| 27 | expansion = 1 |
| 28 | |
| 29 | def __init__(self, in_planes, planes, stride=1): |
| 30 | super(BasicBlock, self).__init__() |
| 31 | self.conv1 = M.Conv2d( |
| 32 | in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False |
| 33 | ) |
| 34 | self.bn1 = M.BatchNorm2d(planes) |
| 35 | self.conv2 = M.Conv2d( |
| 36 | planes, planes, kernel_size=3, stride=1, padding=1, bias=False |
| 37 | ) |
| 38 | self.bn2 = M.BatchNorm2d(planes) |
| 39 | self.shortcut = M.Sequential() |
| 40 | if stride != 1 or in_planes != planes: |
| 41 | self.shortcut = M.Sequential( |
| 42 | M.Conv2d( |
| 43 | in_planes, |
| 44 | self.expansion * planes, |
| 45 | kernel_size=1, |
| 46 | stride=stride, |
| 47 | bias=False, |
| 48 | ), |
| 49 | M.BatchNorm2d(self.expansion * planes), |
| 50 | ) |
| 51 | |
| 52 | def forward(self, x): |
| 53 | out = F.relu(self.bn1(self.conv1(x))) |