File size: 1,399 Bytes
178d33b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | #!/bin/bash
# sh scripts/ood/react/imagenet_test_ood_react.sh
GPU=1
CPU=1
node=63
jobname=openood
PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:${GPU} -n1 \
--cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
--kill-on-bad-exit=1 --job-name=${jobname} -w SG-IDC1-10-51-2-${node} \
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/datasets/imagenet/imagenet_ood.yml \
configs/networks/react_net.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/react.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained False \
--network.backbone.checkpoint 'results/pretrained_weights/resnet50_imagenet1k_v1.pth' \
--merge_option merge
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# available architectures:
# resnet50, swin-t, vit-b-16
# ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor react \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor react \
--save-score --save-csv --fsood
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