| 43 | expansion = 4 |
| 44 | |
| 45 | def __init__(self, in_planes, planes, stride=1, is_last=False): |
| 46 | super(Bottleneck, self).__init__() |
| 47 | self.is_last = is_last |
| 48 | self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) |
| 49 | self.bn1 = nn.BatchNorm2d(planes) |
| 50 | self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) |
| 51 | self.bn2 = nn.BatchNorm2d(planes) |
| 52 | self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) |
| 53 | self.bn3 = nn.BatchNorm2d(self.expansion * planes) |
| 54 | |
| 55 | self.shortcut = nn.Sequential() |
| 56 | if stride != 1 or in_planes != self.expansion * planes: |
| 57 | self.shortcut = nn.Sequential( |
| 58 | nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), |
| 59 | nn.BatchNorm2d(self.expansion * planes) |
| 60 | ) |
| 61 | |
| 62 | def forward(self, x): |
| 63 | out = F.relu(self.bn1(self.conv1(x))) |