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mmdeploy/docs/zh_cn/03-benchmark/benchmark_tvm.md
huayuan4396 840adcfb43
mmcls -> mmpretrain (#2003) 
* mmcls -> mmpretrain

* add constraints.txt

* fix lint

* fix lint

* remove constraints.txt

* fix windows ci

* modify requirements

* fix mdlink and mmpretrain version

* fix dead link

* modify codebase cmakelist

* fix rename
2023-04-28 20:49:22 +08:00

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TVM 测试

支持模型列表

Model Codebase Model config
RetinaNet MMDetection config
Faster R-CNN MMDetection config
YOLOv3 MMDetection config
YOLOX MMDetection config
Mask R-CNN MMDetection config
SSD MMDetection config
ResNet MMPretrain config
ResNeXt MMPretrain config
SE-ResNet MMPretrain config
MobileNetV2 MMPretrain config
ShuffleNetV1 MMPretrain config
ShuffleNetV2 MMPretrain config
VisionTransformer MMPretrain config
FCN MMSegmentation config
PSPNet MMSegmentation config
DeepLabV3 MMSegmentation config
DeepLabV3+ MMSegmentation config
UNet MMSegmentation config

表中仅列出已测试模型，未列出的模型可能同样支持，可以自行尝试转换。

Test

  • Ubuntu 20.04
  • tvm 0.9.0
mmpretrain metric PyTorch TVM
ResNet-18 top-1 69.90 69.90
ResNeXt-50 top-1 77.90 77.90
ShuffleNet V2 top-1 69.55 69.55
MobileNet V2 top-1 71.86 71.86
mmdet(*) metric PyTorch TVM
SSD box AP 25.5 25.5

*: 由于暂时不支持动态转换，因此仅提供 SSD 的精度测试结果。

mmseg metric PyTorch TVM
FCN mIoU 72.25 72.36
PSPNet mIoU 78.55 77.90