This is the adapted from the implementation of STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits. The following citation needs to be used when referencing the code:

@inproceedings{bhattacharya2020step, title={STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits.}, author={Bhattacharya, Uttaran and Mittal, Trisha and Chandra, Rohan and Randhavane, Tanmay and Bera, Aniket and Manocha, Dinesh}, booktitle={AAAI}, pages={1342--1350}, year={2020} }

Instructions of use:

1. Download the data using python3 download\_ebmdb.py inside the 'generate_data' and save it in folder 'data' inside the repo.
2. Navigate to 'generate_data' folder and run python3 load\_data.py to generate all gait data to be used as input to the STEP pipeline. These will be saved inside the 'feature_data' folder
3. Navigate to 'classifier_stgcn_real_only' folder and run python3 main.py to begin training. The final feature vector for all the inputs, post-training, will be saved in an 'output.h5' file.

Instructions to run ZSL:

1. Navigate to the folder titled 'feature_data' and store the 'output.h5' that was generated from the STEP output there.
2. Install transformers module using pip3 install transformers and download the 'bert-base-uncased' for BERT pretrained model and 'NRC-VAD-Lexicon.txt' for VAD and store it in 'feature_data' itself.
3. Run python3 check.py to generate the mat files 'featuresT.mat' and 'labelsT.mat'.
4. Copy these two mat files and navigate to the folder 'Generalized_Zero_Shot/data' and paste them there.
5. Navigate to 'Generalized_Zero_Shot' and run python3 linear_classifier.py to begin training.

Instructions to run AAE:

1. Navigate to the folder titled 'AdversarialAutoencoder'.
2. Run python3 aae.py to start evaluation. Use arguments --dataset_path <location of mat files> --word_vec_loc <location of word to vec googlebin file>