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import itertools |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import torch |
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from torch.nn import functional as F |
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import distutils.util |
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def show_result(num_epoch, G_net, imgs_lr, imgs_hr): |
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with torch.no_grad(): |
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test_images = G_net(imgs_lr) |
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fig, ax = plt.subplots(1, 3) |
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for j in itertools.product(range(3)): |
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ax[j].get_xaxis().set_visible(False) |
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ax[j].get_yaxis().set_visible(False) |
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ax[0].cla() |
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ax[0].imshow(np.transpose(np.clip(imgs_lr.cpu().numpy()[0] * 0.5 + 0.5, 0, 1), [1,2,0])) |
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ax[1].cla() |
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ax[1].imshow(np.transpose(np.clip(test_images.cpu().numpy()[0] * 0.5 + 0.5, 0, 1), [1,2,0])) |
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ax[2].cla() |
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ax[2].imshow(np.transpose(np.clip(imgs_hr.cpu().numpy()[0] * 0.5 + 0.5, 0, 1), [1,2,0])) |
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label = 'Epoch {0}'.format(num_epoch) |
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fig.text(0.5, 0.04, label, ha='center') |
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plt.savefig("results/train_out/epoch_" + str(num_epoch) + "_results.png") |
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plt.close('all') |
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def cvtColor(image): |
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if len(np.shape(image)) == 3 and np.shape(image)[2] == 3: |
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return image |
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else: |
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image = image.convert('RGB') |
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return image |
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def preprocess_input(image, mean, std): |
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image = (image/255 - mean)/std |
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return image |
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def get_lr(optimizer): |
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for param_group in optimizer.param_groups: |
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return param_group['lr'] |
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def print_arguments(args): |
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print("----------- Configuration Arguments -----------") |
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for arg, value in sorted(vars(args).items()): |
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print("%s: %s" % (arg, value)) |
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print("------------------------------------------------") |
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def add_arguments(argname, type, default, help, argparser, **kwargs): |
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type = distutils.util.strtobool if type == bool else type |
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argparser.add_argument("--" + argname, |
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default=default, |
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type=type, |
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help=help + ' 默认: %(default)s.', |
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**kwargs) |
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