| 106 | |
| 107 | |
| 108 | class ResNet(nn.Module): |
| 109 | |
| 110 | def __init__(self, block, layers, width=1): |
| 111 | super(ResNet, self).__init__() |
| 112 | self.inplanes = 64 * 2 |
| 113 | self.conv1_v1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) |
| 114 | self.conv1_v2 = nn.Conv2d(2, 64, kernel_size=7, stride=2, padding=3, bias=False) |
| 115 | self.bn1 = nn.BatchNorm2d(self.inplanes) |
| 116 | self.relu = nn.ReLU(inplace=True) |
| 117 | |
| 118 | self.base = int(64 * width) |
| 119 | |
| 120 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 121 | self.layer1 = self._make_layer(block, self.base, layers[0]) |
| 122 | self.layer2 = self._make_layer(block, self.base * 2, layers[1], stride=2) |
| 123 | self.layer3 = self._make_layer(block, self.base * 4, layers[2], stride=2) |
| 124 | self.layer4 = self._make_layer(block, self.base * 8, layers[3], stride=2) |
| 125 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| 126 | # self.fc = nn.Linear(self.base * 8 * block.expansion, low_dim) |
| 127 | # self.l2norm = Normalize(2) |
| 128 | |
| 129 | for m in self.modules(): |
| 130 | if isinstance(m, nn.Conv2d): |
| 131 | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| 132 | m.weight.data.normal_(0, math.sqrt(2. / n)) |
| 133 | elif isinstance(m, nn.BatchNorm2d): |
| 134 | m.weight.data.fill_(1) |
| 135 | m.bias.data.zero_() |
| 136 | |
| 137 | def _make_layer(self, block, planes, blocks, stride=1): |
| 138 | downsample = None |
| 139 | if stride != 1 or self.inplanes != planes * block.expansion: |
| 140 | downsample = nn.Sequential( |
| 141 | nn.Conv2d(self.inplanes, planes * block.expansion, |
| 142 | kernel_size=1, stride=stride, groups=2, bias=False), |
| 143 | nn.BatchNorm2d(planes * block.expansion), |
| 144 | ) |
| 145 | |
| 146 | layers = list([]) |
| 147 | layers.append(block(self.inplanes, planes, stride, downsample)) |
| 148 | self.inplanes = planes * block.expansion |
| 149 | for i in range(1, blocks): |
| 150 | layers.append(block(self.inplanes, planes)) |
| 151 | |
| 152 | return nn.Sequential(*layers) |
| 153 | |
| 154 | def forward(self, x): |
| 155 | x1, x2 = torch.split(x, [1, 2], dim=1) |
| 156 | x1 = self.conv1_v1(x1) |
| 157 | x2 = self.conv1_v2(x2) |
| 158 | x = torch.cat([x1, x2], dim=1) |
| 159 | x = self.bn1(x) |
| 160 | x = self.relu(x) |
| 161 | x = self.maxpool(x) |
| 162 | |
| 163 | x = self.layer1(x) |
| 164 | x = self.layer2(x) |
| 165 | x = self.layer3(x) |