def __init__(self,
inc,
outc,
inner_inc,
inner_outc,
inner_module=None,
depth=1,
bn_momentum=0.05,
dimension=-1):
ME.MinkowskiNetwork.__init__(self, dimension)
self.depth = depth
self.conv = nn.Sequential(
conv_norm_non(
inc,
inner_inc,
3,
2,
dimension,
region_type=self.REGION_TYPE,
norm_type=self.NORM_TYPE,
nonlinearity=self.NONLINEARITY), *[
get_block(
self.NORM_TYPE,
inner_inc,
inner_inc,
bn_momentum=bn_momentum,
region_type=self.REGION_TYPE,
dimension=dimension) for d in range(depth)
])
self.inner_module = inner_module
self.convtr = nn.Sequential(
conv_tr(
in_channels=inner_outc,
out_channels=inner_outc,
kernel_size=3,
stride=2,
dilation=1,
has_bias=False,
region_type=self.REGION_TYPE,
dimension=dimension),
get_norm(
self.NORM_TYPE, inner_outc, bn_momentum=bn_momentum, dimension=dimension),
get_nonlinearity(self.NONLINEARITY))
self.cat_conv = conv_norm_non(
inner_outc + inc,
outc,
1,
1,
dimension,
norm_type=self.NORM_TYPE,
nonlinearity=self.NONLINEARITY)
def __init__(self,
in_channels=3,
out_channels=32,
bn_momentum=0.1,
conv1_kernel_size=3,
normalize_feature=False,
D=3):
ME.MinkowskiNetwork.__init__(self, D)
NORM_TYPE = self.NORM_TYPE
BLOCK_NORM_TYPE = self.BLOCK_NORM_TYPE
CHANNELS = self.CHANNELS
TR_CHANNELS = self.TR_CHANNELS
REGION_TYPE = self.REGION_TYPE
self.normalize_feature = normalize_feature
self.conv1 = conv(
in_channels=in_channels,
out_channels=CHANNELS[1],
kernel_size=conv1_kernel_size,
stride=1,
dilation=1,
has_bias=False,
region_type=ME.RegionType.HYPERCUBE,
dimension=D)
self.norm1 = get_norm(NORM_TYPE, CHANNELS[1], bn_momentum=bn_momentum, dimension=D)
self.block1 = get_block(
BLOCK_NORM_TYPE,
CHANNELS[1],
CHANNELS[1],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D)
self.pool2 = ME.MinkowskiSumPooling(kernel_size=2, stride=2, dimension=D)
self.conv2 = conv(
in_channels=CHANNELS[1],
out_channels=CHANNELS[2],
kernel_size=3,
stride=1,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
self.norm2 = get_norm(NORM_TYPE, CHANNELS[2], bn_momentum=bn_momentum, dimension=D)
self.block2 = get_block(
BLOCK_NORM_TYPE,
CHANNELS[2],
CHANNELS[2],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D)
self.pool3 = ME.MinkowskiSumPooling(kernel_size=2, stride=2, dimension=D)
self.conv3 = conv(
in_channels=CHANNELS[2],
out_channels=CHANNELS[3],
kernel_size=3,
stride=1,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
self.norm3 = get_norm(NORM_TYPE, CHANNELS[3], bn_momentum=bn_momentum, dimension=D)
self.block3 = get_block(
BLOCK_NORM_TYPE,
CHANNELS[3],
CHANNELS[3],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D)
self.pool4 = ME.MinkowskiSumPooling(kernel_size=2, stride=2, dimension=D)
self.conv4 = conv(
in_channels=CHANNELS[3],
out_channels=CHANNELS[4],
kernel_size=3,
stride=1,
dilation=1,
has_bias=False,
region_type=ME.RegionType.HYPERCUBE,
dimension=D)
self.norm4 = get_norm(NORM_TYPE, CHANNELS[4], bn_momentum=bn_momentum, dimension=D)
self.block4 = get_block(
BLOCK_NORM_TYPE,
CHANNELS[4],
CHANNELS[4],
bn_momentum=bn_momentum,
region_type=ME.RegionType.HYPERCUBE,
dimension=D)
self.conv4_tr = conv_tr(
in_channels=CHANNELS[4],
out_channels=TR_CHANNELS[4],
kernel_size=3,
stride=2,
dilation=1,
has_bias=False,
region_type=ME.RegionType.HYPERCUBE,
dimension=D)
self.norm4_tr = get_norm(
NORM_TYPE, TR_CHANNELS[4], bn_momentum=bn_momentum, dimension=D)
self.block4_tr = get_block(
BLOCK_NORM_TYPE,
TR_CHANNELS[4],
TR_CHANNELS[4],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D)
self.conv3_tr = conv_tr(
in_channels=CHANNELS[3] + TR_CHANNELS[4],
out_channels=TR_CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
self.norm3_tr = get_norm(
NORM_TYPE, TR_CHANNELS[3], bn_momentum=bn_momentum, dimension=D)
self.block3_tr = get_block(
BLOCK_NORM_TYPE,
TR_CHANNELS[3],
TR_CHANNELS[3],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D)
self.conv2_tr = conv_tr(
in_channels=CHANNELS[2] + TR_CHANNELS[3],
out_channels=TR_CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
self.norm2_tr = get_norm(
NORM_TYPE, TR_CHANNELS[2], bn_momentum=bn_momentum, dimension=D)
self.block2_tr = get_block(
BLOCK_NORM_TYPE,
TR_CHANNELS[2],
TR_CHANNELS[2],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D)
self.conv1_tr = conv(
in_channels=CHANNELS[1] + TR_CHANNELS[2],
out_channels=TR_CHANNELS[1],
kernel_size=1,
stride=1,
dilation=1,
has_bias=False,
dimension=D)
# self.block1_tr = BasicBlockBN(TR_CHANNELS[1], TR_CHANNELS[1], bn_momentum=bn_momentum, D=D)
self.final = ME.MinkowskiConvolution(
in_channels=TR_CHANNELS[1],
out_channels=out_channels,
kernel_size=1,
stride=1,
dilation=1,
has_bias=True,
dimension=D)
def __init__(self,
in_channels=3,
out_channels=32,
bn_momentum=0.1,
conv1_kernel_size=3,
normalize_feature=False,
D=3):
ME.MinkowskiNetwork.__init__(self, D)
NORM_TYPE = self.NORM_TYPE
BLOCK_NORM_TYPE = self.BLOCK_NORM_TYPE
CHANNELS = self.CHANNELS
TR_CHANNELS = self.TR_CHANNELS
DEPTHS = self.DEPTHS
REGION_TYPE = self.REGION_TYPE
self.normalize_feature = normalize_feature
self.conv1 = conv(
in_channels=in_channels,
out_channels=CHANNELS[1],
kernel_size=conv1_kernel_size,
stride=1,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
self.norm1 = get_norm(NORM_TYPE, CHANNELS[1], bn_momentum=bn_momentum, dimension=D)
self.block1 = nn.Sequential(*[
get_block(
BLOCK_NORM_TYPE,
CHANNELS[1],
CHANNELS[1],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D) for d in range(DEPTHS[1])
])
self.pool2 = ME.MinkowskiSumPooling(kernel_size=2, stride=2, dimension=D)
self.conv2 = conv(
in_channels=CHANNELS[1],
out_channels=CHANNELS[2],
kernel_size=1,
stride=1,
dilation=1,
has_bias=False,
dimension=D)
self.norm2 = get_norm(NORM_TYPE, CHANNELS[2], bn_momentum=bn_momentum, dimension=D)
self.block2 = nn.Sequential(*[
get_block(
BLOCK_NORM_TYPE,
CHANNELS[2],
CHANNELS[2],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D) for d in range(DEPTHS[2])
])
self.pool3 = ME.MinkowskiSumPooling(kernel_size=2, stride=2, dimension=D)
self.conv3 = conv(
in_channels=CHANNELS[2],
out_channels=CHANNELS[3],
kernel_size=1,
stride=1,
dilation=1,
has_bias=False,
dimension=D)
self.norm3 = get_norm(NORM_TYPE, CHANNELS[3], bn_momentum=bn_momentum, dimension=D)
self.block3 = nn.Sequential(*[
get_block(
BLOCK_NORM_TYPE,
CHANNELS[3],
CHANNELS[3],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D) for d in range(DEPTHS[3])
])
self.pool3_tr = ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D)
self.conv3_tr = conv_tr(
in_channels=CHANNELS[3],
out_channels=TR_CHANNELS[3],
kernel_size=1,
stride=1,
dilation=1,
has_bias=False,
dimension=D)
self.norm3_tr = get_norm(
NORM_TYPE, TR_CHANNELS[3], bn_momentum=bn_momentum, dimension=D)
self.block3_tr = nn.Sequential(*[
get_block(
BLOCK_NORM_TYPE,
TR_CHANNELS[3],
TR_CHANNELS[3],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D) for d in range(DEPTHS[-3])
])
self.pool2_tr = ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D)
self.conv2_tr = conv_tr(
in_channels=CHANNELS[2] + TR_CHANNELS[3],
out_channels=TR_CHANNELS[2],
kernel_size=1,
stride=1,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
self.norm2_tr = get_norm(
NORM_TYPE, TR_CHANNELS[2], bn_momentum=bn_momentum, dimension=D)
self.block2_tr = nn.Sequential(*[
get_block(
BLOCK_NORM_TYPE,
TR_CHANNELS[2],
TR_CHANNELS[2],
bn_momentum=bn_momentum,
region_type=REGION_TYPE,
dimension=D) for d in range(DEPTHS[-2])
])
self.conv1_tr = conv_tr(
in_channels=CHANNELS[1] + TR_CHANNELS[2],
out_channels=TR_CHANNELS[1],
kernel_size=1,
stride=1,
dilation=1,
has_bias=False,
region_type=REGION_TYPE,
dimension=D)
# self.block1_tr = BasicBlockBN(TR_CHANNELS[1], TR_CHANNELS[1], bn_momentum=bn_momentum, dimension=D)
self.final = conv(
in_channels=TR_CHANNELS[1],
out_channels=out_channels,
kernel_size=1,
stride=1,
dilation=1,
has_bias=True,
dimension=D)