#!/usr/bin/env python3 """Generate ClimbingBoardGPT routes and save board visualizations. Examples -------- Generate four TB2 V6 climbs at 40 degrees: python scripts/demo_generate_and_visualize.py --board tb2 --angle 40 --grade 6 --n 4 Generate Kilter climbs with placement labels: python scripts/demo_generate_and_visualize.py --board kilter --angle 35 --grade 5 --annotate The script writes: - generated_routes.csv - generated_route_001.png - generated_route_001.svg - ... """ from __future__ import annotations import argparse import json import sys from pathlib import Path REPO_ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(REPO_ROOT / "src")) import matplotlib.pyplot as plt import pandas as pd from climbingboardgpt.inference import ( generate_route, load_board_for_demo, load_grade_predictor, load_route_generator, predict_route_grade, ) from climbingboardgpt.visualization import load_token_metadata, visualize_route_result def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Generate ClimbingBoardGPT routes and save route visualizations.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument("--board", choices=["tb2", "kilter"], required=True) parser.add_argument("--angle", type=int, default=40) parser.add_argument("--grade", type=int, default=6, help="Target grouped V-grade.") parser.add_argument("--n", type=int, default=4, help="Number of routes to sample.") parser.add_argument("--temperature", type=float, default=0.9) parser.add_argument("--top-k", type=int, default=50) parser.add_argument("--max-new-tokens", type=int, default=40) parser.add_argument("--annotate", action="store_true", help="Label route holds by placement ID.") parser.add_argument("--device", type=str, default=None, help="cpu, cuda, or omit for auto.") parser.add_argument("--torch-threads", type=int, default=None, help="Optional CPU thread cap for VPS demos.") parser.add_argument( "--model-path", type=Path, default=REPO_ROOT / "models" / "joint_route_gpt_generator.pth", ) parser.add_argument( "--grade-model-path", type=Path, default=REPO_ROOT / "models" / "joint_transformer_grade_predictor.pth", help="Optional grade-predictor checkpoint used to score generated routes.", ) parser.add_argument( "--no-grade-prediction", action="store_true", help="Skip grade-predictor scoring even if the checkpoint exists.", ) parser.add_argument( "--tokenized-dir", type=Path, default=REPO_ROOT / "data" / "processed" / "tokenized", ) parser.add_argument( "--out-dir", type=Path, default=REPO_ROOT / "outputs" / "demo_routes", ) parser.add_argument( "--background-image", type=Path, default=None, help=( "Optional board image to draw under the scatter plot. " "If omitted, the script automatically uses images/tb2_board_12x12_composite.png " "for TB2 and images/kilter-original-16x12_composite.png for Kilter when present." ), ) return parser.parse_args() def default_background_for_board(board: str) -> Path | None: candidates = { "tb2": REPO_ROOT / "images" / "tb2_board_12x12_composite.png", "kilter": REPO_ROOT / "images" / "kilter-original-16x12_composite.png", } path = candidates.get(board) return path if path is not None and path.exists() else None def main() -> None: args = parse_args() board_config = load_board_for_demo(args.board, config_dir=REPO_ROOT / "configs") generator = load_route_generator(args.model_path, device=args.device, torch_threads=args.torch_threads) token_meta = load_token_metadata(args.tokenized_dir) background_image = args.background_image or default_background_for_board(args.board) grade_predictor = None if not args.no_grade_prediction: if args.grade_model_path.exists(): grade_predictor = load_grade_predictor( args.grade_model_path, device=args.device, torch_threads=args.torch_threads, ) else: print(f"Grade predictor not found at {args.grade_model_path}; skipping grade prediction.") run_dir = args.out_dir / args.board / f"angle_{args.angle}" / f"V{args.grade}" run_dir.mkdir(parents=True, exist_ok=True) rows = [] for i in range(1, args.n + 1): result = generate_route( generator=generator, board_config=board_config, angle=args.angle, grade=args.grade, temperature=args.temperature, top_k=args.top_k, max_new_tokens=args.max_new_tokens, ) if grade_predictor is not None: grade_result = predict_route_grade(grade_predictor, result["tokens"]) result.update(grade_result) result["critic_v_error"] = ( int(result["predicted_grouped_v"]) - int(result["requested_grouped_v"]) ) rows.append(result) stem = f"generated_route_{i:03d}" png_path = run_dir / f"{stem}.png" svg_path = run_dir / f"{stem}.svg" fig, _, _ = visualize_route_result( result, df_token_meta=token_meta, output_path=png_path, annotate=args.annotate, background_image=background_image, ) fig.savefig(svg_path, bbox_inches="tight") plt.close(fig) print(f"[{i}/{args.n}] {result['frames']}") print(f" valid={result['basic_valid']} holds={result['n_hold_tokens']}") if "predicted_grouped_v" in result: print( f" predicted=V{result['predicted_grouped_v']} " f"(difficulty={result['predicted_display_difficulty']:.2f}, " f"error={result['critic_v_error']:+d} V)" ) try: png_display = png_path.resolve().relative_to(REPO_ROOT.resolve()) except Exception: png_display = png_path print(f" saved {png_display}") df = pd.DataFrame(rows) df["tokens_json"] = df["tokens"].apply(json.dumps) df.drop(columns=["tokens"]).to_csv(run_dir / "generated_routes.csv", index=False) if background_image is not None: try: bg_display = background_image.relative_to(REPO_ROOT) except Exception: bg_display = background_image print(f"Using background image: {bg_display}") else: print("Using background image: none (coordinate-board style only)") print("\nSaved route table:") print(run_dir / "generated_routes.csv") print("\nOutput directory:") print(run_dir) if __name__ == "__main__": main()