Refactor `export.py` (#4080)
* Refactor `export.py` * cleanup * Update check_requirements() * Update export.py
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Glenn Jocher
GitHub
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0cc7c58787
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442a7abdf2
148
export.py
148
export.py
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@ -24,6 +24,78 @@ from utils.general import colorstr, check_img_size, check_requirements, file_siz
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from utils.torch_utils import select_device
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from utils.torch_utils import select_device
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def export_torchscript(model, img, file, optimize):
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# TorchScript model 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 = file.with_suffix('.torchscript.pt')
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ts = torch.jit.trace(model, img, strict=False)
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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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return ts
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except Exception as e:
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print(f'{prefix} export failure: {e}')
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def export_onnx(model, img, file, opset_version, train, dynamic, simplify):
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# ONNX model export
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prefix = colorstr('ONNX:')
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try:
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check_requirements(('onnx', 'onnx-simplifier'))
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import onnx
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print(f'{prefix} starting export with onnx {onnx.__version__}...')
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f = file.with_suffix('.onnx')
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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 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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# Simplify
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if simplify:
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try:
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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(
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model_onnx,
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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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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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def export_coreml(ts_model, img, file, train):
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# CoreML model 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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print(f'{prefix} starting export with coremltools {ct.__version__}...')
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f = file.with_suffix('.mlmodel')
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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_model, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
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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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def run(weights='./yolov5s.pt', # weights path
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def run(weights='./yolov5s.pt', # weights path
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img_size=(640, 640), # image (height, width)
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img_size=(640, 640), # image (height, width)
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batch_size=1, # batch size
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batch_size=1, # batch size
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@ -40,12 +112,13 @@ def run(weights='./yolov5s.pt', # weights path
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t = time.time()
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t = time.time()
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include = [x.lower() for x in include]
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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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img_size *= 2 if len(img_size) == 1 else 1 # expand
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file = Path(weights)
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# Load PyTorch model
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# Load PyTorch model
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device = select_device(device)
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device = select_device(device)
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assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
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assert not (device.type == 'cpu' and 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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model = attempt_load(weights, map_location=device) # load FP32 model
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labels = model.names
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names = model.names
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# Input
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# Input
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gs = int(max(model.stride)) # grid size (max stride)
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gs = int(max(model.stride)) # grid size (max stride)
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@ -57,7 +130,6 @@ def run(weights='./yolov5s.pt', # weights path
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img, model = img.half(), model.half() # to FP16
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img, model = img.half(), model.half() # to FP16
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model.train() if 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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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, Conv): # assign export-friendly activations
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if isinstance(m, Conv): # assign export-friendly activations
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if isinstance(m.act, nn.Hardswish):
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if isinstance(m.act, nn.Hardswish):
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m.act = Hardswish()
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m.act = Hardswish()
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@ -72,73 +144,13 @@ def run(weights='./yolov5s.pt', # weights path
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y = model(img) # dry runs
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y = model(img) # dry runs
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print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(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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# Exports
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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 = 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 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 include:
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if 'onnx' in include:
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prefix = colorstr('ONNX:')
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export_onnx(model, img, file, opset_version, train, dynamic, simplify)
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try:
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if 'torchscript' in include or 'coreml' in include:
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import onnx
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ts = export_torchscript(model, img, file, optimize)
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if 'coreml' in include:
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print(f'{prefix} starting export with onnx {onnx.__version__}...')
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export_coreml(ts, img, file, 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 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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# 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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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=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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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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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 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 = 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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# 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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print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
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