Add VectorizedBaseImageAugmentation layer #1373
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keras_cv/layers/preprocessing/vectorized_base_image_augmentation_layer.py
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ianstenbit
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Grayscale test passed --- root cause is that on GPU tf.image.grayscale is not memory efficient. in practice this barely matters because in tf.data we force CPU scope. If users request we can come up with a fix using pure tf-ops instead of tf.image ops |
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* implement vectorized base image augmentation layer * Implement vectorized RandomContrast layer * KPL performance * Random contrast vectorized * Vectorized contrast * Fix vectorized base layer * Fix vectorized base layer * Add vectorized grayscale layer * Remove random contrast * Remove random contrast * test_preserves_ragged_status_Grayscale * test_preserves_ragged_status_Grayscale * Fix * Fix masks * Fix masks * rename to 'batched' * Fix docstrings * Fix docstrings * Remove ragged method * Begin ragged image support * Begin ragged image support * Begin ragged image support * Begin ragged image support * Begin ragged image support * Performance benchmark * Reformat * Vectorized grayscale * Fix ragged test case * Fix ragged test case * Fix ragged test case * Fix ragged test case * Fix ragged test case
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Old non-graphmode basescale is like 100s runtime so not even comparable.