README.md

Prepare data

Note: every preprocessed file or preextracted features can be found in link.

COCO

Download COCO captions and preprocess them

Download preprocessed coco captions from link from Karpathy's homepage. Extract dataset_coco.json from the zip file and copy it in to data/. This file provides preprocessed captions and also standard train-val-test splits.

Then do:

$ python scripts/prepro_labels.py --input_json data/dataset_coco.json --output_json data/cocotalk.json --output_h5 data/cocotalk

prepro_labels.py will map all words that occur <= 5 times to a special UNK token, and create a vocabulary for all the remaining words. The image information and vocabulary are dumped into data/cocotalk.json and discretized caption data are dumped into data/cocotalk_label.h5.

Download COCO dataset and pre-extract the image features (Skip if you are using bottom-up feature)

Download the coco images from link. We need 2014 training images and 2014 val. images. You should put the train2014/ and val2014/ in the same directory, denoted as $IMAGE_ROOT.

Then:

$ python scripts/prepro_feats.py --input_json data/dataset_coco.json --output_dir data/cocotalk --images_root $IMAGE_ROOT

prepro_feats.py extract the resnet101 features (both fc feature and last conv feature) of each image. The features are saved in data/cocotalk_fc and data/cocotalk_att, and resulting files are about 200GB.

(Check the prepro scripts for more options, like other resnet models or other attention sizes.)

Warning: the prepro script will fail with the default MSCOCO data because one of their images is corrupted. See this issue for the fix, it involves manually replacing one image in the dataset.

Download preextracted features

To skip the preprocessing, you can download and decompress cocotalk_att.tar and cocotalk_fc.tar from the link provided at the beginning.)

Download Bottom-up features (Skip if you are using resnet features)

Convert from peteanderson80's original file

Download pre-extracted features from link. You can either download adaptive one or fixed one.

For example:

mkdir data/bu_data; cd data/bu_data
wget https://imagecaption.blob.core.windows.net/imagecaption/trainval.zip
unzip trainval.zip

Then:

python script/make_bu_data.py --output_dir data/cocobu

This will create data/cocobu_fc, data/cocobu_att and data/cocobu_box. If you want to use bottom-up feature, you can just follow the following steps and replace all cocotalk with cocobu.

Download converted files

bottomup-fc: link (The fc features here are simply the average of the attention features)

bottomup-att: link

Flickr30k.

It's similar.

python scripts/prepro_labels.py --input_json data/dataset_flickr30k.json --output_json data/f30ktalk.json --output_h5 data/f30ktalk

python scripts/prepro_ngrams.py --input_json data/dataset_flickr30k.json --dict_json data/f30ktalk.json --output_pkl data/f30k-train --split train

This is to generate the coco-like annotation file for evaluation using coco-caption.

python scripts/prepro_reference_json.py --input_json data/dataset_flickr30k.json --output_json data/f30k_captions4eval.json

Feature extraction

For resnet feature, you can do the same thing as COCO.

For bottom-up feature, you can download from link

wget https://scanproject.blob.core.windows.net/scan-data/data.zip

and then convert to a pth file using the following script:

import numpy as np
import os
import torch
from tqdm import tqdm

out = {}
def transform(id_file, feat_file):
  ids = open(id_file, 'r').readlines()
  ids = [_.strip('\n') for _ in ids]
  feats = np.load(feat_file)
  assert feats.shape[0] == len(ids)
  for _id, _feat in tqdm(zip(ids, feats)):
    out[str(_id)] = _feat

transform('dev_ids.txt', 'dev_ims.npy')
transform('train_ids.txt', 'train_ims.npy')
transform('test_ids.txt', 'test_ims.npy')

torch.save(out, 'f30kbu_att.pth')