(self, block, layers, width=1)
| 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 |
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