| import gc
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| from glob import glob
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| from io import BytesIO
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| from pathlib import Path
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|
|
| import clip
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| import pandas as pd
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| import torch
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| import ujson
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| import webdataset as wds
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| from PIL import Image
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| from sentence_transformers import SentenceTransformer
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| from torchvision.transforms import (CenterCrop, Compose, InterpolationMode,
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| Normalize, Resize, ToTensor)
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| from tqdm import tqdm
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|
|
| torch.multiprocessing.set_sharing_strategy('file_system')
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|
|
|
|
| def load_image(jpg):
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| return jpg, Image.open(BytesIO(jpg))
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|
|
|
|
| def load_json(json):
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| return ujson.loads(json)
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|
|
|
|
| load_preprocess_map = {
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| 'jpg': load_image,
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| 'json': load_json,
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| }
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|
|
|
|
| def convert_image_to_rgb(im):
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| return im.convert("RGB")
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|
|
|
|
|
|
| image_transforms = Compose([
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| Resize(224, interpolation=InterpolationMode.BICUBIC),
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| CenterCrop(224),
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| convert_image_to_rgb,
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| ToTensor(),
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| Normalize((0.48145466, 0.4578275, 0.40821073),
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| (0.26862954, 0.26130258, 0.27577711)),
|
| ])
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|
|
|
|
| def image_preprocess(jpgs):
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| jpg_orig, im = jpgs
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| im = image_transforms(im)
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| return jpg_orig, im
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|
|
|
|
| texts_to_check = [
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| 'page_title',
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| 'section_title',
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| 'hierarchical_section_title',
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| 'caption',
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| 'caption_attribution_description',
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| 'caption_alt_text_description',
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| 'context_page_description',
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| 'context_section_description'
|
| ]
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|
|
|
|
| def meta_preprocess(meta: dict):
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| return {
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| 'captions': [meta[text] for text in texts_to_check if text in meta and meta[text]],
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| 'orig': meta
|
| }
|
|
|
|
|
| mclip_preprocess_map = {
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| 'jpg': image_preprocess,
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| 'json': meta_preprocess
|
| }
|
|
|
|
|
| def log(msg):
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| print(msg, end='\n\n\n\n')
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| return msg
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|
|
|
|
| def func(wds_dataset_str, device=None, batch_size=4, **kwargs):
|
| nocap = 0
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| if device is None:
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| device = 'cuda' if torch.cuda.is_available() else 'cpu'
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|
|
| print('Loading models:')
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| model, _ = clip.load('ViT-B/32', device=device, jit=False)
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| mclip = SentenceTransformer(
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| 'sentence-transformers/clip-ViT-B-32-multilingual-v1', device=device)
|
| cosine_similarity = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
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| print('Finished loading models')
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|
|
| ds = wds.WebDataset(wds_dataset_str, shardshuffle=False).map_dict(
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| **load_preprocess_map).map_dict(**mclip_preprocess_map).to_tuple('jpg', 'json').batched(batch_size)
|
| dl = wds.WebLoader(ds, batch_size=None, shuffle=False, **kwargs)
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|
|
| writer = wds.ShardWriter('%05d.tar', 10000)
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| for i, batch in enumerate(tqdm(dl)):
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| try:
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| imss, metas = batch
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| orig_jpgs, ims = zip(*imss)
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| ims = torch.stack(ims)
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|
|
| captionss = [meta['captions'] for meta in metas]
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|
|
| with torch.no_grad():
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| image_features = torch.unbind(
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| model.encode_image(ims.to(device)).float())
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| text_featuress = [mclip.encode(captions, convert_to_tensor=True).to(
|
| device).float() for captions in captionss]
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|
|
| similarities = [
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| cosine_similarity(image_feature.repeat(
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| len(text_features), 1), text_features).tolist()
|
| for image_feature, text_features in zip(image_features, text_featuress)
|
| ]
|
|
|
| captionss = [[cap for cap, sim in zip(
|
| captions, similarity) if sim > 0.26] for captions, similarity in zip(captionss, similarities)]
|
|
|
| for orig_jpg, captions, meta in zip(orig_jpgs, captionss, metas):
|
| if len(captions) == 0:
|
| nocap += 1
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| tqdm.write(f'No captions: {nocap}')
|
| continue
|
|
|
| sample = {
|
| '__key__': f'{writer.count:08}',
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| 'jpg': orig_jpg,
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| 'txt': ''.join(captions),
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| 'json': ujson.dumps(meta['orig'])
|
| }
|
| writer.write(sample)
|
| if i % 25 == 0:
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| gc.collect()
|
| torch.cuda.empty_cache()
|
| except Exception as e:
|
| print(f'Error: {e}')
|
| raise e
|
| writer.close()
|
|
|
