File size: 4,414 Bytes
6461f0c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | """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')}"
)
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