def __init__(self):
super(PointNetFeature, self).__init__()
k = self.KERNEL_SIZES
s = self.STRIDES
c = self.CONV_CHANNELS
self.stn = STN3d(D=3)
self.block1 = nn.Sequential(
ME.MinkowskiConvolution(6,
c[0],
kernel_size=k[0],
stride=s[0],
has_bias=False,
dimension=3),
ME.MinkowskiInstanceNorm(c[0]), ME.MinkowskiReLU())
self.block2 = nn.Sequential(
ME.MinkowskiConvolution(c[0],
c[1],
kernel_size=k[1],
stride=s[1],
has_bias=False,
dimension=3),
ME.MinkowskiInstanceNorm(c[1]), ME.MinkowskiReLU())
self.block3 = nn.Sequential(
ME.MinkowskiConvolution(c[1],
c[2],
kernel_size=k[2],
stride=s[2],
has_bias=False,
dimension=3),
ME.MinkowskiInstanceNorm(c[2]), ME.MinkowskiReLU())
self.avgpool = ME.MinkowskiGlobalPooling()
self.concat = ME.MinkowskiBroadcastConcatenation()
def __init__(self):
super(PointNetFeature, self).__init__()
k = self.KERNEL_SIZES
s = self.STRIDES
c = self.CONV_CHANNELS
self.stn = STN3d(D=3)
self.conv1 = ME.MinkowskiConvolution(6,
c[0],
kernel_size=k[0],
stride=s[0],
has_bias=False,
dimension=3)
self.conv2 = ME.MinkowskiConvolution(c[0],
c[1],
kernel_size=k[1],
stride=s[1],
has_bias=False,
dimension=3)
self.conv3 = ME.MinkowskiConvolution(c[1],
c[2],
kernel_size=k[2],
stride=s[2],
has_bias=False,
dimension=3)
self.bn1 = ME.MinkowskiInstanceNorm(c[0], dimension=3)
self.bn2 = ME.MinkowskiInstanceNorm(c[1], dimension=3)
self.bn3 = ME.MinkowskiInstanceNorm(c[2], dimension=3)
self.relu = ME.MinkowskiReLU(inplace=True)
self.avgpool = ME.MinkowskiGlobalPooling()
self.concat = ME.MinkowskiBroadcastConcatenation()
