brocolli
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Everything in Torch Fx
brocolli
torch fx based pytorch model converter, including pytorch2caffe, pytorch2onnx.
torch fx based pytorch model quantizier.
Pytorch version 1.9.0 and above are all supported
installation
pip install brocolli
How to use
import torchvision.models as models
from brocolli.converter.pytorch_caffe_parser import PytorchCaffeParser
net = models.alexnet(pretrained=False)
pytorch_parser = PytorchCaffeParser(net, [(1, 3, 224, 223)])
pytorch_parser.convert()
pytorch_parser.save('alexnet.onnx')
user can run this script until you see "accuracy test passed" on screen, then you can get your caffe or trt model under tmp folder.
Notice
- ✔️ : support
- ❔ : shall support
- ❌ : not support
Curently supported layers
| Caffe | TensorRT | |
|---|---|---|
| Conv | ✔️ | ✔️ |
| PRelu | ✔️ | ❔ |
| MaxPooling | ✔️ | ✔️ |
| Sigmoid | ✔️ | ✔️ |
| BatchNormalization | ✔️ | ✔️ |
| Relu | ✔️ | ✔️ |
| LeakyRelu | ✔️ | ✔️ |
| Add | ✔️ | ✔️ |
| AvgPool | ✔️ | ✔️ |
| Flatten | ✔️ | ✔️ |
| FullyConnected | ✔️ | ✔️ |
| Softmax | ✔️ | ✔️ |
| Upsample | ✔️ | ✔️ |
| Permute | ✔️ | ✔️ |
| Concat | ✔️ | ✔️ |
| Unsqueeze | ✔️ | ❔ |
| Relu6 | ✔️ | ✔️ |
| Pad | ✔️ | ✔️ |
| HardSwish | ✔️ | ✔️ |
| HardSigmoid | ✔️ | ✔️ |
| Mul | ✔️ | ✔️ |
| Slice | ✔️ | ✔️ |
| L2Normalization | ✔️ | ❔ |
| Resize | ✔️ | ✔️ |
| ReduceMean | ✔️ | ✔️ |
| BilinearInterpolate | ✔️ | ✔️ |
| MaxUnPool | ✔️ | ❌ |
| ConvTranspose | ✔️ | ✔️ |
| Gather | ❌ | ✔️ |
| PixelShufle | ✔️ | ❔ |
Curently supported network
| Caffe | TensorRT | |
|---|---|---|
| SSD | ✔️ | ❔ |
| AlexNet | ✔️ | ✔️ |
| ResNet | ✔️ | ✔️ |
| GoogleNet | ✔️ | ✔️ |
| SqueezeNet | ✔️ | ✔️ |
| MobileNet | ✔️ | ✔️ |
| DenseNet | ✔️ | ✔️ |
| ShuffleNet | ✔️ | ✔️ |
| SCNN | ✔️ | ✔️ |
| SegNet | ✔️ | ❌ |
| YoloV5 | ✔️ | ✔️ |
| YoloV3 | ✔️ | ✔️ |
| Realcugan | ✔️ | ❔ |
| Yolo-Lite | ✔️ | ❔ |
| Resa | ❌ | ✔️ |
| YoloX | ✔️ | ✔️ |
| BiSeNet | ❌ | ✔️ |
| fbnet | ✔️ | ❔ |
| regnet | ✔️ | ❔ |
| ghostnet | ✔️ | ❔ |
| tinynet | ✔️ | ❔ |
| YoloV7 | ✔️ | ❔ |
TODO
RNN support
Contact
QQ Group: 597059928
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