4d630ed599
* add: structure inception v4 * fix: camelcase naming * fix and optimize * add: doInference def * define inception layers * fix: spacing * add: inception network def * fix layers * create net * validate * add README * update readme * typo * create dir inception
1.2 KiB
1.2 KiB
Inception v4
Inception v4 model architecture from "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" https://arxiv.org/abs/1602.07261v2.
For the details, you can refer to rwightman/pytorch-image-models
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 [BlueMirrors/torchtrtz](https://github.com/BlueMirrors/torchtrtz/blob/main/generate_weights.py)
// 2. put inception.wts into tensorrtx/inceptionV4
// 3. build and run
cd tensorrtx/inception/inceptionV4
mkdir build
cd build
cmake ..
make
sudo ./inceptionV4 -s // serialize model to plan file i.e. 'inceptionV4.engine'
sudo ./inceptionV4 -d // deserialize plan file and run inference
// 4. see if the output is same as rwightman/pytorch-image-models/inceptionv4