dataset_132648601_nlp_summarization_multimodal3.py

import json, os, hashlib
from pathlib import Path

# nlp_summarization dataset processor
# modality: multimodal3, preprocessing: domain_specific


# --- real data source: nlp_summarization ---
TV_DATASET = None
HF_CANDIDATES = ['xsum', 'cnn_dailymail', 'multi_news']
IMAGE_FIELD = None
TEXT_FIELD = 'document'
LABEL_FIELD = 'summary'
PROMPT_TEMPLATE = 'a photo of a {label}'
DATASET_URL = 'https://huggingface.co/datasets/xsum'

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 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('nlp_summarization', s.get('text', ''))
        item['domain'] = 'nlp_summarization'
        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')