- 21 Oct, 2022 2 commits
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Yih-Dar authored
* update expected values for the correct TF checkpoint * Run test * Clean up * fix Co-authored-by:ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar authored
* First step of PT->TF for composite models * Update the tests * For VisionEncoderDecoderModel * Fix * Fix * Add comment * Fix * clean up import * Save memory * For (TF)EncoderDecoderModel * For (TF)EncoderDecoderModel Co-authored-by:
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- 20 Oct, 2022 1 commit
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Nicolas Patry authored
* Re-enable `small_model_pt`. Re-enable `small_model_pt`. Enabling the current test with the current values. Debugging the values on the CI. More logs ? Printing doesn't work ? Using the CI values instead. Seems to be a Pillow sensitivity. * Update src/transformers/pipelines/image_segmentation.py Co-authored-by:
Alara Dirik <8944735+alaradirik@users.noreply.github.com> Co-authored-by:
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- 19 Oct, 2022 3 commits
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amyeroberts authored
* Add center crop to transforms library * Return PIL images if PIL image input by default * Fixup and add docstring * Trigger CI * Update src/transformers/image_transforms.py Co-authored-by:
Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/image_transforms.py Co-authored-by:
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Yih-Dar authored
Co-authored-by: -
GMFTBY authored
Adding the state-of-the-art contrastive search decoding methods for the codebase of generation_utils.py (#19477) * add: the contrastive search for generaton_utils * add: testing scripts for contrastive search under examples/text-generation * update the quality of codes * revise the docstring; make the generation_contrastive_search.py scripts; * revise the examples/pytorch/text-generation/run_generation_contrastive_search.py to the auto-APIs format * revise the necessary documents * fix: revise the docstring of generation_contrastive_search.py * Fix the code indentation * fix: revise the nits and examples in contrastive_search docstring. * fix the copyright * delete generation_contrastive_search.py * revise the logic in contrastive_search * update the intergration test and the docstring * run the tests over * add the slow decorate to the contrastive_search intergrate test * add more test * do the style, quality, consistency checks
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- 18 Oct, 2022 12 commits
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Sylvain Gugger authored
* Create repo utils test job * Last occurence * Add tests for tests_fetcher * Better filtering * Let's learn more * Should fix * Should fix * Remove debug * Style * WiP WiP WiP WiP WiP WiP WiP WiP WiP * Quality * address review comments * Fix link
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David Yang authored
* Clean up deprecation warnings Notes: Changed some strings in tests to raw strings, which will change the literal content of the strings as they are fed into whatever machine handles them. Test cases for past in the past/past_key_values switch changed/removed due to warning of impending removal * Add PILImageResampling abstraction for PIL.Image.Resampling
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Sylvain Gugger authored
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Yih-Dar authored
Co-authored-by: -
Nicolas Patry authored
This PR (https://github.com/huggingface/transformers/pull/19367) introduced a few breaking changes: - Removed an argument `mask_threshold`. - Broke the default behavior (instance vs panoptic in the function call) https://github.com/huggingface/transformers/pull/19367/files#diff-60f846b86fb6a21d4caf60f5b3d593a04accb8f248de3029cccae2ff898c5bc3R119-R120 - Broke the actual masks: https://github.com/huggingface/transformers/pull/1961 This PR is the start of a handful that will aim at bringing back the old behavior(s). - tests should not have to specify `task` by default, unless we want to modify the behavior and have a lower form of segmentation running) - `test_small_model_pt` should be working. This specific PR starts with adding more information to the masks hash because missing the actual mask was actual easy to miss (the hashes do change, but it was easy to miss that one code path wasn't properly updated). So we go from a simple `hash` to ``` {"hash": #smaller hash, "shape": (h, w), "white_pixels": n} ``` The `shape` should help make sure the interpolation of the mask works correctly, the `white_pixels` hopefully helps detect big regressions in their amount when the hash gets modified.
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Nicolas Patry authored
* add return_tensors parameter for feature_extraction w/ test add return_tensor parameter for feature extraction Revert "Merge branch 'feature-extraction-return-tensor' of https://github.com/ajsanjoaquin/transformers into feature-extraction-return-tensor" This reverts commit d559da743b87914e111a84a98ba6dbb70d08ad88, reversing changes made to bbef89278650c04c090beb65637a8e9572dba222. call parameter directly Co-authored-by:
Nicolas Patry <patry.nicolas@protonmail.com> Fixup. Update src/transformers/pipelines/feature_extraction.py Co-authored-by:
AJ San Joaquin <ajsanjoaquin@gmail.com> Co-authored-by:
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NielsRogge authored
* First draft * Add conversion script * Make conversion work * Upload checkpoints * Add final fixes * Revert changes of conditional and deformable detr * Fix toctree, add and remove copied from * Use model type * Improve docs * Improve code example * Update copies * Add copied formt * Don't update conditional detr * Don't update deformable detr
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Nicolas Patry authored
🚨 🚨 🚨 [Breaking change] Deformable DETR intermediate representations (#19678) * [Breaking change] Deformable DETR intermediate representations - Fixes naturally the `object-detection` pipeline. - Moves from `[n_decoders, batch_size, ...]` to `[batch_size, n_decoders, ...]` instead. * Apply suggestions from code review Co-authored-by: -
Antonio Carlos Falcão Petri authored
* [Wav2Vec2] Allow user-managed Pool in Wav2Vec2ProcessorWithLM.batch_decode * [Wav2Vec2] Add user-managed LM's pool tests and usage examples * Improve styling Co-authored-by:
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NielsRogge authored
* Improve DETR models * Fix Deformable DETR loss and matcher * Fixup * Fix integration tests * Improve variable names * Apply suggestion * Fix copies * Fix DeformableDetrLoss * Make Conditional DETR copy from Deformable DETR * Copy from deformable detr's hungarian matcher * Fix bug
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Arthur authored
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Arthur authored
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- 17 Oct, 2022 6 commits
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amyeroberts authored
* Adapt FE methods to transforms library * Mixin for saving the image processor * Base processor skeleton * BatchFeature for packaging image processor outputs * Initial image processor for GLPN * REmove accidental import * Fixup and docs * Mixin for saving the image processor * Fixup and docs * Import BatchFeature from feature_extraction_utils * Fixup and docs * Fixup and docs * Fixup and docs * Fixup and docs * BatchFeature for packaging image processor outputs * Import BatchFeature from feature_extraction_utils * Import BatchFeature from feature_extraction_utils * Fixup and docs * Fixup and docs * BatchFeature for packaging image processor outputs * Import BatchFeature from feature_extraction_utils * Fixup and docs * Mixin for saving the image processor * Fixup and docs * Add rescale back and remove ImageType * fix import mistake * Fix enum var reference * Can transform and specify image data format * Remove redundant function * Update reference * Data format flag for rescale * Fix typo * Fix dimension check * Fixes to make IP and FE outputs match * Add tests for transforms * Add test for utils * Update some docstrings * Make sure in channels last before converting to PIL * Remove default to numpy batching * Fix up * Add docstring and model_input_types * Use feature processor config from hub * Alias GLPN feature extractor to image processor * Alias feature extractor mixin * Add return_numpy=False flag for resize * Fix up * Fix up * Use different frameworks safely * Safely import PIL * Call function checking if PIL available * Only import if vision available * Address Sylvain PR comments Co-authored-by: Sylvain.gugger@gmail.com * Apply suggestions from code review Co-authored-by:
Sylvain Gugger <Sylvain.gugger@gmail.com> Co-authored-by:
NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Add in docstrings * Fix TFSwinSelfAttention to have relative position index as non-trainable weight (#18226) Signed-off-by:
Seunghwan Hong <seunghwan@scatterlab.co.kr> * Refactor `TFSwinLayer` to increase serving compatibility (#18352) * Refactor `TFSwinLayer` to increase serving compatibility Signed-off-by:
Seunghwan Hong <harrydrippin@gmail.com> Co-Authored-By:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by:
😬 (#18489) * Delete valohai.yaml * NLP => ML * typo * website supports https * datasets * 60k + modalities * unrelated link fixing for accelerate * Ok those links were actually broken * Fix link * Make `AutoTokenizer` auto-link * wording tweak * add at least one non-nlp task * `transformers-cli login` => `huggingface-cli login` (#18490) * zero chance anyone's using that constant no? * `transformers-cli login` => `huggingface-cli login` * `transformers-cli repo create` => `huggingface-cli repo create` * `make style` * Add seed setting to image classification example (#18519) * [DX fix] Fixing QA pipeline streaming a dataset. (#18516) * [DX fix] Fixing QA pipeline streaming a dataset. QuestionAnsweringArgumentHandler would iterate over the whole dataset effectively killing all properties of the pipeline. This restores nice properties when using `Dataset` or `Generator` since those are meant to be consumed lazily. * Handling TF better. * Clean up hub (#18497) * Clean up utils.hub * Remove imports * More fixes * Last fix * update fsdp docs (#18521) * updating fsdp documentation * typo fix * Fix compatibility with 1.12 (#17925) * Fix compatibility with 1.12 * Remove pin from examples requirements * Update torch scatter version * Fix compatibility with 1.12 * Remove pin from examples requirements * Update torch scatter version * fix torch.onnx.symbolic_opset12 import * Reject bad version Co-authored-by:Ankur Goyal <ankur@impira.com> * New cache fixes: add safeguard before looking in folders (#18522) * unpin resampy (#18527) Co-authored-by:
✨ update to use interlibrary links instead of Markdown (#18500) * Add example of multimodal usage to pipeline tutorial (#18498) *📝 add example of multimodal usage to pipeline tutorial *🖍 apply feedbacks *🖍 apply niels feedback * [VideoMAE] Add model to doc tests (#18523) * Add videomae to doc tests * Add pip install decord Co-authored-by:Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local> * Update perf_train_gpu_one.mdx (#18532) * Update no_trainer.py scripts to include accelerate gradient accumulation wrapper (#18473) * Added accelerate gradient accumulation wrapper to run_image_classification_no_trainer.py example script * make fixup changes * PR comments * changed input to Acceletor based on PR comment, ran make fixup * Added comment explaining the sync_gradients statement * Fixed lr scheduler max steps * Changed run_clm_no_trainer.py script to use accelerate gradient accum wrapper * Fixed all scripts except wav2vec2 pretraining to use accelerate gradient accum wrapper * Added accelerate gradient accum wrapper for wav2vec2_pretraining_no_trainer.py script * make fixup and lr_scheduler step inserted back into run_qa_beam_search_no_trainer.py * removed changes to run_wav2vec2_pretraining_no_trainer.py script and fixed using wrong constant in qa_beam_search_no_trainer.py script * Add Spanish translation of converting_tensorflow_models.mdx (#18512) * Add file in spanish docs to be translated * Finish translation to Spanish * Improve Spanish wording * Add suggested changes from review * Spanish translation of summarization.mdx (#15947) (#18477) * Add Spanish translation of summarization.mdx * Apply suggestions from code review Co-authored-by:
Omar U. Espejel <espejelomar@gmail.com> Co-authored-by:
Thomas Wang <24695242+thomasw21@users.noreply.github.com> * more comments - added more comments on why we cast only floating point parameters * Update src/transformers/modeling_utils.py Co-authored-by:
📝 update metric with evaluate (#18535) * Restore _init_weights value in no_init_weights (#18504) * Recover _init_weights value in no_init_weights For potential nested use. In addition, users might modify private no_init_weights as well. * Apply suggestions from code review Co-authored-by:📝 update documentation build section (#18548) * `bitsandbytes` - `Linear8bitLt` integration into `transformers` models (#17901) * first commit * correct replace function * add final changes - works like charm! - cannot implement tests yet - tested * clean up a bit * add bitsandbytes dependencies * working version - added import function - added bitsandbytes utils file * small fix * small fix - fix import issue * fix import issues * Apply suggestions from code review Co-authored-by:Michael Benayoun <mickbenayoun@gmail.com> * add few comments * add more doctring + make style * more docstring * raise error when loaded in 8bit * make style * add warning if loaded on CPU * add small sanity check * fix small comment * add bitsandbytes on dockerfile * Improve documentation - improve documentation from comments * add few comments * slow tests pass on the VM but not on the CI VM * Fix merge conflict * make style * another test should pass on a multi gpu setup * fix bad import in testing file * Fix slow tests - remove dummy batches - no more CUDA illegal memory errors * odify dockerfile * Update docs/source/en/main_classes/model.mdx * Update Dockerfile * Update model.mdx * Update Dockerfile * Apply suggestions from code review * few modifications - lm head can stay on disk/cpu - change model name so that test pass * change test value - change test value to the correct output - torch bmm changed to baddmm in bloom modeling when merging * modify installation guidelines * Apply suggestions from code review Co-authored-by:
Joao Gante <joaofranciscocardosogante@gmail.com> * Update examples/tensorflow/test_tensorflow_examples.py Co-authored-by:
Julien Chaumond <julien@huggingface.co> * Address Julien's comments Co-authored-by:
📝 update philosophy to include other preprocessing classes *🖍 apply feedbacks * Properly move cache when it is not in default path (#18563) * Adds CLIP to models exportable with ONNX (#18515) * onnx config for clip * default opset as 14 * changes from the original repo * input values order fix * outputs fix * remove unused import * ran make fix-copies * black format * review comments: forward ref, import fix, model change revert, .to cleanup * make style * formatting fixes * revert groupvit * comment for cast to int32 * comment fix * make .T as .t() for onnx conversion * ran make fix-copies * remove unneeded comment Co-authored-by:Maxime Gardoni <maxime.gardoni@ecorobotix.com> * Fix resizing bug in OWL-ViT (#18573) * Fixes resizing bug in OWL-ViT * Defaults to square resize if size is set to an int * Sets do_center_crop default value to False * Fix LayoutLMv3 documentation (#17932) * fix typos * fix sequence_length docs of LayoutLMv3Model * delete trailing white spaces * fix layoutlmv3 docs more * apply make fixup & quality * change to two versions of input docstring * apply make fixup & quality * Skip broken tests * Change BartLearnedPositionalEmbedding's forward method signature to support Opacus training (#18486) * changing BartLearnedPositionalEmbedding forward signature and references to it * removing debugging dead code (thanks style checker) * blackened modeling_bart file * removing copy inconsistencies via make fix-copies * changing references to copied signatures in Bart variants * make fix-copies once more * using expand over repeat (thanks @michaelbenayoun) * expand instead of repeat for all model copies Co-authored-by:
Daniel Jones <jonesdaniel@microsoft.com> * german docs translation (#18544) * Create _config.py * Create _toctree.yml * Create index.mdx not sure about "du / ihr" oder "sie" * Create quicktour.mdx * Update _toctree.yml * Update build_documentation.yml * Update build_pr_documentation.yml * fix build * Update index.mdx * Update quicktour.mdx * Create installation.mdx * Update _toctree.yml * Deberta V2: Fix critical trace warnings to allow ONNX export (#18272) * Fix critical trace warnings to allow ONNX export * Force input to `sqrt` to be float type * Cleanup code * Remove unused import statement * Update model sew * Small refactor Co-authored-by:
dependabot[bot] <support@github.com> Signed-off-by:
dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump nbconvert in /examples/research_projects/visual_bert (#18566) Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 6.0.1 to 6.3.0. - [Release notes](https://github.com/jupyter/nbconvert/releases) - [Commits](https://github.com/jupyter/nbconvert/compare/6.0.1...6.3.0 ) --- updated-dependencies: - dependency-name: nbconvert dependency-type: direct:production ... Signed-off-by:
Wang, Yi A <yi.a.wang@intel.com> * Update docs/source/en/perf_train_cpu_many.mdx Co-authored-by:
sanchit-gandhi <sanchit@huggingface.co> * mac m1 `mps` integration (#18598) * mac m1 `mps` integration * Update docs/source/en/main_classes/trainer.mdx Co-authored-by:
Dan Saattrup Nielsen <47701536+saattrupdan@users.noreply.github.com> * resolve comment Co-authored-by:
Stas Bekman <stas00@users.noreply.github.com> * Apply suggestions from code review * Apply suggestions from code review * Apply suggestions from code review * put in one block - put bash instructions in one block * update readme - refactor a bit hardware requirements * change text a bit * Apply suggestions from code review Co-authored-by:
Yih-Dar <2521628+ydshieh@users.noreply.github.com> * apply suggestions Co-authored-by:
lewtun <lewis.c.tunstall@gmail.com> Co-authored-by:
Amy Roberts <amyeroberts@users.noreply.github.com> Co-authored-by:
regisss <15324346+regisss@users.noreply.github.com> Co-authored-by:
Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by:
Ankur Goyal <ankrgyl@gmail.com> Co-authored-by:
Steven Liu <59462357+stevhliu@users.noreply.github.com> Co-authored-by:
Mishig Davaadorj <dmishig@gmail.com> Co-authored-by:
Rasmus Arpe Fogh Jensen <Rasmus.arpe@gmail.com> Co-authored-by:
Ian Castillo <7807897+donelianc@users.noreply.github.com> Co-authored-by:
AguilaCudicio <aguila.cudicio@gmail.com> Co-authored-by:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by:
Niklas Hansson <niklas.sven.hansson@gmail.com> Co-authored-by:
Thomas Chaigneau <t.chaigneau.tc@gmail.com> Co-authored-by:
YouJiacheng <1503679330@qq.com> Co-authored-by:
Matt <Rocketknight1@users.noreply.github.com> Co-authored-by:
Dhruv Karan <k4r4n.dhruv@gmail.com> Co-authored-by:
Michael Wyatt <mrwyattii@gmail.com> Co-authored-by:
Maxime G <joihn@users.noreply.github.com> Co-authored-by:
Wonseok Lee (Jack) <rollerkid02@snu.ac.kr> Co-authored-by:
Dan Jones <dan.j.jones2@gmail.com> Co-authored-by:
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Niklas Muennighoff <n.muennighoff@gmail.com> Co-authored-by:
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Stefan Schweter <stefan@schweter.it> Co-authored-by:
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Sylvain Gugger authored
This reverts commit 35bd089a.
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Ayrton San Joaquin authored
* add return_tensors parameter for feature_extraction w/ test add return_tensor parameter for feature extraction Revert "Merge branch 'feature-extraction-return-tensor' of https://github.com/ajsanjoaquin/transformers into feature-extraction-return-tensor" This reverts commit d559da743b87914e111a84a98ba6dbb70d08ad88, reversing changes made to bbef89278650c04c090beb65637a8e9572dba222. * call parameter directly Co-authored-by:
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Ankur Goyal authored
* Fixes * update expected values * style * fix Co-authored-by: -
Matt authored
* Partial TF port for ESM model * Add ESM-TF tests * Add the various imports for TF-ESM * TF weight conversion almost ready * Stop ignoring the decoder weights in PT * Add tests and lots of fixes * fix-copies * Fix imports, add model docs * Add get_vocab() to tokenizer * Fix vocab links for pretrained files * Allow multiple inputs with a sep * Use EOS as SEP token because ESM vocab lacks SEP * Correctly return special tokens mask from ESM tokenizer * make fixup * Stop testing unsupported embedding resizing * Handle TF bias correctly * Skip all models with slow tokenizers in the token classification test * Fixing the batch/unbatcher of pipelines to accomodate the `None` being passed around. * Fixing pipeline bug caused by slow tokenizer being different. * Update src/transformers/models/esm/modeling_tf_esm.py Co-authored-by:
Your Name <you@example.com> Co-authored-by:
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Sivaudha authored
* Remove key word argument X from pipeline predict and transform methods As __call__ of pipeline clasees require one positional argument, passing the input as a keyword argument inside predict, transform methods, causing __call__ to fail. Hence in this commit the keyword argument is modified into positional argument. * Implement basic tests for scikitcompat pipeline interface * Seperate tests instead of running with parameterized based on framework as both frameworks will not be active at the same time
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- 14 Oct, 2022 8 commits
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Sylvain Gugger authored
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Arthur authored
* simplify loop * fix layer map split * update * update for special variables * add rag test * fixup * revert change : for next PR
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Nicolas Patry authored
* Improve error messaging for ASR pipeline. - Raise error early (in `_sanitize`) so users don't waste time trying to run queries with invalid params. - Fix the error was after using `config.inputs_to_logits_ratio` so our check was masked by the failing property does not exist. - Added some manual check on s2t for the error message. No non ctc model seems to be used by the default runner (they are all skipped). * Removing pdb. * Stop the early error it doesn't really work :(.
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Yih-Dar authored
Co-authored-by: -
Pi Esposito authored
* add suport for non fast tf bert tokenizer * add tests for non fast tf bert tokenizer * fix fast bert tf tokenizer flag * double tokenizers list on tf tokenizers test to aovid breaking zip on test output equivalence * reformat code with black to comply with code quality checks * trigger ci
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Yih-Dar authored
Co-authored-by: -
Sanchit Gandhi authored
* [Whisper] Don't return attention mask in feat extractor * remove attention mask from test * fix failing tests * quality
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amyeroberts authored
* Cast masks to np.unit8 before converting to PIL.Image.Image * Update tests * Fixup
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- 13 Oct, 2022 1 commit
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Sanchit Gandhi authored
* [Whisper] Freeze params of encoder * add tests
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- 12 Oct, 2022 6 commits
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Yih-Dar authored
* return None to avoid recursive call * Give error * Give error * Add test * More tests * Quality Co-authored-by: -
Sylvain Gugger authored
* Add a decorator for flaky tests * Quality * Don't break the rest * Address review comments * Fix test name * Fix typo and print to stderr
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amyeroberts authored
* Adapt FE methods to transforms library * Mixin for saving the image processor * Base processor skeleton * BatchFeature for packaging image processor outputs * Initial image processor for GLPN * REmove accidental import * Fixup and docs * Mixin for saving the image processor * Fixup and docs * Import BatchFeature from feature_extraction_utils * Fixup and docs * Fixup and docs * Fixup and docs * Fixup and docs * BatchFeature for packaging image processor outputs * Import BatchFeature from feature_extraction_utils * Import BatchFeature from feature_extraction_utils * Fixup and docs * Fixup and docs * BatchFeature for packaging image processor outputs * Import BatchFeature from feature_extraction_utils * Fixup and docs * Mixin for saving the image processor * Fixup and docs * Add rescale back and remove ImageType * fix import mistake * Fix enum var reference * Can transform and specify image data format * Remove redundant function * Update reference * Data format flag for rescale * Fix typo * Fix dimension check * Fixes to make IP and FE outputs match * Add tests for transforms * Add test for utils * Update some docstrings * Make sure in channels last before converting to PIL * Remove default to numpy batching * Fix up * Add docstring and model_input_types * Use feature processor config from hub * Alias GLPN feature extractor to image processor * Alias feature extractor mixin * Add return_numpy=False flag for resize * Fix up * Fix up * Use different frameworks safely * Safely import PIL * Call function checking if PIL available * Only import if vision available * Address Sylvain PR comments Co-authored-by: Sylvain.gugger@gmail.com * Apply suggestions from code review Co-authored-by:
😬 (#18489) * Delete valohai.yaml * NLP => ML * typo * website supports https * datasets * 60k + modalities * unrelated link fixing for accelerate * Ok those links were actually broken * Fix link * Make `AutoTokenizer` auto-link * wording tweak * add at least one non-nlp task * `transformers-cli login` => `huggingface-cli login` (#18490) * zero chance anyone's using that constant no? * `transformers-cli login` => `huggingface-cli login` * `transformers-cli repo create` => `huggingface-cli repo create` * `make style` * Add seed setting to image classification example (#18519) * [DX fix] Fixing QA pipeline streaming a dataset. (#18516) * [DX fix] Fixing QA pipeline streaming a dataset. QuestionAnsweringArgumentHandler would iterate over the whole dataset effectively killing all properties of the pipeline. This restores nice properties when using `Dataset` or `Generator` since those are meant to be consumed lazily. * Handling TF better. * Clean up hub (#18497) * Clean up utils.hub * Remove imports * More fixes * Last fix * update fsdp docs (#18521) * updating fsdp documentation * typo fix * Fix compatibility with 1.12 (#17925) * Fix compatibility with 1.12 * Remove pin from examples requirements * Update torch scatter version * Fix compatibility with 1.12 * Remove pin from examples requirements * Update torch scatter version * fix torch.onnx.symbolic_opset12 import * Reject bad version Co-authored-by:✨ update to use interlibrary links instead of Markdown (#18500) * Add example of multimodal usage to pipeline tutorial (#18498) *📝 add example of multimodal usage to pipeline tutorial *🖍 apply feedbacks *🖍 apply niels feedback * [VideoMAE] Add model to doc tests (#18523) * Add videomae to doc tests * Add pip install decord Co-authored-by:📝 update metric with evaluate (#18535) * Restore _init_weights value in no_init_weights (#18504) * Recover _init_weights value in no_init_weights For potential nested use. In addition, users might modify private no_init_weights as well. * Apply suggestions from code review Co-authored-by:📝 update documentation build section (#18548) * `bitsandbytes` - `Linear8bitLt` integration into `transformers` models (#17901) * first commit * correct replace function * add final changes - works like charm! - cannot implement tests yet - tested * clean up a bit * add bitsandbytes dependencies * working version - added import function - added bitsandbytes utils file * small fix * small fix - fix import issue * fix import issues * Apply suggestions from code review Co-authored-by:📝 update philosophy to include other preprocessing classes *🖍 apply feedbacks * Properly move cache when it is not in default path (#18563) * Adds CLIP to models exportable with ONNX (#18515) * onnx config for clip * default opset as 14 * changes from the original repo * input values order fix * outputs fix * remove unused import * ran make fix-copies * black format * review comments: forward ref, import fix, model change revert, .to cleanup * make style * formatting fixes * revert groupvit * comment for cast to int32 * comment fix * make .T as .t() for onnx conversion * ran make fix-copies * remove unneeded comment Co-authored-by: -
NielsRogge authored
* Fix XCLIP doc tests * Add model to doc test list * Fix tests
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Ritik Nandwal authored
* Add initial files for depth estimation pipelines * Add test file for depth estimation pipeline * Update model mapping names * Add updates for depth estimation output * Add generic test * Hopefully fixing the tests. * Check if test passes * Add make fixup and make fix-copies changes after rebase with main * Rebase with main * Fixing up depth pipeline. * This is not used anymore. * Fixing the test. `Image` is a module `Image.Image` is the type. * Update docs/source/en/main_classes/pipelines.mdx Co-authored-by:
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NielsRogge authored
* First draft * Fix more things * Improve more things * Remove some head models * Fix more things * Add missing layers * Remove tokenizer * Fix more things * Fix copied from statements * Make all tests pass * Remove print statements * Remove files * Fix README and docs * Add integration test and fix organization * Add tips * Apply suggestions from code review Co-authored-by:
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- 11 Oct, 2022 1 commit
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Mathieu Jouffroy authored
* implemented TFCvtModel and TFCvtForImageClassification and modified relevant files, added an exception in convert_tf_weight_name_to_pt_weight_name, added quick testing file to compare with pytorch model * added docstring + testing file in transformers testing suite * added test in testing file, modified docs to pass repo-consistency, passed formatting test * refactoring + passing all test * small refacto, removing unwanted comments * improved testing config * corrected import error * modified acces to pretrained model archive list, to pass tf_test * corrected import structure in init files * modified testing for keras_fit with cpu * correcting PR issues + Refactoring * Refactoring : improving readability and reducing the number of permutations * corrected momentum value + cls_token initialization * removed from_pt as weights were added to the hub * Update tests/models/cvt/test_modeling_tf_cvt.py Co-authored-by:
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