libMACE_convert_to_onnx.py

import torch
from torchvision import models
model = models.resnet50(pretrained=True)
data = torch.rand(1, 3, 256, 256)
input_names = ["input"]
output_names = ["output"]
torch.onnx.export(model, data, "model.onnx", verbose=True, input_names=input_names,  output_names=output_names, opset_version=11, export_params=True, keep_initializers_as_inputs=True)