#
# If you need to save the CLIP model for the current stage, you need to use flags --save_hilora_clip
# refcoco
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset unc           --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/unc/best_checkpoint.pth      --eval_set val    --output_dir /patch_to_output/finetuning_base/unc;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset unc           --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/unc/best_checkpoint.pth      --eval_set testA  --output_dir /patch_to_output/finetuning_base/unc;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset unc           --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/unc/best_checkpoint.pth      --eval_set testB  --output_dir /patch_to_output/finetuning_base/unc;
#

## # RefCOCO+
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset unc+          --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/unc+/best_checkpoint.pth     --eval_set val    --output_dir /patch_to_output/finetuning_base/unc+;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset unc+          --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/unc+/best_checkpoint.pth     --eval_set testA  --output_dir /patch_to_output/finetuning_base/unc+;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset unc+          --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/unc+/best_checkpoint.pth     --eval_set testB  --output_dir /patch_to_output/finetuning_base/unc+;


## gref_umd
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset gref          --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/gref_umd/best_checkpoint.pth --eval_set val    --output_dir /patch_to_output/finetuning_base/gref_umd;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset gref_umd      --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/gref_umd/best_checkpoint.pth --eval_set val    --output_dir /patch_to_output/finetuning_base/gref_umd;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset gref_umd      --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/gref_umd/best_checkpoint.pth --eval_set test   --output_dir /patch_to_output/finetuning_base/gref_umd;
##
#
# Referit
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset referit       --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/referit/best_checkpoint.pth  --eval_set val    --output_dir /patch_to_output/finetuning_base/referit;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset referit       --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/referit/best_checkpoint.pth  --eval_set test   --output_dir /patch_to_output/finetuning_base/referit;
##

# Flickr
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset flickr        --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/flickr/best_checkpoint.pth   --eval_set val    --output_dir /patch_to_output/finetuning_base/flickr;
CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=7 --master_port 28888 --use_env hivg_eval.py --num_workers 2 --batch_size 60  --dataset flickr        --vl_hidden_dim 512 --imsize 224 --max_query_len 77 --normalize_before --enable_adaptive_weights --use_mask_loss  --save_hilora_clip --hi_lora_stage 3 --data_root /path_to_image_data --split_root /path_to_split/ref_data_shuffled --eval_model /patch_to_output/finetuning_base/flickr/best_checkpoint.pth   --eval_set test   --output_dir /patch_to_output/finetuning_base/flickr;
##





