def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
bn_momentum=0.1,
D=3):
super(BasicBlockBase, self).__init__()
self.conv1 = ME.MinkowskiConvolution(
inplanes, planes, kernel_size=3, stride=stride, dimension=D)
self.norm1 = get_norm(self.NORM_TYPE, planes, bn_momentum=bn_momentum, D=D)
self.conv2 = ME.MinkowskiConvolution(
planes,
planes,
kernel_size=3,
stride=1,
dilation=dilation,
bias=False,
dimension=D)
self.norm2 = get_norm(self.NORM_TYPE, planes, bn_momentum=bn_momentum, D=D)
self.downsample = downsample
def __init__(self,
in_channels=3,
out_channels=32,
bn_momentum=0.1,
normalize_feature=None,
conv1_kernel_size=None,
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
self.normalize_feature = normalize_feature
self.conv1 = ME.MinkowskiConvolution(in_channels=in_channels,
out_channels=CHANNELS[1],
kernel_size=conv1_kernel_size,
stride=1,
dilation=1,
bias=False,
dimension=D)
self.norm1 = get_norm(NORM_TYPE,
CHANNELS[1],
bn_momentum=bn_momentum,
D=D)
self.block1 = get_block(BLOCK_NORM_TYPE,
CHANNELS[1],
CHANNELS[1],
bn_momentum=bn_momentum,
D=D)
self.conv2 = ME.MinkowskiConvolution(in_channels=CHANNELS[1],
out_channels=CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm2 = get_norm(NORM_TYPE,
CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.block2 = get_block(BLOCK_NORM_TYPE,
CHANNELS[2],
CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.conv3 = ME.MinkowskiConvolution(in_channels=CHANNELS[2],
out_channels=CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm3 = get_norm(NORM_TYPE,
CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.block3 = get_block(BLOCK_NORM_TYPE,
CHANNELS[3],
CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.conv4 = ME.MinkowskiConvolution(in_channels=CHANNELS[3],
out_channels=CHANNELS[4],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm4 = get_norm(NORM_TYPE,
CHANNELS[4],
bn_momentum=bn_momentum,
D=D)
self.block4 = get_block(BLOCK_NORM_TYPE,
CHANNELS[4],
CHANNELS[4],
bn_momentum=bn_momentum,
D=D)
self.conv4_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[4],
out_channels=TR_CHANNELS[4],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm4_tr = get_norm(NORM_TYPE,
TR_CHANNELS[4],
bn_momentum=bn_momentum,
D=D)
self.block4_tr = get_block(BLOCK_NORM_TYPE,
TR_CHANNELS[4],
TR_CHANNELS[4],
bn_momentum=bn_momentum,
D=D)
self.conv3_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[3] + TR_CHANNELS[4],
out_channels=TR_CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm3_tr = get_norm(NORM_TYPE,
TR_CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.block3_tr = get_block(BLOCK_NORM_TYPE,
TR_CHANNELS[3],
TR_CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.conv2_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[2] + TR_CHANNELS[3],
out_channels=TR_CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm2_tr = get_norm(NORM_TYPE,
TR_CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.block2_tr = get_block(BLOCK_NORM_TYPE,
TR_CHANNELS[2],
TR_CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.conv1_tr = ME.MinkowskiConvolution(in_channels=CHANNELS[1] +
TR_CHANNELS[2],
out_channels=TR_CHANNELS[1],
kernel_size=1,
stride=1,
dilation=1,
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,
bias=True,
dimension=D)
def __init__(self,
in_channels=3,
out_channels=32,
bn_momentum=0.1,
D=3,
config=None):
ME.MinkowskiNetwork.__init__(self, D)
NORM_TYPE = self.NORM_TYPE
bn_momentum = config.bn_momentum
CHANNELS = self.CHANNELS
TR_CHANNELS = self.TR_CHANNELS
self.normalize_feature = config.normalize_feature
self.conv1 = ME.MinkowskiConvolution(
in_channels=in_channels,
out_channels=CHANNELS[1],
kernel_size=config.conv1_kernel_size,
stride=1,
dilation=1,
bias=False,
dimension=D)
self.norm1 = get_norm(NORM_TYPE,
CHANNELS[1],
bn_momentum=bn_momentum,
D=D)
self.conv2 = ME.MinkowskiConvolution(in_channels=CHANNELS[1],
out_channels=CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm2 = get_norm(NORM_TYPE,
CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.conv3 = ME.MinkowskiConvolution(in_channels=CHANNELS[2],
out_channels=CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm3 = get_norm(NORM_TYPE,
CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.conv4 = ME.MinkowskiConvolution(in_channels=CHANNELS[3],
out_channels=CHANNELS[4],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm4 = get_norm(NORM_TYPE,
CHANNELS[4],
bn_momentum=bn_momentum,
D=D)
self.conv5 = ME.MinkowskiConvolution(in_channels=CHANNELS[4],
out_channels=CHANNELS[5],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm5 = get_norm(NORM_TYPE,
CHANNELS[5],
bn_momentum=bn_momentum,
D=D)
self.conv5_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[5],
out_channels=TR_CHANNELS[5],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm5_tr = get_norm(NORM_TYPE,
TR_CHANNELS[5],
bn_momentum=bn_momentum,
D=D)
self.conv4_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[4] + TR_CHANNELS[5],
out_channels=TR_CHANNELS[4],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm4_tr = get_norm(NORM_TYPE,
TR_CHANNELS[4],
bn_momentum=bn_momentum,
D=D)
self.conv3_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[3] + TR_CHANNELS[4],
out_channels=TR_CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm3_tr = get_norm(NORM_TYPE,
TR_CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.conv2_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[2] + TR_CHANNELS[3],
out_channels=TR_CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm2_tr = get_norm(NORM_TYPE,
TR_CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.conv1_tr = ME.MinkowskiConvolution(in_channels=CHANNELS[1] +
TR_CHANNELS[2],
out_channels=TR_CHANNELS[1],
kernel_size=1,
stride=1,
dilation=1,
bias=True,
dimension=D)
def __init__(self,
in_channels=3,
out_channels=32,
bn_momentum=0.1,
normalize_feature=None,
conv1_kernel_size=None,
D=3):
super(SimpleNet, self).__init__(D)
NORM_TYPE = self.NORM_TYPE
CHANNELS = self.CHANNELS
TR_CHANNELS = self.TR_CHANNELS
self.normalize_feature = normalize_feature
self.conv1 = ME.MinkowskiConvolution(in_channels=in_channels,
out_channels=CHANNELS[1],
kernel_size=conv1_kernel_size,
stride=1,
dilation=1,
bias=False,
dimension=D)
self.norm1 = get_norm(NORM_TYPE,
CHANNELS[1],
bn_momentum=bn_momentum,
D=D)
self.conv2 = ME.MinkowskiConvolution(in_channels=CHANNELS[1],
out_channels=CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm2 = get_norm(NORM_TYPE,
CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.conv3 = ME.MinkowskiConvolution(in_channels=CHANNELS[2],
out_channels=CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm3 = get_norm(NORM_TYPE,
CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.conv3_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[3],
out_channels=TR_CHANNELS[3],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm3_tr = get_norm(NORM_TYPE,
TR_CHANNELS[3],
bn_momentum=bn_momentum,
D=D)
self.conv2_tr = ME.MinkowskiConvolutionTranspose(
in_channels=CHANNELS[2] + TR_CHANNELS[3],
out_channels=TR_CHANNELS[2],
kernel_size=3,
stride=2,
dilation=1,
bias=False,
dimension=D)
self.norm2_tr = get_norm(NORM_TYPE,
TR_CHANNELS[2],
bn_momentum=bn_momentum,
D=D)
self.conv1_tr = ME.MinkowskiConvolution(in_channels=CHANNELS[1] +
TR_CHANNELS[2],
out_channels=TR_CHANNELS[1],
kernel_size=3,
stride=1,
dilation=1,
bias=False,
dimension=D)
self.norm1_tr = get_norm(NORM_TYPE,
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,
bias=True,
dimension=D)