#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Benchmark Dataset Splitter & Renamer. This script reads the JSON mapping files generated by `trajectory_split_domain_aware.py`, extracts the corresponding samples from the original merged trajectory data, and optionally renames the output files with split-specific prefixes. """ from __future__ import annotations import argparse import json from copy import deepcopy from pathlib import Path from typing import Dict, List # ==================== Default Configuration ==================== DEFAULT_ORIGINAL_DATA_DIR = Path("Data/trajectories/trajectory_splits") DEFAULT_MAPPING_DIR = Path("Data/trajectories/data_splits") DEFAULT_OUTPUT_BASE = Path("Data/trajectories/data_splits") DEFAULT_TRAIN_DIR = DEFAULT_OUTPUT_BASE / "train" DEFAULT_VAL_DIR = DEFAULT_OUTPUT_BASE / "val" DEFAULT_SCENE_UNSEEN_DIR = DEFAULT_OUTPUT_BASE / "test_scene_unseen" DEFAULT_TRAJECTORY_UNSEEN_DIR = DEFAULT_OUTPUT_BASE / "test_trajectory_unseen" DEFAULT_INSTRUCTION_UNSEEN_DIR = DEFAULT_OUTPUT_BASE / "test_instruction_unseen" DEFAULT_PREFIXES = { "train": "train_", "val": "val_", "scene_unseen": "test_", "trajectory_unseen": "test_", "instruction_unseen": "test_", } MAPPING_FILENAMES = { "train": "GSNav-Bench_Train_Split_Domain.json", "val": "GSNav-Bench_Val_Split_Domain.json", "scene_unseen": "GSNav-Bench_Test_Scene_Unseen_Split_Domain.json", "trajectory_unseen": "GSNav-Bench_Test_Trajectory_Unseen_Split_Domain.json", "instruction_unseen": "GSNav-Bench_Test_Instruction_Unseen_Split_Domain.json", } # ==================== Helper Class ==================== class BenchmarkDatasetBuilder: """Extract split data according to mapping files and optionally rename outputs.""" def __init__( self, original_data_dir: Path, mapping_dir: Path, output_dirs: Dict[str, Path], prefixes: Dict[str, str], rename_files: bool = True, ): self.original_data_dir = original_data_dir self.mapping_dir = mapping_dir self.output_dirs = output_dirs self.prefixes = prefixes self.rename_files = rename_files self.split_mappings: Dict[str, Dict] = {} # ---------- Data Loading ---------- def load_split_mappings(self) -> None: """Load all split mapping JSON files.""" print("Loading split mapping files...") for split_name, filename in MAPPING_FILENAMES.items(): file_path = self.mapping_dir / filename if not file_path.exists(): raise FileNotFoundError(f"Mapping file not found: {file_path}") with file_path.open("r", encoding="utf-8") as f: self.split_mappings[split_name] = json.load(f) scene_count = len(self.split_mappings[split_name].get("scenes", {})) print(f" Loaded {split_name:<20s}: {scene_count} scenes") def load_original_scene_data(self, scene_id: str) -> Dict: """Load the original trajectory JSON for a scene.""" scene_dir = self.original_data_dir / scene_id trajectory_files = list(scene_dir.glob("trajectories_overall_*.json")) if not trajectory_files: raise FileNotFoundError(f"No trajectories_overall_*.json found for scene {scene_id}") with trajectory_files[0].open("r", encoding="utf-8") as f: return json.load(f) # ---------- Output Directories ---------- def create_output_directories(self) -> None: """Create output directories for all splits.""" print("Creating output directories...") for split_name, output_path in self.output_dirs.items(): output_path.mkdir(parents=True, exist_ok=True) print(f" {split_name:<20s} -> {output_path}") # ---------- Extraction Helpers ---------- def _get_scene_filename(self, scene_id: str) -> str: scene_dir = self.original_data_dir / scene_id trajectory_files = list(scene_dir.glob("trajectories_overall_*.json")) if trajectory_files: return trajectory_files[0].name return f"trajectories_overall_{scene_id}.json" def _save_scene_data(self, data: Dict, output_dir: Path, filename: str) -> None: output_dir.mkdir(parents=True, exist_ok=True) output_path = output_dir / filename with output_path.open("w", encoding="utf-8") as f: json.dump(data, f, indent=2, ensure_ascii=False) # ---------- Split Extraction ---------- def extract_scene_unseen(self) -> None: """Extract Scene-Unseen test data (full scenes).""" print("\n=== Extracting Scene-Unseen test set ===") split_data = self.split_mappings["scene_unseen"] output_dir = self.output_dirs["scene_unseen"] total_scenes = total_trajectories = total_instructions = 0 for scene_id in split_data["scenes"].keys(): print(f" Scene {scene_id}") original_data = self.load_original_scene_data(scene_id) scene_filename = self._get_scene_filename(scene_id) scene_output_dir = output_dir / scene_id self._save_scene_data(original_data, scene_output_dir, scene_filename) samples = original_data["scenes"][0]["samples"] total_scenes += 1 total_trajectories += len(samples) total_instructions += sum(len(sample["instructions"]) for sample in samples) print( f"Scene-Unseen done: {total_scenes} scenes, {total_trajectories} trajectories, {total_instructions} instructions" ) def extract_val(self) -> None: """Extract validation data (full scenes).""" print("\n=== Extracting validation set ===") split_data = self.split_mappings["val"] output_dir = self.output_dirs["val"] total_scenes = total_trajectories = total_instructions = 0 for scene_id in split_data["scenes"].keys(): print(f" Scene {scene_id}") original_data = self.load_original_scene_data(scene_id) scene_filename = self._get_scene_filename(scene_id) scene_output_dir = output_dir / scene_id self._save_scene_data(original_data, scene_output_dir, scene_filename) samples = original_data["scenes"][0]["samples"] total_scenes += 1 total_trajectories += len(samples) total_instructions += sum(len(sample["instructions"]) for sample in samples) print( f"Validation done: {total_scenes} scenes, {total_trajectories} trajectories, {total_instructions} instructions" ) def extract_trajectory_unseen(self) -> None: """Extract Trajectory-Unseen test data (subset of trajectories).""" print("\n=== Extracting Trajectory-Unseen test set ===") split_data = self.split_mappings["trajectory_unseen"] output_dir = self.output_dirs["trajectory_unseen"] total_scenes = total_trajectories = total_instructions = 0 for scene_id, scene_info in split_data["scenes"].items(): print(f" Scene {scene_id}") original_data = self.load_original_scene_data(scene_id) samples = original_data["scenes"][0]["samples"] trajectory_map = {sample["trajectory_id"]: sample for sample in samples} selected_samples = [] scene_instruction_count = 0 for traj_info in scene_info["trajectories"]: traj_id = traj_info["trajectory_id"] sample = trajectory_map.get(traj_id) if sample: selected_samples.append(deepcopy(sample)) scene_instruction_count += len(sample["instructions"]) else: print(f" [WARN] Trajectory {traj_id} not found in scene {scene_id}") if selected_samples: new_data = deepcopy(original_data) new_data["scenes"][0]["samples"] = selected_samples scene_output_dir = output_dir / scene_id scene_filename = self._get_scene_filename(scene_id) self._save_scene_data(new_data, scene_output_dir, scene_filename) total_scenes += 1 total_trajectories += len(selected_samples) total_instructions += scene_instruction_count print( f" Selected {len(selected_samples)} trajectories, {scene_instruction_count} instructions" ) print( f"Trajectory-Unseen done: {total_scenes} scenes, {total_trajectories} trajectories, {total_instructions} instructions" ) def extract_instruction_unseen(self) -> None: """Extract Instruction-Unseen test data (subset of instructions).""" print("\n=== Extracting Instruction-Unseen test set ===") split_data = self.split_mappings["instruction_unseen"] output_dir = self.output_dirs["instruction_unseen"] total_scenes = total_trajectories = total_instructions = 0 for scene_id, scene_info in split_data["scenes"].items(): print(f" Scene {scene_id}") original_data = self.load_original_scene_data(scene_id) samples = original_data["scenes"][0]["samples"] trajectory_map = {sample["trajectory_id"]: sample for sample in samples} selected_samples = [] scene_instruction_count = 0 for traj_info in scene_info["trajectories"]: traj_id = traj_info["trajectory_id"] indices = traj_info["selected_instruction_indices"] sample = trajectory_map.get(traj_id) if sample: new_sample = deepcopy(sample) new_sample["instructions"] = [ sample["instructions"][idx] for idx in indices if 0 <= idx < len(sample["instructions"]) ] if new_sample["instructions"]: selected_samples.append(new_sample) scene_instruction_count += len(new_sample["instructions"]) else: print(f" [WARN] Trajectory {traj_id} not found in scene {scene_id}") if selected_samples: new_data = deepcopy(original_data) new_data["scenes"][0]["samples"] = selected_samples scene_output_dir = output_dir / scene_id scene_filename = self._get_scene_filename(scene_id) self._save_scene_data(new_data, scene_output_dir, scene_filename) total_scenes += 1 total_trajectories += len(selected_samples) total_instructions += scene_instruction_count print( f" Selected {len(selected_samples)} trajectories, {scene_instruction_count} instructions" ) print( f"Instruction-Unseen done: {total_scenes} scenes, {total_trajectories} trajectories, {total_instructions} instructions" ) def extract_train(self) -> None: """Extract training data (exclude test trajectories/instructions).""" print("\n=== Extracting training set ===") split_data = self.split_mappings["train"] output_dir = self.output_dirs["train"] total_scenes = total_trajectories = total_instructions = 0 for scene_id, scene_info in split_data["scenes"].items(): print(f" Scene {scene_id}") original_data = self.load_original_scene_data(scene_id) samples = original_data["scenes"][0]["samples"] trajectory_map = {sample["trajectory_id"]: sample for sample in samples} selected_samples = [] scene_instruction_count = 0 for traj_info in scene_info["trajectories"]: traj_id = traj_info["trajectory_id"] available_indices = set(traj_info["available_instruction_indices"]) sample = trajectory_map.get(traj_id) if sample and available_indices: new_sample = deepcopy(sample) new_sample["instructions"] = [ sample["instructions"][idx] for idx in sorted(available_indices) if 0 <= idx < len(sample["instructions"]) ] if new_sample["instructions"]: selected_samples.append(new_sample) scene_instruction_count += len(new_sample["instructions"]) else: print(f" [WARN] Trajectory {traj_id} not found or has no available instructions") if selected_samples: new_data = deepcopy(original_data) new_data["scenes"][0]["samples"] = selected_samples scene_output_dir = output_dir / scene_id scene_filename = self._get_scene_filename(scene_id) self._save_scene_data(new_data, scene_output_dir, scene_filename) total_scenes += 1 total_trajectories += len(selected_samples) total_instructions += scene_instruction_count print( f" Selected {len(selected_samples)} trajectories, {scene_instruction_count} instructions" ) print( f"Training set done: {total_scenes} scenes, {total_trajectories} trajectories, {total_instructions} instructions" ) # ---------- Renaming ---------- def rename_split_files(self) -> None: """Rename files in each split directory using configured prefixes.""" if not self.rename_files: return print("\n=== Renaming split files ===") for split_name, directory_path in self.output_dirs.items(): prefix = self.prefixes.get(split_name) if not prefix: continue self._rename_files_in_directory(directory_path, prefix, split_name) self._verify_renaming() def _rename_files_in_directory(self, directory_path: Path, prefix: str, split_name: str) -> None: if not directory_path.exists(): print(f"[WARN] Directory does not exist: {directory_path}") return total_files = renamed_files = 0 for scene_dir in directory_path.iterdir(): if scene_dir.is_dir(): for file_path in scene_dir.iterdir(): if file_path.is_file(): total_files += 1 if not file_path.name.startswith(prefix): new_path = scene_dir / f"{prefix}{file_path.name}" file_path.rename(new_path) renamed_files += 1 print( f" {split_name:<20s}: {renamed_files}/{total_files} files renamed with prefix '{prefix}'" ) def _verify_renaming(self) -> None: print("\n=== Rename verification ===") for split_name, directory_path in self.output_dirs.items(): if not directory_path.exists(): print(f"{split_name:<20s}: directory missing") continue prefix = self.prefixes.get(split_name, "") total_files = prefixed_files = 0 for scene_dir in directory_path.iterdir(): if scene_dir.is_dir(): for file_path in scene_dir.iterdir(): if file_path.is_file(): total_files += 1 if file_path.name.startswith(prefix): prefixed_files += 1 if total_files > 0: ratio = prefixed_files / total_files * 100 print(f"{split_name:<20s}: {prefixed_files}/{total_files} ({ratio:.1f}% prefixed)") else: print(f"{split_name:<20s}: no files") # ---------- Pipeline ---------- def run(self) -> None: """Run the full pipeline: load mappings, extract splits, rename.""" print("=" * 80) print(" Benchmark Dataset Split & Rename") print("=" * 80) self.load_split_mappings() self.create_output_directories() self.extract_scene_unseen() self.extract_trajectory_unseen() self.extract_instruction_unseen() self.extract_train() self.extract_val() self.rename_split_files() print("\nDone. Output directories:") for split_name, path in self.output_dirs.items(): scene_count = len([d for d in path.iterdir() if d.is_dir()]) if path.exists() else 0 print(f" {split_name:<20s}: {path} ({scene_count} scenes)") # ==================== CLI ==================== def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Extract benchmark splits and rename files.") parser.add_argument( "--original-data-dir", type=Path, default=DEFAULT_ORIGINAL_DATA_DIR, help="Directory containing original merged trajectory scenes", ) parser.add_argument( "--mapping-dir", type=Path, default=DEFAULT_MAPPING_DIR, help="Directory containing split mapping JSON files", ) parser.add_argument( "--train-dir", type=Path, default=DEFAULT_TRAIN_DIR, help="Output directory for the training split", ) parser.add_argument( "--val-dir", type=Path, default=DEFAULT_VAL_DIR, help="Output directory for the validation split", ) parser.add_argument( "--scene-unseen-dir", type=Path, default=DEFAULT_SCENE_UNSEEN_DIR, help="Output directory for the scene-unseen split", ) parser.add_argument( "--trajectory-unseen-dir", type=Path, default=DEFAULT_TRAJECTORY_UNSEEN_DIR, help="Output directory for the trajectory-unseen split", ) parser.add_argument( "--instruction-unseen-dir", type=Path, default=DEFAULT_INSTRUCTION_UNSEEN_DIR, help="Output directory for the instruction-unseen split", ) parser.add_argument( "--no-rename", action="store_true", help="Disable renaming of output files with split-specific prefixes", ) return parser.parse_args() def main() -> None: args = parse_args() # Validate directories if not args.original_data_dir.exists(): print(f"[ERROR] Original data dir does not exist: {args.original_data_dir}") return if not args.mapping_dir.exists(): print(f"[ERROR] Mapping dir does not exist: {args.mapping_dir}") return output_dirs = { "train": args.train_dir, "val": args.val_dir, "scene_unseen": args.scene_unseen_dir, "trajectory_unseen": args.trajectory_unseen_dir, "instruction_unseen": args.instruction_unseen_dir, } builder = BenchmarkDatasetBuilder( original_data_dir=args.original_data_dir, mapping_dir=args.mapping_dir, output_dirs=output_dirs, prefixes=DEFAULT_PREFIXES, rename_files=not args.no_rename, ) builder.run() if __name__ == "__main__": main()