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import torch
import argparse
from ormbg.models.ormbg import ORMBG
def export_to_onnx(model_path, onnx_path):
net = ORMBG()
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_path))
net = net.cuda()
else:
net.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
net.eval()
# Create a dummy input tensor. The size should match the model's input size.
# Adjust the dimensions as necessary; here it is assumed the input is a 3-channel image.
dummy_input = torch.randn(
1,
3,
1024,
1024,
device="cuda" if torch.cuda.is_available() else "cpu",
)
torch.onnx.export(
net,
dummy_input,
onnx_path,
export_params=True,
opset_version=11,
do_constant_folding=True,
input_names=["input"],
output_names=["output"],
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Export a trained model to ONNX format."
)
parser.add_argument(
"--model_path",
type=str,
default="models/ormbg.pth",
help="The path to the trained model file.",
)
parser.add_argument(
"--onnx_path",
type=str,
default="models/ormbg.pth",
help="The path where the ONNX model will be saved.",
)
args = parser.parse_args()
export_to_onnx(args.model_path, args.onnx_path)
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