File size: 1,457 Bytes
150d962 08aed96 150d962 08aed96 150d962 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import torch
import argparse
from 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=10,
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/example.onnx",
help="The path where the ONNX model will be saved.",
)
args = parser.parse_args()
export_to_onnx(args.model_path, args.onnx_path)
|