| import torch |
| from annotator.mmpkg.mmcv.cnn import ContextBlock |
|
|
| from ..builder import HEADS |
| from .fcn_head import FCNHead |
|
|
|
|
| @HEADS.register_module() |
| class GCHead(FCNHead): |
| """GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond. |
| |
| This head is the implementation of `GCNet |
| <https://arxiv.org/abs/1904.11492>`_. |
| |
| Args: |
| ratio (float): Multiplier of channels ratio. Default: 1/4. |
| pooling_type (str): The pooling type of context aggregation. |
| Options are 'att', 'avg'. Default: 'avg'. |
| fusion_types (tuple[str]): The fusion type for feature fusion. |
| Options are 'channel_add', 'channel_mul'. Default: ('channel_add',) |
| """ |
|
|
| def __init__(self, |
| ratio=1 / 4., |
| pooling_type='att', |
| fusion_types=('channel_add', ), |
| **kwargs): |
| super(GCHead, self).__init__(num_convs=2, **kwargs) |
| self.ratio = ratio |
| self.pooling_type = pooling_type |
| self.fusion_types = fusion_types |
| self.gc_block = ContextBlock( |
| in_channels=self.channels, |
| ratio=self.ratio, |
| pooling_type=self.pooling_type, |
| fusion_types=self.fusion_types) |
|
|
| def forward(self, inputs): |
| """Forward function.""" |
| x = self._transform_inputs(inputs) |
| output = self.convs[0](x) |
| output = self.gc_block(output) |
| output = self.convs[1](output) |
| if self.concat_input: |
| output = self.conv_cat(torch.cat([x, output], dim=1)) |
| output = self.cls_seg(output) |
| return output |
|
|
