# SPDX-License-Identifier: Apache-2.0
"""CPU fp32 reference of BEVFormer-small (Autoware tensorrt_bevformer): the ground truth every PCC / output-agreement
gate compares against (PLAN.md 0.3 item 1; research/bevformer/SPEC.md section 6). Ported from the verified research
reference ``research/bevformer/scripts/bevformer_ref.py``.

Layout (importable without ttnn; ``config`` / ``weights`` need numpy only, the rest imports torch):

- ``config.py``    every constant of the deployed graph and the node, with its source line (cameras, classes,
                   geometry, image sizes, transformer sizes, post-processing defaults, SCA rebatch capacity).
- ``weights.py``   ``BEVFormerWeights``: the mirror's safetensors (or the official .pth) read as data with the
                   vendored ``ttaw.weights``; strict 1,001-tensor coverage; the backbone BatchNorms folded in float64
                   exactly where the port folds them (``conv(module) -> ttaw.models.resnet.ConvParams``).
- ``ops.py``       the three TensorRT plugins (MSDA, DCNv2, RotateTRT) in torch fp32 + literal float64 CUDA-loop ports.
- ``model.py``     ``BEVFormerNet``: the network with per-module taps; ``forward`` (the deployed graph, geometry
                   inside) and ``forward_port`` (the TT port's decomposition with host geometry: rebatched SCA,
                   rotation by gather).
- ``pipeline.py``  ``BEVFormerReference``: frames from images + calibration + ego pose through ``host`` (the node's
                   pre-processing, temporal state, post-processing) and the network; stateless ``run_frame``.
- ``ort.py``       the ONNX Runtime oracle on the derived pure ONNX (research and tests only; onnxruntime is NOT a
                   runtime dependency of the image).

The ``to_dict()`` of this reference's output on the shipped sample is stored as
``samples/<sample stem>.reference.json``: ``server/smoke_test.py`` compares the served output with it.
"""