GRM

--- license: cc-by-4.0 --- ## Structure of the Project The `embedding` directory within the `datasets` folder stores embeddings of CAD parts obtained through pre-trained models. Specifically, `embedding_comp2vec.json` contains part embeddings generated using comp2vec, while `embedding_uv.json` contains part embeddings derived from UV-Net. The `results.zip` directory stores the trained model weights for 12 different methods. --- ## Environment Setup Create and activate the Conda environment: ```bash conda env create -f environment.yml conda activate CADRec ``` --- ## Training - `--GNN` specifies the chosen graph neural network, which can be selected from GCN or GAT. - `--RM` specifies the chosen recommendation model, which can be selected from dp (dot product), mlp, and ca (cross-attention). - `--embedding` specifies the method for obtaining part embeddings, which can be selected from comp2vec and uv. - `--node_feature_dim` specifies the dimension of the part embeddings: 128 for comp2vec, 64 for uv. - `--output_dim` specifies the output dimension of the graph neural network, which is consistent with the part embedding dimension: 128 for comp2vec, 64 for uv. - `--experiment_name` specifies the name of the experiment, used for storing checkpoints. - `--max_epochs` specifies the maximum number of training epochs. A code example for training the comp2vec + GCN + dp method is as follows: ```bash python main.py train --GNN GCN --RM dp --embedding comp2vec --node_feature_dim 128 --output_dim 128 --experiment_name GCN_dp_comp2vec_450 --max_epochs 450 ``` A code example for training the uv + GCN + ca method is as follows: ```bash python main.py train --GNN GCN --RM ca --embedding uv --node_feature_dim 64 --output_dim 64 --experiment_name GCN_ca_uv_450 --max_epochs 450 ``` --- ## Testing - `--GNN` specifies the chosen graph neural network, which can be selected from GCN or GAT. - `--RM` specifies the chosen recommendation model, which can be selected from dp (dot product), mlp, and ca (cross-attention). - `--embedding` specifies the method for obtaining part embeddings, which can be selected from comp2vec and uv. - `--checkpoint` specifies the storage location of the trained model checkpoints. The `results` folder contains the trained models for 12 different methods. Choose the best performing checkpoint from `last.ckpt` and `best_loss.ckpt`. A code example for testing the comp2vec + GCN + dp method is as follows: ```bash python main.py test --GNN GCN --RM dp --embedding comp2vec --checkpoint ./results/GCN_dp_comp2vec_450/0401/201405/last.ckpt ``` A code example for testing the uv + GCN + dp method is as follows: ```bash python main.py test --GNN GCN --RM dp --embedding uv --checkpoint ./results/GCN_dp_uv_450/0403/163236/best-loss.ckpt ```