def operation_mode():
# Set to share the coords_man by default
ME.set_sparse_tensor_operation_mode(
ME.SparseTensorOperationMode.SHARE_COORDS_MANAGER)
print(ME.sparse_tensor_operation_mode())
coords0, feats0 = to_sparse_coo(data_batch_0)
coords0, feats0 = ME.utils.sparse_collate(coords=[coords0], feats=[feats0])
coords1, feats1 = to_sparse_coo(data_batch_1)
coords1, feats1 = ME.utils.sparse_collate(coords=[coords1], feats=[feats1])
for _ in range(2):
# sparse tensors
A = ME.SparseTensor(coords=coords0, feats=feats0)
B = ME.SparseTensor(
coords=coords1,
feats=feats1,
# coords_manager=A.coords_man, No need to feed the coords_man
force_creation=True)
C = A + B
# When done using it for forward and backward, you must cleanup the coords man
ME.clear_global_coords_man()
def operation_mode():
# Set to share the coords_man by default
ME.set_sparse_tensor_operation_mode(
ME.SparseTensorOperationMode.SHARE_COORDS_MANAGER)
print(ME.sparse_tensor_operation_mode())
coords, feats = data_generation()
new_coords = torch.IntTensor([[0, 1], [2, 3], [4, 5]])
new_feats = torch.rand(len(new_coords), feats.size(1))
for _ in range(2):
# sparse tensors
A = ME.SparseTensor(coords=coords, feats=feats)
B = ME.SparseTensor(
coords=new_coords,
feats=new_feats,
# coords_manager=A.coords_man, No need to feed the coords_man
force_creation=True)
C = A + B
# When done using it for forward and backward, you must cleanup the coords man
ME.clear_global_coords_man()
