| 107 | |
| 108 | |
| 109 | class WRN(nn.Module): |
| 110 | def __init__(self, num_blocks, in_dims, out_dims, wide=10): |
| 111 | super(WRN, self).__init__() |
| 112 | self.in_planes = 16 |
| 113 | self.wide = wide |
| 114 | |
| 115 | block = BasicBlock |
| 116 | |
| 117 | self.conv1 = nn.Conv2d(in_dims, self.in_planes, kernel_size=3, stride=1, padding=1, bias=False) |
| 118 | self.bn1 = nn.BatchNorm2d(16) |
| 119 | self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1) |
| 120 | self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2) |
| 121 | self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2) |
| 122 | self.avgpool = nn.AdaptiveAvgPool2d((1,1)) |
| 123 | self.linear = nn.Linear(64*wide, out_dims) |
| 124 | |
| 125 | def _make_layer(self, block, planes, num_blocks, stride): |
| 126 | strides = [stride] + [1]*(num_blocks-1) |
| 127 | layers = [] |
| 128 | for stride in strides: |
| 129 | layers.append(block(self.in_planes, planes, stride, self.wide)) |
| 130 | self.in_planes = planes * self.wide * block.expansion |
| 131 | |
| 132 | return nn.Sequential(*layers) |
| 133 | |
| 134 | def forward(self, x): |
| 135 | out = F.relu(self.bn1(self.conv1(x))) |
| 136 | out = self.layer1(out) |
| 137 | out = self.layer2(out) |
| 138 | out = self.layer3(out) |
| 139 | # out = F.avg_pool2d(out, 8) |
| 140 | # out = out.view(out.shape[0], -1) |
| 141 | out = self.avgpool(out) |
| 142 | out = torch.flatten(out, 1) |
| 143 | out = self.linear(out) |
| 144 | return out |
| 145 | |
| 146 | |
| 147 | def resnet18(in_dims, out_dims): |