| 41 | |
| 42 | |
| 43 | class Bottleneck(nn.Module): |
| 44 | expansion = 4 |
| 45 | |
| 46 | def __init__(self, in_planes, planes, stride=1): |
| 47 | super(Bottleneck, self).__init__() |
| 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, |
| 51 | stride=stride, padding=1, bias=False) |
| 52 | self.bn2 = nn.BatchNorm2d(planes) |
| 53 | self.conv3 = nn.Conv2d(planes, self.expansion * |
| 54 | planes, kernel_size=1, bias=False) |
| 55 | self.bn3 = nn.BatchNorm2d(self.expansion*planes) |
| 56 | |
| 57 | self.shortcut = nn.Sequential() |
| 58 | if stride != 1 or in_planes != self.expansion*planes: |
| 59 | self.shortcut = nn.Sequential( |
| 60 | nn.Conv2d(in_planes, self.expansion*planes, |
| 61 | kernel_size=1, stride=stride, bias=False), |
| 62 | nn.BatchNorm2d(self.expansion*planes) |
| 63 | ) |
| 64 | self.relu = nn.ReLU(inplace=False) |
| 65 | |
| 66 | def forward(self, x): |
| 67 | out = self.relu(self.bn1(self.conv1(x))) |
| 68 | out = self.relu(self.bn2(self.conv2(out))) |
| 69 | out = self.bn3(self.conv3(out)) |
| 70 | out = out + self.shortcut(x) |
| 71 | out = self.relu(out) |
| 72 | return out |
| 73 | |
| 74 | |
| 75 | class ResNet(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected