Add --include torchscript onnx coreml argument (#3137)
* Allow users to skip exporting in formats that they don't care about * Correct comments * Update export.py renamed --skip-format to --exclude * Switched format from exclude to include (as instructed by @glenn-jocher) * cleanup Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Cristi FatiandGlenn Jocher authored and GitHub committed
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@@ -1,7 +1,7 @@
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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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"""Exports a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1
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$ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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@@ -27,6 +27,7 @@ if __name__ == '__main__':
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats')
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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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@@ -35,6 +36,7 @@ if __name__ == '__main__':
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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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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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opt.include = [x.lower() for x in opt.include]
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print(opt)
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set_logging()
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t = time.time()
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@@ -47,7 +49,7 @@ if __name__ == '__main__':
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# Checks
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gs = int(max(model.stride)) # grid size (max stride)
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opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
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assert not (opt.device.lower() == "cpu" and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
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assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
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# Input
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img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
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@@ -74,62 +76,66 @@ if __name__ == '__main__':
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print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)")
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# TorchScript export -----------------------------------------------------------------------------------------------
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prefix = colorstr('TorchScript:')
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try:
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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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if 'torchscript' in opt.include or 'coreml' in opt.include:
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prefix = colorstr('TorchScript:')
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try:
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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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# ONNX export ------------------------------------------------------------------------------------------------------
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prefix = colorstr('ONNX:')
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try:
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import onnx
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if 'onnx' in opt.include:
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prefix = colorstr('ONNX:')
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try:
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import onnx
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print(f'{prefix} starting export with onnx {onnx.__version__}...')
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f = opt.weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
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'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
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print(f'{prefix} starting export with onnx {onnx.__version__}...')
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f = opt.weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
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'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# print(onnx.helper.printable_graph(model_onnx.graph)) # print
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# print(onnx.helper.printable_graph(model_onnx.graph)) # print
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# Simplify
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if opt.simplify:
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try:
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check_requirements(['onnx-simplifier'])
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import onnxsim
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# Simplify
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if opt.simplify:
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try:
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check_requirements(['onnx-simplifier'])
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import onnxsim
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print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(model_onnx,
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dynamic_input_shape=opt.dynamic,
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input_shapes={'images': list(img.shape)} if opt.dynamic else None)
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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print(f'{prefix} simplifier failure: {e}')
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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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print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(
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model_onnx,
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dynamic_input_shape=opt.dynamic,
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input_shapes={'images': list(img.shape)} if opt.dynamic else None)
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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print(f'{prefix} simplifier failure: {e}')
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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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# CoreML export ----------------------------------------------------------------------------------------------------
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prefix = colorstr('CoreML:')
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try:
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import coremltools as ct
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if 'coreml' in opt.include:
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prefix = colorstr('CoreML:')
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try:
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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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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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print(f'{prefix} starting export with coremltools {ct.__version__}...')
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model = ct.convert(ts, inputs=[ct.ImageType('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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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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# Finish
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print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
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