def run(weights='./yolov5s.pt', # weights path img_size=(640, 640), # image (height, width) batch_size=1, # batch size device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu include=('torchscript', 'onnx', 'coreml'), # include formats half=False, # FP16 half-precision export inplace=False, # set YOLOv5 Detect() inplace=True train=False, # model.train() mode optimize=False, # TorchScript: optimize for mobile dynamic=False, # ONNX: dynamic axes simplify=False, # ONNX: simplify model opset=12, # ONNX: opset version ): 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 names = model.names # Input gs = int(max(model.stride)) # grid size (max stride) img_size = [check_img_size(x, gs) for x in img_size] # verify img_size are gs-multiples img = torch.zeros(batch_size, 3, *img_size).to(device) # image size(1,3,320,192) iDetection # Update model if half: 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(): if isinstance(m, Conv): # assign export-friendly activations if isinstance(m.act, nn.Hardswish): m.act = Hardswish() elif isinstance(m.act, nn.SiLU): m.act = SiLU() elif isinstance(m, Detect): m.inplace = inplace m.onnx_dynamic = dynamic # m.forward = m.forward_export # assign forward (optional) for _ in range(2): y = model(img) # dry runs print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)") # Exports if 'torchscript' in include: export_torchscript(model, img, file, optimize) if 'onnx' in include: export_onnx(model, img, file, opset, train, dynamic, simplify) if 'coreml' in include: export_coreml(model, img, file) # Finish print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
def yolov5_onnx(pretrained=False, progress=True, num_classes=80, **kwargs): model = yolov5s(pretrained=pretrained, progress=progress, num_classes=num_classes, **kwargs) for m in model.modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if isinstance(m, Conv) and isinstance(m.act, nn.Hardswish): m.act = Hardswish() # assign activation return model
def __init__(self, model_path: str, reso: int = 640, cuda: bool = False): self.device = torch.device("cuda:0" if cuda else "cpu") self.half = self.device.type != "cpu" self.reso = (reso, reso) self.cuda = cuda self.model = attempt_load(model_path, map_location=self.device) # Update model for k, m in self.model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish): m.act = Hardswish() # assign activation self.names = ( self.model.module.names if hasattr(self.model, "module") else self.model.names ) if self.half: self.model.half()
def load_model(weights, device): # Load model # model = attempt_load(weights, map_location=device) # load FP32 model with open('data/coco128.yaml') as f: data_dict = yaml.load(f, Loader=yaml.FullLoader) model = Model('models/yolov5s.yaml').to(device) model.names = data_dict['names'] model = model.fuse().eval() ckpt = torch.load(weights, map_location=device) ckpt['model'].float().fuse().eval() print({ k: (v.shape, model.state_dict()[k].shape) for k, v in ckpt['model'].float().state_dict().items() if model.state_dict()[k].shape != v.shape }) print({ k: (v.shape, ckpt['model'].float().state_dict()[k].shape) for k, v in model.state_dict().items() if ckpt['model'].float().state_dict()[k].shape != v.shape }) ckpt['model'] = { k: v for k, v in ckpt['model'].float().state_dict().items() if model.state_dict()[k].shape == v.shape } model.load_state_dict(ckpt['model'], strict=False) for k, m in model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability if isinstance(m, Conv) and isinstance(m.act, nn.Hardswish): m.act = Hardswish() if isinstance(m, Conv) and isinstance(m.act, nn.SiLU): m.act = SiLU() return model
gs = int(max(model.stride)) # grid size (max stride) opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples print('opt:{}'.format(opt)) # Input img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection # Update model for k, m in model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if isinstance( m, models.common.Conv): # assign export-friendly activations if isinstance(m.act, nn.Hardswish): m.act = Hardswish() elif isinstance(m.act, nn.SiLU): m.act = SiLU() # elif isinstance(m, models.yolo.Detect): # m.forward = m.forward_export # assign forward (optional) model.model[-1].export = True # set Detect() layer export=True y = model(img) # dry run # TorchScript export try: print('\nStarting TorchScript export with torch %s...' % torch.__version__) f = opt.weights.replace('.pt', '.torchscript.pt') # filename ts = torch.jit.trace(model, img) ts.save(f) print('TorchScript export success, saved as %s' % f)
set_logging() # Input img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection # Load PyTorch model model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model # Update model for k, m in model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability if isinstance(m, models.common.Conv) and isinstance( m.act, nn.Hardswish): m.act = Hardswish() # assign activation # if isinstance(m, models.yolo.Detect): # m.forward = m.forward_export # assign forward (optional) model.model[-1].export = True # set Detect() layer export=True y = model(img) # dry run # TorchScript export try: print('\nStarting TorchScript export with torch %s...' % torch.__version__) f = opt.weights.replace('.pt', '.torchscript.pt') # filename ts = torch.jit.trace(model, img) ts.save(f) print('TorchScript export success, saved as %s' % f) except Exception as e: print('TorchScript export failure: %s' % e)
def run( weights='./yolov5s.pt', # weights path img_size=(640, 640), # image (height, width) batch_size=1, # batch size device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu include=('torchscript', 'onnx', 'coreml'), # include formats half=False, # FP16 half-precision export inplace=False, # set YOLOv5 Detect() inplace=True train=False, # model.train() mode optimize=False, # TorchScript: optimize for mobile dynamic=False, # ONNX: dynamic axes simplify=False, # ONNX: simplify model opset_version=12, # ONNX: opset version ): t = time.time() include = [x.lower() for x in include] img_size *= 2 if len(img_size) == 1 else 1 # expand # Load PyTorch model device = select_device(device) assert not ( device.type == 'cpu' and opt.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 # Input gs = int(max(model.stride)) # grid size (max stride) img_size = [check_img_size(x, gs) for x in img_size] # verify img_size are gs-multiples img = torch.zeros(batch_size, 3, *img_size).to( device) # image size(1,3,320,192) iDetection # Update model if half: 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() elif isinstance(m.act, nn.SiLU): m.act = SiLU() elif isinstance(m, Detect): m.inplace = inplace m.onnx_dynamic = dynamic # m.forward = m.forward_export # assign forward (optional) for _ in range(2): 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 ------------------------------------------------------------------------------------------------------ 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}') # Finish print( f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.' )
def hardswish(input, inplace=False): return OriginalHardswish.forward(input)