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_v2.cpp | ||
| mobilenet_v2.py | ||
| README.md | ||
mobilenet v2
MobileNetV2 architecture from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" https://arxiv.org/abs/1801.04381.
For the Pytorch implementation, you can refer to pytorchx/mobilenet
Following tricks are used in this mobilenet,
- Relu6 is used in mobilenet v2. We use
Relu6(x) = Relu(x) - Relu(x-6)in tensorrt. - Batchnorm layer, implemented by scale layer.
// 1. generate mobilenet.wts from [pytorchx/mobilenet](https://github.com/wang-xinyu/pytorchx/tree/master/mobilenet)
// 2. put mobilenet.wts into tensorrtx/mobilenet
// 3. build and run
cd tensorrtx/mobilenet/mobilenetv2
mkdir build
cd build
cmake ..
make
sudo ./mobilenet -s // serialize model to plan file i.e. 'mobilenet.engine'
sudo ./mobilenet -d // deserialize plan file and run inference
// 4. see if the output is same as pytorchx/mobilenet
TensorRT Python API
# 1. generate mobilenetv2.wts from [pytorchx/mobilenet](https://github.com/wang-xinyu/pytorchx/tree/master/mobilenet)
# 2. put mobilenetv2.wts into tensorrtx/mobilenet/mobilenetv2
# 3. install Python dependencies (tensorrt/pycuda/numpy)
cd tensorrtx/mobilenet/mobilenetv2
python mobilenet_v2.py -s // serialize model to plan file i.e. 'mobilenetv2.engine'
python mobilenet_v2.py -d // deserialize plan file and run inference
# 4. see if the output is same as pytorchx/mobilenet