112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
"""ADR-117 hardening sweep — Benchmarks for the P3.5 numpy bridge
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and the P4 WS decoder.
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The numpy bridge is the most-likely candidate for a hidden allocation
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hot-spot: every `BfldFrame.from_compressed_feedback()` call copies the
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ndarray into a Vec<Complex64>. Confirm the per-frame cost is
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acceptable for the BFR cadence the AP emits (typically a few
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hundred per second, not thousands).
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The WS decoder runs once per frame the sensing-server emits. At
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worst-case ~100 Hz × number-of-subscribers, the decoder budget is
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tight; make sure dataclass construction doesn't dominate.
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"""
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from __future__ import annotations
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import json
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import numpy as np
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import pytest
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from wifi_densepose import BfldFrame, BfldKind
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@pytest.mark.parametrize("kind,shape", [
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(BfldKind.UncompressedHT20, (1, 1, 52)),
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(BfldKind.CompressedHE20, (2, 1, 242)),
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(BfldKind.CompressedHE80, (2, 1, 996)),
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(BfldKind.CompressedHE160, (2, 2, 1992)),
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])
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def test_bfld_from_compressed_feedback(benchmark, kind: BfldKind, shape: tuple[int, int, int]) -> None:
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rng = np.random.default_rng(seed=42)
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fb = (rng.standard_normal(shape) + 1j * rng.standard_normal(shape)).astype(np.complex128)
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def _build():
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return BfldFrame.from_compressed_feedback(
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timestamp_ms=0,
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sounding_index=0,
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sta_mac="aa:bb:cc:dd:ee:ff",
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kind=kind,
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feedback_matrix=fb,
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)
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benchmark(_build)
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def test_bfld_feedback_matrix_roundtrip(benchmark) -> None:
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"""How expensive is the numpy-out round-trip? Used by clients
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that want to do further analysis in numpy after constructing
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the frame."""
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rng = np.random.default_rng(seed=42)
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fb = (rng.standard_normal((2, 1, 996)) + 1j * rng.standard_normal((2, 1, 996))).astype(np.complex128)
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frame = BfldFrame.from_compressed_feedback(
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timestamp_ms=0,
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sounding_index=0,
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sta_mac="aa:bb:cc:dd:ee:ff",
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kind=BfldKind.CompressedHE80,
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feedback_matrix=fb,
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)
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benchmark(frame.feedback_matrix)
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# ─── WS decoder ──────────────────────────────────────────────────────
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_EDGE_VITALS_FRAME = json.dumps({
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"type": "edge_vitals",
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"node_id": "bench-node",
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"presence": True,
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"fall_detected": False,
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"motion": 0.34,
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"breathing_rate_bpm": 14.2,
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"heartrate_bpm": 72.5,
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"n_persons": 1,
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"motion_energy": 0.04,
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"presence_score": 0.91,
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"rssi": -42.0,
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})
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def test_ws_decoder_edge_vitals(benchmark) -> None:
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from wifi_densepose.client.ws import _decode
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def _decode_one():
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return _decode(_EDGE_VITALS_FRAME)
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benchmark(_decode_one)
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_POSE_FRAME = json.dumps({
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"type": "pose_data",
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"node_id": "bench-node",
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"timestamp": 1700000000.5,
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"persons": [
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{"id": i, "keypoints": [[0.5, 0.5, 0.9] for _ in range(17)]}
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for i in range(3)
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],
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"confidence": 0.85,
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})
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def test_ws_decoder_pose_data(benchmark) -> None:
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"""The pose_data frame is typically the largest one the server
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emits — bench it separately so a future blob-size regression
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in the persons array is visible."""
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from wifi_densepose.client.ws import _decode
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def _decode_one():
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return _decode(_POSE_FRAME)
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benchmark(_decode_one)
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