Test on TVM¶
Supported Models¶
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 |
The table above list the models that we have tested. Models not listed on the table might still be able to converted. Please have a try.
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 |
*: We only test model on ssd since dynamic shape is not supported for now.
mmseg | metric | PyTorch | TVM |
---|---|---|---|
FCN | mIoU | 72.25 | 72.36 |
PSPNet | mIoU | 78.55 | 77.90 |