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| """This module contains simple helper functions """ | |
| from __future__ import print_function | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import os | |
| def tensor2im(input_image, imtype=np.uint8): | |
| """"Converts a Tensor array into a numpy image array. | |
| Parameters: | |
| input_image (tensor) -- the input image tensor array | |
| imtype (type) -- the desired type of the converted numpy array | |
| """ | |
| if not isinstance(input_image, np.ndarray): | |
| if isinstance(input_image, torch.Tensor): # get the data from a variable | |
| image_tensor = input_image.data | |
| else: | |
| return input_image | |
| image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array | |
| if image_numpy.shape[0] == 1: # grayscale to RGB | |
| image_numpy = np.tile(image_numpy, (3, 1, 1)) | |
| image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 # post-processing: transpose and scaling | |
| else: # if it is a numpy array | |
| image_numpy = input_image * 255. | |
| return image_numpy.astype(imtype) | |
| def diagnose_network(net, name='network'): | |
| """Calculate and print the mean of average absolute(gradients) | |
| Parameters: | |
| net (torch network) -- Torch network | |
| name (str) -- the name of the network | |
| """ | |
| mean = 0.0 | |
| count = 0 | |
| for param in net.parameters(): | |
| if param.grad is not None: | |
| mean += torch.mean(torch.abs(param.grad.data)) | |
| count += 1 | |
| if count > 0: | |
| mean = mean / count | |
| print(name) | |
| print(mean) | |
| def save_image(image_numpy, image_path, aspect_ratio=1.0): | |
| """Save a numpy image to the disk | |
| Parameters: | |
| image_numpy (numpy array) -- input numpy array | |
| image_path (str) -- the path of the image | |
| """ | |
| image_pil = Image.fromarray(image_numpy) | |
| h, w, _ = image_numpy.shape | |
| if aspect_ratio > 1.0: | |
| image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC) | |
| if aspect_ratio < 1.0: | |
| image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC) | |
| image_pil.save(image_path) | |
| def print_numpy(x, val=True, shp=False): | |
| """Print the mean, min, max, median, std, and size of a numpy array | |
| Parameters: | |
| val (bool) -- if print the values of the numpy array | |
| shp (bool) -- if print the shape of the numpy array | |
| """ | |
| x = x.astype(np.float64) | |
| if shp: | |
| print('shape,', x.shape) | |
| if val: | |
| x = x.flatten() | |
| print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( | |
| np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) | |
| def mkdirs(paths): | |
| """create empty directories if they don't exist | |
| Parameters: | |
| paths (str list) -- a list of directory paths | |
| """ | |
| if isinstance(paths, list) and not isinstance(paths, str): | |
| for path in paths: | |
| mkdir(path) | |
| else: | |
| mkdir(paths) | |
| def mkdir(path): | |
| """create a single empty directory if it didn't exist | |
| Parameters: | |
| path (str) -- a single directory path | |
| """ | |
| if not os.path.exists(path): | |
| os.makedirs(path) | |