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shufflenet v2

ShuffleNetV2 with 0.5x output channels, as described in "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" https://arxiv.org/abs/1807.11164

For the Pytorch implementation, you can refer to pytorchx/shufflenet

Following tricks are used in this shufflenet,

  • torch.chunk is used in shufflenet v2. We implemented the 'chunk(2, dim=C)' by tensorrt plugin. Which is the simplest plugin in this tensorrtx project. You can learn the basic procedures of build tensorrt plugin.
  • shuffle layer is used, the channel_shuffle() in pytorchx/shufflenet can be implemented by two shuffle layers in tensorrt.
  • Batchnorm layer, implemented by scale layer.
// 1. generate shufflenet.wts from [pytorchx/shufflenet](https://github.com/wang-xinyu/pytorchx/tree/master/shufflenet)

// 2. put shufflenet.wts into tensorrtx/shufflenet

// 3. build and run

cd tensorrtx/shufflenet

mkdir build

cd build

cmake ..

make

sudo ./shufflenet -s   // serialize model to plan file i.e. 'shufflenet.engine'
sudo ./shufflenet -d   // deserialize plan file and run inference

// 4. see if the output is same as pytorchx/shufflenet