Barlow twins for Contrastive SSL migrate to keras 3#2315
Barlow twins for Contrastive SSL migrate to keras 3#2315maitry63 wants to merge 1 commit intokeras-team:masterfrom
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Summary of ChangesHello, 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 modernizes the Barlow Twins for Contrastive SSL tutorial by migrating its codebase to Keras 3. The changes encompass a comprehensive update of API calls, data handling mechanisms, and the core loss function implementation, ensuring the tutorial remains current and functional with the latest Keras version. The refactoring aims to streamline the code, improve compatibility, and leverage Keras 3's unified backend capabilities. Highlights
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Code Review
This pull request refactors the Barlow Twins implementation to be compatible with Keras 3, primarily by migrating from TensorFlow-specific operations and tf.keras layers to Keras 3's multi-backend keras API and NumPy for augmentation logic. Key changes include updating imports, modifying augmentation classes (RandomToGrayscale, RandomColorJitter, RandomFlip, RandomResizedCrop, RandomSolarize, RandomBlur, RandomAugmentor) to use NumPy-based operations and removing tf.function decorators and keras.layers.Layer inheritance where appropriate. The BarlowLoss class was updated to use Keras ops for tensor operations, and the BTDatasetCreator was rewritten to use keras.utils.Sequence for data loading and augmentation. The ResNet34 model and the build_twin projector network were also updated to use Keras 3 layers. Review comments highlight several critical issues, including runtime errors due to incorrect inheritance in augmentation classes, a missing hue jitter component in RandomColorJitter that could impact performance, and an inefficient implementation in BTDatasetCreator. Additionally, there are suggestions to improve documentation, correct grayscale conversion logic to use a weighted average, and remove redundant variable definitions.
| def __init__(self, prob=0.8): | ||
| self.prob = prob |
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The RandomColorJitter class inherits from Augmentation (which is a keras.layers.Layer), but its __init__ method does not call super().__init__(). This will cause a runtime error. To fix this and be consistent with other refactored augmentation classes like RandomFlip, you should remove the inheritance from Augmentation. This also applies to RandomResizedCrop, RandomSolarize, and RandomBlur classes.
| def __init__(self, image_size): | ||
| super().__init__() | ||
| self.image_size = image_size | ||
| self.resize_layer = layers.Resizing(image_size, image_size) |
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The RandomResizedCrop class inherits from Augmentation (a keras.layers.Layer), but super().__init__() was removed from its constructor. This will cause a runtime error. For consistency with the other refactored augmentation classes, the inheritance from Augmentation should be removed from the class definition.
| def __init__(self, prob=0.2, threshold=0.5): | ||
| self.prob = prob | ||
| self.threshold = threshold |
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The RandomSolarize class inherits from Augmentation (a keras.layers.Layer), but its __init__ method does not call super().__init__(). This will cause a runtime error. To fix this, you should remove the inheritance from Augmentation in the class definition, which would be consistent with other refactored augmentation classes.
| def __init__(self, prob=0.2): | ||
| self.prob = prob |
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The RandomBlur class inherits from Augmentation (a keras.layers.Layer), but its __init__ method does not call super().__init__(). This will cause a runtime error. To fix this, you should remove the inheritance from Augmentation in the class definition, which would be consistent with other refactored augmentation classes.
| def __call__(self, x): | ||
| if np.random.rand() < self.prob: | ||
| x = x + np.random.uniform(-0.2, 0.2) | ||
| x = (x - 0.5) * np.random.uniform(0.8, 1.2) + 0.5 | ||
| gray = np.mean(x, axis=-1, keepdims=True) | ||
| x = gray + (x - gray) * np.random.uniform(0.8, 1.2) | ||
| x = np.clip(x, 0, 1) | ||
| return x |
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The new implementation of RandomColorJitter is missing the hue jitter component, which was present in the original implementation (tf.image.random_hue). The class docstring also mentions hue jitter. This change in augmentation logic could negatively impact the model's performance. Please consider adding hue jitter back into this augmentation.
| # Allocates two threads for a gpu private which allows more operations to be | ||
| # done faster | ||
| os.environ["TF_GPU_THREAD_MODE"] = "gpu_private" | ||
| os.environ["KERAS_BACKEND"] = "tensorflow" |
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The comment on lines 144-145 is now misleading as it describes the previous TF_GPU_THREAD_MODE setting, not the new KERAS_BACKEND setting. To avoid confusion, this comment should be removed.
| # Allocates two threads for a gpu private which allows more operations to be | |
| # done faster | |
| os.environ["TF_GPU_THREAD_MODE"] = "gpu_private" | |
| os.environ["KERAS_BACKEND"] = "tensorflow" | |
| os.environ["KERAS_BACKEND"] = "tensorflow" |
| def __call__(self, x): | ||
| if np.random.rand() < self.prob: | ||
| # average channels to get grayscale | ||
| gray = np.mean(x, axis=-1, keepdims=True) | ||
| x = np.repeat(gray, 3, axis=-1) | ||
| return x |
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The grayscale conversion logic has been changed from tf.image.rgb_to_grayscale, which uses a weighted average of channels (luma), to np.mean, which is a simple average. This changes the augmentation behavior and may impact model performance. For consistency with standard image processing libraries, consider using a weighted average similar to tf.image.rgb_to_grayscale (0.299R + 0.587G + 0.114*B).
| def __call__(self, x): | ||
| """call function. | ||
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| Randomly solarizes the image. |
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The docstring for this function seems to be a copy-paste error from RandomSolarize. It should describe the blur operation.
| Randomly solarizes the image. | |
| Randomly blurs the image. |
| return ZippedSequence(seq1, seq2) | ||
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| bt_augmentor = RandomAugmentor(IMAGE_SIZE) |
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The bt_augmentor variable is being redefined here. It was already defined on line 427. This redundant definition should be removed.
| Returns: | ||
| Returns a tf.tensor which represents the cross correlation | ||
| Returns a tensor which represents the cross correlation | ||
| matrix with its diagonals as zeros. |
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The docstring for the return value is incorrect. This method returns a scalar tensor representing the loss, not the cross-correlation matrix.
| Returns: | |
| Returns a tf.tensor which represents the cross correlation | |
| Returns a tensor which represents the cross correlation | |
| matrix with its diagonals as zeros. | |
| Returns: | |
| Returns a scalar tensor which represents the loss. |
2 participants
This PR Migrate the Barlow twins for Contrastive SSL tutorial into Keras 3.
Colab file: Notebook