7f17ce4f9b
* add tensorrt python api sample for resnet50 * update docs * typo * fix typo Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
58 lines
1.5 KiB
Markdown
58 lines
1.5 KiB
Markdown
# resnet
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ResNet-18 and ResNet-50 model from "Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>
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For the Pytorch implementation, you can refer to [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)
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Following tricks are used in this resnet, nothing special, residual connection and batchnorm are used.
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- Batchnorm layer, implemented with scale layer.
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## TensorRT C++ API
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```
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// 1. generate resnet18.wts or resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)
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// 2. put resnet18.wts or resnet50.wts into tensorrtx/resnet
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// 3. build and run
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cd tensorrtx/resnet
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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 ./resnet18 -s // serialize model to plan file i.e. 'resnet18.engine'
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sudo ./resnet18 -d // deserialize plan file and run inference
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or
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sudo ./resnet50 -s // serialize model to plan file i.e. 'resnet50.engine'
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sudo ./resnet50 -d // deserialize plan file and run inference
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// 4. see if the output is same as pytorchx/resnet
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```
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### TensorRT Python API
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```
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# 1. generate resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)
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# 2. put resnet50.wts into tensorrtx/resnet
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# 3. install Python dependencies (tensorrt/pycuda/numpy)
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cd tensorrtx/resnet
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python resnet50.py -s // serialize model to plan file i.e. 'resnet50.engine'
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python resnet50.py -d // deserialize plan file and run inference
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# 4. see if the output is same as pytorchx/resnet
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```
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