| 46 | |
| 47 | |
| 48 | class BasicBlock(nn.Module): |
| 49 | expansion = 1 |
| 50 | |
| 51 | def __init__(self, in_planes, planes, stride=1): |
| 52 | super(BasicBlock, self).__init__() |
| 53 | self.conv1 = conv3x3(in_planes, planes, stride) |
| 54 | self.bn1 = nn.BatchNorm2d(planes) |
| 55 | self.conv2 = conv3x3(planes, planes) |
| 56 | self.bn2 = nn.BatchNorm2d(planes) |
| 57 | |
| 58 | self.shortcut = nn.Sequential() |
| 59 | if stride != 1 or in_planes != self.expansion * planes: |
| 60 | self.shortcut = nn.Sequential( |
| 61 | nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, |
| 62 | stride=stride, bias=False), |
| 63 | nn.BatchNorm2d(self.expansion * planes) |
| 64 | ) |
| 65 | |
| 66 | def forward(self, x): |
| 67 | out = relu(self.bn1(self.conv1(x))) |
| 68 | out = self.bn2(self.conv2(out)) |
| 69 | out += self.shortcut(x) |
| 70 | out = relu(out) |
| 71 | return out |
| 72 | |
| 73 | |
| 74 | class ResNet(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected