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* add mmpose code * update * update * add rewrites * test trt * test litehrnet with trt * revert unused change * add docs about mmpose * add docstring and staticmethod * update * update * update docs * fix config name and docs * add pose_detection ut * add pose data * fix lint of model.py * add pose_detection_model ut * fix docs and docstrinf * add test_mmpose_models.py * fix yapf * fix lint * fix create input * support ort ut * fix yapf * fix docs * fix createinput * test ci bug * rm test1.py * fix yapf * fix flake8 * fix yapf * add config and update benchmark * fix table format * update mmpose benchmark * update benchmark for mmpose * run mmpose tests seperately in ci * fix lint * resolve comments * add trt ut config * fix test * fix tests * resolve comments * resolve comments * update tests Co-authored-by: VVsssssk <shenkun@pjlab.org.cn> Co-authored-by: hanrui1sensetime <hanrui1@sensetime.com>
40 lines
1.8 KiB
Markdown
40 lines
1.8 KiB
Markdown
# MMPose Support
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[MMPose](https://github.com/open-mmlab/mmpose) is an open-source toolbox for pose estimation based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
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## MMPose installation tutorial
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Please refer to [official installation guide](https://mmpose.readthedocs.io/en/latest/install.html) to install the codebase.
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## MMEditing models support
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| Model | Task | ONNX Runtime | TensorRT | NCNN | PPLNN | OpenVINO | Model config |
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|:----------|:--------------|:------------:|:--------:|:----:|:-----:|:--------:|:-------------------------------------------------------------------------------------------:|
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| HRNet | PoseDetection | Y | Y | Y | N | Y | [config](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnet-cvpr-2019) |
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| MSPN | PoseDetection | Y | Y | Y | N | Y | [config](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mspn-arxiv-2019) |
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| LiteHRNet | PoseDetection | Y | Y | N | N | Y | [config](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#litehrnet-cvpr-2021) |
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### Example
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```bash
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python tools/deploy.py \
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configs/mmpose/posedetection_tensorrt_static-256x192.py \
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$MMPOSE_DIR/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \
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$MMPOSE_DIR/checkpoints/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
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$MMDEPLOY_DIR/demo/resources/human-pose.jpg \
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--work-dir work-dirs/mmpose/topdown/hrnet/trt \
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--device cuda
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```
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Note
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- Usually, mmpose models need some extra information for the input image, but we can't get it directly. So, when exporting the model, you can use `$MMDEPLOY_DIR/demo/resources/human-pose.jpg` as input.
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## Reminder
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None
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## FAQs
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None
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