util.py

import os
import torch
import random
import numpy as np

def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)


def unnormalize(tens, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
    # assume tensor of shape NxCxHxW
    return tens * torch.Tensor(std)[None, :, None, None] + torch.Tensor(
        mean)[None, :, None, None]

def setup_for_distributed(is_master):
    """
    This function disables printing when not in master process
    """
    import builtins as __builtin__
    builtin_print = __builtin__.print

    def print(*args, **kwargs):
        force = kwargs.pop('force', False)
        if is_master or force:
            builtin_print(*args, **kwargs)

    __builtin__.print = print 

def set_seeds(seed):
    torch.manual_seed(seed)
    random.seed(seed)
    np.random.seed(seed)