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 torch
import torch.nn as nn


def get_mean_and_std(dataset, max_load=10000):
    '''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..')
    N = min(max_load, len(dataset))
    for i in range(N):
        print(i)
        im,_,_ = dataset.load(1)
        for j in range(3):
            mean[j] += im[:,j,:,:].mean()
            std[j] += im[:,j,:,:].std()
    mean.div_(N)
    std.div_(N)
    return mean, std

def mask_select(input, mask, dim):
    '''
    Select tensor rows/cols using a mask tensor.

    Args:
      input: (tensor) input tensor, sized [N,M].
      mask: (tensor) mask tensor, sized [N,] or [M,].
      dim: (tensor) mask dim.

    Returns:
      (tensor) selected rows/cols.

    Example:
    >>> a = torch.randn(4,2)
    >>> a
    -0.3462 -0.6930
     0.4560 -0.7459
    -0.1289 -0.9955
     1.7454  1.9787
    [torch.FloatTensor of size 4x2]
    >>> i = a[:,0] > 0
    >>> i
    0
    1
    0
    1
    [torch.ByteTensor of size 4]
    >>> masked_select(a, i, 0)
    0.4560 -0.7459
    1.7454  1.9787
    [torch.FloatTensor of size 2x2]
    '''
    index = mask.nonzero().squeeze(1)
    return input.index_select(dim, index)

def msr_init(net):
    '''Initialize layer parameters.'''
    for layer in net:
        if type(layer) == nn.Conv2d:
            n = layer.kernel_size[0]*layer.kernel_size[1]*layer.out_channels
            layer.weight.data.normal_(0, math.sqrt(2./n))
            layer.bias.data.zero_()
        elif type(layer) == nn.BatchNorm2d:
            layer.weight.data.fill_(1)
            layer.bias.data.zero_()
        elif type(layer) == nn.Linear:
            layer.bias.data.zero_()


_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)

TOTAL_BAR_LENGTH = 86.
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)):
        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