Final version of the PixelCNN++ implementation #51
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…in pixel_cnn_pp_2d.py
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Pull request overview
This PR introduces a complete PixelCNN++ implementation for 2D design generation, based on the OpenAI TensorFlow implementation and Lucas Caccia's PyTorch port. The implementation includes training, sampling, and evaluation capabilities for conditional generative modeling of engineering designs.
Key Changes
- Adds a full PixelCNN++ 2D model implementation with shifted convolutions, gated residual blocks, and discretized mixture of logistics loss
- Provides an evaluation script for model assessment using wandb artifact management
- Accidentally modifies cgan_cnn_2d.py to reduce training epochs from 200 to 2 (likely a debugging change)
Reviewed changes
Copilot reviewed 3 out of 4 changed files in this pull request and generated 10 comments.
| File | Description |
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| engiopt/pixel_cnn_pp_2d/pixel_cnn_pp_2d.py | Complete PixelCNN++ implementation including model architecture (Nin, GatedResnet, shifted convolution layers), loss functions, sampling logic, and training loop with wandb integration |
| engiopt/pixel_cnn_pp_2d/evaluate_pixel_cnn_pp_2d.py | Evaluation script that loads trained models from wandb artifacts, generates samples with specified conditions, and computes performance metrics |
| engiopt/pixel_cnn_pp_2d/init.py | Empty init file for the new module |
| engiopt/cgan_cnn_2d/cgan_cnn_2d.py | Reduces default training epochs from 200 to 2 (appears to be an unintended debugging change) |
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| # Optimzer |
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Typo in comment: "Optimzer" should be "Optimizer".
engiopt/cgan_cnn_2d/cgan_cnn_2d.py
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| # Algorithm specific | ||
| n_epochs: int = 200 | ||
| n_epochs: int = 2 |
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The n_epochs default value has been changed from 200 to 2. This appears to be a debugging value that was accidentally left in the code. Training for only 2 epochs is insufficient for model convergence and will result in poor performance. This should be reverted to the original value of 200 or another appropriate value for production use.
ffelten
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ffelten
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Made a quick pass. I think copilot has some good points, also ruff will probably complain
engiopt/cgan_cnn_2d/cgan_cnn_2d.py
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| # Algorithm specific | ||
| n_epochs: int = 200 | ||
| n_epochs: int = 2 |
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| def sample_from_discretized_mix_logistic(l: th.Tensor, nr_mix: int) -> th.Tensor: | ||
| """Sample from a discretized mixture of logistic distributions.""" | ||
| # Tensorflow ordering |
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There are some Tensorflow comments left here and there
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| # unpack parameters | ||
| logit_probs = l[:, :, :, :nr_mix] # mixture probabilities | ||
| l = l[:, :, :, nr_mix:].contiguous().view([*xs, nr_mix * 2]) # for mean, scale |
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This is very cryptic to me, a comment would help understanding what's going on here.
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@ffelten I see that this PR is ready to be merged and that you have approved. Can you confirm that we are all good and that I can merge this? If this is good to go then the only thing I see that needs updating is the README.md to add a row to the table noting the implementation in the library. |
I think it is good to go. Although a bit complex due to the masking ops and slow inference, it is a simple example of an AutoRegressive model--will be a good addition for educational purposes. |
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