| from __future__ import print_function |
| import torch |
| import numpy as np |
| from PIL import Image |
| import os |
|
|
|
|
| |
| |
| def tensor2im(input_image, imtype=np.uint8): |
| if isinstance(input_image, torch.Tensor): |
| image_tensor = input_image.data |
| else: |
| return input_image |
| image_numpy = image_tensor[0].cpu().float().numpy() |
| if image_numpy.shape[0] == 1: |
| image_numpy = np.tile(image_numpy, (3, 1, 1)) |
| image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 |
| return image_numpy.astype(imtype) |
|
|
|
|
| def diagnose_network(net, name='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): |
| image_pil = Image.fromarray(image_numpy) |
| image_pil.save(image_path) |
|
|
|
|
| def print_numpy(x, val=True, shp=False): |
| 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): |
| if isinstance(paths, list) and not isinstance(paths, str): |
| for path in paths: |
| mkdir(path) |
| else: |
| mkdir(paths) |
|
|
|
|
| def mkdir(path): |
| if not os.path.exists(path): |
| os.makedirs(path) |
|
|
