utils.py

"""utils.py"""

import argparse
import subprocess

import torch
import torch.nn as nn
from torch.autograd import Variable


def cuda(tensor, uses_cuda):
    return tensor.cuda() if uses_cuda else tensor


def str2bool(v):
    # codes from : https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse

    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')


def where(cond, x, y):
    """Do same operation as np.where

    code from:
        https://discuss.pytorch.org/t/how-can-i-do-the-operation-the-same-as-np-where/1329/8
    """
    cond = cond.float()
    return (cond*x) + ((1-cond)*y)


def grid2gif(image_str, output_gif, delay=100):
    """Make GIF from images.

    code from:
        https://stackoverflow.com/questions/753190/programmatically-generate-video-or-animated-gif-in-python/34555939#34555939
    """
    str1 = 'convert -delay '+str(delay)+' -loop 0 ' + image_str  + ' ' + output_gif
    subprocess.call(str1, shell=True)

def Cor_CoeLoss( y_pred, y_target):
        x = y_pred
        y = y_target
        x_var = x - torch.mean(x)
        y_var = y - torch.mean(y)
        r_num = torch.sum(x_var * y_var)
        r_den = torch.sqrt(torch.sum(x_var ** 2)) * torch.sqrt(torch.sum(y_var ** 2))
        r = r_num / r_den

        # return 1 - r  # best are 0
        return 1 - r ** 2  # abslute constrain