| 131 | |
| 132 | |
| 133 | class ResNet(nn.Module): |
| 134 | |
| 135 | def __init__(self, latent_size, depth, basicblock=False, norm_type="bin", inputchannels=4): |
| 136 | super(ResNet, self).__init__() |
| 137 | # Model type specifies number of layers for CIFAR-10 model |
| 138 | assert (depth - 2) % 6 == 0, 'depth should be 6n+2' |
| 139 | n = (depth - 2) // 6 |
| 140 | |
| 141 | if basicblock: |
| 142 | block = BasicBlock |
| 143 | else: |
| 144 | block = Bottleneck if depth >=44 else BasicBlock |
| 145 | |
| 146 | if norm_type == 'bn': |
| 147 | from torch.nn import BatchNorm2d |
| 148 | self.normlayer = functools.partial(BatchNorm2d, affine=True) |
| 149 | elif norm_type == 'in': |
| 150 | from torch.nn import InstanceNorm2d |
| 151 | self.normlayer = functools.partial(InstanceNorm2d, affine=True) |
| 152 | elif norm_type == 'bin': |
| 153 | self.normlayer = functools.partial(BatchInstanceNorm2d, affine=True) |
| 154 | else: |
| 155 | raise ValueError('Normalization should be either of type') |
| 156 | |
| 157 | |
| 158 | |
| 159 | self.inplanes = 64 |
| 160 | self.conv1 = nn.Conv2d(inputchannels, 64, kernel_size=3, padding=1, |
| 161 | bias=False) |
| 162 | self.bn1 = self.normlayer(64) |
| 163 | self.relu = nn.ReLU(inplace=True) |
| 164 | self.layer1 = self._make_layer(block, 64, n) |
| 165 | self.layer2 = self._make_layer(block, 128, n, stride=2) |
| 166 | self.layer3 = self._make_layer(block, 256, n, stride=2) |
| 167 | self.layer4 = self._make_layer(block, 256, n, stride=2) |
| 168 | |
| 169 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| 170 | self.fc = nn.Linear(256 * block.expansion, latent_size) |
| 171 | |
| 172 | for m in self.modules(): |
| 173 | if isinstance(m, nn.Conv2d): |
| 174 | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| 175 | m.weight.data.normal_(0, math.sqrt(2. / n)) |
| 176 | elif isinstance(m, self.normlayer.func): |
| 177 | m.weight.data.fill_(1) |
| 178 | m.bias.data.zero_() |
| 179 | |
| 180 | def _make_layer(self, block, planes, blocks, stride=1): |
| 181 | downsample = None |
| 182 | if stride != 1 or self.inplanes != planes * block.expansion: |
| 183 | downsample = nn.Sequential( |
| 184 | nn.Conv2d(self.inplanes, planes * block.expansion, |
| 185 | kernel_size=1, stride=stride, bias=False), |
| 186 | self.normlayer(planes * block.expansion), |
| 187 | ) |
| 188 | |
| 189 | layers = [] |
| 190 | layers.append(block(self.inplanes, planes, stride, downsample, normlayer=self.normlayer)) |
nothing calls this directly
no outgoing calls
no test coverage detected