File size: 2,090 Bytes
babffc8 | 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 | from __future__ import annotations
from pathlib import Path
from typing import Any
from torch.utils.data import Dataset
from .io_utils import read_jsonl, resolve_path
from .qwen_video_utils import build_messages
class VideoJsonlDataset(Dataset):
def __init__(
self,
jsonl_path: str | Path,
*,
video_column: str,
target_column: str,
prompt: str,
video_fps: int | float | None = None,
max_frames: int | None = None,
max_samples: int | None = None,
require_target: bool = True,
) -> None:
rows = read_jsonl(jsonl_path)
if max_samples is not None and max_samples > 0:
rows = rows[:max_samples]
self.rows = rows
self.video_column = video_column
self.target_column = target_column
self.prompt = prompt
self.video_fps = video_fps
self.max_frames = max_frames
self.require_target = require_target
def __len__(self) -> int:
return len(self.rows)
def __getitem__(self, idx: int) -> dict[str, Any]:
row = self.rows[idx]
if self.video_column not in row:
raise KeyError(f"Missing video column '{self.video_column}' in row id={row.get('id', idx)}")
video_path = resolve_path(row[self.video_column])
if not video_path.exists():
raise FileNotFoundError(f"Video file not found for row id={row.get('id', idx)}: {video_path}")
target = row.get(self.target_column)
if self.require_target and (target is None or str(target).strip() == ""):
raise ValueError(f"Missing target '{self.target_column}' in row id={row.get('id', idx)}")
return {
"id": row.get("id", str(idx)),
"video_path": str(video_path),
"target": str(target) if target is not None else None,
"messages": build_messages(
video_path,
self.prompt,
target,
video_fps=self.video_fps,
max_frames=self.max_frames,
),
}
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