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35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
import torch
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import torch.nn as nn
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def gradient_penalty(critic, real, fake, device="cpu"):
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BATCH_SIZE, C, H, W = real.shape
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alpha = torch.rand((BATCH_SIZE, 1, 1, 1)).repeat(1, C, H, W).to(device)
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interpolated_images = real * alpha + fake * (1 - alpha)
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# Calculate critic scores
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mixed_scores = critic(interpolated_images)
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# Take the gradient of the scores with respect to the images
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gradient = torch.autograd.grad(
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inputs=interpolated_images,
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outputs=mixed_scores,
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grad_outputs=torch.ones_like(mixed_scores),
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create_graph=True,
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retain_graph=True,
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)[0]
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gradient = gradient.view(gradient.shape[0], -1)
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gradient_norm = gradient.norm(2, dim=1)
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gradient_penalty = torch.mean((gradient_norm - 1) ** 2)
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return gradient_penalty
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def save_checkpoint(state, filename="celeba_wgan_gp.pth.tar"):
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print("=> Saving checkpoint")
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torch.save(state, filename)
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def load_checkpoint(checkpoint, gen, disc):
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print("=> Loading checkpoint")
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gen.load_state_dict(checkpoint['gen'])
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disc.load_state_dict(checkpoint['disc']) |