(self, num_blocks, in_dims, out_dims, wide=10)
| 108 | |
| 109 | class WRN(nn.Module): |
| 110 | def __init__(self, num_blocks, in_dims, out_dims, wide=10): |
| 111 | super(WRN, self).__init__() |
| 112 | self.in_planes = 16 |
| 113 | self.wide = wide |
| 114 | |
| 115 | block = BasicBlock |
| 116 | |
| 117 | self.conv1 = nn.Conv2d(in_dims, self.in_planes, kernel_size=3, stride=1, padding=1, bias=False) |
| 118 | self.bn1 = nn.BatchNorm2d(16) |
| 119 | self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1) |
| 120 | self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2) |
| 121 | self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2) |
| 122 | self.avgpool = nn.AdaptiveAvgPool2d((1,1)) |
| 123 | self.linear = nn.Linear(64*wide, out_dims) |
| 124 | |
| 125 | def _make_layer(self, block, planes, num_blocks, stride): |
| 126 | strides = [stride] + [1]*(num_blocks-1) |
no test coverage detected