def forward_train(self, img, img_metas, gt_semantic_seg):
        """Forward function for training.

        Args:
            img (Tensor): Input images.
            img_metas (list[dict]): List of image info dict where each dict
                has: 'img_shape', 'scale_factor', 'flip', and may also contain
                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                For details on the values of these keys see
                `mmseg/datasets/pipelines/formatting.py:Collect`.
            gt_semantic_seg (Tensor): Semantic segmentation masks
                used if the architecture supports semantic segmentation task.

        Returns:
            dict[str, Tensor]: a dictionary of loss components
        """

        x = self.extract_feat(img)

        losses = dict()

        loss_decode = self._decode_head_forward_train(x, img_metas,
                                                      gt_semantic_seg)
        losses.update(loss_decode)

        if self.with_auxiliary_head:
            loss_aux = self._auxiliary_head_forward_train(
                x, img_metas, gt_semantic_seg)
            losses.update(loss_aux)

        return losses
Forward function for training.

        Args:
            img (Tensor): Input images.
            img_metas (list[dict]): List of image info dict where each dict
                has: 'img_shape', 'scale_factor', 'flip', and may also contain
                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                For details on the values of these keys see
                `mmseg/datasets/pipelines/formatting.py:Collect`.
            gt_semantic_seg (Tensor): Semantic segmentation masks
                used if the architecture supports semantic segmentation task.

        Returns:
            dict[str, Tensor]: a dictionary of loss components
        
ControlNet-v1-1-nightly/annotator/uniformer/mmseg/models/segmentors/encoder_decoder.py