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 conv_norm_non(inc,
outc,
kernel_size,
stride,
dimension,
bn_momentum=0.05,
region_type=ME.RegionType.HYPER_CUBE,
norm_type='BN',
nonlinearity='ELU'):
return nn.Sequential(
conv(in_channels=inc,
out_channels=outc,
kernel_size=kernel_size,
stride=stride,
dilation=1,
bias=False,
region_type=region_type,
dimension=dimension),
get_norm(norm_type, outc, bn_momentum=bn_momentum,
dimension=dimension), get_nonlinearity(nonlinearity))