File size: 4,912 Bytes
362fd0e | 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 | import json, os, hashlib
from pathlib import Path
# ecommerce dataset processor
# modality: sensor_fusion, preprocessing: minimal
# --- real data source: ecommerce ---
TV_DATASET = None
HF_CANDIDATES = ['ashraq/fashion-product-images-small']
IMAGE_FIELD = 'image'
TEXT_FIELD = None
LABEL_FIELD = 'articleType'
PROMPT_TEMPLATE = 'a product catalog photo of {label}'
DATASET_URL = 'https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset'
def fetch_real_samples(max_samples=5000, cache_dir='./_cache'):
# 本地???有数据时自动下载真实公开数据集: torchvision -> HuggingFace -> 手动说明
out = []
if TV_DATASET is not None:
try:
import torchvision
ctor = getattr(torchvision.datasets, TV_DATASET)
try:
ds = ctor(root=cache_dir, split='train', download=True)
except TypeError:
try:
ds = ctor(root=cache_dir, train=True, download=True)
except TypeError:
ds = ctor(root=cache_dir, download=True)
classes = getattr(ds, 'classes', None)
os.makedirs(os.path.join(cache_dir, 'tv'), exist_ok=True)
for i, item in enumerate(ds):
if len(out) >= max_samples:
break
img, label = item[0], item[1]
name = classes[label] if classes else str(label)
p = os.path.join(cache_dir, 'tv', str(i) + '.png')
try:
img.save(p)
except Exception:
continue
out.append({'image': p, 'text': PROMPT_TEMPLATE.format(label=name)})
if out:
return out
except Exception as e:
print('torchvision load failed:', e)
for repo in HF_CANDIDATES:
try:
from datasets import load_dataset
try:
ds = load_dataset(repo, split='train', streaming=True)
except Exception:
ds = load_dataset(repo, split='train')
img_dir = os.path.join(cache_dir, 'hf_images')
os.makedirs(img_dir, exist_ok=True)
for i, ex in enumerate(ds):
if len(out) >= max_samples:
break
txt = None
if TEXT_FIELD is not None and TEXT_FIELD in ex:
v = ex[TEXT_FIELD]
txt = v if isinstance(v, str) else ' '.join(map(str, v if isinstance(v, (list, tuple)) else [v]))
if txt is None and LABEL_FIELD in ex:
txt = PROMPT_TEMPLATE.format(label=ex[LABEL_FIELD])
if txt is None:
continue
if IMAGE_FIELD not in ex or ex[IMAGE_FIELD] is None:
continue
p = os.path.join(img_dir, str(i) + '.jpg')
try:
ex[IMAGE_FIELD].convert('RGB').save(p)
except Exception:
continue
out.append({'image': p, 'text': txt})
if out:
return out
except Exception as e:
print('HF load failed for', repo, ':', e)
print('Automatic download failed. Please get the data manually from:')
print(' ' + DATASET_URL)
return out
def build_dataset(src, dst, sz=224):
samples = []
for f in Path(src).glob('*.jsonl'):
with open(f) as fp:
for line in fp:
if line.strip():
samples.append(json.loads(line))
if not samples:
samples = fetch_real_samples()
# dedup
seen = set()
unique = []
for s in samples:
fp = s.get('image', s.get('audio', ''))
if fp and os.path.exists(fp):
h = hashlib.md5(open(fp, 'rb').read()).hexdigest()
if h not in seen:
seen.add(h)
unique.append(s)
else:
unique.append(s)
os.makedirs(dst, exist_ok=True)
out = []
for s in unique:
item = {}
if 'image' in s:
from PIL import Image
img = Image.open(s['image']).convert('RGB').resize((sz, sz))
p = os.path.join(dst, os.path.basename(s['image']))
img.save(p, 'JPEG', quality=95)
item['image'] = p
item['text'] = s.get('ecommerce', s.get('text', ''))
item['domain'] = 'ecommerce'
out.append(item)
with open(os.path.join(dst, 'dataset.jsonl'), 'w') as f:
for d in out:
f.write(json.dumps(d, ensure_ascii=False) + '\n')
return out
if __name__ == '__main__':
import sys
result = build_dataset(sys.argv[1], sys.argv[2])
print(f'Processed {len(result)} samples')
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