FIRM-Video-Bench / tools /data_loader.py
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Release FIRM-Video-Bench (part 2)
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"""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')}"
)