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eval_mIoU.sh
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all_args=("$@")
rest_args=("${all_args[@]:2}")
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples 1000 \
--max-prompt-bs 64 \
--img-bs 1 \
--refine-iter 3 \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples 1000 \
--max-prompt-bs 64 \
--img-bs 1 \
--prompt-types 'point' \
--point-from 'mask-center' \
--refine-iter 3 \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--img-bs 1 \
--dataset 'coco' \
--refine-iter 3 \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--img-bs 1 \
--prompt-types 'point' \
--dataset 'coco' \
--point-from 'mask-center' \
--refine-iter 3 \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--refine-iter 1 \
--img-bs 1 \
--dataset 'lvis' \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--dataset 'lvis' \
--point-from 'mask-center' \
${rest_args[@]}
# ---------------------------------------------
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples 1000 \
--max-prompt-bs 64 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--point-from 'mask-center' \
--num-multimask-outputs 3 \
--multimask-select 'score' \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples 1000 \
--max-prompt-bs 64 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--point-from 'mask-center' \
--num-multimask-outputs 3 \
--multimask-select 'oracle' \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--dataset 'coco' \
--point-from 'mask-center' \
--num-multimask-outputs 3 \
--multimask-select 'score' \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--dataset 'coco' \
--point-from 'mask-center' \
--num-multimask-outputs 3 \
--multimask-select 'oracle' \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--dataset 'lvis' \
--point-from 'mask-center' \
--num-multimask-outputs 3 \
--multimask-select 'score' \
${rest_args[@]}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node 8 \
evaluation/eval_mIoU.py --launcher pytorch \
$1 \
--checkpoint $2 \
--num-samples -1 \
--max-prompt-bs -1 \
--refine-iter 1 \
--img-bs 1 \
--prompt-types 'point' \
--dataset 'lvis' \
--point-from 'mask-center' \
--num-multimask-outputs 3 \
--multimask-select 'oracle' \
${rest_args[@]}