| 19 | """ |
| 20 | |
| 21 | def __init__(self, inplanes, outplanes, stride): |
| 22 | super(ResNetLayer, self).__init__() |
| 23 | self.conv1a = nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False) |
| 24 | self.bn1a = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001) |
| 25 | self.conv2a = nn.Conv2d(outplanes, outplanes, kernel_size=3, stride=1, padding=1, bias=False) |
| 26 | self.stride = stride |
| 27 | self.downsample = nn.Conv2d(inplanes, outplanes, kernel_size=(1,1), stride=stride, bias=False) |
| 28 | self.outbna = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001) |
| 29 | |
| 30 | self.conv1b = nn.Conv2d(outplanes, outplanes, kernel_size=3, stride=1, padding=1, bias=False) |
| 31 | self.bn1b = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001) |
| 32 | self.conv2b = nn.Conv2d(outplanes, outplanes, kernel_size=3, stride=1, padding=1, bias=False) |
| 33 | self.outbnb = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001) |
| 34 | return |
| 35 | |
| 36 | |
| 37 | def forward(self, inputBatch): |