Download tools/data_loader.py from VisionXLab/FIRM-Video-Bench: direct link, hf CLI and curl.
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https://huggingface.co/datasets/VisionXLab/FIRM-Video-Bench/resolve/main/tools/data_loader.py
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curl -L -o data_loader.py https://huggingface.co/datasets/VisionXLab/FIRM-Video-Bench/resolve/main/tools/data_loader.py
4.41 kB
| """Point-wise benchmark JSON loader. | |
| The expected schema is a flat JSON list of records of the form:: | |
| { | |
| "video_name": "000434_c.mp4", | |
| "video_path": "videos/000434_c.mp4", # relative to the JSON file | |
| "prompt": "...", | |
| "source": "vs2", | |
| "metadata": { | |
| "visual_score": 4, | |
| "t2v_score": 4, | |
| "phy_score": 4 | |
| } | |
| } | |
| ``video_path`` is interpreted relative to the directory that contains the | |
| JSON file (and may also be absolute). | |
| """ | |
| from __future__ import annotations | |
| import json | |
| import os | |
| from typing import Any, Tuple | |
| def _json_records(payload: Any, data_path: str) -> list[dict[str, Any]]: | |
| if not isinstance(payload, list): | |
| raise ValueError( | |
| f"JSON data must be a list of records: {data_path} " | |
| f"(got {type(payload).__name__})" | |
| ) | |
| if not all(isinstance(item, dict) for item in payload): | |
| raise ValueError(f"JSON data must contain objects only: {data_path}") | |
| return payload | |
| def _resolve_video_path(base_dir: str, video_path: str) -> str: | |
| """Resolve a record's ``video_path`` against the JSON file's directory.""" | |
| if not isinstance(video_path, str) or not video_path: | |
| return "" | |
| if os.path.isabs(video_path): | |
| return video_path | |
| return os.path.normpath(os.path.join(base_dir, video_path)) | |
| def load_pointwise_data( | |
| data_path: str, | |
| num_samples: str = "all", | |
| ) -> Tuple[list[dict[str, Any]], list[dict[str, Any]]]: | |
| """Load a point-wise benchmark JSON. | |
| Parameters | |
| ---------- | |
| data_path : str | |
| Path to the benchmark JSON file. | |
| num_samples : str | |
| Either ``"all"`` or a positive integer (as a string) capping the | |
| number of records. | |
| Returns | |
| ------- | |
| (raw_prompts, expanded) | |
| raw_prompts : list of de-duplicated prompts with their source row | |
| indices (handy for any prompt-level step). | |
| expanded : list of per-video records ready for scoring. | |
| """ | |
| data_path = os.path.abspath(data_path) | |
| base_dir = os.path.dirname(data_path) | |
| with open(data_path, "r", encoding="utf-8") as f: | |
| records = _json_records(json.load(f), data_path) | |
| print(f"[data] Loaded {len(records)} videos from {data_path}") | |
| if num_samples != "all": | |
| records = records[: int(num_samples)] | |
| print(f"[data] Truncated to {len(records)} videos") | |
| prompt_to_index: dict[str, int] = {} | |
| raw_prompts: list[dict[str, Any]] = [] | |
| expanded: list[dict[str, Any]] = [] | |
| for row_idx, item in enumerate(records): | |
| video_name = str(item["video_name"]) | |
| prompt_text = str(item["prompt"]) | |
| source_index = prompt_to_index.get(prompt_text) | |
| if source_index is None: | |
| source_index = len(raw_prompts) | |
| prompt_to_index[prompt_text] = source_index | |
| raw_prompts.append( | |
| {"prompt": prompt_text, "prompt_id": source_index, "source_rows": []} | |
| ) | |
| raw_prompts[source_index]["source_rows"].append(row_idx) | |
| rel_video_path = item.get("video_path", "") | |
| local_path = _resolve_video_path(base_dir, rel_video_path) | |
| source = str(item.get("source", "")).strip() | |
| metadata = item.get("metadata", {}) or {} | |
| if not isinstance(metadata, dict): | |
| metadata = {} | |
| expanded.append( | |
| { | |
| "video_id": f"{row_idx}_{os.path.splitext(video_name)[0]}", | |
| "video_name": video_name, | |
| "caption": prompt_text, | |
| "video_path": rel_video_path, | |
| "video_local_path": local_path, | |
| "source": source, | |
| "source_index": source_index, | |
| "source_row_index": row_idx, | |
| "metadata": metadata, | |
| } | |
| ) | |
| print( | |
| f"[data] Unique prompts: {len(raw_prompts)}; videos to score: {len(expanded)}" | |
| ) | |
| return raw_prompts, expanded | |
| def ensure_video_local(item: dict[str, Any]) -> str: | |
| """Validate that the local video file exists; return its absolute path.""" | |
| local_path = item.get("video_local_path") | |
| if ( | |
| isinstance(local_path, str) | |
| and local_path | |
| and os.path.exists(local_path) | |
| and os.path.getsize(local_path) > 0 | |
| ): | |
| return local_path | |
| raise FileNotFoundError( | |
| f"Video not found: {local_path or item.get('video_name')}" | |
| ) | |