Memory, Attention and Composition (MAC) Network for CLEVR from Compositional Attention Networks for Machine Reasoning (https://arxiv.org/abs/1803.03067) implemented in PyTorch

Requirements:

• Python 3.6
• PyTorch 0.4
• torch-vision
• Pillow
• nltk
• tqdm

To train:

1. Download and extract CLEVR v1.0 dataset from http://cs.stanford.edu/people/jcjohns/clevr/
2. Preprocessing question data and extracting image features using ResNet 101

python preprocess.py [CLEVR directory]
python image_feature.py [CLEVR directory]

!CAUTION! the size of file created by image_feature.py is very large! (~70 GiB) You may use hdf5 compression, but it will slow down feature extraction.

3. Run train.py

python train.py [CLEVR directory]

This implementation produces 95.75% accuracy at epoch 10, 96.5% accuracy at epoch 20.