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 numpy as np

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


def read_unknowns(unknown_list):
    """
    input is of form ['--METHOD.MODEL.LR=0.001758722642964502', '--METHOD.MODEL.NUM_LAYERS=1']
    """
    ret = {}
    for item in unknown_list:
        key, value = item.split('=')
        try:
            value = ast.literal_eval(value)
        except:
            print("MALFORMED ", value)
        k = key[2:]
        ret[k] = value
    return ret

def nest_dict(flat_dict, sep='.'):
    """Return nested dict by splitting the keys on a delimiter.

    >>> from pprint import pprint
    >>> pprint(nest_dict({'title': 'foo', 'author_name': 'stretch',
    ... 'author_zipcode': '06901'}))
    {'author': {'name': 'stretch', 'zipcode': '06901'}, 'title': 'foo'}
    """
    tree = {}
    for key, val in flat_dict.items():
        t = tree
        prev = None
        for part in key.split(sep):
            if prev is not None:
                t = t.setdefault(prev, {})
            prev = part
        else:
            t.setdefault(prev, val)
    return tree

def get_counts(labels):
    values, counts = np.unique(labels, return_counts=True)
    sorted_tuples = zip(*sorted(zip(values, counts))) # this just ensures we are getting the counts in the sorted order of the keys
    values, counts = [ list(tuple) for tuple in  sorted_tuples]
    fracs   = 1 / torch.Tensor(counts)
    return fracs / torch.max(fracs)


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)

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