File size: 6,029 Bytes
dbfcf0b | 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 | import json, os, hashlib, logging
from typing import List, Dict, Optional
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
class CodeDataset:
def __init__(self, data_dir: str, output_dir: str, img_size: int = 224):
self.data_dir = data_dir
self.output_dir = output_dir
self.img_size = img_size
self.samples = []
self.processed = []
def load(self) -> List[Dict]:
from pathlib import Path
for f in Path(self.data_dir).glob('*.jsonl'):
with open(f) as fp:
for line in fp:
if line.strip():
self.samples.append(json.loads(line))
log.info(f'Loaded {len(self.samples)} samples')
return self.samples
def filter(self) -> List[Dict]:
filtered = []
for s in self.samples:
text = s.get('code', s.get('text', ''))
if len(text.split()) >= 3:
filtered.append(s)
self.samples = filtered
return filtered
def deduplicate(self) -> List[Dict]:
seen = set()
result = []
for s in self.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)
result.append(s)
self.samples = result
return result
def process(self) -> List[Dict]:
os.makedirs(self.output_dir, exist_ok=True)
for s in self.samples:
item = {}
if 'image' in s:
try:
from PIL import Image
img = Image.open(s['image']).convert('RGB')
img = img.resize((self.img_size, self.img_size))
p = os.path.join(self.output_dir, os.path.basename(s['image']))
img.save(p, 'JPEG', quality=95)
item['image'] = p
except Exception as e:
log.warning(f'Failed: {e}')
continue
item['text'] = s.get('code', s.get('text', ''))
item['domain'] = 'code'
self.processed.append(item)
return self.processed
def save(self):
path = os.path.join(self.output_dir, 'dataset.jsonl')
with open(path, 'w') as f:
for d in self.processed:
f.write(json.dumps(d, ensure_ascii=False) + '\n')
log.info(f'Saved {len(self.processed)} samples to {path}')
def run(self):
self.load()
if not self.samples:
self.samples = fetch_real_samples()
self.filter()
self.deduplicate()
self.process()
self.save()
if __name__ == '__main__':
import sys
ds = CodeDataset(sys.argv[1] if len(sys.argv) > 1 else './data',
sys.argv[2] if len(sys.argv) > 2 else './output')
ds.run()
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