8cfc8ee03f
* add tensorrt temporal shift module and related pytorch implementations * add .gitignore and getn weights script. * rename get_wts.py script * Add tsm-r50 demo. * update readme * remove useless codes * update readme * update readme * remote video and .gitignore, update tutorial * update readme and tutorial * fix a few bugs and test on tensorrt 5.1 * update readme |
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| .. | ||
| CMakeLists.txt | ||
| logging.h | ||
| README.md | ||
| resnet18.cpp | ||
| resnet50.cpp | ||
| resnet50.py | ||
| resnext50_32x4d.cpp | ||
resnet
ResNet-18 and ResNet-50 model from "Deep Residual Learning for Image Recognition" https://arxiv.org/pdf/1512.03385.pdf
For the Pytorch implementation, you can refer to pytorchx/resnet
Following tricks are used in this resnet, nothing special, residual connection and batchnorm are used.
- Batchnorm layer, implemented with scale layer.
TensorRT C++ API
// 1. generate resnet18.wts or resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)
// 2. put resnet18.wts or resnet50.wts into tensorrtx/resnet
// 3. build and run
cd tensorrtx/resnet
mkdir build
cd build
cmake ..
make
sudo ./resnet18 -s // serialize model to plan file i.e. 'resnet18.engine'
sudo ./resnet18 -d // deserialize plan file and run inference
or
sudo ./resnet50 -s // serialize model to plan file i.e. 'resnet50.engine'
sudo ./resnet50 -d // deserialize plan file and run inference
// 4. see if the output is same as pytorchx/resnet
TensorRT Python API
# 1. generate resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)
# 2. put resnet50.wts into tensorrtx/resnet
# 3. install Python dependencies (tensorrt/pycuda/numpy)
cd tensorrtx/resnet
python resnet50.py -s // serialize model to plan file i.e. 'resnet50.engine'
python resnet50.py -d // deserialize plan file and run inference
# 4. see if the output is same as pytorchx/resnet