52 lines
5.2 KiB
Markdown
52 lines
5.2 KiB
Markdown
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# TVM 测试
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## 支持模型列表
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| Model | Codebase | Model config |
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| :---------------- | :--------------- | :---------------------------------------------------------------------------------------------: |
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| RetinaNet | MMDetection | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet) |
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| Faster R-CNN | MMDetection | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) |
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| YOLOv3 | MMDetection | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo) |
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| YOLOX | MMDetection | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox) |
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| Mask R-CNN | MMDetection | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn) |
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| SSD | MMDetection | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd) |
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| ResNet | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet) |
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| ResNeXt | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext) |
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| SE-ResNet | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet) |
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| MobileNetV2 | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2) |
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| ShuffleNetV1 | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1) |
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| ShuffleNetV2 | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2) |
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| VisionTransformer | MMClassification | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer) |
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| FCN | MMSegmentation | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/fcn) |
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| PSPNet | MMSegmentation | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/pspnet) |
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| DeepLabV3 | MMSegmentation | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/deeplabv3) |
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| DeepLabV3+ | MMSegmentation | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/deeplabv3plus) |
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| UNet | MMSegmentation | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/unet) |
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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。
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## Test
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- Ubuntu 20.04
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- tvm 0.9.0
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| mmcls | metric | PyTorch | TVM |
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| :----------------------------------------------------------------------------------------------------------------------------------------------------: | :----: | :-----: | :---: |
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| [ResNet-18](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet/resnet18_b32x8_imagenet.py) | top-1 | 69.90 | 69.90 |
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| [ResNeXt-50](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext/resnext50_32x4d_b32x8_imagenet.py) | top-1 | 77.90 | 77.90 |
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| [ShuffleNet V2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py) | top-1 | 69.55 | 69.55 |
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| [MobileNet V2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | top-1 | 71.86 | 71.86 |
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<!-- | [Vision Transformer](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | top-1 | 85.43 | 84.01 | -->
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| mmdet(\*) | metric | PyTorch | TVM |
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| :-------------------------------------------------------------------------------------: | :----: | :-----: | :--: |
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| [SSD](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | box AP | 25.5 | 25.5 |
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\*: 由于暂时不支持动态转换，因此仅提供 SSD 的精度测试结果。
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| mmseg | metric | PyTorch | TVM |
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| :------------------------------------------------------------------------------------------------------------------------: | :----: | :-----: | :---: |
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| [FCN](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py) | mIoU | 72.25 | 72.36 |
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| [PSPNet](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py) | mIoU | 78.55 | 77.90 |
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