Add --optimize argument (#3093)
Browse filesFix for c++ runtime errors in https://github.com/ultralytics/yolov5/issues/2973
- models/export.py +2 -2
models/export.py
CHANGED
@@ -30,6 +30,7 @@ if __name__ == '__main__':
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parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
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parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
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parser.add_argument('--train', action='store_true', help='model.train() mode')
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parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
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parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only
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opt = parser.parse_args()
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@@ -78,7 +79,7 @@ if __name__ == '__main__':
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print(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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ts = torch.jit.trace(model, img, strict=False)
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-
optimize_for_mobile(ts).save(f)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'{prefix} export failure: {e}')
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@@ -123,7 +124,6 @@ if __name__ == '__main__':
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import coremltools as ct
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print(f'{prefix} starting export with coremltools {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='image', 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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parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
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parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
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parser.add_argument('--train', action='store_true', help='model.train() mode')
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+
parser.add_argument('--optimize', action='store_true', help='optimize TorchScript for mobile') # TorchScript-only
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parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
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parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only
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opt = parser.parse_args()
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print(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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ts = torch.jit.trace(model, img, strict=False)
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+
(optimize_for_mobile(ts) if opt.optimize else ts).save(f)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'{prefix} export failure: {e}')
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import coremltools as ct
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print(f'{prefix} starting export with coremltools {ct.__version__}...')
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model = ct.convert(ts, inputs=[ct.ImageType(name='image', 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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