Skip to content

Latest commit

 

History

History

ctx

README.md

Cross-modal context

Download the pre-computed context (Recommended)

Visual context

mkdir -p outputs && cd outputs

# download visual context from the following link:
# https://www.dropbox.com/s/r1vkovyhjg4oyxd/image_features.tgz

tar -xzvf image_features.tgz
rm image_features.tgz

Textual context

mkdir -p outputs && cd outputs

# download textual context files from the following links:
# https://www.dropbox.com/s/jw0x3j3wwlykqnn/retrieved_captions.tgzaa
# https://www.dropbox.com/s/l0wkadjw1px4c2h/retrieved_captions.tgzab
# https://www.dropbox.com/s/18125g7mh6qllpm/retrieved_captions.tgzac
# https://www.dropbox.com/s/ykxresinph1rm5h/retrieved_captions.tgzad
# https://www.dropbox.com/s/1birset73vaxi9y/retrieved_captions.tgzae
# https://www.dropbox.com/s/9elfoulxh0wei81/retrieved_captions.tgzaf

cat retrieved_captions.tgza* > retrieved_captions.tgz
tar -xzvf retrieved_captions.tgz
rm retrieved_captions.*

Re-build the context from scratch

⚠️ It's totally safe to skip this step if you've already downloaded the pre-computed visual and textual context in the previous step.

Download datasets

MS-COCO

mkdir -p datasets/coco_captions
cd datasets/coco_captions

# Download the training and validation images
wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip
unzip "*.zip"
rm *.zip

# Download the annotations
gdown --fuzzy https://drive.google.com/file/d/1i8mqKFKhqvBr8kEp3DbIh9-9UNAfKGmE/view?usp=sharing
unzip annotations.zip
rm annotations.zip

Visual Genome

mkdir -p datasets/visual_genome
cd datasets/visual_genome

# Download the images
wget https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip
wget https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip
unzip "*.zip"
rm *.zip

# Download the annotations
wget https://visualgenome.org/static/data/dataset/attributes.json.zip
wget https://visualgenome.org/static/data/dataset/relationships.json.zip
unzip "*.zip"
rm *.zip

Re-build the visual context

Re-build the visual context using GPU#0 (can be any GPU that is available on your machine).

python encode_images.py --device 0

Re-build the textual context

Re-build the textual context using GPU#1 (can be any GPU that is available on your machine).

# First, encode the captions of the Visual Genome dataset
python encode_captions.py --device 1

# Then, perform cross-modal retrieval using CLIP
python retrieve_captions.py --device 1

How to use the context?

We introduce the structure of the visual context vis_ctx.hdf5 and textual context txt_ctx.hdf5, and provide code snippets for how to use them.

Visual context

The visual context encodes all images in MSCOCO with a frozen pre-trained CLIP-I (visual branch of CLIP). The vis_ctx.hdf5 file is structures as:

vis_ctx.hdf5
    |
    +-- img_id (str): encoded features (float numpy array of (768, ) shape)
    |
    ...

For example, the following code snippet allow us to get the features of image 531912:

import h5py

img_id = 531912
with h5py.File("vis_ctx.hdf5", "r") as f:
    feat_img = f[str(img_id)][:]

print(feat_img.shape)  # (768,)
print(type(feat_img))  # <class 'numpy.ndarray'>
print(feat_img.dtype)  # float32

Textual context

The textual context contains the encoded text descriptions retrieved by using the query of the whole image, image five crops, or image nine crops illustrated in the figure below. The retrieved text descriptions are encoded by CLIP-T (textual branch of CLIP). Please see the paper for more details. In this project, we record the top-k (default k=16) results sorted by descending order, including the retrieved text descirptions, similarity scores, and encoded features. txt_ctx

The txt_ctx.hdf5 file is structured as:

txt_ctx.hdf5
    |
    +-- img_id (str)
    |       |
    |       +-- "whole"
    |       |       |
    |       |       +-- "features": encoded top-k texts (float numpy array of (k, d) shape)
    |       |       |
    |       |       +-- "scores": similarity scores (float numpy array of (k, ) shape)
    |       |       |
    |       |       +-- "texts": top-k retrieved texts (list of string of (k, ) shape)
    |       |
    |       +-- "five"
    |       |       |
    |       |       +-- "features": encoded top-k retrieved texts of each crop  (float numpy array of (5, k, d) shape)
    |       |       |
    |       |       +-- "scores": similarity scores of each crop (float numpy array of (5, k) shape)
    |       |       |
    |       |       +-- "texts": top-k retrieved texts of each crop (list of list of string of (5, k) shape)
    |       |
    |       +-- "nine"
    |               |
    |               +-- "features": encoded top-k retrieved texts of each crop  (float numpy array of (9, k, d) shape)
    |               |
    |               +-- "scores": similarity scores of each crop (float numpy array of (9, k) shape)
    |               |
    |               +-- "texts": top-k retrieved texts of each crop (list of list of string of (9, k) shape)
    |
    ...

For example, for image 531912, the following code snippet allow us to access its top-4 retrieval results using the 2nd crop of five crops as the query:

import h5py

img_id = 531912
top_k = 4
crop_n = 2
with h5py.File("txt_ctx.hdf5", "r") as f:
    features = f[f"{img_id}/five/features"][crop_n, :top_k]
    scores = f[f"{img_id}/five/scores"][crop_n, :top_k]
    texts = f[f"{img_id}/five/texts"][crop_n, :top_k]

print(features.shape)  # (4, 512)
print(scores.shape)  # (4,)
print(len(texts))  # 4

Next Step

Once the visual and textual context are downloaded/re-built, it's time to train the model! Check out the haav folder for how to incorporate the visual and textual context.