pnnx

import torch import os # 1. Define a simple PyTorch model class MyModel(torch.nn.Module): def __init__(self): super(MyModel, self).__init__() self.conv = torch.nn.Conv2d(3, 32, 3, padding=1) self.relu = torch.nn.ReLU() def forward(self, x): return self.relu(self.conv(x)) model = MyModel() dummy_input = torch.rand(1, 3, 64, 64) # 2. Trace the model using torch.jit.trace # This creates a TorchScript module which pnnx can convert. traced_model = torch.jit.trace(model, dummy_input) # 3. Save the traced model to a file model_path = "my_model.pt" traced_model.save(model_path) print(f"PyTorch model saved to {model_path}") print("\nNow, open your terminal and run the following command to convert the model:") print(f"pnnx {model_path} inputshape=[{','.join(map(str, dummy_input.shape))}] outputpath=converted_model") print("\nThis will generate NCNN model files (e.g., converted_model.param, converted_model.bin) in the 'converted_model' directory.") # You can also specify ONNX output: # print("Or for ONNX output:") # print(f"pnnx {model_path} inputshape=[{','.join(map(str, dummy_input.shape))}] outputpath=converted_model --onnx")