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
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.init as init
import torchvision
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn

from models import *


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 = int(term_width)

TOTAL_BAR_LENGTH = 65.0
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


def prepare_test_data():
    transform_test = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ]
    )

    testset = torchvision.datasets.CIFAR10(
        root="./data", train=False, download=True, transform=transform_test
    )
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=10, shuffle=False, num_workers=2
    )
    return testloader


def test_model_vis(net, checkpoint_dir: str, testloader: torch.utils.data.DataLoader):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    net = net.to(device)
    if device == "cuda":
        net = torch.nn.DataParallel(net)
        cudnn.benchmark = True
    print("==> Loading from checkpoint..")
    assert os.path.isdir(checkpoint_dir), "Error: no checkpoint directory found!"
    checkpoint = torch.load(f"./{checkpoint_dir}/ckpt.pth")
    net.load_state_dict(checkpoint["net"])
    net.eval()
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            print("batch data shape:", inputs.shape)
            outputs = net(inputs)
            break