|
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) |
|
|
