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import json
from tqdm import tqdm
from transformers import AutoTokenizer
import clip
import torch
import faiss
import os
import numpy as np
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def load_coco_data(coco_data_path):
"""We load in all images and only the train captions."""
annotations = json.load(open(coco_data_path))['images']
images = []
captions = []
for item in annotations:
if item['split'] == 'restval':
item['split'] = 'train'
if item['split'] == 'train':
for sentence in item['sentences']:
captions.append({'image_id': item['cocoid'], 'caption': ' '.join(sentence['tokens'])})
images.append({'image_id': item['cocoid'], 'file_name': item['filename'].split('_')[-1]})
return images, captions
def filter_captions(data):
decoder_name = 'gpt2'
tokenizer = AutoTokenizer.from_pretrained(decoder_name)
bs = 512
image_ids = [d['image_id'] for d in data]
caps = [d['caption'] for d in data]
encodings = []
for idx in range(0, len(data), bs):
encodings += tokenizer.batch_encode_plus(caps[idx:idx+bs], return_tensors='np')['input_ids'].tolist()
filtered_image_ids, filtered_captions = [], []
assert len(image_ids) == len(caps) and len(caps) == len(encodings)
for image_id, cap, encoding in zip(image_ids, caps, encodings):
if len(encoding) <= 25:
filtered_image_ids.append(image_id)
filtered_captions.append(cap)
return filtered_image_ids, filtered_captions
def encode_captions(captions, model, device):
bs = 256
encoded_captions = []
for idx in tqdm(range(0, len(captions), bs)):
with torch.no_grad():
input_ids = clip.tokenize(captions[idx:idx+bs]).to(device)
encoded_captions.append(model.encode_text(input_ids).cpu().numpy())
encoded_captions = np.concatenate(encoded_captions)
return encoded_captions
def encode_images(images, image_path, model, feature_extractor, device):
image_ids = [i['image_id'] for i in images]
bs = 64
image_features = []
for idx in tqdm(range(0, len(images), bs)):
image_input = [feature_extractor(Image.open(os.path.join(image_path, i['file_name'])))
for i in images[idx:idx+bs]]
with torch.no_grad():
image_features.append(model.encode_image(torch.tensor(np.stack(image_input)).to(device)).cpu().numpy())
image_features = np.concatenate(image_features)
return image_ids, image_features
def get_nns(captions, images, k=15):
xq = images.astype(np.float32)
xb = captions.astype(np.float32)
faiss.normalize_L2(xb)
index = faiss.IndexFlatIP(xb.shape[1])
index.add(xb)
faiss.normalize_L2(xq)
D, I = index.search(xq, k)
return index, I
def filter_nns(nns, xb_image_ids, captions, xq_image_ids):
""" We filter out nearest neighbors which are actual captions for the query image, keeping 7 neighbors per image."""
retrieved_captions = {}
for nns_list, image_id in zip(nns, xq_image_ids):
good_nns = []
for nn in zip(nns_list):
if xb_image_ids[nn] == image_id:
continue
good_nns.append(captions[nn])
if len(good_nns) == 7:
break
assert len(good_nns) == 7
retrieved_captions[image_id] = good_nns
return retrieved_captions
def main():
coco_data_path = 'data/dataset_coco.json' # path to Karpathy splits downloaded from Kaggle
image_path = 'data/images/'
print('Loading data')
images, captions = load_coco_data(coco_data_path)
device = "cuda" if torch.cuda.is_available() else "cpu"
clip_model, feature_extractor = clip.load("RN50x64", device=device)
print('Filtering captions')
xb_image_ids, captions = filter_captions(captions)
print('Encoding captions')
encoded_captions = encode_captions(captions, clip_model, device)
print('Encoding images')
xq_image_ids, encoded_images = encode_images(images, image_path, clip_model, feature_extractor, device)
print('Retrieving neighbors')
index, nns = get_nns(encoded_captions, encoded_images)
retrieved_caps = filter_nns(nns, xb_image_ids, captions, xq_image_ids)
print('Writing files')
faiss.write_index(index, "datastore/coco_index")
json.dump(captions, open('datastore/coco_index_captions.json', 'w'))
json.dump(retrieved_caps, open('data/retrieved_caps_resnet50x64.json', 'w'))
if __name__ == '__main__':
main()
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