718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
56 lines
1.2 KiB
Markdown
56 lines
1.2 KiB
Markdown
Here is a practical guide for converting pytorch model to ncnn
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resnet18 is used as the example
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## pytorch to onnx
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The official pytorch tutorial for exporting onnx model
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https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html
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```python
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import torch
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import torchvision
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import torch.onnx
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# An instance of your model
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model = torchvision.models.resnet18()
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# An example input you would normally provide to your model's forward() method
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x = torch.rand(1, 3, 224, 224)
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# Export the model
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torch_out = torch.onnx._export(model, x, "resnet18.onnx", export_params=True)
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```
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## simplify onnx model
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The exported resnet18.onnx model may contains many redundant operators such as Shape, Gather and Unsqueeze that is not supported in ncnn
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```
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Shape not supported yet!
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Gather not supported yet!
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# axis=0
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Unsqueeze not supported yet!
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# axes 7
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Unsqueeze not supported yet!
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# axes 7
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```
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Fortunately, daquexian developed a handy tool to eliminate them. cheers!
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https://github.com/daquexian/onnx-simplifier
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```
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python3 -m onnxsim resnet18.onnx resnet18-sim.onnx
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```
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## onnx to ncnn
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Finally, you can convert the model to ncnn using tools/onnx2ncnn
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```
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onnx2ncnn resnet18-sim.onnx resnet18.param resnet18.bin
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```
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