Sem_seg_dense and sem_seg_sparse are both for the semantic segmentation task.
The difference between them is that the data shape for the graph is different.
As for sem_seg_sparse, data shape is N x C (N is number of nodes, C is feature_size) and there is a batch variable indicating the batch of each node.
But for sem_seg_dense, data shape is B x C x N x 1 (B is batch size, N here means the number of nodes per graph).
In gcn_lib, there are two folders: dense and sparse. They are used for different data shapes above.
We keep using 2 Tesla V100 GPUs for distributed training.
cd examples/sem_seg/dense
Train ResGCN-28 (DeepGCN with 28 residually connected EdgeConv layers, dilated graph convolutions and batch normalization):
CUDA_VISIBLE_DEVICES=0,1 python train.py --multi_gpus --phase train --data_dir /data/deepgcn/S3DIS
Just need to set --data_dir $yourpath/to/data, dataset will be downloaded automatically.
If you want to train model with other gcn layers (for example, mrgcn), run
python train.py --conv mr --multi_gpus --phase train --data_dir /data/deepgcn/S3DIS
Other parameters for changing the architecture are:
--block graph backbone block type {res, plain, dense}
--conv graph conv layer {edge, mr}
--n_filters number of channels of deep features, default is 64
--n_blocks number of basic blocks, default is 28
A shallow version of DeepGCN (ResGCN-7) could be trained by the command below:
CUDA_VISIBLE_DEVICES=0 python train.py --multi_gpus --phase train --data_dir /data/deepgcn/S3DIS --n_blocks 7
Quick test on area 5, run:
python test.py --pretrained_model checkpoints/sem_seg_dense-res-edge-28-64-ckpt_best_model.pth --batch_size 32 --data_dir /data/deepgcn/S3DIS
Our pretrained model is available here google driver.
Note: Please use our Tensorflow code if you want to reproduce the same result in the paper. The performance of pytorch code is slightly worse than tensorflow. mIOU is 52.11% on Area 5 compared to 52.49% in the tensorflow version.
python test.py --pretrained_model checkpoints/sem_seg_dense-res-edge-28-64-ckpt_best_model.pth --batch_size 32 --data_dir /data/deepgcn/S3DIS
Lower the batch size if running out of memory. The batch size will not influence the test results.