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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)
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