155 lines
5.3 KiB
Bash
155 lines
5.3 KiB
Bash
#!/usr/bin/env bash
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set -euo pipefail
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usage() {
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cat <<'EOF'
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Train a local operator YOLO building detector from an exported GeoIntel dataset.
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Environment variables:
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OPERATOR_YOLO_DATASET_DIR Directory containing dataset.yaml.
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Default: /app/storage/operator-data/yolo-building-dataset
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YOLO_BASE_MODEL_PATH Existing local base .pt model path.
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Default: /app/models/yolov8n.pt
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TRAIN_OUTPUT_DIR Ultralytics project output directory.
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Default: /app/storage/training/operator-yolo
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TRAIN_RUN_NAME Ultralytics run name.
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Default: geointel-building-detector
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TRAIN_MODEL_OUTPUT_PATH Destination for the best trained .pt file.
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Default: /app/models/geointel-building-detector.pt
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TRAIN_EPOCHS Training epochs. Default: 8
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TRAIN_IMGSZ Image size. Default: 512
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TRAIN_BATCH Batch size. Default: 2
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TRAIN_WORKERS Data-loader workers. Default: 0
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TRAIN_DEVICE Device passed to Ultralytics. Default: cpu
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PYTHON_BIN Python executable. Default: python3
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This helper is an operator/runtime smoke wrapper. It requires an existing local
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base model and an existing local dataset.yaml. It does not create app features.
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EOF
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}
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if [[ "${1:-}" == "--help" || "${1:-}" == "-h" ]]; then
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usage
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exit 0
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fi
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OPERATOR_YOLO_DATASET_DIR="${OPERATOR_YOLO_DATASET_DIR:-/app/storage/operator-data/yolo-building-dataset}"
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YOLO_BASE_MODEL_PATH="${YOLO_BASE_MODEL_PATH:-/app/models/yolov8n.pt}"
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TRAIN_OUTPUT_DIR="${TRAIN_OUTPUT_DIR:-/app/storage/training/operator-yolo}"
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TRAIN_RUN_NAME="${TRAIN_RUN_NAME:-geointel-building-detector}"
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TRAIN_MODEL_OUTPUT_PATH="${TRAIN_MODEL_OUTPUT_PATH:-/app/models/geointel-building-detector.pt}"
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TRAIN_EPOCHS="${TRAIN_EPOCHS:-8}"
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TRAIN_IMGSZ="${TRAIN_IMGSZ:-512}"
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TRAIN_BATCH="${TRAIN_BATCH:-2}"
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TRAIN_WORKERS="${TRAIN_WORKERS:-0}"
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TRAIN_DEVICE="${TRAIN_DEVICE:-cpu}"
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PYTHON_BIN="${PYTHON_BIN:-python3}"
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DATASET_YAML="${OPERATOR_YOLO_DATASET_DIR%/}/dataset.yaml"
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SUMMARY_PATH="${TRAIN_OUTPUT_DIR%/}/${TRAIN_RUN_NAME}/training_summary.json"
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export DATASET_YAML
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export YOLO_BASE_MODEL_PATH
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export TRAIN_OUTPUT_DIR
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export TRAIN_RUN_NAME
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export TRAIN_MODEL_OUTPUT_PATH
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export TRAIN_EPOCHS
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export TRAIN_IMGSZ
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export TRAIN_BATCH
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export TRAIN_WORKERS
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export TRAIN_DEVICE
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export PYTHON_BIN
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export SUMMARY_PATH
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if [[ ! -f "${DATASET_YAML}" ]]; then
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echo "Dataset YAML not found: ${DATASET_YAML}" >&2
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exit 1
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fi
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if [[ ! -f "${YOLO_BASE_MODEL_PATH}" ]]; then
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echo "Base model file not found: ${YOLO_BASE_MODEL_PATH}" >&2
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exit 1
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fi
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mkdir -p "${TRAIN_OUTPUT_DIR}" "$(dirname "${TRAIN_MODEL_OUTPUT_PATH}")"
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"${PYTHON_BIN}" - <<'PY'
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from __future__ import annotations
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import json
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import os
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import shutil
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from pathlib import Path
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def seed_ultralytics_font() -> None:
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font_candidates = [
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Path("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf"),
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Path("/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf"),
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Path("/usr/share/fonts/truetype/freefont/FreeSans.ttf"),
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]
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source_font = next((font for font in font_candidates if font.exists()), None)
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if source_font is None:
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return
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config_root = Path(os.environ.get("YOLO_CONFIG_DIR", str(Path.home() / ".config")))
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target_font = config_root / "Ultralytics" / "Arial.ttf"
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target_font.parent.mkdir(parents=True, exist_ok=True)
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if not target_font.exists():
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shutil.copy2(source_font, target_font)
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seed_ultralytics_font()
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from ultralytics import YOLO
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dataset_yaml = Path(os.environ["DATASET_YAML"])
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base_model_path = Path(os.environ["YOLO_BASE_MODEL_PATH"])
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train_output_dir = Path(os.environ["TRAIN_OUTPUT_DIR"])
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run_name = os.environ["TRAIN_RUN_NAME"]
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trained_model_output_path = Path(os.environ["TRAIN_MODEL_OUTPUT_PATH"])
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summary_path = Path(os.environ["SUMMARY_PATH"])
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epochs = int(os.environ["TRAIN_EPOCHS"])
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image_size = int(os.environ["TRAIN_IMGSZ"])
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batch_size = int(os.environ["TRAIN_BATCH"])
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workers = int(os.environ["TRAIN_WORKERS"])
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device = os.environ["TRAIN_DEVICE"]
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model = YOLO(str(base_model_path))
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model.train(
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data=str(dataset_yaml),
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epochs=epochs,
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imgsz=image_size,
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batch=batch_size,
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workers=workers,
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device=device,
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project=str(train_output_dir),
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name=run_name,
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exist_ok=True,
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pretrained=True,
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plots=False,
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verbose=True,
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)
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best_path = train_output_dir / run_name / "weights" / "best.pt"
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if not best_path.exists():
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raise SystemExit(f"Expected trained model artifact was not created: {best_path}")
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shutil.copy2(best_path, trained_model_output_path)
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summary = {
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"status": "ok",
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"dataset_yaml": str(dataset_yaml),
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"base_model_path": str(base_model_path),
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"train_output_dir": str(train_output_dir),
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"train_run_name": run_name,
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"trained_model_path": str(trained_model_output_path),
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"best_artifact_path": str(best_path),
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"epochs": epochs,
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"image_size": image_size,
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"batch_size": batch_size,
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"workers": workers,
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"device": device,
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}
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summary_path.parent.mkdir(parents=True, exist_ok=True)
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summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True), encoding="utf-8")
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print(json.dumps(summary, indent=2, sort_keys=True))
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PY
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