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Add BridgeTower model (#20775)

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* Commit with BTModel and latest HF code

* Placeholder classes for BTForMLM and BTForITR

* Importing Bert classes from transformers

* Removed objectives.py and dist_utils.py

* Removed swin_transformer.py

* Add image normalization, BridgeTowerForImageAndTextRetrieval

* Add center_crop

* Removing bert tokenizer and LCI references

* Tested config loading from HF transformers hub

* Removed state_dict updates and added path to hub

* Enable center crop

* Getting image_size from config, renaming num_heads and num_layers

* Handling max_length in BridgeTowerProcessor

* Add BridgeTowerForMaskedLM

* Add doc string for BridgeTowerConfig

* Add doc strings for BT config, processor, image processor

* Adding docs, removed swin

* Removed convert_bridgetower_original_to_pytorch.py

* Added doc files for bridgetower, removed is_vision

* Add support attention_mask=None and BridgeTowerModelOutput

* Fix formatting

* Fixes with 'make style', 'make quality', 'make fixup'

* Remove downstream tasks from BridgeTowerModel

* Formatting fixes, add return_dict to BT models

* Clean up after doc_test

* Update BTModelOutput return type, fix todo in doc

* Remove loss_names from init

* implement tests and update tuples returned by models

* Add image reference to bridgetower.mdx

* after make fix-copies, make fixup, make style, make quality, make repo-consistency

* Rename class names with BridgeTower prefix

* Fix for image_size in BTImageProcessor

* implement feature extraction bridgetower tests

* Update image_mean and image_std to be list

* remove unused import

* Removed old comments

* Rework CLIP

* update config in tests followed config update

* Formatting fixes

* Add copied from for BridgeTowerPredictionHeadTransform

* Update bridgetower.mdx

* Update test_feature_extraction_bridgetower.py

* Update bridgetower.mdx

* BridgeTowerForMaskedLM is conditioned on image too

* Add BridgeTowerForMaskedLM

* Fixes

* Call post_init to init weights

* Move freeze layers into method

* Remove BTFeatureExtractor, add BT under multimodal models

* Remove BTFeatureExtractor, add BT under multimodal models

* Code review feedback - cleanup

* Rename variables

* Formatting and style to PR review feedback

* Move center crop after resize

* Use named parameters

* Style fix for modeling_bridgetower.py

* Update docs/source/en/model_doc/bridgetower.mdx

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Rename config params, copy BERT classes, clean comments

* Cleanup irtr

* Replace Roberta imports, add BTTextConfig and Model

* Update docs, add visionconfig, consistent arg names

* make fixup

* Comments for forward in BTModel and make fixup

* correct tests

* Remove inconsistent roberta copied from

* Add BridgeTowerTextModel to dummy_pt_objects.py

* Add BridgeTowerTextModel to IGNORE_NON_TESTED

* Update docs for BT Text and Vision Configs

* Treat BridgeTowerTextModel as a private model

* BridgeTowerTextModel as private

* Run make fix-copies

* Adding BTTextModel to PRIVATE_MODELS

* Fix for issue with BT Text and Image configs

* make style changes

* Update README_ja.md

Add から to BridgeTower's description

* Clean up config, .mdx and arg names

* Fix init_weights. Remove nn.Sequential

* Formatting and style fixes

* Re-add tie_word_embeddings in config

* update test implementation

* update style

* remove commented out

* fix style

* Update README with abs for BridgeTower

* fix style

* fix mdx file

* Update bridgetower.mdx

* Update img src in bridgetower.mdx

* Update README.md

* Update README.md

* resolve style failed

* Update _toctree.yml

* Update README_ja.md

* Removed mlp_ratio, rename feats, rename BTCLIPModel

* Replace BTCLIP with BTVisionModel,pass in vision_config to BTVisionModel

* Add test_initialization support

* Add support for output_hidden_states

* Update support for output_hidden_states

* Add support for output_attentions

* Add docstring for output_hidden_states

* update tests

* add bridgetowervisionmodel as private model

* rerun the PR test

* Remove model_type, pass configs to classes, renames

* Change self.device to use weight device

* Remove image_size

* Style check fixes

* Add hidden_size and num_hidden_layers to BridgeTowerTransformer

* Update device setting

* cosmetic update

* trigger test again

* trigger tests again

* Update test_modeling_bridgetower.py

trigger tests again

* Update test_modeling_bridgetower.py

* minor update

* re-trigger tests

* Update docs/source/en/model_doc/bridgetower.mdx

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove pad, update max_text_len, doc cleanup, pass eps to LayerNorm

* Added copied to, some more review feedback

* make fixup

* Use BridgeTowerVisionEmbeddings

* Code cleanup

* Fixes for BridgeTowerVisionEmbeddings

* style checks

* re-tests

* fix embedding

* address comment on init file

* retrigger tests

* update import prepare_image_inputs

* update test_image_processing_bridgetower.py to reflect test_image_processing_common.py

* retrigger tests

Co-authored-by: Shaoyen Tseng <shao-yen.tseng@intel.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>
This commit is contained in:
Anahita Bhiwandiwalla
2023-01-25 11:04:32 -08:00
committed by GitHub
parent 39799fbf85
commit 3a6e4a221c
28 changed files with 3696 additions and 1 deletions
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docs/source/en/_toctree.yml
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@@ -516,6 +516,8 @@
title: AltCLIP
- local: model_doc/blip
title: BLIP
- local: model_doc/bridgetower
title: BridgeTower
- local: model_doc/chinese_clip
title: Chinese-CLIP
- local: model_doc/clip
@@ -595,4 +597,4 @@
- local: internal/file_utils
title: General Utilities
title: Internal Helpers
title: API
title: API

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@@ -68,6 +68,7 @@ The documentation is organized into five sections:
1. **[BLIP](model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
1. **[BLOOM](model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
1. **[BridgeTower](model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
1. **[CamemBERT](model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
@@ -250,6 +251,7 @@ Flax), PyTorch, and/or TensorFlow.
| BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ✅ |
| BLIP | ❌ | ❌ | ✅ | ❌ | ❌ |
| BLOOM | ❌ | ✅ | ✅ | ❌ | ❌ |
| BridgeTower | ❌ | ❌ | ✅ | ❌ | ❌ |
| CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
| CANINE | ✅ | ❌ | ✅ | ❌ | ❌ |
| Chinese-CLIP | ❌ | ❌ | ✅ | ❌ | ❌ |

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<!--Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# BridgeTower
## Overview
The BridgeTower model was proposed in [BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder thus achieving remarkable performance on various downstream tasks with almost negligible additional performance and computational costs.
This paper has been accepted to the [AAAI'23](https://aaai.org/Conferences/AAAI-23/) conference.
The abstract from the paper is the following:
*Vision-Language (VL) models with the TWO-TOWER architecture have dominated visual-language representation learning in recent years.
Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder.
Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BRIDGETOWER, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the crossmodal encoder.
This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BRIDGETOWER achieves state-of-the-art performance on various downstream vision-language tasks.
In particular, on the VQAv2 test-std set, BRIDGETOWER achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs.
Notably, when further scaling the model, BRIDGETOWER achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/bridgetower_architecture%20.jpg"
alt="drawing" width="600"/>
<small> BridgeTower architecture. Taken from the <a href="https://arxiv.org/abs/2206.08657">original paper.</a> </small>
## Usage
BridgeTower consists of a visual encoder, a textual encoder and cross-modal encoder with multiple lightweight bridge layers.
The goal of this approach was to build a bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder.
In principle, one can apply any visual, textual or cross-modal encoder in the proposed architecture.
The [`BridgeTowerProcessor`] wraps [`RobertaTokenizer`] and [`BridgeTowerImageProcessor`] into a single instance to both
encode the text and prepare the images respectively.
The following example shows how to run image-text retrieval using [`BridgeTowerProcessor`] and [`BridgeTowerForImageAndTextRetrieval`].
```python
>>> from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval
>>> import requests
>>> from PIL import Image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
>>> model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
>>> # forward pass
>>> scores = dict()
>>> for text in texts:
... # prepare inputs
... encoding = processor(image, text, return_tensors="pt")
... outputs = model(**encoding)
... scores[text] = outputs.logits[0, 1].item()
```
The following example shows how to run masked language modeling using [`BridgeTowerProcessor`] and [`BridgeTowerForMaskedLM`].
```python
>>> from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000360943.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
>>> text = "a <mask> looking out of the window"
>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
>>> model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
>>> # prepare inputs
>>> encoding = processor(image, text, return_tensors="pt")
>>> # forward pass
>>> outputs = model(**encoding)
>>> results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist())
>>> print(results)
.a cat looking out of the window.
```
This model was contributed by [Anahita Bhiwandiwalla](https://huggingface.co/anahita-b), [Tiep Le](https://huggingface.co/Tile) and [Shaoyen Tseng](https://huggingface.co/shaoyent). The original code can be found [here](https://github.com/microsoft/BridgeTower).
Tips:
- This implementation of BridgeTower uses [`RobertaTokenizer`] to generate text embeddings and OpenAI's CLIP/ViT model to compute visual embeddings.
- Checkpoints for pre-trained [bridgeTower-base](https://huggingface.co/BridgeTower/bridgetower-base) and [bridgetower masked language modeling and image text matching](https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm) are released.
- Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other down stream tasks.
- The PyTorch version of this model is only available in torch 1.10 and higher.
## BridgeTowerConfig
[[autodoc]] BridgeTowerConfig
## BridgeTowerTextConfig
[[autodoc]] BridgeTowerTextConfig
## BridgeTowerVisionConfig
[[autodoc]] BridgeTowerVisionConfig
## BridgeTowerImageProcessor
[[autodoc]] BridgeTowerImageProcessor
- preprocess
## BridgeTowerProcessor
[[autodoc]] BridgeTowerProcessor
- __call__
## BridgeTowerModel
[[autodoc]] BridgeTowerModel
- forward
## BridgeTowerForMaskedLM
[[autodoc]] BridgeTowerForMaskedLM
- forward
## BridgeTowerForImageAndTextRetrieval
[[autodoc]] BridgeTowerForImageAndTextRetrieval
- forward