Update main.py #2
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codeant-ai bot added the size:L 
Things to considerBased on the provided PR diff, here are the top probable issues that could be considered as bugs or missed edge cases:
These are potential issues that might arise from the changes in the PR. However, without running the code and writing appropriate tests, it is not possible to guarantee that these are definitive bugs. They are, however, areas that should be carefully reviewed and tested. |
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| return reconstruction_loss + KL | ||
| else: | ||
| if use_mse: | ||
| criterion = nn.MSELoss() | ||
| else: | ||
| criterion = nn.BCELoss(reduction='mean') | ||
| reconstruction_loss = criterion(outputs, inputs) | ||
| # normalize reconstruction loss | ||
| reconstruction_loss *= 28*28 | ||
| KL = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), -1) | ||
| return torch.mean(reconstruction_loss + KL) |
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This rule is about removing unnecessary else statements in your code. An else statement is considered unnecessary when it follows a return statement in the if block. In such cases, the else block can be safely removed without changing the logic of the code. Unnecessary else statements can make your code harder to read and understand. They can also lead to more complex code structures, which can increase the likelihood of introducing bugs. By removing unnecessary else statements, you can make your code simpler and more readable.
| return reconstruction_loss + KL | |
| else: | |
| if use_mse: | |
| criterion = nn.MSELoss() | |
| else: | |
| criterion = nn.BCELoss(reduction='mean') | |
| reconstruction_loss = criterion(outputs, inputs) | |
| # normalize reconstruction loss | |
| reconstruction_loss *= 28*28 | |
| KL = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), -1) | |
| return torch.mean(reconstruction_loss + KL) | |
| if use_mse: | |
| criterion = nn.MSELoss() | |
| else: | |
| criterion = nn.BCELoss(reduction='mean') | |
| reconstruction_loss = criterion(outputs, inputs) | |
| # normalize reconstruction loss | |
| reconstruction_loss *= 28*28 | |
| KL = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), -1) | |
| return torch.mean(reconstruction_loss + KL) | |
| datasets.MNIST('../data', train=False, transform=transforms.ToTensor()), | ||
| batch_size=args.batch_size, shuffle=False, **kwargs) | ||
| train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transforms.ToTensor()) | ||
| train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs) |
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If possible, it is better to rely on automatic pinning in PyTorch to avoid undefined behavior and for efficiency
| train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs) | ||
| | ||
| test_dataset = datasets.MNIST('../data', train=False, transform=transforms.ToTensor()) | ||
| test_loader = DataLoader(test_dataset , batch_size=args.batch_size, shuffle=True, **kwargs) |
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If possible, it is better to rely on automatic pinning in PyTorch to avoid undefined behavior and for efficiency
Description
Changes walkthrough
main.py
Refactor and Enhance VAE Example with Visualization Toolsvae/main.py
dataset.
for reparameterization and decoding.
image generation.
training and testing datasets.
generating manifold visualizations and animations.
removed macOS GPU training flags.
added an option to use MSE loss.
and generate a 2D manifold of digits.
🔍 Anti-patterns Detected:
vae/main.py
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