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155 lines (146 loc) · 5.49 KB
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# Benchmark in hmof
#for target in 'CO2-298-2.5' \
# 'CH4-298-2.5' \
# 'N2-298-0.9' \
# 'Xe-273-10' \
# 'Kr-273-10' \
# 'H2-77-2'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[null]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/hmof/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/hmof/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/hmof/' \
# --trainer.max_epochs=50 \
# --n_frozen_layers=6
# done
# Benchmark in hmofheat
#for target in 'CO2-298K-2.5bar' 'H2-77K-2bar'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[5000]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/hmofheat/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/hmofheat/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/hmofheat/' \
# --trainer.max_epochs=50 \
# --n_frozen_layers=7
# done
# Benchmark in hcof
#for target in 'lowbar' 'highbar'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[null]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/cof/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/cof/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/cof/' \
# --trainer.max_epochs=500 \
# --n_frozen_layers=6
# done
# Benchmark in ppn
#for target in '1bar' '65bar'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[null]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/ppn/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/ppn/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/ppn/' \
# --trainer.max_epochs=100 \
# --n_frozen_layers=2
# done
# Benchmark in zeolite
#for target in 'unitless_KH' 'heat_of_adsorption'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[null]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/zeolite/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/zeolite/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/zeolite/' \
# --trainer.max_epochs=500 \
# --n_frozen_layers=1
# done
# Benchmark in c3h6c3h8coremof
#for target in 'C3H8_C3H6_Selectivity_1bar' \
# 'C3H8_C3H6_Selectivity_infinite' \
# 'C3H6_loadings'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[null]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/c3h6c3h8coremof/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/c3h6c3h8coremof/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/c3h6c3h8coremof/' \
# --trainer.max_epochs=1000 \
# --n_frozen_layers=1
# done
#
## Benchmark in c3h6c3h8coremof
#for target in 'C3H8_loadings' \
# 'TSN_S_1Bar' \
# 'C3H8_Henry_298K' \
# 'C3H6_Henry_298K'; do
#
# echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
#
# python evaluate.py -c configs/evaluate.yaml \
# --ckpt_path='experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt' \
# --train_sizes='[null]' \
# --n_runs='[1]' \
# --target=${target} \
# --voxels_path='/home/asarikas/databases/SpbNet/benchmark/c3h6c3h8coremof/voxels_data_GS32_CB30' \
# --labels_path='/home/asarikas/databases/SpbNet/benchmark/c3h6c3h8coremof/all.csv' \
# --trainer.default_root_dir='experiments/evaluate/benchmark/c3h6c3h8coremof/' \
# --trainer.max_epochs=1000 \
# --n_frozen_layers=2
# done
for target in 'ch4n2ratio-0.1bar' \
'ch4n2ratio-10bar' \
'ch4n2ratio-1bar' \
'ch4uptake-0.1bar' \
'ch4uptake-10bar' \
'ch4uptake-1bar' \
'n2uptake-0.1bar' \
'n2uptake-10bar' \
'n2uptake-1bar'; do
echo -e "\033[31;1mTarget task: ${target}\033[0m\n"
for ckpt_path in experiments/pretrain/early_pretrain/lightning_logs/version_0/checkpoints/best.ckpt; do
python evaluate.py -c configs/evaluate.yaml \
--ckpt_path=${ckpt_path} \
--train_sizes='[null]' \
--n_runs='[1]' \
--target=${target} \
--voxels_path='/home/asarikas/databases/SpbNet/benchmark/ch4n2/voxels_data_GS32_CB30' \
--labels_path='/home/asarikas/databases/SpbNet/benchmark/ch4n2/all.csv' \
--trainer.default_root_dir='experiments/evaluate/benchmark/ch4n2/' \
--trainer.max_epochs=1000 \
--n_frozen_layers=2
done
done