utils.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 os
import sys
import time
import math
import time
import re
import shutil

import torch.nn as nn
import torch.nn.init as init
import torch


class Logger(object):
    def __init__(self, save_dir):
        self.terminal = sys.stdout
        self.log = open(save_dir, 'w')

    def write(self, message):
        self.log.write(message)
        self.terminal.write(message)

    def flush(self):
        #this flush method is needed for python 3 compatibility.
        #this handles the flush command by doing nothing.
        #you might want to specify some extra behavior here.
        pass

def create_result_dir(prefix):
    result_dir = 'results/{}_{}_0'.format(
        prefix, time.strftime('%Y-%m-%d_%H-%M-%S'))
    while os.path.exists(result_dir):
        i = result_dir.split('_')[-1]
        result_dir = re.sub('_[0-9]+$', result_dir, '_{}'.format(i))
    if not os.path.exists(result_dir):
        os.makedirs(result_dir)
    shutil.copy(__file__, os.path.join(result_dir, os.path.basename(__file__)))
    return result_dir


def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

def count_convs(model):
    l = 0
    for name, layer in model._modules.items():
        if isinstance(layer, nn.Conv2d):
            l += 1
        else:
            l += count_convs(layer)
    return l

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 = 200
# print(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