Merge pull request #74 from BloodAxe/develop

Release 0.5.1

.github/workflows/ci.yml

@@ -25,11 +25,11 @@ jobs:
         matrix.operating-system == 'ubuntu-latest' ||
         matrix.operating-system == 'windows-latest'
       run: >
-        pip install torch==1.8.1+cpu torchvision==0.9.1+cpu
+        pip install torch==1.10.1+cpu torchvision==0.11.2+cpu
         -f https://download.pytorch.org/whl/torch_stable.html
     - name: Install PyTorch on MacOS
       if: matrix.operating-system == 'macos-latest'
-      run: pip install torch==1.8.1 torchvision==0.9.1
+      run: pip install torch==1.10.1 torchvision==0.11.2
     - name: Install dependencies
       run: pip install .[${{ matrix.pytorch-toolbelt-version }}]
     - name: Install linters
@@ -59,6 +59,6 @@ jobs:
     - name: Update pip
       run: python -m pip install --upgrade pip
     - name: Install Black
-      run: pip install black==20.8b1
+      run: pip install black==22.3.0
     - name: Run Black
       run: black --config=black.toml --check .

.gitignore

@@ -20,3 +20,4 @@ var/
 .pytest_cache/
 /tests/tta_eval.csv
 /tests/tmp.onnx
+/tests/test_plot_confusion_matrix.png

pytorch_toolbelt/__init__.py

@@ -1,3 +1,3 @@
 from __future__ import absolute_import
 
-__version__ = "0.5.0"
+__version__ = "0.5.1"

pytorch_toolbelt/datasets/segmentation.py

@@ -139,7 +139,7 @@ def __getitem__(self, index):
 
             if self.need_supervision_masks:
                 for i in range(1, 6):
-                    stride = 2 ** i
+                    stride = 2**i
                     mask = block_reduce(mask, (2, 2), partial(_block_reduce_dominant_label))
                     sample[name_for_stride(TARGET_MASK_KEY, stride)] = self.make_target(mask)
 

pytorch_toolbelt/inference/functional.py

@@ -7,26 +7,27 @@
 from ..utils.support import pytorch_toolbelt_deprecated
 
 __all__ = [
+    "geometric_mean",
+    "harmonic_mean",
+    "logodd_mean",
+    "pad_image_tensor",
+    "torch_fliplr",
+    "torch_flipud",
     "torch_none",
+    "torch_rot180",
+    "torch_rot270",
     "torch_rot90",
-    "torch_rot90_cw",
     "torch_rot90_ccw",
-    "torch_transpose_rot90_cw",
-    "torch_transpose_rot90_ccw",
     "torch_rot90_ccw_transpose",
+    "torch_rot90_cw",
     "torch_rot90_cw_transpose",
-    "torch_rot180",
-    "torch_rot270",
-    "torch_fliplr",
-    "torch_flipud",
     "torch_transpose",
     "torch_transpose2",
     "torch_transpose_",
-    "pad_image_tensor",
+    "torch_transpose_rot90_ccw",
+    "torch_transpose_rot90_cw",
     "unpad_image_tensor",
     "unpad_xyxy_bboxes",
-    "geometric_mean",
-    "harmonic_mean",
 ]
 
 
@@ -240,3 +241,23 @@ def harmonic_mean(x: Tensor, dim: int, eps: float = 1e-6) -> Tensor:
     x = torch.mean(x, dim=dim)
     x = torch.reciprocal(x.clamp_min(eps))
     return x
+
+
+def logodd_mean(x: Tensor, dim: int, eps: float = 1e-6) -> Tensor:
+    """
+    Compute log-odd mean along given dimension.
+    logodd = log(p / (1 - p))
+
+    This implementation assume values are in range [0, 1] (Probabilities)
+    Args:
+        x: Input tensor of arbitrary shape
+        dim: Dimension to reduce
+
+    Returns:
+        Tensor
+    """
+    x = x.clamp(min=eps, max=1.0 - eps)
+    x = torch.log(x / (1 - x))
+    x = torch.mean(x, dim=dim)
+    x = torch.exp(x) / (1 + torch.exp(x))
+    return x

pytorch_toolbelt/inference/tiles.py

@@ -60,7 +60,7 @@ class ImageSlicer:
     Helper class to slice image into tiles and merge them back
     """
 
-    def __init__(self, image_shape, tile_size, tile_step=0, image_margin=0, weight="mean"):
+    def __init__(self, image_shape: Tuple[int, int], tile_size, tile_step=0, image_margin=0, weight="mean"):
         """
 
         :param image_shape: Shape of the source image (H, W)
@@ -122,12 +122,6 @@ def __init__(self, image_shape, tile_size, tile_step=0, image_margin=0, weight="
             else:
                 margin_left = margin_right = margin_top = margin_bottom = image_margin
 
-            if (self.image_width + margin_left + margin_right) % self.tile_size[1] != 0:
-                raise ValueError()
-
-            if (self.image_height + margin_top + margin_bottom) % self.tile_size[0] != 0:
-                raise ValueError()
-
             self.margin_left = margin_left
             self.margin_right = margin_right
             self.margin_top = margin_top
@@ -337,6 +331,10 @@ def integrate_batch(self, batch: torch.Tensor, crop_coords):
         if batch.device != self.image.device:
             batch = batch.to(device=self.image.device)
 
+        # Ensure that input batch dtype match the target dtyle of the accumulator
+        if batch.dtype != self.image.dtype:
+            batch = batch.type_as(self.image)
+
         for tile, (x, y, tile_width, tile_height) in zip(batch, crop_coords):
             self.image[:, y : y + tile_height, x : x + tile_width] += tile * self.weight
             self.norm_mask[:, y : y + tile_height, x : x + tile_width] += self.weight

pytorch_toolbelt/inference/tta.py

@@ -79,6 +79,8 @@ def _deaugment_averaging(x: Tensor, reduction: MaybeStrOrCallable) -> Tensor:
         x = F.geometric_mean(x, dim=0)
     elif reduction in {"hmean", "harmonic_mean"}:
         x = F.harmonic_mean(x, dim=0)
+    elif reduction == "logodd":
+        x = F.logodd_mean(x, dim=0)
     elif callable(reduction):
         x = reduction(x, dim=0)
     elif reduction in {None, "None", "none"}:

pytorch_toolbelt/modules/activations.py

@@ -264,7 +264,15 @@ def instantiate_activation_block(activation_name: str, **kwargs) -> nn.Module:
 
     act_params = {}
 
-    if "inplace" in kwargs and activation_name in {ACT_RELU, ACT_RELU6, ACT_LEAKY_RELU, ACT_SELU, ACT_CELU, ACT_ELU}:
+    if "inplace" in kwargs and activation_name in {
+        ACT_RELU,
+        ACT_RELU6,
+        ACT_LEAKY_RELU,
+        ACT_SELU,
+        ACT_SILU,
+        ACT_CELU,
+        ACT_ELU,
+    }:
         act_params["inplace"] = kwargs["inplace"]
 
     if "slope" in kwargs and activation_name in {ACT_LEAKY_RELU}:

pytorch_toolbelt/modules/decoders/unet_v2.py

@@ -100,7 +100,7 @@ def __init__(self, feature_maps: List[int], decoder_features: int, mask_channels
         super().__init__()
 
         if not isinstance(decoder_features, list):
-            decoder_features = [decoder_features * (2 ** i) for i in range(len(feature_maps))]
+            decoder_features = [decoder_features * (2**i) for i in range(len(feature_maps))]
 
         blocks = []
         for block_index, in_enc_features in enumerate(feature_maps[:-1]):

pytorch_toolbelt/modules/dropblock.py

@@ -68,7 +68,7 @@ def _compute_block_mask(self, mask):
         return block_mask, keeped
 
     def _compute_gamma(self, x):
-        return self.drop_prob / (self.block_size ** 2)
+        return self.drop_prob / (self.block_size**2)
 
 
 class DropBlock3D(DropBlock2D):
@@ -131,7 +131,7 @@ def _compute_block_mask(self, mask):
         return block_mask
 
     def _compute_gamma(self, x):
-        return self.drop_prob / (self.block_size ** 3)
+        return self.drop_prob / (self.block_size**3)
 
 
 class DropBlockScheduled(nn.Module):

pytorch_toolbelt/modules/encoders/swin.py

@@ -94,7 +94,7 @@ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, at
         self.window_size = window_size  # Wh, Ww
         self.num_heads = num_heads
         head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
+        self.scale = qk_scale or head_dim**-0.5
 
         # define a parameter table of relative position bias
         self.relative_position_bias_table = nn.Parameter(
@@ -587,7 +587,7 @@ def __init__(
         self.layers = nn.ModuleList()
         for i_layer in range(self.num_layers):
             layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
+                dim=int(embed_dim * 2**i_layer),
                 depth=depths[i_layer],
                 num_heads=num_heads[i_layer],
                 window_size=window_size,
@@ -604,7 +604,7 @@ def __init__(
             )
             self.layers.append(layer)
 
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
         self.num_features = num_features
 
         # add a norm layer for each output

pytorch_toolbelt/modules/encoders/unet.py

@@ -27,8 +27,8 @@ def __init__(
         if pool_block is None:
             pool_block = partial(nn.MaxPool2d, kernel_size=2, stride=2)
 
-        feature_maps = [out_channels * (growth_factor ** i) for i in range(num_layers)]
-        strides = [2 ** i for i in range(num_layers)]
+        feature_maps = [out_channels * (growth_factor**i) for i in range(num_layers)]
+        strides = [2**i for i in range(num_layers)]
         super().__init__(feature_maps, strides, layers=list(range(num_layers)))
 
         input_filters = in_channels

pytorch_toolbelt/modules/ocnet.py

@@ -62,7 +62,7 @@ def forward(self, x):  # skipcq: PYL-W0221
         key = self.f_key(x).view(batch_size, self.key_channels, -1)
 
         sim_map = torch.matmul(query, key)
-        sim_map = (self.key_channels ** -0.5) * sim_map
+        sim_map = (self.key_channels**-0.5) * sim_map
         sim_map = F.softmax(sim_map, dim=-1)
 
         context = torch.matmul(sim_map, value)
@@ -300,7 +300,7 @@ def forward(self, x):
             key_local = key_local.contiguous().view(batch_size, self.key_channels, -1)
 
             sim_map = torch.matmul(query_local, key_local)
-            sim_map = (self.key_channels ** -0.5) * sim_map
+            sim_map = (self.key_channels**-0.5) * sim_map
             sim_map = F.softmax(sim_map, dim=-1)
 
             context_local = torch.matmul(sim_map, value_local)

pytorch_toolbelt/modules/upsample.py

@@ -50,14 +50,14 @@ def icnr_init(tensor: torch.Tensor, upscale_factor=2, initializer=nn.init.kaimin
     .. _Checkerboard artifact free sub-pixel convolution:
         https://arxiv.org/abs/1707.02937
     """
-    new_shape = [int(tensor.shape[0] / (upscale_factor ** 2))] + list(tensor.shape[1:])
+    new_shape = [int(tensor.shape[0] / (upscale_factor**2))] + list(tensor.shape[1:])
     subkernel = torch.zeros(new_shape)
     subkernel = initializer(subkernel)
     subkernel = subkernel.transpose(0, 1)
 
     subkernel = subkernel.contiguous().view(subkernel.shape[0], subkernel.shape[1], -1)
 
-    kernel = subkernel.repeat(1, 1, upscale_factor ** 2)
+    kernel = subkernel.repeat(1, 1, upscale_factor**2)
 
     transposed_shape = [tensor.shape[1]] + [tensor.shape[0]] + list(tensor.shape[2:])
     kernel = kernel.contiguous().view(transposed_shape)
@@ -77,7 +77,7 @@ class DepthToSpaceUpsample2d(nn.Module):
 
     def __init__(self, in_channels: int, out_channels: int, scale_factor: int = 2):
         super().__init__()
-        n = 2 ** scale_factor
+        n = 2**scale_factor
         self.conv = nn.Conv2d(in_channels, out_channels * n, kernel_size=3, padding=1, bias=False)
         self.out_channels = out_channels
         self.shuffle = nn.PixelShuffle(upscale_factor=scale_factor)

pytorch_toolbelt/optimization/functional.py

@@ -46,7 +46,9 @@ def get_optimizable_parameters(model: nn.Module) -> Iterator[nn.Parameter]:
     return filter(lambda x: x.requires_grad, model.parameters())
 
 
-def freeze_model(module: nn.Module, freeze_parameters: Optional[bool] = True, freeze_bn: Optional[bool] = True):
+def freeze_model(
+    module: nn.Module, freeze_parameters: Optional[bool] = True, freeze_bn: Optional[bool] = True
+) -> nn.Module:
     """
     Change 'requires_grad' value for module and it's child modules and
     optionally freeze batchnorm modules.
@@ -70,3 +72,5 @@ def freeze_model(module: nn.Module, freeze_parameters: Optional[bool] = True, fr
         for m in module.modules():
             if isinstance(m, bn_types):
                 module.track_running_stats = not freeze_bn
+
+    return module

pytorch_toolbelt/optimization/lr_schedules.py

@@ -80,7 +80,7 @@ def get_lr(self):
         def compute_lr(base_lr):
             return (
                 self.eta_min
-                + (base_lr * self.gamma ** self.last_epoch - self.eta_min)
+                + (base_lr * self.gamma**self.last_epoch - self.eta_min)
                 * (1 + math.cos(math.pi * self.last_epoch / self.T_max))
                 / 2
             )
@@ -110,7 +110,7 @@ def get_lr(self):
 
         return [
             self.eta_min
-            + (base_lr * self.gamma ** self.last_epoch - self.eta_min)
+            + (base_lr * self.gamma**self.last_epoch - self.eta_min)
             * (1 + math.cos(math.pi * self.T_cur / self.T_i))
             / 2
             for base_lr in self.base_lrs

pytorch_toolbelt/utils/catalyst/metrics.py

@@ -234,7 +234,7 @@ def _f1_from_confusion_matrix(
             true_sum = np.array([true_sum.sum()])
 
         # Finally, we have all our sufficient statistics. Divide! #
-        beta2 = beta ** 2
+        beta2 = beta**2
 
         # Divide, and on zero-division, set scores and/or warn according to
         # zero_division:

pytorch_toolbelt/utils/fs.py

@@ -16,6 +16,7 @@
     "find_images_in_dir",
     "find_in_dir",
     "find_in_dir_glob",
+    "find_subdirectories_in_dir",
     "has_ext",
     "has_image_ext",
     "id_from_fname",
@@ -45,6 +46,19 @@ def find_in_dir(dirname: str) -> List[str]:
     return [os.path.join(dirname, fname) for fname in sorted(os.listdir(dirname))]
 
 
+def find_subdirectories_in_dir(dirname: str) -> List[str]:
+    """
+    Retrieve list of subdirectories (non-recursive) in the given directory.
+    Args:
+        dirname: Target directory name
+
+    Returns:
+        Sorted list of absolute paths to directories
+    """
+    all_entries = find_in_dir(dirname)
+    return [entry for entry in all_entries if os.path.isdir(entry)]
+
+
 def find_in_dir_with_ext(dirname: str, extensions: Union[str, List[str]]) -> List[str]:
     return [os.path.join(dirname, fname) for fname in sorted(os.listdir(dirname)) if has_ext(fname, extensions)]
 
@@ -107,7 +121,7 @@ def read_rgb_image(fname: Union[str, Path]) -> np.ndarray:
     if type(fname) != str:
         fname = str(fname)
 
-    image = cv2.imread(fname, cv2.IMREAD_UNCHANGED)
+    image = cv2.imread(fname, cv2.IMREAD_COLOR)
     if image is None:
         raise IOError(f'Cannot read image "{fname}"')