profile (bool): Print the computation time of each layer if True, defaults to False. Args: x (torch.Tensor): The input tensor to the model. _predict_once ( x, profile, visualize, embed ) def _predict_once ( self, x, profile = False, visualize = False, embed = None ): ''' Perform a forward pass through the network. Returns: (torch.Tensor): The last output of the model. embed (list, optional): A list of feature vectors/embeddings to return. augment (bool): Augment image during prediction, defaults to False. visualize (bool): Save the feature maps of the model if True, defaults to False. predict ( x, * args, ** kwargs ) def predict ( self, x, profile = False, visualize = False, augment = False, embed = None ): ''' Perform a forward pass through the network. loss ( x, * args, ** kwargs ) return self. ''' if isinstance ( x, dict ): # for cases of training and validating while training. Returns: (torch.Tensor): The output of the network. Args: x (torch.Tensor | dict): The input image tensor or a dict including image tensor and gt labels. Module ): '''The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family.''' def forward ( self, x, * args, ** kwargs ): ''' Forward pass of the model on a single scale.

Ultra model nn set