# SPDX-License-Identifier: Apache-2.0
"""CPU reference of Point Transformer V3 (Autoware autoware_ptv3, weights ``AutowareFoundation/ptv3@v4.0``, node
semantics @5eb9286) -- the ground truth every PCC / output-agreement gate compares against (PLAN.md 0.3 item 1). It
runs where ttnn is absent (the research venv) and never imports ttnn.

- ``config.py``    the Autoware parameters this port reproduces (ml_package yamls of the weights, the node's
                   ``ptv3.param.yaml``, class LUTs) and the port's COMPILE constants (stage capacities, patch 512, BEV
                   256 x 256, 500 proposals), each with its source;
- ``weights.py``   the three ONNX graphs read as data with the vendored ``ttaw.weights.OnnxWeights`` (by consuming
                   node), every wiring assumption asserted from the graph, BNs folded in float64 where the port folds
                   them (the pooling BN stays an affine after the max);
- ``model.py``     fp32 torch rebuild of the encoder, seg head and det head on the host tables (gather-form sparse
                   conv, clip-padded patch attention, gather + max pooling / BEV scatter, ONNX-semantics TopK with
                   teacher forcing) with per-module taps (``TAP_NAMES``);
- ``pipeline.py``  ``Reference``: ``host.preprocess`` -> model -> ``host.postprocess`` (golden layout of
                   research/ptv3/public_data);
- ``ort.py``       oracles on the unmodified graphs: ONNX Runtime for the det head (with intermediates), and
                   ``onnx.reference.ReferenceEvaluator`` + numpy ``autoware::`` ops for the encoder / seg head
                   (research / tests only).

Pre- and post-processing live in ``tt_ptv3.host`` (shared with the device path, so TT and CPU see identical tables).
``samples/pandaset_019_f40.reference.json`` (and ``.seg.``) is ``PTv3Output.to_dict()`` of this reference on the
shipped sample (``code/scripts/make_samples.py``): ``server/smoke_test.py`` compares the served output with it.
"""