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@@ -61,11 +61,19 @@ The results of reconstruction after slot-attention and ckps are stored in './out
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  - SlotVLM<sup>4</sup>
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  ```shell
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- python -m train.adversarial_training_clip_with_object_token --clip_model_name ViT-L-14 --slots_ckp ./ckps/model_slots_step_300000.pt --pretrained openai --dataset imagenet --imagenet_root /path/to/imagenet --template std --output_normalize False --steps 20000 --warmup 1400 --batch_size 128 --loss l2 --opt adamw --lr 1e-5 --wd 1e-4 --attack pgd --inner_loss l2 --norm linf --eps 4 --iterations_adv 10 --stepsize_adv 1 --wandb False --output_dir ./output --experiment_name with_OT --log_freq 10 --eval_freq 10
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  ```
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  Set `--eps 2` to obtain SlotVLM<sup>2</sup> models.
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  ## Evaluation
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  Make sure files in `bash` directory are executable: `chmod +x bash/*`
 
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  - SlotVLM<sup>4</sup>
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  ```shell
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+ CUDA_VISIBLE_DEVICES=0,1,2,3 python -m train.adversarial_training_clip_with_object_token --clip_model_name ViT-L-14 --slots_ckp ./ckps/model_slots_step_300000.pt --pretrained openai --dataset imagenet --imagenet_root /path/to/imagenet --template std --output_normalize False --steps 20000 --warmup 1400 --batch_size 128 --loss l2 --opt adamw --lr 1e-5 --wd 1e-4 --attack pgd --inner_loss l2 --norm linf --eps 4 --iterations_adv 10 --stepsize_adv 1 --wandb False --output_dir ./output --experiment_name with_OT --log_freq 10 --eval_freq 10
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  ```
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  Set `--eps 2` to obtain SlotVLM<sup>2</sup> models.
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+ \
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+ If you want to resume your training, just add some params like:
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+ \
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+ --optimizer_state /xxx/checkpoints/fallback_80000_opt.pt --start_step 80000 --pretrained none
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+
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+ ```shell
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+ CUDA_VISIBLE_DEVICES=0,1,2,3 python -m train.adversarial_training_clip_with_object_token --clip_model_name ViT-L-14 --slots_ckp ./ckps/model_slots_step_300000.pt --dataset imagenet --imagenet_root /path/to/imagenet --template std --output_normalize False --steps 20000 --warmup 1400 --batch_size 128 --loss l2 --opt adamw --lr 1e-5 --wd 1e-4 --attack pgd --inner_loss l2 --norm linf --eps 4 --iterations_adv 10 --stepsize_adv 1 --wandb False --output_dir ./output --experiment_name with_OT --log_freq 10 --eval_freq 10 --optimizer_state /home/xxx/RobustVLM/output/ViT-L-14_openai_imagenet_l2_imagenet_with_Object_Token_xxxxx/checkpoints/fallback_80000_opt.pt --start_step 80000 --pretrained none
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+ ```
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  ## Evaluation
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  Make sure files in `bash` directory are executable: `chmod +x bash/*`