(self, block, layers, num_classes=1000)
| 95 | class ResNet(nn.Module): |
| 96 | |
| 97 | def __init__(self, block, layers, num_classes=1000): |
| 98 | self.inplanes = 128 |
| 99 | super(ResNet, self).__init__() |
| 100 | self.conv1 = conv3x3(3, 64, stride=2) |
| 101 | self.bn1 = BatchNorm2d(64) |
| 102 | self.relu1 = nn.ReLU(inplace=True) |
| 103 | self.conv2 = conv3x3(64, 64) |
| 104 | self.bn2 = BatchNorm2d(64) |
| 105 | self.relu2 = nn.ReLU(inplace=True) |
| 106 | self.conv3 = conv3x3(64, 128) |
| 107 | self.bn3 = BatchNorm2d(128) |
| 108 | self.relu3 = nn.ReLU(inplace=True) |
| 109 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 110 | |
| 111 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 112 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| 113 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| 114 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| 115 | self.avgpool = nn.AvgPool2d(7, stride=1) |
| 116 | self.fc = nn.Linear(512 * block.expansion, num_classes) |
| 117 | |
| 118 | for m in self.modules(): |
| 119 | if isinstance(m, nn.Conv2d): |
| 120 | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| 121 | m.weight.data.normal_(0, math.sqrt(2. / n)) |
| 122 | elif isinstance(m, BatchNorm2d): |
| 123 | m.weight.data.fill_(1) |
| 124 | m.bias.data.zero_() |
| 125 | |
| 126 | def _make_layer(self, block, planes, blocks, stride=1): |
| 127 | downsample = None |
no test coverage detected