(self, block, num_blocks, num_classes=10, cfg=None)
| 148 | |
| 149 | class ResNet_basic(nn.Module): |
| 150 | def __init__(self, block, num_blocks, num_classes=10, cfg=None): |
| 151 | super(ResNet_basic, self).__init__() |
| 152 | self.train_sup = (num_classes > 0) |
| 153 | |
| 154 | self.in_planes = 16 |
| 155 | self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) |
| 156 | self.bn1 = nn.BatchNorm2d(16, affine=True) |
| 157 | self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1) |
| 158 | self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2) |
| 159 | self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2) |
| 160 | self.output_dim = 512*block.expansion |
| 161 | if(self.train_sup): |
| 162 | self.linear = nn.Linear(64*block.expansion, num_classes) |
| 163 | |
| 164 | def _make_layer(self, block, planes, num_blocks, stride): |
| 165 | strides = [stride] + [1]*(num_blocks-1) |
nothing calls this directly
no test coverage detected