utils.py

'''Some helper functions for PyTorch, including:
    - progress_bar: progress bar mimic xlua.progress.
    - set_lr : set the learning rate
    - clip_gradient : clip gradient
'''

import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Function

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

TOTAL_BAR_LENGTH = 30.
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 = []
    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 set_lr(optimizer, lr):
    for group in optimizer.param_groups:
        group['lr'] = lr

def clip_gradient(optimizer, grad_clip):
    for group in optimizer.param_groups:
        #print(group['params'])
        for param in group['params']:
            param.grad.data.clamp_(-grad_clip, grad_clip)