e8653a776d
* add: mobilenetv2 Python network definition API * add: mobilenetv3 base code * add: mobilenetv2 Python network definition API * restructure: mobilenetv2 code * add: Alexnet Python Network Definition API * update: README according to new folder architecture * add: mobilenetv3 small and large python network definition API * add: DenseNet121 Python Network Definition API
53 lines
1.4 KiB
Markdown
53 lines
1.4 KiB
Markdown
# mobilenet v2
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MobileNetV2 architecture from
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"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>.
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For the Pytorch implementation, you can refer to [pytorchx/mobilenet](https://github.com/wang-xinyu/pytorchx/tree/master/mobilenet)
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Following tricks are used in this mobilenet,
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- Relu6 is used in mobilenet v2. We use `Relu6(x) = Relu(x) - Relu(x-6)` in tensorrt.
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- Batchnorm layer, implemented by scale layer.
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```
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// 1. generate mobilenet.wts from [pytorchx/mobilenet](https://github.com/wang-xinyu/pytorchx/tree/master/mobilenet)
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// 2. put mobilenet.wts into tensorrtx/mobilenet
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// 3. build and run
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cd tensorrtx/mobilenet/mobilenetv2
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mkdir build
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cd build
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cmake ..
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make
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sudo ./mobilenet -s // serialize model to plan file i.e. 'mobilenet.engine'
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sudo ./mobilenet -d // deserialize plan file and run inference
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// 4. see if the output is same as pytorchx/mobilenet
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```
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### TensorRT Python API
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```
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# 1. generate mobilenetv2.wts from [pytorchx/mobilenet](https://github.com/wang-xinyu/pytorchx/tree/master/mobilenet)
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# 2. put mobilenetv2.wts into tensorrtx/mobilenet/mobilenetv2
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# 3. install Python dependencies (tensorrt/pycuda/numpy)
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cd tensorrtx/mobilenet/mobilenetv2
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python mobilenet_v2.py -s // serialize model to plan file i.e. 'mobilenetv2.engine'
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python mobilenet_v2.py -d // deserialize plan file and run inference
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# 4. see if the output is same as pytorchx/mobilenet
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```
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