fa8cdccd91bcb6803ea50855166ed8254ea70708
7 Commits
| Author | SHA1 | Message | Date | |
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Pavel Iakubovskii
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f42d46ccb4 |
Add common test for torch.export and fix some vision models (#35124)
* Add is_torch_greater_or_equal test decorator * Add common test for torch.export * Fix bit * Fix focalnet * Fix imagegpt * Fix seggpt * Fix swin2sr * Enable torch.export test for vision models * Enable test for video models * Remove json * Enable for hiera * Enable for ijepa * Fix detr * Fic conditional_detr * Fix maskformer * Enable test maskformer * Fix test for deformable detr * Fix custom kernels for export in rt-detr and deformable-detr * Enable test for all DPT * Remove custom test for deformable detr * Simplify test to use only kwargs for export * Add comment * Move compile_compatible_method_lru_cache to utils * Fix beit export * Fix deformable detr * Fix copies data2vec<->beit * Fix typos, update test to work with dict * Add seed to the test * Enable test for vit_mae * Fix beit tests * [run-slow] beit, bit, conditional_detr, data2vec, deformable_detr, detr, focalnet, imagegpt, maskformer, rt_detr, seggpt, swin2sr * Add vitpose test * Add textnet test * Add dinov2 with registers * Update tests/test_modeling_common.py * Switch to torch.testing.assert_close * Fix masformer * Remove save-load from test * Add dab_detr * Add depth_pro * Fix and test RT-DETRv2 * Fix dab_detr |
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Arthur
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b912f5ee43 |
use torch.testing.assertclose instead to get more details about error in cis (#35659)
* use torch.testing.assertclose instead to get more details about error in cis * fix * style * test_all * revert for I bert * fixes and updates * more image processing fixes * more image processors * fix mamba and co * style * less strick * ok I won't be strict * skip and be done * up |
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amyeroberts
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1de7dc7403 |
Skip tests properly (#31308)
* Skip tests properly * [test_all] * Add 'reason' as kwarg for skipTest * [test_all] Fix up * [test_all] |
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amyeroberts
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25245ec26d |
Rename test_model_common_attributes -> test_model_get_set_embeddings (#31321)
* Rename to test_model_common_attributes The method name is misleading - it is testing being able to get and set embeddings, not common attributes to all models * Explicitly skip |
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Lysandre Debut
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39114c0383 |
Remove static pretrained maps from the library's internals (#29112)
* [test_all] Remove static pretrained maps from the library's internals * Deprecate archive maps instead of removing them * Revert init changes * [test_all] Deprecate instead of removing * [test_all] PVT v2 support * [test_all] Tests should all pass * [test_all] Style * Address review comments * Update src/transformers/models/deprecated/_archive_maps.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/deprecated/_archive_maps.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * [test_all] trigger tests * [test_all] LLAVA * [test_all] Bad rebase --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> |
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Yih-Dar
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7b87ecb047 |
Fix PVT v2 tests (#29660)
* update --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> |
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Nate Cibik
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1fc505b816 |
Add PvT-v2 Model (#26812)
* Added pytests for pvt-v2, all passed * Added pvt_v2 to docs/source/end/model_doc * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat. Added additional type support for image size in config * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Reverted batch eval changes for PR * Updated index.md * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat * Ran fix-copies * Fixed PvtV2Backbone tests * Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py * Fixed backbone stuff and fixed tests: all passing * Ran make fixup * Made modifications for code checks * Remove ONNX config from configuration_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Use explicit image size dict in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Make image_size optional in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Remove _ntuple use in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Remove reference to fp16_enabled * Model modules now take config as first argument even when not used * Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling" * All LayerNorm now instantiates with config.layer_norm_eps * Added docstring for depth-wise conv layer * PvtV2Config now only takes Union[int, Tuple[int, int]] for image size * Refactored PVTv2 in prep for gradient checkpointing * Gradient checkpointing ready to test * Removed override of _set_gradient_checkpointing * Cleaned out old code * Applied code fixup * Applied code fixup * Began debug of pvt_v2 tests * Leave handling of num_labels to base pretrained config class * Deactivated gradient checkpointing tests until it is fixed * Removed PvtV2ImageProcessor which duped PvtImageProcessor * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Added pvt_v2 to docs/source/end/model_doc * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat. Added additional type support for image size in config * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat * Ran fix-copies * Fixed PvtV2Backbone tests * Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py * Fixed backbone stuff and fixed tests: all passing * Ran make fixup * Made modifications for code checks * Remove ONNX config from configuration_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Use explicit image size dict in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Make image_size optional in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Remove _ntuple use in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Remove reference to fp16_enabled * Model modules now take config as first argument even when not used * Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling" * All LayerNorm now instantiates with config.layer_norm_eps * Added docstring for depth-wise conv layer * PvtV2Config now only takes Union[int, Tuple[int, int]] for image size * Refactored PVTv2 in prep for gradient checkpointing * Gradient checkpointing ready to test * Removed override of _set_gradient_checkpointing * Cleaned out old code * Applied code fixup * Applied code fixup * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Ran fix-copies and fixup. All checks passed * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Fixed config docstring. Added channels property * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Ran fix-copies and fixup. All checks passed * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Fixed config backbone compat * Ran fix-copies * Began debug of pvt_v2 tests * Leave handling of num_labels to base pretrained config class * Deactivated gradient checkpointing tests until it is fixed * Removed PvtV2ImageProcessor which duped PvtImageProcessor * Fixed issue from rebase * Fixed issue from rebase * Set tests for gradient checkpointing to skip those using reentrant since it isn't supported * Fixed issue from rebase * Fixed issue from rebase * Changed model name in docs * Removed duplicate PvtV2Backbone * Work around type switching issue in tests * Fix model name in config comments * Update docs/source/en/model_doc/pvt_v2.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Changed name of variable from 'attn_reduce' to 'sr_type' * Changed name of variable from 'attn_reduce' to 'sr_type' * Changed from using 'sr_type' to 'linear_attention' for clarity * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Removed old code * Changed from using 'sr_type' to 'linear_attention' for clarity * Fixed Class names to be more descriptive * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Removed outdated code * Moved paper abstract to single line in pvt_v2.md * Added usage tips to pvt_v2.md * Simplified module inits by passing layer_idx * Fixed typing for hidden_act in PvtV2Config * Removed unusued import * Add pvt_v2 to docs/source/en/_toctree.yml * Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive. * Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive. * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Move function parameters to single line Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Update year of copyright to 2024 Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Make code more explicit Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Updated sr_ratio to be more explicit spatial_reduction_ratio * Removed excess type hints in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Move params to single line in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Removed needless comment in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update copyright date in pvt_v2.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Moved params to single line in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Updated copyright date in configuration_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Cleaned comments in modeling_pvt_v2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Renamed spatial_reduction Conv2D operation * Revert "Update src/transformers/models/pvt_v2/modeling_pvt_v2.py " This reverts commit c4a04416dde8f3475ab405d1feb368600e0f8538. * Updated conversion script to reflect module name change * Deprecated reshape_last_stage option in config * Removed unused imports * Code formatting * Fixed outdated decorators on test_inference_fp16 * Added "Copied from" comments in test_modeling_pvt_v2.py * Fixed import listing * Updated model name * Force empty commit for PR refresh * Fixed linting issue * Removed # Copied from comments * Added PVTv2 to README_fr.md * Ran make fix-copies * Replace all FoamoftheSea hub references with OpenGVLab * Fixed out_indices and out_features logic in configuration_pvt_v2.py * Made ImageNet weight conversion verification optional in convert_pvt_v2_to_pytorch.py * Ran code fixup * Fixed order of parent classes in PvtV2Config to fix the to_dict method override --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> |