utils.py

import os

import cv2
import numpy as np
import torch
import torch.nn as nn
from scipy import misc


def load_test_data(image_path, size=256):
  img = misc.imread(image_path, mode='RGB')
  img = misc.imresize(img, [size, size])
  img = np.expand_dims(img, axis=0)
  img = preprocessing(img)

  return img


def preprocessing(x):
  x = x/127.5 - 1  # -1 ~ 1
  return x


def save_images(images, size, image_path):
  return imsave(inverse_transform(images), size, image_path)


def inverse_transform(images):
  return (images+1.) / 2


def imsave(images, size, path):
  return misc.imsave(path, merge(images, size))


def merge(images, size):
  h, w = images.shape[1], images.shape[2]
  img = np.zeros((h * size[0], w * size[1], 3))
  for idx, image in enumerate(images):
    i = idx % size[1]
    j = idx // size[1]
    img[h*j:h*(j+1), w*i:w*(i+1), :] = image

  return img


def ensure_folder_exists(log_dir):
  if not os.path.exists(log_dir):
    os.makedirs(log_dir)
  return log_dir


def str2bool(x):
  return x.lower() in ('true')


def cam(x, size=256):
  x = x - np.min(x)
  cam_img = x / np.max(x)
  cam_img = np.uint8(255 * cam_img)
  cam_img = cv2.resize(cam_img, (size, size))
  cam_img = cv2.applyColorMap(cam_img, cv2.COLORMAP_JET)
  return cam_img / 255.0


def imagenet_norm(x):
  mean = [0.485, 0.456, 0.406]
  std = [0.299, 0.224, 0.225]
  mean = torch.FloatTensor(mean).unsqueeze(
      0).unsqueeze(2).unsqueeze(3).to(x.device)
  std = torch.FloatTensor(std).unsqueeze(
      0).unsqueeze(2).unsqueeze(3).to(x.device)
  return (x - mean) / std


def denorm(x):
  return x * 0.5 + 0.5


def tensor2numpy(x):
  return x.detach().cpu().numpy().transpose(1, 2, 0)


def RGB2BGR(x):
  return cv2.cvtColor(x, cv2.COLOR_RGB2BGR)


def get_total_model_params(model: nn.Module):
  return human_format(sum(p.numel() for p in model.parameters()))


def get_total_trainable_model_params(model: nn.Module):
  return human_format(sum(p.numel() for p in model.parameters() if p.requires_grad))


def human_format(num: float):
  magnitude = 0
  while abs(num) >= 1000:
    magnitude += 1
    num /= 1000.0
  # add more suffixes if you need them
  return '%.2f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])