(self,
in_filters,
filters,
batch_norm=True,
activation=None,
octave_conv=False)
| 154 | class DilatedDCNNV2(nn.Module): |
| 155 | |
| 156 | def __init__(self, |
| 157 | in_filters, |
| 158 | filters, |
| 159 | batch_norm=True, |
| 160 | activation=None, |
| 161 | octave_conv=False): # not supported |
| 162 | super(DilatedDCNNV2, self).__init__() |
| 163 | |
| 164 | if octave_conv: |
| 165 | raise NotImplemented( |
| 166 | "Octave convolution not implemented in Pytorch version of Transnet!") |
| 167 | |
| 168 | assert not (octave_conv and batch_norm) |
| 169 | |
| 170 | self.Conv3D_1 = Conv3DConfigurable( |
| 171 | in_filters, filters, 1, use_bias=not batch_norm) |
| 172 | self.Conv3D_2 = Conv3DConfigurable( |
| 173 | in_filters, filters, 2, use_bias=not batch_norm) |
| 174 | self.Conv3D_4 = Conv3DConfigurable( |
| 175 | in_filters, filters, 4, use_bias=not batch_norm) |
| 176 | self.Conv3D_8 = Conv3DConfigurable( |
| 177 | in_filters, filters, 8, use_bias=not batch_norm) |
| 178 | |
| 179 | self.bn = nn.BatchNorm3d(filters * 4, eps=1e-3) if batch_norm else None |
| 180 | self.activation = activation |
| 181 | |
| 182 | def forward(self, inputs): |
| 183 | conv1 = self.Conv3D_1(inputs) |
nothing calls this directly
no test coverage detected