diff --git a/export.py b/export.py index b7ff0748ba93..34cd21449bc0 100644 --- a/export.py +++ b/export.py @@ -24,6 +24,78 @@ from utils.torch_utils import select_device +def export_torchscript(model, img, file, optimize): + # TorchScript model export + prefix = colorstr('TorchScript:') + try: + print(f'\n{prefix} starting export with torch {torch.__version__}...') + f = file.with_suffix('.torchscript.pt') + ts = torch.jit.trace(model, img, strict=False) + (optimize_for_mobile(ts) if optimize else ts).save(f) + print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') + return ts + except Exception as e: + print(f'{prefix} export failure: {e}') + + +def export_onnx(model, img, file, opset_version, train, dynamic, simplify): + # ONNX model export + prefix = colorstr('ONNX:') + try: + check_requirements(('onnx', 'onnx-simplifier')) + import onnx + + print(f'{prefix} starting export with onnx {onnx.__version__}...') + f = file.with_suffix('.onnx') + torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version, + training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL, + do_constant_folding=not train, + input_names=['images'], + output_names=['output'], + dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640) + 'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85) + } if dynamic else None) + + # Checks + model_onnx = onnx.load(f) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + # print(onnx.helper.printable_graph(model_onnx.graph)) # print + + # Simplify + if simplify: + try: + import onnxsim + + print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') + model_onnx, check = onnxsim.simplify( + model_onnx, + dynamic_input_shape=dynamic, + input_shapes={'images': list(img.shape)} if dynamic else None) + assert check, 'assert check failed' + onnx.save(model_onnx, f) + except Exception as e: + print(f'{prefix} simplifier failure: {e}') + print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') + except Exception as e: + print(f'{prefix} export failure: {e}') + + +def export_coreml(ts_model, img, file, train): + # CoreML model export + prefix = colorstr('CoreML:') + try: + import coremltools as ct + + print(f'{prefix} starting export with coremltools {ct.__version__}...') + f = file.with_suffix('.mlmodel') + assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`' + model = ct.convert(ts_model, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) + model.save(f) + print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') + except Exception as e: + print(f'{prefix} export failure: {e}') + + def run(weights='./yolov5s.pt', # weights path img_size=(640, 640), # image (height, width) batch_size=1, # batch size @@ -40,12 +112,13 @@ def run(weights='./yolov5s.pt', # weights path t = time.time() include = [x.lower() for x in include] img_size *= 2 if len(img_size) == 1 else 1 # expand + file = Path(weights) # Load PyTorch model device = select_device(device) assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0' model = attempt_load(weights, map_location=device) # load FP32 model - labels = model.names + names = model.names # Input gs = int(max(model.stride)) # grid size (max stride) @@ -57,7 +130,6 @@ def run(weights='./yolov5s.pt', # weights path img, model = img.half(), model.half() # to FP16 model.train() if train else model.eval() # training mode = no Detect() layer grid construction for k, m in model.named_modules(): - m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if isinstance(m, Conv): # assign export-friendly activations if isinstance(m.act, nn.Hardswish): m.act = Hardswish() @@ -72,73 +144,13 @@ def run(weights='./yolov5s.pt', # weights path y = model(img) # dry runs print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)") - # TorchScript export ----------------------------------------------------------------------------------------------- - if 'torchscript' in include or 'coreml' in include: - prefix = colorstr('TorchScript:') - try: - print(f'\n{prefix} starting export with torch {torch.__version__}...') - f = weights.replace('.pt', '.torchscript.pt') # filename - ts = torch.jit.trace(model, img, strict=False) - (optimize_for_mobile(ts) if optimize else ts).save(f) - print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - print(f'{prefix} export failure: {e}') - - # ONNX export ------------------------------------------------------------------------------------------------------ + # Exports if 'onnx' in include: - prefix = colorstr('ONNX:') - try: - import onnx - - print(f'{prefix} starting export with onnx {onnx.__version__}...') - f = weights.replace('.pt', '.onnx') # filename - torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version, - training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL, - do_constant_folding=not train, - input_names=['images'], - output_names=['output'], - dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640) - 'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85) - } if dynamic else None) - - # Checks - model_onnx = onnx.load(f) # load onnx model - onnx.checker.check_model(model_onnx) # check onnx model - # print(onnx.helper.printable_graph(model_onnx.graph)) # print - - # Simplify - if simplify: - try: - check_requirements(['onnx-simplifier']) - import onnxsim - - print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') - model_onnx, check = onnxsim.simplify( - model_onnx, - dynamic_input_shape=dynamic, - input_shapes={'images': list(img.shape)} if dynamic else None) - assert check, 'assert check failed' - onnx.save(model_onnx, f) - except Exception as e: - print(f'{prefix} simplifier failure: {e}') - print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - print(f'{prefix} export failure: {e}') - - # CoreML export ---------------------------------------------------------------------------------------------------- - if 'coreml' in include: - prefix = colorstr('CoreML:') - try: - import coremltools as ct - - print(f'{prefix} starting export with coremltools {ct.__version__}...') - assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`' - model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) - f = weights.replace('.pt', '.mlmodel') # filename - model.save(f) - print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - print(f'{prefix} export failure: {e}') + export_onnx(model, img, file, opset_version, train, dynamic, simplify) + if 'torchscript' in include or 'coreml' in include: + ts = export_torchscript(model, img, file, optimize) + if 'coreml' in include: + export_coreml(ts, img, file, train) # Finish print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')