You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 

77 lines
2.2 KiB

import argparse
from io import BytesIO
import onnx
import torch
from ultralytics import YOLO
from models.common import optim
try:
import onnxsim
except ImportError:
onnxsim = None
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-w',
'--weights',
type=str,
required=True,
help='PyTorch yolov8 weights')
parser.add_argument('--opset',
type=int,
default=11,
help='ONNX opset version')
parser.add_argument('--sim',
action='store_true',
help='simplify onnx model')
parser.add_argument('--input-shape',
nargs='+',
type=int,
default=[1, 3, 640, 640],
help='Model input shape only for api builder')
parser.add_argument('--device',
type=str,
default='cpu',
help='Export ONNX device')
args = parser.parse_args()
assert len(args.input_shape) == 4
return args
def main(args):
YOLOv8 = YOLO(args.weights)
model = YOLOv8.model.fuse().eval()
for m in model.modules():
optim(m)
m.to(args.device)
model.to(args.device)
fake_input = torch.randn(args.input_shape).to(args.device)
for _ in range(2):
model(fake_input)
save_path = args.weights.replace('.pt', '.onnx')
with BytesIO() as f:
torch.onnx.export(
model,
fake_input,
f,
opset_version=args.opset,
input_names=['images'],
output_names=['bboxes', 'scores', 'labels', 'maskconf', 'proto'])
f.seek(0)
onnx_model = onnx.load(f)
onnx.checker.check_model(onnx_model)
if args.sim:
try:
onnx_model, check = onnxsim.simplify(onnx_model)
assert check, 'assert check failed'
except Exception as e:
print(f'Simplifier failure: {e}')
onnx.save(onnx_model, save_path)
print(f'ONNX export success, saved as {save_path}')
if __name__ == '__main__':
main(parse_args())