MNASNet with depth multiplier of 0.5 from "MnasNet: Platform-Aware Neural Architecture Search for Mobile" https://arxiv.org/pdf/1807.11626.pdf
For the Pytorch implementation, you can refer to pytorchx/mnasnet
Following tricks are used in this mnasnet, nothing special, group conv and batchnorm are used.
- Batchnorm layer, implemented by scale layer.
// 1. generate mnasnet.wts from [pytorchx/mnasnet](https://github.com/wang-xinyu/pytorchx/tree/master/mnasnet)
// 2. put mnasnet.wts into tensorrtx/mnasnet
// 3. build and run
cd tensorrtx/mnasnet
mkdir build
cd build
cmake ..
make
sudo ./mnasnet -s // serialize model to plan file i.e. 'mnasnet.engine'
sudo ./mnasnet -d // deserialize plan file and run inference
// 4. see if the output is same as pytorchx/mnasnet