EXP.md

Training

1. Download the tsv databases for train/val/test and geodesic dist. matrix from the RICH website. Unzip the zip file and place them under datasets folder following the structure below.

   ${REPO_DIR}  
   |-- models  
   |-- metro 
   |-- datasets 
   |   |-- rich_for_bstro_tsv_db
   |   |   |-- train.img.tsv
   |   |   |-- train.hw.tsv
   |   |   |-- train.label.tsv
   |   |   |-- ...
   |   |   |-- test.img.tsv
   |   |   |-- test.hw.tsv
   |   |   |-- test.label.tsv
   |   |   |-- ...
   |   |   |-- val.img.tsv
   |   |   |-- val.hw.tsv
   |   |   |-- val.label.tsv
   |   |   |-- ...
   |   |-- smpl_neutral_geodesic_dist.npy
   |-- predictions 
   |-- README.md 
   |-- ... 
   

2. Run the following command

   bash scripts/training.sh 32         # 32 is the batch size.
   

   We trained BSTRO with a GPU with 32GB memory. Please adjust this number according to your GPUs.

   One should see the following output:

   ...
   METRO INFO: Update config parameter num_hidden_layers: 12 -> 4
   METRO INFO: Update config parameter hidden_size: 768 -> 1024
   METRO INFO: Update config parameter num_attention_heads: 12 -> 4
   METRO INFO: Init model from scratch.
   METRO INFO: Update config parameter num_hidden_layers: 12 -> 4
   METRO INFO: Update config parameter hidden_size: 768 -> 256
   METRO INFO: Update config parameter num_attention_heads: 12 -> 4
   METRO INFO: Init model from scratch.
   METRO INFO: Update config parameter num_hidden_layers: 12 -> 4
   METRO INFO: Update config parameter hidden_size: 768 -> 128
   METRO INFO: Update config parameter num_attention_heads: 12 -> 4
   ...
   => loading pretrained model models/hrnet/hrnetv2_w64_imagenet_pretrained.pth
   METRO INFO: => loading hrnet-v2-w64 model
   METRO INFO: Transformers total parameters: 102256130
   METRO INFO: Backbone total parameters: 128059944
   METRO INFO: Loading state dict from checkpoint models/metro_release/metro_3dpw_state_dict.bin
   METRO INFO: => initializing with metro weights from models/metro_release/metro_3dpw_state_dict.bin
   ...
   rich_for_bstro_tsv_db/train.yaml
   rich_for_bstro_tsv_db/val.yaml
   ...
   

   The log file log.txt and intermediate training files can be found under ${REPO_DIR}/output.