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