Buckets:
| """Measured, CPU-only pretrained baseline on the approved fixed tile records.""" | |
| import argparse | |
| import copy | |
| import json | |
| import multiprocessing | |
| import platform | |
| import time | |
| from collections import defaultdict | |
| from pathlib import Path | |
| import numpy as np | |
| from PIL import Image, ImageDraw | |
| from training.model_factory import ( | |
| ASSET_BASE, CHECKPOINTS, FRAMEWORK_VERSION, ROOT, detector_transfer_plan, | |
| load_pretrained_detector, load_pretrained_segmenter, public_mapping, sha256, | |
| ) | |
| from evaluation.calibrate_thresholds import _validate_selection_records | |
| from evaluation.manifest import DETECTION_CLASS_NAMES, SEGMENTATION_CLASS_NAMES | |
| from evaluation.metrics import _match_class, evaluate_detection | |
| from load_dataset import dataset | |
| from training.runtime import load_runtime_config | |
| def detection_target(sample: dict, provenance: str) -> dict: | |
| x, y = sample["metadata"]["tile_coordinates"][:2] | |
| annotations = copy.deepcopy(sample["annotations"]) | |
| for annotation in annotations: | |
| annotation["obb_polygon"] = [ | |
| value - (x if index % 2 == 0 else y) | |
| for index, value in enumerate(annotation["obb_polygon"]) | |
| ] | |
| return { | |
| "sample_id": sample["metadata"]["id"], "split": "val", | |
| "provenance": provenance, "annotations": annotations, | |
| "ignored_annotations": copy.deepcopy(sample["ignored_annotations"]), | |
| } | |
| def selection_thresholds(predictions: list[dict], targets: list[dict]) -> dict: | |
| """Equivalent F1 search to Task 2, matching once per class, not per threshold. | |
| Confidence filtering removes a suffix of the greedy ordering, so earlier | |
| assignments cannot change. Ignore-only scores are excluded by the matcher. | |
| """ | |
| _validate_selection_records(predictions, targets) | |
| classes = sorted({str(a["category_id"]) for row in targets for a in row["annotations"]}) | |
| thresholds, f1, sparse = {}, {}, [] | |
| ignored = {row["sample_id"]: row.get("ignored_annotations", []) for row in targets} | |
| for class_id in classes: | |
| by_sample = { | |
| row["sample_id"]: [a for a in row["annotations"] if str(a["category_id"]) == class_id] | |
| for row in targets | |
| } | |
| count = sum(map(len, by_sample.values())) | |
| candidates = [ | |
| (row["sample_id"], a) for row in predictions for a in row["annotations"] | |
| if str(a["category_id"]) == class_id | |
| ] | |
| scores, _, _ = _match_class(candidates, by_sample, ignored, .5) | |
| score_values = np.array([score for score, _ in scores]) | |
| positives = np.array([matched for _, matched in scores], dtype=np.int64) | |
| cumulative = np.cumsum(positives) | |
| scored = [] | |
| for threshold in sorted({0.0, *(float(a["confidence"]) for _, a in candidates)}): | |
| kept = int(np.count_nonzero(score_values >= threshold)) | |
| tp = int(cumulative[kept - 1]) if kept else 0 | |
| scored.append((2 * tp / (count + kept) if count + kept else 0.0, threshold)) | |
| best_f1, threshold = max(scored) | |
| thresholds[class_id], f1[class_id] = threshold, best_f1 | |
| if count < 2: | |
| sparse.append(class_id) | |
| return { | |
| "thresholds": thresholds, "f1": f1, "provenance": "selection_validation", | |
| "split": "val", "sample_ids": sorted(row["sample_id"] for row in targets), | |
| "sparse_class_ids": sparse, | |
| } | |
| def detection_metrics(predictions: list[dict], targets: list[dict], thresholds: dict) -> dict: | |
| ap = evaluate_detection(predictions, targets) | |
| operating = evaluate_detection(predictions, targets, confidence_thresholds=thresholds) | |
| # Do not publish threshold-truncated AP as if it were the full prediction pool. | |
| for metrics in [operating, *operating["per_class"].values()]: | |
| metrics.pop("ap50", None) | |
| metrics.pop("map50_95", None) | |
| return {"ap": ap, "operating": operating} | |
| def segmentation_counts(raw: np.ndarray, target: np.ndarray, mapping: dict, classes) -> dict: | |
| mapped = np.zeros(raw.shape, dtype=np.uint8) | |
| for source_id, canonical_id in mapping.items(): | |
| mapped[raw == source_id] = canonical_id | |
| valid = target != 255 | |
| return { | |
| str(class_id): { | |
| "intersection": int(np.count_nonzero(valid & (mapped == class_id) & (target == class_id))), | |
| "union": int(np.count_nonzero(valid & ((mapped == class_id) | (target == class_id)))), | |
| "targets": int(np.count_nonzero(valid & (target == class_id))), | |
| } for class_id in classes | |
| } | |
| def semantic_array(result) -> np.ndarray: | |
| return result.semantic_mask.data.cpu().numpy() | |
| def _preview(sample, raw, mapping, target, path: Path, thresholds=None, prediction_label="pretrained"): | |
| image = Image.fromarray(sample["image"]).convert("RGB") | |
| truth, prediction = image.copy(), image.copy() | |
| colors = {key: ((key * 53) % 200 + 55, (key * 97) % 200 + 55, (key * 139) % 200 + 55) | |
| for key in range(1, 17)} | |
| if sample["metadata"]["task"] == "detection": | |
| for panel, annotations in ((truth, target["annotations"]), (prediction, raw)): | |
| draw = ImageDraw.Draw(panel) | |
| for a in annotations: | |
| class_id = a["category_id"] | |
| if "confidence" in a and a["confidence"] < thresholds.get(str(class_id), 0): | |
| continue | |
| points = a["obb_polygon"] | |
| points = list(zip(points[::2], points[1::2])) | |
| draw.line(points + points[:1], fill=colors[class_id], width=3) | |
| draw.text(points[0], str(class_id), fill=colors[class_id]) | |
| else: | |
| mapped = np.zeros(raw.shape, dtype=np.uint8) | |
| for source_id, canonical_id in mapping.items(): | |
| mapped[raw == source_id] = canonical_id | |
| for panel, mask in ((truth, target), (prediction, mapped)): | |
| overlay = np.zeros((*mask.shape, 3), dtype=np.uint8) | |
| for class_id, color in colors.items(): | |
| overlay[mask == class_id] = color | |
| panel.paste(Image.blend(image, Image.fromarray(overlay), .5)) | |
| sheet = Image.new("RGB", (1536, 540), "black") | |
| for index, (label, panel) in enumerate((("input", image), ("truth", truth), (prediction_label, prediction))): | |
| sheet.paste(panel.resize((512, 512)), (index * 512, 28)) | |
| ImageDraw.Draw(sheet).text((index * 512 + 8, 8), label, fill="white") | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| sheet.save(path) | |
| def _validate_sizes(detection_imgsz, segmentation_imgsz): | |
| for value in (detection_imgsz, segmentation_imgsz): | |
| if isinstance(value, bool) or not isinstance(value, int) or value <= 0 or value % 32: | |
| raise ValueError("inference sizes must be positive integer multiples of 32") | |
| def _run_paths(phase, output_dir): | |
| output = Path(output_dir).resolve() | |
| if not output.is_relative_to(ROOT): | |
| raise ValueError("evaluation output must remain within the project") | |
| legacy = ROOT / "evaluation" / "results" | |
| previews = (ROOT / "evaluation" / "predictions" / "baseline" | |
| if output == legacy else output / "predictions" / "baseline") | |
| return output, previews | |
| def _check_fresh_output(phase, output_dir): | |
| output, previews = _run_paths(phase, output_dir) | |
| protected = [output / (phase + suffix) for suffix in | |
| (".json", "_STOP.json", "_INCOMPLETE.json", ".json.partial")] | |
| if previews.exists() or any(path.exists() for path in protected): | |
| raise FileExistsError("evaluation evidence already exists; choose a fresh --output-dir") | |
| return output, previews | |
| def calibration_evidence(targets, selection_rows): | |
| scene_by_id = {row["sample_id"]: row["scene_id"] for row in selection_rows} | |
| classes = sorted({int(a["category_id"]) for row in targets for a in row["annotations"]}) | |
| support = { | |
| str(class_id): { | |
| "positive_instances": sum(a["category_id"] == class_id for row in targets for a in row["annotations"]), | |
| "positive_tiles": sum(any(a["category_id"] == class_id for a in row["annotations"]) for row in targets), | |
| "positive_scenes": len({scene_by_id[row["sample_id"]] for row in targets | |
| if any(a["category_id"] == class_id for a in row["annotations"])}), | |
| } for class_id in classes | |
| } | |
| low_support = [key for key, row in support.items() | |
| if row["positive_instances"] < 10 or row["positive_tiles"] < 3] | |
| warnings = [f"Class {key} threshold has limited evidence: {support[key]['positive_instances']} positives " | |
| f"in {support[key]['positive_tiles']} tiles; do not infer stable generalization." | |
| for key in low_support] | |
| return {"per_class": support, "low_support_class_ids": low_support, | |
| "disclosure_rule": "Fewer than 10 positive instances or 3 positive tiles is a disclosure heuristic, not a statistical reliability test; threshold selection is unchanged.", | |
| "warnings": warnings} | |
| def phase_mappings(phase, detector, segmenter): | |
| mappings = {"detection": public_mapping(detector.names, "detection"), | |
| "segmentation": public_mapping(segmenter.names, "segmentation")} | |
| if phase == "BASELINE_ADAPTED": | |
| from training.segmentation_data import CLASS_NAMES, CANONICAL_IDS | |
| if segmenter.names != dict(enumerate(CLASS_NAMES)): | |
| raise ValueError("adapted segmenter does not have the approved seven-class head") | |
| mappings["segmentation"] = dict(enumerate(CANONICAL_IDS)) | |
| return mappings | |
| def run_quick_eval(phase, detector, segmenter, manifest: Path, output_dir: Path, *, timeout_seconds=300, | |
| detection_imgsz=1024, segmentation_imgsz=1024, _loading_seconds=0.0) -> dict: | |
| if not 0 < timeout_seconds <= 300: | |
| raise ValueError("approved timeout must be in (0, 300]") | |
| if phase not in {"BASELINE_PRETRAINED", "BASELINE_ADAPTED"}: | |
| raise ValueError("unsupported baseline phase") | |
| _validate_sizes(detection_imgsz, segmentation_imgsz) | |
| output_dir, previews = _check_fresh_output(phase, output_dir) | |
| started = time.perf_counter() | |
| def budget(): | |
| if time.perf_counter() - started >= timeout_seconds: | |
| raise TimeoutError("approved quick-evaluation budget exhausted") | |
| fixed = ROOT / "evaluation" / "quick_eval_manifest.json" | |
| if manifest.resolve() != fixed or sha256(manifest) != "0a0934ddd79a7536a5bbca3df85e77cb9110ac5d69afb7f524bb276861e4b9de": | |
| raise ValueError("only the approved fixed manifest is accepted") | |
| spec = json.loads(manifest.read_text()) | |
| runtime = load_runtime_config(ROOT / "training" / "configs" / "runtime.json") | |
| import torch | |
| torch.set_num_threads(runtime.num_threads) | |
| torch.manual_seed(runtime.seed) | |
| if (detector.task, segmenter.task) != ("obb", "semantic"): | |
| raise ValueError("requires oriented detection and semantic segmentation checkpoints") | |
| output_dir.mkdir(parents=True, exist_ok=True) | |
| mappings = phase_mappings(phase, detector, segmenter) | |
| adapted = phase == "BASELINE_ADAPTED" | |
| adaptation = {} | |
| if adapted: | |
| from training.model_factory import model_state_sha256 | |
| from training.model_factory import DOTA_NAMES | |
| if detector.names != dict(enumerate(DOTA_NAMES)): | |
| raise ValueError("adapted detector does not have the approved16 source classes") | |
| adaptation["adapted_state_sha256"] = {"detection": model_state_sha256(detector.model), | |
| "segmentation": model_state_sha256(segmenter.model)} | |
| pools = defaultdict(list) | |
| timings = [] | |
| counts = {str(key): {"intersection": 0, "union": 0, "targets": 0} for key in SEGMENTATION_CLASS_NAMES} | |
| preview_rows = [] | |
| previews.mkdir(parents=True, exist_ok=False) | |
| incomplete = output_dir / (phase + "_INCOMPLETE.json") | |
| incomplete.write_text(json.dumps({"status": "INCOMPLETE", "phase": phase}), encoding="utf-8") | |
| raw_dir = previews / "raw" | |
| raw_dir.mkdir() | |
| if adapted: | |
| initial = {} | |
| for task, wrapper in (("detection", detector), ("segmentation", segmenter)): | |
| path = output_dir / (task + "_initial.pt") | |
| with path.open("xb") as handle: | |
| torch.save({"model": copy.deepcopy(wrapper.model).cpu(), | |
| "train_args": {"task": wrapper.task, "imgsz": 1024, "seed": runtime.seed}, | |
| "training_performed": False}, handle) | |
| initial[task] = {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} | |
| adaptation["initial_checkpoints"] = initial | |
| invalid_labeled_semantic_pixels = 0 | |
| calibration = None | |
| subset_order = [ | |
| ("selection_validation", [row for row in spec["selection_validation"] if row["task"] == "detection"]), | |
| ("quick_eval", spec["quick_eval"]["detection"] + spec["quick_eval"]["segmentation"]), | |
| ] | |
| for provenance, rows in subset_order: | |
| for task in ("detection", "segmentation"): | |
| task_rows = [row for row in rows if row["task"] == task] | |
| if not task_rows: | |
| continue | |
| data = dataset(ROOT / "datasets" / "corpus", task, "val", license_scope="all") | |
| indices = {sample["id"]: index for index, sample in enumerate(data.samples)} | |
| for index, row in enumerate(task_rows): | |
| budget() | |
| sample = data[indices[row["sample_id"]]] | |
| for key in ("image", "labels" if task == "detection" else "mask"): | |
| if row[key] != sample["metadata"][key]: | |
| raise ValueError("manifest asset differs from approved corpus record") | |
| model = detector if task == "detection" else segmenter | |
| tick = time.perf_counter() | |
| result = model.predict( | |
| sample["image"][:, :, ::-1].copy(), device="cpu", | |
| imgsz=detection_imgsz if task == "detection" else segmentation_imgsz, | |
| conf=.001, max_det=300, verbose=False, save=False, | |
| )[0] | |
| elapsed = time.perf_counter() - tick | |
| timings.append({"sample_id": row["sample_id"], "task": task, "provenance": provenance, | |
| "wall_seconds": elapsed, "framework_ms": result.speed}) | |
| budget() | |
| if task == "detection": | |
| annotations = [] | |
| original = [] | |
| for points, cls, confidence in zip(result.obb.xyxyxyxy.cpu().numpy(), | |
| result.obb.cls.cpu().numpy(), | |
| result.obb.conf.cpu().numpy()): | |
| source_id = int(cls) | |
| item = {"source_class_id": source_id, "confidence": float(confidence), | |
| "obb_polygon": points.reshape(-1).tolist()} | |
| original.append(item) | |
| if source_id in mappings[task]: | |
| annotations.append({**item, "category_id": mappings[task][source_id]}) | |
| predicted = {**row, "annotations": annotations} | |
| target = detection_target(sample, provenance) | |
| pools[provenance + "_predictions"].append(predicted) | |
| pools[provenance + "_targets"].append(target) | |
| (raw_dir / (row["sample_id"] + ".json")).write_text(json.dumps(original), encoding="utf-8") | |
| raw = annotations | |
| else: | |
| raw = semantic_array(result) | |
| if raw.shape != sample["mask"].shape: | |
| raise RuntimeError("semantic output is not in original tile coordinates") | |
| target = sample["mask"] | |
| if "valid_mask" in sample: | |
| invalid_labeled_semantic_pixels += int(np.count_nonzero((target != 255) & ~sample["valid_mask"])) | |
| np.savez_compressed(raw_dir / (row["sample_id"] + ".npz"), semantic_class_ids=raw) | |
| current = segmentation_counts(raw, target, mappings[task], SEGMENTATION_CLASS_NAMES) | |
| for key in counts: | |
| for field in counts[key]: | |
| counts[key][field] += current[key][field] | |
| if provenance == "quick_eval" and index < 8: | |
| path = previews / (row["sample_id"] + ".jpg") | |
| _preview(sample, raw, mappings[task], target, path, | |
| calibration["thresholds"] if calibration else {}, | |
| prediction_label="adapted, untrained" if adapted else "pretrained") | |
| preview_rows.append(str(path.relative_to(ROOT))) | |
| if provenance == "selection_validation": | |
| budget() | |
| calibration = selection_thresholds(pools["selection_validation_predictions"], | |
| pools["selection_validation_targets"]) | |
| budget() | |
| metrics_tick = time.perf_counter() | |
| detection = detection_metrics(pools["quick_eval_predictions"], pools["quick_eval_targets"], | |
| calibration["thresholds"]) | |
| budget() | |
| mapped_classes = set(mappings["segmentation"].values()) | |
| per_class = { | |
| key: {**values, "name": SEGMENTATION_CLASS_NAMES[int(key)], | |
| "available": int(key) in mapped_classes, | |
| "iou": values["intersection"] / values["union"] if values["union"] and int(key) in mapped_classes else None} | |
| for key, values in counts.items() | |
| } | |
| available_ious = [row["iou"] for row in per_class.values() if row["iou"] is not None] | |
| unavailable = [key for key, value in per_class.items() if not value["available"]] | |
| result = { | |
| "phase": phase, "status": "DONE_WITH_CONCERNS", "training_performed": False, | |
| "runtime": {"python": platform.python_version(), "torch": torch.__version__, | |
| "ultralytics": FRAMEWORK_VERSION, "device": "cpu", | |
| "num_threads": runtime.num_threads, "seed": runtime.seed}, | |
| "checkpoints": { | |
| name: {"sha256": digest, "url": ASSET_BASE + name, | |
| "license": "Ultralytics AGPL-3.0 / enterprise terms; inspect intended distribution"} | |
| for name, digest in CHECKPOINTS.items() | |
| }, | |
| "checkpoint_names": {"detection": detector.names, "segmentation": segmenter.names}, | |
| "head_adaptation_applied": adapted, | |
| "future_detector_transfer": detector_transfer_plan(detector.names), | |
| "public_mapping": mappings, | |
| "manifest_sha256": sha256(manifest), | |
| "corpus_content_sha256": json.loads( | |
| (ROOT / "datasets" / "corpus" / "validation.json").read_text())["content_sha256"], | |
| "runtime_config_sha256": sha256(ROOT / "training" / "configs" / "runtime.json"), | |
| "counts": {"quick_detection": 32, "quick_segmentation": 20, "calibration_detection": 68, | |
| "selection_segmentation_reserved_not_used": 21}, | |
| "inference_options": {"detection_imgsz": detection_imgsz, "segmentation_imgsz": segmentation_imgsz, "ap_confidence_floor": .001, | |
| "max_det": 300, "input": "original RGB converted to framework BGR"}, | |
| "detection": detection, "calibration": calibration, | |
| "calibration_evidence": calibration_evidence(pools["selection_validation_targets"], spec["selection_validation"]), | |
| "segmentation": {"per_class": per_class, "miou_available_aliases": float(np.mean(available_ious)), | |
| "unavailable_class_ids": unavailable, "calibration": "not available: class-map output, no confidence"}, | |
| "timing": {"per_record": timings, "inference_seconds": sum(row["wall_seconds"] for row in timings), | |
| "metrics_seconds": time.perf_counter() - metrics_tick, | |
| "total_seconds": time.perf_counter() - started, "timeout_seconds": timeout_seconds, | |
| "loading_seconds": _loading_seconds}, | |
| "preview_paths": preview_rows, | |
| "prediction_sha256": {str(path.relative_to(ROOT)): sha256(path) | |
| for path in sorted(previews.rglob("*")) if path.is_file()}, | |
| "concerns": [ | |
| "Tile-record-disjoint selection, not scene/geographic independence.", | |
| "Bare-ground class 15 has one quick tile and zero selection positives; no stable threshold.", | |
| "ADE tree/field/earth/land are not exact vegetation/agricultural_area/bare_ground/developed_area aliases.", | |
| "Unmapped semantic predictions count as errors on labeled pixels; unavailable classes have null IoU.", | |
| "Available-alias mIoU is not a seven-class OpenEarthMap score.", | |
| "AP uses the separate confidence >=0.001, max_det=300 prediction pool.", | |
| "DOTA imagery academic-only; pretrained license does not grant corpus commercial permission.", | |
| ], | |
| } | |
| if adapted: | |
| result.update(adaptation) | |
| result["adaptation_reports"] = {"detection": getattr(detector, "adaptation_report", None), | |
| "segmentation": getattr(segmenter, "adaptation_report", None)} | |
| result["future_detector_transfer"] = result["adaptation_reports"]["detection"] | |
| result["segmentation"]["miou"] = result["segmentation"].pop("miou_available_aliases") | |
| result["segmentation"]["target_policy"] = "original canonical mask, ignore255; same as retained pretrained baseline" | |
| result["segmentation"]["invalid_labeled_pixels"] = invalid_labeled_semantic_pixels | |
| result["concerns"] = [c for c in result["concerns"] if not c.startswith(("ADE ", "Unmapped semantic", "Available-alias"))] | |
| result["concerns"].append("Four semantic rows are fresh; adaptation is not a learning gain. Training comparisons apply the same validity masks before and after learning.") | |
| budget() | |
| partial = output_dir / (phase + ".json.partial") | |
| with partial.open("x", encoding="utf-8") as handle: | |
| handle.write(json.dumps(result, indent=2)) | |
| partial.replace(output_dir / (phase + ".json")) | |
| incomplete.unlink() | |
| return result | |
| def _worker(args): | |
| _validate_sizes(args.detection_imgsz, args.segmentation_imgsz) | |
| _check_fresh_output(args.phase, args.output_dir) | |
| try: | |
| cache = ROOT / "training" / "cache" / "checkpoints" | |
| tick = time.perf_counter() | |
| detector = load_pretrained_detector(cache) | |
| segmenter = load_pretrained_segmenter(cache) | |
| if args.phase == "BASELINE_ADAPTED": | |
| from training.detection_loss import build_adapted_detector | |
| from training.segmentation_loss import build_adapted_segmenter | |
| runtime = load_runtime_config(ROOT / "training/configs/runtime.json") | |
| import torch | |
| torch.set_num_threads(runtime.num_threads) | |
| detector.model, detector.adaptation_report = build_adapted_detector(detector, seed=runtime.seed) | |
| segmenter.model, segmenter.adaptation_report = build_adapted_segmenter(segmenter, seed=runtime.seed) | |
| loading = time.perf_counter() - tick | |
| result = run_quick_eval(args.phase, detector, segmenter, Path(args.manifest), | |
| Path(args.output_dir), timeout_seconds=args.timeout_seconds, | |
| detection_imgsz=args.detection_imgsz, segmentation_imgsz=args.segmentation_imgsz, | |
| _loading_seconds=loading) | |
| print(json.dumps({"status": result["status"], "timing": result["timing"]["total_seconds"], | |
| "inference_seconds": result["timing"]["inference_seconds"]}), flush=True) | |
| except FileExistsError: | |
| # Another run may have reserved this output after the initial preflight. | |
| raise | |
| except Exception as error: | |
| _failure(args, f"{type(error).__name__}: {error}") | |
| raise | |
| def _failure(args, diagnosis): | |
| output, _ = _run_paths(args.phase, args.output_dir) | |
| if (output / (args.phase + ".json")).exists(): | |
| return | |
| output.mkdir(parents=True, exist_ok=True) | |
| path = output / (args.phase + "_STOP.json") | |
| try: | |
| with path.open("x", encoding="utf-8") as handle: | |
| json.dump({"status": "BLOCKED", "diagnosis": diagnosis, | |
| "timeout_seconds": args.timeout_seconds}, handle, indent=2) | |
| except FileExistsError: | |
| pass | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--phase", choices=["BASELINE_PRETRAINED", "BASELINE_ADAPTED"], required=True) | |
| parser.add_argument("--manifest", default=str(ROOT / "evaluation" / "quick_eval_manifest.json")) | |
| parser.add_argument("--output-dir", default=str(ROOT / "evaluation" / "results")) | |
| parser.add_argument("--timeout-seconds", type=int, default=300) | |
| parser.add_argument("--detection-imgsz", type=int, default=1024) | |
| parser.add_argument("--segmentation-imgsz", type=int, default=1024) | |
| args = parser.parse_args() | |
| if not 0 < args.timeout_seconds <= 300: | |
| parser.error("approved timeout must be in (0, 300]") | |
| try: | |
| _validate_sizes(args.detection_imgsz, args.segmentation_imgsz) | |
| _check_fresh_output(args.phase, args.output_dir) | |
| except (ValueError, FileExistsError) as error: | |
| parser.error(str(error)) | |
| process = multiprocessing.get_context("spawn").Process(target=_worker, args=(args,)) | |
| process.start() | |
| process.join(args.timeout_seconds) | |
| if process.is_alive(): | |
| process.terminate() | |
| process.join() | |
| _failure(args, "hard wall-clock budget exhausted (includes checkpoint loading); worker terminated") | |
| raise SystemExit(1) | |
| raise SystemExit(process.exitcode) | |
| if __name__ == "__main__": | |
| main() | |
Xet Storage Details
- Size:
- 26.3 kB
- Xet hash:
- ee73ae752895005078f8d39549ddcfe1d7006079251755b4ca0f29150d61df49
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.