NKM

This is the code page for NKM work. Since we are currently working on a relevant project that share the same codebase, we do not release the code yet. The full code will be available soon.

Scene Graph Generation Part

1. Download and unpack Visual Genome images as well as the annotations, class info and image meta-data
2. Get initial scene graph with VCT
3. Next, run train_graph.py to train the scene graph generation

python train_graph.py --input_scene_dir <path/to/input/scene/dir> --output_scene_dir <path/to/output/scene/dir> 

4. Finally, load the images from VQA to first get initial graph and next get the semantic enriched scene graph.

VQA Training Part

1. Download Glove pretrained word vectors
2. Preprocess VQA2.0 questions to obtain train_questions.pt and vocab.json

python preprocess_questions.py --glove_pt </path/to/generated/glove/pickle/file> --input_questions_json </your/path/to/v2_OpenEnded_mscoco_train2014_questions.json> --input_annotations_json </your/path/to/v2_mscoco_train2014_annotations.json> --output_pt </your/output/path/train_questions.pt> --vocab_json </your/output/path/vocab.json> --mode train

3. Download grounded features from paper Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering repo
4. Preprocess featurs

python preprocess_features.py --input_tsv_folder /your/path/to/trainval_36/ --output_h5 /your/output/path/trainval_feature.h5

5. Train the model

python train.py --input_dir <path/to/preprocessed/files> --save_dir </path/for/checkpoint> --val

6. Validate

python train.py --input_dir <path/to/preprocessed/files> --save_dir </path/for/checkpoint> --mode val

7. Test

python train.py --input_dir <path/to/preprocessed/files> --save_dir </path/for/checkpoint> --mode test