From 53f758cc07522254acad9c9ec33c7877b7bfb419 Mon Sep 17 00:00:00 2001 From: srvoyo-cell Date: Tue, 4 Aug 2026 11:14:51 +0400 Subject: [PATCH] Expand application functionality and coverage --- README.md | 32 +- scripts/run_yolo26_smoke_training.py | 38 +- scripts/verify_mlflow_smoke.py | 256 +++++++- src/yolo_webui/mlflow_metrics.py | 533 +++++++++++++++++ src/yolo_webui/trainer.py | 8 +- src/yolo_webui/ultralytics_trainers.py | 72 +++ tests/test_mlflow_metrics.py | 423 +++++++++++++ tests/test_mlflow_smoke_verifier.py | 43 ++ tests/test_trainer.py | 42 +- tests/test_ultralytics_trainers.py | 39 ++ uv.lock | 785 +++++++++++++++++++++++-- 11 files changed, 2213 insertions(+), 58 deletions(-) create mode 100644 src/yolo_webui/mlflow_metrics.py create mode 100644 src/yolo_webui/ultralytics_trainers.py create mode 100644 tests/test_mlflow_metrics.py create mode 100644 tests/test_mlflow_smoke_verifier.py create mode 100644 tests/test_ultralytics_trainers.py diff --git a/README.md b/README.md index f7b8ad3..f353f28 100755 --- a/README.md +++ b/README.md @@ -96,6 +96,31 @@ uv run mlflow ui --backend-store-uri sqlite:///mlflow.db артефакты запуска, а не версии MLflow Model Registry: raw YOLO checkpoint не имеет стандартной MLflow `MLmodel`-упаковки. +WebUI дополняет штатные метрики стабильным namespace `monitor/*`. Значения +собираются после validation текущей эпохи, поэтому не запаздывают на одну эпоху: + +| Задача | Основные дополнительные ряды | +|---|---| +| `detect` | box F1 при оптимальном confidence, mAP@0.75, метрики худшего класса | +| `segment` | F1 при оптимальном confidence и mAP@0.75 отдельно для box и mask | +| `classify` | top-1/top-5 error, macro precision/recall/F1, weighted F1, balanced accuracy | +| `pose` | F1 при оптимальном confidence и mAP@0.75 отдельно для box и keypoints (OKS) | +| `obb` | F1 при оптимальном confidence, mAP@0.75 и метрики худшего класса для oriented boxes | + +Для всех задач также записываются исходный Ultralytics fitness и нормализованный +`task_score` (для `segment`/`pose` сумма box+mask/keypoints приводится к диапазону +0–1), суммарные train/validation loss, gap/ratio между ними, средний learning rate, +время эпохи и скорость validation по стадиям. Теги `yolo.task` и +`monitoring.schema_version` позволяют фильтровать совместимые запуски. Финальные +per-class precision/recall/F1/AP и support сохраняются в артефакте +`monitoring/task_metrics.csv`, чтобы не создавать сотни поэпоховых рядов для +датасетов с большим числом классов. Monitor подключается внутри task-specific +trainer и сохраняется при запуске Ultralytics DDP на нескольких GPU. + +Шаги MLflow остаются совместимыми с Ultralytics: первая эпоха имеет `step=0`, а +финальная проверка лучшего checkpoint добавляется отдельной точкой после последней +эпохи. В WebUI эпохи отображаются привычно, начиная с 1. + Проверка интеграции на минимальных датасетах для всех пяти задач: ```bash @@ -103,8 +128,13 @@ uv run scripts/run_yolo26_smoke_training.py --mlflow uv run scripts/verify_mlflow_smoke.py ``` +Smoke-runner присваивает всей пятёрке запусков уникальный тег `yolo.run_group`; +проверка берёт только самый свежий batch и не смешивает его со старыми успешными +run-ами. + Второй скрипт завершается с ошибкой, если отсутствует experiment/run, параметры, -метрики, `results.csv`, `best.pt` или `last.pt` хотя бы для одной задачи. +task-specific метрики, их история, теги, `monitoring/task_metrics.csv`, +`results.csv`, `best.pt` или `last.pt` хотя бы для одной задачи. ## Проверка diff --git a/scripts/run_yolo26_smoke_training.py b/scripts/run_yolo26_smoke_training.py index 2996a23..6c5179b 100755 --- a/scripts/run_yolo26_smoke_training.py +++ b/scripts/run_yolo26_smoke_training.py @@ -4,8 +4,10 @@ from __future__ import annotations import argparse import json +import os import time import traceback +import uuid from pathlib import Path from yolo_webui import TrainingConfig, TrainingRunner @@ -39,6 +41,26 @@ def main() -> None: results: dict[str, dict[str, object]] = {} default_project = "runs/yolo26_mlflow_smoke/train" if args.mlflow else "runs/yolo26_smoke" project_dir = (args.project or Path(default_project)).resolve() + smoke_batch_id = uuid.uuid4().hex if args.mlflow else "" + if args.mlflow: + import mlflow + + project_dir.parent.mkdir(parents=True, exist_ok=True) + if args.tracking_uri.startswith("sqlite:///"): + tracking_db = Path(args.tracking_uri.removeprefix("sqlite:///")) + tracking_db.parent.mkdir(parents=True, exist_ok=True) + os.environ["YOLO_WEBUI_MLFLOW_RUN_GROUP"] = smoke_batch_id + mlflow.set_tracking_uri(args.tracking_uri) + mlflow.set_experiment(args.experiment) + with mlflow.start_run(run_name=f"smoke-batch-{smoke_batch_id}") as anchor: + mlflow.set_tags( + { + "smoke.anchor": "true", + "yolo.run_group": smoke_batch_id, + } + ) + print(f"MLflow smoke batch: {smoke_batch_id}", flush=True) + for task, (dataset, model) in TASKS.items(): print(f"\n=== {task}: {model} ===", flush=True) config = TrainingConfig( @@ -83,14 +105,24 @@ def main() -> None: "error": f"{type(exc).__name__}: {exc}", } - summary_path = project_dir.parent / "smoke_summary.json" if args.mlflow else project_dir / "smoke_summary.json" + summary_path = ( + project_dir.parent / "smoke_summary.json" + if args.mlflow + else project_dir / "smoke_summary.json" + ) summary_path.parent.mkdir(parents=True, exist_ok=True) + summary = { + "smoke_batch_id": smoke_batch_id or None, + "tracking_uri": args.tracking_uri if args.mlflow else None, + "experiment": args.experiment if args.mlflow else None, + "tasks": results, + } summary_path.write_text( - json.dumps(results, indent=2, ensure_ascii=False) + "\n", + json.dumps(summary, indent=2, ensure_ascii=False) + "\n", encoding="utf-8", ) print(f"\nSummary: {summary_path.resolve()}") - print(json.dumps(results, indent=2, ensure_ascii=False)) + print(json.dumps(summary, indent=2, ensure_ascii=False)) if any(result["status"] != "succeeded" for result in results.values()): raise SystemExit(1) diff --git a/scripts/verify_mlflow_smoke.py b/scripts/verify_mlflow_smoke.py index a32cff1..b2eae21 100755 --- a/scripts/verify_mlflow_smoke.py +++ b/scripts/verify_mlflow_smoke.py @@ -3,15 +3,139 @@ from __future__ import annotations import argparse +import csv import json +import math +import tempfile +from pathlib import Path import mlflow from mlflow.entities import Run from mlflow.tracking import MlflowClient +from yolo_webui.mlflow_metrics import PER_CLASS_FIELDS TASKS = ("detect", "segment", "classify", "pose", "obb") -REQUIRED_ARTIFACTS = {"weights/best.pt", "weights/last.pt", "results.csv"} +REQUIRED_ARTIFACTS = { + "weights/best.pt", + "weights/last.pt", + "results.csv", + "monitoring/task_metrics.csv", +} +BOX_METRICS = { + "metrics/precisionB", + "metrics/recallB", + "metrics/mAP50B", + "metrics/mAP50-95B", +} +REQUIRED_METRICS_BY_TASK = { + "detect": { + *BOX_METRICS, + "train/box_loss", + "train/cls_loss", + "train/dfl_loss", + "val/box_loss", + "val/cls_loss", + "val/dfl_loss", + "monitor/quality/box/f1_at_optimal_confidence", + "monitor/quality/box/map75", + }, + "segment": { + *BOX_METRICS, + "metrics/precisionM", + "metrics/recallM", + "metrics/mAP50M", + "metrics/mAP50-95M", + "train/seg_loss", + "train/box_loss", + "train/cls_loss", + "train/dfl_loss", + "val/seg_loss", + "val/box_loss", + "val/cls_loss", + "val/dfl_loss", + "monitor/quality/box/f1_at_optimal_confidence", + "monitor/quality/mask/f1_at_optimal_confidence", + "monitor/quality/mask/map75", + }, + "classify": { + "metrics/accuracy_top1", + "metrics/accuracy_top5", + "train/loss", + "val/loss", + "monitor/quality/classification/top1_error", + "monitor/quality/classification/macro_f1", + "monitor/quality/classification/balanced_accuracy", + }, + "pose": { + *BOX_METRICS, + "metrics/precisionP", + "metrics/recallP", + "metrics/mAP50P", + "metrics/mAP50-95P", + "train/pose_loss", + "train/box_loss", + "train/kobj_loss", + "train/cls_loss", + "train/dfl_loss", + "val/pose_loss", + "val/box_loss", + "val/kobj_loss", + "val/cls_loss", + "val/dfl_loss", + "monitor/quality/box/f1_at_optimal_confidence", + "monitor/quality/keypoints/f1_at_optimal_confidence", + "monitor/quality/keypoints/map75", + }, + "obb": { + *BOX_METRICS, + "train/box_loss", + "train/cls_loss", + "train/dfl_loss", + "train/angle_loss", + "val/box_loss", + "val/cls_loss", + "val/dfl_loss", + "val/angle_loss", + "monitor/quality/oriented_box/f1_at_optimal_confidence", + "monitor/quality/oriented_box/map75", + }, +} +COMMON_MONITOR_METRICS = { + "monitor/fitness/ultralytics_current", + "monitor/fitness/ultralytics_best", + "monitor/fitness/task_score_current", + "monitor/fitness/task_score_best", + "monitor/loss/train_total", + "monitor/loss/validation_total", + "monitor/loss/generalization_gap", + "monitor/loss/validation_to_train_ratio", + "monitor/optimization/learning_rate_mean", + "monitor/performance/epoch_seconds", + "monitor/performance/validation_preprocess_ms_per_image", + "monitor/performance/validation_inference_ms_per_image", + "monitor/performance/validation_loss_ms_per_image", + "monitor/performance/validation_postprocess_ms_per_image", +} +HISTORY_METRIC_BY_TASK = { + "detect": "monitor/quality/box/f1_at_optimal_confidence", + "segment": "monitor/quality/mask/f1_at_optimal_confidence", + "classify": "monitor/quality/classification/macro_f1", + "pose": "monitor/quality/keypoints/f1_at_optimal_confidence", + "obb": "monitor/quality/oriented_box/f1_at_optimal_confidence", +} +EXPECTED_COMPONENTS = { + "detect": {"box"}, + "segment": {"box", "mask"}, + "classify": {"classification"}, + "pose": {"box", "keypoints"}, + "obb": {"oriented_box"}, +} + + +def require(condition: object, message: str) -> None: + if not condition: + raise AssertionError(message) def artifact_paths(client: MlflowClient, run_id: str, path: str = "") -> set[str]: @@ -24,12 +148,74 @@ def artifact_paths(client: MlflowClient, run_id: str, path: str = "") -> set[str return result -def latest_task_run(runs: list[Run], task: str) -> Run: +def latest_task_run(runs: list[Run], task: str, run_group: str) -> Run: expected_name = f"{task}-smoke" for run in runs: - if run.data.tags.get("mlflow.runName") == expected_name: + if ( + run.data.tags.get("mlflow.runName") == expected_name + and run.data.tags.get("yolo.run_group") == run_group + ): return run - raise AssertionError(f"MLflow run not found: {expected_name}") + raise AssertionError( + f"MLflow run not found: {expected_name}, yolo.run_group={run_group}" + ) + + +def latest_smoke_run_group(runs: list[Run]) -> str: + for run in runs: + if ( + run.data.tags.get("smoke.anchor") == "true" + and run.data.tags.get("yolo.run_group") + ): + return run.data.tags["yolo.run_group"] + raise AssertionError("No MLflow smoke batch with yolo.run_group tag found") + + +def verify_per_class_artifact( + client: MlflowClient, + run_id: str, + task: str, +) -> int: + with tempfile.TemporaryDirectory() as directory: + downloaded = Path( + client.download_artifacts( + run_id, + "monitoring/task_metrics.csv", + dst_path=directory, + ) + ) + with downloaded.open(encoding="utf-8") as stream: + reader = csv.DictReader(stream) + rows = list(reader) + require( + tuple(reader.fieldnames or ()) == PER_CLASS_FIELDS, + f"Invalid task_metrics.csv header for {task}: {reader.fieldnames}", + ) + + require(rows, f"Empty task_metrics.csv for {task}") + components = {row["component"] for row in rows} + require( + components == EXPECTED_COMPONENTS[task], + f"Invalid task_metrics.csv components for {task}: {sorted(components)}", + ) + for row in rows: + require(row["task"] == task, f"Invalid task in task_metrics.csv: {row}") + require(row["class_id"].isdigit(), f"Invalid class_id in task_metrics.csv: {row}") + numeric_fields = ("support", "precision", "recall", "f1") + if task != "classify": + numeric_fields += ("map50", "map50_95") + for field in numeric_fields: + try: + value = float(row[field]) + except (TypeError, ValueError) as exc: + raise AssertionError( + f"Invalid {field} in task_metrics.csv for {task}: {row}" + ) from exc + require( + math.isfinite(value), + f"Non-finite {field} in task_metrics.csv for {task}: {row}", + ) + return len(rows) def main() -> None: @@ -39,6 +225,10 @@ def main() -> None: default="sqlite:///runs/yolo26_mlflow_smoke/mlflow.db", ) parser.add_argument("--experiment", default="yolo26-mlflow-smoke") + parser.add_argument( + "--run-group", + help="Verify this yolo.run_group tag; defaults to the newest smoke batch.", + ) args = parser.parse_args() mlflow.set_tracking_uri(args.tracking_uri) @@ -51,27 +241,75 @@ def main() -> None: [experiment.experiment_id], order_by=["start_time DESC"], ) + run_group = args.run_group + if not run_group: + run_group = latest_smoke_run_group(runs) + summary: dict[str, object] = { "tracking_uri": args.tracking_uri, "experiment_id": experiment.experiment_id, "artifact_location": experiment.artifact_location, + "run_group": run_group, "tasks": {}, } task_summary: dict[str, object] = summary["tasks"] # type: ignore[assignment] for task in TASKS: - run = latest_task_run(runs, task) + run = latest_task_run(runs, task, run_group) artifacts = artifact_paths(client, run.info.run_id) missing = REQUIRED_ARTIFACTS - artifacts - assert run.info.status == "FINISHED", (task, run.info.status) - assert run.data.params, f"No parameters logged for {task}" - assert run.data.metrics, f"No metrics logged for {task}" - assert not missing, f"Missing artifacts for {task}: {sorted(missing)}" + required_metrics = COMMON_MONITOR_METRICS | REQUIRED_METRICS_BY_TASK[task] + missing_metrics = required_metrics - run.data.metrics.keys() + require( + run.info.status == "FINISHED", + f"Unexpected run status for {task}: {run.info.status}", + ) + require(run.data.params, f"No parameters logged for {task}") + require( + not missing_metrics, + f"Missing metrics for {task}: {sorted(missing_metrics)}", + ) + invalid_metrics = { + key: run.data.metrics[key] + for key in required_metrics + if not math.isfinite(run.data.metrics[key]) + } + require( + not invalid_metrics, + f"Non-finite metrics for {task}: {invalid_metrics}", + ) + require(not missing, f"Missing artifacts for {task}: {sorted(missing)}") + for metric_key in required_metrics: + require( + client.get_metric_history(run.info.run_id, metric_key), + f"No metric history for {task}: {metric_key}", + ) + history_key = HISTORY_METRIC_BY_TASK[task] + history = client.get_metric_history(run.info.run_id, history_key) + require( + all(math.isfinite(point.value) for point in history), + f"Non-finite metric history for {task}: {history_key} {history}", + ) + history_steps = [point.step for point in history] + require( + history_steps == [0, 1], + f"Unexpected metric steps for {task}: {history_key} {history_steps}", + ) + require(run.data.tags.get("yolo.task") == task, f"Missing task tag for {task}") + require( + run.data.tags.get("monitoring.schema_version") == "1", + f"Missing monitoring schema tag for {task}", + ) + per_class_rows = verify_per_class_artifact(client, run.info.run_id, task) task_summary[task] = { "run_id": run.info.run_id, "status": run.info.status, "parameters": len(run.data.params), "metrics": len(run.data.metrics), + "required_metrics": sorted(required_metrics), + "history_metric": history_key, + "history_steps": history_steps, + "per_class_rows": per_class_rows, "artifact_uri": run.info.artifact_uri, "required_artifacts": sorted(REQUIRED_ARTIFACTS), } diff --git a/src/yolo_webui/mlflow_metrics.py b/src/yolo_webui/mlflow_metrics.py new file mode 100644 index 0000000..057166a --- /dev/null +++ b/src/yolo_webui/mlflow_metrics.py @@ -0,0 +1,533 @@ +from __future__ import annotations + +import csv +import logging +import math +import os +from collections.abc import Iterable, Mapping, MutableMapping +from pathlib import Path +from typing import Any + +from .config import YoloTask + +logger = logging.getLogger(__name__) + +MONITORING_SCHEMA_VERSION = "1" + +# Public component names intentionally do not mirror Ultralytics' one-letter +# suffixes. In particular, OBB uses "B" upstream even though its boxes are rotated. +TASK_COMPONENTS: dict[YoloTask, tuple[tuple[str, str], ...]] = { + "detect": (("box", "box"),), + "segment": (("box", "box"), ("mask", "seg")), + "classify": (), + "pose": (("box", "box"), ("keypoints", "pose")), + "obb": (("oriented_box", "box"),), +} + +PER_CLASS_FIELDS = ( + "task", + "component", + "class_id", + "class_name", + "support", + "precision", + "recall", + "f1", + "map50", + "map50_95", +) + + +def _finite_float(value: Any) -> float | None: + """Return a finite Python float for scalar-like values.""" + if value is None or isinstance(value, (str, bytes, bool)): + return None + for method in ("detach", "cpu"): + operation = getattr(value, method, None) + if callable(operation): + try: + value = operation() + except Exception: + return None + item = getattr(value, "item", None) + if callable(item): + try: + value = item() + except Exception: + pass + try: + number = float(value) + except (TypeError, ValueError, OverflowError): + return None + return number if math.isfinite(number) else None + + +def _finite_values(value: Any) -> list[float]: + """Flatten array-like values while dropping non-finite entries.""" + if value is None or isinstance(value, (str, bytes, bool)): + return [] + for method in ("detach", "cpu"): + operation = getattr(value, method, None) + if callable(operation): + try: + value = operation() + except Exception: + return [] + tolist = getattr(value, "tolist", None) + if callable(tolist): + try: + value = tolist() + except Exception: + return [] + if isinstance(value, Mapping): + source: Iterable[Any] = value.values() + elif isinstance(value, Iterable): + source = value + else: + number = _finite_float(value) + return [] if number is None else [number] + + result: list[float] = [] + for item in source: + result.extend(_finite_values(item)) + return result + + +def _aligned_values(value: Any) -> list[float | None]: + """Convert a one-dimensional array without shifting non-finite positions.""" + if value is None or isinstance(value, (str, bytes, bool)): + return [] + for method in ("detach", "cpu"): + operation = getattr(value, method, None) + if callable(operation): + try: + value = operation() + except Exception: + return [] + tolist = getattr(value, "tolist", None) + if callable(tolist): + try: + value = tolist() + except Exception: + return [] + if isinstance(value, Mapping): + source: Iterable[Any] = value.values() + elif isinstance(value, Iterable): + source = value + else: + return [_finite_float(value)] + return [_finite_float(item) for item in source] + + +def _attribute(obj: Any, name: str) -> Any: + if obj is None: + return None + value = getattr(obj, name, None) + if callable(value): + try: + return value() + except Exception: + return None + return value + + +def _add_metric(metrics: dict[str, float], key: str, value: Any) -> None: + number = _finite_float(value) + if number is not None: + metrics[key] = number + + +def _mean(values: list[float]) -> float | None: + return sum(values) / len(values) if values else None + + +def _harmonic_mean(precision: float | None, recall: float | None) -> float | None: + if precision is None or recall is None: + return None + denominator = precision + recall + return 0.0 if denominator == 0 else 2 * precision * recall / denominator + + +def _component_metrics(name: str, component: Any) -> dict[str, float]: + prefix = f"monitor/quality/{name}" + result: dict[str, float] = {} + precision = _finite_float(_attribute(component, "mp")) + recall = _finite_float(_attribute(component, "mr")) + f1_values = _finite_values(_attribute(component, "f1")) + ap_values = _finite_values(_attribute(component, "ap")) + mean_f1 = _mean(f1_values) + + _add_metric(result, f"{prefix}/precision", precision) + _add_metric(result, f"{prefix}/recall", recall) + _add_metric( + result, + f"{prefix}/f1_at_optimal_confidence", + mean_f1 if mean_f1 is not None else _harmonic_mean(precision, recall), + ) + _add_metric(result, f"{prefix}/map50", _attribute(component, "map50")) + _add_metric(result, f"{prefix}/map75", _attribute(component, "map75")) + _add_metric(result, f"{prefix}/map50_95", _attribute(component, "map")) + _add_metric( + result, + f"{prefix}/worst_class_f1_at_optimal_confidence", + min(f1_values) if f1_values else None, + ) + _add_metric(result, f"{prefix}/worst_class_map50_95", min(ap_values) if ap_values else None) + _add_metric(result, f"{prefix}/classes_evaluated", len(ap_values) or len(f1_values)) + return result + + +def _matrix_rows(matrix: Any) -> list[list[float]]: + raw_rows = _attribute(matrix, "tolist") + if raw_rows is None: + raw_rows = matrix + if not isinstance(raw_rows, Iterable) or isinstance(raw_rows, (str, bytes)): + return [] + + rows: list[list[float]] = [] + for row in raw_rows: + if not isinstance(row, Iterable) or isinstance(row, (str, bytes)): + return [] + converted: list[float] = [] + for value in row: + number = _finite_float(value) + converted.append(0.0 if number is None else max(0.0, number)) + rows.append(converted) + size = len(rows) + return rows if size and all(len(row) == size for row in rows) else [] + + +def _classification_statistics(metric_set: Any) -> tuple[dict[str, float], list[dict[str, Any]]]: + prefix = "monitor/quality/classification" + result: dict[str, float] = {} + top1 = _finite_float(_attribute(metric_set, "top1")) + top5 = _finite_float(_attribute(metric_set, "top5")) + _add_metric(result, f"{prefix}/top1_accuracy", top1) + _add_metric(result, f"{prefix}/top5_accuracy", top5) + _add_metric(result, f"{prefix}/top1_error", None if top1 is None else 1.0 - top1) + _add_metric(result, f"{prefix}/top5_error", None if top5 is None else 1.0 - top5) + + confusion = _attribute(metric_set, "confusion_matrix") + rows = _matrix_rows(_attribute(confusion, "matrix")) + names = _attribute(confusion, "names") or {} + per_class: list[dict[str, Any]] = [] + if not rows: + return result, per_class + + precisions: list[float] = [] + supported_recalls: list[float] = [] + f1_scores: list[float] = [] + supports: list[float] = [] + for class_id in range(len(rows)): + true_positive = rows[class_id][class_id] + predicted = sum(rows[class_id]) + support = sum(row[class_id] for row in rows) + if support <= 0 and predicted <= 0: + continue + precision = true_positive / predicted if predicted else 0.0 + recall = true_positive / support if support else 0.0 + f1 = _harmonic_mean(precision, recall) or 0.0 + precisions.append(precision) + f1_scores.append(f1) + supports.append(support) + if support > 0: + supported_recalls.append(recall) + per_class.append( + { + "task": "classify", + "component": "classification", + "class_id": class_id, + "class_name": _class_name(names, class_id), + "support": support, + "precision": precision, + "recall": recall, + "f1": f1, + "map50": "", + "map50_95": "", + } + ) + + total_support = sum(supports) + weighted_f1 = ( + sum(score * support for score, support in zip(f1_scores, supports)) / total_support + if total_support + else None + ) + _add_metric(result, f"{prefix}/macro_precision", _mean(precisions)) + _add_metric(result, f"{prefix}/macro_recall", _mean(supported_recalls)) + _add_metric(result, f"{prefix}/macro_f1", _mean(f1_scores)) + _add_metric(result, f"{prefix}/weighted_f1", weighted_f1) + _add_metric(result, f"{prefix}/balanced_accuracy", _mean(supported_recalls)) + _add_metric( + result, + f"{prefix}/worst_class_recall", + min(supported_recalls) if supported_recalls else None, + ) + _add_metric(result, f"{prefix}/classes_evaluated", len(per_class)) + return result, per_class + + +def collect_monitoring_metrics(task: YoloTask, trainer: Any) -> dict[str, float]: + """Collect stable, task-aware metrics from an Ultralytics trainer.""" + result: dict[str, float] = {} + trainer_metrics = getattr(trainer, "metrics", {}) or {} + validator = getattr(trainer, "validator", None) + metric_set = getattr(validator, "metrics", None) + final_validation = ( + validator is not None and getattr(validator, "training", True) is False + ) + + current_fitness = _finite_float(_attribute(metric_set, "fitness")) + if current_fitness is None: + current_fitness = _finite_float(getattr(trainer, "fitness", None)) + best_fitness = _finite_float(getattr(trainer, "best_fitness", None)) + _add_metric(result, "monitor/fitness/ultralytics_current", current_fitness) + _add_metric(result, "monitor/fitness/ultralytics_best", best_fitness) + fitness_scale = max(1, len(TASK_COMPONENTS[task])) + _add_metric( + result, + "monitor/fitness/task_score_current", + None if current_fitness is None else current_fitness / fitness_scale, + ) + _add_metric( + result, + "monitor/fitness/task_score_best", + None if best_fitness is None else best_fitness / fitness_scale, + ) + + if not final_validation: + train_total: float | None = None + validation_total: float | None = None + label_losses = getattr(trainer, "label_loss_items", None) + if callable(label_losses): + try: + train_losses = label_losses(getattr(trainer, "tloss", None), prefix="train") + except Exception: + train_losses = {} + if isinstance(train_losses, Mapping): + values = [ + number + for value in train_losses.values() + if (number := _finite_float(value)) is not None + ] + train_total = sum(values) if values else None + _add_metric(result, "monitor/loss/train_total", train_total) + + if isinstance(trainer_metrics, Mapping): + validation_loss_keys: set[str] = set() + if callable(label_losses): + try: + expected_losses = label_losses(None, prefix="val") + except Exception: + expected_losses = () + if isinstance(expected_losses, Mapping): + validation_loss_keys.update(map(str, expected_losses)) + elif isinstance(expected_losses, Iterable) and not isinstance( + expected_losses, + (str, bytes), + ): + validation_loss_keys.update(map(str, expected_losses)) + if not validation_loss_keys: + validation_loss_keys = { + str(key) + for key in trainer_metrics + if str(key) == "val/loss" + or ( + str(key).startswith("val/") + and str(key).endswith("_loss") + ) + } + val_losses = [ + number + for key, value in trainer_metrics.items() + if str(key) in validation_loss_keys + and (number := _finite_float(value)) is not None + ] + validation_total = sum(val_losses) if val_losses else None + _add_metric(result, "monitor/loss/validation_total", validation_total) + + if train_total is not None and validation_total is not None: + _add_metric( + result, + "monitor/loss/generalization_gap", + validation_total - train_total, + ) + if train_total > 0: + _add_metric( + result, + "monitor/loss/validation_to_train_ratio", + validation_total / train_total, + ) + + learning_rates = _finite_values(getattr(trainer, "lr", None)) + _add_metric(result, "monitor/optimization/learning_rate_mean", _mean(learning_rates)) + _add_metric( + result, + "monitor/performance/epoch_seconds", + getattr(trainer, "epoch_time", None), + ) + + speed = _attribute(metric_set, "speed") + if isinstance(speed, Mapping): + for stage in ("preprocess", "inference", "loss", "postprocess"): + _add_metric( + result, + f"monitor/performance/validation_{stage}_ms_per_image", + speed.get(stage), + ) + + if task == "classify": + classification, _ = _classification_statistics(metric_set) + result.update(classification) + else: + for public_name, attribute_name in TASK_COMPONENTS[task]: + component = _attribute(metric_set, attribute_name) + if component is not None: + result.update(_component_metrics(public_name, component)) + return result + + +def _class_name(names: Any, class_id: int) -> str: + if isinstance(names, Mapping): + return str(names.get(class_id, names.get(str(class_id), class_id))) + if isinstance(names, (list, tuple)) and 0 <= class_id < len(names): + return str(names[class_id]) + return str(class_id) + + +def _value_at(values: list[float | None], index: int) -> float | str: + if index >= len(values) or values[index] is None: + return "" + return values[index] + + +def _component_rows( + task: YoloTask, + public_name: str, + component: Any, + metric_set: Any, +) -> list[dict[str, Any]]: + raw_class_indices = _aligned_values(_attribute(component, "ap_class_index")) + precision = _aligned_values(_attribute(component, "p")) + recall = _aligned_values(_attribute(component, "r")) + f1 = _aligned_values(_attribute(component, "f1")) + map50 = _aligned_values(_attribute(component, "ap50")) + map50_95 = _aligned_values(_attribute(component, "ap")) + count = max( + map(len, (raw_class_indices, precision, recall, f1, map50, map50_95)), + default=0, + ) + class_indices = [ + index if value is None else int(value) + for index, value in enumerate(raw_class_indices) + ] + if not class_indices: + class_indices = list(range(count)) + + names = _attribute(metric_set, "names") or {} + support_by_class = _aligned_values(_attribute(metric_set, "nt_per_class")) + rows: list[dict[str, Any]] = [] + for index in range(min(count, len(class_indices))): + class_id = class_indices[index] + rows.append( + { + "task": task, + "component": public_name, + "class_id": class_id, + "class_name": _class_name(names, class_id), + "support": _value_at(support_by_class, class_id), + "precision": _value_at(precision, index), + "recall": _value_at(recall, index), + "f1": _value_at(f1, index), + "map50": _value_at(map50, index), + "map50_95": _value_at(map50_95, index), + } + ) + return rows + + +def collect_per_class_metrics(task: YoloTask, trainer: Any) -> list[dict[str, Any]]: + """Build final per-class diagnostics for the MLflow CSV artifact.""" + validator = getattr(trainer, "validator", None) + metric_set = getattr(validator, "metrics", None) + if metric_set is None: + return [] + if task == "classify": + _, rows = _classification_statistics(metric_set) + return rows + + result: list[dict[str, Any]] = [] + for public_name, attribute_name in TASK_COMPONENTS[task]: + component = _attribute(metric_set, attribute_name) + if component is not None: + result.extend(_component_rows(task, public_name, component, metric_set)) + return result + + +class TaskMetricsMonitor: + """Enrich Ultralytics metrics before its built-in MLflow callback runs.""" + + def __init__(self, task: YoloTask, *, mlflow_enabled: bool) -> None: + self.task = task + self.mlflow_enabled = mlflow_enabled + + def on_train_start(self, trainer: Any) -> None: + """Attach searchable task/schema tags without ever starting a second run.""" + if not self.mlflow_enabled or not getattr(trainer, "_mlflow_active", False): + return + try: + import mlflow + + if mlflow.active_run() is not None: + tags = { + "monitoring.schema_version": MONITORING_SCHEMA_VERSION, + "yolo.task": self.task, + } + run_group = os.environ.get("YOLO_WEBUI_MLFLOW_RUN_GROUP", "").strip() + if run_group: + tags["yolo.run_group"] = run_group + mlflow.set_tags(tags) + except Exception as exc: + logger.warning( + "Не удалось записать теги мониторинга " + "в MLflow: %s", + exc, + ) + + def on_fit_epoch_end(self, trainer: Any) -> None: + """Add derived metrics to trainer.metrics for the current validation epoch.""" + if not self.mlflow_enabled: + return + metrics = getattr(trainer, "metrics", None) + if not isinstance(metrics, MutableMapping): + return + try: + metrics.update(collect_monitoring_metrics(self.task, trainer)) + except Exception as exc: + logger.warning( + "Не удалось собрать task-specific метрики: %s", + exc, + ) + + def on_train_end(self, trainer: Any) -> None: + """Write and upload final per-class diagnostics to the active MLflow run.""" + if not self.mlflow_enabled: + return + try: + rows = collect_per_class_metrics(self.task, trainer) + output = Path(trainer.save_dir) / "monitoring" / "task_metrics.csv" + output.parent.mkdir(parents=True, exist_ok=True) + with output.open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=PER_CLASS_FIELDS) + writer.writeheader() + writer.writerows(rows) + if getattr(trainer, "_mlflow_active", False): + import mlflow + + if mlflow.active_run() is not None: + mlflow.log_artifact(str(output), artifact_path="monitoring") + except Exception as exc: + logger.warning( + "Не удалось сохранить per-class метрики: %s", + exc, + ) diff --git a/src/yolo_webui/trainer.py b/src/yolo_webui/trainer.py index 95bbea5..99bacf9 100755 --- a/src/yolo_webui/trainer.py +++ b/src/yolo_webui/trainer.py @@ -189,6 +189,7 @@ class TrainingRunner: on_event(TrainingEvent("info", "Загрузка Ultralytics и подготовка модели…")) from ultralytics import YOLO, settings + from .ultralytics_trainers import trainer_for_task settings.update({"mlflow": config.mlflow.enabled}) @@ -198,11 +199,11 @@ class TrainingRunner: self._model = model model.add_callback("on_train_start", self._on_train_start(on_event)) - model.add_callback("on_train_epoch_end", self._on_epoch_end(on_event)) + model.add_callback("on_fit_epoch_end", self._on_epoch_end(on_event)) model.add_callback("on_train_end", self._on_train_end(on_event)) try: - model.train(**train_args) + model.train(trainer=trainer_for_task(config.task), **train_args) trainer = getattr(model, "trainer", None) save_dir = getattr(trainer, "save_dir", None) return Path(save_dir) if save_dir else None @@ -221,6 +222,9 @@ class TrainingRunner: def _on_epoch_end(self, on_event: EventHandler) -> Callable[[Any], None]: def callback(trainer: Any) -> None: + validator = getattr(trainer, "validator", None) + if validator is not None and getattr(validator, "training", True) is False: + return # final best-checkpoint validation is not an additional train epoch epoch = int(getattr(trainer, "epoch", 0)) + 1 total = int(getattr(getattr(trainer, "args", None), "epochs", 0)) metrics = getattr(trainer, "metrics", {}) or {} diff --git a/src/yolo_webui/ultralytics_trainers.py b/src/yolo_webui/ultralytics_trainers.py new file mode 100644 index 0000000..603e854 --- /dev/null +++ b/src/yolo_webui/ultralytics_trainers.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +from typing import Any, ClassVar + +from ultralytics.models.yolo.classify.train import ClassificationTrainer +from ultralytics.models.yolo.detect.train import DetectionTrainer +from ultralytics.models.yolo.obb.train import OBBTrainer +from ultralytics.models.yolo.pose.train import PoseTrainer +from ultralytics.models.yolo.segment.train import SegmentationTrainer +from ultralytics.utils import SETTINGS + +from .config import YoloTask +from .mlflow_metrics import TaskMetricsMonitor + + +class _TaskMetricsTrainerMixin: + """Install monitoring inside the trainer so Ultralytics DDP keeps it.""" + + monitoring_task: ClassVar[YoloTask] + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self._install_task_metrics_monitor() + + def _install_task_metrics_monitor(self) -> None: + monitor = TaskMetricsMonitor( + self.monitoring_task, + mlflow_enabled=SETTINGS["mlflow"] is True, + ) + self._task_metrics_monitor = monitor + callbacks = ( + ("on_train_start", monitor.on_train_start), + ("on_fit_epoch_end", monitor.on_fit_epoch_end), + ("on_train_end", monitor.on_train_end), + ) + for event, callback in callbacks: + # BaseTrainer adds integrations in __init__. Prepending ensures our + # metrics and CSV exist before Ultralytics logs them to MLflow. + self.callbacks.setdefault(event, []).insert(0, callback) + + +class MonitoredDetectionTrainer(_TaskMetricsTrainerMixin, DetectionTrainer): + monitoring_task = "detect" + + +class MonitoredSegmentationTrainer(_TaskMetricsTrainerMixin, SegmentationTrainer): + monitoring_task = "segment" + + +class MonitoredClassificationTrainer(_TaskMetricsTrainerMixin, ClassificationTrainer): + monitoring_task = "classify" + + +class MonitoredPoseTrainer(_TaskMetricsTrainerMixin, PoseTrainer): + monitoring_task = "pose" + + +class MonitoredOBBTrainer(_TaskMetricsTrainerMixin, OBBTrainer): + monitoring_task = "obb" + + +MONITORED_TRAINERS = { + "detect": MonitoredDetectionTrainer, + "segment": MonitoredSegmentationTrainer, + "classify": MonitoredClassificationTrainer, + "pose": MonitoredPoseTrainer, + "obb": MonitoredOBBTrainer, +} + + +def trainer_for_task(task: YoloTask) -> type: + return MONITORED_TRAINERS[task] diff --git a/tests/test_mlflow_metrics.py b/tests/test_mlflow_metrics.py new file mode 100644 index 0000000..671abcc --- /dev/null +++ b/tests/test_mlflow_metrics.py @@ -0,0 +1,423 @@ +from __future__ import annotations + +import csv +import math +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +import pytest + +from yolo_webui.config import YoloTask +from yolo_webui.mlflow_metrics import ( + PER_CLASS_FIELDS, + TaskMetricsMonitor, + collect_monitoring_metrics, + collect_per_class_metrics, +) + + +def _component(offset: float = 0.0) -> SimpleNamespace: + return SimpleNamespace( + mp=0.75 + offset, + mr=0.65 + offset, + p=[0.8 + offset, 0.7 + offset], + r=[0.6 + offset, 0.7 + offset], + f1=[0.685714 + offset, 0.7 + offset], + map50=0.72 + offset, + map75=0.58 + offset, + map=0.61 + offset, + ap50=[0.76 + offset, 0.68 + offset], + ap=[0.64 + offset, 0.58 + offset], + ap_class_index=[0, 1], + ) + + +def _metric_set(task: YoloTask) -> SimpleNamespace: + common: dict[str, Any] = { + "fitness": 0.61, + "speed": { + "preprocess": 0.1, + "inference": 1.2, + "loss": 0.3, + "postprocess": 0.4, + }, + "names": {0: "cat", 1: "dog"}, + "nt_per_class": [7, 5], + } + if task == "classify": + return SimpleNamespace( + **common, + top1=0.8, + top5=0.95, + confusion_matrix=SimpleNamespace( + matrix=[ + [7, 2], + [1, 4], + ], + names=common["names"], + ), + ) + + common["box"] = _component() + if task == "segment": + common["seg"] = _component(0.05) + elif task == "pose": + common["pose"] = _component(0.1) + return SimpleNamespace(**common) + + +def _trainer(task: YoloTask, save_dir: Path | None = None) -> SimpleNamespace: + def label_loss_items( + loss: object, + prefix: str = "train", + ) -> dict[str, float] | list[str]: + keys = [f"{prefix}/first_loss", f"{prefix}/second_loss"] + return keys if loss is None else dict(zip(keys, (0.25, 0.5))) + + return SimpleNamespace( + metrics={ + "val/first_loss": 0.2, + "val/second_loss": 0.3, + "val/future_non_loss_metric": 99.0, + }, + validator=SimpleNamespace(metrics=_metric_set(task), training=True), + fitness=0.6, + best_fitness=0.65, + tloss=[0.25, 0.5], + label_loss_items=label_loss_items, + lr={"lr/pg0": 0.01, "lr/pg1": 0.02}, + epoch_time=12.5, + save_dir=save_dir, + ) + + +@pytest.mark.parametrize( + ("task", "required"), + [ + ( + "detect", + { + "monitor/quality/box/f1_at_optimal_confidence", + "monitor/quality/box/map75", + "monitor/quality/box/worst_class_map50_95", + }, + ), + ( + "segment", + { + "monitor/quality/box/f1_at_optimal_confidence", + "monitor/quality/mask/f1_at_optimal_confidence", + "monitor/quality/mask/map75", + }, + ), + ( + "pose", + { + "monitor/quality/box/f1_at_optimal_confidence", + "monitor/quality/keypoints/f1_at_optimal_confidence", + "monitor/quality/keypoints/map75", + }, + ), + ( + "obb", + { + "monitor/quality/oriented_box/f1_at_optimal_confidence", + "monitor/quality/oriented_box/map75", + "monitor/quality/oriented_box/worst_class_map50_95", + }, + ), + ( + "classify", + { + "monitor/quality/classification/top1_error", + "monitor/quality/classification/macro_f1", + "monitor/quality/classification/balanced_accuracy", + "monitor/quality/classification/worst_class_recall", + }, + ), + ], +) +def test_collect_monitoring_metrics_has_task_specific_contract( + task: YoloTask, + required: set[str], +) -> None: + metrics = collect_monitoring_metrics(task, _trainer(task)) + + assert required <= metrics.keys() + assert metrics["monitor/fitness/ultralytics_current"] == pytest.approx(0.61) + assert metrics["monitor/fitness/ultralytics_best"] == pytest.approx(0.65) + expected_scale = 2 if task in {"segment", "pose"} else 1 + assert metrics["monitor/fitness/task_score_current"] == pytest.approx( + 0.61 / expected_scale + ) + assert metrics["monitor/fitness/task_score_best"] == pytest.approx( + 0.65 / expected_scale + ) + assert metrics["monitor/loss/train_total"] == pytest.approx(0.75) + assert metrics["monitor/loss/validation_total"] == pytest.approx(0.5) + assert metrics["monitor/loss/generalization_gap"] == pytest.approx(-0.25) + assert metrics["monitor/loss/validation_to_train_ratio"] == pytest.approx(2 / 3) + assert metrics["monitor/optimization/learning_rate_mean"] == pytest.approx(0.015) + assert metrics["monitor/performance/epoch_seconds"] == pytest.approx(12.5) + assert metrics["monitor/performance/validation_inference_ms_per_image"] == pytest.approx(1.2) + + +def test_classification_metrics_are_derived_from_confusion_matrix() -> None: + metrics = collect_monitoring_metrics("classify", _trainer("classify")) + + assert metrics["monitor/quality/classification/top1_error"] == pytest.approx(0.2) + assert metrics["monitor/quality/classification/macro_precision"] == pytest.approx( + (7 / 9 + 4 / 5) / 2 + ) + assert metrics["monitor/quality/classification/macro_recall"] == pytest.approx( + (7 / 8 + 4 / 6) / 2 + ) + assert metrics["monitor/quality/classification/classes_evaluated"] == 2 + + +def test_mask_and_keypoint_metrics_use_their_own_components() -> None: + segment_metrics = collect_monitoring_metrics("segment", _trainer("segment")) + pose_metrics = collect_monitoring_metrics("pose", _trainer("pose")) + + assert segment_metrics[ + "monitor/quality/mask/f1_at_optimal_confidence" + ] == pytest.approx((0.735714 + 0.75) / 2) + assert pose_metrics[ + "monitor/quality/keypoints/f1_at_optimal_confidence" + ] == pytest.approx((0.785714 + 0.8) / 2) + assert segment_metrics[ + "monitor/quality/mask/f1_at_optimal_confidence" + ] != segment_metrics["monitor/quality/box/f1_at_optimal_confidence"] + + +def test_classification_macro_metrics_include_false_positive_only_class() -> None: + trainer = _trainer("classify") + trainer.validator.metrics.confusion_matrix = SimpleNamespace( + matrix=[ + [7, 2, 0], + [1, 4, 0], + [1, 0, 0], + ], + names={0: "cat", 1: "dog", 2: "fox"}, + ) + + metrics = collect_monitoring_metrics("classify", trainer) + rows = collect_per_class_metrics("classify", trainer) + + assert metrics["monitor/quality/classification/classes_evaluated"] == 3 + assert metrics["monitor/quality/classification/macro_precision"] == pytest.approx( + (7 / 9 + 4 / 5 + 0.0) / 3 + ) + assert metrics["monitor/quality/classification/macro_recall"] == pytest.approx( + (7 / 9 + 4 / 6) / 2 + ) + assert rows[-1]["class_name"] == "fox" + assert rows[-1]["support"] == 0 + assert rows[-1]["precision"] == 0 + + +def test_monitor_drops_non_finite_and_non_numeric_values() -> None: + trainer = _trainer("detect") + trainer.best_fitness = float("nan") + trainer.epoch_time = float("inf") + trainer.lr = {"lr/pg0": "not-a-number"} + trainer.validator.metrics.box.map75 = None + + metrics = collect_monitoring_metrics("detect", trainer) + + assert "monitor/fitness/ultralytics_best" not in metrics + assert "monitor/fitness/task_score_best" not in metrics + assert "monitor/performance/epoch_seconds" not in metrics + assert "monitor/optimization/learning_rate_mean" not in metrics + assert "monitor/quality/box/map75" not in metrics + assert all( + value == value and value not in (float("inf"), float("-inf")) + for value in metrics.values() + ) + + +def test_callback_enriches_trainer_metrics_before_mlflow_logging() -> None: + trainer = _trainer("obb") + monitor = TaskMetricsMonitor("obb", mlflow_enabled=True) + + monitor.on_fit_epoch_end(trainer) + + assert trainer.metrics["monitor/fitness/ultralytics_current"] == pytest.approx(0.61) + assert trainer.metrics[ + "monitor/quality/oriented_box/f1_at_optimal_confidence" + ] == pytest.approx((0.685714 + 0.7) / 2) + + +def test_final_validation_does_not_repeat_stale_train_diagnostics() -> None: + trainer = _trainer("detect") + trainer.validator.training = False + + metrics = collect_monitoring_metrics("detect", trainer) + + assert "monitor/quality/box/f1_at_optimal_confidence" in metrics + assert "monitor/performance/validation_inference_ms_per_image" in metrics + assert "monitor/loss/train_total" not in metrics + assert "monitor/loss/validation_total" not in metrics + assert "monitor/optimization/learning_rate_mean" not in metrics + assert "monitor/performance/epoch_seconds" not in metrics + + +@pytest.mark.parametrize( + ("task", "expected_components"), + [ + ("detect", {"box"}), + ("segment", {"box", "mask"}), + ("pose", {"box", "keypoints"}), + ("obb", {"oriented_box"}), + ("classify", {"classification"}), + ], +) +def test_final_per_class_rows_cover_every_task_component( + task: YoloTask, + expected_components: set[str], +) -> None: + rows = collect_per_class_metrics(task, _trainer(task)) + + assert {row["component"] for row in rows} == expected_components + assert {row["class_name"] for row in rows} == {"cat", "dog"} + assert all(row["support"] > 0 for row in rows) + + +def test_per_class_rows_preserve_positions_of_non_finite_values() -> None: + trainer = _trainer("detect") + component = trainer.validator.metrics.box + component.ap_class_index = [0, 1, 2] + component.p = [0.8, float("nan"), 0.6] + component.r = [0.7, 0.5, 0.4] + component.f1 = [0.746, 0.5, 0.48] + component.ap50 = [0.75, 0.55, 0.45] + component.ap = [0.65, 0.45, 0.35] + trainer.validator.metrics.names = {0: "first", 1: "second", 2: "third"} + trainer.validator.metrics.nt_per_class = [5, 4, 3] + + rows = collect_per_class_metrics("detect", trainer) + + assert [row["class_id"] for row in rows] == [0, 1, 2] + assert rows[1]["precision"] == "" + assert rows[2]["precision"] == pytest.approx(0.6) + + +def test_train_end_writes_mlflow_uploadable_per_class_csv(tmp_path: Path) -> None: + trainer = _trainer("segment", tmp_path) + monitor = TaskMetricsMonitor("segment", mlflow_enabled=True) + + monitor.on_train_end(trainer) + + output = tmp_path / "monitoring" / "task_metrics.csv" + assert output.exists() + with output.open(encoding="utf-8") as stream: + reader = csv.DictReader(stream) + rows = list(reader) + assert tuple(reader.fieldnames or ()) == PER_CLASS_FIELDS + assert len(rows) == 4 + assert {row["component"] for row in rows} == {"box", "mask"} + assert {row["task"] for row in rows} == {"segment"} + assert {row["class_id"] for row in rows} == {"0", "1"} + assert {row["class_name"] for row in rows} == {"cat", "dog"} + for row in rows: + for field in ("support", "precision", "recall", "f1", "map50", "map50_95"): + assert math.isfinite(float(row[field])) + + +def test_disabled_mlflow_does_not_add_metrics_or_write_artifact(tmp_path: Path) -> None: + trainer = _trainer("detect", tmp_path) + original_metrics = dict(trainer.metrics) + monitor = TaskMetricsMonitor("detect", mlflow_enabled=False) + + monitor.on_fit_epoch_end(trainer) + monitor.on_train_end(trainer) + + assert trainer.metrics == original_metrics + assert not (tmp_path / "monitoring" / "task_metrics.csv").exists() + + +def test_train_end_writes_header_when_no_classes_were_evaluated(tmp_path: Path) -> None: + trainer = _trainer("detect", tmp_path) + component = trainer.validator.metrics.box + for attribute in ("p", "r", "f1", "ap50", "ap", "ap_class_index"): + setattr(component, attribute, []) + + TaskMetricsMonitor("detect", mlflow_enabled=True).on_train_end(trainer) + + output = tmp_path / "monitoring" / "task_metrics.csv" + with output.open(encoding="utf-8") as stream: + reader = csv.DictReader(stream) + rows = list(reader) + assert tuple(reader.fieldnames or ()) == PER_CLASS_FIELDS + assert rows == [] + + +def test_monitoring_contract_is_persisted_by_mlflow( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + import mlflow + from mlflow.tracking import MlflowClient + from ultralytics.utils.callbacks import mlflow as ultralytics_mlflow + from yolo_webui.ultralytics_trainers import trainer_for_task + + previous_uri = mlflow.get_tracking_uri() + tracking_uri = f"sqlite:///{tmp_path / 'mlflow.db'}" + run_id = "" + try: + monkeypatch.setenv("YOLO_WEBUI_MLFLOW_RUN_GROUP", "test-group") + mlflow.set_tracking_uri(tracking_uri) + client = MlflowClient(tracking_uri=tracking_uri) + experiment_id = client.create_experiment( + "task-monitor-test", + artifact_location=(tmp_path / "artifacts").as_uri(), + ) + trainer_class = trainer_for_task("obb") + trainer = trainer_class.__new__(trainer_class) + trainer.__dict__.update(vars(_trainer("obb", tmp_path / "run"))) + trainer._mlflow_active = True + trainer.epoch = 0 + trainer.callbacks = { + "on_train_start": [], + "on_fit_epoch_end": [ultralytics_mlflow.on_fit_epoch_end], + "on_train_end": [], + } + trainer._install_task_metrics_monitor() + trainer._task_metrics_monitor.mlflow_enabled = True + + with mlflow.start_run(experiment_id=experiment_id, run_name="obb-contract") as run: + run_id = run.info.run_id + for callback in trainer.callbacks["on_train_start"]: + callback(trainer) + for callback in trainer.callbacks["on_fit_epoch_end"]: + callback(trainer) + + trainer.epoch = 1 + trainer.validator.training = False + trainer.metrics = {"metrics/mAP50": 0.72} + for callback in trainer.callbacks["on_fit_epoch_end"]: + callback(trainer) + for callback in trainer.callbacks["on_train_end"]: + callback(trainer) + + persisted = client.get_run(run_id) + history = client.get_metric_history( + run_id, + "monitor/quality/oriented_box/f1_at_optimal_confidence", + ) + assert persisted.data.tags["yolo.task"] == "obb" + assert persisted.data.tags["monitoring.schema_version"] == "1" + assert persisted.data.tags["yolo.run_group"] == "test-group" + assert persisted.data.metrics[ + "monitor/fitness/ultralytics_current" + ] == pytest.approx(0.61) + assert [point.step for point in history] == [0, 1] + assert { + artifact.path + for artifact in client.list_artifacts(run_id, "monitoring") + } == {"monitoring/task_metrics.csv"} + finally: + if mlflow.active_run() is not None: + mlflow.end_run(status="KILLED") + mlflow.set_tracking_uri(previous_uri) diff --git a/tests/test_mlflow_smoke_verifier.py b/tests/test_mlflow_smoke_verifier.py new file mode 100644 index 0000000..0fd3921 --- /dev/null +++ b/tests/test_mlflow_smoke_verifier.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import importlib.util +from pathlib import Path +from types import SimpleNamespace + +import pytest + +SPEC = importlib.util.spec_from_file_location( + "verify_mlflow_smoke", + Path(__file__).parents[1] / "scripts" / "verify_mlflow_smoke.py", +) +assert SPEC is not None and SPEC.loader is not None +verify_mlflow_smoke = importlib.util.module_from_spec(SPEC) +SPEC.loader.exec_module(verify_mlflow_smoke) +latest_smoke_run_group = verify_mlflow_smoke.latest_smoke_run_group +latest_task_run = verify_mlflow_smoke.latest_task_run + + +def _run(name: str, group: str, *, anchor: bool = False) -> SimpleNamespace: + tags = { + "mlflow.runName": name, + "yolo.run_group": group, + } + if anchor: + tags["smoke.anchor"] = "true" + return SimpleNamespace(data=SimpleNamespace(tags=tags)) + + +def test_verifier_selects_one_complete_smoke_batch() -> None: + runs = [ + _run("detect-smoke", "new"), + _run("smoke-batch-new", "new", anchor=True), + _run("obb-smoke", "old"), + _run("smoke-batch-old", "old", anchor=True), + ] + + group = latest_smoke_run_group(runs) + + assert group == "new" + assert latest_task_run(runs, "detect", group).data.tags["yolo.run_group"] == "new" + with pytest.raises(AssertionError, match="obb-smoke"): + latest_task_run(runs, "obb", group) diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 8d0b73c..61b7032 100755 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -43,31 +43,46 @@ def test_runner_wires_yolo_callbacks_and_returns_output( class FakeYOLO: def __init__(self, model: str, task: str) -> None: constructed.append((model, task)) - self.callbacks: dict[str, Any] = {} + self.callbacks: dict[str, list[Any]] = {} self.trainer = SimpleNamespace( args=SimpleNamespace(epochs=2), epoch=0, metrics={}, + fitness=0.0, + best_fitness=0.0, stop=False, save_dir=tmp_path / "run", ) def add_callback(self, name: str, callback: Any) -> None: - self.callbacks[name] = callback + self.callbacks.setdefault(name, []).append(callback) + + def run_callbacks(self, name: str) -> None: + for callback in self.callbacks.get(name, []): + callback(self.trainer) def train(self, **kwargs: Any) -> None: train_arguments.append(kwargs) - self.callbacks["on_train_start"](self.trainer) + self.run_callbacks("on_train_start") for epoch in range(2): self.trainer.epoch = epoch self.trainer.metrics = {"metrics/mAP50": 0.5 + epoch / 10} - self.callbacks["on_train_epoch_end"](self.trainer) - self.callbacks["on_train_end"](self.trainer) + self.trainer.fitness = 0.5 + epoch / 10 + self.trainer.best_fitness = self.trainer.fitness + self.run_callbacks("on_fit_epoch_end") + self.run_callbacks("on_train_end") fake_ultralytics = ModuleType("ultralytics") fake_ultralytics.YOLO = FakeYOLO # type: ignore[attr-defined] fake_ultralytics.settings = FakeSettings() # type: ignore[attr-defined] + fake_trainers = ModuleType("yolo_webui.ultralytics_trainers") + + class FakeTrainer: + pass + + fake_trainers.trainer_for_task = lambda _task: FakeTrainer # type: ignore[attr-defined] monkeypatch.setitem(sys.modules, "ultralytics", fake_ultralytics) + monkeypatch.setitem(sys.modules, "yolo_webui.ultralytics_trainers", fake_trainers) config = TrainingConfig( dataset="dataset.yaml", @@ -85,7 +100,24 @@ def test_runner_wires_yolo_callbacks_and_returns_output( assert settings_updates == [{"mlflow": False}] assert train_arguments[0]["data"] == "dataset.yaml" assert train_arguments[0]["verbose"] is True + assert train_arguments[0]["trainer"] is FakeTrainer assert [event.kind for event in events] == ["info", "started", "epoch", "epoch", "success"] + assert "mAP50=0.5" in events[2].message + assert "mAP50=0.6" in events[3].message + + +def test_final_checkpoint_validation_is_not_reported_as_an_extra_epoch() -> None: + events: list[TrainingEvent] = [] + trainer = SimpleNamespace( + args=SimpleNamespace(epochs=1), + epoch=1, + metrics={"metrics/mAP50": 0.75}, + validator=SimpleNamespace(training=False), + ) + + TrainingRunner()._on_epoch_end(events.append)(trainer) + + assert events == [] def test_prepare_run_clears_previous_stop_request() -> None: diff --git a/tests/test_ultralytics_trainers.py b/tests/test_ultralytics_trainers.py new file mode 100644 index 0000000..c476dc5 --- /dev/null +++ b/tests/test_ultralytics_trainers.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from typing import Any + +import pytest + +from yolo_webui.config import YoloTask +from yolo_webui.mlflow_metrics import TaskMetricsMonitor +from yolo_webui.ultralytics_trainers import MONITORED_TRAINERS, trainer_for_task + + +@pytest.mark.parametrize("task", ["detect", "segment", "classify", "pose", "obb"]) +def test_every_task_uses_an_importable_monitored_trainer(task: YoloTask) -> None: + trainer_class = trainer_for_task(task) + + assert trainer_class is MONITORED_TRAINERS[task] + assert trainer_class.monitoring_task == task + assert trainer_class.__module__ == "yolo_webui.ultralytics_trainers" + + +def test_monitor_callbacks_are_prepended_before_integrations() -> None: + trainer_class = trainer_for_task("detect") + trainer = trainer_class.__new__(trainer_class) + integration_callbacks: dict[str, Any] = { + "on_train_start": lambda _trainer: None, + "on_fit_epoch_end": lambda _trainer: None, + "on_train_end": lambda _trainer: None, + } + trainer.callbacks = { + event: [callback] + for event, callback in integration_callbacks.items() + } + + trainer._install_task_metrics_monitor() + + assert isinstance(trainer._task_metrics_monitor, TaskMetricsMonitor) + for event, integration_callback in integration_callbacks.items(): + assert trainer.callbacks[event][0].__self__ is trainer._task_metrics_monitor + assert trainer.callbacks[event][1] is integration_callback diff --git a/uv.lock b/uv.lock index 00e722a..a159bfd 100755 --- a/uv.lock +++ b/uv.lock @@ -1,13 +1,15 @@ version = 1 revision = 3 -requires-python = ">=3.11" +requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'win32'", - "python_full_version < '3.12' and sys_platform != 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'win32'", + "python_full_version < '3.11' and sys_platform != 'win32'", ] 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