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run.sh
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#!/usr/bin/env bash
CURDIR=$(cd $(dirname $0); pwd)
cd $CURDIR
export PYTHONPATH="$PYTHONPATH:$CURDIR"
echo 'The work dir is: ' $CURDIR
TYPE=$1
JOB_NAME=$2
ARCH=$3
KEY=$4
GPUS=$5
if [ $GPUS -lt ${MAX_GPUS:-8} ]; then
GPUS_PER_NODE=${GPUS_PER_NODE:-$GPUS}
else
GPUS_PER_NODE=${GPUS_PER_NODE:-${MAX_GPUS:-8}}
fi
if [ -z $OUTPUT_DIR ];then
OUTPUT_DIR=$CURDIR/work_dirs/$JOB_NAME
fi
if [ -z $PRETRAINED ];then
PRETRAINED=$OUTPUT_DIR/checkpoint.pth
fi
# pre-training
if [[ $TYPE =~ pretrain ]]; then
echo "==> Starting pretrainin iBOT."
python3 -m torch.distributed.launch --nnodes ${TOTAL_NODES:-1} \
--node_rank ${NODE_ID:-0} --nproc_per_node=$GPUS_PER_NODE \
--master_addr=${MASTER_ADDR:-127.0.0.1} \
--master_port=${MASTER_PORT:-29500} \
$CURDIR/main_ibot.py \
--arch $ARCH \
--output_dir $OUTPUT_DIR \
--data_path data/imagenet/train \
${@:6}
fi
# evaluation
if [[ $TYPE =~ imagenet_knn ]] || [[ $TYPE =~ imagenet_reg ]] || \
[[ $TYPE =~ imagenet_linear ]] || [[ $TYPE =~ imagenet_cls ]] || \
[[ $TYPE =~ imagenet_semi_cls ]] || [[ $TYPE =~ imagenet_unsup_cls ]] || \
[[ $TYPE =~ cifar_cls ]] || [[ $TYPE =~ cifar10_cls ]] || [[ $TYPE =~ cars_cls ]] || \
[[ $TYPE =~ flwrs_cls ]] || [[ $TYPE =~ inat_cls ]] || [[ $TYPE =~ inat19_cls ]] || \
[[ $TYPE =~ coco_det ]] || [[ $TYPE =~ ade20k_dense_linear ]] || [[ $TYPE =~ ade20k_seg ]] || \
[[ $TYPE =~ davis_viseg ]] || [[ $TYPE =~ paris_oxford_reid ]] || [[ $TYPE =~ copydays_copydet ]]; then
if [ -z $AVGPOOL ] && [ -z $LINEAR_AVGPOOL ] && [ -z $LINEAR_N_LAST_BLOCKS ]; then
if [[ $ARCH =~ small ]] || [[ $ARCH =~ swin ]]; then
AVGPOOL=0
LINEAR_AVGPOOL=0
LINEAR_N_LAST_BLOCKS=4
else
AVGPOOL=0
LINEAR_AVGPOOL=2
LINEAR_N_LAST_BLOCKS=1
fi
fi
echo "==> Starting evaluating iBOT."
KEY_LIST=($(echo $KEY | tr "," "\n"))
if [[ $TYPE =~ imagenet_knn ]]; then
if [[ $TYPE =~ imagenet_knn ]] && [[ ! $TYPE =~ pretrain ]]; then
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_knn.py \
--pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/imagenet \
${@:6}
done
else
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-${K}] \
$CURDIR/evaluation/eval_knn.py \
--pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/imagenet
done
fi
fi
if [[ $TYPE =~ imagenet_reg ]]; then
if [[ $TYPE =~ imagenet_reg ]] && [[ ! $TYPE =~ pretrain ]]; then
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_logistic_regression.py \
--pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/imagenet \
${@:6}
done
else
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_logistic_regression.py \
--pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/imagenet
done
fi
fi
if [[ $TYPE =~ imagenet_linear ]]; then
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
if [[ $TYPE =~ imagenet_linear_solo ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/linear_solo
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${METIS_WORKER_0_PORT:-29500}-$K] \
${CURDIR}/evaluation/eval_linear.py \
--pretrained_weights $PRETRAINED \
--n_last_blocks $LINEAR_N_LAST_BLOCKS \
--avgpool_patchtokens $LINEAR_AVGPOOL \
--arch ${ARCH} \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--data_path data/imagenet \
${@:6}
elif [[ $TYPE =~ imagenet_linear ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/linear
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_linear_multi.py \
--pretrained_weights $PRETRAINED \
--n_last_blocks $LINEAR_N_LAST_BLOCKS \
--avgpool_patchtokens $LINEAR_AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--data_path data/imagenet \
${@:6}
else
SUB_OUTPUT_DIR=$OUTPUT_DIR/linear
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_linear_multi.py \
--pretrained_weights $PRETRAINED \
--n_last_blocks $LINEAR_N_LAST_BLOCKS \
--avgpool_patchtokens $LINEAR_AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--data_path data/imagenet
fi
done
fi
if [[ $TYPE =~ imagenet_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/imnet
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
WEIGHT_FILE=$SUB_OUTPUT_DIR/checkpoint_${KEY_LIST[$K]}.pth
python3 $CURDIR/evaluation/classification_layer_decay/extract_backbone_weights.py \
$PRETRAINED $WEIGHT_FILE --checkpoint_key ${KEY_LIST[$K]}
python3 -m torch.distributed.launch --nnodes ${TOTAL_NODES:-1} \
--node_rank ${NODE_ID:-0} --nproc_per_node=$GPUS_PER_NODE \
--master_addr=${MASTER_ADDR:-127.0.0.1} \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/classification_layer_decay/run_class_finetuning.py \
--finetune $WEIGHT_FILE \
--model $ARCH \
--epochs 100 \
--warmup_epochs 20 \
--layer_decay 0.65 \
--mixup 0.8 \
--cutmix 1.0 \
--layer_scale_init_value 0.0 \
--disable_rel_pos_bias \
--abs_pos_emb \
--use_cls \
--imagenet_default_mean_and_std \
--output_dir $SUB_OUTPUT_DIR \
--data_path data/imagenet \
${@:6}
done
fi
if [[ $TYPE =~ imagenet_semi_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=${OUTPUT_DIR}/semi_cls
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/semi_supervised/eval_cls.py \
--pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--data_path data/imagenet_split \
${@:6}
done
fi
if [[ $TYPE =~ imagenet_unsup_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/unsupervised/unsup_cls.py \
--pretrained_weights $PRETRAINED \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/imagenet \
${@:6}
done
fi
if [[ $TYPE =~ cifar_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/cifar
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_cls.py --pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--batch-size 96 \
--lr 7.5e-6 \
--epochs 1000 \
--data_set CIFAR \
--data_path data/cifar \
${@:6}
done
fi
if [[ $TYPE =~ cifar10_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/cifar10
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_cls.py \
--pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--batch-size 96 \
--lr 7.5e-6 \
--epochs 1000 \
--data_set CIFAR10 \
--data_path data/cifar \
${@:6}
done
fi
if [[ $TYPE =~ cars_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/cars
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_cls.py --pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--batch-size 96 \
--lr 7.5e-5 \
--epochs 1000 \
--data_set Cars \
--data_path data/cars \
${@:6}
done
fi
if [[ $TYPE =~ flwrs_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/flwrs.
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_cls.py --pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--batch-size 96 \
--lr 7.5e-6 \
--epochs 1000 \
--data_set Flwrs \
--data_path data/flwrs \
${@:6}
done
fi
if [[ $TYPE =~ inat_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/inat
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_cls.py --pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--batch-size 96 \
--lr 7.5e-6 \
--epochs 360 \
--reprob 0.1 \
--data_set INAT \
--data_path data/inat/2018 \
${@:6}
done
fi
if [[ $TYPE =~ inat19_cls ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/cls/inat19
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/eval_cls.py --pretrained_weights $PRETRAINED \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir $SUB_OUTPUT_DIR \
--batch-size 96 \
--lr 7.5e-6 \
--epochs 360 \
--reprob 0.1 \
--data_set INAT19 \
--data_path data/inat/2019 \
${@:6}
done
fi
if [[ $TYPE =~ ade20k_dense_linear ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/seg_linear
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
echo "Starting evaluating ${KEY_LIST[$K]}."
if [ -z $WEIGHT_FILE ]; then
WEIGHT_FILE=$OUTPUT_DIR/checkpoint_${KEY_LIST[$K]}.pth
python3 extract_backbone_weights.py $PRETRAINED $WEIGHT_FILE --checkpoint_key ${KEY_LIST[$K]}
fi
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/semantic_segmentation/train.py \
$CURDIR/evaluation/semantic_segmentation/configs/linear/${ARCH}_512_ade20k_160k.py \
--launcher pytorch \
--work-dir $SUB_OUTPUT_DIR \
--deterministic \
--options model.backbone.use_checkpoint=True \
model.pretrained=$WEIGHT_FILE \
${@:6}
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/semantic_segmentation/test.py \
$CURDIR/evaluation/semantic_segmentation/configs/linear/${ARCH}_512_ade20k_160k.py \
$SUB_OUTPUT_DIR/iter_160000.pth \
--launcher pytorch \
--eval mIoU \
--options model.backbone.use_checkpoint=True \
${@:6}
done
fi
if [[ $TYPE =~ ade20k_seg ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/seg
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
echo "Starting evaluating ${KEY_LIST[$K]}."
if [ -z $WEIGHT_FILE ]; then
WEIGHT_FILE=$OUTPUT_DIR/checkpoint_${KEY_LIST[$K]}.pth
python3 extract_backbone_weights.py $PRETRAINED $WEIGHT_FILE --checkpoint_key ${KEY_LIST[$K]}
fi
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/semantic_segmentation/train.py \
$CURDIR/evaluation/semantic_segmentation/configs/upernet/${ARCH}_512_ade20k_160k.py \
--launcher pytorch \
--work-dir $SUB_OUTPUT_DIR \
--deterministic \
--options model.backbone.use_checkpoint=True \
model.pretrained=$WEIGHT_FILE \
${@:6}
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/semantic_segmentation/test.py \
$CURDIR/evaluation/semantic_segmentation/configs/upernet/${ARCH}_512_ade20k_160k.py \
$SUB_OUTPUT_DIR/iter_160000.pth \
--launcher pytorch \
--eval mIoU \
--options model.backbone.use_checkpoint=True \
${@:6}
done
fi
if [[ $TYPE =~ coco_det ]] && [[ ! $TYPE =~ pretrain ]]; then
SUB_OUTPUT_DIR=$OUTPUT_DIR/det
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
echo "Starting evaluating ${KEY_LIST[$K]}."
if [ -z $WEIGHT_FILE ]; then
WEIGHT_FILE=$OUTPUT_DIR/checkpoint_${KEY_LIST[$K]}.pth
python3 extract_backbone_weights.py $PRETRAINED $WEIGHT_FILE --checkpoint_key ${KEY_LIST[$K]}
fi
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/object_detection/train.py \
$CURDIR/evaluation/object_detection/configs/cascade_rcnn/${ARCH}_giou_4conv1f_coco_3x.py \
--launcher pytorch \
--work-dir $SUB_OUTPUT_DIR \
--deterministic \
--cfg-options model.backbone.use_checkpoint=True \
model.pretrained=$WEIGHT_FILE \
${@:6}
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/evaluation/object_detection/test.py \
$CURDIR/evaluation/object_detection/configs/cascade_rcnn/${ARCH}_giou_4conv1f_coco_3x.py \
$SUB_OUTPUT_DIR/latest.pth \
--launcher pytorch \
--eval bbox segm \
--cfg-options model.backbone.use_checkpoint=True \
${@:6}
done
fi
if [[ $TYPE =~ davis_viseg ]] && [[ ! $TYPE =~ pretrain ]]; then
git clone [email protected]:davisvideochallenge/davis2017-evaluation.git $HOME/davis2017-evaluation
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
echo "Starting evaluating ${KEY_LIST[$K]}."
python3 \
$CURDIR/evaluation/eval_video_segmentation.py \
--arch $ARCH \
--pretrained_weights $PRETRAINED \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/davis/DAVIS \
--output_dir '.viseg/' \
${@:6}
python3 \
$HOME/davis2017-evaluation/evaluation_method.py \
--task semi-supervised \
--davis_path data/davis/DAVIS \
--results_path '.viseg/'
done
fi
if [[ $TYPE =~ paris_oxford_reid ]]; then
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
echo "Starting evaluating paris with ${KEY_LIST[$K]}."
python3 -m torch.distributed.launch --use_env --nproc_per_node=1 \
$CURDIR/evaluation/eval_image_retrieval.py \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--pretrained_weights $PRETRAINED \
--checkpoint_key ${KEY_LIST[$K]} \
--imsize 512 \
--multiscale 1 \
--data_path data/revisited_paris_oxford \
--dataset rparis6k
echo "Starting evaluating oxford with ${KEY_LIST[$K]}."
python3 -m torch.distributed.launch --use_env --nproc_per_node=1 \
$CURDIR/evaluation/eval_image_retrieval.py \
--avgpool_patchtokens $AVGPOOL \
--arch $ARCH \
--pretrained_weights $PRETRAINED \
--checkpoint_key ${KEY_LIST[$K]} \
--imsize 512 \
--multiscale 1 \
--data_path data/revisited_paris_oxford \
--dataset roxford5k
done
fi
if [[ $TYPE =~ copydays_copydet ]]; then
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
echo "Starting evaluating ${KEY_LIST[$K]}."
python3 -m torch.distributed.launch --use_env --nproc_per_node=1 \
$CURDIR/evaluation/eval_copy_detection.py \
--arch $ARCH \
--pretrained_weights $PRETRAINED \
--checkpoint_key ${KEY_LIST[$K]} \
--data_path data/copydays \
# --whitening_path /path/to/whitening_data \
# --distractors_path /path/to/distractors
done
fi
fi
if [[ $TYPE =~ imagenet_part_linear ]]; then
echo "==> Starting analyzing iBOT."
KEY_LIST=($(echo ${KEY} | tr "," "\n"))
TOPK=(1 7 14 56 112 196)
for TK in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#TOPK[@]}-1])
do
for K in $(seq ${NODE_ID:-0} ${TOTAL_NODES:-1} $[${#KEY_LIST[@]}-1])
do
python3 -m torch.distributed.launch --nproc_per_node=$GPUS_PER_NODE \
--master_port=$[${MASTER_PORT:-29500}-$K] \
$CURDIR/analysis/linear_part/eval_linear_part_multi.py \
--pretrained_weights $PRETRAINED \
--arch $ARCH \
--checkpoint_key ${KEY_LIST[$K]} \
--output_dir .linear_part \
--data_path data/imagenet \
--topk ${TOPK[$TK]} \
${@:6}
done
done
fi