| 238 | expansion = 4 |
| 239 | |
| 240 | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, |
| 241 | base_width=64, dilation=1, norm_layer=None, |
| 242 | *, reduction=16): |
| 243 | super(SEBottleneck, self).__init__() |
| 244 | if norm_layer is None: |
| 245 | norm_layer= nn.BatchNorm2d |
| 246 | self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| 247 | self.bn1 = norm_layer(planes) |
| 248 | self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) |
| 249 | self.bn2 = norm_layer(planes) |
| 250 | self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| 251 | self.bn3 = norm_layer(planes * 4) |
| 252 | self.relu = nn.ReLU(inplace=True) |
| 253 | self.se = SELayer(planes * 4, reduction) |
| 254 | self.downsample = downsample |
| 255 | self.stride = stride |
| 256 | |
| 257 | def forward(self, x): |
| 258 | residual = x |