2825efe378
* add DPT head * [fix] fix init error * use mmcv function * delete code * remove transpose clas * support NLC output shape * Delete post_process_layer.py * add unittest and docstring * rename variables * fix project error and add unittest * match dpt weights * add configs * fix vit pos_embed bug and dpt feature fusion bug * match vit output * fix gelu * minor change * update unitest * fix configs error * inference test * remove auxilary * use local pretrain * update training results * update yml * update fps and memory test * update doc * update readme * add yml * update doc * remove with_cp * update config * update docstring * remove dpt-l * add init_cfg and modify readme.md * Update dpt_vit-b16.py * zh-n README * use constructor instead of build function * prevent tensor being modified by ConvModule * fix unittest Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
48 lines
3.0 KiB
Markdown
48 lines
3.0 KiB
Markdown
# Vision Transformer for Dense Prediction
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## Introduction
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<!-- [ALGORITHM] -->
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```latex
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@article{dosoViTskiy2020,
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title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
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author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
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journal={arXiv preprint arXiv:2010.11929},
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year={2020}
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}
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@article{Ranftl2021,
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author = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
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title = {Vision Transformers for Dense Prediction},
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journal = {ArXiv preprint},
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year = {2021},
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}
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```
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## Usage
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To use other repositories' pre-trained models, it is necessary to convert keys.
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We provide a script [`vit2mmseg.py`](../../tools/model_converters/vit2mmseg.py) in the tools directory to convert the key of models from [timm](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) to MMSegmentation style.
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```shell
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python tools/model_converters/vit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}
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```
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E.g.
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```shell
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python tools/model_converters/vit2mmseg.py https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth pretrain/jx_vit_base_p16_224-80ecf9dd.pth
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```
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This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
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## Results and models
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| DPT | ViT-B | 512x512 | 160000 | 8.09 | 10.41 | 46.97 | 48.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-20210809_172025.log.json) |
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