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• Random-Erasing-master
  • utils
    • misc.py
    • eval.py
    • logger.py
    • __init__.py
    • progress
      • test_progress.py
      • LICENSE
      • README.rst
      • setup.py
      • progress
        • bar.py
        • counter.py
        • spinner.py
        • __init__.py
        • helpers.py
      • MANIFEST.in
    • visualize.py
    • transforms.py
  • img
    • exmples
  • LICENSE
  • models
    • cifar
      • wideresnet.py~
      • resnet.py
      • alexnet.py~
      • __init__.py
      • preresnetimagenet.py~
      • wrn.py
      • preresnet.py~
      • __init__.py~
    • fashion
      • resnext.py~
      • wideresnet.py~
      • wrn.py~
      • resnet.py
      • alexnet.py~
      • __init__.py
      • resnet.py~
      • preresnetimagenet.py~
      • wrn.py
      • preresnet.py~
      • __init__.py~
    • __init__.py
  • fashionmnist.py
  • README.md
  • cifar.py
  • .gitignore
  • transforms.py

'''Some helper functions for PyTorch, including:
    - get_mean_and_std: calculate the mean and std value of dataset.
    - msr_init: net parameter initialization.
    - progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import math

import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable

__all__ = ['get_mean_and_std', 'init_params', 'mkdir_p', 'AverageMeter']


def get_mean_and_std(dataset):
    '''Compute the mean and std value of dataset.'''
    dataloader = trainloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)

    mean = torch.zeros(3)
    std = torch.zeros(3)
    print('==> Computing mean and std..')
    for inputs, targets in dataloader:
        for i in range(3):
            mean[i] += inputs[:,i,:,:].mean()
            std[i] += inputs[:,i,:,:].std()
    mean.div_(len(dataset))
    std.div_(len(dataset))
    return mean, std

def init_params(net):
    '''Init layer parameters.'''
    for m in net.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal(m.weight, mode='fan_out')
            if m.bias:
                init.constant(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant(m.weight, 1)
            init.constant(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal(m.weight, std=1e-3)
            if m.bias:
                init.constant(m.bias, 0)

def mkdir_p(path):
    '''make dir if not exist'''
    try:
        os.makedirs(path)
    except OSError as exc:  # Python >2.5
        if exc.errno == errno.EEXIST and os.path.isdir(path):
            pass
        else:
            raise

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count