utils.py

import os
import json
import torch
import shutil
import logging


class 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():
    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 set_logger(log_path):
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    if not logger.handlers:
        # Logging to a file
        file_handler = logging.FileHandler(log_path)
        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_checkpoint(state, is_best, checkpoint):
    filepath = os.path.join(checkpoint, 'last.pth.tar')
    if not os.path.exists(checkpoint):
        print("Checkpoint Directory does not exist! Making directory {}".format(checkpoint))
        os.mkdir(checkpoint)
    torch.save(state, filepath)
    if is_best:
        shutil.copyfile(filepath, os.path.join(checkpoint, 'best.pth.tar'))

def load_checkpoint(checkpoint, model, optimizer=None):
    if not os.path.exists(checkpoint):
        raise ("File doesn't exist {}".format(checkpoint))
    checkpoint = torch.load(checkpoint)
    # model.load_state_dict(checkpoint['state_dict'])
    model.load_state_dict(checkpoint['state_dict'])

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

    return checkpoint