| 57 | expansion = 4 |
| 58 | |
| 59 | def __init__(self, inplanes, planes, stride=1, downsample=None): |
| 60 | super(Bottleneck, self).__init__() |
| 61 | self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| 62 | self.bn1 = BatchNorm2d(planes) |
| 63 | self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
| 64 | padding=1, bias=False) |
| 65 | self.bn2 = BatchNorm2d(planes) |
| 66 | self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| 67 | self.bn3 = BatchNorm2d(planes * 4) |
| 68 | self.relu = nn.ReLU(inplace=True) |
| 69 | self.downsample = downsample |
| 70 | self.stride = stride |
| 71 | |
| 72 | def forward(self, x): |
| 73 | residual = x |