Refactor models/export.py arguments (#3564)
* Refactor models/export.py arguments * cleanup * cleanuppull/3565/head
Glenn Jocher
GitHub
parent
66cf5c28c1
commit
0e5cfdbea7
108
models/export.py
108
models/export.py
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@ -1,4 +1,4 @@
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"""Exports a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
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"""Export a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
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Usage:
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$ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1
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@ -21,42 +21,39 @@ from utils.activations import Hardswish, SiLU
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from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging
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from utils.torch_utils import select_device
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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') # 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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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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parser.add_argument('--opset-version', type=int, default=12, help='ONNX opset version') # 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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def export(weights='./yolov5s.pt', # weights path
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img_size=(640, 640), # image (height, width)
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batch_size=1, # batch size
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device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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include=('torchscript', 'onnx', 'coreml'), # include formats
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half=False, # FP16 half-precision export
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inplace=False, # set YOLOv5 Detect() inplace=True
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train=False, # model.train() mode
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optimize=False, # TorchScript: optimize for mobile
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dynamic=False, # ONNX: dynamic axes
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simplify=False, # ONNX: simplify model
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opset_version=12, # ONNX: opset version
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):
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t = time.time()
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include = [x.lower() for x in include]
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img_size *= 2 if len(img_size) == 1 else 1 # expand
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# Load PyTorch model
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device = select_device(opt.device)
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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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model = attempt_load(opt.weights, map_location=device) # load FP32 model
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device = select_device(device)
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assert not (device.type == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
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model = attempt_load(weights, map_location=device) # load FP32 model
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labels = model.names
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# Input
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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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img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
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img_size = [check_img_size(x, gs) for x in img_size] # verify img_size are gs-multiples
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img = torch.zeros(batch_size, 3, *img_size).to(device) # image size(1,3,320,192) iDetection
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# Update model
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if opt.half:
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if half:
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img, model = img.half(), model.half() # to FP16
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model.train() if opt.train else model.eval() # training mode = no Detect() layer grid construction
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model.train() if train else model.eval() # training mode = no Detect() layer grid construction
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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@ -65,42 +62,42 @@ if __name__ == '__main__':
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elif isinstance(m.act, nn.SiLU):
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m.act = SiLU()
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elif isinstance(m, models.yolo.Detect):
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m.inplace = opt.inplace
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m.onnx_dynamic = opt.dynamic
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m.inplace = inplace
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m.onnx_dynamic = dynamic
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# m.forward = m.forward_export # assign forward (optional)
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for _ in range(2):
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y = model(img) # dry runs
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print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)")
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print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
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# TorchScript export -----------------------------------------------------------------------------------------------
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if 'torchscript' in opt.include or 'coreml' in opt.include:
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if 'torchscript' in include or 'coreml' in 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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f = 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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(optimize_for_mobile(ts) if 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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if 'onnx' in opt.include:
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if 'onnx' in 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=opt.opset_version,
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training=torch.onnx.TrainingMode.TRAINING if opt.train else torch.onnx.TrainingMode.EVAL,
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do_constant_folding=not opt.train,
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f = weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version,
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training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
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do_constant_folding=not train,
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input_names=['images'],
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output_names=['output'],
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dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
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'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
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} if opt.dynamic else None)
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} if dynamic else None)
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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@ -108,7 +105,7 @@ if __name__ == '__main__':
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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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if simplify:
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try:
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check_requirements(['onnx-simplifier'])
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import onnxsim
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@ -116,8 +113,8 @@ if __name__ == '__main__':
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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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dynamic_input_shape=dynamic,
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input_shapes={'images': list(img.shape)} if 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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@ -127,15 +124,15 @@ if __name__ == '__main__':
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print(f'{prefix} export failure: {e}')
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# CoreML export ----------------------------------------------------------------------------------------------------
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if 'coreml' in opt.include:
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if 'coreml' in 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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assert opt.train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
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assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
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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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f = 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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@ -143,3 +140,24 @@ if __name__ == '__main__':
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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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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 (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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parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
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parser.add_argument('--dynamic', action='store_true', help='ONNX: dynamic axes')
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parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
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parser.add_argument('--opset-version', type=int, default=12, help='ONNX: opset version')
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opt = parser.parse_args()
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print(opt)
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set_logging()
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export(**vars(opt))
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