brocolli 1.0.0

everything in pytorch

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