We train each model on one tesla V100.
For training the default ResMRConv-14 with 64 filters, run
python -u examples/ppi/main.py --phase train --data_dir /data/deepgcn/ppi
If you want to train model with other gcn layers (for example EdgeConv, 28 layers, 256 channels in the first layer, with dense connection), run
python -u examples/ppi/main.py --phase train --conv edge --data_dir /data/deepgcn/ppi --block dense --n_filters 256 --n_blocks 28
Just need to set --data_dir into your data folder, dataset will be downloaded automatically.
Other parameters for changing the architecture are:
--block graph backbone block type {res, plain, dense}
--conv graph conv layer {edge, mr, sage, gin, gcn, gat}
--n_filters number of channels of deep features, default is 64
--n_blocks number of basic blocks, default is 28
Our pretrained models can be found from Goolge Cloud.
The Naming format of our pretrained model: task-connection-conv_type-n_blocks-n_filters_phase_best.pth, eg. ppi-res-mr-28-256_val_best.pth, which means PPI node classification task, with residual connection, convolution is MRGCN, 28 layers, 256 channels, the best pretrained model found in validation dataset.
Use parameter --pretrained_model to set the specific pretrained model you want.
python -u examples/ppi/main.py --phase test --pretrained_model checkpoints/ppi-res-mr-28-256_val_best.pth --data_dir /data/deepgcn/ppi --n_filters 256 --n_blocks 28 --conv mr --block res
python -u examples/ppi/main.py --phase test --pretrained_model checkpoints/ppi-dense-mr-14-256_val_best.pth --data_dir /data/deepgcn/ppi --n_filters 256 --n_blocks 14 --conv mr --block dense
Please also specify the number of blocks and filters according to the name of pretrained models.