b671024a27
* add ghostnet * add ghostnet * add ghostnetV1&ghostnetV2 * Fix object destruction order in APIToModel function to avoid undefined behavior * Fix pre-commit errors in ghostnet/README.md * Fix pre-commit errors * Fix pre-commit errors in mobilenetV3 * Add a noqa marker in mobilenet py files |
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| .. | ||
| CMakeLists.txt | ||
| logging.h | ||
| mobilenet_v3.cpp | ||
| mobilenet_v3.py | ||
| README.md | ||
mobilenet v3
MobileNetV3 architecture from "Searching for MobileNetV3" https://arxiv.org/abs/1905.02244?context=cs.
For the Pytorch implementation, you can refer to mobilenetv3.pytorch
Run
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generate mbv3_small.wts/mbv3_large.wts from pytorch implementation
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put mbv3_small.wts/mbv3_large.wts into tensorrtx/mobilenet/mobilenetv3
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build and run
cd tensorrtx/mobilenet/mobilenetv3
mkdir build
cd build
cmake ..
make
sudo ./mobilenetv3 -s small(or large) // serialize model to plan file i.e. 'mobilenetv3_small.engine'
sudo ./mobilenetv3 -d small(or large) // deserialize plan file and run inference
- see if the output is same as pytorch side
TensorRT Python API
# 1. generate mobilenetv3.wts from [mobilenetv3.pytorch](https://github.com/chufei1995/mobilenetv3.pytorch)
# 2. put mobilenetv3.wts into tensorrtx/mobilenet/mobilenetv3
# 3. install Python dependencies (tensorrt/pycuda/numpy)
cd tensorrtx/mobilenet/mobilenetv3
python mobilenet_v2.py -s small(or large) // serialize model to plan file i.e. 'mobilenetv2.engine'
python mobilenet_v2.py -d small(or large) // deserialize plan file and run inference