[OMNIML-5774] Add BEVFormer ONNX PTQ and evaluation example

[ajrasane]

What does this PR do?

Type of change: new example

Adds an end-to-end BEVFormer-tiny ONNX PTQ example under examples/onnx_ptq/bevformer. The example provides a pinned container environment, temporal calibration-data generation, INT8 and FP8 quantization, TensorRT engine building, full nuScenes accuracy evaluation, and latency benchmarking.

The workflow extends NVIDIA's DL4AGX BEVFormer INT8 example with FP8 support and reuses the shared bounded NpzCalibrationReader introduced by #2180. It also adds temporal-state unit coverage, links the example from the ONNX PTQ index, and updates the changelog.

Usage

python examples/onnx_ptq/bevformer/quantize.py \
  --onnx=/artifacts/bevformer_tiny_epoch_24_cp2_op13.onnx \
  --calibration-dir=/artifacts/calibration \
  --trt-plugins=/workspace/BEVFormer_tensorrt/TensorRT/lib/libtensorrt_ops.so \
  --quantization-mode=fp8 \
  --output=/artifacts/bevformer_tiny_epoch_24_cp2_op13.fp8.onnx

Testing

• Ran the targeted CPU-only unit tests: 10 passed.
• Ran all applicable pre-commit hooks, including Ruff, mypy, Bandit, markdownlint, license checks, and requirements validation.
• Generated 600 temporal calibration samples and validated the INT8 and FP8 ONNX graphs.
• Built FP16, INT8, and FP8 TensorRT 10.14.1.48 engines on an NVIDIA RTX 6000 Ada Generation GPU.
• Evaluated all 6,019 nuScenes validation samples.

Precision	Median GPU compute time	NDS	mAP
FP16	4.597 ms	0.3546	0.2515
INT8/FP16	3.180 ms	0.3512	0.2505
FP8/FP16	4.106 ms	0.3526	0.2489

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• Is this change backward compatible?: ✅
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: ✅
• Did you write any new necessary tests?: ✅
• Did you update Changelog?: ✅

Additional Information

This PR is stacked on #2180 to reuse its shared calibration reader and should be retargeted to main after #2180 merges.

Reference workflow: NVIDIA DL4AGX BEVFormer INT8 example.

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