utils.py

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
from shutil import copyfile
from datetime import datetime
import os
import logging
import numpy as np
import math
import torchvision
import pdb
import shutil
from tensorboardX import SummaryWriter
import skimage as sk
import random
    
def get_lr(step, total_steps, lr_max, lr_min):
    """Compute learning rate according to cosine annealing schedule."""
    return lr_min + (lr_max - lr_min) * 0.5 * (1 +
                                             np.cos(step / total_steps * np.pi))

def lr_poly(base_lr, iter, max_iter, power):
    return base_lr * ((1 - float(iter) / max_iter) ** (power))

def seed_torch(seed=0):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.