(self, block, layers, num_classes=1000)
| 138 | Resnet Global Module for Initialization |
| 139 | """ |
| 140 | def __init__(self, block, layers, num_classes=1000): |
| 141 | self.inplanes = 64 |
| 142 | super(ResNet, self).__init__() |
| 143 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| 144 | bias=False) |
| 145 | self.bn1 = mynn.Norm2d(64) |
| 146 | self.relu = nn.ReLU(inplace=True) |
| 147 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 148 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 149 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| 150 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| 151 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| 152 | self.avgpool = nn.AvgPool2d(7, stride=1) |
| 153 | self.fc = nn.Linear(512 * block.expansion, num_classes) |
| 154 | |
| 155 | for m in self.modules(): |
| 156 | if isinstance(m, nn.Conv2d): |
| 157 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
| 158 | elif isinstance(m, nn.BatchNorm2d): |
| 159 | nn.init.constant_(m.weight, 1) |
| 160 | nn.init.constant_(m.bias, 0) |
| 161 | |
| 162 | def _make_layer(self, block, planes, blocks, stride=1): |
| 163 | downsample = None |
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