(self, block, layers, num_classes=1000)
| 94 | class ResNet(nn.Module): |
| 95 | |
| 96 | def __init__(self, block, layers, num_classes=1000): |
| 97 | self.inplanes = 64 |
| 98 | super(ResNet, self).__init__() |
| 99 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| 100 | bias=False) |
| 101 | self.bn1 = NN.BatchNorm2d(64) #NN.BatchNorm2d |
| 102 | self.relu = nn.ReLU(inplace=True) |
| 103 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 104 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 105 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| 106 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| 107 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| 108 | #self.avgpool = nn.AvgPool2d(7, stride=1) |
| 109 | #self.fc = nn.Linear(512 * block.expansion, num_classes) |
| 110 | |
| 111 | for m in self.modules(): |
| 112 | if isinstance(m, nn.Conv2d): |
| 113 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
| 114 | elif isinstance(m, nn.BatchNorm2d): |
| 115 | nn.init.constant_(m.weight, 1) |
| 116 | nn.init.constant_(m.bias, 0) |
| 117 | |
| 118 | def _make_layer(self, block, planes, blocks, stride=1): |
| 119 | downsample = None |
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