mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-31 02:02:21 +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 | ||
| align | ||
| altclip | ||
| audio_spectrogram_transformer | ||
| auto | ||
| autoformer | ||
| bark | ||
| bart | ||
| barthez | ||
| bartpho | ||
| beit | ||
| bert | ||
| bert_generation | ||
| bert_japanese | ||
| bertweet | ||
| big_bird | ||
| bigbird_pegasus | ||
| biogpt | ||
| bit | ||
| blenderbot | ||
| blenderbot_small | ||
| blip | ||
| blip_2 | ||
| bloom | ||
| bridgetower | ||
| bros | ||
| byt5 | ||
| camembert | ||
| canine | ||
| chinese_clip | ||
| clap | ||
| clip | ||
| clipseg | ||
| clvp | ||
| code_llama | ||
| codegen | ||
| conditional_detr | ||
| convbert | ||
| convnext | ||
| convnextv2 | ||
| cpm | ||
| cpmant | ||
| ctrl | ||
| cvt | ||
| data2vec | ||
| deberta | ||
| deberta_v2 | ||
| decision_transformer | ||
| deformable_detr | ||
| deit | ||
| depth_anything | ||
| deta | ||
| detr | ||
| dinat | ||
| dinov2 | ||
| distilbert | ||
| dit | ||
| donut | ||
| dpr | ||
| dpt | ||
| efficientformer | ||
| efficientnet | ||
| electra | ||
| encodec | ||
| encoder_decoder | ||
| ernie | ||
| ernie_m | ||
| esm | ||
| falcon | ||
| fastspeech2_conformer | ||
| flaubert | ||
| flava | ||
| fnet | ||
| focalnet | ||
| fsmt | ||
| funnel | ||
| fuyu | ||
| gemma | ||
| git | ||
| glpn | ||
| gpt_bigcode | ||
| gpt_neo | ||
| gpt_neox | ||
| gpt_neox_japanese | ||
| gpt_sw3 | ||
| gpt2 | ||
| gptj | ||
| gptsan_japanese | ||
| graphormer | ||
| groupvit | ||
| herbert | ||
| hubert | ||
| ibert | ||
| idefics | ||
| imagegpt | ||
| informer | ||
| instructblip | ||
| jukebox | ||
| kosmos2 | ||
| layoutlm | ||
| layoutlmv2 | ||
| layoutlmv3 | ||
| layoutxlm | ||
| led | ||
| levit | ||
| lilt | ||
| llama | ||
| llava | ||
| longformer | ||
| longt5 | ||
| luke | ||
| lxmert | ||
| m2m_100 | ||
| mamba | ||
| marian | ||
| markuplm | ||
| mask2former | ||
| maskformer | ||
| mbart | ||
| mbart50 | ||
| mega | ||
| megatron_bert | ||
| megatron_gpt2 | ||
| mgp_str | ||
| mistral | ||
| mixtral | ||
| mluke | ||
| mobilebert | ||
| mobilenet_v1 | ||
| mobilenet_v2 | ||
| mobilevit | ||
| mobilevitv2 | ||
| mpnet | ||
| mpt | ||
| mra | ||
| mt5 | ||
| musicgen | ||
| mvp | ||
| nat | ||
| nezha | ||
| nllb | ||
| nllb_moe | ||
| nougat | ||
| nystromformer | ||
| oneformer | ||
| openai | ||
| opt | ||
| owlv2 | ||
| owlvit | ||
| patchtsmixer | ||
| patchtst | ||
| pegasus | ||
| pegasus_x | ||
| perceiver | ||
| persimmon | ||
| phi | ||
| phobert | ||
| pix2struct | ||
| plbart | ||
| poolformer | ||
| pop2piano | ||
| prophetnet | ||
| pvt | ||
| pvt_v2 | ||
| qdqbert | ||
| qwen2 | ||
| rag | ||
| realm | ||
| reformer | ||
| regnet | ||
| rembert | ||
| resnet | ||
| roberta | ||
| roberta_prelayernorm | ||
| roc_bert | ||
| roformer | ||
| rwkv | ||
| sam | ||
| seamless_m4t | ||
| seamless_m4t_v2 | ||
| segformer | ||
| seggpt | ||
| sew | ||
| sew_d | ||
| siglip | ||
| speech_encoder_decoder | ||
| speech_to_text | ||
| speech_to_text_2 | ||
| speecht5 | ||
| splinter | ||
| squeezebert | ||
| stablelm | ||
| starcoder2 | ||
| swiftformer | ||
| swin | ||
| swin2sr | ||
| swinv2 | ||
| switch_transformers | ||
| t5 | ||
| table_transformer | ||
| tapas | ||
| time_series_transformer | ||
| timesformer | ||
| timm_backbone | ||
| trocr | ||
| tvlt | ||
| tvp | ||
| udop | ||
| umt5 | ||
| unispeech | ||
| unispeech_sat | ||
| univnet | ||
| upernet | ||
| videomae | ||
| vilt | ||
| vipllava | ||
| vision_encoder_decoder | ||
| vision_text_dual_encoder | ||
| visual_bert | ||
| vit | ||
| vit_hybrid | ||
| vit_mae | ||
| vit_msn | ||
| vitdet | ||
| vitmatte | ||
| vits | ||
| vivit | ||
| wav2vec2 | ||
| wav2vec2_bert | ||
| wav2vec2_conformer | ||
| wav2vec2_phoneme | ||
| wav2vec2_with_lm | ||
| wavlm | ||
| whisper | ||
| x_clip | ||
| xglm | ||
| xlm | ||
| xlm_prophetnet | ||
| xlm_roberta | ||
| xlm_roberta_xl | ||
| xlnet | ||
| xmod | ||
| yolos | ||
| yoso | ||
| __init__.py | ||