| 33 | |
| 34 | class ResNet(nn.Module): |
| 35 | def __init__(self, block, layers, num_classes=1000, in_channels=3): |
| 36 | super().__init__() |
| 37 | self.in_channels = 64 |
| 38 | |
| 39 | self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False) |
| 40 | self.bn1 = nn.BatchNorm2d(64) |
| 41 | self.relu = nn.ReLU(inplace=True) |
| 42 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 43 | |
| 44 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 45 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| 46 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| 47 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| 48 | |
| 49 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| 50 | self.fc = nn.Linear(512, num_classes) |
| 51 | |
| 52 | def _make_layer(self, block, out_channels, blocks, stride=1): |
| 53 | downsample = None |