhWrFhRb3t8fHyDLpWN8RTAYSJ2saTGqwl3ZJprY9WqVQ0aRAEsXLiQadOmXYnbuyr4l6NwDePMmTM0b9680XYZGRmEhbm/Fu8u2rVrR3FxMSkpKX9p3a8sy4x74APOJ+eg1ai4d1J/fv3zGDcP70JkmHPpVGeYOHEicXFx3HLLLSxatIjz6RUsWvYnvTs358E7BzpNh0qShF2SUSkV+Hob+P272WTkFNEkIqCBG5ZQaktp8urnmNPT0TVviyAI3NCnLV+u2omgFJFMjplsXp6RdrM/5pkV35IkKAk2lvHgkd8d6VnrYfSTplNy4jDvRjfhxCGHkJddOkvPQZ04vPY4kVSQZNURoahAWev2rXaBTv4mulSYSFljRll5PVEFhkARUWkhaLgPogryD7+HvaSQ0h8+5RNNd0xhHtxzeya2h8LpU2IiplQmT0zlbOFmgjPKQAYfjYqyCht3t30YtaKGAGgpKEQO74ZYch5t55GIBh8AtE1qfCtAAE9HanrcBAs33HADU6dORalUIpdkIu/7nGDgTgTG9p+Nt8HxXicmJvL777+z8vvXIfVxkEzgcyNm03EsppPoPa5DoQzAVphD0Q9vgGTHZ/xjxPoOpXDNYtR5NsqtZjQKEU2QSFiUGnuwgozfrJhyKuWeK0d3S6mdQz/koCuEQ+3D+TC+FwgCd2ceoV/mOfxaajEEqjGX2DAVWfGuMLLcJ5zMsAr8LKYqWYU6EBSOmborWWYr4KxIz1JaQ24wVlpbl2aa0HTpi6jdj1Rek+WypCUjCgKDw/z4/otl3PVkM5RezslyLVu3Ye/evU73gYMvMHXqVKZPn15v35AhQ5gwYQIzZsyos12pVKJSqTCZTE75VH379mXWrFkNDpK33347I0aM4NZbb/1HzLhfeeUVHnjgAbeqzSoqKjCZTC4lnQFSUlLQaDRXbYn6X1wcrp2Qxk3YbDa36n2Tk5Pd1iq/WDz11FPMmzfvqpzbFSxWOwkpjiyAyWzlup6tWfz6VNq3isRquziGWNu2bVm8eDHTpk1j8v3PsfPAed747Bf2H02q1/Z8Sg7dRr9Im0FzWL3pEAA6rZqmUUFOMzayLLNszW7O77wPEu9GmTcLQ6uWiFI+WHJ57v4bWfPBQzx9zzAiQ/y4ZUQ3usfHEqgPI1upRQYsCgWGcDWeMRpQgO+wcawePJxTkg2QUUh2Inec4WzRYcqnaYgaBipkSiQl5dbKtXAZypRqStMryDlWjDanJv2tD9Kg0ioQlY5ZMYBXrBpBELBLMmYUNG1WgFUuRwZO6tQoVHb0BVlEbipClWfF6KMEBLzCm+At1ixZlGz/hYzXHqHwj81Ikdej6zWh0c9DrVYzY8YMhg8fzhNPPMGRk+dAEBEQEBQqvHSO85tMJh544AFHWZq+JTRfDi1WYtU1Iy/jMUoKPiU34zFkWabs9++wJBzBknSM0g1f0SZwEs1a3IogQ4bRTLhOS0W6ndz9OpKXWZHKQK1XYGjfGd2IJvgP0NDkFj1WrYRWEJE0Ch45so2OuWnsNwSj0IiodArO/VxA6qYSbOUSClmmWcIpuinNBBodA3dV3BbY1gOltub7IrioCFVzEVkICfJWfocxoa6omEd8NwDGdmjJN18u5tx9o0h/59nq/R8s+5224//D1BeX0LxFS/bv3+/yEt7e3pSXlzvVPtDpdGi1WqdS9N26dXNpjhcYGNio5oFWq+WGG27gl19+abDdX4GTJ09y6tSpBmWaa2PVqlWNmjv907MJ8K+E8zWLgoICtyJagKSkJJo0adgk6VLRsWNHLBbLVXVwuxAatZLH7x2Oh17D0H7t0GvVDL7jde56fCGzXvruos8XHR3N6tWrsVVkkX/uNySbGQ9D/dnPil/2k19UhtVm57PvtjR63lUbDvLsm0tp6lfJ47BlQ+YSOHY38uE7Mf/2JP6/L+RmbTJ/fPUE8x8bV7188Uj3QQTLcLuUgF8bHT7NtSg7R/Lkhi0cz8xHrQSVIHN9eToqrZnsAV5IIQraDi3ju+i9fBl0kNXJObSOiOaRnDh+OKqjPMuMzSRhM9oplhSk6vT4dhfRRkuUZpiwmSoHgPChCFode7yakKf2xF/TikqfYyIsNtCHUbJ+H6G7SgjdUQJ5VqxGO1H+viQmJlY/vznpdKVnBZgTXZfJXYgxY8bw22+/MWnSJL74/ieGf5rOG3tEMuLuR1DpkCSJqVOn8uijj9K0aVPHQaIaFHrsthyqEvZ2Wx4go/Dyr65bVHg7shFBN9xJ2Izn0DZp7ejCZMCvJwJStWCV2s+PiI6tCIzToPFXENfLk8wKM12z02hbmM19J3bTL/UcFqNM/gljvVE9qcRIZ139DjL3WFnNe03DXIWL7rRqnctv5C1Ezn6dFot/I7BZKH5qJWllJkp3b0EyGbHa7Cz4ZhNGs5Xf950mIasMs9lMYaFrjZHWrVu75BR07tzZ6TJD3759G1xe8PLyatTr5sYbb3TL++FqQpblahMqd9svXryYSZMmNdhmx44d9O3b90rd5lXBv8qM1yguhsh4NQMFgPfff5/vvvuOl19+2elso7TcxPQ5X3LjPW+z/1jSFbnmg3cM5Nhv8/jklckcP5OO3e647q5DzsmVv2w7xnvfbCIn33mHpNPp2Lx+KbFtepB7aiWvvr2oXpvuHZtSNS/s161xTYqC4nLMVpH1+30AMBNEWsLBSh6fjGg+jpC6g4IViyjdWTfQ6h8aw/vhzRnavCVypXHAOaWCjefPszsxl/iAKG5o0ZrWViW5sh7sjkJ+i13B1vBYNBqB39LyubNvBGZRyb5iz+pxTJLhdyGISMlIwWET+SeNWIrtnPu+kMQNGkrO28Bioq89hZ/Vm3n4/O8MKbieEbEv0L7Dx8idF2DJdth4C4DS4hiEYyPDObL2J87ceyPnHhqHOqY1Sv8QFJ6++N5wm8v3yV5RjmyrSyoVBIFOnTqxYMEC1m78g/bDpzDn9c8YNWoUd955J/3793fKpNfoOqL3HI5CGYZP4MMIgojH9ZPwvmkm3qNn4Dn4Tsf5RRHvPkOIuP9p1C3jCZn2JEG3P4QqsgUgIChEvHoPR+kfjyA47idYoSaj3Fz93ApZpn1mBh59bsRs9ql3L0llRqLU6nrbqyDbK7UPZLg4pYCG4TN8HL43TqL8yD6ylryHqNGii23F6NhAVibmoImMQdBoUSpEWkQFOd43tZLYiAAmTpzIsmXLXJ67oexAp06dnDrMdu3a1eUx4FjCPHr0qMv9AM2bN+fMGdcS7H8FfvzxR7p37+72Muv333/PkCFDGlTd3b59O7169bpokae/Gv9yFK5RHDt2zO1AITExsUFzk/3Hkjh4PIWRA+KryXkXA4PBwKJFi1i0aBE333wzn332GQEBNWv236/axW9/HgUBnn9rBWs+f/Sir9EQhvVvx+IftpKUnsfMu+q7rm0/eI4HXv4GQYBNu0/x87vOveCtNoliixeBrcbw4w/LMJDLG6+/Wr222rdrCzZ8/QSFReV0aR/T6H1NHNmNY6fT+WJnE3ybxzP1P2u4vmUB70wCQQZrnlhvOimZyin5eQEFO3ZjzilEoRGIvbMXuZnpWPfn8p/SDTQpL0Q4F0Rwj0HIfmEUHtiFx9dWDncNp+eBTAwZRn5NyKVbkDfemYV8FlqEyiBirQDJ5oiYx5AJZig7VzMNlu1gNwsYjzoqDASFgNZHDZZSxI0/4OUTBi37k/Hp65jOOpwgDe27og6PQentR7w+iI3PPER80yDsQPbit2ixsOF0ceHGlWQtfB1BoyVsxnN4de1Xr41SqWTYsGEMGzaM0tJSjhw5Qu/evTHuWIb5wFpUrfuhH3B3pQ+GAl/v20HRFXTxlc+hwNCjLutclmVKzQnYFGWoo5vhc72DbBb94meUHd6FqDOgb9keZJkl53chlOSiKS2k3Nsb72ZaTIUWLKUSVpNMye4/qjMntZFSaiLawz2dEyWVnAURpMsxQRQVKD39yFv2KQDmlHN4dOyJJu46Rj4axOdz3yTqpUXVA9P386fxx4GztGsWTligD6NHj2bs2LFOeQjgsJx//fXXnTogduzYka+//rredo1GgyzLLuXm4+Pjqz9TVxAEgaioqKu6jNoQysrK+Oijjxq0x66NiooKFi5c2Gj7r776iscff/xK3OK/uEL4fxUoHD16lFGjRrnVNjc3l8DAQKf7ziXnMOGBD7BLMl/9tJ0t31+8hCk4fshTp06lS5cuTJgwgXnz5lUrUoYE+ThU5GQIC/a56HM3Bn9fDzZ+8ySSJDnlCuQWOlQDZbnmb2eIDvfH21NHcSl0vm4SAwe0ZuTIkbz33nvVehXNooPAzX5Kr9Pw5txbADiVkEm5SWLNYR/2nTfw6yMtUIaakO2l+HWJxrOnI8Ax7lmD+fh2rIWOEkO7WSb3rIHCLUmEAKFVJ8/JomCVo1MWRPBLsjEkJxFRKZBTYuGbs5l81Le1Iw4RZSSlF569ulH8R33WuioyFlGvw3z2FNbsdOwlOmRJRlRWag4oBPT+KixbPsJycDUVx2sULQ3tuyGbyrDlphHdqSVp5prAR3CDeJa/3jF7lc0m0hc8i+6D5ah8a4JMWZIo3/Yzttw0PPqPwzMgjN69eyOVF2Hc4hCwMe/+CU27gSiDYsBaDMfuA3s5aMKh3adOSQCJBT+SkPct2RlGyk0+1dvzfvycvOWLAfAfOR57UQ494jvzU2AJAeO02DZvJaCnCkFQYzfLnF1SjFRUs8YuaLXIlQ6GZruEVum+JLVsB1HhjPboBiqzHsqAoOogoQpFvy2nbP82BKWKXn37sWP37upUt5eHjhv7ta9uq9VqiY6O5vTp006VXF2VSIKDb+DKBKp3795s376dAQMG1NvXoUMHt0SLhgwZwoYNG5g6dWqjba805s2bxxNPPOGWrw7Am2++yUMPPYS6gYySxWIhOTnZLUL6341/BZeuUaSmphIREdF4w0q4Gvwzc4qwVxa5ZmQXIUkyCsWlf6gdOnRgxYoV3H///ezatYtHHnmEkQPikWWZrJxibhl19XQlXJWADu/Tjt2HEziZmMXjd7kWbvH1NvDrksc5dCKFnh2b4u2lp0eP7tx7772MGzeuUR/5htCySQgP3jaAdX8c4c4xvfEa0JuSnZso/uUzLOUCvpIEogLR0w+Q0Qdoqcg3ofFTYD7jqEN39alUJSWsZgmNSskbR5KY2S4KrVKBIAIyqIOjnAYJANb0hDpr25LZiAyYi+xofOwoK10rAeTCVGxVa9gilB34A/O5EyCIqLPSKQpvhme3zshWM4G3OJ+V1oZHfHcK05MrL2zHXlxYJ1AwHd1K8cqPQBCwpJwi6NEPHe+FRo+g80I2loBSg2jwdXALjixClCpZ/+Z0sBtBWSNxLtut2AuzyKv0gBAVUGHOoWTnJgSNjvITByufTaB0i6PcT3FkF0+8/TPHS5axzKCkpMKOt0GJQi1Uu0RWn78ySLBLMuJF/oxkwG65tILzqs/HmptZZ7vvsHGU7v7dcX6blZs7xfHxkiX07tGdwl9+RLbb8Rs+HlFTk/m48847WbJkiVOSsiAIaDQal1UMer2e8vJyDAZDne1Dhgxh2bJlTgOF8PBwt3wZBgwYwIMPPviXBwpnzpzh/Pnz9eTBXSE9PZ09e/Ywd+7cBttt3LjR6fLZPxGSLGD/HwkUrp1FkkZQpWTmzszfZrOhULie1fTq1IyJI7sRFebHvCdudqoFcLHw9vbmm2++QRRFJk2aRHFxMaMGdeTeW6/Dy6O+Mc3VhkatZP6jN/Pzuw/Sp1OzBtuGBHozrH87vL0cLoahoaH8/PPPJCcnM2XKFMrKnNggX4ATZzMYdPvr3HDXWySlOWZYgiDw+D3D2LzkSWxRGkYuWcLJj17j/vIYxp0UmP3OFwBoOwzC66ZZ+A69kZhRXkQP98IQKlYHCbslP77RtMNnZH2ClKCAjedzEbwMHLhxGNmDIlDoBGQJys+ccH3DtVc/RMeSgyCA7OH4V1DL2O0ikl2mKNmIqBLQeClRGxTYUk8jSzKyLDu+OwolEbPmEfHk69gK8yk7vBu7xYwlJ6NGt6EWgu94CN8RE1H6B+E38hY00XU/n9rchdp/C0o1Xne9hX7QvXhNWYBo8Ia803BqO3KZwiF0FDi6bpBgs1D4yQwKP7gX/6MlCIioJCXS+VzS33metNeeRBPVFEGjQ+npQyU5AUEUEQQRf31r/AJ1HNxciCnXTsbvFfiNuNXpW5peYSLMCSEWHNUltVWoq96Vy+lKq9/bWm+xV7/hBE9+GN/h4x19hkZH3IChpKenk/r9Z+R88xG5Sz8ld9lndc7Vo0cPdu/e7dLiuUOHDi7NnKqWEZxtd3WMIAh4eno26hfi5+dHcXGx29bTVwJVBMaL0YuZO3cuL7/8cqP98w8//HDFZPX/xZXD/5uMQmJiotuEmsbaKhQirz7VeNnaxUIQBB5++GG6d+/OTTfdxG233cb48ePrOPtdK1AoFDz33HNs2bKFG24Ywd33zmDybeNcdgTvf7mR88k5CAJ89v0fzHu8ppQqq7SUD3fvBmC7OpB0hWMgW3Umm/+kJZL30bNIFjN2uxJLRjFKTxHvWDWB4+7kzwofjhcrmDyiJ/Ka91F7KLCU1XSaRUYri4+n8cmANmQWJtC/g4nTvztGDp82oA3UkL/XgrWk4VmrZJeQZbD7K9B6aijJtJKfW5NSFmsJNMh2O5JNRuFpIPie2Yi77keSJArXLyPnq/cd75+nF1J5KZ69BhE+4/k61xIEgZA7HiLkjvreDgC6DtdjzU7BlpuG1+Db634uvqEouo2u2aD1BkGBnKEDrQ9Cl7qWvracZOy5DsMprwPJRGzQokzLxZaeA+HejqBAEGj5xW8g2ajY+inlp87j0e9WDhUXoFU0oXOnW0nYt4OQFaWgVCFm1BcZAkgtMxHlhJ+g9VFVSkTLGAutIF9egFANmXqBWMnWX/C/YQKW9CSQQTIZyfvpS0aPHs2vO7bRt/L7ay+tS/AVBIH+/fu7ZON36dKFffv2OVWdrQoiLjTCE0URHx8fCgsL6/lxgIP7sGvXLgYPrs8xuvDae/fupUePHg22u1JYu3YtHTp0cJsMfuTIEVQqFfHx8Q22s1gsZGVl/aX6M5eD/6Wlh/83GYV169a5rX3+888/M2LEiKt8R67Ro0cP1q5di8Fg4K677uK2225j7dq1FyWd/E9BbPN25Cq7MPPJ/9Kuc3+nkrUATaODkGUZSZKJjarLDfHUaDBUrlv+0rYzPmrH17Jf26aU/74Ca0461rwcLBmpANhKJTKRsaSe4saRA5htOILH25MxH9+JqBAd9fgKBbIs88Kec8zuEotGUNDC5ovQ7kUCR9+EIVZF6CAtPu1URI7VgSCi0Lr4OUhQbLThrVKiSLMhIyNf6N7n4Ye1wo7dLGGtqCxFLC2h6M9fkDKSMeZmUX6kRrinaiAq3bER2X5x/H5BocD7hrvxn/wcqrDYhtt6hiIM+g9C+1sQhryMcIFltTIoGkWQg2CiimkP5SaMZhsGlQLRywelTwCGtl0d2bqUbzAo1hEUd5qfCo/z2MafePDXZez7bTOGqrp/mxWpwrlkuCyD4sJAUqjiIDgGY/EylvjcgixTuGkFJdt+qf6/8dQBBg4cyCGbGs8uffDo2JPACfVT+Z07d3ZZiRAbG1unDLY2wsPDyczMdLovPj7epR35ddddx++//97oI40aNcotPsOVwnfffccDDzzgdvtPPvmEhx9+uNF2f/75J9dff/3l3NpfCgnxsl/XCv7fZBQ2bNjAt99+61bbzZs388gjj1zdG2oEer2eSZMmMWnSJDIzM/n222955513iIuL484776RDhw7/+PIggF0Hz2O0CPjFXo+FUu6991769OnDzJkz66zJPnL3EJpFB6FSKRh+Xfs65zCo1Xw1bhzbkpPpFRVFoE5Pck4hrSKCKd+6utIfoFYlAqA+bKZE3EvJ/rF4BKoRJDt2u4yoEFCoFdhsMkvPZ9HO15OWBj2STcZ/zGTw64T/hE6UNPcH61JsFRJFx60gS9hNzp9RBnKMFgJ1agQ7lGYYMZfWDO7qsCgCx99D9lfvI8sy6iAPLGlJaJu3JX/5Yuw5GaR9PB/pdE2aWdBowWrGo2s/BMUl/AztFkjcBEodJjECa3YGhg49EFX1iWJCUBsIakPO95+Qv/pbDHGdiXzyVQSlCkGpxvfe95GKcxB9ghFbbubkq3Pw9dagUApYC3JJe2sOsa9/icaYTtVc/0hWHsgKBCChoIjAoAsIbU74hypRwCpJiHoPpIpyqlQerRV2lDoRu0VylEZWfe2vhha+QoGtKB9RKSBVXkChVtGkaVMS0zKI+Kx+CXAVWrRo4VIbJSIiwiWnICAgwCWhMS4ujmPHjjnNUnTo0IFnn33WyVF10blzZ+bMmdOgkdSVgtFopKSkxCUR/EJUVFSQmJhIXFxco21XrVp1UQHIv/jr8P8iUCguLkapVNYjC1VBlmVe+WANP/2yj+u7RhAZGemWeuNfhdDQUB577DEee+wxDh8+zFdffcXs2bMZOHAgt956K5GRkX/3LbpE/x4tCfL3Iie/hGl3jOHxe4excuVKRo8ezY033sj06dPRarUoFCKjh3RyeZ4YX19iKtOvsizjt24xZ7f+imevgYQ8/CrGM0fI/WkJSDJSuB5lhtERPNgruQAqBbLWE4VvOPr4Xmz/9F22ZRXxbu8aaV57JRPfVlzAgV1nsIkiwv6y+ssOooDCxx+FtRCb2Y5kcQQKQTo1hrYdKT9WRe4TQZKwZKSQv3YpodOfxpR4Gq++Q1F6eFHwy4+Yzh5DIYAxKx2NLKFQi477NWiJfGYh6tDG06z2shJEvYFyayoV5hT8PbqiOPwNJPyKscBC0u8FIMt4dO1H5GOvOD2HZDGT//NXAJQf2UPF6aMY4hyfh6BQovBzyJmrdSB5gXe+EmtBJUFTsmPJTEXT+lYwZoCoZtiRTI4ZQlDbbFiycwhq1hSFUsReVeHhZJBXiSJWSUaqqMtpsRrtWI211tgdjuFOg41LhgiiKODQ0xMQ9R4IFjNKnwCC7ngUQRDw8PBwaf8MEBMT4zJr4OHhQXktmejaaCxQcKWuqFAo8PT0pKioCB8fH5ePJggCrVu35uTJk7Rp08ZluyuBDRs2XBTZ8IcffmD8+PGNtpNlmVOnTjmtKvmn4n9p6eH/RaDw66+/Mnz4cJf7UzLy+ez7PwBYtHgJH7x6ZTULriTi4+OJj4/HZrOxefNmnn32WfLy8hgzZgzjxo1rsMP4OxAc4M3WH+ZQUmYk0M/RwY4ZM4Ybb7yRpUuXMmLECCZOnMiUKVPcDs6s2RkU/7EOgNJtvxF08xSKt6xxVJ4oRTxD4inL3ge2yvVshYCudWd8b51L3spvSPniPebvOcWrvdsg1pphCVotpfu3k7PkLdoW5SADFTYRFfa6a+KSjDo0CntKIQq1iGSRqgMFXZsujkBBEKCWkJYp6Syp82cBULR5NX4jJpL7o2N2qhAEzNkZ6L30DtEGQCovo2D9D4Tc/RiAw3lSkhCUdd+jzM/foui3nxDaR5EzzghIeOvaEl/hAQiYimzVrpOmc8ddvqeCSo0mMhZzagKCVkfZgR2YEk7hN3x8nWuqmnQkN9eKxuooMUWtQ9+mE/q4TshaPULnDwDocOJVPtn3C6ZiK/daLXh6aRCVAhajDWslR0QQQeFpwFbpJurIKLg58l/hbIKoEKo5Cx5dryP8UUdAZSsuqH7+Hj16NMgJUCqV2Gw2yo8foHTPH3h2vw5Dm46NXluv17t0emyotBIcyw9btmxhzJgxDV7jpptuYsWKFVc9UPjpp594+eWX3W6/bNmyBsWqqnD06FHat29/TWRRq/C/FChcO4skDWD16tUNWpb6+3rgVUmispVlMGL4xfu4/9VQKpUMGTKEL774gqVLl2IwGLjnnnuYOHEiK1aswGw2N36SvwgatbI6SKiCQqHg1ltv5ddff0WhUDB06FC++uqranZ2eYWZ97/cyMKlf9bzolD6BaCsLAdU+Pij9AvE0KHSrloG7343EHT3LNQh4eiDtKh8DSijO1Oy5w/yV37Nm0eSmdwijECViGgwgAAqf3+QIe312Viys5EsMpJVRmihQKhd1185kzUeP4C13I61XAIBck1WQkJC8Rs+nvBZ8/AdMrbum1Cr+sCak0H24gVQyTkRBQG7LIMoVo3p2K02ijauxHj6KJbMFBJmjuXstKGU7NpUfR5ZslP0m4MYWGHP4mhuENtym5BdngrtbgXfpnh26IgmuimCRkvgBNfa+IIgEDhpOh5d+qBv2Z6CdUvJ+eZDzs+6jVO3X0/eCof+gsIrgDJDMB5qFYJCxNCqPeUHtnNmylDOPzyWczMnkPrqkyhjOyPrgtG3747SxwfR06v6WauvqRSwl9XMsh0ZhUv0kXaBk4VlVLjhZSIoQKERkGUoWPs95pTzFG74mXP338S5+8dQcfIQffr0Yfv27Q2eRynA+ZcfpvC3n0iZ9yi20uLqfaIoXnT1gSiKTitfqjBw4EA2bdrkcn8V+vTp06jj5OXCYrGQk5Pjdgn6yZMniYqKcpnprY1Vq1a5rYHzL/56XPMZBavVSl5eHiEhIS7beOi1rFn0KEt/3sCR3fl4ezX+xf0nwWAwVPMZcnJyWLZsGaNGjaJdu3bce++9tGjRovGT/E1QKpXcc8893H777Xz22WcMGTKE4cOHcyLVzub92YCI2WLlgVv7U7p/OwoPLwxtOtLk1cVUnDyMvlU8okaLd9/h6JrFYUpNJH/NdxjPHgMZLIDs35KCz98FYEtGASLQP8zh+SGVlyMoVQRPfoyKs7Vm3A51Z3yKFQSN7UL+7sOUpNRNHdcWiMw1mfEzl1B2cAfevQYhWy0U/rq8xu1Qkp37HwNK0REo2CtMeHTsTvnRfSDZQRRR+gVQsnU99pIiQKZw/VK8elRaGgsiSv8ApOI8fDJgyLfnWdS1C1vlXsRtWoLpfDKBE+8l9tXrGv0crHnZpL81x8H3qMqfCALWHIdQVO6yRfiPvh1BVCC37UZAkg6P+DjKDtUQMK05uZX/ZmA+t9ex7JKWjMJUCuYSTCU2PHsOxLp7BwrBhMZbSVktsy2VQsDmbkbBDVTY7Ly4PwEvtZI3e7bAQ+W6O7ObZOyCg8NiSTtP8kszkcorRcdsVlL++zhePv7sP13j6SBLEqW/LsSacAh9n/Ho4q+nSWQk6cmZxHjqwG6vU54aEhJCdnb2RbvSBgUFkZOTQ1BQUL19zZs35+zZs42eQ6lUEh4eflVVGrds2XJRZMOFCxe6re+wbds2nnrqqUbbHTt2jAMHDnDnnXe6fR9XCxKXJ8N8LZEZr507dYHt27fTp0+fRttFhftjKTrP3VP+/i/Y5SAoKIgZM2bw66+/csstt/D6668zatQoli9fju1CJv4/CBqNhhkzZrBmzRpHXfn+7eSc+JncUytZ+cOXrHr+MRJef5qUFx+iZNdmlF6+eHW/DqV3TdlY/urvSF/wTL0Uu/HkYXIqzPyUmMOSs1nMio+ps1+2WUl7aw7lR3bX2S4oBAKa65FzzuDXRIdKV5lZcDKWZRstBOo0iFo9ksmI3ehIJVdNBoVqJaELWf0CokD1AGkzW5GrZsCShDHhNPnrfsButSPLMoZ2XasPLTuwA7kiH5XeQRpUShJd09KxlEHJ1l+xZKSQ+amjll2ymJEbmK1LpgrHUonsiJAM7bvh0akXVJIftU1aVFdElKOk+QPPEP7IPJQBtWx+az2aUgVaLxU5FWZCfbSUZ1uwmyWKtmwg9L456Nt1R+EfjiDUDN5KQcBS6x51AarqHsiVW2RD+OZsJve0CueBuEge3XGaYkvD339RKVRXWcjGuvoEstkEORmYUhOrq4+syccw7vgJW1YCJSveRLbbaRXfgaLeIzC060roA3PqCGFFRESQkpLi9NqCILjMHMTFxXH8uPNlI0EQCA0NdVk1URs33XTTVa1+WL58OWPHjm28IQ6tmmPHjtG5c+dG22ZmZhIcHOyWXfamTZvcJlJebfwvmUJd8xmFvXv3NqiHXhv79+//f6Uh3qVLF7p06UJxcTGLFy9m6NChDBs2jKlTpzqty/4nQKfTMXjwYFq26cRzC1agUsiM7BPB1gX/4d0z57BKMs0KXqDLmFPExcURFxdHQEAA9vJSijavBsGxzmyVZPbnFLMtq4jTReUEatX0ax7NV688iPXXHwAQ1BpkS82M1px8ru7NKFWgUIPkKHmU7HLdIEEAr36DsAgahPPl+PcbRtprTzoW36vSDRJIkoyhU0+sedlYUhLqXkOWMSgVlNtl/EfdTtG2WsQ1hZKsz16rrAAAbbP2+N9cMwOzl5WAHVDWjNGmZm2ZGtkRGw6palVgCIVrllDw82JUweGEP/U+Ck+fOrdQfuIgpbs24z1gFJa0RHwGjca7j2P5zZKdjinpLIb23arbl5SUVGt7xLzwAZkL30C2mPEfOxnj8YOUbvgCpVKu9IawYRDqzjdK/lhOxJPvUnpgB8XHnqzebpFk1LWUQo35VnybGpDtEsVpRsezXgT25BQztVU4giDwaPtoHttxmjd6tsBH44ILU0WSpCbAA/DsOZDSnZsQBGgpmtn0zisMe/x5RIOPg4siyw7xKlGkW7duLFu2jEnzF9Q7fZcuXdizZ49TPYOwsDDS09Odpu19fX0pLi6ut70K4eHh5ObmEhoa6rINOIymVqxwrmFxuTCbzaSkpNQ4kzaCrKwsYmMbLt2twuHDh+natWvjDYFDhw5xyy23uNX2auN/iaNwzQcKx48f56677nKrrcVicalLvmzNHj74ahO9Ojfj5ceujBrjXwVvb28eeeQRHnroIdauXcvkyZMJDw/nwQcfpG3btn/37TlFVLg/X7xRMygOigkg/f2XEAyeSJNmcjY7l127drFo0SLy8/MRAL+sfAJsRo4VVmDX6uigExgRFcCs9tGE3PEQ/iNvcchiy3ZM508SOHEaxvOnyFvq0PkvjAnDJymjpvrOYkUzZj7lGxZRmpCJ3exgphuC1diMElofFUJ5DrvC+zK8SwdKt1XKPTvxQC4/sNPls/poVJSq9RSsW1onVR04/h7y135f/X/Rx68Omcur10AqzhzDkngC7/7D8ejQk9uCHRUw5XPfwZR0Bu8+Q0mZezsgY81Oo+LYHjx71rDS7WUlpL4yy6HVIIg0/3AFSp8aK3Z1cDjq4PA691ub+a8weBHx8IvV+/RN2+DRviPGYzso+OUnLBJgqjtTtiUdRSorQt8qHlVoJNZMh/5FhdWOXlUrdSBDcbIjSJIvUkIku8JMoFZd/X618fXgsfhoHtt5mjd7tnQaLEg2h0eHLEvVAaG+dQfCZjxLamkRxpMH6R3qx4ZNvzPs8edRBkXhNeFZSrb/gqHPSARBID4+njlz5ji9pz59+rBo0SJmzpxZb1/Lli05deqU00ChoWzDxaBqCeNqYM2aNQ3ywC5EXl5eHRO8hnDu3Dm3vR1cLdH8i6uLaz5QaMjcqTZMJpPLIMFmszPnjR+x2SSS0/MZOaADvbv8801JLoRCoWDUqFGMGjWK48eP88EHH5CSksKUKVMYPXq0W6m9vwuGdl1p8cmq6v+3hDodk91uJ+H0Kc5s+Y1btyzHoBBqpoWiiHcfB1O94vgBin77CYD0t59FE1XzOYr5BRi9NOhLKrMMssz555/AXphb515kCbwidNitEkUnz/HVsp08H62HSxQDCm7aHFNoGHJ+LbtvQcCjYw9sRfkU/vIjAEoPnzrHiSo1YdOeqLPNXlZKxekjqHx9seelULp9Dfo2XSg/8CeCWoMmtk3lM0jIFhOy3e4IEmQZZDuyzdLo/TZUIgigiWmDJqYNuvbXsfv+W1FXBtWiSkChFtEGeiOotYhqLSF3PULqfEdlR7nNjuECQ6hLdYXcllVEn1CfOtta+3rwRHyTBoMF2V4zIPsOHUfw5JkIokjw7TNIe2M2HTy9+fKUY3YvyzKZX36MOTWBwq1biX3rG9RBYXh7eztVU/T09MRoNGKz2er91lq1asWpU6cYNGhQvXtqjNDoLq5mxcD333/PRx995HZ7d/tlcAQKDVWtVeFiZPr/CsiycFkcBfnfjMJfA1fOiM5w9uxZp1GryWyltNxEaKAPqZkFKESBkKBrT1L5QsTFxfHhhx9SVFTEl19+ydChQxk4cCBTp069JiNyhUJB8zZxxAT5c+7P5dVBgqFLX/SxLcn45L/oW8Wjb93BcYAgIKq1CLVKMg1GCx4eCiyqmgHqwiABoCLXgrHAimyXyTGaUeSk4xVbQxjVtu6A6eQh5zcqgtqgwFJqr74PQ2EOZU1bI5brkExGPLv1x2/4BLTRld/HSpcqe3Gh83NWQrZZSXrmHqy5mQhKJUotCMgE3Pkk3oNuRhUYhtI3EFtxPvlv340gmRFjehD2wFyK//wFr14DEVRqkv9zH7aCXEKmPomhfX3J4XoGRmVZcH4j+DWFyBoZYk1UMyRPX1Si4z0UdB4EDBuGpULk3KO34dV7EH4jJlbrTTgLFC4VO7OLmNupfmq7la+BJzs4goU3erbE19UyBOA3YiKCKCJLEqrgcGJf/4r8Hz5Es+9Lcs+fIqBJc8zpSYDjvbdkpqIOCqsuWbzpppvqnbNz584cPHiwXiq9VatWrFu3zul9OMiwV6YaRKFQOA1ULge5ubkIguB2hgAuLqPgLgEzMzOz0eWXvxISAtJliI1fzrF/Na6d/LoTJCYmuq03fuLEiWpb5CqkZRXSe/w8uo55keHXtWfujBv54cMZNI269gZSV/Dx8eHhhx9mw4YNdOrUifvvv58pU6awd+/exg/+B0IVEIJXv2E1GySJ/FXfUH5wJ7nffQx2G2EPPY/vkLFEPrMAv6FjHevMgNImIUmCW7PYqpnnmuQ8RkbXnRlZ0pyL7oBD1McQWCtAkWW8BZns0ydo/vFKmn+ymohZ89C3dujee/cegNJDh0Kvxff6YS7PC2ArLqx2QpRtNuRKgqQgCOhaxKP0ddxn+ZbvUSqsiEoBOWU33n2HEvXMAnyuH0nxljWYzh3HVpBD7tJP6l2jZNdmyg7vIeXFGVjP70a2GuGPV+DUatixAHJP1WmvjGrqyCiIDm2IvD92kL9mKbb8bApWfYNUVkLozOdBcFQpeGgufwCzSTKlVrtLLkJLHwOzK4OFApPrD7vi6G4sGUkkPHYbZ6YMIX3BbEq3r6Ob3sqqV+ciiAqCbr0fUe+BR5e+1QJVDZUsDhgwwOm+qKgokpOTnR5zpTIKAJGRkaSmpl6Rc1Xh+++/59ZbnRt9ucLFZBTcDWxOnjxZrw//F38NrulAITs7220jFGeqZZt3nCC/yLFG+vPGA0y9pT+d2l6d0qK/G6IoMmzYMJYvX87cuXP57rvvGD58ON9++y0WS+Pp6H8StJGx1S6G9tJiBLWmOhgQ1Fq8ew8mZMqjaCNjUXh4Efbgc8S8+gXRL3yIurlr0pTo44/vjTUdoizL7MgqolewT512kqnGy8Cz5yCafbQSvxtvRRUYjD4yBH18b5q9u5TgOx369t4aJSVKDTnffkzxH+sc4kqVMB3chM5TRu8N5Xt/Je2tOaS+MRtrfna9+1P6BeLdbzggoIluiqz1RdO6B4Yu19Vpp27mCEIEQQBV3VJgdURNYK2Jqk9My/7qfbBZqTh5mOJv/oNt1RPIljKqF/WtdUtIVfE90fsHODh/Eig8vBENjmULQaNF4emDOiAUQYAKJAzKy+9yjhaU0t7Po8E2LXwMPN2hCXP2uC4tzFz4BinP3YMlyzGwGs8eB1mmT7gvf55xVC/4j5xEy89/IfLx+dhLi6g4sY+m0VGcO3fO6Tl79OjBzp31+SoKhcJl1uBKZhSaNGniUj3yUrF27VpuuOGGizrG3YyCzWZzOyt84sSJqy4odTH4t+rhGkGvXr3o1auXW21PnTrFY489Vvf4zs3Ra9VUmCyMuL5hZ7P/T2jatClvvfUWZWVlfP3119xwww306dOHBx544JpYlvAdPAZTagK2/FyC75wBQNGmVehatkfXtEayuWjLWjI/ng+AqDMQev8c7GU1ZXGCVodca9BXaHUUb6zhSZwRdLTw0aMUL/hBWy2gUIDdTumuTXj3G0LhLz8iWy1Yc8Hv9qdR+gbhd8N4dK3aoc3O5ruHHqBww88gSyg8vZGsFoynjqAJ9EW226kosFCW9UclK18gz2MRoffVJc0JgkDofXMIvGMmZ+8dAXY7RmMBKY+MQNe+F0HTX0QQRXRxfRCYgyXhMHZ9GOVH91aXXXp27kvE029jK8zFs/uAeu+tJrIpsAUAtYcSSjKh/0xI2gx+zSG0rhKhXa0l7NYHCIkOwJKRgu+wccgWM6X7t2OvKCPjo1fwue4GRK2WMrMNvYfenY+4QWzLLKRfmF+j7Zr7GPBWKykyW51nH2SwlpkRlAKyTUYb1w3P1m3xNpaT+fEPdbwTrHmZpD4/BdlsQtemC6GhoaSnpxMeXpcIqtVqkWUZs9lcjxNlMBgoKyvDw6NukHOlyIxw5QOF48eP06xZM9Tq+h4iDcHdjEJqaqrbbpEnT578R4kySfJl6ihcxrF/Na7pQOFiUFZWVo+g1Sw6iG3L5pBbUEqLJsEujvz/Cw8PD+677z6mT5/O5s2bmTZtGi1atOCJJ574RwcMolZP+ANz62yrkkKWTEZS5s/CdP4UiloaDJKxnJyv3yfo1vtJP3MMZKlOkABgzaox9bFJMgt2HmFuvItOrEqBT5bJeP8lZGtNVqa2s6QuthUWq4BO4eAhAJhSEihcv8zBo9B74N1nGNKvK0GoKc+0OMkoVN9nXhZIMoIISrVjzd94ZAfW7BTUoTEAaOP6ULhjJ0Wb3gQg/JEX8erhCAwMca5r2yMeeQHN6j+JGNkdrTUJIboHNOkHsf0dzyZJWNLOo/QPQqH3pLy8HE9PT3yuq+vGqm8VT9IzjqqW8oM7CJ35AuIDU7FfAcGlcyVGprdxTzStiZeOpFIjHRrgKsg2GVGnJ/i2GWhCHVUlvj/8jtFoRK93BDbm5DMOrQXAeOYw8W2HcmTbZsLG34ZwwYw4JiaG9PT0euWBTZs2JSEhgfbt65qiBQYGcuLECZf3FxAQQFZWVr3jnCEsLMylJsOlYN++ffTr1++ijysuLq4usW0ISUlJbi8f5+fn/2M0FP7XcO2ENJcBWZZJzypk7hvLOZ9St3zIz8dAy9iQfwyT9u+AIAgMHDiQlStXMmrUKKZNm8ZDDz10xVOYfwVKdm7GePooss2KLb/uZ62JbIpn9+vx7NZ4x/fB8RRujPClabt4FD7+1dsFQ/1qAMlYk44XPb3Rx3Wk4vRRbEUFgEMUbPDdD+DZ/Xq8B46ipLrMUgZBwKvvyEptBqpfFcf217eyBvJXf0vS7LscgY6EQwcCUPgEovSrG+yaUyrT44KAOdW9z1LQ6FD5B+Nx2xso7/gW5YAn6vw2cj6fT9oL95Dy9K3YCnIoKChAm5dOwlNTyPhwHrnLv6Bk79Z6M+TCNd8RolOTWXH50uM2SaqutGgMPmqlcyEmUawkkeLQ5jAbyfnqverdBoOhjj+Dvk0X1BFNAQHvgWPhz2Ukf/ka+YtfqHdqV8sM0dHRTgWZOnbsyMGDB10+Q5cuXdzmFHl6elJWVtZ4Qzdhs9kuOpsAOM2oOENGRka9rIwr6HQ6jEbnFuZ/B/6upYcPPviAmJgYtFot3bt3Z8+ePW4d9/333yMIQqO+Ic7wPxEo/PbHYc4k5fLtyl1Mf/qLv/t2/tHo27cvK1eu5K677uK5557jtttuY//+/X/3bbkFU2oCmZ+/WbNB4ZhtC0oVQZMfJvyh57FkpFC6e0uD59mRVUSBycqI6ECCJkyto0ioDgxFG9uqTntVaC13T0kibcGzJD9/P2fvH83pe4bz+6qf6TNgAAq9AXPyOeyV/gCCRkvU7NfQN2tNk/9+jiqkVo29qMSYVLO+LlnMmNOTKKgs/XRAQB3Xh+CHXiVs7kJEja7OfQVOvBelfzDaJi3xGeg6ZWsryseUch5ZlqsrHgRBQFDW7+jL9zvM1aTyUoxnj1JQUIBt/VLMSWcp/nM9eT8sJP3Np7GkJ6Fr5VjOU3h4oQ6NJESvIcv41/JhvNUq54GCJKEOr8tHEjXa6r8NBkMdN0hRZyDi+YXEfvwbhjYd8bCbKLXYMB7ZVodzAq4DBVeExsDAQJfuktB4IFEbnp6elJaWNt7QTdhstkty2rVarW4dl56e7rbk9b+BAixdupRZs2bx/PPPc+DAAeLj4xk6dGij+hlJSUk8/vjjTu3M3cH/RKCQnpGBQu2BjIzJXJcFXVxSwcLv/2DDtiuXrvv/gM6dO/PVV18xb948lixZwqhRo1i/fv0VW0u9Gqg4us/BHwBQqvDsMYDAW+8nZv4i/IePR9TqUHh4IVTKFos6PdrmbVGFRRN4xwxQKMk1Wlh4Ko2nOjrSofnrf8BeVFO2aMlKJXxWXfe8gAk1wlFSeSnlh3Y5/iPLSOWlpB47hPLP1RT9vhbT+ZPVxEvvfsPRNXcIYmmjmxE45ja0vmpUegXYbaS9+gSyLGOvKCfxydtJmn0HYlV5oajAs8d1BN3+ELo2XVE4yXQY2nam+QfLafLKQlR+zlO2xoRTnJsxjsQnJ5O77DOn+gC14dnvRgAUvoHoWneioKAAv6D6y3aWjBSin3+fkPueBlHElJJAWIAfWVcgo3Ax8HaVUQC0sS1QhUaiCgjGe8BogqfUuMrq9fp6ttGO4EmFpkkc3gFBlJpt6LsMrJa+roIoii4zCq4qH3Q6nUuHyQuzGw2hyir7SsFqtV5yqaU7WVpnPA9X+KcFClcKJSUldV4NGf699dZbTJs2jSlTptCmTRs+/vhj9Ho9n3/+uctj7HY7t912Gy+88ILbapkX4n8iUIgJ0dK/dycG94nj/RfvqLPvof98zcvvr2baU4vZvMP1OuH/KmJiYnjnnXf44osvOHDgAEOGDOHzzz+npKTk7761evDo3LtGvthmo3THRrDbHVUSlVB6+xLz4scETpyGqNNjOnsMW14W3r0G0eStb3jhUDJPd4xFVzkgG88craPEqIlqRuJTd9e5rjYiFq9egxBUKjTRzcBeMzDlmSz42E0UbVpFtROVLKOJjCX4rkcAsBbkYko6i+3MJgxBarwidSh1IpLZiCxJmJNOY8vLcjxWbgYxryyk+cc/E/HISyg9a9aBJbOJvJVfkb/muzoKkA2h/Oje6ralu35vNFAInPQQ0W8sJ3r+tyi9fCkoKKDd3DfxHTkJQevIaCh8/PAdOhZBECj8dTn2kiIsqecJEGzk/MUZBR+1kiIXgYKuWVuaLfiOZu8tJ3Tq4yi9ap77woxCbYh6T5o++Apyj1H43f50/f0uAoWoqCiXXhDx8fEcPnzY5XNUSUA3hn9KRsFdZGRkuK2N8E8LFKrIjJfzAkdJq7e3d/Vr/vz5Tq9nsVjYv39/HdEuURQZNGiQ00qbKrz44osEBQVxzz33XPKz/k+QGTMyMrjt5kFONcLTMmtmi+nZRZd9raqMhbYB8tS1CD8/P5555hkee+wxli1bxuTJk9HpdEycOJFhw4a5tR55taEODqfZhz+R9tZcx6xelhCczIa0TVqgCokgd+lnAMgWMyWpiTw5+ykGRAbSqkkMokaLNSsN/xtvRTKbKNr4M9qmrR1Zi9oQBFTevoTP/A8ZH82j+I9f6uw+mFdKpwAvhyFTLZhTE5BNFRgzU0l6/gGw29CHeKKrHPdFlYjNaCbxycn4DhyFKiQCa1Yahs590V2w9FGF3O8/peCXHxyZDGMFgeMb7xg8u/Ynf9W3SOWlaJu04PzR/Y36hChrcTbKy8vxjozB+5Z7MbSKp+zgTnwGjULlF0jJnj8wp9SoUapUaofd9l8Ib42SYnOtQKHSuwFBoOLUIXRNW6OJbELRphUIgojPoJsQlKoGAwUAH/8ASq12p7NmV3bTHh4eLs/ZuXNnDhw4QM+ePZ3u79atG3v37m109q1UKi/a6rohXE5GwR2YTCZ0Ol3jDfkHBgpXSHApNTUVLy+v6u2u+tK8vDzsdjvBwXUzeMHBwZw6dcrpMdu2bWPRokUcOnToku8T/kcyCqmpqURGRjrd99JjY2kZG8Kw/u0YO7Rxp7OGsGP/WTrc8BwdbniOHfsbt4a9FqHVarnzzjtZsWIFCxYsIDk5mZtuuol7772XLVu2XLF68EuFqFITNv0pfIeOJeDmKfgOG+e0nUKnJ3DCVES9gWOiB6NuHk/HsgzGRvhgL8hBKisBWaLwtxX4j7wF7+tG1A8ScPAMUl99EltJEbLVWr2sUIUDuSV0CqzpBAS1oxPw6jkAhcHTYTldmYGw2nQoAiKw2rVYSh3bLOlJZC95l8BbH6LJW0sJf2RevXuQ7TZyF8+j5M/K0k5BrOZBNAZNWBTNP/oZXav2lOzYxOmPXsdgvbjO2HjmGKfvGU7aG09RtGklKS88hGQxk7d8cXV1iKAz1FSK/IVwLD3Uyq5UBSqyTOnOzaS++jj5P31O3ncfkfvt++SvXAI0nFEAh7+KKyOnhjQTXKFTp04NcoG6du3qNmntSuJqZxQuBv+4QOEKcRS8vLzqvK7UpKu0tJQ77riDzz777KJUNZ3hfyKjUFRUhI+Pj9N9vbs059clV8ZRcumaPZgrO6XvV++hV+drzy/iYhAcHMzMmTOZOXMm586d47vvvmP+/PlERERwww03MGjQILdKpK4EzOlJlO7bhkeHHmijmxEw5k4KN62ibN9WvHoOrNdekiT2G0J592A6nmX5vNkuGm91zc/BXlrk+Le4gNTXn8J09gIOiyAg+vgjFeZhPHuM9Heeo+LcCQStFlVAML5Dbub0wrc4VWIkNigATJW21IJA809XVy+ReHa/joK132MvK8Vv1O34DRmLKeks9u8+pvzo3upMRNqbcwid9iSydQ9evQcj6vTVs1nT2cNU7NuEUiUjqQT07XthiO9O1uK38OjQA4+ODWuNiGoNlgyH6FCJxUqkzdRge1mWsRcXoPByPEPR72uQa62rSmYTss2KNrZVtWOnXFkZIkAdfYJLwcXkJAxKBRU2FwFK5TKQvaSwMsATHH/j4Cg0xAtoKFBwnNr5XbraHhwc3CAhrVWrVpw5c8bl/trnv5IZhSpJ6IvF1eAyqdXqa04c7koiICAAhUJBdnbd0uns7GxCQkLqtT9//jxJSUnceOON1duqAlilUsnp06fddgP9nwgUQkNDycrKIi4u7qpep1+3lqzc4GAn9+veopHW/7/QrFkznn32WQBSUlJYv349U6dOxWg00rdvX4YPH067du2uShmqZKogae50JGM5ecsX0/zDFaS9PRfj6aMOi2CtHo+OPTEmnCJ59VJ+Tshi4/GzDBw4kJdHD0R76gBVw4/o6Y1UXlo9QGtiW9UPEqCSqFhThlZx/IBjM2BJS8KYcp5nd5xkdvtoBFMFmqimWHMzCZp0P0ovX+zlZSS/PBNLaiJe/Yfh1bV/tdWzNqY5UU+/SdnxA6S+VOlEaLeR+fErAGR9/haqgCCinn0XdVAYSv8QUCiRrRaUHp74DBxNxrvPIVvMFG38mdgF36MOCiN/1TcUblyJV+9BBE6YVuezCJ48k+yv3scUoCC8Wx+X77Usy6QvmEvpnj/QRDd32Gi370rxlrUAqILDCBx3D7LNRtn+bfWP5/LMi2RZvqhkb0PXUkfFEjr1SVR+AdgqgwW/0ZOxFuQh5WVhDXad5lcqlS4HUHdLA2sjNze3Qe0So9FYT6jJGc6dO+d25+8OwsPDSUtLa7zhJUKWZbcDx9q6Fv8EyJcpuCRf5LFqtZrOnTuzadOm6hJHSZLYtGkTM2bMqNe+VatWHD16tM62uXPnUlpayjvvvOMyy+4M13SgUFhYiNlsdhpN1UZ4eLhbRKDLxc3Du9CupaPErUVsw/dUGzabnfkfruHIqTQenjKYPl2v7SAjKiqK6dOnM336dMxmM9u2bWPJkiUcO3aMyMhI+vTpQ/v27WnTps0VSbPZKyqqtQxkixl7WQm2wnyQZXKNFvZ//SWH5jzNkRMnsNnt3BQTxKKJt+DdvgO6Vu1Jfu5+7MUFDkfHdl0p2bGx+tyRT/yX5Bdm1BFjqobFhK5VPAHjppDxwcvYCytL3GSZ/375HYPCfGnl6xAGChg7uVrwCCD11ccxJzpmiMWbVlO8aTW+N0wg5M4ai2KPuE4E3fEQ+Su/QmHwwpJZSYSTJay5WRRv/ZXAm6egCgwneOZbJP1nBrK9hLTXnwIBhMpZnWy1YCvKJ+dbh/tf/oolePcZiqZWeaB378F49x6MPHcuASGuy9Wk8lJK9zhKJM3JZ7HmVODdaxDaqKbIdlu10VXBLz9iLymqOVChdGrPfbGwSjIqNyV/G4PSNwB9C0fVScTjrwNgTkskcc5UCs+nIzZvh33UDVTs34g6vBma5h0bOl01XAUKdrsdhcK5Kdb58+cbHOBLSkrqrGO7wvbt2+ndu7db9+kOoqKiGiTKXS5CQ0PJzMx0q0SyoqLiHxUoXK4M86UcO2vWLCZPnkyXLl3o1q0bb7/9NuXl5UyZMgWAO++8k/DwcObPn49Wq6Vt27Z1jq/KrF+4vTFc04HCyZMn2bx5M3Pnzm2wXUREBL///vsVu25xSQUmi5XggPpp9YsJEKqwcfsJFi3bCsAjL33LvlX/ucw7/OdAo9EwcOBABg50pP+Tk5PZvXs3y5cv56WXXsJsNuPt7U379u2rX+Hh4Rc161T5BeA/8V5OrFpGRkhTfvj0c/afyKM4MYXggACaZ2yii7eeW7s2ra5mqNixkYrtG4h48jXC7p9D2lvPIChV+I2YiKj3oPjP9WiiYlEYPPHuN5y8ZZ85vbZss5H23ydQ+gXgMWg0xRtX8nt6AYXlEg+0qJwhqtR4dK7beZvTkuqdq/C3FRRtXo13n6EEjpuC0scf/xET8R8xkYTZU+q1ryqtBFAGRSDb7dV20sFTHqX84A48OvdBEx6DZDYh6j2QKsoQ1BoUns6XhAoKChokM4oGT5SBIdhyHVUY1kpRK02lh0TJ7i0U/bEObXiN2p7SLxBbQaXD5GU65lklCdWFktqXCLmiAlmWHUGgQoE6KIyKU0eQLWaUgkBJwmnyPn0aW4aDkBn48Puoo1o2el6LxeI0UEhLSyMiIsLJEY5MQLNmzVye091AYceOHdWZvSuByMhIli5detHHaTQatzIrcXFxHD9+/JoMFP4OTJw4kdzcXJ577jmysrLo0KEDv/zySzXBMSUlxW3vjIvBNR0otGvXjnfeeafRdhEREVcsfXbgWDKTZn6E2WJj3uM3c9sY5yzli4GPl+PLLwjg4+WeNO21iujoaKKjo5kwYUL1tqKiIo4ePcqRI0dYuXIlGRkZgCO9Gx0dTWxsbPUrJCSE5ORkTp06xalTpzh9+jQludnYC/KIataMDhEtGNK5M0888QQ6nQ7JYubM1BuQLRZARh/XCVPyOaRKzwepvBTvvkNp+fmvjlm4QknagmeRLWZM505SsG6Z0xQ6gODhja0oD9lmxZqTiVReSkqZke/OZfLR8O5QUbk0YbWQ8PideHW/jsBJ92HNSkOqcKKeZ7Mi26wUbfyZskM7afbusuoafaVfIObkGoJs+KMv49G+xuBK6eVD6P1zKNq8Bq8eA/AbejN+Q2+m4NflnLr9ejSRsUQ9s4CKEwcxtO+GspJfcCEKCwvx83PtoyAIAiF3PUra67MBUOgN2I0VlO3fhiowhPR3nnNYSh/YQcRj85HtNowJpyhY9Q1m++UP8pZLyig4v6bx7DFSX32iWvci9IG5eHTujfLnJagyCtG27YK9sIY3YC/KATcCBbPZ7FTNMCEhwaVc8blz5+qsJV+I4uJitwKFtLS0i0opN4ZLzcZWlWm6GygMHjy40XNWVFS4XSHxV+DvyCgAzJgxw+lSA8CWLVsaPPaLL764pGte04GCu3KlISEhZGZmXpFr/vrnUSxWx9rk8vX7uG1MT46dTuO7NbvpFh/L6EHupSdro0fHpnzw4h0cP5vOpFHuuWH+f4KPjw99+/atpxpmsVhITU0lISGBhIQEtm7dSkZGBjExMbRs2ZIbb7yRx2bNIuvxW7EH+YCcS5Mbh6Gt5YooqjVEPvkaRVvW4hHfHe++Q6k4fZTcHxaiiWhSTXSsKqOUJTtCrfRw8bbf8O47xCGUdAH8R07CmpVC8ZZ1ABSeOcGLh1N5uV8HIibPpGD9j5iTHMsL1ux08ld9g7ZZG+oOXAJefQZj6NiTzPdq5IBteTlIZjMKnSOIDH9wLtlff4A5LRGfQWPw6n5d/fex33B8+g2vsy132UJkmxVT4mnM6Un413LHdIaysjIMhoaDVc/OvYl67j1sedmoX32P1NeexHjyEIgKhzSyLAMC2ibNUQWEIHp4UbDqGwrMVvy1l8egt0oSasWV47lUi2MBmR++TOi9TxI0qB8BWg1m/xh8R1xPyfrFqCJbom1T97fpirDnaiadmJjoUvDmSiw9FBYWXnHysEqluiQyo6enJyUlJY2y7ePi4li2bJlb5/ynZRT+NYW6hlCloNZQ5+bul12SJOYtXs+GHSfo0zaWZ++/EaPJytI1u4mNCmJov7YM6duWz3/YitVq56ahnZAkidsf+4ziUiPfrNxF08hA2rZ0nl5sCCMGxDNiwP+Og6U7UKvVNG3atMEOVLbbyDAZq8veJGN9prqhbWcMbWtKX/Ut2xE9t24mSrbbSH39KcoP7UYdWzNrtBXlU35sPygUiFod6rAY7CVF6Fu1J2DkLZSfOEDxlnXIsszz67dyd9MgWl4/GO/eQ/DuPZi0t+ZSdmB79flyvv0Qa05W9f8Dxt9D4M13OZ43OIzc7z7BlHwO/5GTqoMEcMggh91XX9ynMehbtKPs4A5QKNDGuMd9cbXsI8syZQd3IpWV4NVrIEKbjgivf1AdDCHZCbhpCraifDw69QZBJOGpKVgyU1GFRVF45jy+lfoimqatHRoL1otjsVvsV46jADiCG1mq/v7kr/4aD48ShJwCylIz0T39HLr2zmVv1Wq106DAVaCQkJDAuHHOy3WLiooaXPJxJ1DYtWuXSx2GvxruCj9VcRTcwT8tUPhfwjUfKLRo0YIzZ87QsWPDM3lJkhrVH/9y/S4Wf/cnAN+n5eGhVnPkVBq7DjrWKL94YyrX9WjF7hXPYTRZCA/xxW6XsFht1bMLswsVuH9xdSAolIQ/8iIF65ZhaNsFXYuLI+lUwZR0tnp2aUmoES+Rykqq9ROk8jJMZ48R/fz76Ft3AMB46gjFFhv/PZhAOz9PeoX4UPznLxRvWYdnj+vrBAkA1qy6aVyh1oCibxZH9LPvXtL9u0L4oy9RfmQvot6Ayv/yHEFLtv5KxocO+eqKcycIvXsWoijiP3E6+cs+Q9ukBf433opYqdCY882HmCv9KqwZKVR4+OCtdnAVvHsPJsdJlqYxSBdVHFkF58eIBk/CZjyH8fRR8ld+DbKEoXVbSN2BViliaqRkvyqjeWFQYLfbna4TN8RRaAw2m63R0sAjR47QpUuXSzp/Q/D09HRIdTewJHUhfHx8KCwsbLRdVVDqTuWDzWb7R5n32WWwX8bSg/2fq4ZfD9dO7sMFioqK3PoCDx8+nJ9++qnBNtk5teqi7VBSaiQzp6h6U1auY7+fj4HwEEf0r1CIfPLyZAb2as0zD4ykU9u6RjP/4urDs3Mfop99l4Cb7nS7I5FtNrK+eIfklx/BePYY6uDwGvnnWlB4eDsY+1UQRIr+/JXS/TsA2Fxi59Fd55jYNIRbm1dK0VaWVpbu+t0h6QyOlDygDotGX1kGqQwIxm/IWADsZSVYMlMvuv7ckpWOtcC1oZCo1mDNzSTlxYc4e/8YjAnOFdyqH8/F+2dNO0PRui+qWmFJc7hRhoaGUuAZQLP3fiD62XergwQAdWhdi26DzYzcqS9NXv2Csr1/Nv5wThCu15JW1rDOQ204eBF1uznRwwv/CdMwdOhO+tvPkv/zEkStjpiXPiF46jN4DL+XuGETSVQ2HFiZzWa0Wm297TExMSQlJdXb3pAyY2Pf23HjxvHNN980KJ0+ZMgQ1qxZ0+B5LgXDhg276PO2adOmQevs2ggICCA/P7/Rdp07d/5bRKdcoUqZ8XJe1wqu+UDBXfexyZMn89nnizBaXWvgz5w0gJioAERRIDYykFlTh/HKE+No0zyMUYM6Mnpw3ayFLMs8//7P3PfS1wSH+DB1Yr9/VMT7L1yjZMdGCn/5gYrj+0n/4GUUHl7EvvEVEU++hlffoQAofPyJmb+Ipm98RfCUR/EbdSsIAsW/r+bQS48wtmcXjiWn8fv5FMav2e1IY9eC6OOPrnlbQqY9QbP3fiTmpY9pMn8R0XPeovX322j+/nJEtQZzWiJnH7yZ849OInfZZ8g2G4Ubfqbgt5/qeDaU7ttG2oK5FG/fAEDBbz9x/pGJnJtxs2N5xAWK/nBwKGSLmdK9Wxt9b5wFK8UrFqCSC1BoRJTe3gROnAZAQOY5dsx9kPOP3IIlOx1TynmM504gyzLe149ArGVG5WcpJ3Hnn2iimmLOcO550BgUooAoCFjs7pVa5hgtBOvqEgulshLKdv9O6faNyGZH0CEZy5FtVgRRgaHXWGImPEpRqWtlRnBNrmvVqpVTSd3AwMBGXf5cQafTMXfu3AYrvDp27MjJkycxmdwPpNzBqFGjWLVq1UUd06FDB7dlg2NjY0lISGi03cCBA9m8efNF3ce/uDK45gMFdyVGl507yinMDHvzP2ReIG8ryzI70hLYmnGeUf3iwSwR4KHHy1NH7y7NWbd4Fu/+5zZ02rodTnp2IV+t2oXRZOH7dXtITHc9s/sX/yyIhhrxmirnRVHnEGYKe2AuzT78mebvL0cdGII6NBK/oTfje/2NyHYbq5JymL3rDHeG6XjppZfQ6vQotTpC7p6F0tcfjy59CRh/D1JRPkUbfyb7i3dQeHiha962jpVxFcoO70Y2O/LcJVt/I2/Fl2QteoPsz98id/liwJFxSHvrGUp3byHjvRex5GRSuquy5FeWKNu/vd55q1BFcBRUajy7uBZTAteKhILeE1El4hWhI+LBx9G3bA9AcEEmaeVm7KXFnH94IolPTiZp7r0UrP2eipOHkCrLIgH8tWryiktIe+Mph27FJaKNr4GThQ0P4lXIrjATrK/PF6hSjKyN0kO76/y/ioPgCrIsO11icBUoBAUFkZubW287uDaSqo0BAwZgNBrZtWuXyzY33XQTK1asaPA8FwsfHx8kSWpQifJCuGtiBe4HCo3JXP/V+Dtspv8uXPOBgrtYe+444QP7cOaXzezOSKqzb0PiKZ79YzWvbv+NL/buxGaX2HUwgZ0H6nYmsiyTnJ5Xbfzk7+NBgK9jwPH10hPkV5dsVGE0k5pZ8I+2Zv7/ClmyO6SQK5wPKB6dehN639P4j5xExKMvk/3Nh5y+cyCJT01BMhlR+QXUM5TKtsGTiRVkG6183K8NXfpcV2fG7ztoNM0/Wknk4/Ox5tYQFmWrpUE3R4+OvRD1ju+Rz8AbsRXmgSCCIGLLrxxYBKHWCwRRwOe6ESAICCo1Xr0GuDy/3w0TaPbejw5Ph6atG3zf/Pz8KCioP4j7THgKQ7+JeI15BG27/gCUnzhIpEFNipOlgNwfPiftnf/U2aYUBeyy3GBQ4w46BnhxMN8999Iso4UQff1SxWrU6quLNv5MymuzSXjiTsqPH7io9HltNJRRcBUo+Pj4UFRU1Oi5X331VebOneuSrzBp0iS+++67i7pfdzBq1ChWr17tdntBENyWf27atCnnz59vtJ1SqUSn07lV6fZX4Eq5R14LuKbJjCUlJXh6errVdnCTViwzVSBXGIlV162QSCkpQACyNxVQds4xm1KrlDRvUtel64lXlvLj+n0EB3ixbvEs/H09WP3hTHYePE/3+Fg8as1cMrKLGHnPAgqKyrlnQl+enTn68h72X1wU0t54mrIDO1B4+xH7+pJ6ugGCUDnQ4ggAC9Z8DzhmmhXHD1TPvGVZZvv27Xz66adUVFTwxpLviJLNJL/4EKW7fycdiHj0pXrX9+o5oFrW2LP7ddVZC2fQhEXR/MMVWLLSUQUEYa8ox5KdhizJ+N90J+aMFNRBoUQ++Rol237Fo2s/VAEhePcbhqFjTwSlqk6FhDOoAt0TAqsKFC6sxVd4+uE5pK7oU/nh3UQYdA7OgFoDFjOVbg7IZmN1luRKo72/Bz8kZDXeEEdGoUNAA9UCVW6SODQ1yivJp1mL3iS+5xgOHz7cKFH6QgQFBdXT4wdHoJCYmOj0GH9/f/Lz8xvlW/n5+XHPPffwxhtvMGfOnHr7vby8CAoKarTc8mIxatQopk+fzu233+72MS1btuTMmTO0adOmwXaxsbEsXLjQrXP27duXrVu3Mnz48MYbX2X8XToKfweunZDGCVJTU4mOdo88eF/nPnw1+k4+nPsi65b+WGff6BbxtPIPwZhaMzPqdH1zAvw8ySmombms+M2h55+dV8LeI44ffLC/F2MGdSQ0sG798rZ9Zygocsxml63de/EP9y8uGbLdRtkBB9nQXlzgVAOhNgRBQB/XyfG3WkP2V+9x4rWnefftBQwZMoT169fz4osv8uOPP9KqVSvM6UnVcsQVpw5R8OtySvdtrZM58ojvTuxb3xIz7zPCH6kfSFyI0n1bSXx6Cmemj8Kam0n0s+8R/ey7ZH7yXxJm3UrCU3ejbx1P2IPP4tWtf/VxSk/vRoOEi4G/v7/TjIIzePUciKeXBxV2OyGTHyZq7js0/fAnBI1rURwBkC4zw+ahUlJutbuVqWssoxB0y33oKpdRUNcE+qqgULeNmC6EIAjVHgZ1rtXA0kNVoOAObrnlFvbv38/Zs84daqdOner2wOsu/Pz8sNlsbpU8VsFdnoKrwMoZhg4delH38C+uDK5aoDBv3jx69eqFXq936dx4uUhNTXWLyAiOH2+Ely9jRo5k48aNddYDA/UevD9sItMmODpgQafgqKWA/g++Rc87/8u8hY6Z4ahKMaVAf0+6tI9p8Hq9OzfHx9PRYd40tNPFPtq/uAwICiXe/W8AQBUSga5luwbbm5LPEXz7A/g/9ip/ZBUxe/UWZrzzKT7lRaxbt4558+YRExNT3d6zW380kbGgUKL0CyJ78QLS3niaxKfvIfXNOZRWKjlqwqLQNW3tFsG1eNtvjpmtZKdk5yYAbEUFGE8dBsCSlog5zfls9ErCz8/P7QFLG9Oc5p+sxtC+G74DR2Fo2xlBlutlEgSNFv+bJiN6euOtVlFkvvwS4kgPLUmlDWcszHaJc8UVBGqdBwqCTo9X36GYUx3r4+qAEMIfm0fQHQ8R/tDzxMTEuMwANIaAgADy8upylhpaevD396/X3hUEQWDevHm89tprTvd3796dffv2XXHL95EjR7Ju3Tq323fo0IEDBw402k4QBCRJcivwa9WqVR1V178TMpfHT7g4e7O/F1dt6cFisTB+/Hh69uzJokWLrso12rVrx5IlS5g5c2bjjSshiiKtWrUiJSWluvMvKCrHw6Dh0XuGsrYwiUKzCY1CSV6ug6H83fo9PDN1BI9OHUKR0UiQvxdaTQPrnkB4iC/blj9Dbn4pMRGX5wV+JSHLMidOnEClUhEbG4tSeU2vPrlE6H1PE3jLvSg9fepxDcBR637ixAl+/+ITtv38I1lGC74t4+igVDO9TSSRHlpiJoyvR5S1ZKVR9Mc6gm69H0OHHiQ8fXf1PnPSGcxJZyjbt5Vm7/7gdrofHLPz8kO7QBDRtYzHlJaIbDahDo/Bkp6EOqJJtZ/C1UR4eDjHjh2rs02WZbK/ep+yPX/gO3wc/iNuqd4nqjXoDB7VIkNK3wB0LdpiPHMMVUgEkbNfx15aTMEvPyCVFtPcW8/p4nJ6an0u6z4nNQvlg+OpvNGzrqSyLMsczCtlZVIOWUYLY2KCUCucz4fUEbFYs9Or5bQtGckY4rqg6OpYmsw+d47AwECnx7qSaa5Cfn5+vQlSQ8Jw7no5VGHVqlUul0QEQUCj0VzxCqz4+HjWrl3rdvvWrVvX+y41dO6dO3fSq1fDluj/JPyrzHgF8MILDjnaS9WWdgfh4eFYLBZyc3Nd/qCdoW3bthw7doyYmBjeWvQr7y7eQJC/J6MGd2R4YAyRbUJo4R/Afce+pKTMxPDeDhGflz5aw9b9Z5GBsCAfHr5jUL1z5xeWcd/cL8nMLmL+7PH0/Qc4QZaUlLBx40bWr19PSkpKtd12QkICNpsNrVZLixYtaNWqFa1ataJ58+ao1eo66dOqv2VZRqVSNSrz+3dDEARUvo4AzWazcebMGY4cOcL+/fs5efIkgiDQpk0bmuVncn9cJMF6DT7XDSdo0v0Ub/0FbXSzOqZLUElmfWkmtoJc8mWZJv9djHe368hNuiAFLMsNkhedwaf/cAxxnSg/upfMD16ss8+jc28iHn0ZQXlx8seyLFN+aBeS1YJnl74IbigaNmnSpB5pzZx8jsJ1DmOgnK/ex6f/DSg8agY1Pz8/CgsLHcY0gkD08+9jyUpDHRyOJTOV5OcfqF6qiff3ZE9uMT2DfS7qWS5EM2894QYtf2QU0D/Mj/RyE6uSctmbW0y8vydTWoUT49mwL0DA6NvRNWuDoV1Xyo/uw3f4OBT6mu/1r7/+ynXXXef02IbEk8rLy1Gr1fWCzIaOSU5OZuLEiQ3ebxU+++wz0tPTefvtt53ulyQJQRCueKAQFBR0UeWdoigSHBxMZmYmoaGhDbadPn06zz333DUVKPwv4R81nTSbzXXKkRoSF6nC5MmT+fLLL3n88ceRZZkzSdmEBHjh7el63TYuLo4dO3YwcuRIlix3kJdy8ktZuHQrgigwfdJ13HFfZ/5Y+ASZecUcPZ3G7Nd+wGy2ggCyJKOvlc40W2xs3Hac8BBfdh44x97DiQjAKx+sZv0Xj136G3KJqMoarFu3jj///BONRsOgQYOYO3euU06H0Wjk7NmznDp1il9//ZX3338fq9Va3dFUdTpV/7dYLNUldGq1mqioKKKjo4mJiSE6OpqmTf+PvbOOjurqovjvjcTdCBASCAnuLoUiLe5S3N2Ke7Hi7hR3d3dvC4XgLiECBAIR4jL2vj+GBNKMBQjSj73WrJXMPe+9O/buuUf2zp2h3dGngiiKhIeHc+vWLW7dusXNmzcJCwtDJpORJ08eihQpQocOHcifP3+q3G/czcs8nzUCJFIcqtRFZu+Ic71Weq+hjo1OLX4L37MWzXs964KZOeY5fXGsUg+zrBkT5kl+8ZSkwIdEvydxnTr2PCjDTgJA1Kl9hK6cBYBL0064Nu9i9BhPT0+ePk3LcSC1d0SQmyEqFUjtHNKQKoHWUXh+5SIxe1eAICHHiJlY5s7P663LeHN0Zxp56QKO1qx9+Gkk33sW8KD58Zts9Q/FyUJOAy9XehTwQGLiAqmOiQKpDNsyPyJ1dkstbk3BoUOH9JK0PX36FE9PT51jf/31VzrdEtA6CtmzZ9d5TEhIiN6x97F582YuXbrE8uXL9ToCwcHBaVJlnwqGUif6ULt2bY4ePZoqg6wPOXPmJCEhgdevX+Pm9nEMop8LH0ua9C0RLn1VjsLUqVNTIxGmonbt2syfP5/BgwczcvYuth2+jJ2NBYeWD8DDXXcFccGCBVm+fDkAtX4szNYDl5BJJagREQSIS9De/O1sLAkOiWDo1O0IgoCttTldmvyAna0VCfHJ/Nx+Fq3ql+X2g2fsOabNxWVx1S6QIpAnV8Ylpz8GL168YMOGDZw8eZKCBQtSt25d+vXrp5M97n1YWlqmSjxnFElJSTx79ozg4GCCgoK4evUq/v7+xMTEpC7QBQsWpFChQuTPnz9DkQhRFNFoNKhUKlQqFUqlkqSkJJ4/f05QUFDqNZ8/f45KpaXRdnFxSX0tbdq0MXrTsSlahjyrjiBIJEYXY0EQyN5vHOH7NqJOTCD20jkAzHJ4IzEzJ0u7vljlS6vXkRjwAOWrEGxK/oDETLeSXvSFU7xYME57Dcv33h+5HInMLJXcKKNICvbXVvWn/G0CdOmiyB1dyDlpGfF3tN0g/36fnJ2deXp8H/njtUVmb47tQt66FxF7N6Q7v4VMSrJGNImy1xgsZVK2/VwUQQArmdT4Af+CuUdOEu5cIXSV1pmKu3yWHMNnYZW3ME+ePMHDw0Pvb+fp06d6VRpPnjxJu3bt0j0fEhKiN12gVquNpgH37dvH4cOHWbdunUEp4Tt37lCo0IdRmRuCpaVlhsmcatSoQe/evY06CgBdunRh9erVjBgx4kOn+Fnx/9T1kCFHYcSIEUyfPt2gzf3798mXL98HTWbkyJEMGjQo9f+YmBijkqkymYzy5cvz119/ceistvArJi6JSzcD9DoKKaFSgClDm9K2cQXMZFLmrzuBRJDQv+M7yVOVSg1oFy21WsOwLrV5+iKCqm20hUS/L9yPV9Z313kVHkPLBmUpUdArtfgxM5GcnMz+/fvZsmULlpaWtGvXjiFDhqTumDMbFhYW+Pr64uvrm25MqVTy+PFj7t69y4EDB5gxYwbx8fE65/Z+IdP7/O9SqRS5XI5MJkMmk2Fubk6OHDnw8vKiZMmSNGnSBA8PD5NIt/RB3wKuTogHUZOmtdG2dGXMsuckYGj7d8+VqIBbq57pjk94cJPg8X0BEduyVfAYOEnndcJ3r0n9W0yMJ9f0NShev8SmWDkkcsO1MIbgVPsXEu5eQ6NIxqVhWzSKZF4snkjik/u4teqJfUX90r7/XsgtvHyx8Er/GYP295QQ6wavtc6IZZ5CSKxskNo7oY6ORGJpjVlWD5ICHgKQw9qCZ/FJeNp8vGSwtfzDv+evty1H+epddEOTEE/whL74zN/Gpk2baNOmjd5jnz17plcaWd9C/fz5c4NS0oZw4sQJNmzYwObNm43+tm/fvs2PP/5o0OZzwcnJidjYWKM6O6DtaJg9ezZDhw79bPev7zANGXIUBg8eTMeOHQ3a6JNRNQXm5uZG9ct1oUuXLowfP57W9ZqzfPs53JxtqVTKcG2ARCJBrVYjlUoplEcb8ls8If0uoHhBL8b2a8DlmwG0b1wBqVSCna0l5mYykhUqrC3NGNC5BsOmbUep1DoVtasU4ccyxnXrPwbXrl1j9erV+Pv706BBA1atWmVQfe5LQC6XU6BAAQoUKEDz5s2/9HQyhLhbfjybPhQQ8Rg0BduSFd+NXf0L1Npdt2BhhXND3b3l2l281gFKeqJfY8G6UCkUz4O055ObYZ4jt95FOSMwz+ZJ7jmbU/+PuXiK2EtnAQhdPUevo5BSsW9q3Y+TkxOR5MWrlZbi2ipfUeJuXkKdmIBgYUm2fuOwyleEl39MIdbvPEVc7LgZHoungx1ksJbjUyLx/o1UXY5UaNQoo99w/vx5g3TJ+lIP4eHhODo66hWF0pVeiIqKMigP/eeff7Jw4UK2b99usIAyBXfu3KF3795G7T4EH0IeV7FiRS5cuGDUeZFIJNSqVYujR49St25dg7ZfA74XM+qBq6trhooGPxdy5sxJVFQUPVtUoEvzStjbWmFulvalPXn6mtU7/iRf7qy0bVgeHx8fnjx5Qp48xosNOzX9gU5N31HfOjvYsHNxH878c58aPxQiX+6s1KlalKPnb5PNzYHSRTK3On3y5MkEBQXRv3//TAkxfgfE/HkUNGoQRaL/PJrGUbAuWhZhxypERTJOtZsjtbLReQ67Cj8Rff4oitDnuLbqofdaWTr0J97cmpCH97CtUpebt26lpltSHgA//PDDR3WpyLNkJ4UQycxdv4phrly5CAwMNPm37uzszJMnT1IVNQEiD20DRRKiIBB/6zK2JSrgMXgKGqWCJkHBzJk4HhTB704ilYJa/UGv64MhSIB3joLU1h6Hag24HRFDqVKlDIb3Q0NDtcWb/8KZM2eoVk03S6Y+meTg4GC9fDDXrl1jypQp7Nixw2gKMQXR0dGZ1pKewraYke9hnTp12Lx5s0lRjk6dOtGtW7dvxFH4nnr4aDx9+pTIyEiePn2KWq1OJd7w8fHBxkb3jfVj0Lx5c/bu3as3F9Zj9DoCnoahEUWyZ3Gkdu3abNy4kd9//12nvTEUzutB4bzvbrbmZjIafoZUw4oVK4iNjWXFihWZfq3/GkS1ClVkODJnN6MdADalKmm5DQDbUmkL0yw8c+OzeDfq2GiDRYsyW3tyTU7/OUVERHDt2jWuXr3K9evXiYuLw87cDJvg+0j3HsChXBWssnmmpltkMhlxcXHMnj2bVatWfXCxl6V3PrwmLCYp2B+78tX12mXPnp0XL16Yfl5Ly3T6EFYFihN/67K2A0SjJnzfRmxKViTy4BZsrWwICAwCTxmo1QhW1oh6qLYzExa585P06Hbq/7lmrkfu4MysYcNo1Up/QStoO2l0ORI3btzIcJ//ixcv9PLBHDhwgLFjx5p8z1QoFB+VhjOGMmXKcOLEiQwxIxYtWtRgdOZ9ODs7ozQg3PcdXwaZ5iiMHTuWdevWpf6fUsRz5swZvS1HH4O6devStWtXvY5CskKF+DYMnJyspNZPP7F06VLu3btnlGL0a8HJkyc5e/YsGzakLxL7DsPQKJIJ+q07yU+fYF2kDDlGzjZYTGdXtgoW87cDImZu6W/iMlt7ZLb6w8WgbVMLDg5O7cK4ffs28fHxODs7U6JECSpWrEifPn2wtbXl1YZFRB4OB1HEKrsVXmPGpjvf7du3admyJdOmTaNMmTIZfg8ArPIWSRV00gdHR8fUGh5ToNFo0uWUnRu2xSpvYeLuXCVi1xoQBCKP7kQdFQmiRsuWmf3t707yZfLRSSGBODdqR+ylczjWbIrcwRnQLvaGarEMUS0/f/5cZ12VId6FiIgInJ2ddY75+voSFBRE+fLljb0cAB4+fPjBNWKmoGfPnnTs2DFDjsL7NUemFLCam5ujUChMSrN8SYhiagPUBx//rSDTHIW1a9dmKofCv+Hg4IBKpSIuLk6n971ofFuWbDxN3tzu1KxcCEEQWLx4MW3atGH//v1fPS/AvXv3mDVrFnv27DEYEv0O3Uh+FkDyU63wTPyty6ijI5E56L45p8DMzXDvdwpEUeT169c8fvyY27dvc+vWLQLv30WMjca7SFFKVKhEjRo1GDhwoN6doUWuPKl3DkufgjptChcuzO7du+natSu1atWiaS43Xm9ZiqV3XjwGT9WpTPkhcHJyyhAjoUajSfedFAQBq/zFiLmUonApokl6F3VwMpcTmaTEyUKOGGeawNMnR3wcNkXL4tbyXVro4cOH5MmTx+CCdu3aNUqWLKlzLDIyUmet0LNnz/S2U0ZEROjdrOTJkydDJEe3b9/+oO4lU+Hk5ESuXLnw8/OjdOnSJh+XM2dOk9s2HRwc3vFyfMX47ih8o6hVqxbHjh2jadOm6caK5s/Bsskd0jzn7u7Ob7/9Rr9+/Vi1apXJ7VqiKHLlVhAOdpb4foYWyNevX9O3b9/UzobvyDjMPXIhd/dAGfocyzyFkdplrPBToVAQGBjIkydPCAgIICAggKCgoFQVvyxZspA7d26KFClCjQplSZ4+AEFji0R4hW+H9no7K1IQf/Oy9g9Bgk2p9D34KXBwcGD79u1MmjSJXjPHM7KIJ5pbfsRd+9tgOiEjyGhEQa1W63ReX66eQ9Txd5LH5m/ZJaPPHsLbzpKAmARcPfKgDjeN5/+TwMwCFO9a/JKC/dPUVuzfv58GDRoYPMXVq1cNEgPpuo8EBgaSK5fu2iVjEYWHDx8anM/7uHXrlsFujU+B/v37M3ny5Aw5CkWKFOHWrVsmOQop37+v3VH4f8J/ylFo0KABI0aM0Oko6EPVqlX566+/WL16NV266CekeR0ew+5jVymUJzv/XH/CovWnEASBdbO7UjkTOxwSExNp3749Cxcu/P7D+QhIzC3wnrEORehzzLN76axRUKvVPH36lMePH/Po0SMeP35MUFAQarU6lfI6d+7c+Pj4ULNmTTw9PXV26SQ9CyDwbTW9JjEBUalIIzikC/H33nLivw3LW/nqT4dJJBLGjh3LmoeX6Hv6AhPK5CFn1hy82riIpIBHuDTtiHXBjOuLxN+9RtSp/Ujdc3106kEVFUnU8bRkRbalKuHSsC22pX7AZ+xwnqpESkWapm/wKWCeOz8WOX1JuHcd9ZsIzLJ7YVchLbvqmTOn6dfjHW9F/O0rKMNDsavwU2rE5vr16zq7Cgx1BAQFBeldJCMiInBx0U3zbm9vbxLxXAoePHhA3ryZ23Hl7e1NaGio3uJMXShcuDCnTp0y6oRBxh3V78h8/KcchWzZshEeHm5Sz+77GD16NE2aNKF06dJ6w3btBi3n4VtpW9+cKcVkIpdvBGSao6DRaOjWrRuDBw9OpV3+jg+HxMwcC8+00ruBgYGcOHGCU6dOERsbi7e3N3ny5MHX15fatWvj5eWV4U4Dc49cuLboRsylszjWaGJQYjoFLo07ELpmLnJXd+zKVTHpOu1XbKfYvm0MWbyKgYcPk/u8VhU15EUQeZbuz9CcNUoFz6YPRVQoSFKqCFOZrk/y79RD8ounxN34B5mzG6qI10gsrHCs2xLH2toW2chju8hJEv8EvQTHD2+nzijMXN2JPrUfBAG5W9Z0habPrl1C8vAGgT0bkK3XaKQ2dm9bZLUMnh4DtIXPsbGxOplH37x5o7dFOSgoSG9diaGIAryjTzcl4qlUKj9Lbr9x48bs2bPH5OhFoUKFmD9/vkm234qjIIoC4kd0LnzMsZ8b/ylHAeDHH3/k7NmzeslQdEEikbBixQpatGjBvn37sLVNf2N/+Toq9e8q5fITHBKBna0VjWvpzlV+CowdO5bKlStn6LV8h2HExMRw5swZjh8/zpMnT8iZMyc1atRg2bJln6ylTBAEXBp3wKVxh3Rj6rgY4m5ewtKnAGZZ3vXUO/7UEIdq9UCQmJwCk1pZU7xVZw40bEm96tWY7C7iYGGmt10z3VziY0EU0+g2AFjJJMRHm96F8H7qQR0XQ9CormiSEhCsbHBr34/XW5YRsWs1iQ9vIqqUJD64jYe1Bc/iko2c+RNCIsGufHVi/9HWTMh0pJ72rlhCZVc70GiIPLYLu3JVU8dSlDsNOQOGtBwCAwP1RhSM7czd3d159eoV7u6G05yGiiw/NZo3b06HDh1MdhRsbW2Ji4szyfbbcRS+1yh8s6hfvz4rVqzI8OLq6urK2LFjGTlyJIsWLUo3Pn3EL8xeeZTiBbwY1rMOw3rWQSoRMq2wcP/+/cTFxdG9e/dMOf9/FSl6D0FBQWkeISEhqNVqLCwsqFatGoMGDSJ37tzGT/gp56bREDS2J4oXTxHMLck9b0uqcBWA8IHV/1ZWVoyZMpWNyxYztl41HH9qaPSY+DtXeTZtCKJGTfb+v2NXtgo5hs8k6rRWNpoZ6X8D+mBjY5N6Y1fFRKUWLYoJccTfuQpKbR1Hwr0bWm4KQCYRUIkiMld3VOGv0+hBZAZkbtlQhL3EtsyPKF4+w6l++gXupP8zRmTXLrS2pSph/2MdYq/+jTLsJVna9gG0Og5ly5bVeQ1/f3+9dQjR0dF6SZVSRJz0wcHBgcjISKOOwv79+3VqTGQG7O3tMTMzIyoqymQH28bGxuD7kAJPT09OnDjxCWb5HZ8K/zlHIV++fBkq/nkfVapUYcWKFTx8+DBdnq92lSLUrpJ51cTv4969eyxcuDCdit93pEdCQgIXL17k/PnzXLt2DVEUcXV1JWfOnOTKlYuyZcvSokULsmXL9sUltUVFMooXWsElMTkRZejzNI7Cx6Bq1aqsXbuW5/nKkiWLcXGh6L9PIKpVWkKp80exK1sF64IlUmsbLBeuMiiL/D5KliyZykdiljUHTvVaEv3ncTQJccRfu5BqZ5E7H0kBD0CtxqpgCfJa50HapQ+qeSMhKUHf6T8JVKHPCdu4+O1/Ai+XTMK25GEEqfY7ERISgo1bVkrOnYkmIR6LnFpmzJzj0jpMu3btYtIk3VTcfn5+OhUgDaUNjDkJ8K4TwxBiY2PZuHEjR48eNWj3KVGxYkX++ecfatWqZZL9jz/+yLlz54zWKZQtWzbDmj9fAt8jCt8wJBIJ5ubmJCYmflCHwMSJExkxYgTbt2/PhNkZx+3btxk4cCCbN282mYnt/wkxMTH8/fffnDt3jlu3bmFhYUH58uWpW7cuv/32W6aSzXwsJBaWuDTtRMTBLVgXKZNOxvpDEXf9IrF+5xnfuS3dR4zgyJEjRp0i21KViD57GK0ORXrGvOLFi3Pjxg0qVqyY/uB/QSaTpUYVHB0dydK2L061f8G/T5M0dhILS7zGL0EV8RqLnL54t6zH3t5tqO31udleRTTJiWiSk5FayRBFkTVr1tCpUyednBkpSE5O5vXr13rTC3fu3GHixInpnjekNPnixQuDqpEprJzGPs9JkyYxfPjwz/r9r1ixIvv27TPZUahevTqLFi0y6ihIpVK8vb15/PixTg2Z7/j8+E825JcqVYqrV69+0LHe3t7kyJGDs2fPftpJmYAbN24wcOBAtm7d+s1IrWY2RFHk1q1bTJs2jXr16tGlSxceP35My5YtOXDgALt372bo0KGUKVPmq3YSUuDavAv51p0kx+ApCJ8gwqF8E86zmcOJOnOQpOWTad2sKfPmzTN6nG3Jivgs2knuBTtwqJyePKd06dL4+fmZPI+ffvqJkyffSWTLnd1w/aUrMidXZE7a73LCnatE7N+EXbmqJD6+SxErCTcjYj7N1kqawfdSFEl4dJvEwIc87FaPw0vmUjaLg8FDTpw4QY0aNXSOqdVqRFHUuaAbUnN89OiRwcXwzp07FC5c2OC8Hj16RGBgoN65ZRaKFi3KzZs3Tbb39fXl0aNHJtk2a9aMXbt2fejUvuMT4z/pKJQvX56LFy9+8PGjR49mypQpaP4tGJOJuHbtGgMHDkJwrUKF5rPYsOeC8YP+o4iNjWXv3r10796dWrVqsWHDBsqWLcvu3bvZsWMHv/76K8WKFfuuMAdacSqNRrvYajS0bfkL58+fT0OYFH/bj8e9GxM4qivK99oR5c5uekmlSpUqxZUrV0yeRs2aNdOFvV2adMR3yR6cajVFqzEB6mhtLYNVgeLkdLInKDZjssV6oVbpH5NKMc+VvjMp8cEtXm9awsUnwZRytiHqmOGFaefOnTRr1kzn2P3798mfP7/Osbt37+rtWnr8+LHBtMI///xDuXLlDM5r1KhRTJ061aBNZkAulyOKYjpZcn0QBIFs2bIREhJi1DalKP1rRkrq4WMe3wr+k45CmTJluHz58gcf7+TkRO3atdm8ebNx4/cgiiKb9//D6Nm7eBxkOomMn58fI0eOpF2PUTx+FoVCqWLuqmMZnfY3C1EUefjwIXPnzqVBgwZ06NCBFy9eMHLkSI4dO8bMmTOpWrXqV0/p+iUgd3Ena+/RWJeoQPZfxyG3d2LOnDkMGjQota//9dblqCLDSAp8SNQZ0+pe3pdi1wVRFAnfvZbgSf2Ju34RT09Pnj9/noZLQBXzhtA1c1FGRWJbrgpW+Yrg3mkgAFIHZ+TWNsglAsnqzHPI5VmyY1euGnKXLOQYOQebUpWQWFghd/cgYu96Eu5cZW/gaxrlcsMqn/4aJGNph8uXL+stcjTmKBiKKPzzzz96zwtw5MgR8uTJ89kLc1OQQqRkKqpXr86pU6eM2slkMjw9PTPEEPq58f/kKPznahRAW10bFxdncu+xLvTu3Zt27drx8OFDRo0aZVK9w19XHjNqpraX/cRf97i8Z4zRYy5evMiECRPYvn07Ia/jkUqPo1ZrKFk45wfN+1tBUlIS586d49ChQ6kkMXXq1KFnz57f2SczCIfKtdOkD3Jlz0bp4sXYvHkzbdq0wcLLR6utIIqY5zCdt8De3l5vVXvig1uEbV+p/fvRHfKuPU6hQoW4e/duapj91boFxFw4BYi4NOuC64B3+fuoU/tQR7+hgKMN997EUdwlPS/Bx0JiZYN57vzE/K2toNckJuD12zwAAoa2BwSikhWoXbNSbvpKLA04CobSDgCXLl1i9OjROsfCw8P1Eio9efIEb2/9n4mhtkiFQsGsWbPYt2+f3uMzGxUrVuTvv/+mRAnTCL6qV6/O8OHDad++vVHblPTDkCFDPnaa3/GR+E86CpAxbnFdMDc3Z9u2bezfv5+6deuyadMmsmY1zP3/4MlL3upOERYejVqtQSrVH7S5ffs2v//+O9u3b8fOzg57e3uOrR9C0LMwKmUi22Nm4uXLl1y8eDGdTHLKQ6lU8s8//xAXF0eVKlXo1asX+fLl+2CH7nNDFEWUSiUKhSL1kfK/KIpkzZr1i+qGxN+9xrNpg6mnVPLrwf3Url0b986DsCpQHJmDM9aFTOf9KF26NJcvX9a5QEos3/b9C4L2b0FCzZo1OXLkSKqjIL4vG61JKyGd/PI5AMVdbPk7NCpTHAVNQhzxN/5J/V8VGZb6t3vnQYSunceR+8/pNHgAVvmLpj1WkUzSo+uYZcuFzCkLO3fu1FmomIKgoCCdYlBqtdpgiswQQVJUVJRezgaAzZs307p160xR4zUV5cqVY8OGDfTr188k+xROCFM2cVWrVmXBggVfr6PwkYRLfCdc+vLImzcv/v7+H+wogDan1rBhQ/LmzUunTp3Yt2+fTsreFJQu8u5aluaGw+RxcXH079+frVu3pmF58/Fyw8fr2ypkFEWR8+fPs2zZMtRqNTVq1MDc3ByZTIaVlVUauWSpVErLli2/CTrqFy9ecOnSJS5dusTdu3cRRRGpVIpcLsfMzCz1kfK/KIq8fPmShISE1BthlixZyJ49Ox4eHmTPnp2sWbOSNWtWXF1dM6XGIubvE4gqFXKgX6n8jB07lkWLFmH/Q8YL3SpXrszu3bt1OgoWOX3JPmgyifdvYl+tHoIgULlyZRYtWsTQoVo2Q8eaTdEkxiN398C5flrZZuc6vxB1fDdl3OzZ6h/KnchYCjkZZ7A0ipSixrc1C4KYohkLipfPSHx8F0vfgljlL4btkJlcbN2a8b+kb2l8tWgEyY9vIsjNcR6xlLCwMJ2OAGjrE3x9fXUufIaKFXWJab0PYwJPgYGBNGrUSO/454CTkxNRUVEZit56eXnx9OlTvLy8DNrJ5XLkcjnJyckG77tfCiKp+8IPPv5bwX/WUfD29v5k+a18+fLRr18/+vbty/Lly/X+IIoV8GL6iOb8c/0JbRqW1xtNUCqVdO3albFjx37T3Q0xMTFs3LiRPXv2UL58eaZPn673Zvq1Iz4+nuvXr3Pp0iUuX75MbGwsWbNmpVy5crRu3ZqCBQtmeGHXaDSEhYXx/PlzQkJCCA4O5uKFCzy9d4vI+KRU/QepVIqbmxtNmjQx2GqminlD+J71SK1tcW7YFok8vTNqU7IiUWcOAlD9l1YcWLuDGzduUKxYsQzNHaBYsWKMHZte7joFdmV+xK7Mj7w5fYBXa+Zi/0NNZDIZcXFxSEICeTqxH2g0OLhlQ2LxjnlQFRVBxJ512FeqSfTZQ4wvlZuBFx4yt0JeHMw/snPlX0WNmsR4pHYOqGOiQNQQeXQn8qt/ER8VSe9dp5kzZ47OxTo54A4AojKZIzu2Gkw7bNu2jZYtW+ocu379OsWLF9c59uLFC7Jl09+Oefv2bb3dEmCc+vlzoWDBgmlSTsZQtmxZLl++bNRRAG2b7vXr140WdH4R/B95Cv9pR+FjOh/+jbp163L79m0WLVpkMMzWol5ZWtTTX3yUlJRE+/btadmyJVWqVPlk8/ucuHv3LkuWLCE4OJh27dpx6NChb6rQUKlUcvv2bS5fvoyfnx+hoaFYWVlRvHhxypYtS9euXY2yx5kCiURClixZyJIlS6osccjCCcT4R4KFlJyTF2KZKy8qlYpXr16xaNEi1qxZw/Tp03VGwkLXzCX2n7MgighyM1watk1nY1vyB3wW7EBUqzBz92Bq3hJ06dKFQ4cOZTi9I5VKsbKyIjY2VietOYAyMozQ5dMBSLh3nR/L1uP06dNUJFbbjQHEXfub51ERWOYphFO9VoSum699HYhIbB1wIIrBRb0Yf+UJcyrkRfKJ01DmXr4k3Na2esb8fQKNCCMuPaJtzZ/0th461GlP1MF1mHnl4YDfbaZOn67TThRF/v77b70O1fXr1+nYsaPOsYCAAIP1Cbdv36Zu3bp6xz8nZbMhVKtWjdOnT2fIUVizZg3Nmzc3ydaUzo/vyFz8Zx2FXLlyERAQ8EnPmVKEc/r0aapVq5bh4+Pi4mjTpg29e/emZs2an3RunwMJCQmMGjWK6OhoBg0aZLS/+2vC8+fPWbNmDf/88w9yuZxChQpRunRpJk6caHBX96mRcO+69g+NmqTH97DMlReZTEb27NmZOnUqDx8+ZMCAAZQoUYJhw4alId0SlUrtHwKIKqXea8hd3xW/Zc2alerVq7Nx40batWuX4fn+8MMP/PXXX9SunZ5rAUCQybXhfo0aQSqjVq1azFu6jNoTxxF5fDeqNxGo42KJ9TtPrN95LHLlRZ2UkErZLLN3QBEbRSEnWyrkcGPl/RC6F9DdWfChSHESUrDgTjCl3eypnCen3mMc6rTDvlZrkhVKIpo21UuKdPPmTYoWLao3haCL5TUFAQEBBrsVnj17ppeoCTCZOTOzUalSJdasWcOvv/5qkn2+fPm4f/++SbZlypRh3bp1HzO9TMP/EzPjf7I9EsDa2pqEhE9LCysIAsuWLWPatGk6nRCNRsOaHX8ydclBXkeklYaNioqiefPmDBky5Jt0Ei5fvkz9+vWpVasWa9as+SacBKVSyb59+2jatCkjRoygYsWKHDhwgL179zJp0iQaNmz4WZ0E0HILIJVhls0T2zLpGRHz5s3Lnj17KFy4MHXq1ElD4+3esT92FX/C8efGONdNn1fXh19//ZW1a9eSlJRxzoKqVaty+vRpveMyOwe8fpuHY61meI6eR96ixQgICEDmnIXcc7eSe+4WBAtLSIkSSGUkPbqTerzieRBy9xxk6TKYjk0bERSbyKVXUdpBSyOL4AdEHo7aeGKWKx9dO3fCveMAw6eXSDl+/LjB3+vWrVv1ph1EUUStVutlVTTU8WCqYuTXUARsa2tLQkKCyXwKUqkUiURikr2joyNRUVEfOcPMwff2yP8IDOnDfyisrKxYuXIlnTt3Zu/evWkqjncevsKE+fuQCAL3/V+yfo5W1/7169e0adOGqVOnUqpUqU8+p8yEQqFg4sSJBAcHs2PHjq8i1GkMT548YdWqVfj5+VG3bl2WLVumtz3tc8Px50Y4VK9vUCVSEAQaN25MzZo1mTp1KmvXrmX69On4+PiQva/+mgF9kMvldO/enSVLljBo0KAMHVuoUCHu3Llj0MYqfzGs8hcDtN0CefPm5d7NG1isn4nixVOsi5VD7uSKhU8Bkp8HoolPqyKoinhF9JmDJAU85LeS3vT96z657CwxWr2Twd/3pUQBPwls2HsY5Yun6VQz1XExBE8agCIkkCztfsWxRmN27typl8xIFEWuXr2qd/zZs2cGa3YMpR4MKVF+jShZsiTXr1+ndOnSJtmnfK9MqZ3JkiULoaGhRkWxviPz8J+NKIC2Dzw6OvqTn9fT05MJEybQqFEjRo4cmUpME5fwTjY3LkG7e4uMjKRly5bMmzfvm3MSAgMDqVevHkWKFGH9+vVftZOg0WjYs2cPDRs2ZMqUKdSrV4/jx48zYMCAr8ZJSIEgkZq0E7SysmLixIlMnz6dgQMHsnfv3g++ZvPmzTl69CgxMTHGjd+DRCLB3t7eJNnf0PULeNi+OsVfP2b/+tWpAljxN/4hy1uipVerZr09sRTB3ALBwhLr4uVICtAKuVnJpIwp4c1YvyeoNTocAXMLkGe8Aj4gJoFVfneZ3rwOITOGEzisAwGD26JOeOe0xPqdJznoEaJSSdiOVSQlJREZGak37XDp0iXKlCmj97O8du2a3kJG0EYZ9dXCpKQ0DCEzNkIfipQ6BVOREVK8smXLcunSpQ+dWqZB/ASPbwX/aUche/bsJtGFfggqVqzIiRMnqFmzJs2aNWPDhg20alCWto3KU6V8PqYN0xbqjB07lokTJ+plZvtacf/+fbp06cLKlStNKjr6UlCpVGzcuJEaNWrw5MkTNmzYwKpVq6hQocJXEZb9FPDx8WH37t1s2rQpjZ5CRiCRSOjfvz/z58/P8LG1a9fm0KFDBm1ElYo3h7VCasXV0Zy7fAXZW2VM66JlkcjNtJ0Hbz8Tqa09qNXYlqlC3JW/351IJsc3bz5q5nBmzUMdv93kJFAmp3/+XxDMzHGq1wqHGo1Jllsw6VoAk0v7ELd9GfG3tAuUMjyU11uWIb4turTwzgtvpb4t8xZm//79BrtQDhw4YLA98dixY1SvXl3nWEREhEH+gwsXLhgs4IuNjf2i/An/RqlSpbh27ZrJ9gUKFODBgwcm2ebPn5/Hjx9/6NQyD6Lw8Y9vBP9pRyFnzpwEBQVl2vkFQaBKlSocPXqU0NBQfmnelDZ1C7B6RhfyeLtz48YN4uLiTFLg+5pw/fp1+vXrx5YtWwwWU31JJCcns3z5cmrWrElMTAwHDx5kyJAhaTgp/kuQy+WsX7+euXPnfjA9eZ06dfj7778znPOtX79+OslzURQJ27WGgBGdiTp7CKRSLPNo61Ysbe2wd3PHbtQCck1bQ47hMwBwrNEYu/LVsfApgDo6ElGlJOb8EaQ273VUqJQIEgmNcrpxJzKOR1HxGX6dDtUbkmvqKrK07YN7p0HMuhVMp7zZcbU0Q2Jli03pyqm2USf2EHv5LAAWXr54z1yPx9BpZPt1PCtWrNDbsQBw9erV1G6Wf0Oj0fDkyRN8fHx0ju/fv5+GDRvqPbexiEJISMhXlZqwsbEhPt70z8rLy4vg4GCTbTPzPv4dxvGfdhRy5cr1WbjC5XI5Q4cOZfHixUyYMIFRo0YRHx//xcRaPgavXr1i8ODBbN++/askRYqPj2fevHnUqVMHMzMzjh49Su/evf8vJLktLS3ZvHkzo0eP5t69exk+XhAEBgwYwLx580h+Hkjk8d0ow41rkjg5OZGUlJSmODjxyX3Cd6wiOegRL5dNR5OUiOdv8/AcPY/cczbToGkzjpw6jUVOX4S3u3SplQ3Zfx2P15gFSKy0u2HBwhJ5dq/UnTyAMiQIQRAYXcKb6TcCUejSgpC941uwLfMjlgVKYPdDDbx+X4p7l0G83ryE+60rMb9yAcwViVTKqmU4lDm54jFoMvKsHqnRDfG9ojrz7F7YlvyBQ0eP8WOF8ijOHyb+bvqdcmhoKG5ubnq7Ha5cuWIwX3/o0CHq1auncywhIQFzc3ODvB1fYw2DpaWlyQXkFhYWJCcbjwyBNjL8/Pnzj5lapuB7MeN/BLly5TJJgORTwdPTk61bt3Lw4EGqVKlCixYtjNI+f00QRZE+ffowb968r64eQa1Ws2rVKrZv30737t05fvz4Z1ePTEpKIjY2lri4OJ0PURQpWrQoBQsW1Fvp/rGwt7dn06ZNtGzZkjVr1phEWvM+atasybxZs7hx/zR2goaI3evwWbTLqOR1rVq1OHbsGI0bN0ZUq3j5x+TUMYmlFYJMhkRuhnVhbR1O3bp16datm84duTo2Ck2idvcpJiWSdF+3VLGrpRld8nnQ+exd2vi6U8PDBankbbhWo9Eu9KJI/O0r+C7bj+QtgVXE/o3c/fM0q+89J1mtYWJpH614pQiWufOR/PQJbi16EPPPGcyzeWJXIW16QBRFFi9ezKyi2Xi1Xpt39/p9KVZ53vEEHD161GBaYu/evTRu3FjnWExMDBqNRm/0yxTeAGNkTV8CBQsW5N69exmqxTKls0Mul5vcUfE58f/UHvmfdhRS9B4+N+rVq0eNGjWQyz+SZe4zY/ny5ZQrV84gbeyXwLlz55g0aRJNmzbl6NGjmbIIi6LI9evXuX//Pi9fvkx9xMTEpN7IzM3Nsbe3x8bGRudDo9GwdetW7t27h0qlwtfXl9KlS1O6dGl8fHwM0vVmBG5ubqxZs4ZOnTqxZcuWDEV+BEGgX4fWrJ02jl8Le6GKikCTlJCuA+DfaNSoESNGjKBx48YoI8JQhLz7XWXtNSodS6SDgwNKpVJnr7+oUpl8l6zg7kAxF1t2Bbyi67m71MrhQqNcbrxfzqhJSkAdH8fz2aO4euki6wLCkMTH0ClfdnztrRA12tdtmbcwlj4FCBzeEQD3niNxrJKe0Oj48eNUqFAB85ArpChUKF+/gPcchePHj7Nw4UK9875y5QqTJk3SOXb48GGDRErnz5/Xy1uRgvDwcJOFmD4XUpQkTXUUXF1diYiIMLnY+GNE/r7j4/CfdhQsLCxITEz8Itf+lpgKQSt3e/DgwS+qRPdvBAUFMWrUKLJkycL27dsNCuR8CERR5MqVK+zYsYPr169TokQJihcvTunSpXF3dydr1qzY2tpm6ObUooWW30Cj0eDv74+fnx9LlizB398fCwsL6tevT8OGDXUqMmYEXl5eLFq0iLZt27Jz584MMUnWbt2BuTNnEmNhQ+5GbYw6CaAlbgoLC0OhUKCKisAse04UIUFY5imMbYkKOo+pWbMmR/ftpXTQFRSvX+LeaRA2Rctg5u5Bli5DeLVmbjqxqH9D7pELq+eBtMuTjZY+7hwODqfXn/eomMWBrm1aIQ99itwtK/tG9mPZwWO4WZrRN58HuQv/gOpNOBa5CxJ37QKCmRlurXsTeWQHICBI4fXKaUSd2IvH4KnInbSLlSiKLFiwgI0bNyJ9dJPXW5Zi4eWbhvNCpVIRHR2tlz45Rd9Bn2O4b98+g07G1atX9SpRpsCQIuWXQpEiRVi0aJHJ9imkeKa8DkdHR968efNVRTr/jxic/9uOwneYBrVaTb9+/VixYsUn2/V+DBQKBb///jv37t1jypQp5MuX75Oe/+bNm2zYsIGbN29SqlQpWrZsyfTp0z/pbkUikZAnTx7y5MlDmzZtAIiOjmb//v107doVjUZDgwYNaNiw4Qc7QAUKFGDy5Mm0bt2anTt3mizPLZFKGT1/CXuPH2dqI9PZGqtVq8aRzevJc2wdiCIWPvnxGr+YpABt9bqlT4E09g0aNGBIhzYUcNGGjV+tX4DN7I0AOP3cCLlLFl7MHYZGoeOWKZODpRXK5+9qjOQSCQ1zuVE/pytnXkTSfeEqvK3lBMYmksfOijElc+NqaQYCJAc/waZsNbL3HYMmIV6bGrGwwkGRTKzfWRA1iCIkBz0gdM1ssvcZi8TCkrNnz1KsWDHtZ1K2CnZlq6Sb2qVLlyhbVj9N+759+/R2QyQmJhIfH693cUxOTkYikRiNRoaHh38VOg/vw9vbmydPnmTIPiAggDJlyhi1TSl+/OochY9JPXyymWQ+vvyq8BGIjo4mIiLCoI2lpeUHMdL9PyE0NBRPT8+vRtBpwoQJqS2Bn9JJ8Pf3p1mzZixbtow2bdpw/Phxpk6dSokSJT5LSNPe3p527dqxc+dO1qxZg1QqpXv37jRq1IiVK1cSGxub4XOWKVOGAQMG0KVLFzQaHUV/elC1alVu3LiRoUr1n376ifOnT6XeHZOfBfLm9AGCfutO0G/deXPyXTRKVKlwlUuISHxXsGaWLe33S/r8LFkK6YmEqFUQq5sDRSIIVM/uzNIKvtTO4cL0cnkYUioXWezMtbUIgKjREHvxJHHXLiC1sUNiYYUqKhKJpRXZfv0dQXhbyyiKxPn9SeDIzmiUCmbPns3AgQMNvg+7d++mQYMGesf//PNPKleurHPs+vXrBhfGx48fkz9/foPXBy2Jm6urq1G7zwmJRIJGozGZ38Hd3Z3Q0FCTbLNmzWqy7WfD/xGRwjftKFy8eJGtW7catHFwcPhqKUC/FpiZmaFWGw4Bfy74+fnx7Nkzg21pH4ILFy7Qq1cvZs+ezZIlSyhevPgXzXemOA07duxgw4YNWFlZ0aBBA44ePZrhc/38889Ur16dESNGZOi41q1bs2XLFpPt8+XLR0BMApK3qQoxOYk3R7aTsuom3L8BgCYpkYBhHXjya3OsEqKx6T0W9y5DyN5njPa4twqPsugHWDrJkVlqb0OCmTlmOXJpL2bCYiMIAkWcbXG0MkcQBO1DIqT+jQiaRG0VfnJIMP6/NidodDfirl/Ec8JybMpWT5GcQPHyGSf3aamzDYXCNRoNt27d0tu6qFarUavVemWRDWk/AAQHB+sUBPs3VCpVphXMfgyyZMnC69evTbK1trY2OTVsb2+fYbKw7/h0+KYdhWzZshklVErJbX2HfpiZmaFQKL70NFCr1YwaNYq5c+d+0vPu2bOHmTNnsnv37gx3CXwO2Nra0rp1aw4ePMjx48fp0qVLhr+zXbp0wdzcnCVLlph8TPPmzdmxY0eqrkDSswBU0fqva2FhgUKpQubgTMrWXRESDBIJEksrHGtoq/yTAh+heKEtdvTWJPAkSUTulhX/vs142LUOD9pWJWBEJxIFD6RyCTnKOyGzkuL6SzfU/6J3BkAux7JgcRxrN8csW1peD4mVFW6te717QhQRNdqH9l+tJ5Bw/waiQhvdiLvyF5a585Otz1isi2h391ZFSrNgzXoGDx5s8D37888/qVSpkl4n8969ewbJ1R49emTQUQgKCjLqKGg0mq+2qM8Uyu8UWFlZmRzRsrOzyxSW3Y+BKAof/fhW8E07CqYwL353FIzD3Nzc5J7mzMSaNWto0qTJJ829LliwgMOHD7N9+3a9UslfC6ytrZkzZw5du3alWbNmGaZs/v333/Hz80tHjqQPFhYWFClSBD8/P15vWUrg0Pb492tGUrB+FjwbGxtsWvdF5vqu00Jq70SelUewyqvtljHP6YvcTdu6V6RoUW4/fET47nWo42LQxMWAKJIc9JhEpQtPbwoEnQtHlaDm9eYlyGx1FFYqlSTevc6bIztwa9sH1xbdydq5D/Y5LXHILiA+v4Tn7yuw+6lpaoQAwCy7FzbFtYWWNiUqInPShuqd6miZRiOjE4huPBCf5Yd4Uq4+RYoWNVpYt2XLFlq1aqV3/MKFC1SooLu4E7SpBV9fX73jwcHBRp3Zly9ffnWtkSkoVKgQd+/eNcnWysrKZN6FrzGi8H+Uefi2ixmdnJyIjIw0aPM99WAccrn8i0cUYmJi2Lp16weF3nVBo9EwbNgwbG1tWb58+Ve7A9OF8uXLc+jQISZOnMjOnTuZO3euSfnoFHXTpk2bkjVrVpPa1Hr06MGUKVMYZa2tjxAVycTfvoKFl3YxU4aHErZ9JVJrO1xbdqdgwYIEKcHF/F3hpNPPjVC+fsHTaUPQJMSR/dcJeM/agPJVCLbI+H3iJH6pXITEh7fSXPvN0V1pJyORoow0XHMkKpJxadyepFunUZhLEUVQBN/HPCGBLO1+xfatY2CZryiCTJ76ucudXPBZuANNUiJSa1uehkRQu9Mc4hOSaVKzBI8ubTYqZ6xUKgkKCjK40F+8eJHp06frHU9MTDRYdBocHGyUDdWYPPWXRKFChdi5c6dJthlR+M0s3Z7vMA3fdETBlJv/94iCcUil0i9eozBlyhSGDx/+SfKuSUlJtG/fngIFCjBu3LhvyklIgYWFBZMnT2bQoEG0bt3aaC1OCszMzNiwYQPDhw83iZXUx8dH+/so/zMAUhs7bEv+kDoeumYu0X8eJ/LoDiKP7EgNLSvD3hWWmXvk4s2JvShfhaCOiSJ8zzokZuaY5/Amu0cOnj17hlvrXmQfNBnbH3RLNlt45yNbv7FoYqMMT1gQUEaGYZ7/B1SiNRqlhrjQRCL2bUQQBKwLl8a6cGkkcrN0n7sglSG11kaVLt8KIP6tiNvufYcpWrSo0UjWiRMnqFGjhkGbV69e6eW1UKvVRruKEhIS0vFO/BuGVCe/NLJmzWqyvk5GIgpfZ+rhOzPjfwYODg74+/t/6Wl8hwEEBATg7+/PtGnTPvpckZGRtGvXjn79+hlkzssINBoNN27c4Ny5c6hUKiwtLXU+HB0dyZ8//ycl2ipRogSHDx9m5MiRvHz50mhFPmi/86tXr6Zz587s3LnTaPtlhw4dOBgURN/lB5BYWKUyHAKp9MuIIEilFC5YmMWLF9Os6xDCtq/E0rcgNsXLo1EqUu9877dJCoKAg4MDATvXo9izWu/dMUunAVj6FCTuhxrE/HVc71xfLBiLIAi4tOxJ9KN39NNmgoBGkYwgNwNRRDCyIP9YNh9Z3Rx48eoN1opHDBpkXCxr27ZtTJkyRe94WFiYwdSFKfUHpiAgIMCgTsSXREohqSnkSN966uH/iUjhm3cUNBoNGo1Gr6ceFxf3zZEffQkoFIovxnw2b948xo0b99HnSU5OpnXr1kyZMuWjWes0Gg1///03O3bs4P79+5QsWZJq1aqlknilPN68ecOLFy9ITEwkIiKCu3fvolQqyZ07N6VKlaJUqVLkz5//o+im5XI5M2fOpFu3bhw5csQoax9o+85nzJhBv3792Lhxo0HbOnXqUKdOHfr375/u83fvMhiZowtSGzscazXDQSLl1q1bWM+ahX0lbXRAk5SIdYHieI1bjDohNrUuIAUNGjRg5dxJtPf6l8MiSEgpKgge0xPb8tUQzMyR2Nqjeb818i39MqAtVEQkbNfaNKeKv/EPIfPGIMaEoI58jUu7odiW17/7d3Wy5fz2kRw8dISrfpZGowmJiYkGJadBW+hoSADu/v37Blsf4+LisLKyMjgP0Lb5fq0RBdAW58bFxRmtCcpIytPS0vKLked9xzfuKCQkJCCXyw2G83bs2JEhtrBPAaVSyZ9//knevHkN3li+JhQoUIDbt29/EfrmR48eUahQIeOGRjBo0CD69ev3wU6CWq1OdQ4ePnxIhQoV6NGjR4YlwkVRJDAwED8/P9atW8f9+/cRRZEyZcrQo0cP3N3dMzw3QRBYuHAhDRo0oGDBgiapepYsWRJnZ+fUSn19MDMzo3r16jqphWUOzrh3HpTmuRSdiW7dupEcEkTQmB5oEuJxa9Mb5/qt052/RYsWrPx9DDVck3G30kYr5O4e2Ff8mfBda1LtYi+e/teLliBIRCRS7Q5VoxLfOQyJ6XeiyUH3kKi0XRNRh9YbdBQAZFIJK5YtYdOmTQbtwLi2A2ipmceMGaN3/Pr163plp8F4R0QKoqOjP5rZMzMRFxdnkgS2Wq02OdUYHx//Vclqw/+X1sM3XaPw4MEDatbUnfMErea7RqP5LMQkSUlJ7N+/n06dOtGgQQMuXrxIly5dWL9+vckEJF8SLVq0YNu2bZ/9uiEhIWTLlu2jIxkbN27EwcHBIIe+Ifj5+VGzZk3Onj1L7969OX78OOPHj8+wkwDaRd3b25sWLVowc+ZMDh48yIEDB6hUqRL9+vWjU6dOXLuWXpHQGCwtLVm6dCndu3c3eSc2btw4Jk6caJSMqWfPnvzxxx8mnbNz585s3bqV+Ph4Yq/8hSZB2+L25tR+nfYSiYSRDaox62ZQ6nPK1y+wLlQK3+UHkTq8ZduTpl00pHYOCG/vUNqQ9tu/JaQ+DyDP5oXExg6rwmURNVoji7z6JZpTsHz5cn7++WeTmDF37dpF06ZN9Y6LosjTp08NdiwYk45+8OCBUUfhW9A7SOWxMAKFQmFymi42NvbrcxQ+weNbwTftKJQoUYJevXrpHd+2bRstW7bMtOvHxsaybds2WrduTfPmzQkODub333/nyJEjjB49moMHD/L8+XNatGjBq1fG5Xy/JMqWLcvly5c/u1Nz6tQpfvrpp486x927d9m2bRsTJkzI8LFJSUmMHDmShQsXsm3bNsaOHWsSM15GIZFIqFatGjt27GD06NGsW7eOevXqsWfPngwVkubOnZsePXowbNgwk+ydnJyoX7++0Yp+R0dH8uXLx8WLF42eUyaT0b9/f+bOnYtNsbIIb2sa7Cv+nMbufflmz6iXZLM25+yLt11Kokj03yeQ2TmQa/o6rEtX1rIxvoXETIaoUSCKkrc3VRFRlCCYmyM1kyCRC0jeZhTFpAQsvPMRfe4YikQNTq0G4tImbRTk33j06BGHDh2if//+Rl9vYmIiUVFRBiNBN27coHjx4gbPY6xQ8eHDh0aZSL9G1cgPhVKpNNlRMCWV8R2Zh2/aUTCG06dPf7KCtn/j+PHjNG7cmNjYWObNm8eBAwfo169fGhpkmUzGqFGjGD16dKp4z9cKQRAoXrw4169f/6zXPXXqlMFwrDHExsby66+/snLlygx3TFy6dIm6detStmxZ1q9f/9m48318fJg/fz6bNm0iKCiIGjVqMGfOHJPJZxo3boxUKmXHjh0m2ffs2ZONGzcajSoMGDCAefPmmXTOWj+U5+LffxFjaY/Pol14z9kMUin321QhcHwfns0exYO2VXg+ZzSiRoNLk470KpSTdU/CSFCpQYSEBzd53KsRoSumE+93Ps35NQoVmtg4VI4iMUPtiB5lj82Crpi5vVusU3atqsgwEh+8lapWq1GGh6OK0t82rVQq6devH4sXLzZJ2+To0aNG60IOHz5MnTp19I5HRkYajVwYY20EbdThU2uffErExcUZ7dpIgVKpNLl+zNR0xueENvXwMYRLX/oVmI7/tKNgSqvRh0CtVjNjxgz27NlD165dcXNzM2hftGhRDh06xM2bN2nXrp1RfYovhc+dfhBFkdDQ0AzJJP/7+F69ejF+/PgMnSMpKYnhw4fzxx9/sGPHDr0CPpkNe3t7Bg4cyPHjx8mRIwdNmzY1uZV32rRprF69mkePHhm1lcvl/PTTTxw/rr+bAMDDwwMbGxsePHhg0C7y2C6e9G5MO/E1g3+uyJujO5G7exC+czWoVSQ9uEnc24U/9vI5kkOCcarZlOJbzzF2xTq2uhfHtsyPKJ4HonoTTtyVv3ReJ1BpyZ5Qd64/sAYVxEXfwdJOQKXUaIsa395pNQjsjnREjYBgbkn4rjX4925M7NW/dZ537Ljx1KjbiKwm7sx37txpMO0AxomWbty4QbFixQyew5TF0JSow5dERsigvvnUw/9Re+R/1lFIyX1nBjZt2kSjRo0yFAozMzNj4sSJ9OvXj+bNm3Pw4MFPMhe1WsP4uXuo22kuR8/d/qhzlShRgmvXrn229MP9+/cpUKCAcUM9WLx4McWKFTNYqPdvXLx4kbp16/LDDz+wdu3ar0KNTiqV0rx5c6ZMmcIvv/xiUppKLpezYsUKevfubVKLWdeuXVmxYoVRuyFDhjB79myDNpFHtZGxog5WvImK4sq6P4j95zTyLB7vjCzekQq9/GMSokaNIJFSp04dAu/c4trxQwavoZRIsZGoaSZ9gdXmOGJ+T8T++G0U4S9BAxoliEixbNKNrq+KsjwmFw1elNW2ab7Fm+O70p335OmzrN55jD9Oh9Bx/Dqj3/XExESio6MNph0iIiKws7MzuOilyJjrgykcC2BaHcOXREZSI9986uH/qEjhP+so3Lhx46Nb5HQhLCyMdevW0b179w86vkyZMhw6dIgNGzZw5syZj57P+csPWbvrb+4+DmHwJNMFfnRBEATKli3L33/r3ol9aly4cCFDi/z7CA4O5tSpU0a5+d/Hvn37WLhwITt37qR+/fofdN3MRIkSJViwYAGtWrXi6dOnRu09PDwYPnw43bt3N1rn4Orqir29PQ8fPjRolz9/fqKionh686rO8L3i9QuUoe8IdfoW8mTF/ee8WDAeTdJ7qZOkd61sSQEPUwmaRJWKX7PKmX/H8OsT1RpcpdpFv6R5NElqKZoABfCu80GhBGmlejh5+wDg5mKHzEUb3ZNZCZxTuFK+ySSWbNB2UyiVSkaMHIWVbxUA/rkdQFyiYerygwcPGkwpgDYNaYyI6fr16wYjCqZQN2fE7kvh2bNnJnd6JSYmYmFhYZLtmzdvsLe3/5ipfcdH4D/rKERERHzybofk5GQ6duzIvHnzPoqbwdLSktWrVzN16lQeP9bPq28KXJ3eedkuTh/vcffs2ZOFCxd+9HlMgUQi+eAK7hMnTtC2bdsMHb906VKWL19uUpX7l0L+/PlZtWoVHTt2NJoCAK1yZM2aNenXr5/R3fGIESOYNGmS0XM2zuvJog7N8O/blGezR/KoW13CdqwC0DoPKYIKgkCBytWISFICoNEl6ARY+hZEYmOHKi6G0DVzcBWVZLUy596bOLDUzRtgJogkCNrf2DOVBd6yBOKT4ewr29SNWJIaZq04yq4lfdg+twv7ehVClhRGlkqW+Hayo2bpi2iSwpix7DAHT91g06ZNdGjXhrze2jqimuULYGtleKHasGEDrVunb/l8H6dPnzZaZxMREWGwBub69etGiyFBy+/xMZwcmY07d+6Y3Cn07Nkzk6Xtr127ZjR187nxfxRQ+LZ5FAwhNjbWaO1ARiCKIr1796ZXr14ULlz4o89nbW3N2rVrU6WGPzQEXiivB2tndeXm/ac0rW2c298YPDw8sLCw+GQscoZga2tLbGzsBx179uzZDKlM+vn5kT9//g/Oc96/f59Ro0ahVqvTLMiCIGBubo6ZmRmOjo5Uq1aN6tWrf9TuJ1euXGzevJm2bdsybdo0o5oN7dq1IyAggI0bN9KuXTu9dnny5MHc3Jzbt28b/A6XSg5jUegbOubNRpzfnwCE71qDY80mWPoUwLF2c+KvX8SpXitsSlTEbOlGFGoNLqUro4x8jczWgUT/e8jdspGlXT9iLp3mcZfaSKxs0CRqow7t8mRl6d3nzCpeAjExHlGtQhWeNuWSq+sAXq+fTw5pMqhBHZZMDgREtBxMthI1DR5sJnTK39g+vkOsrTUSQcA+rzac7WarpnHeN6geJ3JqxjROhPqzedtOevXKQdibONyddQhQvYe7d++SNWtWo5wFz549M8hrYUoh47Vr12jevLlBm4zk9L8UMlJsGRgYaDJxVGBg4FcXSfnOo/AfQGxsLHZ2hm8EGcHMmTMpUKAA9erV+2TnzJYtG7NmzaJDhw4fJcpUpVw++neqgYf7p8m3d+/eneXLl3+ScxnChzoKoigSHh6eoYjR4sWL6dOnzwdda9GiRYwcOZJFixaxf/9+Dhw4kPrYu3cvGzZs4I8//mDAgAG8fv2arl27Uq9ePaZOncr169c/qObD3d2dnTt3MnbsWJNSVKNHj2bLli0EBAQYtPvtt9+MRhVcf25MPgdr7sWp4G3ro9TBGamVDYJEgnuH/uSetxXHnxoid3KhRPO2xDfqRsyFkyTev0ns5XN4DJxEzvGLscydj8jD2u4MTUJc6t3R08aSRLWakPt3cG3WGbnjO+pjmYs7WNrwavUcREtzkGiPEQB7lCQhIQHtrjqbGK0Vm9JoUEbHImTPy5Mn2v1PvELG/YdSVkZ5sTs4Eo3SisZDlrPtmB9ZXeyNRqMmT57M0KFDDdo8e/YMDw8PgzZXrlyhdOnSBm3u3r1rtF7na9Z4SEFG6g4CAgLIlSuXUbv4+HisrKy+ev6Iz4XFixeTM2dOLCwsUtva9WHFihVUqlQJR0dHHB0d+emnnwza68N/2lH4VMUv+/btw9/fn0GDDPdmfwiKFy9Ot27d6NOnz1dDzFShQgX8/PwyXXr6Qx0Ff39/8uTJY7L969eviY2NzbDi3osXL2jcuDEajYbdu3frzL1KJBIsLCyws7PDx8eHnj17smPHDnbv3k25cuXYunUrtWrVolOnTpw+fVrHVfTDwcGBnTt3smjRIqOS0zKZjCVLltC7d2+USqVeu5w5c+Li4sKVK1f02jjWaMyAdTs5naMY3tPW4N51KLkmr0CQ6V4AihQtxqXNK9NskQSZHE1yEopXIVjk1c322donK5v9XyJ3zYpT3ZYIMjkSK2tU4aGQGAdqFWJUDHEqWeqdSkDEUtRgyduajPd/MhIp7q364tpiM/cdxoDnOP6OcUQUNbwOe0SiVzkATl0yntLx8/PD0dERHx8fg3ZnzpyhatWqBm0uX75MmTJl9I6LomhSq6C/v7/R+XxJGNO6+DcCAwNNchSuXr1qkhLqZ8fHdjx8wO1+27ZtDBo0iHHjxnHt2jWKFi1KzZo1ef36tU77s2fP0qpVK86cOcPFixfJkSMHNWrUMFm4KwXfHQUjuHnzJitWrGDRokWZ5tE2aNCAfPnyMWPGjEw5f0YhCAJNmjRh9+7dmXodOzu7DxJ6OXPmDFWqVDHZfvny5RkuPt25cycdOnRgypQp/PrrryZVpL8PMzMzqlatyvTp0zl27BiTJk3iwIEDdOnSJUNqplZWVmzZsoWdO3ca5U3ImTMn7du3NxoxGD16tEFxI4AiZcsTGPwU0ckNh+oNiPX7kxdLJpEYmL4YsnDhwvjHv3NOzD19kNg68KR/C570b4FUzw6zlKsddyLjeDJvLLalK5F37XHMvdJKOAvAeTEL4yTFOSnNhiUaBEH7PIDUyhy5oxOOv3Qn8Jcx3FPY4OhgQ/7C5VBHRFPNKpyEiEdYu/siecv8WK2M8a6BSZMm8dtvvxm1S3EUkpKVeh39a9euGaw/eP78udGoBMDjx48NSlx/aRhjnvw3TG1fv3TpEuXKlfuYqWUOROHjH0BMTEyah6EN2pw5c+jWrRudOnWiQIECLF26FCsrK1avXq3TftOmTfTu3ZtixYqRL18+Vq5ciUaj4dSpUxl6qZnmKAQFBdGlSxdy5cqFpaUluXPnZty4cR8VYs8IYmJiPtpRCA0NZeDAgaxbty7ThaUGDRpEYGBgpi/OpqJt27YmceB/DD40onDu3DkqV65skq1SqeTs2bP8/PPPxo3Rfm86d+7MtWvXOHTo0Ee1b76P7NmzM3fuXLp27UqzZs0y9DmbmZmxdu1adu/ezf79ummSU9C6dWuePn3KX3/p5iYAbcorV65cBm0AGjZsyP79+0m4e41Xa+cS/ecxns8Yns4uX758hNhmQWqnzcMnP/Xn6e99UUVp+ULib19Jy7n8FoIgUN/LlZ1XbpNw7wZIZSTYO/LYSVv0J4qgFuGOYE9BdSRF1OHvdmRvz6FOSEb5JpLw7csZufgwzfv/wV9XtQXCEkHBqNJqPGMu0/BtsWGVQg60KvAYlOkli+MTklGp1Jw8eZKCBQuSNWtWg+8PaHkDNh+/TYGGY6nbewGx8UlpxkVRJDEx0aDY07Vr1yhZsqTRaz1+/PirjijcunUrQ46CqfDz8/s6IwqfCDly5MDe3j71MXXqVJ12CoWCq1evpmGylUgk/PTTTyYxqoLWOVMqlRmuics0R+HBgwdoNBqWLVvG3bt3mTt3LkuXLmXUqFGZdck0iI6O/ihHIT4+no4dO7J48eLPwtiXIvqzdu1azp07l+nXMwZbW1u8vLxM/gJ+COzs7DK0u05BZGSkySHO/fv307BhQ5MiArdv36ZRo0Z07tyZKVOmZIpzWL58eQ4fPsz169dp1aoVQUFBJh0nk8lYt24d69evN1qzMH/+fMaOHWuQ6XH48OFGI1gtW7Zky5Yt77ocRBGNKv3O2czMjGSVClH67j1WhoUitXUAwLpoGQRz89TMxPuH1/Z04cTzCJ7+fZrQozsZY2fNnsJvd99vwwbDxHs01wTjhkIbTRBAkMpItDHjTvFshOSwRyJCnjLgaK9i4rw9+B0/RdTWmZy4dZt6hVxYNb0Vx2bmYVX9c0hebIfHaWWl1+78i0I1f6N800lMnjLNJIrslND58h1acqkHgaH8fT2tpP3Tp0+NCngZizikwNTIw5fCzZs3TRaVy8hG7qvkUODTdT08e/aM6Ojo1MfIkSN1Xi88PBy1Wp2OXC5LliyEhoaaNOfhw4eTLVu2DNPmZ5qjUKtWLdasWUONGjXw9vamQYMGDBky5LPsmGNiYlCpVB98o1coFLRp04ZRo0ZlCu+/PsjlcjZt2sTy5cuZN2/eF69ZmDhxIhMmTMg0XgVHR0fCwsIyLB+bkfawEydO0KBBA6N2oigyZMgQtm3bxg8//JCh+WQU5ubmTJw4kd9++41hw4YxcOBAwsPDjR5nZmbG+vXrmTRpkkF2Tzs7O3r06KE3HAng5uaGl5cXN2/e1Gvj4OCAi4sLwVJrXFr0QLCwRBMbzaMutXhzfE8a2+rVq/NPzlKpkQObsj8iyN8WFd74B7sKP70TdXovgyeXSOhf2IsxS1fzbNdq+v99hjLPglhXpCTnE5zYHPO2LkR4d2MVRXiWJONk7bxcLe/JyQYFuF4gG9nV8US8EngU+Ir+C7Sh1X0PX/FLAVcSAqZxPfSftxEJkdt371Gl5TQOn7nJlMUHWLTuJKIo8vJFCCrMTVJn/PPPP6lcuTLli2lrX6wtzSicJ+1CfuzYMaM35eDgYJPqZ0RR/KpbIzPSkn79+nWTog+RkZGfjVo9o/hUzIx2dnZpHubm5pky32nTprF161b27NljMn9FCj5rjUJ0dLTBkEdycnK6fM2HYMOGDbRv3/6DjlWr1XTu3JkuXbqYHN7+lLC1tWXjxo0kJyfTq1cvg4VpmQ0nJyd27NjB9OnTjdL/fggEQeCXX34xWbPg/eOM6RakwJQdHcDevXupUqXKZ1EaTUHBggXZvn07TZs2pV27dmzYsMHoMVZWVkyfPt0ob0LTpk3Zu3cvqveEmf6NPn36sGTJEoPXGzhwIPPmzcPKJz/iWxIlTUI8oatn86hHfRL876OKjqR3r56s3rwtlfhJGfL0HWGTRkP8zUuYOb9z3JVm7249RZxtcVTEcflhEK7x8VR7/gSP8DdMi8zD9tjsHIpzI0YtI14jIUhhyfpET17aOmAT/+61xbuZ4xsalvq/qFSyOUqCT3ZLctcwJ1mUcE7lxgW1C1df2TJsmytBz8Pp//tmVmw9R/gbLQeEMvYZDeubpkB67tw5KlWqxIrx7dk0vSunVg4mu5tDGptjx44ZVLgF09onv3ZkdFNz6dIlgwWeKfjaKas/J1xcXJBKpemYW1+9emVUun7WrFlMmzaN48ePmxz1eR+fzVHw9/dn4cKF9OjRQ6/N1KlT0+RqTCXjeB+iKLJ3794P4u8XRZF+/fpRo0aNL8rcJwgCw4cP5+eff6ZZs2ZER6fPp34u2Nrasn37dlauXMmuXekpcT8WrVq10oa3M4AcOXLw/Plzo3YJCQlYWloaLUJVq9UsXryYX3/9NUPz+FT44YcfOHjwINevX2fChAlGb7qlSpWiQIECBh0LmUxGkyZNDDph+fLlIzQ01GD6p0CBAkRERBBl7YjMKS0viTomipeLxvJsWFMiZ/1KgxrVORCsXazNvXLj3PAdp4Mq4jWOHpa4FrBByGqBRPGeoyeBYTV92fT4JYnmIvY5rGhqEcqsHA9QIWFxXG6OKbNwOsGVpbG56J0njGou0dS/FYTseRK5H7zGK/wNrmWk9HUIoJb1K8pHHObq3/eYPGUQFkWaYu0zhmLuxThlXoGjb5pzP1S7a1OrNak7u5mjWlCxoA0d2/2i9/14HyEhIXh4eGAml1G+aG7c/sXLEB8fj1qtNqlN+1tv/Xvx4oXJjIygbRk1pe7g/v37X62j8Lm1HszMzChZsmSaQsSUwsTy5cvrPW7GjBlMnDiRo0ePfnCtR4YdhREjRqTqjet7/JtRLiQkhFq1atG8eXO6deum99wjR45Mk6t59uxZhl/Q+fPnqVChwgcRk4wdO5Y8efJ8cDTiU6Np06aMHj2aJk2amJzLzgxYWFiwadMmDh48yJo1az7pua2srMiXLx/Xrl0z+RgfHx/8/f2N2l29etWkIrGNGzfSpEmTjxIQU6vVJqUP9EEqlTJnzhycnJzo0qWL0aLfESNGsGXLFoPfi06dOrFmzRqDjkfHjh1Zu3atwWsNHz6cqXPmknveFrL1G4dV4bc3G1FEE6Xd3ShfBNK1fRuOYY9z12HYV/wJTXIiFr5alr44pLy6Gc3re3FoXiaRJoCuAblKYFTz+kz1C0ydbzHHBJo7h9LaMYTm5iHUt3nFjKz3kSCmdj1ItoZT+VwAUVmtCSjlyt+ObsjePMA/PIzfinkRfT0GPHpgZuFO18JNGFuuB791acLyqR1ZNqUjC8a3oXSRXAztXpsmNUuQmBBnUv2LKa19J0+eNLmI9ltHRlUtTa07+JrVMr+EKNSgQYNYsWIF69at4/79+/Tq1Yv4+Hg6deoEQPv27dPUOEyfPp0xY8awevVqcubMSWhoKKGhocTF6WZR1YcMMzMOHjyYjh07GrR5nxTkxYsXVK1alQoVKhgl8TE3N//o/Mzy5cuZPn16ho9LkdcdMGDAR13/U6NMmTKsXr2aLl26MHnyZMqWLftF5iGXy1m1ahX9+/cnLi6Ofv36fbJz9+zZk9mzZ5tM8uTr68vjx4+pVq2aQbtLly4Zfb+Sk5NZt24dR48eNXm+oigSFBSEn58ffn5+qY6xg4MDYWFheHl5Ua1aNapWrZphdtB+/fqxf/9+mjZtyvr16/WGpGUyGYsWLaJPnz7s379fZ+7aysqKChUqGFyw6tevT82aNenfv7/egs8yZcowb948ngQ/xbfiz9hV+ImEO1cREYk5tonkxzeROrhgk7sAHXv1Yfu1+9TyP4yZvUDySw0RrYfzYvMyChEFvGttTAO5LRUqVuTguXMce/yamj5uaOJUdLYMSjURJCCqRZLeKMFCxtUYG3J5iMTEy8l9KwpbhZrYsBB2Pw1lW2UfZBIBq4JpHUWlUsWiTad58TqKni2rkNvTjXrVigEZowk+ffq00e/fgQMHGDt2rEEbU8WgvnY8ePDA5HquFy9emNRRAl9/p8fnRosWLQgLC2Ps2LGEhoZSrFgxjh49mlrg+PTp0zTfpz/++AOFQkGzZs3SnGfcuHGMHz/e5Otm2FFwdXU1OY8bEhJC1apVKVmyJGvWrMn0H8TLly9Rq9UZrgzesGEDjx49YvHixZk0s4+Dl5cXu3btomPHjrRu3doo1WtmQSKRsGDBAvr06cO2bdto0aLFJzlv3rx5CQ0NJSoqyqQiMh8fH86fP2/Uzs/Pzyh/wrJly+jcubPRwldRFFmyZAknT55EoVCQM2dOSpcuTceOHcmXL1+ahTo4OJjTp08zePBgwsLCyJcvX6rjYMouqkGDBmTPnp1mzZqxatUqvVTauXPnpkmTJsyaNYvhw9O3LYK2DqFbt256HQWZTEa1atU4fvw4tWrV0jun8ePHM378eDZt2oQgCFi/jSpYFyhOUtBDNEoVglRG27Zt+blyOTqNcsXaWkbcMyW3zx7nIDkoQBT/dmc0AiTZyhEUchJ3bKRXXg96nr9HXrU5DmbaqKDMQoK5nYzESG3HhSpJjXm2vBSJuAcpulWvFSS/DOfq0+f8NbcLVmXaI0gkmOfwJv7edczcPUi2sOXn9rN4FaVtyb335AWHlg1IncuJEydMjgCcOXPGIIW4RqMxSu0MmMww+rU7FPfv36duXdNqO0xx4FOgVCozrbjvW0Xfvn3p27evzrGzZ8+m+f9TRaIz7ZsXEhJClSpV8PT0ZNasWYSFhaWGPTILK1euNJja0IUDBw5w7NgxFi5c+FXnCe3t7dmxYwdnzpxhypQpJhfzfWoIgsCCBQvYtm3bJ+2G6NixI+vWrTPJ1tvb2yhVMRin8Y6Li+PAgQO0atXK6LlmzJhBZGQkW7du5dChQyxevJiOHTtSsGDBdLt5Ly8vOnXqxIYNGzhy5Ag9e/bk2bNnNG3alOnTp5vEeFmyZElWr15N165duXr1ql67FM6H69ev6xx3dXXFw8ND7ziYRtmdJ08eHBwc0tO/CgKhK2fzdHwfAoZ3RBH0kLaFHVh6KBgAK3cZhcLvU5HXbHUojcrNEyRSzG1lSO1knG/jy4mu+blUzgwRMJdKGFjEi5m3gpBaCAhSsPeywsJJjo27OaIaApIsWHtPib9Cy00gAo+i4pnxTxCb+pXljLIg6/55icI1ByHzx/Bs0q8EDG7N1XOXePlaW+8jvk/G8BYXLlygYsWKBt+HlGONVfibyo0QGhqart1NF4xxMXxpmFo0DKY7CsnJyZnOX/MxEEXhox/fCjLNUThx4gT+/v6cOnUKDw8PsmbNmvrIDCQnJ3Pu3Dmj4cD3sWvXLjZs2MDq1au/6rajFMhkMhYvXoyjoyONGjUiODj4i81j3bp1TJo0iUuXLn2SczZo0IADBw6YVLhpbm5OUlKSUTtjn+nly5epVauWUbuYmBj++usvfvvttwzvbgRBIF++fPTp04djx46RM2dO6tevb5IypJeXFzt37mTYsGF6HWxBEFi0aBEjRozQ6zwOGjQoNbWmC66urjg4OBAYGGhwPv37909XQKmKDCcp8BEAytDnvFwyhQoJSRy58BpRhJhXWUCRRGVe0SbKD3t7MyztpQhSgQhzGdFulsgUaoqcfpGakijibEt2S3MORUdhbicjOVpJdFAiiZFKZJYSZr3xZd0rJwa/LkikRsYpa3dG33qOZ6+O+Bd0w9J6Bxt37WbE9B3E3brC6qgc9Arywf/uI+yszNEoNbg52rLgt3eqkEqlNlphyucbGBhotJ3x6tWrJrEJ3rx506SQfURExFfbGZHC12FKxEOj0eDn50ehQoWM2m7dujXD/f6fE1+iRuFLIdMchY4dOyKKos5HZmDTpk20bt3a5KjArVu32LRpE5s3b/6qvdZ/QxAEevXqxZw5c+jVqxcrV678InwLtra2bNu2jfHjx/PPP/989PlkMhm//fab0Zzu+9f/0PbZFDx//tykXdDBgwdp3LjxR0ecBEGgRYsWrF27luHDh7Ns2TKjn52DgwMLFiygW7duqa2H/4arqys///yz3q4Ub29vkpKSePHihd7rdOnShZUrVxqci6+vb7oiUpmzG9ZFtG1uFj4FkDo6g0rALUHOX3/lISY0bY2GJvxp6t9O8UpsIpOwilZgkZC2jbNngRycexjGXw8jiAt9F4ERBIGClvGAgAIpR+OyMPpIIGEOlbBIUlDI4wlVikczrOVzQl69IaR0I7bHZCNAacWkvQ/J6ubA77824s8tI8nt+W5uGaEfNkXkKaUjwhhOnjxp0mJo6vm+BPbt20fDhg1Nsk3hlTDmnCsUCtatW0fXrl0/xRS/4yPx9Sa9MgCNRsPmzZtp06aNSfaJiYkMGjSIpUuXIpN9m0rbPj4+HDhwgKioKJo1a2ZwEcgs2NnZfVJnoUqVKoSFhZmUVytWrBg3btz4qOuFhISY1NL1oe22+pAtWzb27NlDXFwcLVq0MNotUbBgQerXr8+iRYv02vTu3Ztly5bpdSaMcSZUqFCBy5cvG+RdEAQBe3t7oqKiiLl4ilcbFqJ4+QyP4TPJvXgPOX9fSva+Y3Gu14qfixfiwO7dxF/9M805RM07x0iiEflx/WMqnHyGWXYr5FbvFg+JTOC3/LlYcu8ZO5+Ess//FYcDwzgQGMGrV4Fkj7uDfexDdgaGIWpUWDp5E/VKg+StDkSyYEZS6UTO53R/S/Cklad+FBjKjJVHaD9hLf7P3wnpXLx40WQ9AVMohU35bomiyOvXr01KPXzNrIy7du2iadOmJtmuWrXKpMV/1apVtGvX7qvexH2PKHxjOHLkCNWrVzc5LDxs2DCGDBmS4Yr0rw1SqZQhQ4YwceJEOnXqxMaNGz97dMHOzo7t27czYcKET+IsdO/e3aRaheLFixvMu6fA0Pthys03JiYGhUKRYW50Y5BIJAwePJiRI0fSokULTpw4YdC+a9eunDhxQq8TZWVlRYMGDdi6davO8UqVKnH58mW9LJiCIFCnTh0OHTpkcB4lc+Vgd4+WhMwfR+ShbQSP783jLjV5OrY7itDnyB1dcGvTm7LuzviFvU0jCWDrLcfaQ0ZSjBKpuYBaqUGVpMFMAt6elrh5muNW2A5BCtsVOfBXWmMtlzKjXB48y5pj7SFBcNMQnAz5eEkVySOyq57yKkmDi3dlHO2VtGoUyrEbuTlysxAbowtimUXOjvVXEQUBqUx7qxNFiFcouPbgKTM2vCMRy4jwkCkte2FhYUaLFO/evWtSCB6+3ohCWFgYMpnMpCLkwMBArK2tjb4viYmJ7Ny5k3bt2hm0+ypgCk+zvsc3hP+Eo7B06VJ69uxpku3BgweRyWQGK7y/NRQoUIBDhw4RFBREq1at9EqOZhZSIgufwlmoXLkyf/75p9FizeLFixvlXrC2tiYhIUHvuCltWgcPHsxU8q3ixYtz4MABtm/fzqpVq/TaSSQS5s6dy8CBA/U6P927d2f16tU6owKCINC2bVs2btyo9xrt27dn/fr1Bufr+/ASF2/fSf1fEx+LqEhGFRVB9LnDAMRe/RtpfBTmUgnRChUyawGXEhZ41rXFzscMQSagUWk/X0EqIJFq+VckUoHfkwtxXeOE0lmGtIMl7u1sadbQjfJ5slG0sTdta7nRysedtnmyMb2QLT07dWL70rKsnx+Eu4snA2Y7MHi2OTfXxhCwK5KEV0oEiYBaFOnX8Sd+rloIiZ0ZoiiSxeldoev7VMHJChX7Tlzn8s30BbMajQZTqJRFUTSasz9x4oTJOfjnz59niNDoc2H79u388otpBFV//PEHvXv3Nmq3ZMkSunfv/s1Ge/+L+OYdhStXrpA7d26TCn1CQ0OZN2+eXnWubxkpOf4RI0bQunVrNm/e/FmjC+87Cx9T4CiRSPjxxx/Ttfn8G25ubkZD9i4uLgZtVCqVUWKuT5120AUrKyuWLVvGkSNHDHY4+Pr6Uq5cOb2LvYWFBc2bN9c73qJFC7Zt26b3e+Hs7IyVlZVBorP82bPwKErrfJnn9MW2/NviYVHEMk9hAMK2LkMTH0cld0f+fPkGVZzIqwuJiKKIYx5LQm0cUKu09R7qZA1RwQm8SJQz46Yr1rGJjDG7TcFaSUh8JEiKynn1UuD5L/ZEF7UgrJpN6lxkAvQrlUzpIm0plHsvNuZpuT1iniTzU4UCSABvDxda1ivL0vHtmdy7ESM61GJUR+1m4dWrV2miiyOm76D/hE380mcJZy7eT3POR48ekSdPHr3vT0Zw/vx5k2niTU2TfW4cOnTIpLbIxMREbt68aZS2OSoqiqNHj36xFvCMQNSIH/34VvDNOwrz5s0ziSRJo9HQq1cv5s2bl2FBjG8JxYoV4/Dhw4SEhFC/fn1u3br12a6d4iyMHDmSyMhI4wfoQYcOHUxKPxjrfnB2djYonmQMsbGxKBSKzyJKI5FIWLFiBUOHDjX43g0ePJgNGzbojRp17tyZDRs26NQIMTc3p3Llypw8eVLv+Tt37pxOTCr5xVOUEdrr5Rw5Gyt3Dxy7Dcd72hqy9RlLjtHzyDllNbYlta2F5jl9AfgxmyPnXmhfi9Qckt9oWHvJkRGXs2Ll9G63+CYkiX7JxXkVomSQ4xMsBQ2av97OXwVm4XK876qQJYuonGUk5tbmrUUHOY6hR1m2/hgAJQvnIk+utJz3TWuURIJAQHAYbQYsRSIRaPBDYWwlcm7c1RZWXrx4MQ0F7r3HIal/P3jyMs35TClkTExMNHqPUavVKJVKk1se4+PjsbGxMW74GREQEEC2bNlMup9u3bqVli1bGi0InjJlCiNGjPiqOSNSoRE//vGN4Bv4NAwjIiJCLyHN+5g+fTo///yzyTnBbxlmZmYMHTqU5cuXM2PGDEaPHm1S7/6ngJ2dHTNnzqRFixY8fPjwg87h6elJfHw8ISEhBu1+/vlng/3/uXLlMjgHYzTJt27dMroofEo4Ojoyc+ZMunfvrnfXL5PJmD59Ov3799dpY2ZmRvv27fnjjz90Ht+rVy8WLFig9/xVqlThwoULqe995OHtBAxqjX+/5kQe3UnY9pVULFmMS3FvUweCgHXBkli8dQ4AsnYfTtbuw/Aq4EmcSk2CSk1soIrAHTFEP00iVinBxtMKlwK2WOS0ZmyRGkiVarxkCYjiW3XJEA3qPxKxWK3CWikja5Aa+78F9m51wtJRgyYLuHla8jJJzttecEwAAG9XSURBVMKb16k4YRG3nr6kXeN3C/6grjWxs7VEqdIWeD578QaVWsOA37cwbNp2Wv26lNMX7zN60h9MW3OZxRu0HPrDetTB1cmWIvlz0KxO2s//8uXLRr8TQUFBeHl5GbR5+vSpUQro9/E1crysXr3aZLr7vXv30rJlS4M24eHhPHr0iOrVq3+K6X3HJ8Q37yiYoulw+PBhAgIC6NWr12eY0deDbNmysXHjRkqWLEmdOnW4cuXKZ7luChPn4MGDjea89WHkyJFMmjTJoE3Pnj3x8/Pj9OnTOsfr1q3L/v379R6fI0cOgx0WLi4uBgWT3odarWbBggVs2bKFa9euZZhLPQUlS5akWrVqzJkzR69N8eLFKVq0qF7djXbt2nHkyBGdUQdXV1eKFCmiN6qQQqjVvXt3VCoVMZfOagdEDa83/0HspTNUC3/IqoXztE+LIhH7NxH8+6/EXD5HzIVTPJ00gMQ7F0h49oqaHi4cffou/eMpScBZncirh/EoYpQ8Ulgx8dYJZj4+QWOb0HcS1IJA9Espkx558tu1rEQkS4kNkCPc0vDHpVw0uVaaakd9OXhWjUaE6IREJmw4wLHzd1IO51FAKDsO+5HPJyt29lZ0aVkJuUzK48B3nBSn/r5HwJNHaKQOzF5xFJVKTfWKBfDbP479K/rj6pSWSdPf398oh8LNmzeNUkFnNELwtTkKcXFx+Pn5UalSJaO2oiiiUqmwtLQ0aLdmzZoME+Z9UWj4yIjCl34BpuObdxSM4dGjRyxYsOCrZ17MTDRp0oRt27Yxf/78zxZd8PDwYN++fQQGBtKpU6cML5wlS5YkJiaGx48f67URBIFly5YxdepUnWRBrq6uxMfH6y1obNasmUF1RXd3d5OZRFPSASqVit27d9OlSxfq1atH/fr16d27N/Pnz+fevXsmnatXr15cu3aNixcv6rUZOnQou3fv1imOJZFImDhxIqNHj9Z77MyZM/UWjObNm5e2bdsybtw4HKs3AEFAMDNHYq4NMTuay7G3tSUgIICkgIe83vwHCfeuETJ/HCELxpH48BbRl/4GEWrmcObos3CQalMNJW1jmOfxAEmCkpjnSXi8CEOKiARIkJnhUF3bj//A3JH1UR4cDXfkSIg9M2+4Io1UoPJ25GyMM8mihFiNjIfRFnRX3MP85kuuHX3ErfvBSCQCUqmEKw+fsv/EdR4EhRKbmMSK3X+yfs8FRvetT7YsDpQvkZv6VfIjN9MqjObLnRWZTH+RYnx8PFZWVkbvIzdu3DDKyZCUlGR04fyasWbNGjp37mzSPfXJkydGnSuNRsOxY8eMFpknJiYaLFD+nBA1H//4VvCfdhRiYmLo1asXq1evzvS6hIiICM6cOcOCBQvo2rUrdevWpWXLluzYseOr+GK7uLiwYcOG1OhCOireTIBUKmXcuHF07NiRhg0bmtTO+D7Gjh3L77//btDGysqKVatW0a1bN53OSO3atfUKPlWvXl1vNAK0aRRTmCITEhLYsGED48aNo127dkyaNIlt27Zx8OBB9u3bx/DhwylQoABjx47ljz/+MFpkKggCS5YsYfTo0XrrFaRSKUuWLKFv37466xFKlSqFRCLRWVjq4OBgkKAJtPLfERER/JMokGfFIfIsO4DnqLnY/1gb965D6DtiNMuWLUNiYQkIIAipjgRoxZs0ahErczk+hYuhGDqXq3a5QRS0r//tneeZ0hINsLtUMWbX/4ldoVrGylPO3ly1dEMENAg8kjngkhRJ48hHYCtHy4sgUNgqlprqF0TGSwEBL8c4zIo6IslhSaQ6CY0ogkpEVGgQlSKLNp7ipx8KcmHXb2xZ0ItXIY8Z2r8jCye0ZetCwxHHy5cvGy3GA9OkkRMTEzPkKHwJUjV9UKlU7N2712TuBFMkpU+cOEG1atWMdpPMmTPHYI3NZ8X3GoX/BkaOHMnvv/9OtmzZPul54+PjOXDgAMOGDaNRo0bUq1ePgQMHcvXqVfLly8ekSZM4ePAgCxcuJCIighYtWtCuXTv27dv32WoF9KFJkyZs376dJUuWMGTIkM/ixPz4449s27aNyZMnG8yP/xt58+bFysrKqIPh6enJuHHj6Nq1a7pdcuPGjdm9e7fO4+RyOdmzZ9ebfjA1AjVv3jz69OmjMw0mkUjw8vLi559/Zvv27cTHx9O2bVujDoi9vT0zZ86kR48eet8vT09POnTooDdFM3nyZMaOHaszctC3b1+WLl1qkGBp7ty5zJw5k9DoWCQWlljkykO2nqNw/KkRZcqU4dip87SYtZ+7ldrhVKs5XmMX4tqmtzYCIdGqPdqUrkyfKTOZM3ceO0LfyniLIKBlRjqX6MxYSVEu+3oTb2HBmYL5UJlL6PPsCgvkV2hlH0I11yhGCreYFunL9Kc5KeMuodyPnkzzfkKNLDHIbGWYSTUgQFXrCDQxShSB8ShC/8UZoRF5/Soav1uBvImOJzY+idOnT9O8SQPqVy+GnY0loiiydNMZOg1dyV9+j9IcfuHCBSpUqGDgU9PClG4aUwoev1bs3r2bhg0bmty+aIqjsGrVKrp06WLQ5vXr11y4cCFT25W/Qzf+s46CQqHgyZMnJom8mILHjx8zf/58GjZsSNu2bQkKCqJ9+/bs2LGDgwcPsn79eoYMGUKNGjVwd3dHEARcXV3p2bMnBw4cYPr06QQHB9OkSRM6derE0aNHv9guwdnZmbVr11KtWjXq1atntBXxU8DFxYUdO3aQnJzMsGHDTD5uzJgxTJw40ahdpUqVqFq1KjNnzkzzvLu7O2/evNHbHWEs/WAMr1+/5s8//6Rx48ZGbSUSCUOGDKFv3740btzYYCskaNMvP/74o0GVwlatWhEcHMyFCxfSjbm4uNCgQYN0XQwAlpaWqXTS+mBpacnixYtT6xXeR2x8Ei9UTlw4e4Lhx4KJqdoSi5y+uJQQyDfQDs/mlkjkAgm3L5E7NhT/kwdpY/aEMLVZGka6xvaveJlojkahdWY8QsORJWv/thHUdHB4zjDrh1xUOnMp2YmXagvuBmtY1qgA8eXKc9wnKzZVBObVfo25RGTxQ3e8YyPQyWrz9t/mvRdTqv4ECtf8jW17TuCd+52Msd+tQKb9cYgzFx/QY9S6NE7WlStXjAo9hYaG4u7ubtAGMh5R+FogiiKrV6+mc+fOJh/z8OFD8ubNq3f82bNnmJmZGWWonDhxImPGjPl6UsjfIwrfPo4dO0bNmjU/+HhRFDl58iT9+/fn559/Zt68efj4+LBlyxb27NlDv379KFSokEnFlKAtLPz11185dOgQEyZM4OrVq9StW5ebN29+8Bw/FnXq1GHv3r3s2LGDXr16mRRm/xgIgsDQoUOxtLQ0SCf8Pjw8PPD29ubcuXNGbbt3787p06fTRUlq1qypl/mwevXqnDp1Su855XK5ztB+Cj7k5lW+fHl27NjBtGnTWLRokUGHsU+fPvj5+Rkkspo/fz6//fabTtbFHj16sG3bNp0pjE6dOrFlyxa9bI2gjeo0bNgwnQ6EXC7FzbsYcS/uIpVKsLEy1yZdQzYioMEquwwrDyly5yyEbVnKj1nsCQ19ilnO7ChcXFG9XcfvZctGm9yRlIh/jqMskUgHS3D813upEckrpuh6CGQT3zB/xhombn7FtL1ZWXPJlRUPcqDQCKgRkGDDT8Wy4+7qkO71yKTaW55ao0GtiCcuSeC+/zv6c0tzbeulIAiYm8tSP1eNRoNCoTC6uN+4ccNoISN8u47CX3/9RYkSJUwuxDSFoGrFihVGixgfPXpERESEyeyZnwPfeRS+Ebx580bvTXbdunW0aNHig86rVqsZPHgwx44do2fPnhw/fpzFixdTt27dTyL16unpyejRo1m5ciVz5syhU6dORneXmQU7OzsWL15Mq1ataNKkCYsWLcr0dMSECRO4fPmyUbrgFIwcOZKJEycaTdsIgkDr1q3TRQhSijl1QS6X4+npqVfNMWvWrDx//lznWHx8PE+ePDEpHP1vODs7s337dpKTk2nSpAl3797VaScIAkuXLmXUqFGEhYXptLG3t6dfv37MmDEj3ZhMJmPcuHGMGzcu3ZhcLqdHjx5GnbbOnTuzffv2NL81S3Mzts3sQ7HCBZnQ4Qeyuti/zTVod44aZFzxKM+RGi2Re/pQ38uVQ8FhFOk2CuXwyQyvVI+pZapxPpc3HWuFM8H3MfPs/JA6yrhdwROzhtoFW6MGjQaKmMcwxNmfNq4vmeAVQIRShoC2uyEs1oxe7Wqkzq1FmzqsXDSIf/aMYcbIXyiUJ3vqe+nqbPf2b0h8E0BWr4J4vycOVTifB/PHtaFNw3JsmNs91VEICAjAx+dd5EEf7ty5Y1ILdkJCQobuJV9LjcLixYvp27evyfZ+fn4ULFhQ77hCoeDChQtGiafGjx9vtF7pOzIP37SjYGVlpVM+98iRI/j4+HxQbUJCQgJt2rShWLFizJw5k/z582daqCtbtmysW7eO0aNHs3HjRurUqcPWrVsN7mAzC5UrV+bYsWM4OzvTsGFDxo0bx6tXrzLlWindCkuWLDFJ2MnZ2ZmBAwfy66+/GrVt2LBhOgfEw8MDtVqtl3Fw6NChevP8lStX5syZMzrHPtZpFASBwYMHM3fuXKZMmcLixYt12tnb2zN37ly6du2qV/SpUaNG3Lp1S2eXyA8//EBUVJTOrotmzZpx7Ngxg0qcMpkMX1/fdA5TwdzZGD2wB+HP3svl550CvmOZENGUGbLsrH/8hPYPnVimKsQT6yzcCgvk2clx1DW7Tx+bJ0zhGtaW2kXQQaLEOTmRvIHRXLdxJFkUENBGaF+ozDka68pDqR2hSjMKWsfj5mRNhSKuDOr3KzWqV2X/moGsWdSV9k206UZRFPnz8iPuPArBxdGGovk88M7h8nYMcrrJ2fTHSGyt09YKNPy5OJOGNKWg7zsmxFevXhml+wbt5sXFxcWonSlaEO/jawi3v3z5EkEQMnRfnTp1Kv369dM7vnv3bpo0aWLw9V25cgUHBweTHLXPiu+ph28D5ubm6dpuEhMTmTlzJmPGjMnw+V69ekWTJk3o3r27yUQinwI+Pj7MnTuX7du3ExkZSe3atZk8efIH9+J/KGQyGa1ateL48eNUrlyZPn360KVLl0xJj5ibm7N+/XoGDRpklFgJtJwI2bNnN0iwBNqK/qSkpHQ1CQMGDGD+/Pk6j/H19cXKykqn0/LTTz8Z5BwwlpowBTlz5mTjxo1cu3aNvXv36rQpWrQoTZo0Yfz48XrnMnv2bIYMGaJz9zlp0iSdvwmJRMKgQYOYNWuWwTmWKlUKPz+/dM9XrFiRv/76690TUgtwLE+s4IQgQHKYgpCwRM4r3YiW5+SPLVPxz+PIjzERFFBF4ZZdTvRLgdgkCeuvONHgzG1eyWX4bzLDXNC+DqkAv4fn5XaCLX6hVvSOLcGMl7np3LoacitXxiy+zOQVB+kwfw2Dd+5hypFj3Pd/wYT5+zhw6gYA4W/iyJHdmb+vvmsnDX35nCKF8ht83Sl48+aNSTTxsbGxJoXlQ0NDTVKN/Jqwdu1aOnXqZLL9yZMnyZ8/v0Exqw0bNhi9106ePFlvq++XhCh+ZHvkt+MnfNuOgi5Mnz6dX3/9FWtr6wwd9+DBA1q1asWcOXOoVq1aJs3OMGxsbOjduzcnTpygSJEiNGrUiNWrV+vdRWYWBEGgevXq7Ny5k2HDhrF8+XIaNGjAwYMHjYo1ZQTOzs4sW7bMZJ6F3377jcOHD/Po0SODdlWrVk1XoFm2bFnu37+vtw7jt99+0xlVcHZ2JioqSu9nULBgQb1pg4xAEAT++OMP1qxZo1cro0OHDoSHh+tN2eTMmZMKFSqwefPmdGNeXl7kzZuXY8eOpRurWbMmN27cMChVXrp0aZ2EXQ4ODsTExKDRaFBFRaIIDSHi0DZ6ntjO0IQoulQph421VtXVvVR+blx/Tq7Dr5HfiCb0cixPHmlICIbC4/IwYYcrfW96c+xvKxqI76I/b9Qynqu0+XxBJuAgaMBOzsytJzl37RHnLj1gxbqzRF6PIvJsBCumnqRhtwWs3fnOgXF3tcdMLksldJJJJeTI5mBy50FGHAVbW1ujdq9evfqmHAWNRsPp06dNFrHSaDTMmDGD4cOH67W5du0avr6+Bh2r8+fP4+Pj81XqXPw/4T/lKDx+/Jg7d+7QsGHDDB137tw5+vfvz+bNmylQoEAmzc50CIJA/fr1OXLkCElJSdSqVctgwV1mIm/evCxevJi1a9dy9+7dVNpkQ211GYGvry9jxoyhU6dORh0iiUTC/PnzGTRokEGHpV69ehw4cCDd87169dLbQeDp6Ym7u7vOqEKpUqX0slqWKFHik9WXmJmZsX79ekaPHs2TJ0902sydO5f58+cTEJBe2RBg0KBBrF69Wiej5MiRI5k5c2a6z04QBMaPH683WgGGHaIKFSpwbMNqHvdpwpOBLXi9YSHqkCDy/n2U7vlyc3ZFf/q3TKRZUzmCUoUqOCE18no3WM7x5LLIZSJqBGJVMu4kaBdaUYRguS3TS1bA3kOCYC4BcwlhESqt+qQIyIT0OzMRFMp3rzFfbncOrOxPkXw5qFDCl8Y1S3Bh12hUKg2jZ+3i7mPjEa1P7SiYave14MyZM/z4448mazBs3bqV2rVrG5SfXrx4MX369NE7Looi06dPN+hsfFF8Tz18exBFkWHDhjFjxowM5fM2bdrE4sWL2b17t0ltTZ8Tcrmc3r17s2PHDo4dO0bz5s31Ft1lNpycnBg+fDhHjx5FEARq1ar1ydoqK1WqRKNGjRg8eLBRWy8vL2rWrMmKFSv02vj6+uLv758uBF+3bl29eXzQFu1t2rQp3fM1a9bUuRMHbQujMbnrjMDe3p61a9fSrVs3ncqXFhYWLF++nB49eujsVpDL5fz++++MGjUq3ZitrS0tW7ZM18EAWodHoVBw586ddGMp51WpVDrTGm3btmXDmtVY2AhYu5hjZivTajbIzZFa2fAyMobXT1QUDHpJ1Uqu7FXGE4IVAdiQ1ceMOnXO0a2RtlDT2k5DtFSuZccVBK5Wzo1NYRtytXLDpaAFYrQKMV6FJkqBVKOmQzFH+ravhmc2pzRzKpo/R+rfrRqUY/rSQ4ybu4e/rz6m1o9FuHH7AQ+Cotm87x/aDVxutFjQVEchOTkZc3Nzo3bwddQdmIrVq1ebnHZITk5m5cqVBiWlw8LCePPmDb6+vnptjh49SpkyZUyq+fgiUIsf//hG8J9xFHbt2kXJkiXx9vY2yV4URSZPnszly5fZsmVLhlMVnxMODg7MmDGDGTNmMHHiRPr162dUYjmzIJfL6datGzt37mTv3r20adOGp0+ffvR527Rpg6OjI4sWLTJq27t3b/bu3au3GwGgSJEi6ZQzBUFg1qxZevP4xYsX58aNG+miFWXLltWbDsiZM6dBvYgUBAYG8scffxAbG2vU1sPDg7lz59KuXTudzkDOnDkZPHgw/fv313l8xYoV0Wg0OimgO3XqxK5du3SmYCZMmMDYsWP1zsvb21tnJCNXrlxEKNQo3tYUyC2k7E7Mhvvo+UisbekybQs7Hjoy8lwO/rJuxFn/WKwaWJHXOhG3h6FEr9NQpXokffsn0HNSDM3GJjE+R1l6Fa/HkxxuxCgsUWqkZPnRAYnZu8W1mfQpbd4cJS4sjOehbxAEkEi04zfvPyO/bzZ2LulDXEIyO4+8i/o8DgzlyZMnyC0dEUURhUK3A/Q+THUUMgtf0qkICwtDoVCYHP6fP38+Xbp0MegwGWuJ1Gg0zJ07l4EDB2Z4vt/x6fGfcBSSk5NZsmQJQ4cONcleFEUGDx6MmZkZ8+fPN0ob+rUgV65cbNq0iVatWtG5c2cGDBjwxSIMDg4OzJs3j1GjRtGvXz9+//13g/34pmDs2LEGRZ5SIJVKmT17NgMGDNB7g69fv75OimJvb2/Kli2rc0wQBCpWrJhugZXJZNja2hIVFaXzGJlMZvC1q1QqOnfujKWlJS1btmTUqFFGazKKFi3KwIED9SpJ1qpVi+zZs+sV3Zo6dapOVkapVMqIESOYNm1aumO8vLzw9fXVGykqXbq0XurvJm3bczxUW0AaqLDmVo7K/L7jNs9D36BUqVPTA/HxchLjVDgLsQhvM03SBJEEtRRzL21RqLk1uJWHHNnjiHojA1FEFEGjFHHLqsTZXQUS2BSZjakvchEZEYVGIyIgpPIkANx//IKHAaEsXvcubeeR1ZF61YshUcfSovFPVK+Qn2VTOhoNqUdGRn4yR0GlUmX4nvMl2yPXr19vcnH37du3uXLlCq1bt9Zro1KpOHXqlEGem127dvHzzz9jZ2eX4fl+LnznUfjGcOvWLSpVqmRyyG/MmDHkyJHDZMfia0OFChXYv38/7du3Z/78+dStW5cNGzZ89EL9IShYsCB79+6lcOHC1K1blx07dnzwTS1F42DKlCm8fPnSoG2BAgUoW7as3hTEDz/8wK1bt3Q6Uv369WPDhg06jytTpky6SARo5Zf1LaCtWrVi6dKleucqkUiwsrKiY8eOHDp0iCpVqtCgQQP27dun9xiAGjVqkDdvXp3FiQCjR49m3bp1Op0OJycnqlWrxvHjx9ONVa9enWvXrumsM+nRowdbt27Veb3q1avrPB9AyzZtOaFywW3APO5V6I3frWD2nrjGuLl7GNS5BhI7OTJ3SwQrOeY2+eixPgGb4uZorGREljdHYykh+L72dqRUCti5SsjqoSSLIpKgneGEX40j8tAr5HKR6DdaXQdEkVMxzuy//AypVELlcnmpW60oFubvqIVz5XChfAltW51cJuXFqyhqtp/F7n2HmTCiO6tmdKFiKf3h7xSY6iiYsvOXSqWftCg4M6FSqTh8+DB16tQxyX7hwoVMmjTJ4Puwd+9eGjZsqNc5U6vVLF261GD9wlcBzSd4fCP4TzgKV69epUSJEibZzpw5E5lM9p8IaZUoUYI//viDbdu2oVQqadKkCf369dO50GUmBEGgcePGHD58GH9/f+rXr28SP4IuWFtbs3DhQrp162a0YHLw4MEcPHiQhw8fphuTSCQsWrSI/v37p2tftLW1xdraWqcypJeXl85UgiEGx19++YXDhw/rjRJIJBIEQUhdHGrUqMGhQ4e4cuUKLVq00MvvADB8+HDWrVun03FK+R7Pnj1b57Fdu3bVWY+Q8np0RW68vb0JDAzU6exlz56d0NBQnUWn9vb2OLu64p8s535geOrxcpmUaI0SM29bZK4WIIKlvTcPrkdwNVdHaO6IZylzNqx2Ydcae9ZOs+fAVntUohREEXWsitinCkLPxfAqUM0zfzkqhfCut+zteqRWazh78QF7jl0DBEb0qsvm+T2oUNKXPya3Z+Pc7pQs5IVGI5KsUPD4yVPc3Ny4fjeYH5pP4ed2Mwl89o7Q6tmLCJ6HpmWz/FThf0EQvhoCJWPYs2cPDRs2NCkCEhcXx4sXL8iTJ49BuzVr1tCxY0e949u2baNhw4afhNwuU/G9mPHbwrVr14xysIM2L/bixQudLHXfMmxsbOjcuTNHjhyhe/furFy5ktq1a7Nx48bPKkJlYWHByJEjWb58OfPnz6d3794fVEuRP39+2rRpY5QLQyKRsGTJEn799VcUCkW6cQ8PDzp06MDUqVPTjf3yyy86NR68vLwIDg5O97yPj4/eIkiJREK/fv308jSAtsDy/eMtLS2ZOHEi48ePp1evXsyZM0enYySXy5kzZw79+/fXubjUrVsXPz8/neRYrq6u2NjY6KwraNmyJVu2bNE510KFCuntcDBUr9GpUyd+aT+Q/SdvIAgC9aoXo0PTH5g9aR9yhRK5RoX6ajgWyLB39aHDoB3UGunBmwgFvnaJiGqRWBt7Xisd8bjwiuz3I7C5HUXJgrG42Sj5pVQsCGIaCQeJfXoK9aRkJSULeVGhpDZSYCaXceV2EP/c0L4PybGhlCypFSlavP4UIaGRPAl+ndpOue/EdSr9MpVKzadw+MxNoqOjP3kI/FspZMxIEeOOHTto3ry5QZt//vmHAgUK6H0/NRoNq1evpnv37hme63dkHv4TjsLz58+NFtps27aNS5cuMXv27M/+I/2cu4fChQuzYMECdu7cSUJCAvXr12fUqFGfpODQVGTLlo01a9bQrl072rRpw8KFCzPcTtmqVStiYmI4ePCgQTsPDw+6deumt7WvVatWPHz4MF17Y+3atTly5Eg6exsbG+Lj49M9LwgC+fLl00s+Vb9+fc6fP6/zWNBGf3SpYObPn58DBw6kdivoQqFChShSpAg7d+7UOa8xY8boPbZ379788ccf6Z739PTk9evXOtNVderU4fDhwzrPV79+fZ2tp6DtXnka+ABR1PL716xUiLCIGNRJanrc+JvV9w/QSXjEgCwhaKxzExt6C6Vawv1nljx4ZoU8uxUWeeyRZrfmbEIOlm2xZfddZ67etWHRb/78XjOEHQ2fk9VW6yzIpCJSBzMwT38bO3ruNlOXHORVuLZoc82OP1PHEt8EcuFeAif+ukte76za+geNSF5vLfPi8fO3U21P/HWXx48fG90lg3aRy8x7y+eOQly8eJHChQub3Ma5c+dOmjVrZtBm/vz5BhlWT548SbVq1b4JZc3vNQrfEBQKBXK53OAP9PDhw+zdu5elS5ea3Af8KRAbn0TDbvPxrTKcZZt10wBnFqytrenevTvHjh2jbt26jB49ml9++YUTJ058tvxo+fLlOXLkCNbW1h/EBTF79mwWLFhgtKugWbNmvH79WqdwlCAILFiwgOHDh6dZFM3NzcmaNatJHQsp6NKli95QviAIdO7cmTVr1ugcL1++vF5hKkEQ6NevH/fu3dMbtRg6dChLly7VSbVctmxZwsLCdHaBpNRc6NLvqFevnk7yph9++IE///wz3fOg7QzR1w4qCAJ169RCkvySiqV8qVYhPz/9UJCyxbxJlpizPDYXf8R5M/NFFqZkDaC2QyztbW/wo7nIn3dsU9MIApCokaIQBUAgr0cSJVwTMPdNwLNsLHuX/UrbRuVo3egHhCgVEjMpLq62uLvZA+DkYM2q7X+yYss5Og9dRYNu8zGTa+sWpFIJybGhyK2zsPf4NQZ1rcni39uxekZnWjUoC0CTWqWQSSXIpFIa1yxpsqOQEaEnURQztPDb2tp+dqbWhQsXmkSbDlrCuhw5chjsHvP398fc3JwcOXLotVm5cqVRuemvBt9TD98O7t69a1B05Pz58yxfvpy1a9earJ9uDAkJCSxZsoR27drRt29fRo8ezYwZM1i2bBlbt27lyJEjXLx4kf3HLnLz/jNUag3zVusuAstspFTyb9iwgQULFnDp0iVq1KjB0qVLP0vxo0QioXPnzuzatYsjR47QsmVLo4WKKbCwsGDp0qX06NFDZ2rhfcydO5cJEybo7ExwdnZmyJAh6bgFWrZsqbNwz9zcXKcsdeHChfH399f7vjVt2pS9e/fqjJ74+PiQmJiol0gJYMaMGQwbNkznAmJubs6YMWP0pmMGDRqkl0yqdevWOtMMzZs315l+MTc3x8LCQmcLpUQiwcvLi8DAQJ3XmjdjLOXzCGya1wNLCzOsIm+xtL0z8dWbcEdwRQDyyGIpwBva+2blif9d9vjZkdcuEbMXMZR8FkilN8EMf3YRF4kCEMnrkYjsbYrcwk5BHJZMGtKM3wc05sCCvswe2pxKJXzxcHekR+sqJCZpvysaUeTu4xfcvv+M8Dex5PV2R5EUh0RmAYKEOlWKIJVKqFutKNUqFEjdbFSvWICrBydw9eB4KpfJy6NHjwz2+6cgPj7e5DZrS0tLvdLnuuDg4KDzu51ZCAwMRCaTGaRffh+mSE8ba3c0VW76Oz4/vnlH4erVq3rrE65du8bUqVPZtGmTyR0RhvDq1SvGjBlDo0aNcHR0ZMGCBQwbNoyWLVtSoUIFsmfPjlqtJjAwkDNnzrBz0x+E3d9D+KPD2KgC8fPzM7rgZSbc3d357bffOHLkCDY2NjRo0IApU6bolCD+1LC3t2fWrFmMGTOGNm3aGKVhToG3tze9e/dm0KBBBndgtra2TJkyhQEDBugcr127NklJSWmiDlWrVuX06dPpzuvp6amzTgG00QtdiytoiwsbNWqkd3z06NFMmTJF72vw9vamePHiOls3Qdt5ERsbq5MJsly5cjx8+FDnZ9miRQudaQsXFxeUSqXOBahGjRp6Oxx0pR/iLx0mbMkg7IKvoVQqCQ8PRwy5hnhuOnaPdlAh8TDPNXIcHcxRO9mTIJGRx8GawHgVE687IlZ0pOnv0FN1mz5P/RBUShQW2u6GUzfsuPzQhrgkCWvO5MU7xzsxpbw53YmPSWLPsWtcuRXE8i1nSUxKW7wqoq19fBgQSuKbYCwdczJrVAvqViuq8/UB2NtaYmejjQ5kxFEwVX7Z0dFRJ3um3vnY239WR2HBggV6eTr+DaVSyfXr1yldurRem/DwcEJCQihaVP97nkIkZghHjhwxWXU2s/E99fCNQKVSsXXrVp3aDBqNhuHDh7N58+aPJlO6d+8e3bp1o0+f/7V35nEx7W8c/5yZ9r20UaLFGtmVuGSPpJQW2ogoyRJJyR6ikMqSrWxJaLHHtV87175kV6SoRCWVmfP7Y+5MjXNmmpCu+zvv12te9zrne858zzRzvs95ls/jj379+iEzMxOjRo2Curo6DAwM0L59e/Tq1QvDhg2Dm5sbJk2ahNDQUKTtT8GDO1ewPWET5kwfjUOHDsHJyQk2NjaYOXNmg2kgSEtLw93dHcePH0eHDh3g7e0NX1/fn9KzoDZMTU2xfft2BAcHIzw8XKJkSzs7O5iYmMDHx0fseAsLC2hpaeHYsWO0+5ctW4aFCxcKPAJSUlKws7PDkiVLhMY5Oztj7ty5tNn9rq6uSExMFOlV8Pb2xtatW2mNGlNTU3z8+BGFhYUir2HWrFmIj48XKdHMl7Sl+xxCQ0Mxc+ZMynY5OTmYmZnRNrfy8vKiLe20s7MT2Zp74MCBOHz4sOAaOR8LULxvNSpf3EXx/mh4Oo3gla2WVVcR6CtVQbmDKmRtdFA6UB977cbhrwHWkB35B7Q07qF1V4ArxcK1sf2gFrwS8XpdYOPAc7WXVbDhGdMS7Xw7oeBrPxRePoOI6eFYsWo3xs3eKtToic6WbG1c3fWx/MNzqOu0EJRMSsKnT5/EShHzKSgogIaGRq3jAF7ooS75DGpqanUyLH6UrKwsiRLEAeDQoUMYNmyY2OvZuXOnWI9DRUUFLl++jF69eokcw+FwEBUVhT59+kg0r3qHxI+VRv4+dsLvbSgUFhZixowZtPXNu3fvhq2t7Q+JpJw+fRqOjo6Ijo5GYGAg9u3bh759+wr9IAo+lOBNnugfcDM9TTjY9IS93XAsXLgQGRkZOHjwINzc3LB8+XKMHj26XrozSgJBELCxsUF6ejoCAgIQGxsLOzs7HDp0qF4bUenr6yM1NRUtW7bE0KFDJZKCnjZtGkaMGAFHR0ex6oYLFizAihUraJMK1dTUMHPmTISFhQm2TZo0Cfn5+UIhCAsLCwwbNgyzZ8+mnENRURHTpk2jGBd8FBQUBC276Rg1apRInQKAt6hv3rwZEyZMoL0GLS0tTJ48mbZyx9LSErq6urTeg5CQEERERFD+rsOHD8fJkycp8W9tbW0oKirShhj4hge/myQhLQNISQMgALYUrIcMwcmTJ1Gi1RUfVNqhjKOAPX/LQV/1KwgALIKAXnNDeHuHYrmxIWQu/A33tEvw/qsYwx4qYEXmA9x+K4t8eTVYOnNh2odEh0Ekmut/wcGTl9Fj1kFsuFaMdRl/4+Slhzh2Xlh2evAfppjuPQiTvfojfqkXtkWNh5QUCySXAy6nAiFTRkJPV/i+wOFwUfypDF8qvr8T6OvXr8XG32tS1zbT6urqIhua/Wzy8vLqNLft27fDw8ND7Jhjx47B2tpa5H5+xYQ4Y2P79u1wdHSU2GvD8PP4rQ0FHR0dDBkyhLK9srISW7ZsqdWNJYqXL1/CyckJmZmZWLduHTZu3Ig2bajtaC/9/RQW9ovRc+QS7Eq/hPC4A7AYsRixifRtifmwWCx06tQJCQkJCA8Px4YNGzBy5Ehcvnz5u+b7MzA1NcWGDRuQkJCAhw8fYvDgwQgPD5c4n6CuEAQBZ2dnpKamYv/+/Rg7dizev38v9hgbGxuEhITAzc1NZHxXUVERwcHBIktghw4dik+fPgmS9QiCELT4rqnI6OnpCWlpadrkxeHDh+PJkyd4+PAh7Xv4+/tj7dq1tPuGDRtWayVHs2bNEBYWJlKV0d7eHu/evcPFixcp+xYsWID4+HhKJ0g1NTU4ODggMTFRaDtBEPDz86P1KkyaNAnr1q2jnaOvr6+gmoKloALNCSug+IcDNCesgLSyOkaOHIlewwPQJbIMg5Ibw+bLK0x9fQNNwUU7bV04m3YAm8WC3J27CO3XCuFnH0LuQw4qL2RA+68jKH9Zjjtn2WhqSqJ9PxKttNpCXp7ExzISVaTo25aGmiJWhLpiqvcgzPQZgsG920NHSxXDB3RCxac3kFPWw6E/hQ3zouIy/OG8BB2HzkebASHYkXpB5PnF8fr1a4lj+l+/fq1TztSv9CgcOnQINjY2Eo19+fIlVFRUxHpSHj16BGNjY8jIyIgcs2vXLri5uYncX15ejh07doiVff7lMMmMvzebNm3CmDFj6pyXUFlZKYhzL1myBBEREWITa46fvw/OPxUEKYevYnPyOeS9/4iVm4/hfRH9U++XiiocOX0b9x7zOtYZGRlh/fr1WLNmDfbs2YPhw4fjzJkzDSbIoqGhgaCgIBw/fhwWFhYIDAyEq6srjh8/Xi/VEqqqqoiNjYW/vz/c3NywefNmsdfes2dPBAQEwN3dnSKkxGfw4MF4//69yOz8qKgozJ8/X/DELiUlhcTERMydO1co2TA8PBwnT57E6dPUipWoqCgEBQXRzlVDQwMtW7akNfxkZWVhaGhYa9jJysoK5ubmWLFiBe3+VatWISwsjOJ14MuS+/v7U+Y2YcIEJCUlUbwH9vb2OHr0KCWcIa5iwtjYGMXFxYIwiqxhO6gN94WsUXsAgLWNPZ7cuwSSJJFXxkVh8y5oa9gCgS174G5iFhy9Y5F24gZGv+gAWRVttNBUxMFH+bhWrgYZgvc9K3hFYG+kLFJWyeHl48+QluIKq9lxeB0kSQ4XBAHIyUpj5RxXqCpTKw9s+3dE+YfnkNcwgmUX4bDD2SuPkJvPe1onSWBD0hnBvoqKCrELXE1ycnIk9ijUlV+ZzHjs2DGx8so12bJlC8aPHy92TG1GwLVr19C2bVuxIeK1a9fC19cX0tJU3YyGgslR+I0pLS1FWlqa2C8mHadOncKQIUNgYmKCtLQ0icqhhlqZQVqK1+PewboLFOR5NxQ1FQUoK9LXAU9blIRJc3fAdlw0btx7Kdiup6eHVatWYUbIYqSlH8SQIUOwbt06WiGdXwGLxcKAAQOwe/duREdH4/r16xg0aBAiIiLqZU5du3bF0aNHUVxcDA8PD5F6BAAvRu7u7i62NXVkZCRmz55NW4GgqqqKkJAQhISECLapqKgIujbyFRtZLBY2b96MZcuWUZIvmzZtiv79+1Oe0PlMnz5dZBWCp6enyB4NNQkICMDjx49pcy7U1NQQGhpK24K3bdu26Nu3L8UbICUlhRkzZlCMDxaLhdGjR1M6ZxIEATc3N5ES0uLKQZvp66BtOzNUfMxB9w6G+GN+BPRnRiJ820W8yfuA17lFCN+2B6WVJOZnGcDYsBNWXH6Po/myUFEFLNQ/Q0NVHtzSr+AWV+Hc2WzcyfrW5UwAXBJSLBbiFnngbmY4+vagev4AwMqiNdoZKmL/lrmY4cNzge85dAVTF+6CFJstJPtsZd5a8P/v37+HtrY27Tm/pS4ehbryqwyFz58/o7KyEqqqqrWO/fr1Ky5duiQ2r4AkSVy6dAk9evQQOWbt2rViO01++PABJ06cqFWjgaH++M8ZCnxBD0mbruTl5cHT0xMHDx5EWloanJ2dJU4y0mqkjMWBI3By1yx4OfbCgU1TsWCaPQ5smgo5WXrL9/ZDnvARSZJ48ETYPbx07SF4Be3A0dvSCF8RBx0dHUyfPh3Dhw/Hpk2bxCbB1Se6uroIDQ1FZmYmOnbsiMmTJ8PLy0tkg6Dvhc1mY+bMmfD29oa9vb1YjQN7e3tYW1vDz8+P9qleW1sbo0ePRkxMDO3xAwcOxNevX4VkjPX19QVlr/ybsqKiIhITE+Hn50epKAgICEBSUhKt+qS+vj6UlZVpwxPm5ua4du1arR4agiCwdu1arF69Gk+fPqXsHzBgAAiCoNVnmDx5Mo4fP07xXAwZMgR37tyhaC64u7sjKSmJMidnZ2fs2bOH9jMeOnQojh49SnsdBEHgUMoGWBhXYU+MH6Sl2Mh7/xE66vJorfgZMiwuGrdlQVoeyCmXxbpXTcFtYoW8p+fRm/0e8zXugai5MIr5TX7lcJGbXwxpKdG/+aysLHTuZIYenU1AEATuZb1GcMReHPjzJmYv34vTySFYH+6JPbF+WDxjhOC4d+/eSWwolJSUSCROVFpaWucE619lKJw8eRIDBgyQaOzhw4dhY2Mj9n558eJFWFpaihzz7t07lJSUwNjYWOQ5+Am8v1IDRyKY0MPvSWFhIS5cuABbW1uJxm/cuBF2I5xg2dcOq1atEikrml/wEY6+ceg3ernAC/Am7wOsvVYiePleTJq7AyRJwqS5DsaM7AUDvUaUc2Q9f4t7Wa8xa+JQKCvKwaxNUwz7pjzr2FmeIlxl1VfcvP8ajo6OSEpKQlJSEpSVleHn54cRI0YgMTHxlyU21YTNZsPa2hp79+7F/PnzkZKSAmtr658uFd2vXz9s3LgR48ePF5vo6O7ujk6dOmHGjBm0C5mXlxfOnDkjsuZ/+fLlCA8PF0qObN26NZYuXQo3NzdBZUOTJk0QGRmJMWPGCHkwpKSksGTJEtqkRwCYOXMmoqKiKNsJgoCVlRVtSONb5OTkBPk2dF6WiIgIREREUBYRvrz1t70uCILAokWLKHoMsrKyGDx4MKXsUU5ODubm5rRiVmw2GwMHDhRZRtm4cWM00lDHkydPcOzsXfRwCMeflx7DXe8dtnV5huad5NF/uiy85xuBKy0LJSVtGCtL4WR2AaQIQEW2enGRkmaBJc0CoSgFyLEpd66b9+nLWfkcPHhQ5H2Bw+XCcWIM1u04BQO9RkILUl0MBUnJz8+vs1bArzIUxH1O37Jt27Zau0rWFnbYtGmTWLnmN2/eICsri7ayrcFhmkL9noSFhWHOnDkSeQSSk5OxL+ME8ljdEbHlEtbtFN3aePv+i7h5/xVe5LxHZDxP9vdZ9jtBhnTW8zxwOKL/6gf+vInBnisxbFw0Ssu+4G5mOA5smgp1VeGnijFOvUAQvISswb3bC7YrKSnB1dUVKSkp2LZtG9hsNlxcXBAZGflLeznUxMjICFFRUdi/fz8+f/4MGxsbLFu27KdpMhgaGiIjIwPr168X6foGAD8/P+jp6SEwMJDyZEsQBFatWgUfHx+8e/eOcqyysrKg10JNQ6Nbt26YMmWKkCZD586d4ebmBl9fXyFjoXv37tDS0qL1XLRu3RqysrK0YkdjxowR2d/hW/T19TF9+nRERkZS9ikqKmLx4sWYNWsWZZ+enh6cnZ2xbds2oe1mZmb48uULJYQ0ceJE2uTNgIAAREdH087N29tbpFolwEvs3Lhx4z9GMAkSwOlCVWQ2M8H9AjXklTaFr6U7FBTkUMKVRmXj3tjwMB/nmpjC0X0YCBkWWLJsTB8zCMoqvNwDgk2AkBP2Hty4+1LkHABeBVPNsrp2rfQREewE2/6d0ERXDbnvPuJu1mvsTBNOEM3Pz//phkJdhJn4/ApD4ePHj8jOzoahoWGtY1+8eAFlZWU0akR9KOLz6dMnvHz5Eq1ataLdX1VVhVOnTmHgwIEiz7FkyZJae740FEyOwm/IiRMnIC0tjZ49e9Y69s6dO0hKSkIHSwewWSwQBChhgJoYGmiCS5IgScC4Ge+mYd7RGFYWrSEvJ42ZPtaQEuP2vHD9icBzev4aT6L36ct8+M/bgaVrD6KyirdYjHPujVtHFuNy2lxarwTAi6V7eHjg6NGjMDAwwJAhQ36otfOPwpeK5msyeHl5wd/f/6d0sFRUVERSUhLOnj2LuLg4keNmzJiBtm3bYvz48ZQERxMTE6xZswaurq548+YN5djevXvDwsKCUu44ePBgqKio4MCBA4JtLi4usLKywtixY4XeZ+nSpcjJyaFNPIyNjcWff/6JrVu3Cm3X09ODq6srwsPDxX8I/2BjY4PLly/TLhaWlpb4+vUrrQ7GqFGjkJaWRtk+YMAAikyziooKmjRpQglX6OjooFmzZrShJm1tbcjLy4vsJdKpUyfcunULQ61MwWKxwCIIDNQsxl8avKS/jxUVeFCQD21NXkz8zGdd9PUOw+X3X3Hs1B2QlVxwKziIjD+Kj0U8jwrBJqBipQPFbtWZ9nq66jDuHYT21mEw+oP336QMXjLps2fPoKenB1lZWZAkiZKyLyBJEq625ohZ4IaPn6o1McorhA23t2/fokmTJrTX9r1oaWnVOYwoLS0tMnn3Z8FvPiYJsbGxmDx5stgxGzZsEOstqK3d9Js3b/Du3TuJOwMz1B//CUPh06dPWLZsGW2XwG/58OEDpk2bxsvWHWUF/SYaaKLNk34VhaN1V2xcNgaB4wbDuk97kCQJWRkpJEaNx8M/l8Hfs7/IYzPP3QNJkpCXlYGcrDTcR/CSeoKWpeDo6TvYuPssUg5X34BVleUFuvTiIAgCLi4uOHLkCJ4/fw4bGxuRXf1+BSwWC0OHDsXBgwfh5+cn6GCZkJBAmzUvKWw2Gxs2bMDr16+xcOFCkQaRj4+PQPDq29JJU1NTbNy4ER4eHrR5D/7+/njz5g1lQQ0PD0dMTIyQN8LNzQ12dnbw8PAQqGwSBCHQbpg/f77QHNlsNjZt2oQbN25gy5YtQuf38PBAdnY2rVv/WwiCwJQpU0TmXMyZM4f2+6+goABdXV2KgFPv3r1p33fChAk8saRvCAoKovVoALzPfuPGjSLnbWtri7LC57h+YD6uH1wAp9WbMMC4LQBAhsvG2tXHoa3Bi+2TAPr1tYKSkhI+f3gp6P8AAOCQIMu+gvupCpVvPkPWmHcMiwX8fe8VOFwSJaVfwCV5/w2N3IeJoYmYErQITk4uIEkSPrMT0H5wGDxnbBJ4AdWUFQRv07ZFY9Tk7du3aNxYeNuPoqmpSevhqo36bDj1/v17XL9+XazWAZ+SkhI8evRIrBLjly9fcPz4cQwfPlzkmG3btoltNx0ZGUnrKfvXwOQo/F7w6+Zrc+dxOByMGzcOK1asgJaWFloZNca5PSG4sH8OzFqLLmsiCAJystJYvTUTHtM3YsUG+s5633Ll1jNMDE3E3iPX0LSJBm4fXYTHL/IxKWw7KiqrBDdBSQwDUcjJySE4OBiJiYmCmOH3dookSfKHBGf4tGvXDjExMdi/fz8IgsDIkSMREBCAu3fv1n4wDQRBICIiAgoKCpg6darIJEAHBweMGzcOrq6ulHi+iYkJEhIS4O3tTUk+JAgCa9aswebNm4WqG2RlZbFq1SpMnjxZaPF3dHSEm5sbRo0aJTBKCILAwoULoaioSCmbZLFYiIuLQ2ZmJuWpPCYmBosWLZLoCXPIkCG4cOECbWMoY2NjyMnJ4d69e5R9Hh4e2LFjB+XzoEuQ7NKlC+7du0cxtvT19aGpqYlbt25Rjvnjjz9w+fJlkfLknp6e2LZtG9RVFaGhpgS2igaCLQfCp7EFHie8wrXrL3A36zUiQ12QEDkOdgM7ITw8HK8enAGXWyVsLPwDWclFfyNjdGnfHCBFL6CZ5+7i1OkzePiai5zcIvx54QEA4PzVx3iezVusV811RW/zVvB2/gN2A4WfXnNzcyU2FAiCkMizJy0tXeduqvXNsmXLEBISIpExkpiYKHaB54/x9PQU6S24ffs2DA0NReeF5efj1atX6N69e63zaSiY0MNvBD/kIIms58KFC2Fra4uuXbvWOraisgr2E2LQ2WY+Nu0+i2u3qxPiLtyg7/AHAIdP3cZAj0jMXr4X7wp4N3SSBAqLS3HzfjbCYw/g6Nk7eJ1bCDf7HpjtZwNH69rnUxva2tpYt24dgoODMW3aNISGhtIuKKIo/1IJO581aN0/BItjMn54PgDvaXbMmDE4cuQIfHx8EB8fj6FDh4pMfquNoKAgdOrUCWPGjBG5KA0ePBjTp0+Hs7Mz5fqbNWuGNWvWwN3dnVZ/IC4uDsHBwUI3ezMzM3Tr1o0SOrC1tcXEiRPh4uIidK5Zs2bB0NAQkydPFjJoCILAxo0bMXv2bCFDRUlJCZGRkfD19a11keF3mYyNjaXdHxoaSttLonfv3kJiUvxzqaur0+aUODo60oYrgoODacMrBEHA3t4e6enptPNSV1eHurq6kFeDIAjoKauCrS4LaX0FSKnIwGloN3TvYISjZ+7g1sNcuLqPR/HL87yKB35nSQKw6GyM3WHeWD5kCLQbKYP7z+empaEEVo11Tl1VAZWl+ZBV1MGXyq/Q1VaF4T99Ipo21kDTJrzwXoc2Bti20gfzpthRKicqKiok7gippKT0yzs8/gxycnLw6tUrsWWOfLhcLtLT0+Hg4CByzNevX7F3716MGjVK5Ji4uDixoYuVK1dixowZtc6H4dfwWxsKdQk5ZGRkoKioCGPHjpXo3K6T1+PWg2wUfSzDkrUHYWXRGtqNVCAjLYWxTn/ANWA9WlgFU1QYA8N348mLfCQfvIKVmzPhOqw7OrY1wMo5oyDF5n/cBKSlpbFo+gj4uvUFm/3z/gympqZITU2FlZUVHB0dsW7dOqHY5p1HOViTcByPngkrLl659Rx3HvFK5raknEfV158r4WxmZoa4uDikpKQgMzMTY8aMqVWJkY6xY8fC0dGR1mvAp0+fPpg3bx6cnJwoC2H79u0xe/ZseHl5UZ7qDA0N0bFjR8qCFxgYiNTUVErnx0GDBiEwMBDOzs5ClRP+/v7o1q0bxo8fL/QeampqguTKmgmRnTt3xoABA+Dj41PrQmNjY4Nz587RylgbGRlBUVGR4rlhsVi07u5evXrhr7/+opxn1KhRtAmkzZs3h4KCAh48eEDZR+e1qAldSOMLwQVLQQoEm4VyGd5i7z49Hn5h2+E2LR47M1+BJDkoe/9AYESRJPDpUzk6NdPHm7dFOHG+Oi+jsLgM3Tsaw6CJBpxtuiNj01RoyxXA3sEJvm59ISMthUNbpiFpjS82LBWfrf89NGrUqMFKmH+ExYsXC8mai+PQoUOwtrYWqyqZkpKCESNGiBRHKiwsREFBgcgkx4KCAjx8+FAiw6VB4eIHQw8NfQGS81sbCmw2G2vXrq015JCVlYUNGzZg1apVtPu37DmHwZ5R2JpSndyVnVv9gycAtDDUweW0uXhwYglUleVx+eYzVH3lYPXWTHC5XDx4kgsr1whUVFYvDC9fF2BAL1Okb5yCPuat0NXMEBHBThg5pCsSo8bXa8xx0KBBOHbsGOTl5TF48GBkZGSgoKgEI/3WYvWW43D0jUPZ5+qKibYmTaCixBOJ6mZmKLYm/UdQUlLCypUrERAQADc3N2zbtq3OiZh2dnaYOnUqRo4cKbLKwtzcHMuXL4ezszMlu9/KygrOzs606oWzZs1CbGysUF4F/3vm7+9Pccn36dMHYWFhcHJyEko0HDNmDAYPHgxPT08h70fHjh1ha2tLSWKcOHGiIP9BXK4JQRDw9/cXmdwpyqswcOBAit5Cnz59cO7cOcpYZWVlNG7cGFlZWZR9wcHBWL58OWW7qqoqmjRpggcPHuArjQiWhYUFbty4IfRZNNdtBNY/LoAWTbVx+2E2bt6vETYjCGgY9cPX8k94//AAqsp5EsYmzXmlhTl5H/C1RrURl0vCopMxzqWEYvlsJ2QeTkXr5mpIjAkSqDUqKsgiYe952IyNhsWIxZi1bA9evaHqYACo8/eyLoaCvLx8ndu8s1isn96D5fHjx/j8+TM6deok0fjNmzeLVWKsqqrC5s2bMW7cOJFjtmzZInZ/dHS0yC6w/yqYplC/B4qKirQ9GGpSWlqKSZMmYcuWLbRSrK/zirA49gCynudhUUwG9h25hpzcQsz242mds1gE5k4ZDhUleRAEARaLQFpmtTSwWSt9sFgsRG89jpevhW84CvIyaGMinDHtamuOyFAXtG9dreD2ruATLxlLTIllbXA4XKGFH+AtcGPHjsXBgwdx584djHS0x8f8xyBJEmXlFfj8pfqmra2pgpNJwdgVPRE7VovOVP5ZdOnSBUeOHEFBQQHs7OxoFyVx9OnTB8uWLYOLi4tIDfyOHTsiNjYWbm5ulDJSZ2dntG3bltITQl5eHoGBgRRthubNmyMwMBBubm6U7PMePXpgyZIlcHNzEwo3uLi4YNSoUZRMcm9vb2RnZ+PCBeGeAn379sW+ffsQHR2NlJQUkdduY2Mjsl+EoaEhFBUV8eSJcHhs8ODBFE9JmzZtcP36ddoSWx8fH9qyxxYtWoDL5dK24R7l5gVrp4loYxOGhDRhTwVBEBgxYoRQC+1WBjrYt8gHS3yGY0uwO/YeviZ8QpIEQbChqm8OdcM++JTzF0x1S8HhcrEr/RK6mxlixODO0FRXgnWfdlg0fQT8Pfvj3bt3cHNzQ2FhocAzkv2mEAM9ImFuvwgn/uJ5IYo/fcbeI9cwed5O2s+ytLS0Tg2IlJWVJQ73fU/baBUVlZ+un7J06VKJyw8fPXoEfX19sY32kpOT4eLiIjJcw+FwkJmZSdujB+B5G/7++2+RugmVlZX1Xv3BQOW3NhRqgyRJ+Pn5Yf78+SJLnORkhN1jM5fuQb/RK6CjqQJ5OWlwuSQ2JZ8TLAB3s17j4MlbqCjNx8fXV2Gi9hazZs3C3Yv7UPDkGN5nHULR4wOQLT6HHs0K8fzJPXC5XKRl3sDclakUl/+z7Hfo7bIMDr6xCI4QvTiIo+BDCaxcI9Bu8BzEbaM2pFJUVMTcuXORlroP3U3VUPr8INrrvMeFcyeRk5MjWBC1NJTRs2sLkaqSPxu+pHBMTAzCwsIwb968Oj1ldezYEREREXB3dxfZJKpNmzbw9fWlfQqeOnUqSJKkeJqGDRuGVq1aISQkRMhYGDRoEFxdXSnJjQDP8OnXrx+lM6StrS0KCwsFstB8li9fjiVLllDOo66ujh07dmD79u20Ln6AJ1Lj7u5Ouw8AnJycKMJJurq6aNy4sVCogcViYfr06ZgzZw7lHF27dsXNmzdpE0c9PDxoO2Dml0mhIP8NqqoqsTaJKibl6emJxMREoWtuZ9gETladoaooj17dqmXTZ00cil2rJwieuqTl1JCUtBvXbt7Hjs2rERq5F6u3ZqK/ZVt4OfZEZKgrPBwskbp/H9zc3DBr1iz4TJyM9ON/I+99MXakXcTTl+/wruATNGrol5AkhLyANfn8+TMUFBRo99FRF3GmunaPBHhGoKj2499DXl4ePn/+LDIE8C3p6em1yiinp6fD1dVV5P7MzEwMGjRIpHLu4sWLERoaKtLbGh0djcOHD0s033rnRxMZmWTGfwebNm2CmZkZevfuDQBIy7yBbsMXotfIJYhYfxgcDheaGsow72gkdFzVVw5CIvej/AvPcn37rhjnrz3GX9cfY39qOgoepaPs3X00bmoCZydHeHt7Y2dCLBYtWgKL/h6wdpqCE5lHETh9CnanpKO7xR/wGjMem7elwGlSLJatO4Tc/GJkvymE6+T1gkoDfkZ2XTl18SFy3haBJIH4pDO4l/Wa1m2qrq6Og3u34u2re1gQOgXv3r1DeHg4hg0bBnt7e4SFhSE1NRU5OTnfNY/vpXnz5ti7dy+6dOkCGxubOklDd+nSBQEBAfD29hZZDeHo6Ijbt29TnrIBYNGiRXj58iWldHHatGlQUlKiuPGdnJzQsmVL2ryYyZMnY8uWLRRjx9fXl9KdUVNTEz179hTSaeAjJSWFTZs2ISAggJKzkJeXh/T0dLGdUfv06UNb+jh//nwsWrRI6HOys7NDSUmJkJQ1wPMAiMph6NevH2U8AHRobQCVJqYoy3sIiw5GlP2Kioro3r27SLVN6z7t8efOIBzaMg2TPPqhZ9eWGNjLFADQWFsNvbq1RCPDXpBV0cP7RwcQu/UIJs/fiZWbM2ExPBQuLq549OgRZsyJxNrkm+g5cgkCw5Nh6bgEuloqIEme4NPIoV3x584gTBxthaFWZlg9bxRu3H2JWw+Eq4UqKyvr1FguOztb4qZQde0eCfC8OXTf4e8lPj5erM7Bt5w/fx5//PGHyP0lJSXgcrli+0SICzs8e/YMb9++FZmbkJ+fjzNnzsDOzk7iOdcrTHnk709VVRVSUlKEMmfnrUrD+6ISvM77gA27TmPmEt5TkZudhWAMQQBNdNTwJq/anU1yv2LxkigMHDAQsZv2YkbIEqxftxanUleje/duaN26NYyNDPGuuBIvcotx/e4rrEk8gW0H7uPITQIfFXtBuXFHlH/MwdOrSVgWPhfO4+ZiV8YlFHyoTkob79Jb5PWQJIn5q9PQbfhCrN1+Umhfl/bNBV6A0rIvGDYuGlEbqY2E+MjIyKBLly6YMGEC4uPjcfjwYaSkpMDR0RFFRUWYN28e7O3tceTIkXrpGCkKOzs7pKamYuXKlYiNjZU4RmxtbS1ILKQ7hiAIrFy5krbbI0EQiI6Oxl9//YW9e/cK7ZszZw5KS0spqoSBgYF4+/YtpYmSrKws/Pz8sGbNGqHtffv2xYULFygu/mnTpiEmJoa2VK5x48aYN28eJk2aJDTnWbNmISIiQqzuvZycHKSlpSkJj40aNYKtrS2lIVVUVBTCw8MpXplRo0bRqkpKSUnBxMSEIszU2lAX59LXw0i1FKtn0z9VTp48Wax4lklzHZg018GqrZlYsvYgls92wp87g3AqaRbUVZUQMcsJSpqtoNq0B4qeHELV50KUvX+I5zf2w7SbNTzG+sE3bCdO/HVfYIBzuSS+ckjsWD0B6xZ7INjXBibNdRAyaRjWhXvi6q3ncPSLg/2EGCFNk4qKijoZCqWlpSLL/WrC4XAk7kVTk59pKFRWVuL8+fPo31+0BkxN3r9/DzU1NbHdG48cOSK2PfXz58+hrKwMTU1N2v18Q1YUc+fOxaJFi+o1t4uBnv+sobBv3z44ODgI3VCNDYRdffwyx7NXHwuUE1sZNUZi1HiYdzQCp+ozyt5egcyHs7j16A202zpAVd8cT7I/wM2+B3S1qi3nB09ycfP+K3C5JLj/dLTbe4R306mq4sC8W2eYmA2EjulIKDfuiNcvH2Dv1qX4XPQCJEnCy7EnJnuJbsby4Gkutu2/gPdFJYjceBQfPlZn/BsbaOPsntlwHWYuyI/hx2ElRUZGBp06dcL48eORkJCALVu24Pbt2xg4cCDWr18vtpvjz0RNTQ3JycmorKyEu7u7xDHfMWPGiJRSBnheC0tLS0qZI8BzwW/atAn79u3D0aNHBdsJgsDSpUvx/PlzoYx9vnGRnp5O6dfg6OiIc+fOCVUY8MWxvnXXKyoq0kos8+nTpw9MTU2xfv16AMDx48ehpaWFjh07iv8wwFNe/PNPahjK19cXO3bsEPJUKCsrY+rUqRTvSatWrfDixQvamLAoI6KlkT56WnTFndu3aOelo6MDTU1NWhVJPvG7zyAm8QQ2p5zD0nWHYdJcB/JyvPwiu0GdsX9DAKb5umJK0GJ8eHEaVV+Kod3WAdsOPsCWvecp52OzWBj0hyn+6NYSQ/t2oFQZXbv9AgR4f6eaZdD8FtMkSeLpy3yh3xwdkhq239uOukWLFpQupt9LamoqHBwcJF50jxw5gqFDh4odk5GRIVZgacOGDfDz86Pdd+XKFaiqqooMg9y8eRMcDkei0vZfBuNR+DkMHz4cBgYGkJOTQ+PGjeHh4YHcXNFSyT8LkiRpG5bMnjRM6N/tWvESCkcO6SoQPXr07C3sJ8TAwvgrmss+QmxEIDz8FkBJ2xQEi/cU8CKnuqyPJEncvP8K44O3IOt5dRz6zJVH4N83pNgs/H3/FQo+lEJeTgZWvSyQmrwVp08exfDe+tD+egWWbRXF3mj4ORMAoKmhDCk2G5uTz2LDrtP4UlEFHU1V+IzuA3VVRRAEgbFOP1Za1KhRI4SEhODo0aNQVVWFg4MDZs+eTek6WB8QBIEZM2Zg0qRJGDFihMRy0KGhoTh16hRtoh3Ak3o+duwYrTtdSkoK27ZtQ3x8vJDbnm8UXL16VciDwGazkZiYiIiICKFFjyAIzJ8/HwsWLBA6v5ubG3bu3En5G3t7eyMpKUlkbkZQUBBOnz6Nc+fOISIiAgsXLqz1cwB4nR3pYrnS0tIICgqihE74CaXfdrscMGAAbXdKS0tLXLx4kfY7O3HiRJFKjQCvBTddBRKHw0X68b9x+NRtgAuQHBKvcgsEpbokSYLL5UJXSxVbU84j6dBttOnpBbWmPUCw2OBySezOuCyUgzB8QEdc2D9HoJ9Ah6djTygqykJVWV6gnApUexSWrTuEAe6R6DlyCZ6+pG+xXhcvwbNnz2BiYiLR2JpoaGj8lH4PpaWliI+Pr7WhU02OHj0qMgER4Ckxfvr0SWSORnl5OW7fvk0roESSJBYvXox58+bRHkuSJMLCwiSWO/9VkNwff/0u1Kuh0LdvX6SkpCArKwv79+/Hs2fPfklP8YsXL6JDhw6UjOW/rlVb49JSbITPcMSbvA/YkXYRVha8HvTcr1/w6lYa3hUU4vjxY4jb80CobBIA3hfxXLpFxWUYM2MzRkyMRe474Wzk/ILqJ2EH6y6C/+dwuNi+yged2zWDqqoqNq1fg8yjB3D+/DnY2tpSYsufyyuwavMxJO79C6kbArA4cAQyNk7BpuQzCI87iOUbDmPVZl6YwdhAG1fS5+Ju5mKMGm6Bn4GMjAxGjx6NY8eOYdiwYZg5cyZcXFyQmJiIt2/f1n6CH6Bnz55ITk7GvHnzaD0B30IQhEBhjg42m42tW7diwYIFtElhcnJy2LVrF5YvXy5UoshisbBhwwYcOXIER45Uq3IqKipi+/btCAgIEPoszM3NUVJSIpSMKC8vDysrKyGPBcBbuP38/ESKKLFYLGzcuBETJkzAlClTJM7CNzAwEEpUrYm1tTUePnxIkbOOioqihGdcXFywZ88e2nl16tQJf//9N2Vfq1at8PbtW5EZ+q1bt0ZJSQnloSFu+5+YtigJj2sY3NduvYDzpLUIW7kfnYfNh+mgMKQfvyFIQCz65infpLkO+vZoDRaLAEEQMGyqJeT5o8OikzHuHF2Mm4cXokMbA8F2fo7CgT9vAQA+l1fi3FX6J/q6SD0/ffpUbFvl+qSsrAyurq5YvHixxN+liooKlJaWQkNDQ+SYEydOYNCgQSL3p6SkwMXFhdaDcfDgQZibm4vsqLlv3z707Nnzp0tp/zAc8sdfvwn1aihMnz4dFhYWaNasGSwtLTF79mxcvny53stb1q9fT6v6ZWXRRhBi6Ni2KZroqGHJ2oM4euYOMs/dA7eyDO8epEKjaVdwlNpiQkgiXtfIVeDTWFsNADAxNBFnr1aX9bHZ9G68+VPt4OPaG21NGmNVmCvF9amqqoZBwz0QMjcC8fHxGDhwoCBuHRyxFzGJfyJu+0nMX50GD4ee0NNVR2FxmeBmWFhcfbOUkZaCkoJcnT4vSeAntyUnJyMuLg6ysrIICQnBwIEDxQrt/ChaWlrYv38/Xr9+jdDQ0FrHt23bFlpaWhQlQj7Kysq8Ph/jx9MuooqKiti9ezfmzp0rJIXNNzLWrVsn5P7V0dHB+vXr4e3tLXSexYsXU+KtkyZNoiQ1ArxwxalTp0SGWdTV1XHz5k3Y29uLvG46OnXqJLKPxJIlSzBt2jShvISmTZtiwIABglAHf1thYSGtONaoUaMo+Q58PDw8xHaVnDJlCiIiIoS2vcgpAItmIbn5IBs70y7hw8fPKP9SiV3p1X/b5vqaPOFGAmjaRAMpcZMQFjAcXo69MMm9H3zd+tK+/5VbzxAwfyd2H+A1jmKxWJRF7O3bt5CTk4ObHc/LoKYsj/496cuxs7KyYGBgQLvvWx48ePBdHoWqqiqUlZXVSXH1W9zc3BAQEFAnMaMLFy7A0tJS7JgDBw6IDTvs2bNHZDVEdHQ0AgMDRR4bGxsrdj9D/fPLchSKioqwa9cuWFpaikyIqaiowKdPn4Re34Ouri4KCgrwpaJKqPRJQV5GEA64duclYhJP4OXrAsE2QloebFkVVH4pQ8rhazh/jf7pgUUQsPZaiZffCLVwaliI7H9yI9q30kdZeSWOnb2Hh8/eCgyPx8/zcObyI3z9ysGC6P1w9AzCoKEjUFT8GWFhYXj0PB8D3Fbg+F/V2v33Hld3P5wyZgD69WiD3t1bIXDc4O/6nL4XLS0tjBo1Cn5+fuBwOOjSpUvtB/0AbDYbvXr1kjhL/PPnzyKfTgCelLOCgoLI+KyqqiqGDRtGiQfLysqid+/eFIVDIyMjyrmKioooT0A3b95Eu3btKO9HEATs7OxoKwn4SCojXJPg4GBERETg+vXrlH1t2rTBxIkTYWtrK6Q4GRAQgGvXrgnlUyxbtoxWNrtjx4549+4dbf8HBwcH/PXXX7h58ybt3Hr16gVZWVnEx8cLtvl79EMrY120NWmCJTMd0VyfvoOqspI8WP9ompi11sfg3u3RoXVTxC10h5qKAtRVFTF/qh2CJg4R5DbUhMPhwjtoKw6duoWQFftw5xG1yuf69etYv349xowZg4AxA3A1Yx4upc1FMz1qIl55eTmWLl1KMRbpePXqFV68eIHWrVvXOrYm58+fx5AhQ+Dj4yNRwqQoJkyYgNWrV9N2UhVF+/bta204p6qqKjaPycjISKRWSteuXXHy5EnafQDPWP2ZZaE/C6bXw08kODgYioqKaNSoEbKzs5GRIbqPwLJly6Cqqip4SZrwQ5IkTl96iCOnb4PD4cLJyQlR0ethZj0XnWzmCXrV6+moCRTaAGDV5kx8qPE0ThAsaLawRkn+HXz5SF8iKCPNRnZuER4/z4NxU23o6ajRjlNTUYBNXzOsCHGmlC9evf0c1mNWwm3ScnSytMH6lWHgVJVBo8VgDHGahD59+iAm4QSeZb9HZSVH4AVxtukmEGXS0VTF5uXeSIwaDz1dngDK6UsPEbJ8Ly7+TW3287PZunUrIiMjkZqairZt29b7+9WmDc+nvLwcubm5MDKilufVBRkZGdp+Eu/evaPUv589e5bSa4TvteBDkiSioqIwc+ZM2vfr168fJTFSUi5dugRnZ2eKK19dXR179uzB3LlzcfnyZcpxQ4YMwZYtW+Dv74/MzEwA1d0uDx8+LBBHat++PXx9fSlKlvz8jZkzZ1IqOthsNhYsXoGx433xJldYQ4IPP8TDF0VqYaiLo4kzcCQxEG72PXByVzD+PrQQi2c4oI1JY3g6WiIpxhdxi9xh2koP7VrqYerYwdiwxAvpm6YKwgY7Ui9g6JhV2LBL9OdZ064jvuk6deXKFYSFhSElJUXgbtdupEJrdAC8e1xgYCB0dXVFvh+foKAgrFixQuIkwoKCAvj4+GDPnj3Yt28fRo8eLdFxohg6dChiYmLg5uYmUtX0W7S0tGBkZCTWWOjRowftd4xPYGCgSGXcRYsWISYmRqSk+5IlSySWmP6lMMmMopk9ezYIghD7qlk2FRQUhJs3b+L48eNgs9nw9PQUmbQXEhKCjx8/Cl6S1vMnZVzG2KAtmDR3B6K3HkeLVqbIPHEGFZVVKP9Shb1HeIpvqioKOJo4A4P+MBUcW1YuvBgQLClotrTBxzfXUFFCvcFVVnF4SVUkCfNORjixi74NamFxKQ6fvoPxsxPQ2dRA0KyGzSJw4sw15D/IwKfcv1FQoYlGreyh0rgT2FJyUFaSx+bks7j9MJtX902SmONvy5N93ncBA91XIGxlKuauTBXKws57/xHjgrci+dAVeM3YhI8lvOS4v649xrRFScg4To0lfw+5ubmYNGkSXr58iZSUFKipqf2U84rj7t270NHRkUjMJiMjo1YXvSSKe+IMhW/nkZGRIVTb/fnzZ7x48QKmptXfs7Nnz6Jz584i47wtW7asc0b758+fERgYiM2bN2PmzJlwd3enCDupqqpiz549iIyMxMaNGym/PQMDA2RkZGDDhg0CfQMpKSls3boV+/btEwg32draokWLFpRSUR0dHQQEBFDaAb/J+wDXaVuR/7UFuvUcTFsCymKxEB8fj6tXryI4OJgyhs1mQUNNER4jLHE0cQYWTXeAZWcTmDTTwcHN03Bg8zQYfVPJ9L6oBHNXpeHB01xErD9MUUvlnzchcjzsB3XG8tlOQiqpFy5cwIIFCyT+bh86dAgsFktsWSCfAwcOoGXLlhJ5E0iSREJCAkaPHg0fHx/ExcX9tN9ay5YtsXz5cpFGKx0zZ85EVFSUyP0WFhYiw30ABIb7ixcvKPvk5OQQFRWFgIAA2rWhefPmaNOmDSW/h+HXUWdDYcaMGXj48KHYV82nOU1NTbRs2RIDBw5EcnIyjhw5ItLylJWVhYqKitBLErKevwWLIEAQwIMnbzBiYiwqCRVUfX4PkiTRu3u14puqsrxAHVFJQRYtmlMXHxZbGsadHVCSex1Fz0/iayW1UU/v7i3h5dgLoSv2UvbVJO99MTL+vCUwHnNe3EfE4hBoNO+FRiYDIausi4qqav32i9efIDzuoCAZcuUcV4x16oX9x24AAJ7nFGBX+kXsyriE5TXaXVd95YDLJUGSPNcql8tF+ZdKeM/aiowTf2PqoiQ8z657EyaAJ6Sybds22NnZYdasWXB2dsaiRYvE1vL/TFatWiVxjDIlJQVOTk5ixzx//rxWj4MoQ+HDhw9CN2ySJJGVlSV089+/fz8laTchIUGsuA1BELSNm0Rx7tw5DBs2DAMHDsSWLVvQvXt3rFu3Dm5ubpQnMxUVFezduxeFhYUYM2YMRcRJVlYWu3btQmRkpOCpUVpaGtu2bcP27dsFN+igoCDcvXuXcsO2s7MDm81GamqqYFvW87co/1IFWWVdcOSaYcKECbSaHNLS0oiOjoaZmRkcHBy+q1FYTeTlpKEgLwMC/+TrKNLrIHTrYIjVc0fDZZi5YNu5c+ewdOlSpKSkSHTvefv2LdasWUPJtaCjrKwM0dHRtCqYdMTGxuL58+c4cuRIvbRaNjc3h7q6usCTVBt6enrQ1NTE7du3Re6vraJNnFehY8eOMDMzw86d9HLas2fPRlRU1L9LvpnxKIhGS0sLrVu3Fvui66kAQHCjoNOW/xHGOv0Bo2ba0NNRx8TRffHqTSEUNVuirOAxxrn0xtC+HQRjHzzJRXYuz+VW+rkCeQX0eRAKSioY6zcHilptUfTsT3x4cRacqupGQeeuPkbnYfORfpw+BsuHwyGxdvtJkCQXxdkX8bkgC+otbMGWq36ybKbXCGw2S1CiyUdWho1BvU3BZrMFxo6sjBQAqtuyaWMNRIa6oJ9lG8QucIe6KrXckqylC0nBhxI4TVqL3s7LcO7KQxw+fBju7u7w8PAAQRDYuXMndu7cCSsrK7Hn+Zm8fv0a5eXlaNGiRa1jc3NzIS8vX+uT148YCgCE3Ma3bt2i6Brs2bMHzs7Ogn8XFxfj06dPtSa79e3bV6RqIZ/S0lIEBAQgKSkJaWlpQiVrrVu3RkxMDEaNGkVpTsRisRASEoJx48bBzs4O9+7dE9qvoKCApKQkzJ07V1COKiMjg507d2LTpk04f/48CILAunXrEBMTQ5GXjojgJeLyY8mWXVqgewdDsNkshARNhrm5OWbNmiXSm+jm5obw8HC4uLjUSZnzW5QU5LB3nT8CxgzAnjg/aKorS3TcqVOnEBUVhZSUFCgr135MVVUVfHx8EBMTAzm52pOHFy9ejKCgIIlyTe7cuYOzZ89i0aJFdVZvrAuLFi3C8uXLaTuR0jFr1izaNuN8autF0alTJ+Tk5Ig0BmfNmoVdu3bRepKVlZXh5uYmtuz2V0OSP1ge+fvYCfWXo3DlyhXExcXh1q1bePXqFU6dOoVRo0bB2NgYPXr0qP0EdcCwqRb+3BmEv/bNQfeORlBRkoOMUmNUlrxFW2PhhDLTlk0ENdWWXUzw8dNnulPiXcEnHD17FzbW/aDX3hHyGkYoeHIMxdkXwP1aN0OHU1mGwqyDkJJTRSOTQWCxq5M5mzbWgLQ0GxwOFyyWsIxzRSUHk+ftxJu8D9i83BupGwJwMmkWxjr1gptdDwT7CgugOA3thq0rxsGmH88wUpCXxeblYzGsX0esnjsKxgbiXffJB67g6s0s3L6wHyMdHfD48WNERkYiPT0dnp6eEt1AJaWsvAKPXuTV2ggrJiaG0lRJFBs3bhTb2Y7PjxoKNUlPTxcKOzx+/BhNmjQRCm0kJSVJFFuuLU/h5MmTsLW1hZ2dHTZs2EArlWtqaoqVK1fC1dWVtllW7969sXv3bsyZMwcJCQlC+1RVVbF7924EBgYKwiCysrLYvn07Fi1ahBcvXkBOTg6JiYmYPHkyCgqq3foyMjKIj4+Hr68vKioqICcrjZS1/nh6ZjmmeQ/CxIkToa6uLnahMTMzQ2pqKlasWEEbJqGj6isHN+6+RFGNXCPTFnoIHG+NTqbNxB577/EbPHiSixMnTiAmJgbJycm1dqLlExgYCG9v71qb0gHAvXv38OrVK7E6BHzKy8sxffp0rF+/vt4VCBUVFREWFiZRNRHA6zUhIyMjMinR3Nwc165do93HJyAgQGQpsJSUFGJiYuDv70/rffLy8kJGRobIJnAM9Ue9GQoKCgpITU1F//790apVK4wbNw5mZmY4e/ZsnWRR60rVVw6iQl3Ro7MxDE1aY3tyBk5+00Nh2tiB6GvRGk201VD6WfSi/5XDRW/z1qj6yoGcalNotxkBWeUmeJ91ECVvxXsS+FSU5qPg8RGoGPSCkrYpZX/O2yI8fclzN3+p+CqUbAkAZ69koZfTEpy88ACd2zWDvq4G5k2xQ8gkGygr0j/JRMYfRceh8zDSLw4rNx1Dr24tMGJw7ZUJRNUH5D9Mh7y6ETx852H69On1Urv84VMZ+o+NwtCJ0fBdKLq0sqysDPfv35fIsORwOLhw4YJE3o7vNRToFq6rV6/CwqJasyIxMZFirBw+fFhs6Rif5s2bixSLWr16tSBnYMAA0QqeANChQwcsX74crq6utK5abW1tQU8PX19foetq1KgRduzYAV9fX8GTn5KSEjZv3gwfHx9UVVVBR0cHq1evppSYNm/eHP7+/kLCOTUXu9DQUOTl5dEqOvJRU1NDSkoK8vPzERAQIPY6AcBndgIc/eLQd1QE8gsk76yYlHEZw7xXo59DIOYuWIbk5GSJG0AlJSVBXV0dDg4OEo0PCwtDZGSkRGPnzp2LoKAgiZtL/Sj9+vVDZWWlxF6cWbNmiQwfWFhYiE1o5L/f1atXhdq416Rly5awtbWlVVhls9kICwur1Rj5ZfwfhR4Isq5N138hnz59gqqqKj5+/ChRzPBjSTnsfKLx8nUhrPu0x5FTf4NgsSEjLY1bRxaBzWZhsGckXr2pzvZVlJehJDTWpJmeJrVfPUmi6ksxpOVFt1vlw/36BSDYQl4EURAEzx2lrCQHNWUF5Lytnmd/y7bYsoJXfrVt/19YEJ0B7UbK2L9hMvR1q8MY+QUfYW6/mHLeGwcXQkNN/NNScXExLlx/itIvHNgN7FxvXSTPXM2Cd1j10+zjo0sgJULVrqysTOKnPEnHVlZWQkpKSmyOBYfDS1qt6folSZLSKKi8vFzInVxWVkYpvaxLF8IvX77QurIrKyshLS1dp6dMSToU5ubm0nZWLSwshIaGhtD7FRUVCSVjfvtvPh8+fBDZipjL5aK4uFiseE9tc+NDkiSM+8wC958b7sZlYzDoD2r5KR3+83bgyKnb4HIqYDe4G2IWSK5SWFxcDBUVFYlzdD59+iRxvlVxcfEvSRCuSVlZGWRlZSUOc4hKBuZwOOByuWL7QQC8nCdx3kmSJPHx48fv+hzqumZ8D/z3aNZnKlhS3//Qy/1agVdn19TrXH8W/6leDzfuvsDL17zYbObZu2CxpcFiscBms8BiEXj5ukDISAB4vR34iZA6mioYYtUeng6WaGWoi8UzHChGQqi/DeTlZSUyEgCAJSUHFlsanU2b4VxKCExokif58E22ktIvSN8YAEWF6lwPw6bV9dubks+CJEnkF3zCsbPCsWZVZQVoqgv/iOXlZP7JbRCPmpoabAZ0hcsw83ptNd3ZtBkMmvAWCvv+nUQaCQAkNhLqMlZGRqbWmzybzabcOAmCoHjDvo05KyoqUhbzurQqFhXvlpGRqbMrWpI2xqIW4kaNGlHe79vFXdRiL8pIAHi5EpIYCeLmxocgCIwZyRMOMmmmDYtOkqsdutlZQE5OBkpKKvAYIbojIh1qamp1SuStyyLwq40EgPedrUsuhKiKITabXauRAKDWECZBEA3yOTCIpv4yZRqADm0MoKmuhIIPpejfsy1GDu2G05cewsG6C+TlZGDSTBtd2jXDjXs89662pgpWzHbG9PDdKK+oQvS80WjXUk/onFduPsOhU7ehoiQPZ5tuGDPyDxQUlWLj7mrFOzUVBRTXyHXQ0lCGn3tfPM9+h/yCEnRo0xR+7v3AZrNwfPtMXL39Agf+vAn7QZ3BZrGwcfcZtG+tj5zcIhw/fw9ejr3QSF0Zs31tsDAmA8YG2kINo/pbtsW2/RcgLcWGxTctsuVkpXFg8zScvfIIigqyePAkF8P6dYCiQv2Fe+qKiqIcTmyegYIPpdDV/Hdb0gz/buZNscM070FQUpCt0+Jt2aUF7hzjed6kpereyZGB4UdFk34nwaX/VOgBAErKviA7txCtjRpTpJIBnlvrTd4HFH0sQ2vjxpRKA7rxhcWl0FBVFLoRvS8qAcnlouRzBYyaaqGk7AveFZaAy+GihaHOT0tEIkmSci4ul4tbD3Kgo6kiEFtiYGBg+H/nV4YeDHpO+eHQQ/aFGCb00BAoK8rBtIUerZEA8Nxa+o01YNa6aa1GAn+8proy5WlFS0MZ2pqqMDbQBkEQUFGSh0kzbbQ00v2p2cp052KxWOjcrhljJDAwMDA0EA0l4bx27Vo0b94ccnJyMDc3rzURde/evWjdujXk5OTQvn17ocZ2kvKfMxQYGBgYGBj+i+zZsweBgYGYP38+/v77b3To0AGDBw8WKdR28eJFjBo1CuPGjRM0lrO3t6foqNTGvzr0wM98zcnJ+de7ZhgYGBgYGpZPnz6hadOmKC4uptUZ+VnvoaqqCr2uE8Bi04sLSgKXU4k31zdS1jdZWVmREgLm5ubo1q0b4uLieOfgctG0aVMEBARg9uzZlPEuLi4oKyvDoUOHBNssLCzQsWNH2k62IiH/xeTk5JAAmBfzYl7Mi3kxL4lfOTk59bYulZeXk7q6uj9lnkpKSpRt8+fPp33fiooKks1mk2lpaULbPT09yeHDh9Me07RpU3L16tVC2+bNm0eamZnV6Zr/1VUPTZo0QU5ODpSVlesc9+dblv9P3gjmmplr/q/CXDNzzZJAkiRKSkpqLa39EeTk5PDixQuJlFtrg6RJVhflTSgoKACHw4GOjo7Qdh0dHaFGjDXJy8ujHf9t87ja+FcbCiwWC/r6+rUPFENdmkv9V2Cu+f8D5pr/P2CuuW7UV8ihJnJychL1+PivwCQzMjAwMDAw/MvR1NQEm81Gfn6+0Pb8/Hzo6urSHqOrq1un8aJgDAUGBgYGBoZ/OTIyMujSpQtOnjwp2MblcnHy5EmR/XB69OghNB4ATpw4UefGjP/q0MOPICsri/nz59drA6p/G8w1/3/AXPP/B8w1M3xLYGAgvLy80LVrV3Tv3h3R0dEoKyvD2LFjAQCenp7Q09PDsmXLAABTp05Fnz59sHLlStjY2CA5ORnXr1+vc7vuf3V5JAMDAwMDA0M1cXFxiIyMRF5eHjp27IiYmBiYm5sDAKysrNC8eXMkJiYKxu/duxdhYWF4+fIlWrRogRUrVmDo0KF1ek/GUGBgYGBgYGAQCZOjwMDAwMDAwCASxlBgYGBgYGBgEAljKDAwMDAwMDCIhDEUGBgYGBgYGETyf2EoDB8+HAYGBpCTk0Pjxo3h4eGB3Nzchp5WvfHy5UuMGzcOhoaGkJeXh7GxMebPn/9TJEf/zSxZsgSWlpZQUFCAmppaQ0+nXqhri9nfnXPnzsHW1hZNmjQBQRBIT09v6CnVK8uWLUO3bt2grKwMbW1t2NvbIysrq6GnVa+sX78eZmZmAjXGHj164OjRow09LYYa/F8YCn379kVKSgqysrKwf/9+PHv2DCNHjmzoadUbjx49ApfLRXx8PO7fv4/Vq1djw4YNCA0Nbeip1SuVlZVwcnKCn59fQ0+lXqhri9n/AmVlZejQoQPWrl3b0FP5JZw9exb+/v64fPkyTpw4gaqqKgwaNAhlZWUNPbV6Q19fHxEREbhx4wauX7+Ofv36wc7ODvfv32/oqTHwqVMLqf8IGRkZJEEQZGVlZUNP5ZexYsUK0tDQsKGn8UtISEggVVVVG3oaP53u3buT/v7+gn9zOByySZMm5LJlyxpwVr8OAJTOef913r17RwIgz54929BT+aWoq6uTmzdvbuhpMPzD/4VHoSZFRUXYtWsXLC0tIS0t3dDT+WV8/PgRGhoaDT0Nhu+ksrISN27cwIABAwTbWCwWBgwYgEuXLjXgzBjqk48fPwLA/81vl8PhIDk5GWVlZXWWGWaoP/5vDIXg4GAoKiqiUaNGyM7ORkZGRkNP6Zfx9OlTxMbGYuLEiQ09FYbvRFyL2bq2jGX4PeByuZg2bRp69uyJdu3aNfR06pW7d+9CSUkJsrKy8PX1RVpaGtq2bdvQ02L4h9/WUJg9ezYIghD7qtmjOygoCDdv3sTx48fBZrPh6ekJ8jcTpazrNQPAmzdvYG1tDScnJ/j4+DTQzL+f77lmBob/Av7+/rh37x6Sk5Mbeir1TqtWrXDr1i1cuXIFfn5+8PLywoMHDxp6Wgz/8NtKOL9//x6FhYVixxgZGUFGRoay/fXr12jatCkuXrz4W7m36nrNubm5sLKygoWFBRITE8Fi/X524ff8nRMTEzFt2jQUFxfX8+x+HZWVlVBQUMC+fftgb28v2O7l5YXi4uL/Cw8ZQRBIS0sTuv7/KpMnT0ZGRgbOnTsHQ0PDhp7OL2fAgAEwNjZGfHx8Q0+FAb9x90gtLS1oaWl917FcLhcAUFFR8TOnVO/U5ZrfvHmDvn37okuXLkhISPgtjQTgx/7O/yVqtpjlL5T8FrOTJ09u2Mkx/DRIkkRAQADS0tJw5syZ/0sjAeB9t3+3+/N/md/WUJCUK1eu4Nq1a+jVqxfU1dXx7NkzzJ07F8bGxr+VN6EuvHnzBlZWVmjWrBmioqLw/v17wT5dXd0GnFn9kp2djaKiImRnZ4PD4eDWrVsAABMTEygpKTXs5H4CtbWY/S9SWlqKp0+fCv794sUL3Lp1CxoaGjAwMGjAmdUP/v7+SEpKQkZGBpSVlQX5J6qqqpCXl2/g2dUPISEhGDJkCAwMDFBSUoKkpCScOXMGmZmZDT01Bj4NW3RR/9y5c4fs27cvqaGhQcrKypLNmzcnfX19ydevXzf01OqNhIQEEgDt67+Ml5cX7TWfPn26oaf204iNjSUNDAxIGRkZsnv37uTly5cbekr1yunTp2n/pl5eXg09tXpB1O82ISGhoadWb3h7e5PNmjUjZWRkSC0tLbJ///7k8ePHG3paDDX4bXMUGBgYGBgYGOqf3zNwzcDAwMDAwPBLYAwFBgYGBgYGBpEwhgIDAwMDAwODSBhDgYGBgYGBgUEkjKHAwMDAwMDAIBLGUGBgYGBgYGAQCWMoMDAwMDAwMIiEMRQYGBgYGBgYRMIYCgwMDAwMDAwiYQwFBgYGBgYGBpEwhgIDAwMDAwODSP4HByJ5HxL5XXcAAAAASUVORK5CYII=", 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", 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      " ] @@ -375,7 +402,7 @@ "plt.contour(ref['bins1'], ref['bins2'], ref['fes'], 15, colors='k', linewidths=0.5)\n", "# plt.hist(out_graph.detach().squeeze())\n", "# plt.scatter(torch.arange(len(dataset)).numpy(), out_graph.detach().squeeze(), c=[dataset['data_list'][i]['graph_labels'] for i in range(len(dataset))])\n", - "cp = plt.scatter(colvar['phi'], colvar['psi'], c=out_graph[:, 1].detach().squeeze().numpy(), cmap='fessa', s=2)\n", + "cp = plt.scatter(colvar['phi'], colvar['psi'], c=out_graph[:].detach().squeeze().numpy(), cmap='fessa', s=2)\n", "plt.colorbar(cp)\n", "# plt.ylim(0,1)\n", "plt.show()" From b92e6ef4009665717429fdc460eb841d57ebb0f8 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 4 Apr 2025 11:51:08 +0200 Subject: [PATCH 237/267] Move torch_scatter fucntions to utils --- mlcolvar/core/loss/committor_loss.py | 33 +--------------- mlcolvar/core/nn/graph/gvp.py | 6 +-- mlcolvar/core/nn/graph/schnet.py | 6 +-- mlcolvar/utils/_code.py | 59 ++++++++++++++++++++++++++++ 4 files changed, 67 insertions(+), 37 deletions(-) create mode 100644 mlcolvar/utils/_code.py diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index 86661584..fb942a06 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -18,6 +18,8 @@ from typing import Union, Optional import torch_geometric import warnings + +from mlcolvar.utils._code import scatter_sum # ============================================================================= # LOSS FUNCTIONS # ============================================================================= @@ -96,37 +98,6 @@ def forward(self, z_regularization=self.z_regularization ) -def broadcast(src: torch.Tensor, other: torch.Tensor, dim: int): - """Broadcast util, from torch_scatter""" - if dim < 0: - dim = other.dim() + dim - if src.dim() == 1: - for _ in range(0, dim): - src = src.unsqueeze(0) - for _ in range(src.dim(), other.dim()): - src = src.unsqueeze(-1) - src = src.expand(other.size()) - return src - -def scatter_sum(src: torch.Tensor, - index: torch.Tensor, - dim: int = -1, - out: Optional[torch.Tensor] = None, - dim_size: Optional[int] = None) -> torch.Tensor: - """Scatter sum function, from torch_scatter module (https://github.com/rusty1s/pytorch_scatter/blob/master/torch_scatter/scatter.py)""" - index = broadcast(index, src, dim) - if out is None: - size = list(src.size()) - if dim_size is not None: - size[dim] = dim_size - elif index.numel() == 0: - size[dim] = 0 - else: - size[dim] = int(index.max()) + 1 - out = torch.zeros(size, dtype=src.dtype, device=src.device) - return out.scatter_add_(dim, index, src) - else: - return out.scatter_add_(dim, index, src) def committor_loss(x: torch.Tensor, z: torch.Tensor, diff --git a/mlcolvar/core/nn/graph/gvp.py b/mlcolvar/core/nn/graph/gvp.py index eeb4e36d..59a6c96e 100644 --- a/mlcolvar/core/nn/graph/gvp.py +++ b/mlcolvar/core/nn/graph/gvp.py @@ -2,7 +2,7 @@ import math import torch from torch import nn -import torch_scatter +from mlcolvar.utils import _code from torch_geometric.nn import MessagePassing from typing import Tuple, Callable, Optional, List, Dict @@ -167,10 +167,10 @@ def forward( if scatter_mean: if 'system_masks' not in data.keys(): - out = torch_scatter.scatter_mean(out, batch_id, dim=0) + out = _code.scatter_mean(out, batch_id, dim=0) else: out = out * data['system_masks'] - out = torch_scatter.scatter_sum(out, batch_id, dim=0) + out = _code.scatter_sum(out, batch_id, dim=0) out = out / data['n_system'] return out diff --git a/mlcolvar/core/nn/graph/schnet.py b/mlcolvar/core/nn/graph/schnet.py index 0bdef0f3..541ed330 100644 --- a/mlcolvar/core/nn/graph/schnet.py +++ b/mlcolvar/core/nn/graph/schnet.py @@ -1,6 +1,6 @@ import math import torch -import torch_scatter # TODO check this is equivalent in torch scatter +from mlcolvar.utils import _code from torch import nn from torch_geometric.nn import MessagePassing @@ -158,11 +158,11 @@ def forward( if scatter_mean: if 'system_masks' not in data.keys(): # TODO check this is equivalent in torch scatter - out = torch_scatter.scatter_mean(out, batch_id, dim=0) + out = _code.scatter_mean(out, batch_id, dim=0) else: out = out * data['system_masks'] # TODO check this is equivalent in torch scatter - out = torch_scatter.scatter_sum(out, batch_id, dim=0) + out = _code.scatter_sum(out, batch_id, dim=0) out = out / data['n_system'] if self._w_out_after_sum: diff --git a/mlcolvar/utils/_code.py b/mlcolvar/utils/_code.py new file mode 100644 index 00000000..499609de --- /dev/null +++ b/mlcolvar/utils/_code.py @@ -0,0 +1,59 @@ +import torch +from typing import Optional + +def broadcast(src: torch.Tensor, other: torch.Tensor, dim: int): + """Broadcast util, from torch_scatter""" + if dim < 0: + dim = other.dim() + dim + if src.dim() == 1: + for _ in range(0, dim): + src = src.unsqueeze(0) + for _ in range(src.dim(), other.dim()): + src = src.unsqueeze(-1) + src = src.expand(other.size()) + return src + +def scatter_sum(src: torch.Tensor, + index: torch.Tensor, + dim: int = -1, + out: Optional[torch.Tensor] = None, + dim_size: Optional[int] = None) -> torch.Tensor: + """Scatter sum function, from torch_scatter module (https://github.com/rusty1s/pytorch_scatter/blob/master/torch_scatter/scatter.py)""" + index = broadcast(index, src, dim) + if out is None: + size = list(src.size()) + if dim_size is not None: + size[dim] = dim_size + elif index.numel() == 0: + size[dim] = 0 + else: + size[dim] = int(index.max()) + 1 + out = torch.zeros(size, dtype=src.dtype, device=src.device) + return out.scatter_add_(dim, index, src) + else: + return out.scatter_add_(dim, index, src) + +def scatter_mean(src: torch.Tensor, + index: torch.Tensor, + dim: int = -1, + out: Optional[torch.Tensor] = None, + dim_size: Optional[int] = None) -> torch.Tensor: + """Scatter mean function, from torch_scatter module (https://github.com/rusty1s/pytorch_scatter/blob/master/torch_scatter/scatter.py)""" + out = scatter_sum(src, index, dim, out, dim_size) + dim_size = out.size(dim) + + index_dim = dim + if index_dim < 0: + index_dim = index_dim + src.dim() + if index.dim() <= index_dim: + index_dim = index.dim() - 1 + + ones = torch.ones(index.size(), dtype=src.dtype, device=src.device) + count = scatter_sum(ones, index, index_dim, None, dim_size) + count[count < 1] = 1 + count = broadcast(count, out, dim) + if out.is_floating_point(): + out.true_divide_(count) + else: + out.div_(count, rounding_mode='floor') + return out \ No newline at end of file From b8061c41f52e9c7105f43c06a404e2933be5c031 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 4 Apr 2025 13:32:09 +0200 Subject: [PATCH 238/267] Added buffers with quantities needed for graph models, see #171 --- mlcolvar/cvs/cv.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index cab47eea..9863b527 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -72,7 +72,7 @@ def example_input_array(self): return create_test_graph_input(output_type='tracing_example', n_atoms=3, n_samples=1, n_states=1) - # TODO add general torch.nn.Module TODO TODO TODO TODO + # TODO add general torch.nn.Module def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): if isinstance(model, list): self.layers = model @@ -85,6 +85,11 @@ def parse_model(self, model: Union[List[int], FeedForward, BaseGNN]): self._override_model = True self.in_features = model.in_features self.out_features = model.out_features + # save buffers for the interface for PLUMED + if isinstance(model, BaseGNN): + self.register_buffer('n_out', model.n_out) + self.register_buffer('cutoff', model.cutoff) + self.register_buffer('atomic_numbers', model.atomic_numbers) else: raise ValueError( f"Keyword model can either accept type list, FeedForward or BaseGNN. Found {type(model)}" From c64b41b04db9d021c63963777c41d3e945374fe9 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 9 Apr 2025 17:40:46 +0200 Subject: [PATCH 239/267] Added roundign to prevent presumed issue from #170 --- mlcolvar/data/graph/utils.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 49d95f43..19706a91 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -193,7 +193,10 @@ def create_dataset_from_configurations( for i in range(len(data_list)): data_list[i].cell = cell_list[i] # get and save the original index before removing isolated nodes for each entry - original_idx = torch.unique( torch.where(torch.isin(_aux_pos[i], data_list[i]['positions']))[0] ) + original_idx = torch.unique( torch.where(torch.isin(torch.round(_aux_pos[i], decimals=5), + torch.round(data_list[i]['positions'], decimals=5)) + )[0] + ) data_list[i]['names_idx'] = original_idx.to(torch.int64) # update if needed the overall list @@ -310,7 +313,8 @@ def create_test_graph_input(output_type: str, n_atoms: int = 3, n_samples: int = 60, n_states: int = 2, - random_weights = False ) -> torch_geometric.data.Batch: + random_weights = False, + add_noise = True ): if n_atoms == 3: numbers = [8, 1, 1] node_labels = np.array([[0], [1], [1]]) @@ -320,7 +324,7 @@ def create_test_graph_input(output_type: str, [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], - [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07], [-0.07, 0.0, 0.07]], + [[0.0, 0.0, 0.0], [0.11, 0.11, 0.11], [-0.07, 0.0, 0.07]], [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], ], dtype=np.float64 @@ -335,7 +339,7 @@ def create_test_graph_input(output_type: str, [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0], [0.05, 0.05, 0.0]], [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0], [0.05, 0.05, 0.0]], [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07], [0.0, 0.05, 0.05]], - [[0.0, 0.0, 0.0], [0.07, 0.0, 0.07] , [-0.07, 0.0, 0.07], [-0.05, 0.0, 0.05]], + [[0.0, 0.0, 0.0], [0.11, 0.11, 0.11] , [-0.07, 0.0, 0.07], [-0.05, 0.0, 0.05]], [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1] , [0.17, -0.07, 1.1], [0.15, -0.05, 1.1]], ], dtype=np.float64 @@ -345,6 +349,11 @@ def create_test_graph_input(output_type: str, idx = np.random.randint(low=0, high=6, size=(n_samples*n_states)) positions = _ref_positions[idx, :, :] + # let's add some noise to the positions for fun + if add_noise: + noise = np.random.randn(*positions.shape)*1e-5 + positions = positions + noise + cell = np.identity(3, dtype=float) * 0.2 graph_labels = np.zeros((n_samples*n_states, 1, 1)) for i in range(1, n_states): @@ -373,7 +382,7 @@ def create_test_graph_input(output_type: str, return config dataset = create_dataset_from_configurations( - config, z_table, 0.1, show_progress=False + config, z_table, 0.1, show_progress=False, remove_isolated_nodes=True ) if output_type == 'dataset': From f0ec4f4c751f485fead92f1f02e4cc8d099525a5 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 9 Apr 2025 17:41:23 +0200 Subject: [PATCH 240/267] Added graph sensitivity tests --- mlcolvar/explain/graph_sensitivity.py | 42 ++++++++++++++++++++-- mlcolvar/tests/test_explain_sensitivity.py | 2 ++ 2 files changed, 42 insertions(+), 2 deletions(-) diff --git a/mlcolvar/explain/graph_sensitivity.py b/mlcolvar/explain/graph_sensitivity.py index 34116b9c..2edd56ae 100644 --- a/mlcolvar/explain/graph_sensitivity.py +++ b/mlcolvar/explain/graph_sensitivity.py @@ -68,7 +68,7 @@ def graph_node_sensitivity( results = {} results['atoms_list'] = np.array(dataset.metadata['used_names']) results['node_labels'] = [str(a) for a in results['atoms_list']] - results['node_labels_components'] = np.array([np.array(dataset.metadata['used_names'])[dataset[i]['data_list']['names_idx']] for i in range(len(dataset))]) + # results['node_labels_components'] = np.array([np.array(dataset.metadata['used_names'])[dataset[i]['data_list']['names_idx']] for i in range(len(dataset))]) results['sensitivities'] = sensitivities_components.mean(axis=0) results['sensitivities_components'] = sensitivities_components @@ -199,4 +199,42 @@ def get_dataset_cv_gradients( aux[batch_dict[i]['names_idx'], :] = g cv_value_gradients.extend(aux.unsqueeze(0).cpu().numpy()) - return np.array(cv_value_gradients) \ No newline at end of file + return np.array(cv_value_gradients) + + +def test_graph_sensitivity(): + import lightning + from mlcolvar.cvs import DeepTDA + from mlcolvar.core.nn.graph import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + + # create data, we need the dataset for sensitivity analysis later + dataset = create_test_graph_input(output_type='dataset', n_samples=100, n_states=2, n_atoms=3) + datamodule = DictModule(dataset=dataset, lengths=[0.8, 0.2], shuffle=[1, 0]) + + # create model + gnn_model = SchNetModel(n_out=1, cutoff=0.1, atomic_numbers=[8, 1]) + model = DeepTDA( + n_states=2, + n_cvs=1, + target_centers=[-5, 5], + target_sigmas=[0.2, 0.2], + model=gnn_model + ) + + # train model + trainer = lightning.Trainer( + accelerator="cpu", max_epochs=2, logger=False, enable_checkpointing=False, enable_model_summary=False + ) + trainer.fit(model, datamodule) + + # do analysis + test_sensitivity = graph_node_sensitivity(model=model, + dataset=dataset, + batch_size=0) + + # print results + print(test_sensitivity) + +if __name__ == '__main__': + test_graph_sensitivity() \ No newline at end of file diff --git a/mlcolvar/tests/test_explain_sensitivity.py b/mlcolvar/tests/test_explain_sensitivity.py index 0b80078d..67e8ef29 100644 --- a/mlcolvar/tests/test_explain_sensitivity.py +++ b/mlcolvar/tests/test_explain_sensitivity.py @@ -1,6 +1,8 @@ import pytest from mlcolvar.explain.sensitivity import test_sensitivity_analysis +from mlcolvar.explain.graph_sensitivity import test_graph_sensitivity if __name__ == "__main__": test_sensitivity_analysis() + test_graph_sensitivity() From 16f427116f62f5beda1b5f81ac25623510e90072 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 9 Apr 2025 17:42:29 +0200 Subject: [PATCH 241/267] Added dataset mode for timelagged utils also for descriptors datasets --- mlcolvar/utils/timelagged.py | 73 ++++++++++++++++++++++++++---------- 1 file changed, 54 insertions(+), 19 deletions(-) diff --git a/mlcolvar/utils/timelagged.py b/mlcolvar/utils/timelagged.py index 59a1297e..5c26b825 100644 --- a/mlcolvar/utils/timelagged.py +++ b/mlcolvar/utils/timelagged.py @@ -225,8 +225,8 @@ def create_timelagged_dataset( Parameters ---------- - X : array-like - input descriptors + X : torch.Tensor or np.ndarray or DictDataset + Input data, graph data can only be provided as DictDataset t : array-like, optional time series, by default np.arange(len(X)) reweight_mode: str, optional @@ -327,46 +327,81 @@ def create_timelagged_dataset( return dataset elif isinstance(X, DictDataset): - # we use deepcopy to avoid editing the original dataset - dataset = DictDataset(dictionary={"data_list" : copy.deepcopy(X[x_t.numpy().tolist()]["data_list"]), - "data_list_lag" : copy.deepcopy(X[x_lag.numpy().tolist()]["data_list"])}, - metadata={"z_table" : X.metadata["z_table"], - "cutoff" : X.metadata["cutoff"]}, - data_type="graphs") - # update weights - for i in range(len(dataset)): - dataset['data_list'][i]['weight'] = w_t[i] - dataset['data_list_lag'][i]['weight'] = w_lag[i] + if X.metadata["data_type"] == "descriptors": + dataset = DictDataset({"data": X['data'][x_t], + "data_lag": X['data'][x_lag], + "weights": w_t, + "weights_lag": w_lag}, + data_type='descriptors') + + elif X.metadata["data_type"] == "graphs": + # we use deepcopy to avoid editing the original dataset + dataset = DictDataset(dictionary={"data_list" : copy.deepcopy(X[x_t.numpy().tolist()]["data_list"]), + "data_list_lag" : copy.deepcopy(X[x_lag.numpy().tolist()]["data_list"])}, + metadata={"z_table" : X.metadata["z_table"], + "cutoff" : X.metadata["cutoff"]}, + data_type="graphs") + # update weights + for i in range(len(dataset)): + dataset['data_list'][i]['weight'] = w_t[i] + dataset['data_list_lag'][i]['weight'] = w_lag[i] return dataset def test_create_timelagged_dataset(): in_features = 2 - n_points = 100 + n_points = 20 X = torch.rand(n_points, in_features) * 100 + dataset = DictDataset(data=X, data_type='descriptors') + # unbiased case t = np.arange(n_points) - dataset = create_timelagged_dataset(X, t, lag_time=10) - print(len(dataset)) + lagged_dataset_1 = create_timelagged_dataset(X, t, lag_time=10) + print(len(lagged_dataset_1)) + lagged_dataset_2 = create_timelagged_dataset(dataset, t, lag_time=10) + print(len(lagged_dataset_2)) + assert(torch.allclose(lagged_dataset_1['data'], lagged_dataset_2['data'])) + assert(torch.allclose(lagged_dataset_1['data_lag'], lagged_dataset_2['data_lag'])) + assert(torch.allclose(lagged_dataset_1['weights'], lagged_dataset_2['weights'])) + # reweight mode rescale_time (default) logweights = np.random.rand(n_points) - dataset = create_timelagged_dataset(X, t, logweights=logweights) - print(len(dataset)) + lagged_dataset_1 = create_timelagged_dataset(X, t, logweights=logweights) + print(len(lagged_dataset_1)) + lagged_dataset_2 = create_timelagged_dataset(dataset, t, logweights=logweights) + print(len(lagged_dataset_2)) + assert(torch.allclose(lagged_dataset_1['data'], lagged_dataset_2['data'])) + assert(torch.allclose(lagged_dataset_1['data_lag'], lagged_dataset_2['data_lag'])) + assert(torch.allclose(lagged_dataset_1['weights'], lagged_dataset_2['weights'])) + # reweight mode weights_t logweights = np.random.rand(n_points) - dataset = create_timelagged_dataset( + lagged_dataset_1 = create_timelagged_dataset( X, t, logweights=logweights, reweight_mode="weights_t" ) - print(len(dataset)) + print(len(lagged_dataset_1)) + lagged_dataset_2 = create_timelagged_dataset( + dataset, t, logweights=logweights, reweight_mode="weights_t" + ) + print(len(lagged_dataset_2)) + assert(torch.allclose(lagged_dataset_1['data'], lagged_dataset_2['data'])) + assert(torch.allclose(lagged_dataset_1['data_lag'], lagged_dataset_2['data_lag'])) + assert(torch.allclose(lagged_dataset_1['weights'], lagged_dataset_2['weights'])) + + # graph data from mlcolvar.data.graph.utils import create_test_graph_input dataset = create_test_graph_input('dataset') + print(dataset['data_list'][0]) lagged_dataset = create_timelagged_dataset(dataset, logweights=torch.randn(len(dataset))) + # lagged_dataset['data_list'][0] + print(dataset['data_list'][0]) + print(len(dataset)) From d4391cbea551818f1fae35a08f4c49ae42c7f5fe Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 9 Apr 2025 17:43:36 +0200 Subject: [PATCH 242/267] Added support for xyz trajectories in create dataset from trajectories util --- mlcolvar/tests/data/Cu.xyz | 54 ++++ mlcolvar/tests/data/Cu_top.pdb | 19 ++ mlcolvar/tests/test_utils_io.py | 2 + mlcolvar/utils/io.py | 510 ++++++++++++++++++-------------- 4 files changed, 361 insertions(+), 224 deletions(-) create mode 100644 mlcolvar/tests/data/Cu.xyz create mode 100644 mlcolvar/tests/data/Cu_top.pdb diff --git a/mlcolvar/tests/data/Cu.xyz b/mlcolvar/tests/data/Cu.xyz new file mode 100644 index 00000000..111af6a3 --- /dev/null +++ b/mlcolvar/tests/data/Cu.xyz @@ -0,0 +1,54 @@ +16 +Lattice="7.15486134 0.0 0.0 0.0 3.57743067 0.0 0.0 0.0 7.15486134" Properties=species:S:1:pos:R:3 +Cu 0.0 0.0 0.0 +Cu 0.0 1.78871534 1.78871534 +Cu 1.78871534 0.0 1.78871534 +Cu 1.78871534 1.78871534 0.0 +Cu 0.0 0.0 3.57743067 +Cu 0.0 1.78871534 5.36614601 +Cu 1.78871534 0.0 5.36614601 +Cu 1.78871534 1.78871534 3.57743067 +Cu 3.57743067 0.0 0.0 +Cu 3.57743067 1.78871534 1.78871534 +Cu 5.36614601 0.0 1.78871534 +Cu 5.36614601 1.78871534 0.0 +Cu 3.57743067 0.0 3.57743067 +Cu 3.57743067 1.78871534 5.36614601 +Cu 5.36614601 0.0 5.36614601 +Cu 5.36614601 1.78871534 3.57743067 +16 +Lattice="7.15486134 0.0 0.0 0.0 3.57743067 0.0 0.0 0.0 7.15486134" Properties=species:S:1:pos:R:3 +Cu 0.0 0.0 0.0 +Cu 0.0 1.78871534 1.78871534 +Cu 1.78871534 0.0 1.78871534 +Cu 1.78871534 1.78871534 0.0 +Cu 0.0 0.0 3.57743067 +Cu 0.0 1.78871534 5.36614601 +Cu 1.78871534 0.0 5.36614601 +Cu 1.78871534 1.78871534 3.57743067 +Cu 3.57743067 0.0 0.0 +Cu 3.57743067 1.78871534 1.78871534 +Cu 5.36614601 0.0 1.78871534 +Cu 5.36614601 1.78871534 0.0 +Cu 3.57743067 0.0 3.57743067 +Cu 3.57743067 1.78871534 5.36614601 +Cu 5.36614601 0.0 5.36614601 +Cu 5.36614601 1.78871534 3.57743067 +16 +Lattice="7.15486134 0.0 0.0 0.0 3.57743067 0.0 0.0 0.0 7.15486134" Properties=species:S:1:pos:R:3 +Cu 0.0 0.0 0.0 +Cu 0.0 1.78871534 1.78871534 +Cu 1.78871534 0.0 1.78871534 +Cu 1.78871534 1.78871534 0.0 +Cu 0.0 0.0 3.57743067 +Cu 0.0 1.78871534 5.36614601 +Cu 1.78871534 0.0 5.36614601 +Cu 1.78871534 1.78871534 3.57743067 +Cu 3.57743067 0.0 0.0 +Cu 3.57743067 1.78871534 1.78871534 +Cu 5.36614601 0.0 1.78871534 +Cu 5.36614601 1.78871534 0.0 +Cu 3.57743067 0.0 3.57743067 +Cu 3.57743067 1.78871534 5.36614601 +Cu 5.36614601 0.0 5.36614601 +Cu 5.36614601 1.78871534 3.57743067 \ No newline at end of file diff --git a/mlcolvar/tests/data/Cu_top.pdb b/mlcolvar/tests/data/Cu_top.pdb new file mode 100644 index 00000000..9e7a14a1 --- /dev/null +++ b/mlcolvar/tests/data/Cu_top.pdb @@ -0,0 +1,19 @@ +CRYST1 7.155 3.577 7.155 90.00 90.00 90.00 P 1 +MODEL 1 +ATOM 1 Cu MOL 1 0.000 0.000 0.000 1.00 0.00 CU +ATOM 2 Cu MOL 1 0.000 1.789 1.789 1.00 0.00 CU +ATOM 3 Cu MOL 1 1.789 0.000 1.789 1.00 0.00 CU +ATOM 4 Cu MOL 1 1.789 1.789 0.000 1.00 0.00 CU +ATOM 5 Cu MOL 1 0.000 0.000 3.577 1.00 0.00 CU +ATOM 6 Cu MOL 1 0.000 1.789 5.366 1.00 0.00 CU +ATOM 7 Cu MOL 1 1.789 0.000 5.366 1.00 0.00 CU +ATOM 8 Cu MOL 1 1.789 1.789 3.577 1.00 0.00 CU +ATOM 9 Cu MOL 1 3.577 0.000 0.000 1.00 0.00 CU +ATOM 10 Cu MOL 1 3.577 1.789 1.789 1.00 0.00 CU +ATOM 11 Cu MOL 1 5.366 0.000 1.789 1.00 0.00 CU +ATOM 12 Cu MOL 1 5.366 1.789 0.000 1.00 0.00 CU +ATOM 13 Cu MOL 1 3.577 0.000 3.577 1.00 0.00 CU +ATOM 14 Cu MOL 1 3.577 1.789 5.366 1.00 0.00 CU +ATOM 15 Cu MOL 1 5.366 0.000 5.366 1.00 0.00 CU +ATOM 16 Cu MOL 1 5.366 1.789 3.577 1.00 0.00 CU +ENDMDL diff --git a/mlcolvar/tests/test_utils_io.py b/mlcolvar/tests/test_utils_io.py index 20822210..1828bc23 100644 --- a/mlcolvar/tests/test_utils_io.py +++ b/mlcolvar/tests/test_utils_io.py @@ -4,6 +4,7 @@ from mlcolvar.utils.io import test_datasetFromFile from mlcolvar.utils.io import test_datasesetFromTrajectories from mlcolvar.utils.io import test_create_dataset_from_trajectories +from mlcolvar.utils.io import test_dataset_from_xyz example_files = { "str": "mlcolvar/tests/data/state_A.dat", @@ -26,6 +27,7 @@ def test_loadDataframe(file_type): if __name__ == "__main__": + # test_dataset_from_xyz() test_datasetFromFile() test_datasesetFromTrajectories() text = """ diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index fbc7b698..28675a3e 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -13,6 +13,14 @@ from typing import Union, List, Tuple import mdtraj +# Import ASE for xyz to pdb conversion. +try: + from ase.io import read, write + from ase import Atoms +except ImportError as e: + raise ImportError("ASE is required for xyz to pdb conversion.", e) + + from mlcolvar.data import DictDataset from mlcolvar.data.graph.atomic import AtomicNumberTable, Configuration, Configurations from mlcolvar.data.graph.utils import create_dataset_from_configurations @@ -262,15 +270,41 @@ def create_dataset_from_files( else: return dataset +def create_pdb_from_xyz(input_filename: str, output_filename: str) -> str: + """ + Convert the first frame of an XYZ file into a PDB file using ASE. + This pdb file can then serve as the topology for MDTraj. + + Parameters: + input_filename: Path to the input .xyz file. + output_filename: Path to the output .pdb file. + + Returns: + The path to the generated PDB file. + """ + atoms: Atoms = read(input_filename, index=0) + + if (atoms.cell == 0).all(): + warn("A topology file was generated from the xyz trajectory file but no cell information were provided!") + if not atoms.pbc.any(): + warn("A topology file was generated from the xyz trajectory file but no PBC information were provided!") + elif not atoms.pbc.all(): + warn( f"Partial PBC are not supported! The provided input has pbc {atoms.pbc}") + + write(output_filename, atoms, format='proteindatabank') + return output_filename + + + def create_dataset_from_trajectories( - trajectories: Union[List[List[str]], List[str], str], - top: Union[List[List[str]], List[str], str], + trajectories: Union[List[str], str], + top: Union[List[str], str, None], cutoff: float, buffer: float = 0.0, z_table: AtomicNumberTable = None, load_args: list = None, folder: str = None, - create_labels: bool = True, + labels: list = None, system_selection: str = None, environment_selection: str = None, return_trajectories: bool = False, @@ -289,10 +323,10 @@ def create_dataset_from_trajectories( Parameters ---------- - trajectories: Union[List[List[str]], List[str], str] - Names of trajectories files. - top: Union[List[List[str]], List[str], str] - Names of topology files. + trajectories: Union[List[str], str] + Paths to trajectories files. + top: Union[List[str], str, None] + Path to topology files. Only for .xyz files it can be set to None or empty to generate automatically a topology file. cutoff: float (units: Ang) The graph cutoff radius. buffer: float @@ -300,12 +334,14 @@ def create_dataset_from_trajectories( z_table: mlcolvar.graph.data.atomic.AtomicNumberTable The atomic number table used to build the node attributes. If not given, it will be created from the given trajectories. + load_args: list[dict], optional + List of dictionaries for loading options for each file (keys: start,stop,stride), by default None folder: str Common path for the files to be imported. If set, filenames become `folder/file_name`. - create_labels: bool - Assign a label to each file according to the total number of files. - If False, labels of all files will be `-1`. + labels: list + List of labels to be assigned to the given files. by default None. + If None, it simply enumerates the files. system_selection: str MDTraj style atom selections [1] of the system atoms. If given, only selected atoms will be loaded from the trajectories. This option may @@ -320,6 +356,8 @@ def create_dataset_from_trajectories( If remove isolated nodes from the dataset. show_progress: bool If show the progress bar. + save_names: bool + If to save names from topology file, by default True Returns ------- @@ -343,6 +381,7 @@ def create_dataset_from_trajectories( .. [1] https://www.mdtraj.org/1.9.8.dev0/atom_selection.html """ + # check if using truncated graph if environment_selection is not None: assert system_selection is not None, ( 'the `environment_selection` argument requires the' @@ -355,87 +394,84 @@ def create_dataset_from_trajectories( selection = system_selection else: selection = None - + if environment_selection is None: assert buffer == 0, ( 'Not `environment_selection` given! Cannot define buffer size!' ) - # fmt: off - assert type(trajectories) is type(top), ( - 'The `trajectories` and `top` parameters should have the same type!' - ) + # initiliaze simple labels if not provided + if labels is None: + labels = [i for i in range(len(trajectories))] + else: + assert len(labels) == len(trajectories), ( + "Number of labels and trajectories must be the same!" + ) + + # check topologies if given + if top is not None: + assert len(trajectories) == len(top) or len(top)==1 or isinstance(top, str), ( + 'Either a single topology file or as many as the trajectory files must be provided!' + ) + + # ensure trajectories is a list if isinstance(trajectories, str): trajectories = [trajectories] - top = [top] - assert len(trajectories) == len(top), ( - 'Numbers of trajectories and topology files should be the same!' - ) - # fmt: on - + + # --- Handle topologies input --- + # Allow top to be None or empty. In that case, create a list of empty strings. + if isinstance(top, str): + top = [top for _ in trajectories] + if top is None or (isinstance(top, list) and len(top) == 0): + top = ["" for _ in trajectories] + elif len(top) == 1 and len(trajectories) > 1: + top = [top for _ in trajectories] + + # For each trajectory file (and its associated topology), if the trajectory file + # has a ".xyz" extension and no topology is provided, convert it. for i in range(len(trajectories)): - assert type(trajectories[i]) is type(top[i]), ( - 'Each element of `trajectories` and `top` parameters ' - + 'should have the same type!' - ) - if isinstance(trajectories[i], list): - assert len(trajectories[i]) == len(top[i]), ( - 'Numbers of trajectories and topology files should be ' - + 'the same!' - ) - for j in range(len(trajectories[i])): - if folder is not None: - trajectories[i][j] = folder + '/' + trajectories[i][j] - top[i][j] = folder + '/' + top[i][j] - assert isinstance(trajectories[i][j], str) - assert isinstance(top[i][j], str) - else: - if folder is not None: - trajectories[i] = folder + '/' + trajectories[i] - top[i] = folder + '/' + top[i] - assert isinstance(trajectories[i], str) - assert isinstance(top[i], str) + if folder is not None: + trajectories[i] = os.path.join(folder, trajectories[i]) + if top[i]: + top[i] = os.path.join(folder, top[i]) + assert isinstance(trajectories[i], str) + _, ext = os.path.splitext(trajectories[i]) + if (ext.lower() == ".xyz") and (not top[i]): + pdb_file = trajectories[i].replace('.xyz', '_top.pdb') + top[i] = create_pdb_from_xyz(trajectories[i], pdb_file) # check if per file args are given, otherwise set to {} if load_args is not None: if (not isinstance(load_args, list)) or (len(trajectories) != len(load_args)): raise TypeError( - "load_args should be a list of dictionaries of arguments of same length as trajectories. If you want to use the same args for all file pass them directly as **kwargs." + "load_args should be a list of dictionaries of arguments of same length as trajectories." ) + + # load topologies and trajectories topologies = [] trajectories_in_memory = [] - for i in range(len(trajectories)): - if isinstance(trajectories[i], list): - traj = [ - mdtraj.load(trajectories[i][j], top=top[i][j]) - for j in range(len(trajectories[i])) - ] - for j,t in enumerate(traj): - t.top = mdtraj.core.trajectory.load_topology(top[i][j]) - if selection is not None: - for j in range(len(traj)): - subset = traj[j].top.select(selection) - assert len(subset) > 0, ( - 'No atoms will be selected with selection string ' - + '"{:s}"!'.format(selection) - ) - traj[j] = traj[j].atom_slice(subset) - trajectories_in_memory.append(traj) - topologies.extend([t.top for t in traj]) - else: - traj = mdtraj.load(trajectories[i], top=top[i]) - traj.top = mdtraj.core.trajectory.load_topology(top[i]) - if selection is not None: - subset = traj.top.select(selection) - assert len(subset) > 0, ( - 'No atoms will be selected with selection string ' - + '"{:s}"!'.format(selection) - ) - traj = traj.atom_slice(subset) - trajectories_in_memory.append(traj) - topologies.append(traj.top) + # load trajectory + traj = mdtraj.load(trajectories[i], top=top[i]) + traj.top = mdtraj.core.trajectory.load_topology(top[i]) + + # mdtraj does not load cell info from xyz, so we use ASE and add it + _, ext = os.path.splitext(trajectories[i]) + if (ext.lower() == ".xyz"): + ase_atoms = read(trajectories[i], index=':') + ase_cells = np.array([a.get_cell().array for a in ase_atoms], dtype=float) + traj.unitcell_vectors = ase_cells + + if selection is not None: + subset = traj.top.select(selection) + assert len(subset) > 0, ( + 'No atoms will be selected with selection string ' + + '"{:s}"!'.format(selection) + ) + traj = traj.atom_slice(subset) + trajectories_in_memory.append(traj) + topologies.append(traj.top) if z_table is None: z_table = _z_table_from_top(topologies) @@ -445,25 +481,12 @@ def create_dataset_from_trajectories( else: atom_names = None - + # create configurations objects from trajectories configurations = [] for i in range(len(trajectories_in_memory)): - if isinstance(trajectories_in_memory[i], list): - for j in range(len(trajectories_in_memory[i])): - configuration = _configures_from_trajectory( - trajectory=trajectories_in_memory[i][j], - label=i if create_labels else -1, # NOTE: all these configurations have a label `i` - system_selection=system_selection, - environment_selection=environment_selection, - start=load_args[i][j]['start'] if load_args is not None else 0, - stop=load_args[i][j]['stop'] if load_args is not None else None, - stride=load_args[i][j]['stride'] if load_args is not None else 1, - ) - configurations.extend(configuration) - else: configuration = _configures_from_trajectory( trajectory=trajectories_in_memory[i], - label=i if create_labels else -1, + label=labels[i], system_selection=system_selection, environment_selection=environment_selection, start=load_args[i]['start'] if load_args is not None else 0, @@ -472,14 +495,15 @@ def create_dataset_from_trajectories( ) configurations.extend(configuration) + # convert configurations into DictDataset dataset = create_dataset_from_configurations( - configurations, - z_table, - cutoff, - buffer, - atom_names, - remove_isolated_nodes, - show_progress + config=configurations, + z_table=z_table, + cutoff=cutoff, + buffer=buffer, + atom_names=atom_names, + remove_isolated_nodes=remove_isolated_nodes, + show_progress=show_progress ) if return_trajectories: @@ -525,7 +549,8 @@ def _configures_from_trajectory( environment_selection: str = None, start: int = 0, stop: int = None, - stride: int = 1) -> Configurations: + stride: int = 1, + lengths_conversion : float = 10.0) -> Configurations: """ Create configurations from one trajectory. @@ -543,6 +568,9 @@ def _configures_from_trajectory( MDTraj style atom selections of the environment atoms. If given, only the system atoms and [the environment atoms within the cutoff radius of the system atoms] will be kept in the graph. + lengths_conversion: float, + Conversion factor for length units, by default 10. + MDTraj uses nanometers, the default sends to Angstroms. """ if label is not None: label = np.array([[label]]) @@ -578,8 +606,8 @@ def _configures_from_trajectory( for i in range(start,stop,stride): configuration = Configuration( atomic_numbers=atomic_numbers, - positions=trajectory.xyz[i] * 10, - cell=cell[i] * 10, + positions=trajectory.xyz[i] * lengths_conversion, + cell=cell[i] * lengths_conversion, pbc=pbc, graph_labels=label, node_labels=None, # TODO: Add supports for per-node labels. @@ -590,6 +618,7 @@ def _configures_from_trajectory( return configurations + # ================================================================================================= # ============================================= TESTS ============================================= # ================================================================================================= @@ -659,7 +688,7 @@ def test_datasesetFromTrajectories(): 'p.pdb'], folder="mlcolvar/tests/data", cutoff=8.0, # Ang - create_labels=True, + labels=None, system_selection='all and not type H', show_progress=False, ) @@ -671,7 +700,7 @@ def test_datasesetFromTrajectories(): 'p.pdb'], folder="mlcolvar/tests/data", cutoff=8.0, # Ang - create_labels=True, + labels=[0,1], system_selection='all and not type H', show_progress=False, load_args=[{'start' : 0, 'stop' : 10, 'stride' : 1}, @@ -680,17 +709,17 @@ def test_datasesetFromTrajectories(): assert(len(dataset)==12) dataset = create_dataset_from_trajectories( - trajectories=[['r.dcd', 'r.dcd'], - ['p.dcd', 'p.dcd']], - top=[['r.pdb', 'r.pdb'], - ['p.pdb', 'p.pdb']], + trajectories=['r.dcd', 'r.dcd', + 'p.dcd', 'p.dcd'], + top=['r.pdb', 'r.pdb', + 'p.pdb', 'p.pdb'], folder="mlcolvar/tests/data", cutoff=8.0, # Ang - create_labels=True, + labels=[0,1,2,3], system_selection='all and not type H', show_progress=False, - load_args=[[{'start' : 0, 'stop' : 10, 'stride' : 1}, {'start' : 0, 'stop' : 10, 'stride' : 1}], - [{'start' : 6, 'stop' : 10, 'stride' : 2}, {'start' : 6, 'stop' : 10, 'stride' : 2}]] + load_args=[{'start' : 0, 'stop' : 10, 'stride' : 1}, {'start' : 0, 'stop' : 10, 'stride' : 1}, + {'start' : 6, 'stop' : 10, 'stride' : 2}, {'start' : 6, 'stop' : 10, 'stride' : 2}] ) assert(len(dataset)==24) @@ -708,142 +737,173 @@ def test_create_dataset_from_trajectories(text: str = """ """, system_selection: str = None ) -> None: - with open('test_dataset.pdb', 'w') as fp: - print(text, file=fp) - - dataset, trajectories = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection=system_selection, - return_trajectories=True, - show_progress=False - ) - - assert len(dataset) == 6 - assert dataset.metadata["cutoff"] == 1.0 - assert dataset.metadata["z_table"] == [1, 8] - assert len(trajectories) == 2 - assert len(trajectories[0]) == 2 - assert len(trajectories[1]) == 2 - assert len(trajectories[1][0]) == 2 - assert len(trajectories[1][1]) == 2 - - assert dataset[0]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) - assert dataset[1]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) - assert dataset[2]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) - assert dataset[3]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) - assert dataset[4]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) - assert dataset[5]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) - - dataset, trajectories = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - create_labels=False, - system_selection=system_selection, - return_trajectories=True, - show_progress=False - ) - - assert dataset[0]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[1]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[2]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[3]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[4]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) - assert dataset[5]["data_list"]['graph_labels'] == torch.tensor([[-1.0]]) - - def check_data_1(data) -> None: - assert(torch.allclose(data["data_list"]['edge_index'], torch.tensor([[0, 0, 1, 1, 2, 2], - [2, 1, 0, 2, 1, 0]]) - ) - ) - assert(torch.allclose(data["data_list"]['shifts'], torch.tensor([[0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 2.0, 0.0], - [0.0, -2.0, 0.0], - [0.0, 0.0, 0.0]]) - ) - ) - assert(torch.allclose(data["data_list"]['unit_shifts'], torch.tensor([[0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, -1.0, 0.0], - [0.0, 0.0, 0.0]]) - ) - ) - assert(torch.allclose(data["data_list"]['positions'], torch.tensor([[0.0, 0.0, 0.0], - [0.7, 0.7, 0.0], - [0.7, -0.7, 0.0]]) - ) - ) - assert(torch.allclose(data["data_list"]['cell'], torch.tensor([[2.0, 0.0, 0.0], - [0.0, 2.0, 0.0], - [0.0, 0.0, 2.0]]) - ) - ) - assert(torch.allclose(data["data_list"]['node_attrs'], torch.tensor([[0.0, 1.0], - [1.0, 0.0], - [1.0, 0.0]]) - ) - ) - - for i in range(6): - check_data_1(dataset[i]) - - if system_selection is not None: - - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection='type O and {:s}'.format(system_selection), - environment_selection='type H and {:s}'.format(system_selection), + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + test_dataset_path = "test_dataset.pdb" + test_dataset_path = os.path.join(tmpdir, test_dataset_path) + with open(test_dataset_path, 'w') as fp: + print(text, file=fp) + + dataset, trajectories = create_dataset_from_trajectories( + trajectories=[test_dataset_path, test_dataset_path, test_dataset_path], + top=[test_dataset_path, test_dataset_path, test_dataset_path], + cutoff=1.0, + system_selection=system_selection, + return_trajectories=True, show_progress=False ) - for i in range(6): - check_data_1(dataset[i]) - - dataset = create_dataset_from_trajectories( - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - ['test_dataset.pdb', ['test_dataset.pdb', 'test_dataset.pdb']], - 1.0, - system_selection='name H1 and {:s}'.format(system_selection), - environment_selection='name H2 and {:s}'.format(system_selection), + assert len(dataset) == 6 + assert dataset.metadata["cutoff"] == 1.0 + assert dataset.metadata["z_table"] == [1, 8] + assert len(trajectories[0]) == 2 + assert len(trajectories[1]) == 2 + assert len(trajectories[2]) == 2 + + assert dataset[0]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[1]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[2]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[3]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[4]["data_list"]['graph_labels'] == torch.tensor([[2.0]]) + assert dataset[5]["data_list"]['graph_labels'] == torch.tensor([[2.0]]) + + dataset, trajectories = create_dataset_from_trajectories( + trajectories=[test_dataset_path, test_dataset_path, test_dataset_path], + top=test_dataset_path, + cutoff=1.0, + labels=None, + system_selection=system_selection, + return_trajectories=True, show_progress=False ) - def check_data_2(data) -> None: - assert(torch.allclose(data["data_list"]['edge_index'], torch.tensor([[0, 1], [1, 0]]))) - assert(torch.allclose(data["data_list"]['shifts'], torch.tensor([[0.0, 2.0, 0.0], - [0.0, -2.0, 0.0]]) + assert dataset[0]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[1]["data_list"]['graph_labels'] == torch.tensor([[0.0]]) + assert dataset[2]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[3]["data_list"]['graph_labels'] == torch.tensor([[1.0]]) + assert dataset[4]["data_list"]['graph_labels'] == torch.tensor([[2.0]]) + assert dataset[5]["data_list"]['graph_labels'] == torch.tensor([[2.0]]) + + def check_data_1(data) -> None: + assert(torch.allclose(data["data_list"]['edge_index'], torch.tensor([[0, 0, 1, 1, 2, 2], + [2, 1, 0, 2, 1, 0]]) ) ) - assert(torch.allclose(data["data_list"]['unit_shifts'], torch.tensor([[0.0, 1.0, 0.0], - [0.0, -1.0, 0.0]]) + assert(torch.allclose(data["data_list"]['shifts'], torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, -2.0, 0.0], + [0.0, 0.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['unit_shifts'], torch.tensor([[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0]]) ) ) - assert(torch.allclose(data["data_list"]['positions'], torch.tensor([[0.7, 0.7, 0.0], + assert(torch.allclose(data["data_list"]['positions'], torch.tensor([[0.0, 0.0, 0.0], + [0.7, 0.7, 0.0], [0.7, -0.7, 0.0]]) ) ) assert(torch.allclose(data["data_list"]['cell'], torch.tensor([[2.0, 0.0, 0.0], - [0.0, 2.0, 0.0], - [0.0, 0.0, 2.0]]) - ) + [0.0, 2.0, 0.0], + [0.0, 0.0, 2.0]]) + ) ) - assert(torch.allclose(data["data_list"]['node_attrs'], torch.tensor([[1.0], - [1.0]]) - ) + assert(torch.allclose(data["data_list"]['node_attrs'], torch.tensor([[0.0, 1.0], + [1.0, 0.0], + [1.0, 0.0]]) + ) ) for i in range(6): - check_data_2(dataset[i]) + check_data_1(dataset[i]) + + if system_selection is not None: + + dataset = create_dataset_from_trajectories( + trajectories=[test_dataset_path, test_dataset_path, test_dataset_path], + top=[test_dataset_path, test_dataset_path, test_dataset_path], + cutoff=1.0, + system_selection='type O and {:s}'.format(system_selection), + environment_selection='type H and {:s}'.format(system_selection), + show_progress=False + ) - __import__('os').remove('test_dataset.pdb') + for i in range(6): + check_data_1(dataset[i]) + + dataset = create_dataset_from_trajectories( + trajectories=[test_dataset_path, test_dataset_path, test_dataset_path], + top=[test_dataset_path, test_dataset_path, test_dataset_path], + cutoff=1.0, + system_selection='name H1 and {:s}'.format(system_selection), + environment_selection='name H2 and {:s}'.format(system_selection), + show_progress=False + ) + + def check_data_2(data) -> None: + assert(torch.allclose(data["data_list"]['edge_index'], torch.tensor([[0, 1], [1, 0]]))) + assert(torch.allclose(data["data_list"]['shifts'], torch.tensor([[0.0, 2.0, 0.0], + [0.0, -2.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['unit_shifts'], torch.tensor([[0.0, 1.0, 0.0], + [0.0, -1.0, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['positions'], torch.tensor([[0.7, 0.7, 0.0], + [0.7, -0.7, 0.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['cell'], torch.tensor([[2.0, 0.0, 0.0], + [0.0, 2.0, 0.0], + [0.0, 0.0, 2.0]]) + ) + ) + assert(torch.allclose(data["data_list"]['node_attrs'], torch.tensor([[1.0], + [1.0]]) + ) + ) + + for i in range(6): + check_data_2(dataset[i]) + + +def test_dataset_from_xyz(): + # load single file + load_args = [{'start' : 0, 'stop' : 2, 'stride' : 1}] + dataset = create_dataset_from_trajectories(trajectories="Cu.xyz", + folder="mlcolvar/tests/data", + top=None, + cutoff=3.5, # Ang + labels=None, + system_selection="index 0", + environment_selection="not index 0", + show_progress=False, + load_args=load_args, + buffer=1, + ) + + # load multiple files + load_args = [{'start' : 0, 'stop' : 2, 'stride' : 1}, + {'start' : 0, 'stop' : 4, 'stride' : 2}] + dataset = create_dataset_from_trajectories(trajectories=["Cu.xyz", "Cu.xyz"], + folder="mlcolvar/tests/data", + top=None, + cutoff=3.5, # Ang + labels=None, + system_selection="index 0 or index 1", + environment_selection="not index 0 and not index 1", + show_progress=False, + load_args=load_args, + buffer=1, + ) if __name__ == "__main__": @@ -900,3 +960,5 @@ def check_data_2(data) -> None: """ test_create_dataset_from_trajectories(text, 'not resname XXXX') + # test_with_xyz() + From df6c7ffb5460247a73375ba6a9453db1af3e8b47 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Wed, 9 Apr 2025 17:50:02 +0200 Subject: [PATCH 243/267] Updated test notebook --- test_graphs/test_explain.ipynb | 1057 ++++++++++++++++---------------- 1 file changed, 533 insertions(+), 524 deletions(-) diff --git a/test_graphs/test_explain.ipynb b/test_graphs/test_explain.ipynb index b090dfd9..1803ca1e 100644 --- a/test_graphs/test_explain.ipynb +++ b/test_graphs/test_explain.ipynb @@ -2,15 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9']\n", - "DictModule(dataset -> DictDataset( \"data_list\": 2000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"used_idx\": tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \"used_names\": ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9'], \"data_type\": graphs ),\n", + "[ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C]\n", + "DictModule(dataset -> DictDataset( \"data_list\": 2000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"used_idx\": tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \"used_names\": [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], \"data_type\": graphs ),\n", "\t\t train_loader -> DictLoader(length=0.8, batch_size=2000, shuffle=1),\n", "\t\t valid_loader -> DictLoader(length=0.2, batch_size=2000, shuffle=0))\n" ] @@ -56,7 +56,7 @@ " system_selection='all and not type H',\n", " show_progress=False,\n", " load_args=load_args,\n", - " save_names=False\n", + " save_names=True\n", " )\n", "\n", "datamodule_graph = DictModule(dataset_graph, lengths=[0.8, 0.2], shuffle=[1, 0])\n", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -123,7 +123,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31b618dd7728447e9348e7a49c3335ed", + "model_id": "708b7e1a3f5749dbb58b493ec25d2a24", "version_major": 2, "version_minor": 0 }, @@ -146,7 +146,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e14cadbbc8a4dbb8695b9da0dd95723", + "model_id": "fbd3d6eaba8f4dd297c7960cc30cccba", "version_major": 2, "version_minor": 0 }, @@ -160,7 +160,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c4d6c8b1858447da56f8b6076158b44", + "model_id": "7393269fabf84b5f998cec2f67027bcc", "version_major": 2, "version_minor": 0 }, @@ -174,7 +174,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c0e6573a120d4d86a6224fb0e4a13c31", + "model_id": "c058d74f752e4450a9a03b3118a44564", "version_major": 2, "version_minor": 0 }, @@ -188,7 +188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e21064f0782b4e3b8bd3996af9d3f661", + "model_id": "af9378e8e81b48e2b3c146732fa1f564", "version_major": 2, "version_minor": 0 }, @@ -202,7 +202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1d8677fda8f94929aeca108630ab2b40", + "model_id": "4e19b46d1e1c4e78bf438add7c4f94ee", "version_major": 2, "version_minor": 0 }, @@ -216,7 +216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9034e78e6b494f66a9e0e0f900895597", + "model_id": "920de4bc93b84ff4acddc865a95322c7", "version_major": 2, "version_minor": 0 }, @@ -230,7 +230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1461a2662c646e5b0a00bd385b8ea06", + "model_id": "885ffe863bde4137a2cfa83f85adfa7a", "version_major": 2, "version_minor": 0 }, @@ -244,7 +244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e1d4eb0b22d4767b10ba241058c943a", + "model_id": "5ab9c1991c274c5aa72aaea056b8627f", "version_major": 2, "version_minor": 0 }, @@ -258,7 +258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "799e1ed754af4dd987874334a1f1d6ce", + "model_id": "bf165acda1cb4960b0aa5b3d9cbd2882", "version_major": 2, "version_minor": 0 }, @@ -272,7 +272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a4b2accba094ec6a71473580e5ef7e2", + "model_id": "bd1ae5c9d97547208bec34e6e62fe20f", "version_major": 2, "version_minor": 0 }, @@ -286,7 +286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd5cce3de8794428bf79b88eebe13d4d", + "model_id": "29b51067dafd45629fc3dc933c0982f2", "version_major": 2, "version_minor": 0 }, @@ -300,7 +300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60bd846995a64144b0ae602ecb64cc73", + "model_id": "d0b0ce1034a848568db37a18a527e039", "version_major": 2, "version_minor": 0 }, @@ -314,7 +314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05bcd71c3f524816989415e670639b8d", + "model_id": "9755dd0e3f9647138e608ccfb4028553", "version_major": 2, "version_minor": 0 }, @@ -328,7 +328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e61e1a63aa0b4316ab1aa38079d63bcc", + "model_id": "72801b4387d3420caeec77cbd1fb1eb9", "version_major": 2, "version_minor": 0 }, @@ -342,7 +342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3359ad5962e84dd9b92db0f77e56a5f5", + "model_id": "4ed3b73e94d841e4aa3d73d8d71f8914", "version_major": 2, "version_minor": 0 }, @@ -356,7 +356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04f270fa4bad4342a6d739f5dea9216a", + "model_id": "f4a8b5816d0444debf132040d456da2e", "version_major": 2, "version_minor": 0 }, @@ -370,7 +370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1782b04994eb49e4b3d4ef15ccf715fd", + "model_id": "1a0170c9f64c44d5a1027da5e4a765d4", "version_major": 2, "version_minor": 0 }, @@ -384,7 +384,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c84efc2dcbd34c638617f5e00b38d1b7", + "model_id": "0704775cfe964a49b8a68b91da0dd84d", "version_major": 2, "version_minor": 0 }, @@ -398,7 +398,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21d879f2836449c8b56e94f2b3656a00", + "model_id": "8d7d6e2ebc154dd890223c82891d3d93", "version_major": 2, "version_minor": 0 }, @@ -412,7 +412,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc087708393041b191e5409fb2892c78", + "model_id": "60ddacc45c38464aae3aaf3de3de1168", "version_major": 2, "version_minor": 0 }, @@ -426,7 +426,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac95f275345b4918ab62c2ae440c1b0b", + "model_id": "3abc99bad0684adcab0a227add49931d", "version_major": 2, "version_minor": 0 }, @@ -440,7 +440,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8771bddb664b4e0e9b3845c8b89daa2d", + "model_id": "b1310290ff1042bab9ddc89b6215773e", "version_major": 2, "version_minor": 0 }, @@ -454,7 +454,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2e56f4a21f14bf599029b4e73a80ade", + "model_id": "f2210f6155bb405fb006f32c4d5a3c8a", "version_major": 2, "version_minor": 0 }, @@ -468,7 +468,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e6a1b9bfd4414387a90cff46b514e330", + "model_id": "2f3a24da2d4a4e83b2d4bff5b6c6d875", "version_major": 2, "version_minor": 0 }, @@ -482,7 +482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "311cbd57e4174e2a87be31b6c383aa59", + "model_id": "d11d1b99bd754c1e98bc260bfcec9589", "version_major": 2, "version_minor": 0 }, @@ -496,7 +496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d701e3091bd54c92b224c7beb9f5ed36", + "model_id": "dd391730a3c04ea28985a27614c82ff2", "version_major": 2, "version_minor": 0 }, @@ -510,7 +510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc02672713f643ca8044ff5b077f9ea5", + "model_id": "ac14333d8ccf48d488fdf1eeda777d1f", "version_major": 2, "version_minor": 0 }, @@ -524,7 +524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35141b995b5244369927afbdb9908e79", + "model_id": "6edba26d87aa41808e81a2b567c3dbdb", "version_major": 2, "version_minor": 0 }, @@ -538,7 +538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bafe972c1fad42d5bc22a4f04c42af2a", + "model_id": "536b70793b1a4e749706a827f5145832", "version_major": 2, "version_minor": 0 }, @@ -552,7 +552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7293866ee254cadacc3e5676a5ca24c", + "model_id": "66aaf7e10ceb4b0c98bb2405ae7ff270", "version_major": 2, "version_minor": 0 }, @@ -566,7 +566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96c7ecb6e8ef493b8abb39eebbdc06ca", + "model_id": "0cc0375c8e4b4cfc929ebdb0f2d697fc", "version_major": 2, "version_minor": 0 }, @@ -580,7 +580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3740af669764aea8f35b85866076f47", + "model_id": "5f7356b089694d3a9b3d7bb99fd56b56", "version_major": 2, "version_minor": 0 }, @@ -594,7 +594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a105860854f405c9a12ff624b1e0703", + "model_id": "ab56a1cf5f6a467d90a7fd7daff19ade", "version_major": 2, "version_minor": 0 }, @@ -608,7 +608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a69d13cf0e084f288e55d7073dabbb74", + "model_id": "bda9cbe79b384e4ca403016502f12b82", "version_major": 2, "version_minor": 0 }, @@ -622,7 +622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8a6b47afa31411883f0dc222e7188b5", + "model_id": "711f6163d62d49d1b3dd6aa8d7e3b9b2", "version_major": 2, "version_minor": 0 }, @@ -636,7 +636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8e04642867549dba81aa7318d6e6638", + "model_id": "d55f5e525d104690b8bad8d346a9392c", "version_major": 2, "version_minor": 0 }, @@ -650,7 +650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67feef0634544c9f8aee63a077480f73", + "model_id": "208a801be1ed45068881580ece1fcdda", "version_major": 2, "version_minor": 0 }, @@ -664,7 +664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "27444815c0b448a0ba736ef6d260b049", + "model_id": "312c584f9d0847ff80a3a1232a806507", "version_major": 2, "version_minor": 0 }, @@ -678,7 +678,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1e85d8323624ab7a04c6e52cb354c9b", + "model_id": "4aa4ca1c052c47cc9fa43c32e1a1abc4", "version_major": 2, "version_minor": 0 }, @@ -692,7 +692,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6877ddcb3dc54f36ad77b760bfe1ae51", + "model_id": "699e8878936b40cd949ddc1cc5393376", "version_major": 2, "version_minor": 0 }, @@ -706,7 +706,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34d4cde255f849869e77a7360f14a2d5", + "model_id": "43ebb83c8d5e46dbb16dbda5d38da71e", "version_major": 2, "version_minor": 0 }, @@ -720,7 +720,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2fd40b463a649108b728e8939b53a6f", + "model_id": "bd6102ad635c4d6da4cbe4cdb86717ef", "version_major": 2, "version_minor": 0 }, @@ -734,7 +734,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "72b1ca0e9dd94a078b516eb0a7f5eb43", + "model_id": "e08858e8bc4f4342b99f8fd644cdc1dc", "version_major": 2, "version_minor": 0 }, @@ -748,7 +748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82e2f8e31e5d400c8b82658824192bca", + "model_id": "9345b01d00b347439ba3e093e5293418", "version_major": 2, "version_minor": 0 }, @@ -762,7 +762,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97416da8949941d58c95ae8d458db145", + "model_id": "a13310653f024aac9c3c51eb6901469a", "version_major": 2, "version_minor": 0 }, @@ -776,7 +776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b66a9bf8b88340ed81e8a7fcbe9eb088", + "model_id": "00953c65df5e44268888446878682358", "version_major": 2, "version_minor": 0 }, @@ -790,7 +790,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63dc032754464260a34f4efb691c3c96", + "model_id": "ab95fa8a4a82417cba83e64b4e482960", "version_major": 2, "version_minor": 0 }, @@ -804,7 +804,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76992198b674461b9acfeb57b6b47f5d", + "model_id": "79d0413a35fc434fb71da0bf751c1a3e", "version_major": 2, "version_minor": 0 }, @@ -818,7 +818,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02c2578d6a3343d7bbc61ca2ca4748ec", + "model_id": "9976227647024e6a89815510feef9d31", "version_major": 2, "version_minor": 0 }, @@ -832,7 +832,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9d349ada70604fd0b12f4c1f524b38ad", + "model_id": "83ec9df6238f41729e44331a2f7084c0", "version_major": 2, "version_minor": 0 }, @@ -846,7 +846,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2ea9301e95c4989a2cf639edb57091a", + "model_id": "3d485c910d38483c9051fbbb6b4f532b", "version_major": 2, "version_minor": 0 }, @@ -860,7 +860,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91ce030db3204a56bf844e68966873e1", + "model_id": "d4400d6ad4c34d9c94d65c3f23880a51", "version_major": 2, "version_minor": 0 }, @@ -874,7 +874,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73e9363132da4c6a902966cef3cb9370", + "model_id": "30957498c0684c79a1c0acfb233131e2", "version_major": 2, "version_minor": 0 }, @@ -888,7 +888,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5beae8eed0144673b2aac672d1d9e8f0", + "model_id": "c2106b6645854afd8e015758c6db17ee", "version_major": 2, "version_minor": 0 }, @@ -902,7 +902,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74f5218126a74479816592a6c623c98a", + "model_id": "93fafdf1628f421696cae42ffad4888a", "version_major": 2, "version_minor": 0 }, @@ -916,7 +916,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2bef0f33c7bd4573aab25d46c56d0b53", + "model_id": "0a52b66e2e7b438db7dca10aea5f4e8d", "version_major": 2, "version_minor": 0 }, @@ -930,7 +930,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f0a6143a627450ab81932e4cb874910", + "model_id": "28a0e010aeee42c4a14e2603dce19fe2", "version_major": 2, "version_minor": 0 }, @@ -944,7 +944,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce9494a529ec45fa8c84357e19534fb8", + "model_id": "c065f352ceb04375bf07edc22b22ce69", "version_major": 2, "version_minor": 0 }, @@ -958,7 +958,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c66caf351c964c3fae1b060a62221589", + "model_id": "33de61c8a77e4630a60e1f3392d70a98", "version_major": 2, "version_minor": 0 }, @@ -972,7 +972,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c89b75f9feb4cae8e0e7683d21b35ff", + "model_id": "7c419880d0e34ef5ace8f8f2ba523a65", "version_major": 2, "version_minor": 0 }, @@ -986,7 +986,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "36c46e947c9b44b9bf4745b9116c079d", + "model_id": "b5b1efa2f8104c2da411e979645c1826", "version_major": 2, "version_minor": 0 }, @@ -1000,7 +1000,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e7763b929824eb78d71a0562a8be4d2", + "model_id": "0367d5a8ab1f4a88ae7a5525cb6c8621", "version_major": 2, "version_minor": 0 }, @@ -1014,7 +1014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "11bf50ab3a574fcb9e379979ce9ef8f0", + "model_id": "f3f651eb82f84af8b93a29a03967c1f4", "version_major": 2, "version_minor": 0 }, @@ -1028,7 +1028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "efb9666482674b188d62b8368a938cf9", + "model_id": "459c548e1a974ec8af64dbd4ffa663b4", "version_major": 2, "version_minor": 0 }, @@ -1042,7 +1042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "27205b7d5e77407a96f548544f2a0f1f", + "model_id": "bb674a15eca94dfbacc873f87f0bfa1c", "version_major": 2, "version_minor": 0 }, @@ -1056,7 +1056,7 @@ { "data": { 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-7191,12 +7191,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -7214,7 +7214,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -7244,7 +7244,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -7253,7 +7253,7 @@ "(2000, 10)" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -7264,16 +7264,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9']" + "['ACE1-CH3',\n", + " 'ACE1-C',\n", + " 'ACE1-O',\n", + " 'ALA2-N',\n", + " 'ALA2-CA',\n", + " 'ALA2-CB',\n", + " 'ALA2-C',\n", + " 'ALA2-O',\n", + " 'NME3-N',\n", + " 'NME3-C']" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -7284,12 +7293,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -7308,7 +7317,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -7340,16 +7349,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=[1, 1], n_system=[1, 1], weight=1.0, names_idx=[10])" + "Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=[1, 1], n_system=[1, 1], n_env=[1, 1], weight=1.0, names_idx=[10])" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } From 2e84eba7dbd160a0c340adb65a97ffcf40d75187 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 14 Apr 2025 16:10:32 +0200 Subject: [PATCH 244/267] Added example input method for graphs --- mlcolvar/data/datamodule.py | 17 +++++++++++++++++ mlcolvar/data/dataset.py | 11 ++++++++++- 2 files changed, 27 insertions(+), 1 deletion(-) diff --git a/mlcolvar/data/datamodule.py b/mlcolvar/data/datamodule.py index 85ffaed3..9dcb1d4c 100644 --- a/mlcolvar/data/datamodule.py +++ b/mlcolvar/data/datamodule.py @@ -262,6 +262,23 @@ def _check_setup(self): "outside a Lightning trainer please call .setup() first." ) + def get_graph_inputs(self, mode='train'): + """Generate an input that can be used as input for a GNN model + + Parameters + ---------- + mode : str, optional + Type of loader to be used, either 'train' or 'val'/'valid', by default 'train' + """ + self.setup() + if mode == 'train': + loader=self.train_dataloader + elif (mode=='val' or mode=='valid'): + loader=self.val_dataloader + else: + raise ValueError(f"Mode can either be 'train', 'val', 'valid', found {mode}!") + + return next(iter(loader()))['data_list'] def split_dataset( dataset, diff --git a/mlcolvar/data/dataset.py b/mlcolvar/data/dataset.py index 59f7bb95..41f0ea92 100644 --- a/mlcolvar/data/dataset.py +++ b/mlcolvar/data/dataset.py @@ -1,4 +1,5 @@ import torch +import torch_geometric import numpy as np from mlcolvar.core.transform.utils import Statistics from torch.utils.data import Dataset @@ -66,7 +67,7 @@ def __init__(self, metadata = {} if 'data_type' in metadata.keys(): - if not metadata['data_type'] == metadata: + if not metadata['data_type'] == data_type: raise ValueError(f"Two different data_type specified. Found {metadata['data_type']} in metadata and {data_type} as keyword") else: metadata['data_type'] = data_type @@ -167,6 +168,14 @@ def feature_names(self, value): np.asarray(value, dtype=str) if value is not None else value ) + def get_graph_inputs(self): + assert self.metadata['data_type'] == 'graphs', ( + 'Graph inputs can only be generated for graph-based datasets' + ) + loader = torch_geometric.loader.DataLoader(self, + batch_size=len(self), + shuffle=False ) + return next(iter(loader))['data_list'] def test_DictDataset(): # descriptors based From ff48440888adf1409a96c708e5398cd59c8bdcdf Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 14 Apr 2025 16:11:49 +0200 Subject: [PATCH 245/267] FIxed tracing for graphs + docs --- mlcolvar/cvs/cv.py | 4 +- mlcolvar/data/graph/utils.py | 103 +++++++++++++++++++++++++++++++++-- mlcolvar/utils/_code.py | 3 + 3 files changed, 102 insertions(+), 8 deletions(-) diff --git a/mlcolvar/cvs/cv.py b/mlcolvar/cvs/cv.py index 9863b527..a746ee66 100644 --- a/mlcolvar/cvs/cv.py +++ b/mlcolvar/cvs/cv.py @@ -2,7 +2,7 @@ from mlcolvar.core.transform import Transform from typing import Union, List from mlcolvar.core.nn import FeedForward, BaseGNN -from mlcolvar.data.graph.utils import create_test_graph_input +from mlcolvar.data.graph.utils import create_graph_tracing_example class BaseCV: @@ -69,7 +69,7 @@ def example_input_array(self): else self.preprocessing.in_features ) else: - return create_test_graph_input(output_type='tracing_example', n_atoms=3, n_samples=1, n_states=1) + return create_graph_tracing_example(n_species=len(self.atomic_numbers)) # TODO add general torch.nn.Module diff --git a/mlcolvar/data/graph/utils.py b/mlcolvar/data/graph/utils.py index 19706a91..5fc16561 100644 --- a/mlcolvar/data/graph/utils.py +++ b/mlcolvar/data/graph/utils.py @@ -172,8 +172,6 @@ def create_dataset_from_configurations( atom_names_env = [f"Y{i}" for i in range(data_list[0]['n_env'].to(torch.int64).item())] atom_names = atom_names_system + atom_names_env - print(atom_names) - # this is only to check what isolated nodes have been removed _aux_pos = torch.Tensor((np.array([d['positions'].numpy() for d in data_list]))) if remove_isolated_nodes: @@ -314,7 +312,31 @@ def create_test_graph_input(output_type: str, n_samples: int = 60, n_states: int = 2, random_weights = False, - add_noise = True ): + add_noise = True): + """ + Util function to generate several types of mock graph data objects for testing purposes. + The graphs are created drawing positions from a predefined set of positions that cover most use cases. + It can generate: one or some configuration objects, a dataset, a datamodule, a batch of example inputs or a single item. + + Parameters + ---------- + output_type : str + Type of graph data object to create. Can be: 'configuration', 'configurations', 'datamodule', 'dataset', 'batch', 'example' + n_atoms : int, optional + Number of atoms for creating the graph, either 3 or 4, by default 3 + n_samples : int, optional + Number of samples per state to create, by default 60 + n_states : int, optional + Number of states for which to create data, by default 2. Configurations are then labelled accordingly. + random_weights : bool, optional + If to assign random weights to the entries, otherwise unitary weights are given, by default False + add_noise : bool, optional + If to add a random noise for each entry to the predefined positions, by default True + + Returns + ------- + Graph data object of the chosen type + """ if n_atoms == 3: numbers = [8, 1, 1] node_labels = np.array([[0], [1], [1]]) @@ -330,7 +352,7 @@ def create_test_graph_input(output_type: str, dtype=np.float64 ) - if n_atoms == 4: + elif n_atoms == 4: numbers = [8, 1, 1, 8] node_labels = np.array([[0], [1], [1], [0]]) _ref_positions = np.array( @@ -344,6 +366,8 @@ def create_test_graph_input(output_type: str, ], dtype=np.float64 ) + else: + raise ValueError(f'Example input can be generated either with 3 or 4 atoms, found {n_atoms}') idx = np.random.randint(low=0, high=6, size=(n_samples*n_states)) @@ -406,9 +430,76 @@ def create_test_graph_input(output_type: str, example['batch'] = torch.zeros(len(example['positions']), dtype=torch.int64) if output_type == 'example': return example - if output_type == 'tracing_example': - return example.to_dict() + + +def create_graph_tracing_example(n_species : int): + """ + Util to create a tracing example for graph based models. + + Parameters + ---------- + n_species : int + Number of chemical species to be considered in the model. + + Returns + ------- + dict + Tracing graph input example as dict. + """ + numbers = [1, 1, 1] + node_labels = np.array([[0], [0], [0]]) + _ref_positions = np.array( + [ + [[0.0, 0.0, 0.0], [0.07, 0.07, 0.0], [0.07, -0.07, 0.0]], + [[0.0, 0.0, 0.0], [-0.07, 0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.07, -0.07, 0.0], [0.07, 0.07, 0.0]], + [[0.0, 0.0, 0.0], [0.0, -0.07, 0.07], [0.0, 0.07, 0.07]], + [[0.0, 0.0, 0.0], [0.11, 0.11, 0.11], [-0.07, 0.0, 0.07]], + [[0.1, 0.0, 1.1], [0.17, 0.07, 1.1], [0.17, -0.07, 1.1]], + ], + dtype=np.float64 + ) + + idx = np.random.randint(low=0, high=6, size=1) + positions = _ref_positions[idx, :, :] + cell = np.identity(3, dtype=float) * 0.2 + graph_labels = np.zeros((1, 1, 1)) + + z_table = atomic.AtomicNumberTable.from_zs(numbers) + + weights = np.ones((1, 1, 1)) + config = [ + atomic.Configuration( + atomic_numbers=numbers, + positions=positions[i], + cell=cell, + pbc=[True] * 3, + node_labels=node_labels, + graph_labels=graph_labels[i], + weight=weights[i] + ) for i in range(0, 1) + ] + + # here we do not remove isolated nodes + dataset = create_dataset_from_configurations( + config, z_table, 0.1, show_progress=False, remove_isolated_nodes=False + ) + + datamodule = DictModule( + dataset, + lengths=(0.8, 0.2), + batch_size=0, + shuffle=False, + ) + + datamodule.setup() + batch = next(iter(datamodule.train_dataloader())) + example = batch['data_list'].get_example(0) + example['batch'] = torch.zeros(len(example['positions']), dtype=torch.int64) + example = example.to_dict() + example['node_attrs'] = torch.cat((example['node_attrs'], torch.zeros(3, n_species - 1)), 1) + return example # =============================================================================== # =============================================================================== diff --git a/mlcolvar/utils/_code.py b/mlcolvar/utils/_code.py index 499609de..e4102702 100644 --- a/mlcolvar/utils/_code.py +++ b/mlcolvar/utils/_code.py @@ -1,6 +1,7 @@ import torch from typing import Optional +@torch.jit.script_if_tracing def broadcast(src: torch.Tensor, other: torch.Tensor, dim: int): """Broadcast util, from torch_scatter""" if dim < 0: @@ -13,6 +14,7 @@ def broadcast(src: torch.Tensor, other: torch.Tensor, dim: int): src = src.expand(other.size()) return src +@torch.jit.script_if_tracing def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1, @@ -33,6 +35,7 @@ def scatter_sum(src: torch.Tensor, else: return out.scatter_add_(dim, index, src) +@torch.jit.script_if_tracing def scatter_mean(src: torch.Tensor, index: torch.Tensor, dim: int = -1, From 11e2e70e2f2587f36a92740b992d6f1431546024 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 14 Apr 2025 16:12:56 +0200 Subject: [PATCH 246/267] Added conversion key to loader --- mlcolvar/utils/io.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/mlcolvar/utils/io.py b/mlcolvar/utils/io.py index 28675a3e..7aba62b8 100644 --- a/mlcolvar/utils/io.py +++ b/mlcolvar/utils/io.py @@ -12,6 +12,7 @@ import urllib.request from typing import Union, List, Tuple import mdtraj +from warnings import warn # Import ASE for xyz to pdb conversion. try: @@ -310,7 +311,8 @@ def create_dataset_from_trajectories( return_trajectories: bool = False, remove_isolated_nodes: bool = True, show_progress: bool = True, - save_names=True + save_names=True, + lengths_conversion : float = 10.0, ) -> Union[ DictDataset, Tuple[ @@ -358,6 +360,9 @@ def create_dataset_from_trajectories( If show the progress bar. save_names: bool If to save names from topology file, by default True + lengths_conversion: float, + Conversion factor for length units, by default 10. + MDTraj uses nanometers, the default sends to Angstroms. Returns ------- @@ -492,6 +497,7 @@ def create_dataset_from_trajectories( start=load_args[i]['start'] if load_args is not None else 0, stop=load_args[i]['stop'] if load_args is not None else None, stride=load_args[i]['stride'] if load_args is not None else 1, + lengths_conversion=lengths_conversion, ) configurations.extend(configuration) From cde077f4c3e4226ced70a0dd59ed995a18aa948d Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Mon, 14 Apr 2025 16:13:26 +0200 Subject: [PATCH 247/267] Added example notebook for gnn based CVs --- .../tutorials/adv_gnn_based_cvs.ipynb | 7469 +++++++++++++++++ .../tutorials/data/alanine_gnn/alad.gro | 25 + .../tutorials/data/alanine_gnn/alad_A.trr | Bin 0 -> 3840384 bytes .../tutorials/data/alanine_gnn/alad_B.trr | Bin 0 -> 3840384 bytes 4 files changed, 7494 insertions(+) create mode 100644 docs/notebooks/tutorials/adv_gnn_based_cvs.ipynb create mode 100644 docs/notebooks/tutorials/data/alanine_gnn/alad.gro create mode 100644 docs/notebooks/tutorials/data/alanine_gnn/alad_A.trr create mode 100644 docs/notebooks/tutorials/data/alanine_gnn/alad_B.trr diff --git a/docs/notebooks/tutorials/adv_gnn_based_cvs.ipynb b/docs/notebooks/tutorials/adv_gnn_based_cvs.ipynb new file mode 100644 index 00000000..2edb13f2 --- /dev/null +++ b/docs/notebooks/tutorials/adv_gnn_based_cvs.ipynb @@ -0,0 +1,7469 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using `mlcolvar` with graph neural networks (GNNs)\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/luigibonati/mlcolvar/blob/main/docs/notebooks/tutorials/adv_gnn_based_cvs.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### NOTE\n", + "Most of the workings of the library are the same using standard feed-forward-nn-based machine-learning CVs or GNN-based ones.\n", + "Thus, it is recommended to first go through the basic tutorials for the standard scenario before moving to this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feed-Forward-based CVs vs GNN-based CVs\n", + "\n", + "The default setting of `mlcolvar` is to represent the CVs as the output nodes of Feed-Forward Neural Networks (FFNNs or NNs, for simplicity) which take as input a set of physical descriptors (e.g., distances, angles, etc.).\n", + "The code is thus designed to reflect this choice, with the default values of the classes set to intilialize the CV model in this framework, which is the most diffused for the time being in the field of machine-learning CVs and suits the needs of most users.\n", + "\n", + "However, recently a different approach have been proposed, in which the CVs are represented as Graph Neural Networks (GNNs) which directly take as input the Cartesian coordinates of the atoms in the studied system and return the CV space after a node-pooling operation on the output layer.\n", + "This approach is thus descriptor-free and goes in the direcion of a more automated way of desgining CVs.\n", + "Unfortunately, it typically comes at a higher computational cost (i.e., slower trainign and evaluation fo the CV) and the underlying codebase is more complex (i.e., more complex models and data format.)\n", + "\n", + "In this tutorial, we show how GNN models can be used within `mlcolvar` to build CVs using the implemented CV methods.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Colab setup\n", + "import os\n", + "\n", + "if os.getenv(\"COLAB_RELEASE_TAG\"):\n", + " import subprocess\n", + " subprocess.run('wget https://raw.githubusercontent.com/luigibonati/mlcolvar/main/colab_setup.sh', shell=True)\n", + " cmd = subprocess.run('bash colab_setup.sh TUTORIAL', shell=True, stdout=subprocess.PIPE)\n", + " print(cmd.stdout.decode('utf-8'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outline\n", + "Typically, the process of constructing a GNN-based CV requires the following ingredients;\n", + "1. A **dataset** of attributed connected graphs (nodes and edges), which are constructed from the atomic positions\n", + "2. A **GNN-model** to represent the CV. Different architectures can be used in this regard.\n", + "3. A **CV method** and the associated **loss function**. These are all the methods implemented for *standard* machine-learning CVs, except for those based on autoencoders. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data\n", + "#### The inputs of GNNs CVs\n", + "The input of GNN models are attributed and connected graphs, in which nodes (representing the atoms, in our case) are connected by edges (the lines of the graph).\n", + "Nodes and edges are then assigned with scalar and, eventually, vector features that are then processed through the layers of the GNN.\n", + "\n", + "In the context of GNN-CVs, such graphs most likely are created directly from the atomic coordinates from a trajectory file and the connectivity between the nodes is determined according to a radial `cutoff`.\n", + "\n", + "#### Truncated graphs\n", + "In some cases, graphs can be built focusing the attention on a subset of the whole system, e.g., a molecule on a surface, but still keeping into account the interaction with the environment, e.g., the surface.\n", + "In this case, only the ndoes from the `system_selection` will be used for the final pooling, whereas the nodes from the `enviroment_selection` will be used only to update the information through the layers.\n", + "Moreover, to reduce the computational costs, only the atoms closer to the `system_selection` atoms will be included in the graphs, according to the set `cutoff` and a `buffer` value to ensure stability e continuity. \n", + "For example, this setup is useful when treting solvent or surface interactions.\n", + "\n", + "#### Create dataset from trajectory files\n", + "To make this process easier, in `mlcolvar` there is an util function to do this under-the-hood: `create_dataset_from_trajectories`, which is analogous to the create_dataset_from_files used with descriptors.\n", + "The loading process is built on the external library [`MDTraj`](https://www.mdtraj.org/), which can natively load most common trajectory+topology format used in biophysics.\n", + "On the other hand, for less-bio applications (e.g., solids, surfaces, molecules) we recommend using the `.xyz` file format.\n", + "\n", + "One advantage of MDTraj, is that it comes with a simple and user friendly synthax for atom selection, which can be used also here.\n", + "\n", + "Here, as an example, we load some data about the state A and B of Alanine Dipeptide." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset info:\n", + " DictDataset( \"data_list\": 2000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"used_idx\": tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \"used_names\": [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], \"data_type\": graphs )\n", + "\n", + "Datamodule info:\n", + " DictModule(dataset -> DictDataset( \"data_list\": 2000, \"z_table\": [6, 7, 8], \"cutoff\": 10.0, \"used_idx\": tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \"used_names\": [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=0.8, batch_size=2000, shuffle=True),\n", + "\t\t valid_loader -> DictLoader(length=0.2, batch_size=2000, shuffle=True))\n" + ] + } + ], + "source": [ + "from mlcolvar.data import DictModule\n", + "from mlcolvar.utils.io import create_dataset_from_trajectories\n", + "\n", + "# loading arguments \n", + "# same as to laod_dataframe\n", + "load_args = [{'start' : 0, 'stop' : 10000, 'stride' : 10},\n", + " {'start' : 0, 'stop' : 10000, 'stride' : 10}]\n", + "\n", + "# create dataset\n", + "dataset = create_dataset_from_trajectories(\n", + " trajectories=[\"alad_A.trr\", \n", + " \"alad_B.trr\"],\n", + " top=\"alad.gro\", \n", + " folder=\"data/alanine_gnn\", \n", + " cutoff=10.0, # Angstrom \n", + " labels=None, \n", + " system_selection='all and not type H',\n", + " show_progress=False,\n", + " load_args=load_args,\n", + " lengths_conversion=10.0, # MDTraj uses nm by defualt, we use Angstroms\n", + " )\n", + "print('Dataset info:\\n', dataset, end=\"\\n\\n\")\n", + "\n", + "# load dataset into a DictModule\n", + "datamodule = DictModule(dataset=dataset)\n", + "print('Datamodule info:\\n', datamodule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Accessing graph data\n", + "The built graphs are then stored as `torch_geometric.Data` objects into the usual `DictDataset` with the information about each graph entry (e.g., nodes positons, edges, weights, elabels etc.) under tehe key `data_list` and the common information for all the graphs (e.g., map from types to chemical species, cutoff) in the `metadata` attribute dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example of a graph entry:\n", + " Data(edge_index=[2, 90], shifts=[90, 3], unit_shifts=[90, 3], positions=[10, 3], cell=[3, 3], node_attrs=[10, 3], graph_labels=[1, 1], n_system=[1, 1], n_env=[1, 1], weight=1.0, names_idx=[10])\n", + "\n", + "Dataset metadata:\n", + " {'z_table': [6, 7, 8], 'cutoff': 10.0, 'used_idx': tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), 'used_names': [ACE1-CH3, ACE1-C, ACE1-O, ALA2-N, ALA2-CA, ALA2-CB, ALA2-C, ALA2-O, NME3-N, NME3-C], 'data_type': 'graphs'}\n" + ] + } + ], + "source": [ + "print('Example of a graph entry:\\n', dataset['data_list'][0], end='\\n\\n')\n", + "print('Dataset metadata:\\n', dataset.metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initializing the GNN model\n", + "At variance with the procedure with FFNNs, here the model is initialized **outside** the CV class, to which is then passed only later as an input.\n", + "GNN architectures are indeed much more complex than FFNNs and have many parameters that can be set.\n", + "In addition, when introducing GNN models into the code, we maintained the standard CVs as the defualt, which still covers most of the users.\n", + "\n", + "Here, for example, we initialize a `SchNetModel`.\n", + "Many other architectures are available in [`pytorch_geometric`](https://pytorch-geometric.readthedocs.io/en/latest/) and can be readily adapted to this library.\n", + "\n", + "#### NOTE\n", + "As the input graph are built with the dataset and then processed in the GNN-model, it is wise to initilize the model directly refering to the values stored in the `dataset.metadata` (e.g., cutoff, z_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset.metadata['cutoff'],\n", + " atomic_numbers=dataset.metadata['z_table'],\n", + " n_bases=16,\n", + " n_layers=2,\n", + " n_filters=16,\n", + " n_hidden_channels=16,\n", + " w_out_after_sum=True,\n", + " aggr='sum'\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initializing CV class\n", + "The initalization of the CV class is almost identical to the standard case, with the only difference that we provide the initialized GNN object as model.\n", + "\n", + "Here, for example, we use the `DeepTDA` CV." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test_2.5/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + ] + } + ], + "source": [ + "import torch\n", + "from mlcolvar.cvs import DeepTDA\n", + "\n", + "# we can still set the options for the optimizer the usual way\n", + "# options for the BLOCKS of the cv are disabled when passing an external model\n", + "options = {'optimizer' : {'lr' : 1e-3}}\n", + "\n", + "torch.manual_seed(42)\n", + "model = DeepTDA(n_states=2,\n", + " n_cvs=1,\n", + " target_centers=[-7, 7],\n", + " target_sigmas=[0.2, 0.2],\n", + " model=gnn_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the CV\n", + "Here, everything works the same!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "115976f0d1894e5297888aae5b84ce84", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1,figsize=(4,3))\n", + "plot_metrics(metrics.metrics,\n", + " keys=['train_loss', 'valid_loss'],\n", + " colors=['fessa1', 'fessa5'],\n", + " yscale='linear',\n", + " ax = ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing the model\n", + "As the graph data are stored as `torch_geometric.Data` they need to be loaded using a loader object.\n", + "For convenience, we implemented both in `DictDataset ` and `DictModule` a method `.get_graph_data` to do it so that one can simply evaluate the model calling either:\n", + "- `model(dataset.get_graph_data())` \n", + "- `model(datamodule.get_graph_data())`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1,2, figsize=(10,3))\n", + "out_graph = model(dataset.get_graph_inputs())\n", + "ax = axs[0]\n", + "ax.hist(out_graph.detach().squeeze(), bins=100)\n", + "ax.set_title('From Dataset')\n", + "ax.set_xlabel('GNN CV')\n", + "\n", + "out_graph = model(datamodule.get_graph_inputs())\n", + "ax = axs[1]\n", + "ax.hist(out_graph.detach().squeeze(), bins=100)\n", + "ax.set_title('From Datamodule')\n", + "ax.set_xlabel('GNN CV')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save the model to TorchScript\n", + "As for normal CVs, the frozen model can be saved to TorchScript suing the `Lightning` util `to_torchscript` using `method=trace`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traced_model = model.to_torchscript('gnn_model.pt', method='trace')\n", + "\n", + "# we can also check the outputs coincide\n", + "torch.allclose(model(dataset.get_graph_inputs()), traced_model(dataset.get_graph_inputs()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "graph_mlcolvar_test_2.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/tutorials/data/alanine_gnn/alad.gro b/docs/notebooks/tutorials/data/alanine_gnn/alad.gro new file mode 100644 index 00000000..c633b39b --- 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