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[Doc] update benchmark add supported-model-list (#286)

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* update benchmark add supported-model-list

* fix lint

* fix lint

* loc mmocr maximum version
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AllentDan 2022-04-01 12:26:34 +08:00 committed by GitHub
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6 changed files with 52 additions and 52 deletions
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1
csrc/core/utils/device_utils.h
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@ -29,7 +29,6 @@ MMDEPLOY_API Result<Mat> MakeAvailableOnDevice(const Mat& src, const Device& dev
MMDEPLOY_API Result<Tensor> MakeAvailableOnDevice(const Tensor& src, const Device& device,
Stream& stream);
// Calls stream.Wait() on destruction if active is true. This class is used to force a wait
// operation before intermediate variables goes out of scope. Add variables in consideration to the
// tailing parameter pack to ensure correctness (this make sure SyncOnScopeExit is created later

8
docs/en/benchmark.md
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@ -326,10 +326,10 @@ Users can directly test the speed through [how_to_measure_performance_of_models.
<td align="center">Mask-RCNN</td>
<td align="center">COCO</td>
<td align="center">1x3x800x1344</td>
<td align="center">320.86 </td>
<td align="center">3.12</td>
<td align="center">241.32</td>
<td align="center">4.14</td>
<td align="center">104.83</td>
<td align="center">9.54</td>
<td align="center">58.27</td>
<td align="center">17.16</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>

4
docs/en/supported_models.md
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@ -3,7 +3,7 @@
The table below lists the models that are guaranteed to be exportable to other backends.
| Model | Codebase | OnnxRuntime | TensorRT | NCNN | PPLNN | OpenVINO | Model config |
|:--------------------------|:-----------------|:-----------:|:--------:|:----:|:-----:|:--------:|:----------------------------------------------------------------------------------------------:|
| :------------------------ | :--------------- | :---------: | :------: | :---: | :---: | :------: | :--------------------------------------------------------------------------------------------: |
| RetinaNet | MMDetection | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet) |
| Faster R-CNN | MMDetection | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) |
| YOLOv3 | MMDetection | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo) |
@ -16,7 +16,7 @@ The table below lists the models that are guaranteed to be exportable to other b
| ATSS | MMDetection | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss) |
| GFL | MMDetection | Y | Y | N | ? | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl) |
| Cascade R-CNN | MMDetection | Y | Y | N | Y | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn) |
| Cascade Mask R-CNN | MMDetection | Y | N | N | N | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn) |
| Cascade Mask R-CNN | MMDetection | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn) |
| VFNet | MMDetection | N | N | N | N | Y | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/vfnet) |
| ResNet | MMClassification | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet) |
| ResNeXt | MMClassification | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext) |

8
docs/zh_cn/benchmark.md
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@ -327,10 +327,10 @@ GPU: ncnn, TensorRT, PPLNN
<td align="center">Mask-RCNN</td>
<td align="center">COCO</td>
<td align="center">1x3x800x1344</td>
<td align="center">320.86 </td>
<td align="center">3.12</td>
<td align="center">241.32</td>
<td align="center">4.14</td>
<td align="center">104.83</td>
<td align="center">9.54</td>
<td align="center">58.27</td>
<td align="center">17.16</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>

2
requirements/optional.txt
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@ -1,7 +1,7 @@
mmcls>=0.15.0,<=0.19.0
mmdet>=2.19.0,<=2.20.0
mmedit
mmocr>=0.3.0
mmocr>=0.3.0,<=0.4.1
mmpose>=0.24.0
mmsegmentation
onnxruntime>=1.8.0

3
tools/deploy.py
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@ -276,7 +276,8 @@ def main():
show_result=args.show),
ret_value=ret_value)
else:
logger.warning('\"visualize_model\" has been skipped may be because it\'s \
logger.warning(
'\"visualize_model\" has been skipped may be because it\'s \
running on a headless device.')
logger.info('All process success.')