utils.py

import json
import logging
import os
import shutil
import math

import numpy as np
import torch
import matplotlib
import matplotlib.pyplot as plt


class Params():
    """Class that loads hyperparameters from a json file.

    Example:
    ```
    params = Params(json_path)
    print(params.learning_rate)
    params.learning_rate = 0.5  # change the value of learning_rate in params
    ```
    """

    def __init__(self, json_path):
        with open(json_path) as f:
            params = json.load(f)
            self.__dict__.update(params)

    def save(self, json_path):
        with open(json_path, 'w') as f:
            json.dump(self.__dict__, f, indent=4)

    def update(self, json_path):
        """Loads parameters from json file"""
        with open(json_path) as f:
            params = json.load(f)
            self.__dict__.update(params)

    @property
    def dict(self):
        """Gives dict-like access to Params instance by `params.dict['learning_rate']"""
        return self.__dict__


class RunningAverage():
    """A simple class that maintains the running average of a quantity

    Example:
    ```
    loss_avg = RunningAverage()
    loss_avg.update(2)
    loss_avg.update(4)
    loss_avg() = 3
    ```
    """
    def __init__(self):
        self.steps = 0
        self.total = 0

    def update(self, val):
        self.total += val
        self.steps += 1

    def __call__(self):
        return self.total/float(self.steps)


def requires_grad(parameters, flag=True):
    for p in parameters:
        p.requires_grad = flag


def set_logger(log_path):
    """Set the logger to log info in terminal and file `log_path`.

    In general, it is useful to have a logger so that every output to the terminal is saved
    in a permanent file. Here we save it to `model_dir/train.log`.

    Example:
    ```
    logging.info("Starting training...")
    ```

    Args:
        log_path: (string) where to log
    """
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    if not logger.handlers:
        # Logging to a file
        file_handler = logging.FileHandler(log_path, 'w+')
        file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
        logger.addHandler(file_handler)

        # Logging to console
        stream_handler = logging.StreamHandler()
        stream_handler.setFormatter(logging.Formatter('%(message)s'))
        logger.addHandler(stream_handler)


def save_dict_to_json(d, json_path):
    """Saves dict of floats in json file

    Args:
        d: (dict) of float-castable values (np.float, int, float, etc.)
        json_path: (string) path to json file
    """
    with open(json_path, 'w') as f:
        # We need to convert the values to float for json (it doesn't accept np.array, np.float, )
        d = {k: float(v) for k, v in d.items()}
        json.dump(d, f, indent=4)


def save_checkpoint(state, is_best, checkpoint, filename=None):
    """Saves model and training parameters at checkpoint + 'last.pth.tar'. If is_best==True, also saves
    checkpoint + 'best.pth.tar'

    Args:
        state: (dict) contains model's state_dict, may contain other keys such as epoch, optimizer state_dict
        is_best: (bool) True if it is the best model seen till now
        checkpoint: (string) folder where parameters are to be saved
    """
    filename = 'last.pth.tar' if filename is None else filename
    filepath = os.path.join(checkpoint, filename)
    if not os.path.exists(checkpoint):
        print("Checkpoint Directory does not exist! Making directory {}".format(checkpoint))
        os.mkdir(checkpoint)
    else:
        print("Checkpoint Directory exists! ")
    torch.save(state, filepath)
    if is_best:
        shutil.copyfile(filepath, os.path.join(checkpoint, 'best.pth.tar'))


def load_checkpoint(checkpoint, model, optimizer=None):
    """Loads model parameters (state_dict) from file_path. If optimizer is provided, loads state_dict of
    optimizer assuming it is present in checkpoint.

    Args:
        checkpoint: (string) filename which needs to be loaded
        model: (torch.nn.Module) model for which the parameters are loaded
        optimizer: (torch.optim) optional: resume optimizer from checkpoint
    """
    if not os.path.exists(checkpoint):
        raise("File doesn't exist {}".format(checkpoint))
    checkpoint = torch.load(checkpoint)
    model.load_state_dict(checkpoint['state_dict'], strict=False)

    if optimizer:
        optimizer.load_state_dict(checkpoint['optim_dict'])

    return checkpoint


def plot_spatial_rf(U, n=None, color='black', size=(10,10)):
    # assume U is num_neuron x dim
    fig, ax = plt.subplots(figsize=size)
    ax.cla()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    if n is None:
        n = U.shape[0]

    M = int(math.sqrt(n))
    N = M if M ** 2 == n else M + 1
    D = int(math.sqrt(U.shape[1]))
    # draw with black border 
    panel = np.zeros([M * D + M-1, N * D + N-1])
    # draw
    for i in range(M):
        for j in range(N):
            if i * M + j < n:
                row_start = i * D + i 
                col_start = j * D + j
                panel[row_start:row_start+D, col_start:col_start+D] = U[i * M + j].reshape(D, D)
    # all black border
    panel[panel == 0] = np.nan
    current_cmap = matplotlib.cm.get_cmap("gray")
    current_cmap.set_bad(color='black')
    ax.imshow(panel, cmap=current_cmap)
    plt.setp(ax.spines.values(), color=color)
    return fig, ax


def plot_r2_loss(r2):
    fig = plt.figure()
    plt.plot(r2)
    return fig


def set_seed(seed):
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)