| 101 | |
| 102 | |
| 103 | class ResNet(nn.Module): |
| 104 | |
| 105 | def __init__(self, block, layers, num_classes=1000,deep_base=False,stem_width=32): |
| 106 | self.inplanes = stem_width*2 if deep_base else 64 |
| 107 | |
| 108 | super(ResNet, self).__init__() |
| 109 | if deep_base: |
| 110 | self.conv1= nn.Sequential( |
| 111 | nn.Conv2d(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False), |
| 112 | nn.BatchNorm2d(stem_width), |
| 113 | nn.ReLU(inplace=True), |
| 114 | nn.Conv2d(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False), |
| 115 | nn.BatchNorm2d(stem_width), |
| 116 | nn.ReLU(inplace=True), |
| 117 | nn.Conv2d(stem_width, stem_width*2, kernel_size=3, stride=1, padding=1, bias=False), |
| 118 | ) |
| 119 | else: |
| 120 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| 121 | bias=False) |
| 122 | |
| 123 | self.bn1 = nn.BatchNorm2d(self.inplanes) |
| 124 | self.relu = nn.ReLU(inplace=True) |
| 125 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 126 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 127 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| 128 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| 129 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| 130 | self.avgpool = nn.AvgPool2d(7, stride=1) |
| 131 | self.fc = nn.Linear(512 * block.expansion, num_classes) |
| 132 | |
| 133 | for m in self.modules(): |
| 134 | if isinstance(m, nn.Conv2d): |
| 135 | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| 136 | m.weight.data.normal_(0, math.sqrt(2. / n)) |
| 137 | elif isinstance(m, nn.BatchNorm2d): |
| 138 | m.weight.data.fill_(1) |
| 139 | m.bias.data.zero_() |
| 140 | |
| 141 | def _make_layer(self, block, planes, blocks, stride=1): |
| 142 | downsample = None |
| 143 | if stride != 1 or self.inplanes != planes * block.expansion: |
| 144 | downsample = nn.Sequential( |
| 145 | nn.Conv2d(self.inplanes, planes * block.expansion, |
| 146 | kernel_size=1, stride=stride, bias=False), |
| 147 | nn.BatchNorm2d(planes * block.expansion), |
| 148 | ) |
| 149 | |
| 150 | layers = [] |
| 151 | layers.append(block(self.inplanes, planes, stride, downsample)) |
| 152 | self.inplanes = planes * block.expansion |
| 153 | for i in range(1, blocks): |
| 154 | layers.append(block(self.inplanes, planes)) |
| 155 | |
| 156 | return nn.Sequential(*layers) |
| 157 | |
| 158 | def forward(self, x): |
| 159 | x = self.conv1(x) |
| 160 | x = self.bn1(x) |