training.md

Prerequirement

Make sure you have already generated all the required synthetic data (refer to Dataset Instruction) under ./data/thuman2_{num_views}views, which includes the rendered RGB (render/), normal images(normal_B/, normal_F/, T_normal_B/, T_normal_F/), corresponding calibration matrix (calib/) and pre-computed visibility arrays (vis/).

👀 Test your dataloader with vedo

# visualization for SMPL-X mesh
python -m lib.dataloader_demo -v -c ./configs/train/icon-filter.yaml

# visualization for voxelized SMPL
python -m lib.dataloader_demo -v -c ./configs/train/pamir.yaml

⚠️ Don't support headless mode currently, unset PYOPENGL_PLATFORM before training.

Command

conda activate icon

# model_type: 
#   "pifu"            reimplemented PIFu
#   "pamir"           reimplemented PaMIR
#   "icon-filter"     ICON w/ global encoder (continous local wrinkles)
#   "icon-nofilter"   ICON w/o global encoder (correct global pose)
#   "icon-mvp"        minimal viable product, simple yet efficient

# Training for implicit MLP
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-filter.yaml

# Training for normal network
CUDA_VISIBLE_DEVICES=0 python -m apps.train-normal -cfg ./configs/train/normal.yaml

Tensorboard

cd ICON/results/{name}
tensorboard --logdir .

Checkpoint

All the checkpoints are saved at ./data/ckpt/{name}