File size: 1,490 Bytes
7a00a5e df7988d 7a00a5e df7988d |
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 |
# Exports a pytorch *.pt model to *.onnx format. Example usage:
# $ export PYTHONPATH="$PWD"
# $ python models/onnx_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
import argparse
import onnx
from models.common import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default='./weights/yolov5s.pt', help='model path RELATIVE to ./models/')
parser.add_argument('--img-size', default=640, help='inference size (pixels)')
parser.add_argument('--batch-size', default=1, help='batch size')
opt = parser.parse_args()
# Parameters
f = opt.weights.replace('.pt', '.onnx') # onnx filename
img = torch.zeros((opt.batch_size, 3, opt.img_size, opt.img_size)) # image size, (1, 3, 320, 192) iDetection
# Load pytorch model
google_utils.attempt_download(opt.weights)
model = torch.load(opt.weights)['model']
model.eval()
# model.fuse() # optionally fuse Conv2d + BatchNorm2d layers TODO
# Export to onnx
model.model[-1].export = True # set Detect() layer export=True
torch.onnx.export(model, img, f, verbose=False, opset_version=11)
# Check onnx model
model = onnx.load(f) # load onnx model
onnx.checker.check_model(model) # check onnx model
print(onnx.helper.printable_graph(model.graph)) # print a human readable representation of the graph
print('Export complete. ONNX model saved to %s\nView with https://github.com/lutzroeder/netron' % f)
|