update presets to point to the main Keras Kaggle page #1921
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ushareng
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Oct 24, 2024
* update presets to point to the main keras page * update mit path
ushareng
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Oct 24, 2024
BytePairTokenizer must not split sequences of \n (keras-team#1910) * fix for loading of special tokens in Llama tokenizer * fix for Llama tokenizer which can have multiple end tokens * bug fix * adding some missing tokens to Llama3 tokenizer * fixed tests and Llama3Tokenizer init. * now loading correct eos_token config from Hugging Face checkpoint. Using hack for Keras checkpoint because it does not have this info * fix for BytePairTokenizer to make Lllama3-instruct work in chat: \n\n sequences are significant in the chat template and must be preserved by the tokenizer --------- Co-authored-by: Martin Görner <[email protected]> fix for generation that never stops in Llama3-Instruct variants (keras-team#1904) * fix for loading of special tokens in Llama tokenizer * fix for Llama tokenizer which can have multiple end tokens * bug fix * adding some missing tokens to Llama3 tokenizer * fixed tests and Llama3Tokenizer init. * now loading correct eos_token config from Hugging Face checkpoint. Using hack for Keras checkpoint because it does not have this info --------- Co-authored-by: Martin Görner <[email protected]> fix failing JAX GPU test (keras-team#1911) * fix tests * fix test Refactor `MMDiT`, add `ImageToImage` and `Inpaint` for SD3 (keras-team#1909) * Refactor `MMDiT` and add `ImageToImage` * Update model version * Fix minor bugs. * Add `Inpaint` for SD3. * Fix warnings of MMDiT. * Addcomment to Inpaint * Simplify `MMDiT` implementation and info of `summary()`. * Refactor `generate()` API of `TextToImage`, `ImageToImage` and `Inpaint`. Minor bug fix (keras-team#1915) Change to image_converter.image_size since it is a tuple and it's not a callable function. [Mix Transformer] Add Presets for MiTB0...MiTB5 (keras-team#1893) * add presets for mit * add standin paths * register presets in __init__.py * fix op in overlapping patching and embedding, start adding conversion utils * style * add padding to MiT patchingandembedding * update to support other presets * update conversin script * fix link for b5 * add cityscapes weights * update presets * update presets * update conversion script to make directories * use save_preset * change name of output dir * add preprocessor flow * api gen and add preprocessor to mits * conform to new image classifier style * format * resizing image converter -> ImageConverter * address comments refactoring remove default resizing for vision backbones (keras-team#1916) * remove defailt resizing * fix GPU test Update VGG model to be compatible with HF and add conversion scripts (keras-team#1914) Deeplab presets (keras-team#1918) * add preset configurations for deeplabv3 * fix uri * Add training details update presets to point to the main Keras Kaggle page (keras-team#1921) * update presets to point to the main keras page * update mit path Added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates (keras-team#1912) * added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates * un commented the test lines that were commented by mistake * fixed linter errors Task models fix (keras-team#1922) * added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates * fix for wrongly configured task models LLama, PaliGemma, Mistral and Phi3 + test * comments * un commented the test lines that were commented by mistake * fixed linter errors adding option strip_prompt to generate() (keras-team#1913) * added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates * un commented the test lines that were commented by mistake * fixed linter errors * added options strip_prompt to generate() * fix for tensorflow: the compiled version of generate(strip_prompt=True) now works + code refactoring to make it more understandable * added test for generate(strip_prompt=True) * minor edits Layout map for Llama (keras-team#1923) * added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates * un commented the test lines that were commented by mistake * fixed linter errors * added default layout map for Llama * minor fixes in tests Update deeplab_v3_presets.py (keras-team#1924) Add paths to get SAM weights from (keras-team#1925) Two fixes for image resizing in preprocessing (keras-team#1927) 1. Properly display when are not resizing the input image in `model.summary()` 2. Allow setting the `image_size` directly on a preprocessing layer. 2. is just to allow a more consistent way to set the input shape across tasks. We now have: ```python text_classifier = keras_hub.models.TextClassifer.from_preset( "bert_base_en", ) text_classifier.preprocessor.sequence_length = 256 image_classifier = keras_hub.models.TextClassifer.from_preset( "bert_base_en", ) image_classifier.preprocessor.image_size = (256, 256) multi_modal_lm = keras_hub.models.CausalLM.from_preset( "some_preset", ) multi_modal_lm.preprocessor.sequence_length = 256 multi_modal_lm.preprocessor.image_size = (256, 256) ``` add back default image resizing (keras-team#1926) Update deeplab_v3_presets.py (keras-team#1928) * Update deeplab_v3_presets.py * Update deeplab_v3_presets.py Update PaliGemma to remove `include_rescaling` arg (keras-team#1917) * update PaliGemma * update conversion script * fix GPU tests fix path (keras-team#1929) * fix path * nit Fix paligemma checkpoint conversion script (keras-team#1931) * add back default image resizing * fix bug in image converter * fix paligemma checkpoint conversion file * fix preset name * remove debug code * revert unintended changes update preset path to point to latest version of models (keras-team#1932) Update sdv3 path (keras-team#1934) update sam docstring to show correct backbone in docstring (keras-team#1936) Convert input dict to tensors during train_on_batch (keras-team#1919) Register VGG presets. (keras-team#1935) * register vgg preset * nit * nit * nit Add ResNetVD presets (keras-team#1897) * Add ResNetVD presets * Updated Kaggle handles * Add weight conversion script for ResNet_vd * Add usage rebase conflict resolved conflict resolve Update sam_presets.py (keras-team#1940) Update vit_det_backbone.py (keras-team#1941) fix gpu test (keras-team#1939) * fix gpu test * cast input * update dtype * change to resnet preset * remove arg Added Support for Returning Attention Scores in TransformerEncoder call (keras-team#1879) * Added: Return attention scores argument to transformer encoder * Added: docstring for return_attention_scores and added a test to chek the working of the argument * Fixed: Test case by removing print stmts and using self.assertAllEqual * Fixed: Linting Mark preset tests as large (keras-team#1942) * fix tests * fix test * Update preset_utils_test.py version bump to 0.17.0.dev0 (keras-team#1944) Update stable_diffusion_3_presets.py (keras-team#1946) [Semantic Segmentation] - Add SegFormer Architecture, Weight Conversion Script and Presets (keras-team#1883) * initial commit - tf-based, kcv * porting to keras_hub structure - removing aliases, presets, etc. * enable instantiation of segformer backbone with custom MiT backbone * remove num_classes from backbone * fix input * add imports to __init__ * update preset * update docstrings * add basic tests * remove redundant imports * update docstrings * remove unused import * running api_gen.py * undo refactor of mit * update docstrings * add presets for mit * add standin paths * add presets for segformer backbone * register presets in __init__.py * addressing comments * addressing comments * addressing comments * update most tests * add remaining tests * remove copyright * fix test * override from_config * fix op in overlapping patching and embedding, start adding conversion utils * style * add padding to MiT patchingandembedding * update to support other presets * update conversin script * fix link for b5 * add cityscapes weights * update presets * update presets * update conversion script to make directories * use save_preset * change name of output dir * add preprocessor flow * api gen and add preprocessor to mits * conform to new image classifier style * format * resizing image converter -> ImageConverter * merge mit branch into segformer branch * add preprocessor and converter * address comments * clarify backbone usage * add conversion script * numerical equivalence changes * fix numerical inaccuracies * update conversion script * update conversion script * remove transpose * add preprocessor to segformer class * fix preset path * update test shape * update presets * update test shape * expand docstrings * add rescaling and normalization to preprocessor * remove backbone presets, remove copyrights, remove backbone cls from segmenter * remove copyright and unused import * apply same transformation to masks as input images * fix import * fix shape in tests Update readme (keras-team#1949) * Update README.md * Update README.md Update llama_backbone.py docstring (keras-team#1950) Update path (keras-team#1953) Update preset path for keras.io. There is no LLaMA2 in keras.io https://keras.io/api/keras_hub/models/llama2 This is the actual link: https://keras.io/api/keras_hub/models/llama2 For Vicuna it does not have it's own model direcotry, since it is also the part of Llama,, updated the path. Update SD3 init parameters (replacing `height`, `width` with `image_shape`) (keras-team#1951) * Replace SD3 `height` and `width` with `image_shape` * Update URI * Revert comment * Update SD3 handle * Replace `height` and `width` with `image_shape` * Update docstrings * Fix CI Update docstring (keras-team#1954) AudioConverter is registered as "keras_hub.layers.WhisperAudioConverter" and not as part of models. updated Mobilenet backbone to match it with torch implementation timm script added checkpoint conversion added Refactoring
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