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utils.py
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utils.py
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'''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 logging
import math
import os
import shutil
import sys
import time
import torch
import torch.nn as nn
import torch.nn.init as init
def get_mean_and_std(dataset):
'''Compute the mean and std value of dataset.'''
dataloader = 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)
# _, term_width = os.popen('stty size', 'r').read().split()
# term_width = int(term_width)
# TOTAL_BAR_LENGTH = 65.
# last_time = time.time()
# begin_time = last_time
# def progress_bar(current, total, msg=None):
# global last_time, begin_time
# if current == 0:
# begin_time = time.time() # Reset for new bar.
# cur_len = int(TOTAL_BAR_LENGTH*current/total)
# rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
# sys.stdout.write(' [')
# for i in range(cur_len):
# sys.stdout.write('=')
# sys.stdout.write('>')
# for i in range(rest_len):
# sys.stdout.write('.')
# sys.stdout.write(']')
# cur_time = time.time()
# step_time = cur_time - last_time
# last_time = cur_time
# tot_time = cur_time - begin_time
# L = []
# L.append(' Step: %s' % format_time(step_time))
# L.append(' | Tot: %s' % format_time(tot_time))
# if msg:
# L.append(' | ' + msg)
# msg = ''.join(L)
# sys.stdout.write(msg)
# for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
# sys.stdout.write(' ')
# # Go back to the center of the bar.
# for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
# sys.stdout.write('\b')
# sys.stdout.write(' %d/%d ' % (current+1, total))
# if current < total-1:
# sys.stdout.write('\r')
# else:
# sys.stdout.write('\n')
# sys.stdout.flush()
# def format_time(seconds):
# days = int(seconds / 3600/24)
# seconds = seconds - days*3600*24
# hours = int(seconds / 3600)
# seconds = seconds - hours*3600
# minutes = int(seconds / 60)
# seconds = seconds - minutes*60
# secondsf = int(seconds)
# seconds = seconds - secondsf
# millis = int(seconds*1000)
# f = ''
# i = 1
# if days > 0:
# f += str(days) + 'D'
# i += 1
# if hours > 0 and i <= 2:
# f += str(hours) + 'h'
# i += 1
# if minutes > 0 and i <= 2:
# f += str(minutes) + 'm'
# i += 1
# if secondsf > 0 and i <= 2:
# f += str(secondsf) + 's'
# i += 1
# if millis > 0 and i <= 2:
# f += str(millis) + 'ms'
# i += 1
# if f == '':
# f = '0ms'
# return f
def get_logger(file_path):
""" Make python logger """
# [!] Since tensorboardX use default logger (e.g. logging.info()), we should use custom logger
logger = logging.getLogger('cifar10-training')
log_format = '%(asctime)s | %(message)s'
formatter = logging.Formatter(log_format, datefmt='%m/%d %I:%M:%S %p')
file_handler = logging.FileHandler(file_path)
file_handler.setFormatter(formatter)
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(stream_handler)
logger.setLevel(logging.INFO)
return logger
class AverageMeter(object):
"""
Computes and stores the average and current value
Copied from: https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
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
def accuracy(output, target, topk=(1,)):
""" Computes the precision@k for the specified values of k """
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(1.0 / batch_size))
return res
def save_checkpoint(state, ckpt_dir, is_best=False):
filename = os.path.join(ckpt_dir, 'checkpoint.pth.tar')
torch.save(state, filename)
if is_best:
best_filename = os.path.join(ckpt_dir, 'best.pth.tar')
shutil.copyfile(filename, best_filename)