def network_initialization(self, in_channels, out_channels, D):
self.inplanes = self.init_dim
self.conv1 = ME.MinkowskiConvolution(
in_channels, self.inplanes, kernel_size=5, stride=2, dimension=D)
self.bn1 = ME.MinkowskiBatchNorm(self.inplanes)
self.relu = ME.MinkowskiReLU(inplace=True)
self.pool = ME.MinkowskiAvgPooling(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 = ME.MinkowskiConvolution(
self.inplanes, self.inplanes, kernel_size=3, stride=3, dimension=D)
self.bn5 = ME.MinkowskiBatchNorm(self.inplanes)
self.glob_avg = ME.MinkowskiGlobalMaxPooling()
self.final = ME.MinkowskiLinear(self.inplanes, out_channels, bias=True)
def avg_pool(kernel_size,
stride=1,
dilation=1,
conv_type=ConvType.HYPERCUBE,
in_coords_key=None,
D=-1):
assert D > 0, 'Dimension must be a positive integer'
region_type, axis_types, kernel_size = convert_conv_type(conv_type, kernel_size, D)
kernel_generator = ME.KernelGenerator(
kernel_size, stride, dilation, region_type=region_type, axis_types=axis_types, dimension=D)
return ME.MinkowskiAvgPooling(
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
kernel_generator=kernel_generator,
dimension=D)
def network_initialization(self, in_channels, out_channels, D):
self.inplanes = self.INIT_DIM
self.conv1 = ME.MinkowskiConvolution(in_channels,
self.inplanes,
kernel_size=5,
stride=1,
dimension=D)
self.bn1 = ME.MinkowskiBatchNorm(self.inplanes)
self.relu = ME.MinkowskiReLU(inplace=True)
self.pool = ME.MinkowskiAvgPooling(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.glob_avg = ME.MinkowskiGlobalPooling(dimension=D)
self.classification_block = nn.Sequential(
ME.MinkowskiLinear(self.inplanes, self.inplanes, bias=False),
ME.MinkowskiBatchNorm(self.inplanes), ME.MinkowskiReLU(),
ME.MinkowskiLinear(self.inplanes, self.inplanes, bias=False),
ME.MinkowskiBatchNorm(self.inplanes))
self.final = ME.MinkowskiLinear(self.inplanes, out_channels, bias=True)
