def _make_deblock(self, num_out_filters, idx):
print("CUSTOM MAKE DEBLOCK")
stride = self._upsample_strides[idx - self._upsample_start_idx]
if self._use_norm:
if self._use_groupnorm:
SparseBatchNorm2d = change_default_args(
num_groups=self._num_groups, eps=1e-3)(GroupNorm)
DenseBatchNorm2d = change_default_args(
num_groups=self._num_groups, eps=1e-3)(GroupNorm)
else:
SparseBatchNorm2d = change_default_args(
eps=1e-3, momentum=0.01)(ME.MinkowskiBatchNorm)
DenseBatchNorm2d = change_default_args(
eps=1e-3, momentum=0.01)(nn.BatchNorm2d)
SparseConvTranspose2d = change_default_args(bias=False, dimension=2)(
ME.MinkowskiConvolutionTranspose)
DenseConvTranspose2d = change_default_args(bias=False)(
nn.ConvTranspose2d)
else:
SparseBatchNorm2d = Empty
DenseBatchNorm2d = Empty
SparseConvTranspose2d = change_default_args(bias=True, dimension=2)(
ME.MinkowskiConvolutionTranspose)
DenseConvTranspose2d = change_default_args(bias=True)(
nn.ConvTranspose2d)
ReLU = ME.MinkowskiReLU()
stride = np.round(stride).astype(np.int64)
if (idx <= LAST_SPARSE_IDX):
deblock = Sequential(
SparseConvTranspose2d(
num_out_filters,
self._num_upsample_filters[idx - self._upsample_start_idx],
stride,
stride=stride),
SparseBatchNorm2d(
self._num_upsample_filters[idx -
self._upsample_start_idx]),
ReLU,
ME.ToDense()
)
else:
stride = np.round(stride).astype(np.int64)
deblock = Sequential(
DenseConvTranspose2d(
num_out_filters,
self._num_upsample_filters[idx - self._upsample_start_idx],
stride,
stride=stride),
DenseBatchNorm2d(
self._num_upsample_filters[idx -
self._upsample_start_idx]),
ReLU,
)
return deblock
