| 83 | return out |
| 84 | |
| 85 | class ResNet(nn.Module): |
| 86 | def __init__(self, |
| 87 | block, |
| 88 | blocks_num, |
| 89 | num_classes=1000, |
| 90 | include_top=True, |
| 91 | groups=1, |
| 92 | width_per_group=64): |
| 93 | super(ResNet, self).__init__() |
| 94 | self.include_top = include_top |
| 95 | self.in_channel = 64 |
| 96 | |
| 97 | self.groups = groups |
| 98 | self.width_per_group = width_per_group |
| 99 | |
| 100 | self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, |
| 101 | padding=3, bias=False) |
| 102 | self.bn1 = nn.BatchNorm2d(self.in_channel) |
| 103 | self.relu = nn.ReLU(inplace=True) |
| 104 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 105 | self.layer1 = self._make_layer(block, 64, blocks_num[0]) |
| 106 | self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) |
| 107 | self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) |
| 108 | self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) |
| 109 | if self.include_top: |
| 110 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| 111 | self.fc = nn.Linear(512 * block.expansion, num_classes) |
| 112 | |
| 113 | for m in self.modules(): |
| 114 | if isinstance(m, nn.Conv2d): |
| 115 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # pytorch新版本已经更新nn.init.kaiming_nomal为nn.kaiming_normal_ |
| 116 | |
| 117 | def _make_layer(self, block, channel, block_num, stride=1): |
| 118 | downsample = None |
| 119 | if stride != 1 or self.in_channel != channel * block.expansion: |
| 120 | downsample = nn.Sequential( |
| 121 | nn.Conv2d(self.in_channel, channel*block.expansion, kernel_size=1, stride=stride, bias=False), |
| 122 | nn.BatchNorm2d(channel * block.expansion)) |
| 123 | layers = [] |
| 124 | layers.append(block(self.in_channel, |
| 125 | channel, |
| 126 | downsample = downsample, |
| 127 | stride = stride, |
| 128 | groups = self.groups, |
| 129 | width_per_group = self.width_per_group)) |
| 130 | self.in_channel = channel * block.expansion |
| 131 | |
| 132 | for _ in range(1, block_num): |
| 133 | layers.append(block(self.in_channel, |
| 134 | channel, |
| 135 | groups=self.groups, |
| 136 | width_per_group=self.width_per_group)) |
| 137 | |
| 138 | return nn.Sequential(*layers) |
| 139 | |
| 140 | def forward(self, x): |
| 141 | x = self.conv1(x) |
| 142 | x = self.bn1(x) |
no outgoing calls
no test coverage detected