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evaluation.sh
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python -u evaluation.py --crop_height=576 \
--crop_width=960 \
--max_disp=12 \
--data_path='/ds-av/public_datasets/freiburg_sceneflow_flyingthings3d/raw/' \
--test_list='lists/sceneflow_test_select.list' \
--save_path='./result/refined/sceneflow/' \
--resume='checkpoint/GANetMS_hourglass_best.pth' \
--threshold=1.0 2>&1 |tee logs/multi_scale/GANetMS_hourglass_sceneflow.txt
python -u evaluation.py --crop_height=384 \
--crop_width=1152 \
--max_disp=12 \
--data_path='/ds-av/public_datasets/kitti2015/raw/training/' \
--test_list='lists/kitti2015_val.list' \
--save_path='./result/refined/kitti2015/' \
--resume='checkpoint/GANetMS_hourglass_ft_best.pth' \
--threshold=3.0 \
--benchmark=0\
--kitti2015=1 2>&1 |tee logs/multi_scale/GANetMS_hourglass_kitti2015.txt
# srun -K --ntasks=1 --gpus-per-task=1 --cpus-per-gpu=1 -p RTX3090 --mem=32000\
# --container-mounts=/netscratch:/netscratch,/ds-av:/ds-av,/netscratch/kraza/gaflow:/netscratch/kraza/gaflow \
# --container-image=/netscratch/enroot/dlcc_pytorch_20.07.sqsh \
# --container-workdir=/netscratch/kraza/gaflow \
# --export="WANDB_API_KEY=406ca7642d853cdfbad965c078cf5c240a43a99e" \
# python -u evaluation.py --crop_height=384 \
# --crop_width=768 \
# --max_disp=12 \
# --data_path='/ds-av/public_datasets/eth3d_low_res_two_view/raw/training/' \
# --test_list='lists/eth3d.list' \
# --save_path='./result/GANetMS/eth3d/' \
# --resume='checkpoint/GANetMS_best_refine_ft_best.pth' \
# --threshold=3.0 \
# --benchmark=0\
# --eth3d=1 2>&1 |tee logs/multi_scale/GANetMS_ft_refine_eth3d_best.txt
## srun -K --ntasks=1 --gpus-per-task=1 --cpus-per-gpu=1 -p RTX3090 --mem=32000\
## --container-mounts=/netscratch:/netscratch,/ds-av:/ds-av,/netscratch/kraza/gaflow:/netscratch/kraza/gaflow \
## --container-image=/netscratch/enroot/dlcc_pytorch_20.07.sqsh \
## --container-workdir=/netscratch/kraza/gaflow \
## --export="WANDB_API_KEY=406ca7642d853cdfbad965c078cf5c240a43a99e" \
# python -u evaluation.py --crop_height=576 \
# --crop_width=768 \
# --max_disp=12 \
# --data_path='/ds-av/public_datasets/middlebury_stereo_2014/raw/trainingH/' \
# --test_list='lists/middlebury_q.list' \
# --save_path='./result/GANetMS/middlebury/' \
# --resume='checkpoint/GANetMS_SDRNet_refine_ms_best.pth' \
# --threshold=3.0 \
# --benchmark=0\
# --middlebury=1 2>&1 |tee logs/multi_scale/GANetMS_ft_refine_middlebury_best.txt
#
##exit
## srun -K --ntasks=1 --gpus-per-task=1 --cpus-per-gpu=1 -p RTX3090 --mem=32000\
## --container-mounts=/netscratch:/netscratch,/ds-av:/ds-av,/netscratch/kraza/gaflow:/netscratch/kraza/gaflow \
## --container-image=/netscratch/enroot/dlcc_pytorch_20.07.sqsh \
## --container-workdir=/netscratch/kraza/gaflow \
## --export="WANDB_API_KEY=406ca7642d853cdfbad965c078cf5c240a43a99e" \
# python -u evaluation.py --crop_height=384 \
# --crop_width=1152 \
# --max_disp=12 \
# --data_path='/ds-av/public_datasets/kitti2012/raw/training/' \
# --test_list='lists/kitti2012_val24.list' \
# --save_path='./result/GANetMS/kitti2012/' \
# --resume='checkpoint/GANetMS_best_refine_ft_best.pth' \
# --threshold=3.0 \
# --benchmark=0\
# --kitti=1 2>&1 |tee logs/multi_scale/GANetMS_ft_refine_kitti2012_best.txt
# CUDA_VISIBLE_DEVICES=5 python -u evaluation.py --crop_height=576 \
# --crop_width=960 \
# --max_disp=192 \
# --test_list='lists/middlebury_q.list' \
# --save_path='./result/sceneflow_retrain_finetune/middlebury/' \
# --resume='./checkpoint/multi_training_all_epoch_600.pth' \
# --data_path='../gaflow/datasets/data.mb/unzip/vision.middlebury.edu/stereo/data/' \
# --middlebury=1 \
# --threshold=1.0 2>&1 | tee logs/multi/log_eval_all_middlebury_600.txt
# CUDA_VISIBLE_DEVICES=5 python -u evaluation.py --crop_height=576 \
# --crop_width=960 \
# --max_disp=192 \
# --test_list='lists/eth3d.list' \
# --save_path='./result/sceneflow_retrain_finetune/eth3d' \
# --resume='./checkpoint/multi_training_all_epoch_600.pth' \
# --data_path='../gaflow/datasets/eth3d/' \
# --eth3d=1 \
# --threshold=1.0 2>&1 |tee logs/multi/log_eval_all_eth3d_600.txt
# exit
# CUDA_VISIBLE_DEVICES=3 python -u evaluation.py --crop_height=384 \
# --crop_width=1344 \
# --max_disp=192 \
# --data_path='../gaflow/datasets/kitti2015/training/' \
# --test_list='lists/kitti2015_test.list' \
# --save_path='./result/multi_scale/kitti2015/' \
# --resume='./checkpoint/finetune_kitti2015_retrain_epoch_50.pth' \
# --threshold=3.0 \
# --kitti2015=1 2>&1 |tee logs/multi_scale/log_eval_kitti_50.txt
# CUDA_VISIBLE_DEVICES=4 python -u evaluation.py --crop_height=576 \
# --crop_width=960 \
# --max_disp=192 \
# --test_list='lists/middlebury_q.list' \
# --save_path='./result/multi_scale/middlebury/' \
# --resume='./checkpoint/finetune_kitti2015_retrain_epoch_50.pth' \
# --data_path='../gaflow/datasets/data.mb/unzip/vision.middlebury.edu/stereo/data/' \
# --middlebury=1 \
# --threshold=1.0 2>&1 | tee logs/multi_scale/log_eval_middlebury_50.txt
# CUDA_VISIBLE_DEVICES=4 python -u evaluation.py --crop_height=576 \
# --crop_width=960 \
# --max_disp=192 \
# --test_list='lists/eth3d.list' \
# --save_path='./result/multi_scale/eth3d/' \
# --resume='./checkpoint/finetune_kitti2015_retrain_epoch_50.pth' \
# --data_path='../gaflow/datasets/eth3d/' \
# --eth3d=1 \
# --threshold=1.0 2>&1 |tee logs/multi_scale/log_eval_eth3d_50.txt
# exit