Inception v3 model architecture from "Rethinking the Inception Architecture for Computer Vision" http://arxiv.org/abs/1512.00567.
For the details, you can refer to pytorchx/inception
Following tricks are used in this inception:
- For pooling layer with padding, we need pay attention to see if padding is included or excluded while calculating average number. Pytorch includes padding while doing avgPool by default, but Tensorrt doesn't. So for pooling layer with padding, we need
setAverageCountExcludesPadding(false)in tensorrt. - Batchnorm layer, implemented by scale layer.
// 1. generate inception.wts from [pytorchx/inception](https://github.com/wang-xinyu/pytorchx/tree/master/inception)
// 2. put inception.wts into tensorrtx/inception
// 3. build and run
cd tensorrtx/inception
mkdir build
cd build
cmake ..
make
sudo ./inception -s // serialize model to plan file i.e. 'inception.engine'
sudo ./inception -d // deserialize plan file and run inference
// 4. see if the output is same as pytorchx/inception