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mmdet.models.utils.se_layer 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule


[文档]class SELayer(BaseModule): """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 configurated by this dict. If act_cfg is a sequence of dicts, the first activation layer will be configurated by the first dict and the second activation layer will be configurated by the second dict. Default: (dict(type='ReLU'), dict(type='Sigmoid')) init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ def __init__(self, channels, ratio=16, conv_cfg=None, act_cfg=(dict(type='ReLU'), dict(type='Sigmoid')), init_cfg=None): super(SELayer, self).__init__(init_cfg) 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=int(channels / ratio), kernel_size=1, stride=1, conv_cfg=conv_cfg, act_cfg=act_cfg[0]) self.conv2 = ConvModule( in_channels=int(channels / ratio), out_channels=channels, kernel_size=1, stride=1, conv_cfg=conv_cfg, act_cfg=act_cfg[1])
[文档] def forward(self, x): out = self.global_avgpool(x) out = self.conv1(out) out = self.conv2(out) return x * out
[文档]class DyReLU(BaseModule): """Dynamic ReLU (DyReLU) module. See `Dynamic ReLU <https://arxiv.org/abs/2003.10027>`_ for details. Current implementation is specialized for task-aware attention in DyHead. HSigmoid arguments in default act_cfg follow DyHead official code. https://github.com/microsoft/DynamicHead/blob/master/dyhead/dyrelu.py Args: channels (int): The input (and output) channels of DyReLU module. ratio (int): Squeeze ratio in Squeeze-and-Excitation-like module, the intermediate channel will be ``int(channels/ratio)``. Default: 4. 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 configurated by this dict. If act_cfg is a sequence of dicts, the first activation layer will be configurated by the first dict and the second activation layer will be configurated by the second dict. Default: (dict(type='ReLU'), dict(type='HSigmoid', bias=3.0, divisor=6.0)) init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ def __init__(self, channels, ratio=4, conv_cfg=None, act_cfg=(dict(type='ReLU'), dict(type='HSigmoid', bias=3.0, divisor=6.0)), init_cfg=None): super().__init__(init_cfg=init_cfg) 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.channels = channels self.expansion = 4 # for a1, b1, a2, b2 self.global_avgpool = nn.AdaptiveAvgPool2d(1) self.conv1 = ConvModule( in_channels=channels, out_channels=int(channels / ratio), kernel_size=1, stride=1, conv_cfg=conv_cfg, act_cfg=act_cfg[0]) self.conv2 = ConvModule( in_channels=int(channels / ratio), out_channels=channels * self.expansion, kernel_size=1, stride=1, conv_cfg=conv_cfg, act_cfg=act_cfg[1])
[文档] def forward(self, x): """Forward function.""" coeffs = self.global_avgpool(x) coeffs = self.conv1(coeffs) coeffs = self.conv2(coeffs) - 0.5 # value range: [-0.5, 0.5] a1, b1, a2, b2 = torch.split(coeffs, self.channels, dim=1) a1 = a1 * 2.0 + 1.0 # [-1.0, 1.0] + 1.0 a2 = a2 * 2.0 # [-1.0, 1.0] out = torch.max(x * a1 + b1, x * a2 + b2) return out
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