Datasets:
File size: 1,798 Bytes
bd00f85 | 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 | """Case loaders for the released benchmark data.
Each loader reads one file under ``data/`` so a case list is identical across
runs and across re-scoring: 145 fonts (one pinned word each), 100 colors, and
200 single-instruction edits.
"""
from __future__ import annotations
import json
from dataclasses import dataclass
from pathlib import Path
DATA_DIR = Path(__file__).resolve().parents[1] / "data"
@dataclass(frozen=True)
class FontCase:
"""One Google font and one word to render. The ground truth ships as a
rendered image under ``references/``; ``noise_floor`` is that reference
scored against itself rendered at another size and position."""
family: str
word: str
noise_floor: float
variant: str = "Regular"
@property
def case_id(self) -> str:
return f"{self.family}/{self.word}"
@dataclass(frozen=True)
class ColorCase:
hex: str
@property
def case_id(self) -> str:
return self.hex.lstrip("#").lower()
@dataclass(frozen=True)
class EditCase:
"""One design and the single edit asked of it. ``start_png``/``start_svg``
name the shared starting design under ``starts/``; every arm edits that
same design."""
design_id: str
width: int
height: int
category: str
instruction: str
start_svg: str
start_png: str
def _read_jsonl(name: str) -> list[dict]:
path = DATA_DIR / name
return [json.loads(line) for line in path.read_text().splitlines() if line]
def font_cases() -> list[FontCase]:
return [FontCase(**row) for row in _read_jsonl("fonts.jsonl")]
def color_cases() -> list[ColorCase]:
return [ColorCase(**row) for row in _read_jsonl("colors.jsonl")]
def edit_cases() -> list[EditCase]:
return [EditCase(**row) for row in _read_jsonl("edits.jsonl")]
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