Adding GroupViT Models (#17313)
* add group vit and fixed test (except slow) * passing slow test * addressed some comments * fixed test * fixed style * fixed copy * fixed segmentation output * fixed test * fixed relative path * fixed copy * add ignore non auto configured * fixed docstring, add doc * fixed copies * Apply suggestions from code review merge suggestions Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * resolve comment, renaming model * delete unused attr * use fix copies * resolve comments * fixed attn * remove unused vars * refactor tests * resolve final comments * add demo notebook * fixed inconsitent default * Apply suggestions from code review Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * rename stage->stages * Create single GroupViTEncoderLayer class * Update conversion script * Simplify conversion script * Remove cross-attention class in favor of GroupViTAttention * Convert other model as well, add processor to conversion script * addressing final comment * fixed args * Update src/transformers/models/groupvit/modeling_groupvit.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
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Jerry Jiarui XU
@@ -316,6 +316,16 @@
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title: OpenAI GPT
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- local: model_doc/gpt2
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title: OpenAI GPT2
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- local: model_doc/gptj
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title: GPT-J
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- local: model_doc/gpt_neo
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title: GPT Neo
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- local: model_doc/gpt_neox
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title: GPT NeoX
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- local: model_doc/groupvit
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title: GroupViT
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- local: model_doc/hubert
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title: Hubert
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- local: model_doc/opt
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title: OPT
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- local: model_doc/pegasus
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@@ -98,6 +98,7 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
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1. **[GPT NeoX](model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
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1. **[GPT-2](model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
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1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
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1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
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1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
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1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
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1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
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@@ -230,6 +231,7 @@ Flax), PyTorch, and/or TensorFlow.
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| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
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| GPT NeoX | ❌ | ✅ | ✅ | ❌ | ❌ |
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| GPT-J | ❌ | ❌ | ✅ | ✅ | ✅ |
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| GroupViT | ❌ | ❌ | ✅ | ❌ | ❌ |
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| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
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| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
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| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |
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61
docs/source/en/model_doc/groupvit.mdx
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docs/source/en/model_doc/groupvit.mdx
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<!--Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# GroupViT
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## Overview
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The GroupViT model was proposed in [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
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Inspired by [CLIP](clip), GroupViT is a vision-language model that can perform zero-shot semantic segmentation on any given vocabulary categories.
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The abstract from the paper is the following:
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*Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision.*
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Tips:
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- You may specify `output_segmentation=True` in the forward of `GroupViTModel` to get the segmentation logits of input texts.
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- The quickest way to get started with GroupViT is by checking the [example notebooks](https://github.com/xvjiarui/GroupViT/blob/main/demo/GroupViT_hf_inference_notebook.ipynb) (which showcase zero-shot segmentation inference). One can also check out the [HuggingFace Spaces demo](https://huggingface.co/spaces/xvjiarui/GroupViT) to play with GroupViT.
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This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui).
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The original code can be found [here](https://github.com/NVlabs/GroupViT).
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## GroupViTConfig
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[[autodoc]] GroupViTConfig
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- from_text_vision_configs
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## GroupViTTextConfig
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[[autodoc]] GroupViTTextConfig
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## GroupViTVisionConfig
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[[autodoc]] GroupViTVisionConfig
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## GroupViTModel
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[[autodoc]] GroupViTModel
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- forward
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- get_text_features
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- get_image_features
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## GroupViTTextModel
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[[autodoc]] GroupViTTextModel
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- forward
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## GroupViTVisionModel
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[[autodoc]] GroupViTVisionModel
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- forward
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