def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
conv_type=ConvType.HYPERCUBE,
bn_momentum=0.1,
D=3):
super(BasicBlockBase, self).__init__()
self.conv1 = conv(
inplanes, planes, kernel_size=3, stride=stride, dilation=dilation, conv_type=conv_type, D=D)
self.norm1 = get_norm(self.NORM_TYPE, planes, D, bn_momentum=bn_momentum)
self.conv2 = conv(
planes,
planes,
kernel_size=3,
stride=1,
dilation=dilation,
bias=False,
conv_type=conv_type,
D=D)
self.norm2 = get_norm(self.NORM_TYPE, planes, D, bn_momentum=bn_momentum)
self.relu = MinkowskiReLU(inplace=True)
self.downsample = downsample
def test_network(self):
dense_tensor = torch.rand(3, 4, 11, 11, 11, 11) # BxCxD1xD2x....xDN
dense_tensor.requires_grad = True
# Since the shape is fixed, cache the coordinates for faster inference
coordinates = dense_coordinates(dense_tensor.shape)
network = nn.Sequential(
# Add layers that can be applied on a regular pytorch tensor
nn.ReLU(),
MinkowskiToSparseTensor(remove_zeros=False,
coordinates=coordinates),
MinkowskiConvolution(4, 5, stride=2, kernel_size=3, dimension=4),
MinkowskiBatchNorm(5),
MinkowskiReLU(),
MinkowskiConvolutionTranspose(5,
6,
stride=2,
kernel_size=3,
dimension=4),
MinkowskiToDenseTensor(
dense_tensor.shape), # must have the same tensor stride.
)
for i in range(5):
print(f"Iteration: {i}")
output = network(dense_tensor)
output.sum().backward()
assert dense_tensor.grad is not None
def __init__(self, in_channels, out_channels, config, D=3, **kwargs):
super(RecUNetBase, self).__init__(in_channels, out_channels, config, D)
PLANES = self.PLANES[::-1]
bn_momentum = config.bn_momentum
if D == 4:
self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1, D)
# Output of the first conv concated to conv6
self.conv1 = conv(in_channels,
PLANES[-1][0],
kernel_size=space_n_time_m(3, 1, D),
conv_type=self.CONV_TYPE,
D=D)
self.norm1 = get_norm(self.NORM_TYPE, PLANES[-1][0], D, bn_momentum)
interm = self.BLOCK(PLANES[0][0],
PLANES[0][0],
conv_type=self.CONV_TYPE,
D=self.D)
for i, inoutplanes in enumerate(PLANES[1:]):
interm = UBlock(inoutplanes[0],
PLANES[i][0],
PLANES[i][1],
inoutplanes[1],
intermediate_module=interm,
BLOCK=self.BLOCK,
reps=self.REPS[len(self.REPS) - i - 1],
conv_type=self.CONV_TYPE,
bn_momentum=bn_momentum,
D=D)
self.unet = interm
self.final = conv(PLANES[-1][1],
out_channels,
kernel_size=1,
stride=1,
dilation=1,
bias=True,
D=D)
self.relu = MinkowskiReLU(inplace=True)
def network_initialization(self, in_channels, out_channels, D):
# Setup net_metadata
dilations = self.DILATIONS
bn_momentum = 0.02
def space_n_time_m(n, m):
return n if D == 3 else [n, n, n, m]
if D == 4:
self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1)
# Output of the first conv concated to conv6
self.inplanes = self.INIT_DIM
self.conv0p1s1 = conv(
in_channels,
self.inplanes,
kernel_size=space_n_time_m(3, 1),
stride=1,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bn0 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.conv1p1s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bn1 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block1 = self._make_layer(
self.BLOCK,
self.PLANES[0],
self.LAYERS[0],
dilation=dilations[0],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.conv2p2s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block2 = self._make_layer(
self.BLOCK,
self.PLANES[1],
self.LAYERS[1],
dilation=dilations[1],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.conv3p4s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block3 = self._make_layer(
self.BLOCK,
self.PLANES[2],
self.LAYERS[2],
dilation=dilations[2],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.conv4p8s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block4 = self._make_layer(
self.BLOCK,
self.PLANES[3],
self.LAYERS[3],
dilation=dilations[3],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.convtr4p16s2 = conv_tr(
self.inplanes,
self.PLANES[4],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bntr4 = get_norm(self.NORM_TYPE, self.PLANES[4], D, bn_momentum=bn_momentum)
self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion
self.block5 = self._make_layer(
self.BLOCK,
self.PLANES[4],
self.LAYERS[4],
dilation=dilations[4],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.convtr5p8s2 = conv_tr(
self.inplanes,
self.PLANES[5],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bntr5 = get_norm(self.NORM_TYPE, self.PLANES[5], D, bn_momentum=bn_momentum)
self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion
self.block6 = self._make_layer(
self.BLOCK,
self.PLANES[5],
self.LAYERS[5],
dilation=dilations[5],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.convtr6p4s2 = conv_tr(
self.inplanes,
self.PLANES[6],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bntr6 = get_norm(self.NORM_TYPE, self.PLANES[6], D, bn_momentum=bn_momentum)
self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion
self.block7 = self._make_layer(
self.BLOCK,
self.PLANES[6],
self.LAYERS[6],
dilation=dilations[6],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.convtr7p2s2 = conv_tr(
self.inplanes,
self.PLANES[7],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D,
)
self.bntr7 = get_norm(self.NORM_TYPE, self.PLANES[7], D, bn_momentum=bn_momentum)
self.inplanes = self.PLANES[7] + self.INIT_DIM
self.block8 = self._make_layer(
self.BLOCK,
self.PLANES[7],
self.LAYERS[7],
dilation=dilations[7],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum,
)
self.final = conv(self.PLANES[7], out_channels, kernel_size=1, stride=1, bias=True, D=D)
self.relu = MinkowskiReLU(inplace=True)
def network_initialization(self, in_channels, out_channels, config, D):
# Setup net_metadata
dilations = self.DILATIONS
bn_momentum = config.bn_momentum
def space_n_time_m(n, m):
return n if D == 3 else [n, n, n, m]
if D == 4:
self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1)
# Output of the first conv concated to conv6
self.inplanes = self.INIT_DIM
self.conv1p1s1 = conv(in_channels,
self.inplanes,
kernel_size=space_n_time_m(
config.conv1_kernel_size, 1),
stride=1,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn1 = get_norm(self.NORM_TYPE,
self.PLANES[0],
D,
bn_momentum=bn_momentum)
self.block1 = self._make_layer(self.BLOCK,
self.PLANES[0],
self.LAYERS[0],
dilation=dilations[0],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv2p1s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn2 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block2 = self._make_layer(self.BLOCK,
self.PLANES[1],
self.LAYERS[1],
dilation=dilations[1],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv3p2s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn3 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block3 = self._make_layer(self.BLOCK,
self.PLANES[2],
self.LAYERS[2],
dilation=dilations[2],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv4p4s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn4 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block4 = self._make_layer(self.BLOCK,
self.PLANES[3],
self.LAYERS[3],
dilation=dilations[3],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.pool_tr4 = ME.MinkowskiPoolingTranspose(kernel_size=8,
stride=8,
dimension=D)
out_pool4 = self.inplanes
self.convtr4p8s2 = conv_tr(self.inplanes,
self.PLANES[4],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr4 = get_norm(self.NORM_TYPE,
self.PLANES[4],
D,
bn_momentum=bn_momentum)
self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion
self.block5 = self._make_layer(self.BLOCK,
self.PLANES[4],
self.LAYERS[4],
dilation=dilations[4],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.pool_tr5 = ME.MinkowskiPoolingTranspose(kernel_size=4,
stride=4,
dimension=D)
out_pool5 = self.inplanes
self.convtr5p4s2 = conv_tr(self.inplanes,
self.PLANES[5],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr5 = get_norm(self.NORM_TYPE,
self.PLANES[5],
D,
bn_momentum=bn_momentum)
self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion
self.block6 = self._make_layer(self.BLOCK,
self.PLANES[5],
self.LAYERS[5],
dilation=dilations[5],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.pool_tr6 = ME.MinkowskiPoolingTranspose(kernel_size=2,
stride=2,
dimension=D)
out_pool6 = self.inplanes
self.convtr6p2s2 = conv_tr(self.inplanes,
self.PLANES[6],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr6 = get_norm(self.NORM_TYPE,
self.PLANES[6],
D,
bn_momentum=bn_momentum)
self.relu = MinkowskiReLU(inplace=True)
self.final = nn.Sequential(
conv(out_pool5 + out_pool6 + self.PLANES[6] +
self.PLANES[0] * self.BLOCK.expansion,
512,
kernel_size=1,
bias=False,
D=D), ME.MinkowskiBatchNorm(512), ME.MinkowskiReLU(),
conv(512, out_channels, kernel_size=1, bias=True, D=D))
def network_initialization(self, in_channels, out_channels, config, D):
# Setup net_metadata
dilations = self.DILATIONS
bn_momentum = config['bn_momentum']
def space_n_time_m(n, m):
return n if D == 3 else [n, n, n, m]
if D == 4:
self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1)
# Output of the first conv concated to conv6
self.inplanes = self.INIT_DIM
self.conv0p1s1 = conv(
in_channels,
self.inplanes,
kernel_size=space_n_time_m(config['conv1_kernel_size'], 1),
stride=1,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn0 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.conv1p1s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn1 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block1 = self._make_layer(
self.BLOCK,
self.PLANES[0],
self.LAYERS[0],
dilation=dilations[0],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv2p2s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block2 = self._make_layer(
self.BLOCK,
self.PLANES[1],
self.LAYERS[1],
dilation=dilations[1],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv3p4s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block3 = self._make_layer(
self.BLOCK,
self.PLANES[2],
self.LAYERS[2],
dilation=dilations[2],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv4p8s2 = conv(
self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
self.block4 = self._make_layer(
self.BLOCK,
self.PLANES[3],
self.LAYERS[3],
dilation=dilations[3],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.relu = MinkowskiReLU(inplace=True)
# add a classification head here
self.clf_glob_avg = ME.MinkowskiGlobalPooling(dimension=D)
self.clf_glob_max=ME.MinkowskiGlobalMaxPooling(dimension=D)
self.clf_conv0 = conv(
256,
512,
kernel_size=3,
stride=2,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.clf_bn0 = get_norm(self.NORM_TYPE, 512, D, bn_momentum=bn_momentum)
self.clf_conv1 = conv(
512,
512,
kernel_size=3,
stride=2,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.clf_bn1 = get_norm(self.NORM_TYPE, 512, D, bn_momentum=bn_momentum)
self.clf_conv2 = conv(
512,
config['clf_num_labels'],
kernel_size=1,
stride=1,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
def network_initialization(self, in_channels, out_channels, D):
# Setup net_metadata
dilations = self.DILATIONS
bn_momentum = 0.02
def space_n_time_m(n, m):
return n if D == 3 else [n, n, n, m]
if D == 4:
self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1)
# Output of the first conv concated to conv6
conv1_kernel_size = 3
self.inplanes = self.INIT_DIM
self.conv0p1s1 = conv(in_channels,
self.inplanes,
kernel_size=space_n_time_m(conv1_kernel_size, 1),
stride=1,
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn0 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.conv1p1s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn1 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block1 = self._make_layer(self.BLOCK,
self.PLANES[0],
self.LAYERS[0],
dilation=dilations[0],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv2p2s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn2 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block2 = self._make_layer(self.BLOCK,
self.PLANES[1],
self.LAYERS[1],
dilation=dilations[1],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv3p4s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn3 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block3 = self._make_layer(self.BLOCK,
self.PLANES[2],
self.LAYERS[2],
dilation=dilations[2],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.conv4p8s2 = conv(self.inplanes,
self.inplanes,
kernel_size=space_n_time_m(2, 1),
stride=space_n_time_m(2, 1),
dilation=1,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bn4 = get_norm(self.NORM_TYPE,
self.inplanes,
D,
bn_momentum=bn_momentum)
self.block4 = self._make_layer(self.BLOCK,
self.PLANES[3],
self.LAYERS[3],
dilation=dilations[3],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.convtr4p16s2 = conv_tr(self.inplanes,
self.PLANES[4],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr4 = get_norm(self.NORM_TYPE,
self.PLANES[4],
D,
bn_momentum=bn_momentum)
self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion
self.block5 = self._make_layer(self.BLOCK,
self.PLANES[4],
self.LAYERS[4],
dilation=dilations[4],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.convtr5p8s2 = conv_tr(self.inplanes,
self.PLANES[5],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr5 = get_norm(self.NORM_TYPE,
self.PLANES[5],
D,
bn_momentum=bn_momentum)
self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion
self.block6 = self._make_layer(self.BLOCK,
self.PLANES[5],
self.LAYERS[5],
dilation=dilations[5],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.convtr6p4s2 = conv_tr(self.inplanes,
self.PLANES[6],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr6 = get_norm(self.NORM_TYPE,
self.PLANES[6],
D,
bn_momentum=bn_momentum)
self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion
self.block7 = self._make_layer(self.BLOCK,
self.PLANES[6],
self.LAYERS[6],
dilation=dilations[6],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.convtr7p2s2 = conv_tr(self.inplanes,
self.PLANES[7],
kernel_size=space_n_time_m(2, 1),
upsample_stride=space_n_time_m(2, 1),
dilation=1,
bias=False,
conv_type=self.NON_BLOCK_CONV_TYPE,
D=D)
self.bntr7 = get_norm(self.NORM_TYPE,
self.PLANES[7],
D,
bn_momentum=bn_momentum)
self.inplanes = self.PLANES[7] + self.INIT_DIM
self.block8 = self._make_layer(self.BLOCK,
self.PLANES[7],
self.LAYERS[7],
dilation=dilations[7],
norm_type=self.NORM_TYPE,
bn_momentum=bn_momentum)
self.all_feat_names = [
"en0",
"en1",
"en2",
"en3",
"en4",
"plane4",
"plane5",
"plane6",
"plane7",
]
self.relu = MinkowskiReLU(inplace=True)
self.maxpool = ME.MinkowskiGlobalMaxPooling()
self.avgpool = ME.MinkowskiGlobalAvgPooling()
if self.use_mlp:
self.head = SMLP(self.mlp_dim)
