def network_initialization(self, in_channels, out_channels, D):
self.inplanes = self.INIT_DIM
self.conv1 = nn.Sequential(
ME.MinkowskiConvolution(in_channels,
self.inplanes,
kernel_size=3,
stride=2,
dimension=D),
ME.MinkowskiBatchNorm(self.inplanes),
ME.MinkowskiReLU(inplace=True),
ME.MinkowskiMaxPooling(kernel_size=2, stride=2, dimension=D),
)
self.layer1 = self._make_layer(self.BLOCK,
self.PLANES[0],
self.LAYERS[0],
stride=2)
self.layer2 = self._make_layer(self.BLOCK,
self.PLANES[1],
self.LAYERS[1],
stride=2)
self.layer3 = self._make_layer(self.BLOCK,
self.PLANES[2],
self.LAYERS[2],
stride=2)
self.layer4 = self._make_layer(self.BLOCK,
self.PLANES[3],
self.LAYERS[3],
stride=2)
self.conv5 = nn.Sequential(
ME.MinkowskiDropout(),
ME.MinkowskiConvolution(self.inplanes,
self.inplanes,
kernel_size=3,
stride=3,
dimension=D),
ME.MinkowskiBatchNorm(self.inplanes),
ME.MinkowskiGELU(),
)
self.glob_pool = ME.MinkowskiGlobalMaxPooling()
self.final = ME.MinkowskiLinear(self.inplanes, out_channels, bias=True)
