Upload filter_wit.py

filter_wit.py

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