Coordinate Management¶

CoordinateMapKey¶

class MinkowskiEngine.CoordinateMapKey¶

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: MinkowskiEngineBackend._C.CoordinateMapKey, arg0: int) -> None
__init__(self: MinkowskiEngineBackend._C.CoordinateMapKey, arg0: List[int], arg1: str) -> None

get_coordinate_size(self: MinkowskiEngineBackend._C.CoordinateMapKey) → int¶

get_key(self: MinkowskiEngineBackend._C.CoordinateMapKey) → Tuple[List[int], str]¶

get_tensor_stride(self: MinkowskiEngineBackend._C.CoordinateMapKey) → List[int]¶

is_key_set(self: MinkowskiEngineBackend._C.CoordinateMapKey) → bool¶

set_key(*args, **kwargs)¶

Overloaded function.

set_key(self: MinkowskiEngineBackend._C.CoordinateMapKey, arg0: List[int], arg1: str) -> None
set_key(self: MinkowskiEngineBackend._C.CoordinateMapKey, arg0: Tuple[List[int], str]) -> None

CoordinateManager¶

class MinkowskiEngine.CoordinateManager(D: int = 0, num_threads: int = - 1, coordinate_map_type: Optional[MinkowskiEngineBackend._C.CoordinateMapType] = None, allocator_type: Optional[MinkowskiEngineBackend._C.GPUMemoryAllocatorType] = None, minkowski_algorithm: Optional[MinkowskiEngineBackend._C.MinkowskiAlgorithm] = None)¶

__init__(D: int = 0, num_threads: int = - 1, coordinate_map_type: Optional[MinkowskiEngineBackend._C.CoordinateMapType] = None, allocator_type: Optional[MinkowskiEngineBackend._C.GPUMemoryAllocatorType] = None, minkowski_algorithm: Optional[MinkowskiEngineBackend._C.MinkowskiAlgorithm] = None)¶: D: The order, or dimension of the coordinates.

exists_field_to_sparse(field_map_key: MinkowskiEngineBackend._C.CoordinateMapKey, sparse_map_key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

field_to_sparse_insert_and_map(field_map_key: MinkowskiEngineBackend._C.CoordinateMapKey, sparse_tensor_stride: Union[int, collections.abc.Sequence, numpy.ndarray], sparse_tensor_string_id: str = '') → Tuple[MinkowskiEngineBackend._C.CoordinateMapKey, Tuple[torch.IntTensor, torch.IntTensor]]¶

Create a sparse tensor coordinate map with the tensor stride.

field_map_key (CoordinateMapKey): field map that a new sparse tensor will be created from.

tensor_stride (list): a list of D elements that defines the tensor stride for the new order-D + 1 sparse tensor.

string_id (str): string id of the new sparse tensor coordinate map key.

Example:

>>> manager = CoordinateManager(D=1)
>>> coordinates = torch.FloatTensor([[0, 0.1], [0, 2.3], [0, 1.2], [0, 2.4]])
>>> key, (unique_map, inverse_map) = manager.insert(coordinates, [1])

field_to_sparse_keys(field_map_key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

field_to_sparse_map(field_map_key: MinkowskiEngineBackend._C.CoordinateMapKey, sparse_map_key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

get_coordinate_field(coords_key_or_tensor_strides) → torch.Tensor¶

get_coordinates(coords_key_or_tensor_strides) → torch.Tensor¶

get_field_to_sparse_map(field_map_key: MinkowskiEngineBackend._C.CoordinateMapKey, sparse_map_key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

get_kernel_map(in_key: MinkowskiEngineBackend._C.CoordinateMapKey, out_key: MinkowskiEngineBackend._C.CoordinateMapKey, stride=1, kernel_size=3, dilation=1, region_type=<RegionType.HYPER_CUBE: 0>, region_offset=None, is_transpose=False, is_pool=False) → dict¶: Alias of CoordinateManager.kernel_map. Will be deprecated in the next version.

get_unique_coordinate_map_key(tensor_stride: Union[int, list]) → MinkowskiEngineBackend._C.CoordinateMapKey¶

Returns a unique coordinate_map_key for a given tensor stride.

tensor_stride (list): a list of D elements that defines the tensor stride for the new order-D + 1 sparse tensor.

insert_and_map(coordinates: torch.Tensor, tensor_stride: Union[int, collections.abc.Sequence, numpy.ndarray] = 1, string_id: str = '') → Tuple[MinkowskiEngineBackend._C.CoordinateMapKey, Tuple[torch.IntTensor, torch.IntTensor]]¶

create a new coordinate map and returns (key, (map, inverse_map)).

coordinates: torch.Tensor (Int tensor. CUDA if coordinate_map_type == CoordinateMapType.GPU) that defines the coordinates.

tensor_stride (list): a list of D elements that defines the tensor stride for the new order-D + 1 sparse tensor.

Example:

>>> manager = CoordinateManager(D=1)
>>> coordinates = torch.IntTensor([[0, 0], [0, 0], [0, 1], [0, 2]])
>>> key, (unique_map, inverse_map) = manager.insert(coordinates, [1])
>>> print(key) # key is tensor_stride, string_id [1]:""
>>> torch.all(coordinates[unique_map] == manager.get_coordinates(key)) # True
>>> torch.all(coordinates == coordinates[unique_map][inverse_map]) # True

insert_field(coordinates: torch.Tensor, tensor_stride: collections.abc.Sequence, string_id: str = '') → Tuple[MinkowskiEngineBackend._C.CoordinateMapKey, Tuple[torch.IntTensor, torch.IntTensor]]¶

create a new coordinate map and returns

coordinates: torch.FloatTensor (CUDA if coordinate_map_type == CoordinateMapType.GPU) that defines the coordinates.

tensor_stride (list): a list of D elements that defines the tensor stride for the new order-D + 1 sparse tensor.

Example:

>>> manager = CoordinateManager(D=1)
>>> coordinates = torch.FloatTensor([[0, 0.1], [0, 2.3], [0, 1.2], [0, 2.4]])
>>> key, (unique_map, inverse_map) = manager.insert(coordinates, [1])
>>> print(key) # key is tensor_stride, string_id [1]:""
>>> torch.all(coordinates[unique_map] == manager.get_coordinates(key)) # True
>>> torch.all(coordinates == coordinates[unique_map][inverse_map]) # True

interpolation_map_weight(key: MinkowskiEngineBackend._C.CoordinateMapKey, samples: torch.Tensor)¶

kernel_map(in_key: MinkowskiEngineBackend._C.CoordinateMapKey, out_key: MinkowskiEngineBackend._C.CoordinateMapKey, stride=1, kernel_size=3, dilation=1, region_type=<RegionType.HYPER_CUBE: 0>, region_offset=None, is_transpose=False, is_pool=False) → dict¶

Get kernel in-out maps for the specified coords keys or tensor strides.

returns dict{kernel_index: in_out_tensor} where in_out_tensor[0] is the input row indices that correspond to in_out_tensor[1], which is the row indices for output.

number_of_unique_batch_indices() → int¶

origin() → MinkowskiEngineBackend._C.CoordinateMapKey¶

origin_field() → MinkowskiEngineBackend._C.CoordinateMapKey¶

origin_field_map(key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

origin_map(key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

size(coordinate_map_key: MinkowskiEngineBackend._C.CoordinateMapKey) → int¶

stride(coordinate_map_key: MinkowskiEngineBackend._C.CoordinateMapKey, stride: Union[int, collections.abc.Sequence, numpy.ndarray, torch.Tensor], string_id: str = '') → MinkowskiEngineBackend._C.CoordinateMapKey¶

Generate a new coordinate map and returns the key.

coordinate_map_key (MinkowskiEngine.CoordinateMapKey): input map to generate the strided map from.

stride: stride size.

stride_map(in_key: MinkowskiEngineBackend._C.CoordinateMapKey, stride_key: MinkowskiEngineBackend._C.CoordinateMapKey)¶

union_map(in_keys: list, out_key)¶

GPU Memory Allocator¶

class MinkowskiEngine.GPUMemoryAllocatorType¶

Members:

PYTORCH

CUDA

property name¶

MinkowskiEngine.set_gpu_allocator(backend: MinkowskiEngineBackend._C.GPUMemoryAllocatorType)¶

Set the GPU memory allocator

By default, the Minkowski Engine will use the pytorch memory pool to allocate temporary GPU memory slots. This allows the pytorch backend to effectively reuse the memory pool shared between the pytorch backend and the Minkowski Engine. It tends to allow training with larger batch sizes given a fixed GPU memory. However, pytorch memory manager tend to be slower than allocating GPU directly using raw CUDA calls.

By default, the Minkowski Engine uses ME.GPUMemoryAllocatorType.PYTORCH for memory management.

Example:

>>> import MinkowskiEngine as ME
>>> # Set the GPU memory manager backend to raw CUDA calls
>>> ME.set_gpu_allocator(ME.GPUMemoryAllocatorType.CUDA)
>>> # Set the GPU memory manager backend to the pytorch c10 allocator
>>> ME.set_gpu_allocator(ME.GPUMemoryAllocatorType.PYTORCH)