Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364 values |
|---|---|---|---|---|---|---|
null | OpenOOD-main/scripts/ad/patchcore/cifar100_test_ood_patchcore.sh | #!/bin/bash
# sh scripts/ad/patchcore/cifar100_test_ood_patchcore.sh
GPU=1
CPU=1
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=${jobname} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/patchcore_net.yml \
configs/pipelines/test/test_patchcore.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/patch.yml \
--num_workers 8 \
--merge_option merge
| 584 | 26.857143 | 56 | sh |
null | OpenOOD-main/scripts/ad/patchcore/cifar10_test_ood_patchcore.sh | #!/bin/bash
# sh scripts/ad/patchcore/cifar10_test_ood_patchcore.sh
# GPU=1
# CPU=1
# node=30
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/patchcore_net.yml \
configs/pipelines/test/test_patchcore.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/patch.yml \
--num_workers 8 \
--merge_option merge
| 453 | 21.7 | 55 | sh |
null | OpenOOD-main/scripts/ad/patchcore/osr_cifar50_test_ood_patchcore.sh | #!/bin/bash
# sh scripts/ad/patchcore/osr_cifar50_test_ood_patchcore.sh
GPU=1
CPU=1
node=30
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=${jobname} \
python main.py \
--config configs/datasets/osr_cifar50/cifar50_seed1.yml \
configs/datasets/osr_cifar50/cifar50_seed1_ood.yml \
configs/networks/patchcore_net.yml \
configs/pipelines/test/test_patchcore.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/patch.yml \
--network.backbone.name resnet18_32x32 \
--network.backbone.checkpoint 'results/checkpoints/osr/cifar50_seed1_acc80.24.ckpt' \
--num_workers 8 \
--merge_option merge &
| 740 | 29.875 | 85 | sh |
null | OpenOOD-main/scripts/ad/patchcore/osr_cifar6_test_ood_patchcore.sh | #!/bin/bash
# sh scripts/ad/patchcore/osr_cifar6_test_ood_patchcore.sh
GPU=1
CPU=1
node=30
jobname=openood
PYTHONPATH='.':$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} \
python main.py \
--config configs/datasets/osr_cifar6/cifar6_seed1.yml \
configs/datasets/osr_cifar6/cifar6_seed1_ood.yml \
configs/networks/patchcore_net.yml \
configs/pipelines/test/test_patchcore.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/patch.yml \
--network.backbone.name resnet18_32x32 \
--network.backbone.checkpoint 'results/checkpoints/osr/cifar6_seed1_acc97.57.ckpt' \
--num_workers 8 \
--merge_option merge &
| 734 | 29.625 | 84 | sh |
null | OpenOOD-main/scripts/ad/patchcore/osr_tin20_test_ood_patchcore.sh | #!/bin/bash
# sh scripts/ad/patchcore/osr_tin20_test_ood_patchcore.sh
GPU=1
CPU=1
node=30
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=${jobname} \
python main.py \
--config configs/datasets/osr_tin20/tin20_seed1.yml \
configs/datasets/osr_tin20/tin20_seed1_ood.yml \
configs/networks/patchcore_net.yml \
configs/pipelines/test/test_patchcore.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/patch.yml \
--network.backbone.name resnet18_64x64 \
--network.backbone.checkpoint 'results/checkpoints/osr/tin20_seed1_acc77.23.ckpt' \
--num_workers 8 \
--merge_option merge &
| 728 | 29.375 | 83 | sh |
null | OpenOOD-main/scripts/ad/rd4ad/cifar10_test.sh | #!/bin/bash
# sh scripts/ad/rd4ad/cifar10_test.sh
PYTHONPATH='.':$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-${node} \
#--config configs/datasets/mvtec/cable.yml \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/rd4ad_net.yml \
configs/pipelines/test/test_rd4ad.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/rd4ad.yml \
| 560 | 34.0625 | 72 | sh |
null | OpenOOD-main/scripts/ad/rd4ad/cifar10_train.sh | #!/bin/bash
# sh scripts/ad/rd4ad/cifar10_train.sh
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/pipelines/train/train_rd4ad.yml \
configs/networks/rd4ad_net.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/rd4ad.yml
| 516 | 33.466667 | 72 | sh |
null | OpenOOD-main/scripts/basics/cifar10/train_cifar10.sh | #!/bin/bash
# sh scripts/basics/cifar10/train_cifar10.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/preprocessors/base_preprocessor.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml \
--seed 0
| 484 | 24.526316 | 51 | sh |
null | OpenOOD-main/scripts/basics/cifar10/train_cifar10_dist.sh | #!/bin/bash
# sh scripts/basics/cifar10/train_cifar10_dist.sh
GPU=1
CPU=1
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=${jobname} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml \
--dataset.image_size 32 \
--optimizer.num_epochs 100 \
--num_workers 8 \
--num_gpus 2 \
--num_machines 1 \
--machine_rank 0 \
--mark 0 &
| 543 | 22.652174 | 49 | sh |
null | OpenOOD-main/scripts/basics/cifar100/train_cifar100.sh |
#!/bin/bash
# sh scripts/basics/cifar100/train_cifar100.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/preprocessors/base_preprocessor.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml \
--seed 0
| 519 | 22.636364 | 53 | sh |
null | OpenOOD-main/scripts/basics/covid/train_covid.sh | #!/bin/bash
# sh scripts/0_basics/covid_train.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/covid/covid.yml \
configs/networks/resnet18_224x224.yml \
configs/pipelines/train/baseline.yml \
--optimizer.num_epochs 200 \
--optimizer.lr 0.0001 \
--optimizer.weight_decay 0.0005 \
--num_workers 8
| 531 | 23.181818 | 49 | sh |
null | OpenOOD-main/scripts/basics/imagenet/test_imagenet.sh | #!/bin/bash
# sh scripts/basics/imagenet/test_imagenet.sh
GPU=1
CPU=1
node=76
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/networks/resnet50.yml \
configs/pipelines/test/test_acc.yml \
configs/preprocessors/base_preprocessor.yml \
--num_workers 20 \
--dataset.test.batch_size 512 \
--dataset.val.batch_size 512 \
--network.pretrained True \
--network.checkpoint "./results/checkpoints/imagenet_res50_acc76.10.pth" \
--save_output True \
--num_gpus 1
| 697 | 26.92 | 74 | sh |
null | OpenOOD-main/scripts/basics/imagenet200/train_imagenet200.sh | #!/bin/bash
# sh scripts/basics/imagenet200/train_imagenet200.sh
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/preprocessors/base_preprocessor.yml \
configs/networks/resnet18_224x224.yml \
configs/pipelines/train/baseline.yml \
--seed 0
| 293 | 28.4 | 59 | sh |
null | OpenOOD-main/scripts/basics/mnist/test_mnist.sh | #!/bin/bash
# sh scripts/0_basics/mnist_test.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/mnist/mnist.yml \
configs/preprocessors/base_preprocessor.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_acc.yml \
--network.checkpoint ./results/checkpoints/mnist_lenet.ckpt
| 519 | 26.368421 | 71 | sh |
null | OpenOOD-main/scripts/basics/mnist/train_mnist.sh | #!/bin/bash
# sh scripts/basics/mnist/train_mnist.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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} \
python main.py \
--config configs/datasets/mnist/mnist.yml \
configs/preprocessors/base_preprocessor.yml \
configs/networks/lenet.yml \
configs/pipelines/train/baseline.yml
| 436 | 23.277778 | 49 | sh |
null | OpenOOD-main/scripts/basics/osr_cifar50/train_cifar50.sh | #!/bin/bash
# sh scripts/basics/osr_cifar50/train_cifar50.sh
GPU=1
CPU=1
node=66
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=${jobname} \
python main.py \
--config configs/datasets/osr_cifar50/cifar50_seed1.yml \
configs/networks/resnet18_32x32.yml \
configs/preprocessors/base_preprocessor.yml \
configs/pipelines/train/baseline.yml \
--network.pretrained False \
--dataset.image_size 32 \
--optimizer.num_epochs 100 \
--num_workers 4 \
--mark 4 &
| 582 | 24.347826 | 57 | sh |
null | OpenOOD-main/scripts/basics/osr_cifar6/osr_cifar6_test_msp.sh | #!/bin/bash
# sh scripts/basics/osr_cifar6/osr_cifar6_test_msp.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/osr_cifar6/cifar6_seed1.yml \
configs/datasets/osr_cifar6/cifar6_seed1_osr.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/msp.yml \
--num_workers 8 \
--network.checkpoint './results/cifar6_seed1_resnet18_32x32_base_e100_lr0.1_default/best.ckpt'
| 695 | 30.636364 | 94 | sh |
null | OpenOOD-main/scripts/basics/osr_cifar6/train_cifar6.sh | #!/bin/bash
# sh scripts/basics/osr_cifar6/train_cifar6.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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} \
python main.py \
--config configs/datasets/osr_cifar6/cifar6_seed5.yml \
configs/preprocessors/base_preprocessor.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml &
| 465 | 24.888889 | 55 | sh |
null | OpenOOD-main/scripts/basics/osr_mnist6/train_mnist6.sh | #!/bin/bash
# sh scripts/basics/osr_mnist6/train_mnist6.sh
GPU=1
CPU=1
node=78
jobname=openood
if [ $USER == "jkyang" ]; then
PYTHONPATH='.':$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} \
python main.py \
--config configs/datasets/osr_mnist6/mnist6_seed2.yml \
configs/networks/lenet.yml \
configs/preprocessors/base_preprocessor.yml \
configs/pipelines/train/baseline.yml \
--network.pretrained False
else
PYTHONPATH='.':$PYTHONPATH \
python main.py \
--config configs/datasets/osr_mnist6/mnist6_seed1.yml \
configs/networks/lenet.yml \
configs/preprocessors/base_preprocessor.yml \
configs/pipelines/train/baseline.yml \
--network.pretrained False \
--dataset.image_size 28 \
--optimizer.num_epochs 100 \
--num_workers 4
fi
cp ./results/cifar6_seed5_resnet18_32x32_base_e100_lr0.1_default/best.ckpt ./results/checkpoints/osr/cifar6_seed5.ckpt
| 1,038 | 29.558824 | 118 | sh |
null | OpenOOD-main/scripts/basics/osr_tin20/train_tin20.sh | #!/bin/bash
# sh scripts/basics/osr_tin20/train_tin20.sh
# python -m pdb -c continue main.py \
GPU=1
CPU=1
node=75
jobname=openood
PYTHONPATH='.':$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} \
python main.py \
--config configs/datasets/osr_tin20/tin20_seed1.yml \
configs/networks/resnet18_64x64.yml \
configs/preprocessors/base_preprocessor.yml \
configs/pipelines/train/baseline.yml \
--network.pretrained False \
--dataset.image_size 64 \
--optimizer.num_epochs 100 \
--num_workers 4 \
--mark 5 &
| 612 | 24.541667 | 53 | sh |
null | OpenOOD-main/scripts/download/download.py | import argparse
import os
import zipfile
import gdown
benchmarks_dict = {
'bimcv': [
'bimcv', 'ct', 'xraybone', 'actmed', 'mnist', 'cifar10', 'texture',
'tin'
],
'mnist': [
'mnist', 'notmnist', 'fashionmnist', 'texture', 'cifar10', 'tin',
'places365', 'cinic10'
],
'cifar-10': [
'cifar10', 'cifar100', 'tin', 'mnist', 'svhn', 'texture', 'places365',
'tin597'
],
'cifar-100':
['cifar100', 'cifar10', 'tin', 'svhn', 'texture', 'places365', 'tin597'],
'imagenet-200': [
'imagenet_1k', 'ssb_hard', 'ninco', 'inaturalist', 'texture',
'openimage_o', 'imagenet_v2', 'imagenet_c', 'imagenet_r'
],
'imagenet-1k': [
'imagenet_1k', 'ssb_hard', 'ninco', 'inaturalist', 'texture',
'openimage_o', 'imagenet_v2', 'imagenet_c', 'imagenet_r'
],
'misc': [
'cifar10c',
'fractals_and_fvis',
'usps',
'imagenet10',
'hannover',
# 'imagenet200_cae', 'imagenet200_edsr', 'imagenet200_stylized'
],
}
dir_dict = {
'images_classic/': [
'cifar100', 'tin', 'tin597', 'svhn', 'cinic10', 'imagenet10', 'mnist',
'fashionmnist', 'cifar10', 'cifar100c', 'places365', 'cifar10c',
'fractals_and_fvis', 'usps', 'texture', 'notmnist'
],
'images_largescale/': [
'imagenet_1k',
'species_sub',
'ssb_hard',
'ninco',
'inaturalist',
'places',
'sun',
'openimage_o',
'imagenet_v2',
'imagenet_c',
'imagenet_r',
# 'imagenet200_cae', 'imagenet200_edsr', 'imagenet200_stylized'
],
'images_medical/': ['actmed', 'bimcv', 'ct', 'hannover', 'xraybone'],
}
download_id_dict = {
'osr': '1L9MpK9QZq-o-JrFHrfo5lM4-FsFPk0e9',
'mnist_lenet': '13mEvYF9rVIuch8u0RVDLf_JMOk3PAYCj',
'cifar10_res18': '1rPEScK7TFjBn_W_frO-8RSPwIG6_x0fJ',
'cifar100_res18': '1OOf88A48yXFw4fSU02XQT-3OQKf31Csy',
'imagenet_res50': '1tgY_PsfkazLDyI1pniDMDEehntBhFyF3',
'cifar10_res18_v1.5': '1byGeYxM_PlLjT72wZsMQvP6popJeWBgt',
'cifar100_res18_v1.5': '1s-1oNrRtmA0pGefxXJOUVRYpaoAML0C-',
'imagenet200_res18_v1.5': '1ddVmwc8zmzSjdLUO84EuV4Gz1c7vhIAs',
'imagenet_res50_v1.5': '15PdDMNRfnJ7f2oxW6lI-Ge4QJJH3Z0Fy',
'benchmark_imglist': '1XKzBdWCqg3vPoj-D32YixJyJJ0hL63gP',
'usps': '1KhbWhlFlpFjEIb4wpvW0s9jmXXsHonVl',
'cifar100': '1PGKheHUsf29leJPPGuXqzLBMwl8qMF8_',
'cifar10': '1Co32RiiWe16lTaiOU6JMMnyUYS41IlO1',
'cifar10c': '170DU_ficWWmbh6O2wqELxK9jxRiGhlJH',
'cinic10': '190gdcfbvSGbrRK6ZVlJgg5BqqED6H_nn',
'svhn': '1DQfc11HOtB1nEwqS4pWUFp8vtQ3DczvI',
'fashionmnist': '1nVObxjUBmVpZ6M0PPlcspsMMYHidUMfa',
'cifar100c': '1MnETiQh9RTxJin2EHeSoIAJA28FRonHx',
'mnist': '1CCHAGWqA1KJTFFswuF9cbhmB-j98Y1Sb',
'fractals_and_fvis': '1EZP8RGOP-XbMsKex3r-BGI5F1WAP_PJ3',
'tin': '1PZ-ixyx52U989IKsMA2OT-24fToTrelC',
'tin597': '1R0d8zBcUxWNXz6CPXanobniiIfQbpKzn',
'texture': '1OSz1m3hHfVWbRdmMwKbUzoU8Hg9UKcam',
'imagenet10': '1qRKp-HCLkmfiWwR-PXthN7-2dxIQVKxP',
'notmnist': '16ueghlyzunbksnc_ccPgEAloRW9pKO-K',
'places365': '1Ec-LRSTf6u5vEctKX9vRp9OA6tqnJ0Ay',
'places': '1fZ8TbPC4JGqUCm-VtvrmkYxqRNp2PoB3',
'sun': '1ISK0STxWzWmg-_uUr4RQ8GSLFW7TZiKp',
'species_sub': '1-JCxDx__iFMExkYRMylnGJYTPvyuX6aq',
'imagenet_1k': '1i1ipLDFARR-JZ9argXd2-0a6DXwVhXEj',
'ssb_hard': '1PzkA-WGG8Z18h0ooL_pDdz9cO-DCIouE',
'ninco': '1Z82cmvIB0eghTehxOGP5VTdLt7OD3nk6',
'imagenet_v2': '1akg2IiE22HcbvTBpwXQoD7tgfPCdkoho',
'imagenet_r': '1EzjMN2gq-bVV7lg-MEAdeuBuz-7jbGYU',
'imagenet_c': '1JeXL9YH4BO8gCJ631c5BHbaSsl-lekHt',
'imagenet_o': '1S9cFV7fGvJCcka220-pIO9JPZL1p1V8w',
'openimage_o': '1VUFXnB_z70uHfdgJG2E_pjYOcEgqM7tE',
'inaturalist': '1zfLfMvoUD0CUlKNnkk7LgxZZBnTBipdj',
'actmed': '1tibxL_wt6b3BjliPaQ2qjH54Wo4ZXWYb',
'ct': '1k5OYN4inaGgivJBJ5L8pHlopQSVnhQ36',
'hannover': '1NmqBDlcA1dZQKOvgcILG0U1Tm6RP0s2N',
'xraybone': '1ZzO3y1-V_IeksJXEvEfBYKRoQLLvPYe9',
'bimcv': '1nAA45V6e0s5FAq2BJsj9QH5omoihb7MZ',
}
def require_download(filename, path):
for item in os.listdir(path):
if item.startswith(filename) or filename.startswith(
item) or path.endswith(filename):
return False
else:
print(filename + ' needs download:')
return True
def download_dataset(dataset, args):
for key in dir_dict.keys():
if dataset in dir_dict[key]:
store_path = os.path.join(args.save_dir[0], key, dataset)
if not os.path.exists(store_path):
os.makedirs(store_path)
break
else:
print('Invalid dataset detected {}'.format(dataset))
return
if require_download(dataset, store_path):
print(store_path)
if not store_path.endswith('/'):
store_path = store_path + '/'
gdown.download(id=download_id_dict[dataset], output=store_path)
file_path = os.path.join(store_path, dataset + '.zip')
with zipfile.ZipFile(file_path, 'r') as zip_file:
zip_file.extractall(store_path)
os.remove(file_path)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Download datasets and checkpoints')
parser.add_argument('--contents',
nargs='+',
default=['datasets', 'checkpoints'])
parser.add_argument('--datasets', nargs='+', default=['default'])
parser.add_argument('--checkpoints', nargs='+', default=['all'])
parser.add_argument('--save_dir',
nargs='+',
default=['./data', './results'])
parser.add_argument('--dataset_mode', default='default')
args = parser.parse_args()
if args.datasets[0] == 'default':
args.datasets = ['mnist', 'cifar-10', 'cifar-100']
elif args.datasets[0] == 'ood_v1.5':
args.datasets = [
'cifar-10', 'cifar-100', 'imagenet-200', 'imagenet-1k'
]
elif args.datasets[0] == 'all':
args.datasets = list(benchmarks_dict.keys())
if args.checkpoints[0] == 'ood':
args.checkpoints = [
'mnist_lenet', 'cifar10_res18', 'cifar100_res18', 'imagenet_res50'
]
elif args.checkpoints[0] == 'ood_v1.5':
args.checkpoints = [
'cifar10_res18_v1.5', 'cifar100_res18_v1.5',
'imagenet200_res18_v1.5', 'imagenet_res50_v1.5'
]
elif args.checkpoints[0] == 'all':
args.checkpoints = [
'mnist_lenet', 'cifar10_res18', 'cifar100_res18', 'imagenet_res50',
'osr'
]
for content in args.contents:
if content == 'datasets':
store_path = args.save_dir[0]
if not store_path.endswith('/'):
store_path = store_path + '/'
if not os.path.exists(os.path.join(store_path,
'benchmark_imglist')):
gdown.download(id=download_id_dict['benchmark_imglist'],
output=store_path)
file_path = os.path.join(args.save_dir[0],
'benchmark_imglist.zip')
with zipfile.ZipFile(file_path, 'r') as zip_file:
zip_file.extractall(store_path)
os.remove(file_path)
if args.dataset_mode == 'default' or \
args.dataset_mode == 'benchmark':
for benchmark in args.datasets:
for dataset in benchmarks_dict[benchmark]:
download_dataset(dataset, args)
if args.dataset_mode == 'dataset':
for dataset in args.datasets:
download_dataset(dataset, args)
elif content == 'checkpoints':
if 'v1.5' in args.checkpoints[0]:
store_path = args.save_dir[1]
else:
store_path = os.path.join(args.save_dir[1], 'checkpoints/')
if not os.path.exists(store_path):
os.makedirs(store_path)
if not store_path.endswith('/'):
store_path = store_path + '/'
for checkpoint in args.checkpoints:
if require_download(checkpoint, store_path):
gdown.download(id=download_id_dict[checkpoint],
output=store_path)
file_path = os.path.join(store_path, checkpoint + '.zip')
with zipfile.ZipFile(file_path, 'r') as zip_file:
zip_file.extractall(store_path)
os.remove(file_path)
| 8,694 | 37.303965 | 79 | py |
null | OpenOOD-main/scripts/download/download.sh | # sh ./scripts/download/dowanload.sh
# download the up-to-date benchmarks and checkpoints
# provided by OpenOOD v1.5
python ./scripts/download/download.py \
--contents 'datasets' 'checkpoints' \
--datasets 'ood_v1.5' \
--checkpoints 'ood_v1.5' \
--save_dir './data' './results' \
--dataset_mode 'benchmark'
| 313 | 27.545455 | 52 | sh |
null | OpenOOD-main/scripts/ood/ash/cifar100_test_ood_ash.sh | #!/bin/bash
# sh scripts/ood/ash/cifar100_test_ood_ash.sh
# GPU=1
# CPU=1
# node=73
# 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/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ash.yml \
--network.checkpoint 'results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt'
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor ash \
--save-score --save-csv
| 1,106 | 31.558824 | 95 | sh |
null | OpenOOD-main/scripts/ood/ash/cifar10_test_ood_ash.sh | #!/bin/bash
# sh scripts/ood/ash/cifar10_test_ood_ash.sh
# GPU=1
# CPU=1
# node=73
# 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/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ash.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 1
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor ash \
--save-score --save-csv
| 1,135 | 30.555556 | 96 | sh |
null | OpenOOD-main/scripts/ood/ash/imagenet200_test_ood_ash.sh | #!/bin/bash
# sh scripts/ood/ash/imagenet200_test_ood_ash.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor ash \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor ash \
--save-score --save-csv --fsood
| 708 | 28.541667 | 74 | sh |
null | OpenOOD-main/scripts/ood/ash/imagenet_test_ood_ash.sh | #!/bin/bash
# sh scripts/ood/ash/imagenet_test_ood_ash.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/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ash.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.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 ash \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor ash \
--save-score --save-csv --fsood
| 1,386 | 27.895833 | 82 | sh |
null | OpenOOD-main/scripts/ood/cider/cifar100_test_cider.sh | #!/bin/bash
# sh scripts/ood/cider/cifar100_test_cider.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_cider_net_cider_e100_lr0.5_protom0.5_default \
--postprocessor cider \
--save-score --save-csv
| 471 | 30.466667 | 75 | sh |
null | OpenOOD-main/scripts/ood/cider/cifar100_train_cider.sh | #!/bin/bash
# sh scripts/ood/cider/cifar100_train_cider.sh
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/cider_net.yml \
configs/pipelines/train/train_cider.yml \
configs/preprocessors/base_preprocessor.yml \
--preprocessor.name cider \
--network.backbone.name resnet18_32x32 \
--dataset.train.batch_size 512 \
--trainer.trainer_args.proto_m 0.5 \
--num_workers 8 \
--optimizer.num_epochs 100 \
--seed 0
| 487 | 29.5 | 53 | sh |
null | OpenOOD-main/scripts/ood/cider/cifar10_test_cider.sh | #!/bin/bash
# sh scripts/ood/cider/cifar10_test_cider.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_cider_net_cider_e100_lr0.5_protom0.95_default \
--postprocessor cider \
--save-score --save-csv
| 469 | 30.333333 | 75 | sh |
null | OpenOOD-main/scripts/ood/cider/cifar10_train_cider.sh | #!/bin/bash
# sh scripts/ood/cider/cifar10_train_cider.sh
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/networks/cider_net.yml \
configs/pipelines/train/train_cider.yml \
configs/preprocessors/base_preprocessor.yml \
--preprocessor.name cider \
--network.backbone.name resnet18_32x32 \
--dataset.train.batch_size 512 \
--trainer.trainer_args.proto_m 0.95 \
--num_workers 8 \
--optimizer.num_epochs 100 \
--seed 0
| 485 | 29.375 | 51 | sh |
null | OpenOOD-main/scripts/ood/cider/imagenet200_test_cider.sh | #!/bin/bash
# sh scripts/ood/cider/imagenet200_test_cider.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_cider_net_cider_e10_lr0.01_protom0.95_default \
--postprocessor cider \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_cider_net_cider_e10_lr0.01_protom0.95_default \
--postprocessor cider \
--save-score --save-csv --fsood
| 716 | 28.875 | 79 | sh |
null | OpenOOD-main/scripts/ood/cider/imagenet200_train_cider.sh | #!/bin/bash
# sh scripts/ood/cider/imagenet200_train_cider.sh
SEED=0
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/cider_net.yml \
configs/pipelines/train/train_cider.yml \
configs/preprocessors/base_preprocessor.yml \
--preprocessor.name cider \
--network.backbone.name resnet18_224x224 \
--network.backbone.pretrained True \
--network.backbone.checkpoint ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default/s${SEED}/best.ckpt \
--optimizer.lr 0.01 \
--optimizer.num_epochs 10 \
--dataset.train.batch_size 512 \
--trainer.trainer_args.proto_m 0.95 \
--num_gpus 1 --num_workers 16 \
--merge_option merge \
--seed ${SEED}
| 736 | 34.095238 | 116 | sh |
null | OpenOOD-main/scripts/ood/cider/imagenet_test_cider.sh | #!/bin/bash
# sh scripts/ood/cider/imagenet_test_cider.sh
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# available architectures:
# resnet50
# ood
python scripts/eval_ood_imagenet.py \
--ckpt-path ./results/imagenet_cider_net_cider_e10_lr0.001_protom0.95_default/s0/best.ckpt \
--arch resnet50 \
--postprocessor cider \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--ckpt-path ./results/imagenet_cider_net_cider_e10_lr0.001_protom0.95_default/s0/best.ckpt \
--arch resnet50 \
--postprocessor cider \
--save-score --save-csv --fsood
| 715 | 27.64 | 94 | sh |
null | OpenOOD-main/scripts/ood/cider/imagenet_train_cider.sh | #!/bin/bash
# sh scripts/ood/cider/imagenet_train_cider.sh
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/networks/cider_net.yml \
configs/pipelines/train/train_cider.yml \
configs/preprocessors/base_preprocessor.yml \
--preprocessor.name cider \
--network.backbone.name resnet50 \
--network.backbone.pretrained True \
--network.backbone.checkpoint ./results/pretrained_weights/resnet50_imagenet1k_v1.pth \
--optimizer.lr 0.001 \
--optimizer.num_epochs 10 \
--dataset.train.batch_size 512 \
--trainer.trainer_args.proto_m 0.95 \
--num_gpus 1 --num_workers 16 \
--merge_option merge \
--seed 0
| 682 | 33.15 | 91 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/cifar100_test_conf_branch.sh | #!/bin/bash
# sh scripts/ood/conf_branch/cifar100_test_conf_branch.sh
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/conf_branch.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/conf_branch.yml \
--network.backbone.name resnet18_32x32 \
--network.backbone.pretrained False \
--network.pretrained True \
--network.checkpoint 'results/cifar100_conf_branch_net_conf_branch_e100_lr0.1/s0/best.ckpt'
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_conf_branch_net_conf_branch_e100_lr0.1_default \
--postprocessor conf_branch \
--save-score --save-csv
| 1,210 | 36.84375 | 95 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/cifar100_train_conf_branch.sh | #!/bin/bash
# sh scripts/ood/conf_branch/cifar100_train_conf_branch.sh
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/conf_branch.yml \
configs/pipelines/train/train_conf_branch.yml \
configs/preprocessors/base_preprocessor.yml \
--network.backbone.name resnet18_32x32 \
--seed 0
| 545 | 33.125 | 72 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/cifar10_test_conf_branch.sh | #!/bin/bash
# sh scripts/ood/conf_branch/cifar10_test_conf_branch.sh
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/conf_branch.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/conf_branch.yml \
--network.backbone.name resnet18_32x32 \
--network.backbone.pretrained False \
--network.pretrained True \
--network.checkpoint 'results/cifar10_conf_branch_net_conf_branch_e100_lr0.1/s0/best.ckpt' \
--mark epoch_100
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_conf_branch_net_conf_branch_e100_lr0.1_default \
--postprocessor conf_branch \
--save-score --save-csv
| 1,225 | 36.151515 | 96 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/cifar10_train_conf_branch.sh | #!/bin/bash
# sh scripts/ood/conf_branch/cifar10_train_conf_branch.sh
PYTHONPATH='.':$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/cifar10/cifar10.yml \
configs/networks/conf_branch.yml \
configs/pipelines/train/train_conf_branch.yml \
configs/preprocessors/base_preprocessor.yml \
--network.backbone.name resnet18_32x32 \
--seed ${SEED}
| 548 | 33.3125 | 72 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/imagenet200_test_conf_branch.sh | #!/bin/bash
# sh scripts/ood/conf_branch/imagenet200_test_conf_branch.sh
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_conf_branch_net_conf_branch_e90_lr0.1_default \
--postprocessor conf_branch \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_conf_branch_net_conf_branch_e90_lr0.1_default \
--postprocessor conf_branch \
--save-score --save-csv --fsood
| 733 | 29.583333 | 80 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/imagenet200_train_conf_branch.sh | python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/conf_branch.yml \
configs/pipelines/train/train_conf_branch.yml \
configs/preprocessors/base_preprocessor.yml \
--network.backbone.name resnet18_224x224 \
--optimizer.num_epochs 90 \
--dataset.train.batch_size 128 \
--num_gpus 2 --num_workers 16 \
--merge_option merge \
--seed 0
| 410 | 33.25 | 59 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/imagenet_test_conf_branch.sh | #!/bin/bash
# sh scripts/ood/conf_branch/imagenet_test_conf_branch.sh
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# ood
python scripts/eval_ood_imagenet.py \
--ckpt-path ./results/imagenet_conf_branch_net_conf_branch_e30_lr0.001_default/s0/best.ckpt \
--arch resnet50 \
--postprocessor conf_branch \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--ckpt-path ./results/imagenet_conf_branch_net_conf_branch_e30_lr0.001_default/s0/best.ckpt \
--arch resnet50 \
--postprocessor conf_branch \
--save-score --save-csv --fsood
| 718 | 31.681818 | 97 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/imagenet_train_conf_branch.sh | python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/networks/conf_branch.yml \
configs/pipelines/train/train_conf_branch.yml \
configs/preprocessors/base_preprocessor.yml \
--network.backbone.name resnet50 \
--network.backbone.pretrained True \
--network.backbone.checkpoint ./results/pretrained_weights/resnet50_imagenet1k_v1.pth \
--optimizer.lr 0.001 \
--optimizer.num_epochs 30 \
--dataset.train.batch_size 128 \
--num_gpus 2 --num_workers 16 \
--merge_option merge \
--seed 0
| 556 | 36.133333 | 91 | sh |
null | OpenOOD-main/scripts/ood/conf_branch/train_conf_branch.sh | #!/bin/bash
# sh scripts/ood/train_conf_branch.sh
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-${node} \
python main.py \
--config configs/datasets/digits/mnist.yml \
configs/pipelines/train/train_conf_esti.yml \
configs/networks/conf_net.yml
| 388 | 31.416667 | 71 | sh |
null | OpenOOD-main/scripts/ood/csi/cifar100_test_ood_csi.sh | #!/bin/bash
# sh scripts/ood/csi/cifar100_test_ood_csi.sh
# GPU=1
# CPU=1
# node=36
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/csi_net.yml \
configs/pipelines/test/test_ood.yml \
configs/postprocessors/msp.yml \
configs/preprocessors/base_preprocessor.yml \
--network.pretrained True \
--network.checkpoint 'results/cifar100_csi_net_csi_step2_e100_lr0.1/s0/best.ckpt' \
--merge_option merge
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_csi_net_csi_step2_e100_lr0.1 \
--postprocessor msp \
--save-score --save-csv
| 1,135 | 30.555556 | 87 | sh |
null | OpenOOD-main/scripts/ood/csi/cifar100_train_csi_step1.sh | #!/bin/bash
# sh scripts/ood/csi/cifar100_train_csi_step1.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/csi_net.yml \
configs/pipelines/train/train_csi.yml \
configs/preprocessors/csi_preprocessor.yml \
--network.pretrained False \
--optimizer.num_epochs 100 \
--dataset.train.batch_size 64 \
--merge_option merge \
--mode csi_step1 \
--seed 0
| 681 | 25.230769 | 53 | sh |
null | OpenOOD-main/scripts/ood/csi/cifar100_train_csi_step2.sh | #!/bin/bash
# sh scripts/ood/csi/cifar100_train_csi_step2.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
SEED=0
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/csi_net.yml \
configs/pipelines/train/train_csi.yml \
configs/preprocessors/base_preprocessor.yml \
--network.pretrained True \
--network.checkpoint ./results/cifar100_csi_net_csi_step1_e100_lr0.1/s${SEED}/best.ckpt \
--optimizer.num_epochs 100 \
--dataset.train.batch_size 128 \
--mode csi_step2 \
--seed ${SEED}
| 762 | 27.259259 | 93 | sh |
null | OpenOOD-main/scripts/ood/csi/cifar10_test_ood_csi.sh | #!/bin/bash
# sh scripts/ood/csi/cifar10_test_ood_csi.sh
GPU=1
CPU=1
node=36
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/csi_net.yml \
configs/pipelines/test/test_ood.yml \
configs/postprocessors/msp.yml \
configs/preprocessors/base_preprocessor.yml \
--network.pretrained True \
--network.checkpoint 'results/cifar10_csi_net_csi_step2_e100_lr0.1/s0/best.ckpt' \
--merge_option merge
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_csi_net_csi_step2_e100_lr0.1 \
--postprocessor msp \
--save-score --save-csv
| 1,121 | 31.057143 | 86 | sh |
null | OpenOOD-main/scripts/ood/csi/cifar10_train_csi_step1.sh | #!/bin/bash
# sh scripts/ood/csi/cifar10_train_csi_step1.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/networks/csi_net.yml \
configs/pipelines/train/train_csi.yml \
configs/preprocessors/csi_preprocessor.yml \
--network.pretrained False \
--optimizer.num_epochs 100 \
--dataset.train.batch_size 64 \
--merge_option merge \
--mode csi_step1 \
--seed 0
| 678 | 25.115385 | 51 | sh |
null | OpenOOD-main/scripts/ood/csi/cifar10_train_csi_step2.sh | #!/bin/bash
# sh scripts/ood/csi/cifar10_train_csi_step2.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
SEED=0
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/networks/csi_net.yml \
configs/pipelines/train/train_csi.yml \
configs/preprocessors/base_preprocessor.yml \
--network.pretrained True \
--network.checkpoint ./results/cifar10_csi_net_csi_step1_e100_lr0.1/s${SEED}/best.ckpt \
--optimizer.num_epochs 100 \
--dataset.train.batch_size 128 \
--mode csi_step2 \
--seed ${SEED}
| 758 | 27.111111 | 92 | sh |
null | OpenOOD-main/scripts/ood/dice/cifar100_test_ood_dice.sh | #!/bin/bash
# sh scripts/ood/dice/cifar100_test_ood_dice.sh
# GPU=1
# CPU=1
# node=36
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/dice.yml \
--num_workers 8 \
--network.checkpoint './results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor dice \
--save-score --save-csv
| 1,146 | 30.861111 | 99 | sh |
null | OpenOOD-main/scripts/ood/dice/cifar10_test_ood_dice.sh | #!/bin/bash
# sh scripts/ood/dice/cifar10_test_ood_dice.sh
# GPU=1
# CPU=1
# node=36
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/dice.yml \
--num_workers 8 \
--network.checkpoint './results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor dice \
--save-score --save-csv
| 1,138 | 30.638889 | 98 | sh |
null | OpenOOD-main/scripts/ood/dice/imagenet200_test_ood_dice.sh | #!/bin/bash
# sh scripts/ood/dice/imagenet200_test_ood_dice.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor dice \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor dice \
--save-score --save-csv --fsood
| 712 | 28.708333 | 74 | sh |
null | OpenOOD-main/scripts/ood/dice/imagenet_test_ood_dice.sh | #!/bin/bash
# sh scripts/ood/dice/imagenet_test_ood_dice.sh
GPU=1
CPU=1
node=35
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/datasets/imagenet/imagenet_ood.yml \
configs/networks/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/dice.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.checkpoint 'results/pretrained_weights/resnet50_imagenet1k_v1.pth' \
--merge_option merge
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# available architectures:
# resnet50, swin-t, vit-b-16
# ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor dice \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor dice \
--save-score --save-csv --fsood
| 1,391 | 28 | 82 | sh |
null | OpenOOD-main/scripts/ood/dice/mnist_test_ood_dice.sh | #!/bin/bash
# sh scripts/ood/dice/mnist_test_ood_dice.sh
# GPU=1
# CPU=1
# node=36
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/mnist/mnist.yml \
configs/datasets/mnist/mnist_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/dice.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/mnist_lenet_acc99.30.ckpt' \
--mark 0
| 651 | 26.166667 | 72 | sh |
null | OpenOOD-main/scripts/ood/dice/mnist_test_osr_dice.sh | #!/bin/bash
# sh scripts/ood/dice/mnist_test_osr_dice.sh
# GPU=1
# CPU=1
# node=36
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/osr_mnist6/mnist6_seed1.yml \
configs/datasets/osr_mnist6/mnist6_seed1_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/dice.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/osr/mnist6_seed1.ckpt' \
--mark 0
| 671 | 27 | 72 | sh |
null | OpenOOD-main/scripts/ood/dice/sweep_osr.py | # python scripts/ood/dice/sweep_osr.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar6_seed1.ckpt'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar50_seed1.ckpt'
],
[
'osr_tin20/tin20_seed1.yml', 'osr_tin20/tin20_seed1_ood.yml',
'resnet18_64x64', 'results/checkpoints/osr/tin20_seed1.ckpt'
],
[
'osr_mnist6/mnist6_seed1.yml', 'osr_mnist6/mnist6_seed1_ood.yml',
'lenet', 'results/checkpoints/osr/mnist6_seed1.ckpt'
],
]
for [dataset, ood_dataset, network, pth] in config:
command = (f"PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:1 -n1 \
--cpus-per-task=1 --ntasks-per-node=1 \
--kill-on-bad-exit=1 --job-name=openood \
python main.py \
--config configs/datasets/{dataset} \
configs/datasets/{ood_dataset} \
configs/networks/{network}.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/dice.yml \
--network.pretrained True \
--network.checkpoint {pth} \
--num_workers 8 \
--merge_option merge &")
os.system(command)
| 1,324 | 32.125 | 77 | py |
null | OpenOOD-main/scripts/ood/ebo/cifar100_test_ood_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/cifar100_test_ood_ebo.sh
# GPU=1
# CPU=1
# node=73
# 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/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--network.checkpoint 'results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt'
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor ebo \
--save-score --save-csv
| 1,106 | 31.558824 | 95 | sh |
null | OpenOOD-main/scripts/ood/ebo/cifar100_train_ood_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/cifar100_train_ood_ebo.sh
# GPU=1
# CPU=1
# node=73
# 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/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml
| 577 | 26.52381 | 73 | sh |
null | OpenOOD-main/scripts/ood/ebo/cifar10_test_ood_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/cifar10_test_ood_ebo.sh
# GPU=1
# CPU=1
# node=73
# 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/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 1 \
--postprocessor.postprocessor_args.temperature 1
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor ebo \
--save-score --save-csv
| 1,190 | 31.189189 | 96 | sh |
null | OpenOOD-main/scripts/ood/ebo/imagenet200_test_ood_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/imagenet200_test_ood_ebo.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor ebo \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor ebo \
--save-score --save-csv --fsood
| 708 | 28.541667 | 74 | sh |
null | OpenOOD-main/scripts/ood/ebo/imagenet_test_ood_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/imagenet_test_ood_ebo.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/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.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 ebo \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor ebo \
--save-score --save-csv --fsood
| 1,386 | 27.895833 | 82 | sh |
null | OpenOOD-main/scripts/ood/ebo/mnist_test_ood_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/mnist_test_ood_ebo.sh
GPU=1
CPU=1
node=73
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/mnist/mnist.yml \
configs/datasets/mnist/mnist_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/mnist_lenet_acc98.50.ckpt' \
--mark 0
| 643 | 25.833333 | 73 | sh |
null | OpenOOD-main/scripts/ood/ebo/mnist_test_ood_ebo_aps.sh | #!/bin/bash
# sh scripts/ood/ebo/mnist_test_ood_ebo_aps.sh
GPU=1
CPU=1
node=73
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/mnist/mnist.yml \
configs/datasets/mnist/mnist_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_ood_aps.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/mnist_lenet_acc98.50.ckpt' \
--mark 0
| 651 | 26.166667 | 73 | sh |
null | OpenOOD-main/scripts/ood/ebo/mnist_test_osr_ebo.sh | #!/bin/bash
# sh scripts/ood/ebo/mnist_test_osr_ebo.sh
GPU=1
CPU=1
node=73
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/osr_mnist6/mnist6_seed1.yml \
configs/datasets/osr_mnist6/mnist6_seed1_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/osr/mnist6_seed1.ckpt' \
--mark 0
| 663 | 26.666667 | 73 | sh |
null | OpenOOD-main/scripts/ood/ebo/sweep_osr.py | # python scripts/ood/ebo/sweep_osr.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar6_seed1.ckpt'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar50_seed1.ckpt'
],
[
'osr_tin20/tin20_seed1.yml', 'osr_tin20/tin20_seed1_ood.yml',
'resnet18_64x64', 'results/checkpoints/osr/tin20_seed1.ckpt'
],
[
'osr_mnist6/mnist6_seed1.yml', 'osr_mnist6/mnist6_seed1_ood.yml',
'lenet', 'results/checkpoints/osr/mnist6_seed1.ckpt'
],
]
for [dataset, ood_dataset, network, pth] in config:
command = (f"PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:1 -n1 \
--cpus-per-task=1 --ntasks-per-node=1 \
--kill-on-bad-exit=1 --job-name=openood \
python main.py \
--config configs/datasets/{dataset} \
configs/datasets/{ood_dataset} \
configs/networks/{network}.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/ebo.yml \
--network.pretrained True \
--network.checkpoint {pth} \
--num_workers 8 \
--merge_option merge &")
os.system(command)
| 1,322 | 32.075 | 77 | py |
null | OpenOOD-main/scripts/ood/godin/cifar100_test_ood_godin.sh | #!/bin/bash
# sh scripts/ood/godin/cifar100_test_ood_godin.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/godin_net.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/godin.yml \
--network.backbone.name resnet18_32x32 \
--num_workers 8 \
--network.checkpoint 'results/cifar100_godin_net_godin_e100_lr0.1_default/s0/best.ckpt' \
--merge_option merge
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_godin_net_godin_e100_lr0.1_default \
--postprocessor godin \
--save-score --save-csv
| 1,194 | 31.297297 | 93 | sh |
null | OpenOOD-main/scripts/ood/godin/cifar100_train_godin.sh | #!/bin/bash
# sh scripts/ood/godin/cifar100_train_godin.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/godin_net.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/godin.yml \
--network.backbone.name resnet18_32x32 \
--num_workers 8 \
--trainer.name godin \
--optimizer.num_epochs 100 \
--merge_option merge \
--seed 0
| 721 | 26.769231 | 53 | sh |
null | OpenOOD-main/scripts/ood/godin/cifar10_test_ood_godin.sh | #!/bin/bash
# sh scripts/ood/godin/cifar10_test_ood_godin.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/godin_net.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/godin.yml \
--network.backbone.name resnet18_32x32 \
--num_workers 8 \
--network.checkpoint 'results/cifar10_godin_net_godin_e100_lr0.1_default/s0/best.ckpt' \
--mark epoch_100
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_godin_net_godin_e100_lr0.1_default \
--postprocessor godin \
--save-score --save-csv
| 1,182 | 30.972973 | 92 | sh |
null | OpenOOD-main/scripts/ood/godin/cifar10_train_godin.sh | #!/bin/bash
# sh scripts/ood/godin/cifar10_train_godin.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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/cifar10/cifar10.yml \
configs/networks/godin_net.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/godin.yml \
--network.backbone.name resnet18_32x32 \
--num_workers 8 \
--trainer.name godin \
--optimizer.num_epochs 100 \
--merge_option merge \
--seed 0
| 718 | 26.653846 | 51 | sh |
null | OpenOOD-main/scripts/ood/godin/imagenet200_test_ood_godin.sh | #!/bin/bash
# sh scripts/ood/godin/imagenet200_test_ood_godin.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_godin_net_godin_e90_lr0.1_default \
--postprocessor godin \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_godin_net_godin_e90_lr0.1_default \
--postprocessor godin \
--save-score --save-csv --fsood
| 696 | 28.041667 | 67 | sh |
null | OpenOOD-main/scripts/ood/godin/imagenet200_train_godin.sh | #!/bin/bash
# sh scripts/ood/godin/imagenet200_train_godin.sh
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/godin_net.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/godin.yml \
--network.backbone.name resnet18_224x224 \
--trainer.name godin \
--optimizer.num_epochs 90 \
--dataset.train.batch_size 128 \
--num_gpus 2 --num_workers 16 \
--merge_option merge \
--seed 0
| 528 | 30.117647 | 59 | sh |
null | OpenOOD-main/scripts/ood/godin/imagenet_test_ood_godin.sh | #!/bin/bash
# sh scripts/ood/godin/imagenet_test_ood_godin.sh
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# available architectures:
# resnet50
# ood
python scripts/eval_ood_imagenet.py \
--ckpt-path ./results/imagenet_godin_net_godin_e30_lr0.001_default/s0/best.ckpt \
--arch resnet50 \
--postprocessor godin \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--ckpt-path ./results/imagenet_godin_net_godin_e30_lr0.001_default/s0/best.ckpt \
--arch resnet50 \
--postprocessor godin \
--save-score --save-csv --fsood
| 704 | 28.375 | 84 | sh |
null | OpenOOD-main/scripts/ood/godin/imagenet_train_godin.sh | #!/bin/bash
# sh scripts/ood/godin/imagenet_train_godin.sh
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/networks/godin_net.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/godin.yml \
--network.backbone.name resnet50 \
--network.backbone.pretrained True \
--network.backbone.checkpoint ./results/pretrained_weights/resnet50_imagenet1k_v1.pth \
--trainer.name godin \
--optimizer.lr 0.001 \
--optimizer.num_epochs 30 \
--dataset.train.batch_size 128 \
--num_gpus 2 --num_workers 16 \
--merge_option merge \
--seed 0
| 671 | 32.6 | 91 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/cifar100_test_ood_gradnorm.sh | #!/bin/bash
# sh scripts/ood/gradnorm/cifar100_test_ood_gradnorm.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
# srun -p mediasuper -x SZ-IDC1-10-112-2-17 --gres=gpu:${GPU} \
# --cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
# --kill-on-bad-exit=1 --job-name=${jobname} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gradnorm.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor gradnorm \
--save-score --save-csv
| 1,150 | 30.972222 | 97 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/cifar10_test_ood_gradnorm.sh | #!/bin/bash
# sh scripts/ood/gradnorm/cifar10_test_ood_gradnorm.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
# srun -p mediasuper -x SZ-IDC1-10-112-2-17 --gres=gpu:${GPU} \
# --cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
# --kill-on-bad-exit=1 --job-name=${jobname} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gradnorm.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor gradnorm \
--save-score --save-csv
| 1,142 | 30.75 | 96 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/imagenet200_test_ood_gradnorm.sh | #!/bin/bash
# sh scripts/ood/ebo/imagenet200_test_ood_gradnorm.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor gradnorm \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor gradnorm \
--save-score --save-csv --fsood
| 723 | 29.166667 | 74 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/imagenet_test_ood_gradnorm.sh | #!/bin/bash
# sh scripts/ood/gradnorm/imagenet_test_ood_gradnorm.sh
GPU=1
CPU=1
node=39
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/datasets/imagenet/imagenet_ood.yml \
configs/networks/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gradnorm.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.checkpoint 'results/pretrained_weights/resnet50_imagenet1k_v1.pth' \
--merge_option merge
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# available architectures:
# resnet50, swin-t, vit-b-16
# ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor gradnorm \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor gradnorm \
--save-score --save-csv --fsood
| 1,404 | 27.673469 | 82 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/mnist_test_ood_gradnorm.sh | #!/bin/bash
# sh scripts/ood/gradnorm/mnist_test_ood_gradnorm.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/mnist/mnist.yml \
configs/datasets/mnist/mnist_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gradnorm.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/mnist_lenet_acc99.30.ckpt' \
--mark 0
| 658 | 26.458333 | 73 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/mnist_test_osr_gradnorm.sh | #!/bin/bash
# sh scripts/ood/gradnorm/mnist_test_osr_gradnorm.sh
GPU=1
CPU=1
node=73
jobname=openood
PYTHONPATH='.':$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-${node} \
python main.py \
--config configs/datasets/osr_mnist6/mnist6_seed1.yml \
configs/datasets/osr_mnist6/mnist6_seed1_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gradnorm.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/osr/mnist6_seed1.ckpt' \
--mark 0
| 678 | 27.291667 | 73 | sh |
null | OpenOOD-main/scripts/ood/gradnorm/sweep_osr.py | # python scripts/ood/gradnorm/sweep_osr.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar6_seed1.ckpt'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar50_seed1.ckpt'
],
[
'osr_tin20/tin20_seed1.yml', 'osr_tin20/tin20_seed1_ood.yml',
'resnet18_64x64', 'results/checkpoints/osr/tin20_seed1.ckpt'
],
[
'osr_mnist6/mnist6_seed1.yml', 'osr_mnist6/mnist6_seed1_ood.yml',
'lenet', 'results/checkpoints/osr/mnist6_seed1.ckpt'
],
]
for [dataset, ood_dataset, network, pth] in config:
command = (f"PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:1 -n1 \
--cpus-per-task=1 --ntasks-per-node=1 \
--kill-on-bad-exit=1 --job-name=openood \
python main.py \
--config configs/datasets/{dataset} \
configs/datasets/{ood_dataset} \
configs/networks/{network}.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gradnorm.yml \
--network.pretrained True \
--network.checkpoint {pth} \
--num_workers 8 \
--merge_option merge &")
os.system(command)
| 1,332 | 32.325 | 77 | py |
null | OpenOOD-main/scripts/ood/gram/cifar100_test_ood_gram.sh | #!/bin/bash
# sh scripts/ood/gram/cifar100_test_ood_gram.sh
# GPU=1
# CPU=1
# node=36
# 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/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gram.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor gram \
--save-score --save-csv
| 1,140 | 30.694444 | 97 | sh |
null | OpenOOD-main/scripts/ood/gram/cifar10_test_ood_gram.sh | #!/bin/bash
# sh scripts/ood/gram/cifar10_test_ood_gram.sh
# GPU=1
# CPU=1
# node=36
# 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/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gram.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor gram \
--save-score --save-csv
| 1,132 | 30.472222 | 96 | sh |
null | OpenOOD-main/scripts/ood/gram/imagenet200_test_ood_gram.sh | #!/bin/bash
# sh scripts/ood/ebo/imagenet200_test_ood_gram.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor gram \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor gram \
--save-score --save-csv --fsood
| 711 | 28.666667 | 74 | sh |
null | OpenOOD-main/scripts/ood/gram/imagenet_test_ood_gram.sh | #!/bin/bash
# sh scripts/ood/gram/imagenet_test_ood_gram.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/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gram.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.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
# ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor gram \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor gram \
--save-score --save-csv --fsood
| 1,365 | 27.458333 | 82 | sh |
null | OpenOOD-main/scripts/ood/gram/mnist_test_osr_gram.sh | #!/bin/bash
# sh scripts/ood/gram/mnist_test_osr_gram.sh
GPU=1
CPU=1
node=73
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/osr_mnist6/mnist6_seed1.yml \
configs/datasets/osr_mnist6/mnist6_seed1_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gram.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/osr/mnist6_seed1.ckpt' \
--mark 0
| 666 | 26.791667 | 73 | sh |
null | OpenOOD-main/scripts/ood/gram/sweep_osr.py | # python scripts/ood/gram/sweep_osr.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar6_seed1.ckpt'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar50_seed1.ckpt'
],
[
'osr_tin20/tin20_seed1.yml', 'osr_tin20/tin20_seed1_ood.yml',
'resnet18_64x64', 'results/checkpoints/osr/tin20_seed1.ckpt'
],
[
'osr_mnist6/mnist6_seed1.yml', 'osr_mnist6/mnist6_seed1_ood.yml',
'lenet', 'results/checkpoints/osr/mnist6_seed1.ckpt'
],
]
for [dataset, ood_dataset, network, pth] in config:
command = (f"PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:1 -n1 \
--cpus-per-task=1 --ntasks-per-node=1 \
--kill-on-bad-exit=1 --job-name=openood \
python main.py \
--config configs/datasets/{dataset} \
configs/datasets/{ood_dataset} \
configs/networks/{network}.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/gram.yml \
--network.pretrained True \
--network.checkpoint {pth} \
--num_workers 8 \
--merge_option merge &")
os.system(command)
| 1,324 | 32.125 | 77 | py |
null | OpenOOD-main/scripts/ood/kl_matching/cifar100_test_ood_kl_matching.sh | #!/bin/bash
# sh scripts/ood/kl_matching/cifar100_test_ood_kl_matching.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
# srun -p mediasuper -x SZ-IDC1-10-112-2-17 --gres=gpu:${GPU} \
# --cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
# --kill-on-bad-exit=1 --job-name=${jobname} \
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/klm.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor ebo \
--save-score --save-csv
| 1,142 | 30.75 | 97 | sh |
null | OpenOOD-main/scripts/ood/kl_matching/cifar10_test_ood_kl_matching.sh | #!/bin/bash
# sh scripts/ood/kl_matching/cifar10_test_ood_kl_matching.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
# srun -p mediasuper -x SZ-IDC1-10-112-2-17 --gres=gpu:${GPU} \
# --cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
# --kill-on-bad-exit=1 --job-name=${jobname} \
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/klm.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor klm \
--save-score --save-csv
| 1,134 | 30.527778 | 96 | sh |
null | OpenOOD-main/scripts/ood/kl_matching/imagenet200_test_ood_kl_matching.sh | #!/bin/bash
# sh scripts/ood/ebo/imagenet200_test_ood_kl_matching.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor klm \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor klm \
--save-score --save-csv --fsood
| 716 | 28.875 | 74 | sh |
null | OpenOOD-main/scripts/ood/kl_matching/imagenet_test_ood_kl_matching.sh | #!/bin/bash
# sh scripts/ood/kl_matching/imagenet_test_ood_kl_matching.sh
GPU=1
CPU=1
node=63
jobname=openood
PYTHONPATH='.':$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-${node} \
python -m pdb -c continue main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/datasets/imagenet/imagenet_ood.yml \
configs/networks/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/klm.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.checkpoint 'results/pretrained_weights/resnet50_imagenet1k_v1.pth' \
--merge_option merge
############################################
# we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood_imagenet.py
# available architectures:
# resnet50, swin-t, vit-b-16
# ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor klm \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor klm \
--save-score --save-csv --fsood
| 1,413 | 28.458333 | 82 | sh |
null | OpenOOD-main/scripts/ood/kl_matching/mnist_test_ood_kl_matching.sh | #!/bin/bash
# sh scripts/ood/kl_matching/mnist_test_ood_kl_matching.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
# srun -p mediasuper -x SZ-IDC1-10-112-2-17 --gres=gpu:${GPU} \
# --cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
# --kill-on-bad-exit=1 --job-name=${jobname} \
python main.py \
--config configs/datasets/mnist/mnist.yml \
configs/datasets/mnist/mnist_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/klm.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/mnist_lenet_acc99.30.ckpt' \
--mark 0
| 654 | 26.291667 | 70 | sh |
null | OpenOOD-main/scripts/ood/kl_matching/mnist_test_osr_kl_matching.sh | #!/bin/bash
# sh scripts/ood/kl_matching/mnist_test_osr_kl_matching.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$PYTHONPATH \
# srun -p mediasuper -x SZ-IDC1-10-112-2-17 --gres=gpu:${GPU} \
# --cpus-per-task=${CPU} --ntasks-per-node=${GPU} \
# --kill-on-bad-exit=1 --job-name=${jobname} \
python main.py \
--config configs/datasets/osr_mnist6/mnist6_seed1.yml \
configs/datasets/osr_mnist6/mnist6_seed1_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/klm.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/osr/mnist6_seed1.ckpt' \
--mark 0
| 674 | 27.125 | 66 | sh |
null | OpenOOD-main/scripts/ood/kl_matching/sweep_osr.py | # python scripts/ood/kl_matching/sweep_osr.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar6_seed1.ckpt'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar50_seed1.ckpt'
],
[
'osr_tin20/tin20_seed1.yml', 'osr_tin20/tin20_seed1_ood.yml',
'resnet18_64x64', 'results/checkpoints/osr/tin20_seed1.ckpt'
],
[
'osr_mnist6/mnist6_seed1.yml', 'osr_mnist6/mnist6_seed1_ood.yml',
'lenet', 'results/checkpoints/osr/mnist6_seed1.ckpt'
],
]
for [dataset, ood_dataset, network, pth] in config:
command = (f"PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:1 -n1 \
--cpus-per-task=1 --ntasks-per-node=1 \
--kill-on-bad-exit=1 --job-name=openood \
python main.py \
--config configs/datasets/{dataset} \
configs/datasets/{ood_dataset} \
configs/networks/{network}.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/klm.yml \
--network.pretrained True \
--network.checkpoint {pth} \
--num_workers 8 \
--merge_option merge &")
os.system(command)
| 1,330 | 32.275 | 77 | py |
null | OpenOOD-main/scripts/ood/knn/cifar100_test_ood_knn.sh | #!/bin/bash
# sh scripts/ood/knn/cifar100_test_ood_knn.sh
# GPU=1
# CPU=1
# node=36
# 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/cifar100/cifar100.yml \
configs/datasets/cifar100/cifar100_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/knn.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar100_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar100 \
--root ./results/cifar100_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor knn \
--save-score --save-csv
| 1,136 | 30.583333 | 97 | sh |
null | OpenOOD-main/scripts/ood/knn/cifar10_test_ood_knn.sh | #!/bin/bash
# sh scripts/ood/knn/cifar10_test_ood_knn.sh
# GPU=1
# CPU=1
# node=36
# 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/cifar10/cifar10.yml \
configs/datasets/cifar10/cifar10_ood.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/knn.yml \
--num_workers 8 \
--network.checkpoint 'results/cifar10_resnet18_32x32_base_e100_lr0.1_default/s0/best.ckpt' \
--mark 0
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
python scripts/eval_ood.py \
--id-data cifar10 \
--root ./results/cifar10_resnet18_32x32_base_e100_lr0.1_default \
--postprocessor knn \
--save-score --save-csv
| 1,128 | 30.361111 | 96 | sh |
null | OpenOOD-main/scripts/ood/knn/imagenet200_test_ood_knn.sh | #!/bin/bash
# sh scripts/ood/ebo/imagenet200_test_ood_knn.sh
############################################
# alternatively, we recommend using the
# new unified, easy-to-use evaluator with
# the example script scripts/eval_ood.py
# especially if you want to get results from
# multiple runs
# ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor knn \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood.py \
--id-data imagenet200 \
--root ./results/imagenet200_resnet18_224x224_base_e90_lr0.1_default \
--postprocessor knn \
--save-score --save-csv --fsood
| 708 | 28.541667 | 74 | sh |
null | OpenOOD-main/scripts/ood/knn/imagenet_test_ood_knn.sh | #!/bin/bash
# sh scripts/ood/knn/imagenet_test_ood_knn.sh
GPU=1
CPU=1
node=37
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/resnet50.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/knn.yml \
--num_workers 4 \
--ood_dataset.image_size 256 \
--dataset.test.batch_size 256 \
--dataset.val.batch_size 256 \
--network.pretrained True \
--network.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 knn \
--save-score --save-csv #--fsood
# full-spectrum ood
python scripts/eval_ood_imagenet.py \
--tvs-pretrained \
--arch resnet50 \
--postprocessor knn \
--save-score --save-csv --fsood
| 1,378 | 27.729167 | 82 | sh |
null | OpenOOD-main/scripts/ood/knn/mnist_test_ood_knn.sh | #!/bin/bash
# sh scripts/ood/knn/mnist_test_ood_knn.sh
# GPU=1
# CPU=1
# node=36
# 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/mnist/mnist.yml \
configs/datasets/mnist/mnist_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_ood.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/knn.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/mnist_lenet_acc99.30.ckpt' \
--mark 0
| 648 | 26.041667 | 72 | sh |
null | OpenOOD-main/scripts/ood/knn/mnist_test_osr_knn.sh | #!/bin/bash
# sh scripts/ood/knn/mnist_test_osr_knn.sh
# GPU=1
# CPU=1
# node=36
# 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/osr_mnist6/mnist6_seed1.yml \
configs/datasets/osr_mnist6/mnist6_seed1_ood.yml \
configs/networks/lenet.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/knn.yml \
--num_workers 8 \
--network.checkpoint 'results/checkpoints/osr/mnist6_seed1.ckpt' \
--mark 0
| 668 | 26.875 | 72 | sh |
null | OpenOOD-main/scripts/ood/knn/sweep_osr.py | # python scripts/ood/knn/sweep_osr.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar6_seed1.ckpt'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_seed1_ood.yml',
'resnet18_32x32', 'results/checkpoints/osr/cifar50_seed1.ckpt'
],
[
'osr_tin20/tin20_seed1.yml', 'osr_tin20/tin20_seed1_ood.yml',
'resnet18_64x64', 'results/checkpoints/osr/tin20_seed1.ckpt'
],
[
'osr_mnist6/mnist6_seed1.yml', 'osr_mnist6/mnist6_seed1_ood.yml',
'lenet', 'results/checkpoints/osr/mnist6_seed1.ckpt'
],
]
for [dataset, ood_dataset, network, pth] in config:
command = (f"PYTHONPATH='.':$PYTHONPATH \
srun -p dsta --mpi=pmi2 --gres=gpu:1 -n1 \
--cpus-per-task=1 --ntasks-per-node=1 \
--kill-on-bad-exit=1 --job-name=openood \
python main.py \
--config configs/datasets/{dataset} \
configs/datasets/{ood_dataset} \
configs/networks/{network}.yml \
configs/pipelines/test/test_osr.yml \
configs/preprocessors/base_preprocessor.yml \
configs/postprocessors/knn.yml \
--network.pretrained True \
--network.checkpoint {pth} \
--num_workers 8 \
--merge_option merge &")
os.system(command)
| 1,322 | 32.075 | 77 | py |