| import annotator.mmpkg.mmcv as mmcv |
| import torch.nn as nn |
| from annotator.mmpkg.mmcv.cnn import ConvModule |
|
|
| from .make_divisible import make_divisible |
|
|
|
|
| class SELayer(nn.Module): |
| """Squeeze-and-Excitation Module. |
| |
| Args: |
| channels (int): The input (and output) channels of the SE layer. |
| ratio (int): Squeeze ratio in SELayer, the intermediate channel will be |
| ``int(channels/ratio)``. Default: 16. |
| conv_cfg (None or dict): Config dict for convolution layer. |
| Default: None, which means using conv2d. |
| act_cfg (dict or Sequence[dict]): Config dict for activation layer. |
| If act_cfg is a dict, two activation layers will be configured |
| by this dict. If act_cfg is a sequence of dicts, the first |
| activation layer will be configured by the first dict and the |
| second activation layer will be configured by the second dict. |
| Default: (dict(type='ReLU'), dict(type='HSigmoid', bias=3.0, |
| divisor=6.0)). |
| """ |
|
|
| def __init__(self, |
| channels, |
| ratio=16, |
| conv_cfg=None, |
| act_cfg=(dict(type='ReLU'), |
| dict(type='HSigmoid', bias=3.0, divisor=6.0))): |
| super(SELayer, self).__init__() |
| if isinstance(act_cfg, dict): |
| act_cfg = (act_cfg, act_cfg) |
| assert len(act_cfg) == 2 |
| assert mmcv.is_tuple_of(act_cfg, dict) |
| self.global_avgpool = nn.AdaptiveAvgPool2d(1) |
| self.conv1 = ConvModule( |
| in_channels=channels, |
| out_channels=make_divisible(channels // ratio, 8), |
| kernel_size=1, |
| stride=1, |
| conv_cfg=conv_cfg, |
| act_cfg=act_cfg[0]) |
| self.conv2 = ConvModule( |
| in_channels=make_divisible(channels // ratio, 8), |
| out_channels=channels, |
| kernel_size=1, |
| stride=1, |
| conv_cfg=conv_cfg, |
| act_cfg=act_cfg[1]) |
|
|
| def forward(self, x): |
| out = self.global_avgpool(x) |
| out = self.conv1(out) |
| out = self.conv2(out) |
| return x * out |
|
|
