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