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utils_pytorch.py
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#!/usr/bin/env python
# coding=utf-8
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.nn.init as init
from collections import OrderedDict
import numpy as np
import os
import os.path as osp
import sys
import time
import math
import subprocess
try:
import cPickle as pickle
except:
import pickle
def savepickle(data, file_path):
mkdir_p(osp.dirname(file_path), delete=False)
print('pickle into', file_path)
with open(file_path, 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
def unpickle(file_path):
with open(file_path, 'rb') as f:
data = pickle.load(f)
return data
def mkdir_p(path, delete=False, print_info=True):
if path == '': return
if delete:
subprocess.call(('rm -r ' + path).split())
if not osp.exists(path):
if print_info:
print('mkdir -p ' + path)
subprocess.call(('mkdir -p ' + path).split())
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 is not None:
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('../../Downloads')
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