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75 lines
2.8 KiB
75 lines
2.8 KiB
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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import torch
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from utils.google_utils import attempt_download
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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opt = parser.parse_args()
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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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print(opt)
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# Input
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img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection
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# Load PyTorch model
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attempt_download(opt.weights)
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model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
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model.eval()
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model.model[-1].export = True # set Detect() layer export=True
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y = model(img) # dry run
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# TorchScript export
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try:
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print('\nStarting TorchScript export with torch %s...' % torch.__version__)
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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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ts = torch.jit.trace(model, img)
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ts.save(f)
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print('TorchScript export success, saved as %s' % f)
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except Exception as e:
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print('TorchScript export failure: %s' % e)
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# ONNX export
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try:
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import onnx
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print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
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f = opt.weights.replace('.pt', '.onnx') # filename
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model.fuse() # only for ONNX
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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output_names=['classes', 'boxes'] if y is None else ['output'])
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# Checks
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onnx_model = onnx.load(f) # load onnx model
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onnx.checker.check_model(onnx_model) # check onnx model
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print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
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print('ONNX export success, saved as %s' % f)
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except Exception as e:
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print('ONNX export failure: %s' % e)
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# CoreML export
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try:
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import coremltools as ct
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print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
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# convert model from torchscript and apply pixel scaling as per detect.py
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model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
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f = opt.weights.replace('.pt', '.mlmodel') # filename
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model.save(f)
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print('CoreML export success, saved as %s' % f)
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except Exception as e:
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print('CoreML export failure: %s' % e)
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# Finish
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print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')
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