Inconsistency with bias enabled in the pytorch model (Conv2d) for image classification
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Assessment
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Newbie friendliness
- 35/100
- Issue type
- Bug
- Clarity
- Clearly specified
- Activity status
- Stale
- Tech stack
- keras, pytorch
- Domain
- machine-learning
Research direction
Start with /tiny/benchmark/experimental/training_torch/image_classification/utils/model.py and inspect the ResNetBlock Conv2d and BatchNorm definitions. Compare their configuration with the corresponding Keras model, then run the relevant image-classification checks if available. Done means the PyTorch and Keras convolution configurations are consistent around batch normalization.
Written by the indexing model from the issue text.
Description
Hello,
I am interested in running the image classification model to benchmark our accelerator and, currently, my environment is in pytorch therefore, I had a look at your experimental model under:
/tiny/benchmark/experimental/training_torch/image_classification/utils/model.py
The model contains ResNetBlock with two Conv2d convolutions followed per Batch Normalization. However, each Conv2d layer is configured with the bias enabled (bias=True) which is inconsistent with the Con2d layers in the Keras model that don't have the use_bias flag enabled (also it is not coherent with the purpose of batch normalization layer that follows).
Thank you,
Best regards,
Jean-Baptiste
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- Stars
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- 116
- PR merge metrics
- No merged PRs in 30d
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