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# Exports a pytorch *.pt model to *.onnx format
# Example usage (run from ./yolov5 directory):
# $ 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', type=str, default='./weights/yolov5s.pt', help='weights path')
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
opt = parser.parse_args()
print(opt)
# 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()
# 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)
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