mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 06:10:04 +06:00
1fc505b816
* 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
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| .. | ||
| albert.md | ||
| align.md | ||
| altclip.md | ||
| audio-spectrogram-transformer.md | ||
| auto.md | ||
| autoformer.md | ||
| bark.md | ||
| bart.md | ||
| barthez.md | ||
| bartpho.md | ||
| beit.md | ||
| bert-generation.md | ||
| bert-japanese.md | ||
| bert.md | ||
| bertweet.md | ||
| big_bird.md | ||
| bigbird_pegasus.md | ||
| biogpt.md | ||
| bit.md | ||
| blenderbot-small.md | ||
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| blip-2.md | ||
| blip.md | ||
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| bort.md | ||
| bridgetower.md | ||
| bros.md | ||
| byt5.md | ||
| camembert.md | ||
| canine.md | ||
| chinese_clip.md | ||
| clap.md | ||
| clip.md | ||
| clipseg.md | ||
| clvp.md | ||
| code_llama.md | ||
| codegen.md | ||
| conditional_detr.md | ||
| convbert.md | ||
| convnext.md | ||
| convnextv2.md | ||
| cpm.md | ||
| cpmant.md | ||
| ctrl.md | ||
| cvt.md | ||
| data2vec.md | ||
| deberta-v2.md | ||
| deberta.md | ||
| decision_transformer.md | ||
| deformable_detr.md | ||
| deit.md | ||
| deplot.md | ||
| depth_anything.md | ||
| deta.md | ||
| detr.md | ||
| dialogpt.md | ||
| dinat.md | ||
| dinov2.md | ||
| distilbert.md | ||
| dit.md | ||
| donut.md | ||
| dpr.md | ||
| dpt.md | ||
| efficientformer.md | ||
| efficientnet.md | ||
| electra.md | ||
| encodec.md | ||
| encoder-decoder.md | ||
| ernie_m.md | ||
| ernie.md | ||
| esm.md | ||
| falcon.md | ||
| fastspeech2_conformer.md | ||
| flan-t5.md | ||
| flan-ul2.md | ||
| flaubert.md | ||
| flava.md | ||
| fnet.md | ||
| focalnet.md | ||
| fsmt.md | ||
| funnel.md | ||
| fuyu.md | ||
| gemma.md | ||
| git.md | ||
| glpn.md | ||
| gpt_bigcode.md | ||
| gpt_neo.md | ||
| gpt_neox_japanese.md | ||
| gpt_neox.md | ||
| gpt-sw3.md | ||
| gpt2.md | ||
| gptj.md | ||
| gptsan-japanese.md | ||
| graphormer.md | ||
| groupvit.md | ||
| herbert.md | ||
| hubert.md | ||
| ibert.md | ||
| idefics.md | ||
| imagegpt.md | ||
| informer.md | ||
| instructblip.md | ||
| jukebox.md | ||
| kosmos-2.md | ||
| layoutlm.md | ||
| layoutlmv2.md | ||
| layoutlmv3.md | ||
| layoutxlm.md | ||
| led.md | ||
| levit.md | ||
| lilt.md | ||
| llama.md | ||
| llama2.md | ||
| llava.md | ||
| longformer.md | ||
| longt5.md | ||
| luke.md | ||
| lxmert.md | ||
| m2m_100.md | ||
| madlad-400.md | ||
| mamba.md | ||
| marian.md | ||
| markuplm.md | ||
| mask2former.md | ||
| maskformer.md | ||
| matcha.md | ||
| mbart.md | ||
| mctct.md | ||
| mega.md | ||
| megatron_gpt2.md | ||
| megatron-bert.md | ||
| mgp-str.md | ||
| mistral.md | ||
| mixtral.md | ||
| mluke.md | ||
| mms.md | ||
| mobilebert.md | ||
| mobilenet_v1.md | ||
| mobilenet_v2.md | ||
| mobilevit.md | ||
| mobilevitv2.md | ||
| mpnet.md | ||
| mpt.md | ||
| mra.md | ||
| mt5.md | ||
| musicgen.md | ||
| mvp.md | ||
| nat.md | ||
| nezha.md | ||
| nllb-moe.md | ||
| nllb.md | ||
| nougat.md | ||
| nystromformer.md | ||
| oneformer.md | ||
| open-llama.md | ||
| openai-gpt.md | ||
| opt.md | ||
| owlv2.md | ||
| owlvit.md | ||
| patchtsmixer.md | ||
| patchtst.md | ||
| pegasus_x.md | ||
| pegasus.md | ||
| perceiver.md | ||
| persimmon.md | ||
| phi.md | ||
| phobert.md | ||
| pix2struct.md | ||
| plbart.md | ||
| poolformer.md | ||
| pop2piano.md | ||
| prophetnet.md | ||
| pvt_v2.md | ||
| pvt.md | ||
| qdqbert.md | ||
| qwen2.md | ||
| rag.md | ||
| realm.md | ||
| reformer.md | ||
| regnet.md | ||
| rembert.md | ||
| resnet.md | ||
| retribert.md | ||
| roberta-prelayernorm.md | ||
| roberta.md | ||
| roc_bert.md | ||
| roformer.md | ||
| rwkv.md | ||
| sam.md | ||
| seamless_m4t_v2.md | ||
| seamless_m4t.md | ||
| segformer.md | ||
| seggpt.md | ||
| sew-d.md | ||
| sew.md | ||
| siglip.md | ||
| speech_to_text_2.md | ||
| speech_to_text.md | ||
| speech-encoder-decoder.md | ||
| speecht5.md | ||
| splinter.md | ||
| squeezebert.md | ||
| stablelm.md | ||
| starcoder2.md | ||
| swiftformer.md | ||
| swin.md | ||
| swin2sr.md | ||
| swinv2.md | ||
| switch_transformers.md | ||
| t5.md | ||
| t5v1.1.md | ||
| table-transformer.md | ||
| tapas.md | ||
| tapex.md | ||
| time_series_transformer.md | ||
| timesformer.md | ||
| trajectory_transformer.md | ||
| transfo-xl.md | ||
| trocr.md | ||
| tvlt.md | ||
| tvp.md | ||
| udop.md | ||
| ul2.md | ||
| umt5.md | ||
| unispeech-sat.md | ||
| unispeech.md | ||
| univnet.md | ||
| upernet.md | ||
| van.md | ||
| videomae.md | ||
| vilt.md | ||
| vipllava.md | ||
| vision-encoder-decoder.md | ||
| vision-text-dual-encoder.md | ||
| visual_bert.md | ||
| vit_hybrid.md | ||
| vit_mae.md | ||
| vit_msn.md | ||
| vit.md | ||
| vitdet.md | ||
| vitmatte.md | ||
| vits.md | ||
| vivit.md | ||
| wav2vec2_phoneme.md | ||
| wav2vec2-bert.md | ||
| wav2vec2-conformer.md | ||
| wav2vec2.md | ||
| wavlm.md | ||
| whisper.md | ||
| xclip.md | ||
| xglm.md | ||
| xlm-prophetnet.md | ||
| xlm-roberta-xl.md | ||
| xlm-roberta.md | ||
| xlm-v.md | ||
| xlm.md | ||
| xlnet.md | ||
| xls_r.md | ||
| xlsr_wav2vec2.md | ||
| xmod.md | ||
| yolos.md | ||
| yoso.md | ||