| import json, os, hashlib
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| from pathlib import Path
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| TV_DATASET = None
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| HF_CANDIDATES = ['code_search_net', 'codeparrot/github-code']
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| IMAGE_FIELD = None
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| TEXT_FIELD = 'func_code_string'
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| LABEL_FIELD = 'func_documentation_string'
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| PROMPT_TEMPLATE = 'a photo of a {label}'
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| DATASET_URL = 'https://huggingface.co/datasets/code_search_net'
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|
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| def fetch_real_samples(max_samples=5000, cache_dir='./_cache'):
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|
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| out = []
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| if TV_DATASET is not None:
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| try:
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| import torchvision
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| ctor = getattr(torchvision.datasets, TV_DATASET)
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| try:
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| ds = ctor(root=cache_dir, split='train', download=True)
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| except TypeError:
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| try:
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| ds = ctor(root=cache_dir, train=True, download=True)
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| except TypeError:
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| ds = ctor(root=cache_dir, download=True)
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| classes = getattr(ds, 'classes', None)
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| os.makedirs(os.path.join(cache_dir, 'tv'), exist_ok=True)
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| for i, item in enumerate(ds):
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| if len(out) >= max_samples:
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| break
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| img, label = item[0], item[1]
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| name = classes[label] if classes else str(label)
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| p = os.path.join(cache_dir, 'tv', str(i) + '.png')
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| try:
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| img.save(p)
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| except Exception:
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| continue
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| out.append({'image': p, 'text': PROMPT_TEMPLATE.format(label=name)})
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| if out:
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| return out
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| except Exception as e:
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| print('torchvision load failed:', e)
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| for repo in HF_CANDIDATES:
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| try:
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| from datasets import load_dataset
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| try:
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| ds = load_dataset(repo, split='train', streaming=True)
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| except Exception:
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| ds = load_dataset(repo, split='train')
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| img_dir = os.path.join(cache_dir, 'hf_images')
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| os.makedirs(img_dir, exist_ok=True)
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| for i, ex in enumerate(ds):
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| if len(out) >= max_samples:
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| break
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| txt = None
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| if TEXT_FIELD is not None and TEXT_FIELD in ex:
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| v = ex[TEXT_FIELD]
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| txt = v if isinstance(v, str) else ' '.join(map(str, v if isinstance(v, (list, tuple)) else [v]))
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| if txt is None and LABEL_FIELD in ex:
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| txt = PROMPT_TEMPLATE.format(label=ex[LABEL_FIELD])
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| if txt is None:
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| continue
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| out.append({'text': txt})
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| if out:
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| return out
|
| except Exception as e:
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| print('HF load failed for', repo, ':', e)
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| print('Automatic download failed. Please get the data manually from:')
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| print(' ' + DATASET_URL)
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| return out
|
|
|
| def build_dataset(src, dst, sz=224):
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| samples = []
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| for f in Path(src).glob('*.jsonl'):
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| with open(f) as fp:
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| for line in fp:
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| if line.strip():
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| samples.append(json.loads(line))
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|
|
| if not samples:
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| samples = fetch_real_samples()
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|
|
|
|
| seen = set()
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| unique = []
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| for s in samples:
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| fp = s.get('image', s.get('audio', ''))
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| if fp and os.path.exists(fp):
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| h = hashlib.md5(open(fp, 'rb').read()).hexdigest()
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| if h not in seen:
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| seen.add(h)
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| unique.append(s)
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| else:
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| unique.append(s)
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|
|
| os.makedirs(dst, exist_ok=True)
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| out = []
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| for s in unique:
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| item = {}
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| if 'image' in s:
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| from PIL import Image
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| img = Image.open(s['image']).convert('RGB').resize((sz, sz))
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| p = os.path.join(dst, os.path.basename(s['image']))
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| img.save(p, 'JPEG', quality=95)
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| item['image'] = p
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| item['text'] = s.get('code', s.get('text', ''))
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| item['domain'] = 'code'
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| out.append(item)
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|
|
| with open(os.path.join(dst, 'dataset.jsonl'), 'w') as f:
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| for d in out:
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| 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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|
|
