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Training

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

# ICON w/ filter (name: icon-filter)
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-filter.yaml

# ICON w/o filter (name: icon-nofilter)
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-nofilter.yaml

# ICON-MVP (name: icon-mvp), mvp = minimal viable product, simple (used features) yet efficient (GPU usage)
# https://en.wikipedia.org/wiki/Minimum_viable_product
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-mvp.yaml

# PIFu (name: pifu)
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pifu.yaml

# PaMIR (name: pamir)
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pamir.yaml

# Normal network Training
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}