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name: sparse-tensor-data description: "Helps with MinkowskiEngine SparseTensor, TensorField, batching, quantization, coordinate-manager, and dense/sparse conversion workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Sparse Tensor Data

Use this sub-skill when the task is about coordinates, sparse tensor construction, voxelization, batching, TensorField slicing/splatting, or dense/sparse conversion.

What This Route Covers

• SparseTensor and TensorField creation.
• sparse_quantize, sparse_collate, batched_coordinates, and SparseCollation.
• Coordinate-manager/key sharing and sparse tensor arithmetic.
• Batch-wise decomposition and coordinate lookup.
• Dense-to-sparse and sparse-to-dense conversion helpers.
• Continuous-coordinate workflows that start from TensorField.

What It Excludes

• Build/install problems; use ../build-and-install/SKILL.md.
• Layer/network construction; use ../layers-and-networks/SKILL.md.
• Training/demo pipelines; use ../training-and-demos/SKILL.md.

Read These Bundled Files First

• references/api-reference.md for verified signatures and public API names.
• references/workflows.md for step-by-step recipes.
• references/troubleshooting.md for coordinate, dtype, quantization, and manager/key failures.
• scripts/sparse_tensor_smoke.py for a safe synthetic smoke test.

Typical Triggers

• You need to turn points into batched sparse tensors.
• You need to quantize coordinates or recover inverse maps.
• You see coordinate-manager/key mismatch errors.
• You need to slice a sparse output back onto an input field.
• You want to inspect batch-wise coordinates or features.

Fast Workflow

1. Read the API reference to confirm the exact constructor and helper signatures.
2. Decide whether the input is discrete coordinates or continuous coordinates.
3. Pick sparse_collate/batched_coordinates for batches and sparse_quantize for voxelization.
4. Share the coordinate manager or map key when tensors must interact.
5. Run the bundled smoke script to verify a minimal path.

Public Semantic Rules

• Batch indices are prepended in the first coordinate column.
• Coordinates and features must be 2D matrices.
• TensorField is the right starting point for continuous coordinates that need quantization or splatting.
• Sparse tensor arithmetic requires compatible coordinate managers or keys.

Related Helpers

• ../../scripts/check_minkowski_engine.py — import and tiny package smoke.
• scripts/sparse_tensor_smoke.py — tiny sparse tensor workflow smoke.

When to Stop

If the issue is really about a convolution or pooling operator, stop here and route to layers-and-networks. If it is about dataset loading, collate functions, or demos, route to training-and-demos.