import torch
import torch.nn as nn
import numpy as np

def count_parameters_in_MB(model):
  if isinstance(model, nn.Module):
    return np.sum(np.prod(v.size()) for v in model.parameters())/1e6
  else:
    return np.sum(np.prod(v.size()) for v in model)/1e6


class Cutout(object):
  def __init__(self, length):
    self.length = length

  def __repr__(self):
    return ('{name}(length={length})'.format(name=self.__class__.__name__, **self.__dict__))

  def __call__(self, img):
    h, w = img.size(1), img.size(2)
    mask = np.ones((h, w), np.float32)
    y = np.random.randint(h)
    x = np.random.randint(w)

    y1 = np.clip(y - self.length // 2, 0, h)
    y2 = np.clip(y + self.length // 2, 0, h)
    x1 = np.clip(x - self.length // 2, 0, w)
    x2 = np.clip(x + self.length // 2, 0, w)

    mask[y1: y2, x1: x2] = 0.
    mask = torch.from_numpy(mask)
    mask = mask.expand_as(img)
    img *= mask
    return img