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import itertools
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.nn import functional as F
import distutils.util
def show_result(num_epoch, G_net, imgs_lr, imgs_hr):
with torch.no_grad():
test_images = G_net(imgs_lr)
fig, ax = plt.subplots(1, 3)
for j in itertools.product(range(3)):
ax[j].get_xaxis().set_visible(False)
ax[j].get_yaxis().set_visible(False)
ax[0].cla()
ax[0].imshow(np.transpose(np.clip(imgs_lr.cpu().numpy()[0] * 0.5 + 0.5, 0, 1), [1,2,0]))
ax[1].cla()
ax[1].imshow(np.transpose(np.clip(test_images.cpu().numpy()[0] * 0.5 + 0.5, 0, 1), [1,2,0]))
ax[2].cla()
ax[2].imshow(np.transpose(np.clip(imgs_hr.cpu().numpy()[0] * 0.5 + 0.5, 0, 1), [1,2,0]))
label = 'Epoch {0}'.format(num_epoch)
fig.text(0.5, 0.04, label, ha='center')
plt.savefig("results/train_out/epoch_" + str(num_epoch) + "_results.png")
plt.close('all') #避免内存泄漏
#---------------------------------------------------------#
# 将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
def cvtColor(image):
if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
return image
else:
image = image.convert('RGB')
return image
def preprocess_input(image, mean, std):
image = (image/255 - mean)/std
return image
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def print_arguments(args):
print("----------- Configuration Arguments -----------")
for arg, value in sorted(vars(args).items()):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def add_arguments(argname, type, default, help, argparser, **kwargs):
type = distutils.util.strtobool if type == bool else type
argparser.add_argument("--" + argname,
default=default,
type=type,
help=help + ' 默认: %(default)s.',
**kwargs)
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