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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
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1.2 KiB
Here is a practical guide for converting pytorch model to ncnn
resnet18 is used as the example
pytorch to onnx
The official pytorch tutorial for exporting onnx model
https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html
import torch
import torchvision
import torch.onnx
# An instance of your model
model = torchvision.models.resnet18()
# An example input you would normally provide to your model's forward() method
x = torch.rand(1, 3, 224, 224)
# Export the model
torch_out = torch.onnx._export(model, x, "resnet18.onnx", export_params=True)
simplify onnx model
The exported resnet18.onnx model may contains many redundant operators such as Shape, Gather and Unsqueeze that is not supported in ncnn
Shape not supported yet!
Gather not supported yet!
# axis=0
Unsqueeze not supported yet!
# axes 7
Unsqueeze not supported yet!
# axes 7
Fortunately, daquexian developed a handy tool to eliminate them. cheers!
https://github.com/daquexian/onnx-simplifier
python3 -m onnxsim resnet18.onnx resnet18-sim.onnx
onnx to ncnn
Finally, you can convert the model to ncnn using tools/onnx2ncnn
onnx2ncnn resnet18-sim.onnx resnet18.param resnet18.bin