Adding Video Classifier wrapper #1805
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* Agg Vgg16 backbone * update names * update tests * update test * add image classifier * incorporate review comments * Update test case * update backbone test * add image classifier * classifier cleanup * code reformat * add vgg16 image classifier * make vgg generic * update doc string * update docstring * add classifier test * update tests * update docstring * address review comments * code reformat * update the configs * address review comments * fix task saved model test * update init * code reformatted
* Add ResNetV1 and ResNetV2 * Address comments
* Add CSP DarkNet * Add CSP DarkNet * snake_case function names * change use_depthwise to block_type
…Backbone` (keras-team#1769) * Add FeaturePyramidBackbone and update ResNetBackbone * Simplify the implementation * Fix CI * Make ResNetBackbone compatible with timm and add FeaturePyramidBackbone * Add conversion implementation * Update docstrings * Address comments
* Add DenseNet * fix testcase * address comments * nit * fix lint errors * move description
* add vit det vit_det_backbone * update docstring * code reformat * fix tests * address review comments * bump year on all files * address review comments * rename backbone * fix tests * change back to ViT * address review comments * update image shape
* Add MixTransformer * fix testcase * test changes and comments * lint fix * update config list * modify testcase for 2 layers
* update input_image_shape -> image_shape * update docstring example * code reformat * update tests
add missing __init__ file to vit_det
This is a temporary way to test out the keras-hub branch. - Does a global rename of all symbols during package build. - Registers the "old" name on symbol export for saving compat. - Adds a github action to publish every commit to keras-hub as a new package. - Removes our descriptions on PyPI temporarily, until we want to message this more broadly.
* Add `CLIPTokenizer`, `T5XXLTokenizer`, `CLIPTextEncoder` and `T5XXLTextEncoder`. * Make CLIPTextEncoder as Backbone * Add `T5XXLPreprocessor` and remove `T5XXLTokenizer` Add `CLIPPreprocessor` * Use `tf = None` at the top * Replace manual implementation of `CLIPAttention` with `MultiHeadAttention`
* Bounding box utils * - Correct test cases * - Remove hard tensorflow dtype * - fix api gen * - Fix import for test cases - Use setup for converters test case * - fix api_gen issue * - FIx api gen * - Fix api gen error * - Correct test cases as per new api changes
* mobilenet_v3 added in keras-nlp * minor bug fixed in mobilenet_v3_backbone * formatting corrected * refactoring backbone * correct_pad_downsample method added * refactoring backbone * parameters updated * Testcaseupdated, expected output shape corrected * code formatted with black * testcase updated * refactoring and description added * comments updated * added mobilenet v1 and v2 * merge conflict resolved * version arg removed, and config options added * input_shape changed to image_shape in arg * config updated * input shape corrected * comments resolved * activation function format changed * minor bug fixed * minor bug fixed * added vision_backbone_test * channel_first bug resolved * channel_first cases working * comments resolved * formatting fixed * refactoring --------- Co-authored-by: ushareng <[email protected]>
* migrating efficientnet models to keras-hub * merging changes from other sources * autoformatting pass * initial consolidation of efficientnet_backbone * most updates and removing separate implementation * cleanup, autoformatting, keras generalization * removed layer examples outside of effiicient net * many, mainly documentation changes, small test fixes
* Add ResNet_vd to ResNet backbone * Addressed requested parameter changes * Fixed tests and updated comments * Added new parameters to docstring
* Add `VAEImageDecoder` for StableDiffusionV3 * Use `keras.Model` for `VAEImageDecoder` and follows the coding style in `VAEAttention`
mattdangerw
reviewed
Sep 3, 2024
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| @keras_nlp_export("keras_nlp.models.VideoClassifier") | ||
| class VideoClassifier(Task): |
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Why do we need this? What models use this? What are the expected input formats?
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This wrapper will be used by video classifier models like video_swin, input format will be (depth, height, width, channel)
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@ushareng has the VideoSwin model been added yet? The video classifier should be added once we have the VideoSwin backbone in. That will help us verify the implementation. |
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