(self, block, layers, last_conv_stride=2, last_conv_dilation=1)
| 95 | class ResNet(nn.Module): |
| 96 | |
| 97 | def __init__(self, block, layers, last_conv_stride=2, last_conv_dilation=1): |
| 98 | |
| 99 | self.inplanes = 64 |
| 100 | super(ResNet, self).__init__() |
| 101 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| 102 | bias=False) |
| 103 | self.bn1 = nn.BatchNorm2d(64) |
| 104 | self.relu = nn.ReLU(inplace=True) |
| 105 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 106 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 107 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| 108 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| 109 | self.layer4 = self._make_layer(block, 512, layers[3], stride=last_conv_stride, dilation=last_conv_dilation) |
| 110 | |
| 111 | for m in self.modules(): |
| 112 | if isinstance(m, nn.Conv2d): |
| 113 | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| 114 | m.weight.data.normal_(0, math.sqrt(2. / n)) |
| 115 | elif isinstance(m, nn.BatchNorm2d): |
| 116 | m.weight.data.fill_(1) |
| 117 | m.bias.data.zero_() |
| 118 | |
| 119 | def _make_layer(self, block, planes, blocks, stride=1, dilation=1): |
| 120 | downsample = None |
no test coverage detected