MobileNetV2 architecture from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" https://arxiv.org/abs/1801.04381.
For the Pytorch implementation, you can refer to pytorchx/mobilenet
Following tricks are used in this mobilenet,
- Relu6 is used in mobilenet v2. We use
Relu6(x) = Relu(x) - Relu(x-6)in tensorrt. - Batchnorm layer, implemented by scale layer.
// 1. generate mobilenet.wts from [pytorchx/mobilenet](https://github.com/wang-xinyu/pytorchx/tree/master/mobilenet)
// 2. put mobilenet.wts into tensorrtx/mobilenet
// 3. build and run
cd tensorrtx/mobilenet
mkdir build
cd build
cmake ..
make
sudo ./mobilenet -s // serialize model to plan file i.e. 'mobilenet.engine'
sudo ./mobilenet -d // deserialize plan file and run inference
// 4. see if the output is same as pytorchx/mobilenet