def create(name, pretrained, channels, classes): """Creates a specified YOLOv5 model Arguments: name (str): name of model, i.e. 'yolov5s' pretrained (bool): load pretrained weights into the model channels (int): number of input channels classes (int): number of model classes Returns: pytorch model """ config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name) # model.yaml path try: model = Model(config, channels, classes) if pretrained: ckpt = '%s.pt' % name # checkpoint filename attempt_download(ckpt) # download if not found locally state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict() # to FP32 state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter model.load_state_dict(state_dict, strict=False) # load m = NMS() m.f = -1 # from m.i = model.model[-1].i + 1 # index model.model.add_module(name='%s' % m.i, module=m) # add NMS model.eval() return model except Exception as e: help_url = 'https://github.com/ultralytics/yolov5/issues/36' s = 'Cache maybe be out of date, deleting cache and retrying may solve this. See %s for help.' % help_url raise Exception(s) from e
def add_nms(self): # fuse model Conv2d() + BatchNorm2d() layers if type(self.model[-1]) is not NMS: # if missing NMS print('Adding NMS module... ') m = NMS() # module m.f = -1 # from m.i = self.model[-1].i + 1 # index self.model.add_module(name='%s' % m.i, module=m) # add return self
def nms(self, mode=True): # add or remove NMS module present = type(self.model[-1]) is NMS # last layer is NMS if mode and not present: print('Adding NMS... ') m = NMS() # module m.f = -1 # from m.i = self.model[-1].i + 1 # index self.model.add_module(name='%s' % m.i, module=m) # add self.eval() elif not mode and present: print('Removing NMS... ') self.model = self.model[:-1] # remove return self