Add UperNet (#20648)
* First draft * More improvements * Add convnext backbone * Add conversion script * Add more improvements * Comment out to_dict * Add to_dict method * Add default config * Fix config * Fix backbone * Fix backbone some more * Add docs, auto mapping, tests * Fix some tests * Fix more tests * Fix more tests * Add conversion script * Improve conversion script * Add support for getting reshaped undownsampled hidden states * Fix forward pass * Add print statements * Comment out set_shift_and_window_size * More improvements * Correct downsampling layers conversion * Fix style * First draft * Fix conversion script * Remove config attribute * Fix more tests * Update READMEs * Update ConvNextBackbone * Fix ConvNext tests * Align ConvNext with Swin * Remove files * Fix index * Improve docs * Add output_attentions to model forward * Add backbone mixin, improve tests * More improvements * Update init_weights * Fix interpolation of logits * Add UperNetImageProcessor * Improve image processor * Fix image processor * Remove print statements * Remove script * Update import * Add image processor tests * Remove print statements * Fix test * Add integration test * Add convnext integration test * Update docstring * Fix README * Simplify config * Apply suggestions * Improve docs * Rename class * Fix test_initialization * Fix import * Address review * Fix confg * Convert all checkpoints * Fix default backbone * Usage same processor as segformer * Apply suggestions * Fix init_weights, update conversion scripts * Improve config * Use Auto API instead of creating a new image processor * Fix docs * Add doctests * Remove ResNetConfig dependency * Add always_partition argument * Fix rebaseé * Improve docs * Convert checkpoints Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local> Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
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NielsRogge
@@ -450,6 +450,8 @@
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title: Table Transformer
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- local: model_doc/timesformer
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title: TimeSformer
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- local: model_doc/upernet
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title: UperNet
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- local: model_doc/van
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title: VAN
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- local: model_doc/videomae
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@@ -194,6 +194,7 @@ The documentation is organized into five sections:
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1. **[UL2](model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
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1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
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1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
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1. **[UPerNet](model_doc/upernet)** (from Peking University) released with the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun.
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1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
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1. **[VideoMAE](model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
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1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
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@@ -363,6 +364,7 @@ Flax), PyTorch, and/or TensorFlow.
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| TrOCR | ❌ | ❌ | ✅ | ❌ | ❌ |
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| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
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| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
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| UPerNet | ❌ | ❌ | ✅ | ❌ | ❌ |
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| VAN | ❌ | ❌ | ✅ | ❌ | ❌ |
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| VideoMAE | ❌ | ❌ | ✅ | ❌ | ❌ |
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| ViLT | ❌ | ❌ | ✅ | ❌ | ❌ |
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docs/source/en/model_doc/upernet.mdx
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docs/source/en/model_doc/upernet.mdx
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<!--Copyright 2022 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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# UPerNet
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## Overview
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The UPerNet model was proposed in [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221)
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by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
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a wide range of concepts from images, leveraging any vision backbone like [ConvNeXt](convnext) or [Swin](swin).
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The abstract from the paper is the following:
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*Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/transformers/model_doc/upernet_architecture.jpg"
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alt="drawing" width="600"/>
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<small> UPerNet framework. Taken from the <a href="https://arxiv.org/abs/1807.10221">original paper</a>. </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code is based on OpenMMLab's mmsegmentation [here](https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/uper_head.py).
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## Usage
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UPerNet is a general framework for semantic segmentation. It can be used with any vision backbone, like so:
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```py
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from transformers import SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
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backbone_config = SwinConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
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config = UperNetConfig(backbone_config=backbone_config)
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model = UperNetForSemanticSegmentation(config)
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```
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To use another vision backbone, like [ConvNeXt](convnext), simply instantiate the model with the appropriate backbone:
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```py
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from transformers import ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation
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backbone_config = ConvNextConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
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config = UperNetConfig(backbone_config=backbone_config)
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model = UperNetForSemanticSegmentation(config)
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```
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Note that this will randomly initialize all the weights of the model.
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## UperNetConfig
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[[autodoc]] UperNetConfig
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## UperNetForSemanticSegmentation
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[[autodoc]] UperNetForSemanticSegmentation
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- forward
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