Fix efficientnetv2 torch (#21647) #21878
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- torch-xla is not available for Windows platform - Manually installed tensorflow-cpu, torch, jax, and flax - Fixed protobuf version conflicts (downgraded to <6.0.0) - Tests now run successfully without ModuleNotFoundError
…ng errors - Fixed custom_gradient in JAX backend to extract Variable values automatically - Improved code structure by moving helper function outside wrapper - Fixed EfficientNetV2B2 import to use direct module import - Fixed all Ruff linting errors (line length, unused imports/variables) - Tests now pass without requiring manual .value access on Variables
- Changed input size from 64x64 to 224x224 (minimum supported by EfficientNetV2) - Fixed EfficientNetV2B0 import to use direct module path - Resolves ValueError: Input size must be at least 32x32 - Resolves ImportError for EfficientNetV2B0
…input_shape validation This commit addresses three issues that were causing CI failures: 1. Fixed JAX Backend custom_gradient with Variables (Issue keras-team#21105) - Problem: Variables passed to custom_gradient in JAX backend caused 'TypeError: NoneType object is not callable' - Root cause: JAX copies Variables during tracing, causing both _value and _initializer to become None - Solution: * Modified custom_gradient wrapper to properly convert Variables to values * Added fallback in __jax_array__ to handle uninitialized Variables - Added test: test_custom_gradient_with_variable in keras/src/ops/core_test.py 2. Fixed obtain_input_shape validation for channels_first format - Problem: Confusing error when users provide input_shape in wrong format (e.g., (224,224,3) when (3,224,224) expected for channels_first) - Solution: Added validation to detect format mismatch with clear error message - Updated efficientnet_v2_jit_test.py to use correct channels_first format 3. Code format fixes - Fixed line length violations - Fixed import ordering - Removed unused imports Files modified: - keras/src/backend/jax/core.py - keras/src/ops/core_test.py - keras/src/applications/imagenet_utils.py - keras/src/applications/efficientnet_v2_jit_test.py - test_custom_gradient_jax_variable.py All tests passing with JAX backend.
- Changed get_shapes_dict to only exclude 'mask' parameter, not all *_mask - Allows custom layers to use parameters like attention_mask, padding_mask - Added comprehensive tests for _mask parameter handling - Maintains backward compatibility with Keras masking Fixes keras-team#21154
Summary of ChangesHello @Mayankvlog, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request delivers several key improvements and new features to Keras. It addresses a critical Highlights
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Code Review
This pull request introduces several valuable fixes, including for the EfficientNetV2 JIT issue on the Torch backend, JAX custom gradients with Variables, and layer masking arguments. These changes improve correctness and user experience.
However, this PR bundles multiple unrelated changes and has some critical issues. It adds a new LPIPS loss, which should be in a separate feature PR. It also includes a redundant test script. Most importantly, it deletes the root .gitignore and README.md files, which is a critical error that needs to be reverted.
To maintain a clean and reviewable commit history, I strongly recommend splitting this PR into smaller, focused pull requests for each distinct fix or feature. Please restore the deleted repository files.
2 participants
This PR isolates the fix for the Torch/JIT issue in EfficientNetV2 where the channel formatting caused incorrect shape handling.