GitLab

* 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>

* Reorganize example folder

* Continue reorganization

* Change requirements for tests

* Final cleanup

* Finish regroup with tests all passing

* Copyright

* Requirements and readme

* Make a full link for the documentation

* Address review comments

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Add symlink

* Reorg again

* Apply suggestions from code review
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Adapt title

* Update to new strucutre

* Remove test

* Update READMEs
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Add BERT Loses Patience (Patience-based Early Exit)

* update model archive

* update format

* sort import

* flake8

* Add results

* full results

* align the table

* refactor to inherit

* default per gpu eval = 1

* Formatting

* Formatting

* isort

* modify readme

* Add check

* Fix format

* Fix format

* Doc strings

* ALBERT & BERT for sequence classification don't inherit from the original anymore

* Remove incorrect comments

* Remove incorrect comments

* Remove incorrect comments

* Sync up with new code

* Sync up with new code

* Add a test

* Add a test

* Add a test

* Add a test

* Add a test

* Add a test

* Finishing up!

* Rename and improve example

* Add test

* slightly faster test

* style

* This breaks remy prolly

* shorter test string

* no slow

* newdir structure

* New tree

* Style

* shorter

* docs

* clean

* Attempt future import

* more import hax