| 72 | |
| 73 | |
| 74 | class ResNet(nn.Module): |
| 75 | def __init__(self, block, num_blocks, num_classes, nf, bias): |
| 76 | super(ResNet, self).__init__() |
| 77 | self.in_planes = nf |
| 78 | |
| 79 | self.conv1 = conv3x3(3, nf * 1) |
| 80 | self.bn1 = nn.BatchNorm2d(nf * 1) |
| 81 | self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1) |
| 82 | self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2) |
| 83 | self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2) |
| 84 | self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2) |
| 85 | print("BIAS IS", bias) |
| 86 | self.linear = nn.Linear(nf * 8 * block.expansion, num_classes, bias=bias) |
| 87 | |
| 88 | def _make_layer(self, block, planes, num_blocks, stride): |
| 89 | strides = [stride] + [1] * (num_blocks - 1) |
| 90 | layers = [] |
| 91 | for stride in strides: |
| 92 | layers.append(block(self.in_planes, planes, stride)) |
| 93 | self.in_planes = planes * block.expansion |
| 94 | return nn.Sequential(*layers) |
| 95 | |
| 96 | def forward(self, x): |
| 97 | bsz = x.size(0) |
| 98 | out = relu(self.bn1(self.conv1(x.view(bsz, 3, 32, 32)))) |
| 99 | out = self.layer1(out) |
| 100 | out = self.layer2(out) |
| 101 | out = self.layer3(out) |
| 102 | out = self.layer4(out) |
| 103 | out = avg_pool2d(out, 4) |
| 104 | out = out.view(out.size(0), -1) |
| 105 | out = self.linear(out) |
| 106 | return out |
| 107 | |
| 108 | |
| 109 | def ResNet18(nclasses, nf=20, bias=True): |