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Greedy Gerbil (Working title)


Create one-hot vectors from data sets

To execute this step, make sure the following files are located inside the /data/ folder:

  • vqa_annotations_train.gzip
  • vqa_annotations_valid.gzip
  • vqa_annotations_test.gzip
  • vqa_questions_train.gzip
  • vqa_questions_valid.gzip
  • vqa_questions_test.gzip

These files contain questions and answers concerning the images as well as some additional information, divided into three splits for training, validation and testing.

To convert this data into one-hot vectors, execute the one-hot module:


More information about how this module works can be found in the documentation added inside the module.

After a successful execution of the module, the /data/ folder should now also contain the following files:

  • qa_vocab.pickle
  • vqa_vecs_train.pickle
  • vqa_vecs_valid.pickle
  • vqa_vecs_test.pickle

Load a data set with one-hot vectors and image features

For the next step, place the following files containing the image features and the resolution of their indices into /data/:

  • VQA_image_features.h5
  • VQA_img_features2id.json

Now, in your python code, you can use the VQADataset class:

from data_loading import VQADataset
vec_collection = VQADataset(

dataset_loader = DataLoader(vec_collection, batch_size=4, shuffle=True, num_workers=4)
for i_batch, sample_batched in enumerate(dataset_loader):
    print(i_batch, sample_batched)

Using the arguments image_features_path and image_features2id_path for VQADataset is optional, but omitting these will not load the image features to the data set.

Also, inflate_vectors can be set False to load vectors in their dense format and save memory. In this case, the first number in the vector determines its lengths, the remaining numbers determine the indices which are one.


See this GitHUb issue describing the project's milestones.