| from abc import ABCMeta, abstractmethod |
|
|
| from .decode_head import BaseDecodeHead |
|
|
|
|
| class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta): |
| """Base class for cascade decode head used in |
| :class:`CascadeEncoderDecoder.""" |
|
|
| def __init__(self, *args, **kwargs): |
| super(BaseCascadeDecodeHead, self).__init__(*args, **kwargs) |
|
|
| @abstractmethod |
| def forward(self, inputs, prev_output): |
| """Placeholder of forward function.""" |
| pass |
|
|
| def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg, |
| train_cfg): |
| """Forward function for training. |
| Args: |
| inputs (list[Tensor]): List of multi-level img features. |
| prev_output (Tensor): The output of previous decode head. |
| 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. |
| train_cfg (dict): The training config. |
| |
| Returns: |
| dict[str, Tensor]: a dictionary of loss components |
| """ |
| seg_logits = self.forward(inputs, prev_output) |
| losses = self.losses(seg_logits, gt_semantic_seg) |
|
|
| return losses |
|
|
| def forward_test(self, inputs, prev_output, img_metas, test_cfg): |
| """Forward function for testing. |
| |
| Args: |
| inputs (list[Tensor]): List of multi-level img features. |
| prev_output (Tensor): The output of previous decode head. |
| 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`. |
| test_cfg (dict): The testing config. |
| |
| Returns: |
| Tensor: Output segmentation map. |
| """ |
| return self.forward(inputs, prev_output) |
|
|
