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command.txt
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command.txt
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cd 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-keypoint" ICON w/ relative-spatial encoding (insight from KeypointNeRF)
python -m apps.infer -cfg ./configs/icon-filter.yaml -gpu 0 -in_dir ./examples -out_dir ./results -loop_smpl 100 -loop_cloth 200 -hps_type pymaf
python -m apps.infer -cfg ./configs/icon-nofilter.yaml -gpu 0 -in_dir ./examples -out_dir ./results -loop_smpl 100 -loop_cloth 200 -hps_type pymaf
python -m apps.infer -cfg ./configs/pifu.yaml -gpu 0 -in_dir ./examples -out_dir ./results -loop_smpl 100 -loop_cloth 200 -hps_type pymaf
python -m apps.infer -cfg ./configs/pifu_N.yaml -gpu 0 -in_dir ./examples -out_dir ./results -loop_smpl 1 -loop_cloth 1 -hps_type pymaf
python -m apps.infer -cfg ./configs/pamir.yaml -gpu 0 -in_dir ./test_image1 -out_dir ./results -loop_smpl 1 -loop_cloth 1 -hps_type pymaf
python -m apps.infer -cfg ./configs/pamir_N.yaml -gpu 0 -in_dir ./test_image1 -out_dir ./results -loop_smpl 1 -loop_cloth 1 -hps_type pymaf
python -m apps.infer -cfg ./configs/refine-pifu.yaml -gpu 0 -in_dir ./test_image -out_dir ./results -loop_smpl 1 -loop_cloth 1 -hps_type pymaf
python -m apps.infer -cfg ./configs/high-refine-pifu.yaml -gpu 0 -in_dir ./examples/test -out_dir ./results -loop_smpl 1 -loop_cloth 1 -hps_type pymaf
=========================>>>>>>>
# 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
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
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/refine-pifu.yaml
CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pamir.yaml
# Training for normal network
CUDA_VISIBLE_DEVICES=0 python -m apps.train-normal -cfg ./configs/train/normal.yaml
===============================
python -m lib.dataloader_demo -v -c ./configs/train/icon-filter.yaml -d cape
python -m lib.dataloader_demo -v -c ./configs/train/refine-pifu.yaml -d cape
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)
python -m apps.train -cfg ./configs/train/icon-filter.yaml -test
python -m apps.train -cfg ./configs/train/icon-nofilter.yaml -test
python -m apps.train -cfg ./configs/train/refine-pifu.yaml -test
python -m apps.train -cfg ./configs/train/high-refine-pifu.yaml -test
python -m apps.train -cfg ./configs/train/pamir.yaml -test
python -m apps.train -cfg ./configs/train/pifu.yaml -test
# TIP: reduce "mcube_res" as 128 in apps/train.py for faster evaluation
===================********* Metrics ************==========================
late fused
{'cape-easy-chamfer': tensor(1.1866, device='cuda:0'),
'cape-easy-p2s': tensor(0.9913, device='cuda:0'),
'cape-easy-NC': tensor(0.0813, device='cuda:0'),
'cape-hard-chamfer': tensor(1.3984, device='cuda:0'),
'cape-hard-p2s': tensor(1.1782, device='cuda:0'),
'cape-hard-NC': tensor(0.0843, device='cuda:0'),
'cape-chamfer': tensor(1.2370, device='cuda:0'),
'cape-p2s': tensor(1.0253, device='cuda:0'),
'cape-NC': tensor(0.0806, device='cuda:0')}
early fused
{'cape-easy-chamfer': tensor(1.0985, device='cuda:0'),
'cape-easy-p2s': tensor(0.9105, device='cuda:0'),
'cape-easy-NC': tensor(0.0749, device='cuda:0'),
'cape-hard-chamfer': tensor(1.2773, device='cuda:0'),
'cape-hard-p2s': tensor(1.0420, device='cuda:0'),
'cape-hard-NC': tensor(0.0776, device='cuda:0'),
'cape-chamfer': tensor(1.1470, device='cuda:0'),
'cape-p2s': tensor(0.9439, device='cuda:0'),
'cape-NC': tensor(0.0753, device='cuda:0')}