(self, pic, lbl)
Note that labels will not be normalized to [0, 1].
        Args:
            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
            lbl (PIL Image or numpy.ndarray): Label to be converted to tensor. 
        Returns:
            Tensor: Converted image and label
Note that labels will not be normalized to [0, 1].
        Args:
            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
            lbl (PIL Image or numpy.ndarray): Label to be converted to tensor. 
        Returns:
            Tensor: Converted image and label
Note that labels will not be normalized to [ 0 1 ] . Args : pic ( PIL Image or numpy . ndarray ) : Image to be converted to tensor . lbl ( PIL Image or numpy . ndarray ) : Label to be converted to tensor . Returns : Tensor : Converted image and label
def __call__(self, pic, lbl):
        """
        Note that labels will not be normalized to [0, 1].
        Args:
            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
            lbl (PIL Image or numpy.ndarray): Label to be converted to tensor. 
        Returns:
            Tensor: Converted image and label
        """
        if self.normalize:
            return F.to_tensor(pic), torch.from_numpy( np.array( lbl, dtype=self.target_type) )
        else:
            return torch.from_numpy( np.array( pic, dtype=np.float32).transpose(2, 0, 1) ), torch.from_numpy( np.array( lbl, dtype=self.target_type) )
def __call__ ( self , pic , lbl ) : if self . normalize : return F . to_tensor ( pic ) , torch . from_numpy ( np . array ( lbl , dtype = self . target_type ) ) else : return torch . from_numpy ( np . array ( pic , dtype = np . float32 ) . transpose ( 2 , 0 , 1 ) ) , torch . from_numpy ( np . array ( lbl , dtype = self . target_type ) )