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Migration Guide from v0.4.x to v0.5.0

Summary

# 0.4
ME.SparseTensor(feats=feats, coords=coords, D=3)
# 0.5
ME.SparseTensor(feats=feats, coords=coords, D=3)
# 0.4
ME.MinkowskiConvolution(..., has_bias=True)
# 0.5
ME.MinkowskiConvolution(..., bias=True)

# 0.4
RegionType.HYPERCUBE
# 0.5
RegionType.HYPER_CUBE

Definitions

CoordinateMap

A coordinate map refers to a map object that converts a D-dimensional coordinate into a row index for a feature matrix where the corresponding feature for the coordinate is located. This can be implemented using std::map, std::unordered_map or a hash-table with the right hash function and the equality function.

CoordinateKey

A CoordinateKey or CoordinateMapKey refers to a unique identifier that can be used to retrieve a CoordinateMap.

tensor_stride

A tensor stride is a minimum distance between non-zero elements in a sparse tensor. If we take a stride-2 convolution on a sparse tensor with tensor stride 1, the resulting sparse tensor will have tensor stride 2. If we apply two stride-2 convolutions on a sparse tensor with tensor stride 3, the resulting sparse tensor will have the tensor stride 2 x 2 x 3 = 12.

From CoordsKey to CoordinateMapKey

CoordsKey should not be called in most cases, but in rare cases where you used it. Please review this section to update your code.

One of the major difference is that we expose the pybind11 object directly to the python side to remove the redundant abstraction layer.

In v0.4, Minkowski Engine uses a uint64_t hash key to identify a CoordinateMap, but from v0.5, we use a tensor stride

From CoordsManager to CoordinateManager

CoordinateManager should not be called in most cases, but if you do please re

Initialization

# 0.4.x
manager = CoordsManager(D=3)
# 0.5.x
manager = CoordinateManager(D=3)

Initializing a new CoordinateMap

# 0.4.x
manager = CoordsManager(D = 3)
manager.initialize(torch.IntTens
    def initialize(self,
                   coords: torch.IntTensor,
                   coords_key: CoordsKey,
                   force_creation: bool = False,
                   force_remap: bool = False,
                   allow_duplicate_coords: bool = False,
                   return_inverse: bool = False) -> torch.LongTensor:

Consistent Layer Arguments