def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
bn_momentum=0.1,
leakiness=0,
dimension=-1):
super(BasicBlock, self).__init__()
assert dimension > 0
self.conv1 = ME.MinkowskiConvolution(inplanes,
planes,
kernel_size=3,
stride=stride,
dilation=dilation,
dimension=dimension)
self.norm1 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
self.conv2 = ME.MinkowskiConvolution(planes,
planes,
kernel_size=3,
stride=1,
dilation=dilation,
dimension=dimension)
self.norm2 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
self.relu = ME.MinkowskiLeakyReLU(negative_slope=leakiness)
self.downsample = downsample
def get_conv_block(self, in_channel, out_channel, kernel_size, stride):
return nn.Sequential(
ME.MinkowskiConvolution(
in_channel,
out_channel,
kernel_size=kernel_size,
stride=stride,
dimension=self.D,
),
ME.MinkowskiBatchNorm(out_channel),
ME.MinkowskiLeakyReLU(),
)
def get_mlp_block(self, in_channel, out_channel):
return nn.Sequential(
ME.MinkowskiLinear(in_channel, out_channel, bias=False),
ME.MinkowskiBatchNorm(out_channel),
ME.MinkowskiLeakyReLU(),
)
def network_initialization(self, in_channels, out_channels, D):
# Output of the first conv concated to conv6
self.inplanes = self.INIT_DIM
self.conv0p1s1 = ME.MinkowskiConvolution(
in_channels, self.inplanes, kernel_size=5, dimension=D)
self.bn0 = ME.MinkowskiBatchNorm(self.inplanes)
self.conv1p1s2 = ME.MinkowskiConvolution(
self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D)
self.bn1 = ME.MinkowskiBatchNorm(self.inplanes)
self.block1 = self._make_layer(self.BLOCK, self.PLANES[0],
self.LAYERS[0], leakiness=self.leakiness)
self.conv2p2s2 = ME.MinkowskiConvolution(
self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D)
self.bn2 = ME.MinkowskiBatchNorm(self.inplanes)
self.block2 = self._make_layer(self.BLOCK, self.PLANES[1],
self.LAYERS[1], leakiness=self.leakiness)
self.conv3p4s2 = ME.MinkowskiConvolution(
self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D)
self.bn3 = ME.MinkowskiBatchNorm(self.inplanes)
self.block3 = self._make_layer(self.BLOCK, self.PLANES[2],
self.LAYERS[2], leakiness=self.leakiness)
self.conv4p8s2 = ME.MinkowskiConvolution(
self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D)
self.bn4 = ME.MinkowskiBatchNorm(self.inplanes)
self.block4 = self._make_layer(self.BLOCK, self.PLANES[3],
self.LAYERS[3], leakiness=self.leakiness)
self.convtr4p16s2 = ME.MinkowskiConvolutionTranspose(
self.inplanes, self.PLANES[4], kernel_size=2, stride=2, dimension=D)
self.bntr4 = ME.MinkowskiBatchNorm(self.PLANES[4])
self.inplanes = self.PLANES[4]
if self.skipconnections:
self.inplanes += self.PLANES[2] * self.BLOCK.expansion
self.block5 = self._make_layer(self.BLOCK, self.PLANES[4],
self.LAYERS[4], leakiness=self.leakiness)
self.convtr5p8s2 = ME.MinkowskiConvolutionTranspose(
self.inplanes, self.PLANES[5], kernel_size=2, stride=2, dimension=D)
self.bntr5 = ME.MinkowskiBatchNorm(self.PLANES[5])
self.inplanes = self.PLANES[5] #+ self.PLANES[1] * self.BLOCK.expansion
if self.skipconnections:
self.inplanes += self.PLANES[1] * self.BLOCK.expansion
self.block6 = self._make_layer(self.BLOCK, self.PLANES[5],
self.LAYERS[5], leakiness=self.leakiness)
self.convtr6p4s2 = ME.MinkowskiConvolutionTranspose(
self.inplanes, self.PLANES[6], kernel_size=2, stride=2, dimension=D)
self.bntr6 = ME.MinkowskiBatchNorm(self.PLANES[6])
self.inplanes = self.PLANES[6] #+ self.PLANES[0] * self.BLOCK.expansion
if self.skipconnections:
self.inplanes += self.PLANES[0] * self.BLOCK.expansion
self.block7 = self._make_layer(self.BLOCK, self.PLANES[6],
self.LAYERS[6], leakiness=self.leakiness)
self.convtr7p2s2 = ME.MinkowskiConvolutionTranspose(
self.inplanes, self.PLANES[7], kernel_size=2, stride=2, dimension=D)
self.bntr7 = ME.MinkowskiBatchNorm(self.PLANES[7])
self.inplanes = self.PLANES[7] #+ self.INIT_DIM
if self.skipconnections:
self.inplanes += self.INIT_DIM
self.block8 = self._make_layer(self.BLOCK, self.PLANES[7],
self.LAYERS[7], leakiness=self.leakiness)
self.final = ME.MinkowskiConvolution(
self.PLANES[7],
out_channels,
kernel_size=1,
has_bias=True,
dimension=D)
self.relu = ME.MinkowskiLeakyReLU(negative_slope=self.leakiness)
self.dropout = ME.MinkowskiDropout(self.dropout_p)
