File size: 7,576 Bytes
7eb63a1 4a027f2 7eb63a1 4a027f2 7eb63a1 4a027f2 7eb63a1 4a027f2 7eb63a1 4a027f2 7eb63a1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import argparse
import io
import itertools
import json
from pathlib import Path
from PIL import Image
from selectground import SelectGround
from self_contrast import SelfContrastGrounder
def load_cases(name: str, root: Path):
if name == "screenspot_pro":
parquet_files = sorted((root / "data").glob("*.parquet"))
if parquet_files:
import pyarrow.parquet as parquet
for path in parquet_files:
for batch in parquet.ParquetFile(path).iter_batches(batch_size=1):
row = batch.to_pylist()[0]
encoded = row["image"]
image = Image.open(io.BytesIO(encoded["bytes"])).convert("RGB")
yield {
"id": row["id"],
"image": image,
"image_name": encoded.get("path") or row["id"],
"instruction": row["instruction"],
"target": row["bbox"],
"type": "xyxy",
"group": row.get("group"),
}
else:
for annotation in sorted((root / "annotations").glob("*.json")):
for row in json.loads(annotation.read_text()):
yield {
"id": row["id"],
"image": root / "images" / row["img_filename"],
"image_name": row["img_filename"],
"instruction": row["instruction"],
"target": row["bbox"],
"type": "xyxy",
"group": row.get("group"),
}
elif name == "ui_vision":
for split in ("basic", "functional", "spatial"):
path = root / "annotations" / "element_grounding" / f"element_grounding_{split}.json"
for index, row in enumerate(json.loads(path.read_text())):
yield {
"id": f"{split}-{index}",
"image": root / "images" / row["image_path"],
"image_name": row["image_path"],
"instruction": row["prompt_to_evaluate"],
"target": row["bbox"],
"type": "xyxy",
"group": split,
}
else:
benchmark = root / "benchmark" if (root / "benchmark").is_dir() else root
for row in json.loads((benchmark / "OSWorld-G.json").read_text()):
if row["box_type"] == "refusal":
continue
yield {
"id": row["id"],
"image": benchmark / "images" / row["image_path"],
"image_name": row["image_path"],
"instruction": row["instruction"],
"target": row["box_coordinates"],
"type": row["box_type"],
"group": None,
}
def contains(point, target, target_type):
if point is None:
return False
x, y = point
if target_type in {"bbox", "xyxy"}:
if target_type == "xyxy":
left, top, right, bottom = target
else:
left, top, width, height = target[:4]
right, bottom = left + width, top + height
center_x, center_y = (left + right) / 2, (top + bottom) / 2
half_width, half_height = abs(right - left) / 2, abs(bottom - top) / 2
return (
center_x - half_width <= x <= center_x + half_width
and center_y - half_height <= y <= center_y + half_height
)
vertices = list(zip(target[0::2], target[1::2]))
previous, inside = vertices[-1], False
for current in vertices:
x1, y1 = current
x2, y2 = previous
cross = (x - x1) * (y2 - y1) - (y - y1) * (x2 - x1)
if abs(cross) <= 1e-7 and min(x1, x2) - 1e-7 <= x <= max(x1, x2) + 1e-7 and min(y1, y2) - 1e-7 <= y <= max(y1, y2) + 1e-7:
return True
if (y1 > y) != (y2 > y) and x < (x2 - x1) * (y - y1) / (y2 - y1) + x1:
inside = not inside
previous = current
return inside
def metrics(rows, benchmark):
if benchmark != "ui_vision":
return {"total": len(rows), "correct": sum(row["correct"] for row in rows), "accuracy": 100 * sum(row["correct"] for row in rows) / len(rows)}
splits = {}
for split in ("basic", "functional", "spatial"):
selected = [row for row in rows if row["group"] == split]
if selected:
splits[split] = 100 * sum(row["correct"] for row in selected) / len(selected)
return {"total": len(rows), "accuracy": sum(splits.values()) / len(splits), "splits": splits}
parser = argparse.ArgumentParser(description="Evaluate SelectGround on a GUI grounding benchmark.")
parser.add_argument("--model", default="ruotian/SelectGround-8B")
parser.add_argument("--benchmark", choices=("screenspot_pro", "ui_vision", "osworld_g"), required=True)
parser.add_argument("--data", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--lcr", action="store_true")
parser.add_argument("--self-contrast", action="store_true")
parser.add_argument(
"--self-contrast-variant",
choices=(
"full",
"no_latent_distractors",
"one_latent_distractor",
"no_recurrent_anchor",
"no_cross_view_evidence",
"no_anchor_proximity",
),
default="full",
)
parser.add_argument(
"--lcr-variant",
choices=("full", "no_competitor", "one_competitor", "no_incumbent"),
default="full",
)
parser.add_argument("--limit", type=int)
parser.add_argument("--num-shards", type=int, default=1)
parser.add_argument("--shard", type=int, default=0)
args = parser.parse_args()
if args.lcr and args.self_contrast:
parser.error("--lcr and --self-contrast are mutually exclusive")
cases = (
case for index, case in enumerate(load_cases(args.benchmark, args.data))
if index % args.num_shards == args.shard
)
if args.limit is not None:
cases = itertools.islice(cases, args.limit)
existing = []
if args.output.exists():
existing = [json.loads(line) for line in args.output.read_text().splitlines() if line.strip()]
done = {row["id"] for row in existing}
grounder = SelfContrastGrounder(args.model) if args.self_contrast else SelectGround(args.model)
args.output.parent.mkdir(parents=True, exist_ok=True)
with args.output.open("a") as output:
for number, case in enumerate(cases, 1):
if case["id"] in done:
continue
if args.self_contrast:
prediction = grounder.predict(
case["image"],
case["instruction"],
variant=args.self_contrast_variant,
)
else:
prediction = grounder.predict(
case["image"],
case["instruction"],
lcr=args.lcr,
benchmark=args.benchmark,
lcr_variant=args.lcr_variant,
)
row = {
"id": case["id"],
"instruction": case["instruction"],
"image": case["image_name"],
"point": prediction["point"],
"correct": contains(prediction["point"], case["target"], case["type"]),
"group": case["group"],
"prediction": prediction,
}
output.write(json.dumps(row) + "\n")
output.flush()
existing.append(row)
print(f"[{number}] {case['id']} correct={int(row['correct'])}", flush=True)
print(json.dumps(metrics(existing, args.benchmark), indent=2))
|