flowgmm命令

  1. python3 experiments/train_flows/train_sup.py --dataset=imagenette --data_path=/root/autodl-tmp/data/imagenette --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=16 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=3 --num_classes=10 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/imagenette_GMVAE_epoch_20.pt

python3 experiments/train_flows/encrypt_train_sup.py --dataset=imagenette --data_path=/root/autodl-tmp/data/imagenette --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=26 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=3 --num_classes=10 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/imagenette_GMVAE_epoch_20.pt --resume=/root/autodl-tmp//flowgmm/checkpoints/15.pt

  1. python3 experiments/train_flows/train_sup.py --dataset=vggface2 --data_path=/root/autodl-tmp/data/chooseVCGFace2 --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=16 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=3 --num_classes=5 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/vggface2_GMVAE_epoch_30.pt

python3 experiments/train_flows/encrypt_train_sup.py --dataset=vggface2 --data_path=/root/autodl-tmp/data/chooseVCGFace2 --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=26 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=3 --num_classes=5 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/vggface2_GMVAE_epoch_30.pt --resume=/root/autodl-tmp//flowgmm/checkpoints/15.pt

  1. python3 experiments/train_flows/train_sup.py --dataset=cxr --data_path=/root/autodl-tmp/data/cxrTopFour --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=6 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=1 --num_classes=4 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/CXR_GMVAE_epoch_5.pt

python3 experiments/train_flows/encrypt_train_sup.py --dataset=cxr --data_path=/root/autodl-tmp/data/cxrTopFour --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=11 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=1 --num_classes=4 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/CXR_GMVAE_epoch_5.pt --resume=/root/autodl-tmp//flowgmm/checkpoints/5.pt

  1. python3 experiments/train_flows/train_sup.py --dataset=DukeiAMD --data_path=/root/autodl-tmp/data/OCT_all/Duke_iAMD --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=16 --lr=1e-4 --eval_freq=5 --batch_size=8 --flow=Glow --in_channels=1 --num_classes=2 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/DukeiAMD_GMVAE_best_model.pt

python3 experiments/train_flows/encrypt_train_sup.py --dataset=DukeiAMD --data_path=/root/autodl-tmp/data/OCT_all/Duke_iAMD --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=21 --lr=1e-4 --eval_freq=5 --batch_size=8 --flow=Glow --in_channels=1 --num_classes=2 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/DukeiAMD_GMVAE_best_model.pt --resume=/root/autodl-tmp//flowgmm/checkpoints/15.pt

  1. python3 experiments/train_flows/train_sup.py --dataset=Kermany --data_path=/root/autodl-tmp/data/OCT_all/OCT2017 --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=6 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=1 --num_classes=4 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/Kermany_epoch_5.pt

python3 experiments/train_flows/encrypt_train_sup.py --dataset=Kermany --data_path=/root/autodl-tmp/data/OCT_all/OCT2017 --logdir=/root/autodl-tmp/flowgmm/logs --ckptdir=/root/autodl-tmp/flowgmm/checkpoints --save_freq=5 --num_epochs=11 --lr=1e-4 --eval_freq=5 --batch_size=128 --flow=Glow --in_channels=1 --num_classes=4 --load_GMVAE_model=/root/autodl-tmp/GMVAE_flowGMM/Models/Best_Model/Kermany_epoch_5.pt --resume=/root/autodl-tmp//flowgmm/checkpoints/5.pt

posted @ 2025-08-23 17:46  zzzzzzz286972  阅读(13)  评论(0)    收藏  举报