| 198 | |
| 199 | |
| 200 | class SEBasicBlock(nn.Module): |
| 201 | expansion = 1 |
| 202 | |
| 203 | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, |
| 204 | base_width=64, dilation=1, norm_layer=None, |
| 205 | *, reduction=16): |
| 206 | super(SEBasicBlock, self).__init__() |
| 207 | if norm_layer is None: |
| 208 | norm_layer = nn.BatchNorm2d |
| 209 | self.conv1 = conv3x3(inplanes, planes, stride) |
| 210 | self.bn1 = norm_layer(planes) |
| 211 | self.relu = nn.ReLU(inplace=True) |
| 212 | self.conv2 = conv3x3(planes, planes, 1) |
| 213 | self.bn2 = norm_layer(planes) |
| 214 | self.se = SELayer(planes, reduction) |
| 215 | self.downsample = downsample |
| 216 | self.stride = stride |
| 217 | |
| 218 | def forward(self, x): |
| 219 | residual = x |
| 220 | out = self.conv1(x) |
| 221 | out = self.bn1(out) |
| 222 | out = self.relu(out) |
| 223 | |
| 224 | out = self.conv2(out) |
| 225 | out = self.bn2(out) |
| 226 | out = self.se(out) |
| 227 | |
| 228 | if self.downsample is not None: |
| 229 | residual = self.downsample(x) |
| 230 | |
| 231 | out += residual |
| 232 | out = self.relu(out) |
| 233 | |
| 234 | return out |
| 235 | |
| 236 | |
| 237 | class SEBottleneck(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected