| 124 | m.bias.data.zero_() |
| 125 | |
| 126 | def _make_layer(self, block, planes, blocks, stride=1): |
| 127 | downsample = None |
| 128 | if stride != 1 or self.inplanes != planes * block.expansion: |
| 129 | downsample = nn.Sequential( |
| 130 | nn.Conv2d(self.inplanes, planes * block.expansion, |
| 131 | kernel_size=1, stride=stride, bias=False), |
| 132 | BatchNorm2d(planes * block.expansion), |
| 133 | ) |
| 134 | |
| 135 | layers = [] |
| 136 | layers.append(block(self.inplanes, planes, stride, downsample)) |
| 137 | self.inplanes = planes * block.expansion |
| 138 | for i in range(1, blocks): |
| 139 | layers.append(block(self.inplanes, planes)) |
| 140 | |
| 141 | return nn.Sequential(*layers) |
| 142 | |
| 143 | def forward(self, x): |
| 144 | x = self.relu1(self.bn1(self.conv1(x))) |