use ncnn with pytorch or onnx

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