128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
from __future__ import annotations
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import importlib.util
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from pathlib import Path
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SCRIPT = Path(__file__).parents[2] / "scripts" / "build_failure_driven_yolo_sampling.py"
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SPEC = importlib.util.spec_from_file_location("failure_sampling", SCRIPT)
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assert SPEC and SPEC.loader
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MODULE = importlib.util.module_from_spec(SPEC)
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SPEC.loader.exec_module(MODULE)
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def test_dataset_validation_source_preserves_manifest_path(tmp_path: Path):
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source = tmp_path / "dataset.yaml"
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source.write_text(
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"path: /data/source\ntrain: /data/source/train.txt\n"
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"val: /data/source/internal-val.txt\nnames:\n 0: building\n",
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encoding="utf-8",
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)
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assert MODULE.dataset_validation_source(source) == "/data/source/internal-val.txt"
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def test_sampling_repeats_only_failed_region_train_tiles() -> None:
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manifest = {
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"samples": [
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{"sample_slug": "train-fl", "split": "train", "region": "flanders"},
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{"sample_slug": "train-wa", "split": "train", "region": "wallonia"},
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{"sample_slug": "test-fl", "split": "test", "region": "flanders"},
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]
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}
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summary = {
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"tiles": [
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{"sample_slug": "train-fl", "split": "train", "label_count": 2, "image_path": "/tmp/fl-pos.png"},
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{"sample_slug": "train-fl", "split": "train", "label_count": 0, "image_path": "/tmp/fl-neg.png"},
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{"sample_slug": "train-wa", "split": "train", "label_count": 1, "image_path": "/tmp/wa-pos.png"},
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{"sample_slug": "test-fl", "split": "val", "label_count": 1, "image_path": "/tmp/protected.png"},
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]
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}
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assessment = {
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"status": "continue_training_loop",
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"gates": {
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"min_region_f1": 0.45,
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"min_region_precision": 0.5,
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"min_region_recall": 0.4,
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"max_pure_empty_false_positives": 0,
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},
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"test": {
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"regions": {
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"flanders": {"f1": 0.2, "precision": 0.3, "recall": 0.2},
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"wallonia": {"f1": 0.6, "precision": 0.6, "recall": 0.6},
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}
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},
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"background": {"pure_empty_false_positives": 2},
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}
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paths, metadata = MODULE.build_sampling(
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summary=summary, manifest=manifest, assessment=assessment
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)
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assert paths.count(str(Path("/tmp/fl-pos.png").resolve())) == 3
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assert paths.count(str(Path("/tmp/fl-neg.png").resolve())) == 4
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assert paths.count(str(Path("/tmp/wa-pos.png").resolve())) == 1
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assert not any("protected" in path for path in paths)
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assert metadata["protected_samples_in_training"] == []
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assert metadata["weak_recall_regions"] == ["flanders"]
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def test_sampling_can_use_calibration_before_test_is_opened() -> None:
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manifest = {"samples": [{"sample_slug": "train-fl", "split": "train", "region": "flanders"}]}
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summary = {
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"tiles": [
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{"sample_slug": "train-fl", "split": "train", "label_count": 1, "image_path": "/tmp/fl.png"}
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]
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}
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assessment = {
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"status": "continue_training_loop",
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"gates": {
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"min_region_f1": 0.45,
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"min_region_precision": 0.5,
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"min_region_recall": 0.4,
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"max_pure_empty_false_positives": 0,
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},
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"calibration": {
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"regions": {"flanders": {"f1": 0.4, "precision": 0.6, "recall": 0.35}}
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},
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"test": None,
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"background": None,
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}
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paths, metadata = MODULE.build_sampling(
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summary=summary, manifest=manifest, assessment=assessment
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)
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assert len(paths) == 3
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assert metadata["failure_evidence_source"] == "calibration"
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def test_precision_correction_can_balance_positive_and_negative_tiles() -> None:
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manifest = {"samples": [{"sample_slug": "train-fl", "split": "train", "region": "flanders"}]}
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summary = {
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"tiles": [
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{"sample_slug": "train-fl", "split": "train", "label_count": 2, "image_path": "/tmp/fl-pos.png"},
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{"sample_slug": "train-fl", "split": "train", "label_count": 0, "image_path": "/tmp/fl-neg.png"},
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]
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}
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assessment = {
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"status": "continue_training_loop",
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"gates": {
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"min_region_f1": 0.45,
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"min_region_precision": 0.5,
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"min_region_recall": 0.4,
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"max_pure_empty_false_positives": 0,
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},
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"calibration": {
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"regions": {"flanders": {"f1": 0.46, "precision": 0.45, "recall": 0.46}}
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},
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}
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paths, metadata = MODULE.build_sampling(
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summary=summary,
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manifest=manifest,
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assessment=assessment,
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precision_positive_repeat=2,
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negative_repeat=3,
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)
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assert paths.count(str(Path("/tmp/fl-pos.png").resolve())) == 2
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assert paths.count(str(Path("/tmp/fl-neg.png").resolve())) == 3
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assert metadata["precision_positive_repeat"] == 2
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