INSTALLATION
1. Setup Conda
# Conda installation
# For Linux
curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
# For OSX
curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
chmod +x ~/miniconda.sh
./miniconda.sh
source ~/.bashrc # For Linux
source ~/.bash_profile # For OSX
2. Setup Python environment for CPU
# Clone GitHub repo
git clone https://github.com/vijaydwivedi75/emotion-gnn.git
cd emotion-gnn
# Install python environment
conda env create -f env_cpu.yml
# Activate environment
conda activate emotion_gnn
Then install Pytorch Geometric using the following commannds.
python -m pip install torch-scatter==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.4.0.html
python -m pip install torch-sparse==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.4.0.html
python -m pip install torch-cluster==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.4.0.html
python -m pip install torch-spline-conv==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.4.0.html
python -m pip install torch-geometric
3. Reproduce results of the DialogueGCN paper in terminal
python pytorchgeometric/train.py --graph-model --no-cuda
4. Fine-tune BERT-base model on IEMOCAP dataset
python fine_tune_bert.py
5. Generate the text embedding and export to pickle file
python generate_text_embedding.py
6. Change line #40 in data/iemocap.py to load the newly generated pickle file, eg:
self.testVid = pickle.load(open('data/IEMOCAP_features_BERT_FT.pkl', 'rb'), encoding='latin1')
7. Run specific 4-layer GNN model on the newly constructed graphs
Note: For now, sequential encoding is not being used
python main_emotion_node_classification.py --config configs/emotion_node_classification_MLP_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_MLP_GATED_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_GCN_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_GAT_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_GraphSage_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_GIN_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_MoNet_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_GatedGCN_IEMOCAP.json
python main_emotion_node_classification.py --config configs/emotion_node_classification_GatedGCN_E_IEMOCAP.json