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| """HIL feature: ablation attribution, sequential/fallback verdicts, sweep.""" | |
| import numpy as np | |
| from sensorflow.vitis.hil import ( | |
| _fallback_verdict, run_bitwidth_sweep, run_hil, sequential_verdict, | |
| ) | |
| class TestAblationAttribution: | |
| def test_planted_precision_regression_attributed_to_precision(self, vitis_root): | |
| # W=8 with generous depth and LUTs on: precision is the only real | |
| # constraint that bites at this image size. | |
| run = run_hil(n_sequences=2, frames_per_sequence=8, seed=7, | |
| width_bits=8, int_bits=4, | |
| max_line_buffer_depth=2048, use_lut_approx=True) | |
| attr = run["ablation"]["attribution"] | |
| assert attr["precision_only"] > 0.8 | |
| assert attr["precision_only"] > attr["streaming_only"] | |
| assert attr["precision_only"] > attr["hls_approx_only"] | |
| def test_high_precision_run_has_negligible_gap(self, vitis_root): | |
| run = run_hil(n_sequences=2, frames_per_sequence=8, seed=7, | |
| width_bits=16, int_bits=4, run_ablation=False) | |
| assert run["comparison"]["gap_score"] < 0.01 | |
| assert run["comparison"]["totals"]["dropped_by_vitis"] == 0 | |
| class TestSequentialVerdicts: | |
| def test_fallback_regression_on_planted_regression(self): | |
| v = _fallback_verdict([-0.25 + 0.01 * (i % 3) for i in range(40)], | |
| delta=0.02, alpha=0.05) | |
| assert v["decision"] == "REGRESSION" | |
| def test_fallback_pass_on_no_regression(self): | |
| v = _fallback_verdict([0.002 - 0.001 * (i % 2) for i in range(40)], | |
| delta=0.02, alpha=0.05) | |
| assert v["decision"] == "PASS" | |
| def test_fallback_insufficient_on_tiny_sample(self): | |
| assert _fallback_verdict([0.0], 0.02, 0.05)["decision"] == \ | |
| "INSUFFICIENT_EVIDENCE" | |
| def test_sequential_verdict_detects_planted_regression(self): | |
| # Strong planted regression: vitis path loses ~half its objects. | |
| rng = np.random.default_rng(5) | |
| deltas = list(np.clip(rng.normal(-0.5, 0.05, 120), -1, 1)) | |
| pairs = [(True, bool(rng.random() > 0.5)) for _ in range(600)] | |
| v = sequential_verdict(deltas, pairs, delta=0.02, alpha=0.05) | |
| assert v["decision"] == "REGRESSION" | |
| assert v["method"] in ("seqeval_anytime_valid", "paired_t_fallback") | |
| def test_sequential_verdict_no_false_regression(self): | |
| deltas = [0.0] * 120 | |
| pairs = [(True, True)] * 600 | |
| v = sequential_verdict(deltas, pairs, delta=0.02, alpha=0.05) | |
| assert v["decision"] != "REGRESSION" | |
| class TestSweep: | |
| def test_sweep_monotone_gap_and_minimal_config(self, vitis_root): | |
| sw = run_bitwidth_sweep(n_sequences=2, frames_per_sequence=8, seed=7, | |
| widths=[6, 8, 12, 16]) | |
| pts = sw["points"] # sorted ascending by width | |
| gaps = [p["gap_score"] for p in pts] | |
| assert gaps == sorted(gaps, reverse=True), f"gap not monotone: {gaps}" | |
| assert pts[0]["decision"] == "REGRESSION" # W=6 is a planted disaster | |
| mp = sw["minimal_passing_config"] | |
| assert mp is not None and mp["width_bits"] > 6 | |
| def test_run_persisted(self, vitis_root): | |
| run = run_hil(n_sequences=1, frames_per_sequence=6, width_bits=12, | |
| run_ablation=False) | |
| assert (vitis_root / "hil" / f"{run['run_id']}.json").exists() | |