File size: 5,456 Bytes
b2beb8e | 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 | import json, os, hashlib, logging
from pathlib import Path
log = logging.getLogger(__name__)
# --- real data source: code ---
TV_DATASET = None
HF_CANDIDATES = ['code_search_net', 'codeparrot/github-code']
IMAGE_FIELD = None
TEXT_FIELD = 'func_code_string'
LABEL_FIELD = 'func_documentation_string'
PROMPT_TEMPLATE = 'a photo of a {label}'
DATASET_URL = 'https://huggingface.co/datasets/code_search_net'
def fetch_real_samples(max_samples=5000, cache_dir='./_cache'):
# 本地没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
out.append({'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 load(data_dir):
files = list(Path(data_dir).glob('*.jsonl'))
if not files:
files = list(Path(data_dir).glob('*.json'))
samples = []
for f in files:
with open(f) as fp:
if f.suffix == '.json':
d = json.load(fp)
samples.extend(d if isinstance(d, list) else [d])
else:
samples.extend(json.loads(l) for l in fp if l.strip())
return samples
def filter_quality(samples, min_score=0.5):
results = []
for s in samples:
text = s.get('code', s.get('text', ''))
if len(text.split()) >= 3:
results.append(s)
return results
def dedup(samples):
seen = set()
out = []
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 in seen:
continue
seen.add(h)
out.append(s)
return out
def preprocess(samples, out_dir, img_size=224):
os.makedirs(out_dir, exist_ok=True)
processed = []
for s in samples:
item = {}
if 'image' in s:
try:
from PIL import Image as IM
img = IM.open(s['image']).convert('RGB')
img = img.resize((img_size, img_size))
p = os.path.join(out_dir, os.path.basename(s['image']))
img.save(p, 'JPEG', quality=95)
item['image'] = p
except Exception:
continue
text = s.get('code', s.get('text', ''))
item['text'] = text
item['domain'] = 'code'
processed.append(item)
return processed
def save_jsonl(data, path):
with open(path, 'w') as f:
for d in data:
f.write(json.dumps(d, ensure_ascii=False) + '\n')
def main():
import sys
data_dir = sys.argv[1] if len(sys.argv) > 1 else './data'
out = sys.argv[2] if len(sys.argv) > 2 else './output'
samples = load(data_dir) or fetch_real_samples()
samples = filter_quality(samples)
samples = dedup(samples)
result = preprocess(samples, out)
save_jsonl(result, os.path.join(out, 'dataset.jsonl'))
print(f'Done: {len(result)} samples')
if __name__ == '__main__':
main()
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