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Running on Zero
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d0e7c02 | 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 | """Evaluation sets outside the training data.
screenspot ScreenSpot (bevaya/ScreenSpot, test, 1,272), shuffled with seed 0: clicks and element boxes
coco17 COCO val2017 photos (RefCOCO is all train2014): objects prompted by category name, for
categories with exactly one instance in the image, at least 1% of it, polygon masks only
fashion Fashionpedia val: one garment per image, by name (a new domain; boxes only)
Point COCO17_DIR at a folder with annotations/instances_val2017.json and val2017/
(recipes/fetch_coco17.sh).
"""
import json, os, random
COCO17 = os.environ.get("COCO17_DIR", "/workspace/data/coco17")
MIN_AREA = 0.01
GARMENTS = 27 # Fashionpedia classes 0-26 are whole garments and accessories; the rest are parts
def screenspot(n=0):
from datasets import load_dataset
ds = load_dataset("bevaya/ScreenSpot", split="test").shuffle(seed=0)
ds = ds.select(range(n)) if n else ds
return [dict(image=r["image"], instruction=r["instruction"], box=list(r["bbox"]), norm=True,
group=(r["data_source"], r["data_type"])) for r in ds]
def coco17(n, seed=0):
d = json.load(open(f"{COCO17}/annotations/instances_val2017.json"))
cats = {c["id"]: c["name"] for c in d["categories"]}
imgs = {i["id"]: i for i in d["images"]}
by = {}
for a in d["annotations"]:
by.setdefault((a["image_id"], a["category_id"]), []).append(a)
out = []
for (iid, cid), anns in sorted(by.items()):
if len(anns) != 1:
continue
a, im = anns[0], imgs[iid]
W, H = im["width"], im["height"]
if a["iscrowd"] or not isinstance(a["segmentation"], list) or a["area"] < MIN_AREA * W * H:
continue
x, y, w, h = a["bbox"]
out.append(dict(image=f"{COCO17}/val2017/{im['file_name']}", size=(W, H), sentence=cats[cid],
instruction=cats[cid], polys=a["segmentation"], bbox=[x, y, x + w, y + h],
box=[x / W, y / H, (x + w) / W, (y + h) / H], norm=True, ref_id=a["id"], source="coco17"))
random.Random(seed).shuffle(out)
return out[:n] if n else out
def fashion(n, seed=0):
from datasets import load_dataset
ds = load_dataset("detection-datasets/fashionpedia", split="val")
names = ds.features["objects"]["category"].feature.names
idx = list(range(len(ds)))
random.Random(seed).shuffle(idx)
out = []
for i in idx:
r = ds[i]
W, H, o = r["width"], r["height"], r["objects"]
counts = {}
for c in o["category"]:
counts[c] = counts.get(c, 0) + 1
for c, (x1, y1, x2, y2) in zip(o["category"], o["bbox"]): # x1, y1, x2, y2 in pixels
if c < GARMENTS and counts[c] == 1 and (x2 - x1) * (y2 - y1) >= MIN_AREA * W * H:
out.append(dict(image=r["image"], size=(W, H), instruction=f"the {names[c].split(',')[0]}",
box=[x1 / W, y1 / H, x2 / W, y2 / H], norm=True, source="fashionpedia"))
break # one object per image
if n and len(out) >= n:
break
return out
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