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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
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import torch
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import config
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from torchvision.utils import save_image
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def save_some_examples(gen, val_loader, epoch, folder):
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x, y = next(iter(val_loader))
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x, y = x.to(config.DEVICE), y.to(config.DEVICE)
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gen.eval()
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with torch.no_grad():
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y_fake = gen(x)
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y_fake = y_fake * 0.5 + 0.5 # remove normalization#
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save_image(y_fake, folder + f"/y_gen_{epoch}.png")
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save_image(x * 0.5 + 0.5, folder + f"/input_{epoch}.png")
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if epoch == 1:
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save_image(y * 0.5 + 0.5, folder + f"/label_{epoch}.png")
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gen.train()
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def save_checkpoint(model, optimizer, filename="my_checkpoint.pth.tar"):
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print("=> Saving checkpoint")
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checkpoint = {
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"state_dict": model.state_dict(),
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"optimizer": optimizer.state_dict(),
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}
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torch.save(checkpoint, filename)
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def load_checkpoint(checkpoint_file, model, optimizer, lr):
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print("=> Loading checkpoint")
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checkpoint = torch.load(checkpoint_file, map_location=config.DEVICE)
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model.load_state_dict(checkpoint["state_dict"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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# If we don't do this then it will just have learning rate of old checkpoint
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# and it will lead to many hours of debugging \:
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for param_group in optimizer.param_groups:
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param_group["lr"] = lr
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