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name: layers-and-networks description: "Guides agents building MinkowskiEngine sparse layers, networks, convolution, pooling, broadcast, pruning, interpolation, union, KernelGenerator, and quick-start architectures." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Layers and networks

Use this operating sub-skill when the task is to assemble, debug, or adapt MinkowskiEngine sparse layers or network blocks: sparse convolutions and transposed convolutions, custom kernels, pooling and global heads, broadcast, normalization, nonlinearities, pruning, interpolation, union, sparse matrix multiplication, feature utilities, and MinkowskiNetwork subclasses.

Route

• For callable signatures and return semantics, load references/api-reference.md.
• For end-to-end layer patterns, coordinate-output choices, quick-start network skeletons, and performance guidance, load references/workflows.md.
• For dimension, channel, coordinate manager/key, device, CPU/GPU, kernel-map, and memory failures, load references/troubleshooting.md.
• To sanity-check an installed package without downloads, run python scripts/layer_smoke.py --help then python scripts/layer_smoke.py --device cpu from this sub-skill directory.

Operating defaults

• Prefer ME.utils.batched_coordinates(...) or ME.utils.sparse_collate(...) when constructing inputs; do not guess the batch-coordinate column manually.
• Pass dimension=D consistently to every spatial layer and to each MinkowskiNetwork subclass.
• Reuse a tensor's coordinate_manager or coordinate_map_key deliberately whenever layers or feature utilities require identical coordinates.
• Treat the bundled script as CPU-first verification. Only use CUDA paths when the user's installed MinkowskiEngine build reports CUDA support and the user explicitly wants GPU execution.