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/uncertainty/mc_dropout/cifar10_train_mc_dropout.sh | #!/bin/bash
# sh scripts/uncertainty/mc_dropout/cifar10_train_mc_dropout.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=${jobname} \
# -w SG-IDC1-10-51-... | 642 | 24.72 | 63 | sh |
null | OpenOOD-main/scripts/uncertainty/mc_dropout/mnist_test_mc_dropout.sh | #!/bin/bash
# sh scripts/uncertainty/mc_dropout/mnist_test_mc_dropout.sh
#GPU=1
#CPU=1
#node=73
#jobname=openood
PYTHONPATH='.':$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-${node} \
pyt... | 717 | 28.916667 | 84 | sh |
null | OpenOOD-main/scripts/uncertainty/mc_dropout/mnist_train_mc_dropout.sh | #!/bin/bash
# sh scripts/uncertainty/mc_dropout/mnist_train_mc_dropout.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-... | 627 | 24.12 | 61 | sh |
null | OpenOOD-main/scripts/uncertainty/mc_dropout/osr_mnist6_test_mc_dropout.sh | #!/bin/bash
# sh scripts/uncertainty/mc_dropout/osr_mnist6_test_mc_dropout.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=${jobname} \
# -w SG-IDC1-10-5... | 770 | 27.555556 | 95 | sh |
null | OpenOOD-main/scripts/uncertainty/mc_dropout/osr_mnist6_train_mc_dropout.sh | #!/bin/bash
# sh scripts/uncertainty/mc_dropout/osr_mnist6_train_mc_dropout.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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-... | 644 | 24.8 | 66 | sh |
null | OpenOOD-main/scripts/uncertainty/mc_dropout/sweep.py | # python scripts/uncertainty/mc_dropout/sweep.py
import os
config = [
['osr_cifar6/cifar6_seed1.yml', 'resnet18_32x32'],
['osr_cifar50/cifar50_seed1.yml', 'resnet18_32x32'],
['osr_tin20/tin20_seed1.yml', 'resnet18_64x64'],
['osr_mnist4/mnist4_seed1.yml', 'lenet'],
['mnist/mnist.yml', 'lenet'],
]
f... | 884 | 31.777778 | 56 | py |
null | OpenOOD-main/scripts/uncertainty/mc_dropout/sweep_test.py | # python scripts/uncertainty/mc_dropout/sweep_test.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32',
'osr_cifar6_seed1_dropout_net_base_e100_lr0.1_default'
],
[
'osr_cifar50/cifar50_seed1.yml', 'osr_cifar50/cifar50_... | 1,401 | 32.380952 | 79 | py |
null | OpenOOD-main/scripts/uncertainty/mixup/cifar100_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/cifar100_test_ood_msp.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} \
pyth... | 707 | 29.782609 | 92 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/cifar100_train_mixup.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/cifar100_train_mixup.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... | 580 | 26.666667 | 54 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/cifar10_test_ood_mixup.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/cifar10_test_ood_mixup.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} \
pyt... | 711 | 29.956522 | 99 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/cifar10_train_mixup.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/cifar10_train_mixup.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 ... | 578 | 25.318182 | 53 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/mnist_test_ood_mixup.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/mnist_test_ood_mixup.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} \
pytho... | 681 | 28.652174 | 88 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/mnist_train_mixup.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/mnist_train_mixup.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 ma... | 563 | 24.636364 | 51 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/osr_mnist6_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/osr_mnist6_test_ood_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... | 715 | 28.833333 | 99 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/osr_mnist6_train_mixup.sh | #!/bin/bash
# sh scripts/uncertainty/mixup/osr_mnist6_train_mixup.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} \
pyth... | 580 | 25.409091 | 56 | sh |
null | OpenOOD-main/scripts/uncertainty/mixup/sweep.py | # python scripts/uncertainty/mixup/sweep.py
import os
config = [
['osr_cifar6/cifar6_seed1.yml', 'resnet18_32x32'],
['osr_cifar50/cifar50_seed1.yml', 'resnet18_32x32'],
['osr_tin20/tin20_seed1.yml', 'resnet18_64x64'],
['osr_mnist4/mnist4_seed1.yml', 'lenet'],
['mnist/mnist.yml', 'lenet'],
]
for [d... | 868 | 31.185185 | 56 | py |
null | OpenOOD-main/scripts/uncertainty/mixup/sweep_test.py | # python scripts/uncertainty/mixup/sweep_test.py
import os
config = [
[
'osr_cifar6/cifar6_seed1.yml', 'osr_cifar6/cifar6_seed1_ood.yml',
'resnet18_32x32',
'./results/cifar10_osr_resnet18_32x32_base_e100_lr0.1_default/best_epoch94_acc0.9773.ckpt'
],
[
'osr_cifar50/cifar50_se... | 1,125 | 32.117647 | 98 | py |
null | OpenOOD-main/scripts/uncertainty/pixmix/cifar100_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/cifar100_test_ood_msp.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} \
pyt... | 1,149 | 31.857143 | 96 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/cifar100_train_pixmix.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/cifar100_train_pixmix.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... | 621 | 24.916667 | 56 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/cifar10_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/cifar10_test_ood_msp.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} \
pyth... | 1,191 | 32.111111 | 95 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/cifar10_train_pixmix.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/cifar10_train_pixmix.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}... | 618 | 24.791667 | 55 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/imagenet200_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/imagenet200_test_ood_msp.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 scri... | 717 | 28.916667 | 73 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/imagenet200_train_pixmix.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/imagenet200_train_pixmix.sh
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/resnet18_224x224.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/pixmix_preprocessor.yml \
--preprocessor.preprocessor... | 560 | 32 | 59 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/imagenet_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/imagenet_test_ood_msp.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 \
--... | 681 | 27.416667 | 71 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/mnist_test_ood_pixmix.sh | !/bin/bash
# sh scripts/uncertainty/pixmix/mnist_test_ood_pixmix.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} \
pyth... | 672 | 28.26087 | 77 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/mnist_train_pixmix.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/mnist_train_pixmix.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} \... | 578 | 23.125 | 53 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/osr_mnist6_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/osr_mnist6_test_ood_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} \
pytho... | 717 | 28.916667 | 88 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/osr_mnist6_train_pixmix.sh | #!/bin/bash
# sh scripts/uncertainty/pixmix/osr_mnist6_train_pixmix.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=${jobname} \
# -w SG-IDC1-10-51-2-${no... | 657 | 25.32 | 58 | sh |
null | OpenOOD-main/scripts/uncertainty/pixmix/sweep.py | # python scripts/uncertainty/pixmix/sweep.py
import os
config = [
['osr_cifar6/cifar6_seed1.yml', 'resnet18_32x32', 'cifar10'],
['osr_cifar50/cifar50_seed1.yml', 'resnet18_32x32', 'cifar100'],
['osr_tin20/tin20_seed1.yml', 'resnet18_64x64', 'tin'],
]
for [dataset, network, od] in config:
command = (f"... | 789 | 31.916667 | 68 | py |
null | OpenOOD-main/scripts/uncertainty/randaugment/cifar100_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/cifar100_test_ood_msp.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/e... | 488 | 31.6 | 78 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/cifar100_train_randaugment.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/cifar100_train_randaugment.sh
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/randaugment_preprocessor.yml \
--seed 0 \
--mark r... | 336 | 29.636364 | 66 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/cifar10_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/cifar10_test_ood_msp.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/ev... | 485 | 31.4 | 77 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/cifar10_train_randaugment.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/cifar10_train_randaugment.sh
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/randaugment_preprocessor.yml \
--seed 0 \
--mark rand... | 333 | 29.363636 | 65 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/imagenet200_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/imagenet200_test_ood_msp.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... | 742 | 29.958333 | 83 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/imagenet200_train_randaugment.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/imagenet200_train_randaugment.sh
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/resnet18_224x224.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/randaugment_preprocessor.yml \
--preprocess... | 505 | 30.625 | 69 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/imagenet_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/imagenet_test_ood_msp.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 \
... | 724 | 29.208333 | 90 | sh |
null | OpenOOD-main/scripts/uncertainty/randaugment/imagenet_train_randaugment.sh | #!/bin/bash
# sh scripts/uncertainty/randaugment/imagenet_train_randaugment.sh
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/networks/resnet50.yml \
configs/pipelines/train/baseline.yml \
configs/preprocessors/randaugment_preprocessor.yml \
--preprocessor.n 2 \
--pr... | 653 | 31.7 | 82 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/cifar100_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/cifar100_test_ood_msp.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... | 488 | 31.6 | 81 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/cifar100_train_regmixup.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/cifar100_train_regmixup.sh
python main.py \
--config configs/datasets/cifar100/cifar100.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/train_regmixup.yml \
configs/preprocessors/base_preprocessor.yml \
--trainer.trainer_args.alpha 1... | 337 | 29.727273 | 60 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/cifar10_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/cifar10_test_ood_msp.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_... | 485 | 31.4 | 80 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/cifar10_train_regmixup.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/cifar10_train_regmixup.sh
python main.py \
--config configs/datasets/cifar10/cifar10.yml \
configs/networks/resnet18_32x32.yml \
configs/pipelines/train/train_regmixup.yml \
configs/preprocessors/base_preprocessor.yml \
--trainer.trainer_args.alpha 20 \... | 334 | 29.454545 | 59 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/imagenet200_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/imagenet200_test_ood_msp.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 sc... | 745 | 30.083333 | 86 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/imagenet200_train_regmixup.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/imagenet200_train_regmixup.sh
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/resnet18_224x224.yml \
configs/pipelines/train/train_regmixup.yml \
configs/preprocessors/base_preprocessor.yml \
--trainer.trainer_a... | 480 | 31.066667 | 63 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/imagenet_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/imagenet_test_ood_msp.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 \
... | 729 | 29.416667 | 94 | sh |
null | OpenOOD-main/scripts/uncertainty/regmixup/imagenet_train_regmixup.sh | #!/bin/bash
# sh scripts/uncertainty/regmixup/imagenet_train_regmixup.sh
python main.py \
--config configs/datasets/imagenet/imagenet.yml \
configs/networks/resnet50.yml \
configs/pipelines/train/train_regmixup.yml \
configs/preprocessors/base_preprocessor.yml \
--trainer.trainer_args.alpha 10 \
... | 605 | 32.666667 | 82 | sh |
null | OpenOOD-main/scripts/uncertainty/rts/cifar100_test_ood_rts.sh | #!/bin/bash
# sh scripts/uncertainty/rts/cifar100_test_rts_msp.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} \
pyth... | 736 | 31.043478 | 100 | sh |
null | OpenOOD-main/scripts/uncertainty/rts/cifar100_train_rts.sh | #!/bin/bash
# sh scripts/uncertainty/rts/cifar100_train_rts.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} \
py... | 643 | 25.833333 | 51 | sh |
null | OpenOOD-main/scripts/uncertainty/styleaug/imagenet200_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/deepaugment/imagenet200_test_ood_msp.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... | 726 | 29.291667 | 75 | sh |
null | OpenOOD-main/scripts/uncertainty/styleaug/imagenet200_train_styleaug.sh | #!/bin/bash
# sh scripts/uncertainty/styleaug/imagenet200_train_styleaug.sh
# the model sees twice the data as the baseline
# so only trains for 90/2=45 epochs
python main.py \
--config configs/datasets/imagenet200/imagenet200.yml \
configs/networks/resnet18_224x224.yml \
configs/pipelines/train/baseline.y... | 644 | 34.833333 | 101 | sh |
null | OpenOOD-main/scripts/uncertainty/styleaug/imagenet_test_ood_msp.sh | #!/bin/bash
# sh scripts/uncertainty/styleaug/imagenet_test_ood_msp.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 \
... | 687 | 27.666667 | 73 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/0_tempscaling.sh | #!/bin/bash
# sh scripts/d_uncertainty/0_tempscaling.sh
# mnist
# 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} \
# p... | 1,518 | 30.645833 | 90 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/cifar100_test_ood_tempscaling.sh | #!/bin/bash
# sh scripts/uncertainty/temp_scaling/cifar100_test_ood_tempscaling.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=${jobname} -w SG-IDC1-10-5... | 1,182 | 31.861111 | 97 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/cifar10_test_ood_tempscaling.sh | #!/bin/bash
# sh scripts/uncertainty/temp_scaling/cifar10_test_ood_tempscaling.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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... | 1,174 | 31.638889 | 96 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/imagenet200_test_ood_tempscaling.sh | #!/bin/bash
# sh scripts/ood/temp_scaling/imagenet200_test_ood_tempscaling.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
pytho... | 743 | 30 | 74 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/imagenet_test_ood_tempscaling.sh | #!/bin/bash
# sh scripts/ood/temp_scaling/imagenet_test_ood_tempscaling.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} \
pytho... | 1,427 | 28.142857 | 82 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/mnist_test_ood_tempscaling.sh | #!/bin/bash
# sh scripts/uncertainty/temp_scaling/mnist_test_ood_tempscaling.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=${jobname} -w SG-IDC1-10-51-2... | 685 | 27.583333 | 73 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/osr_mnist6_test_ood_tempscaling.sh | #!/bin/bash
# sh scripts/uncertainty/temp_scaling/osr_mnist6_test_ood_tempscaling.sh
# GPU=1
# CPU=1
# node=73
# jobname=openood
PYTHONPATH='.':$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... | 710 | 28.625 | 73 | sh |
null | OpenOOD-main/scripts/uncertainty/temp_scaling/sweep_osr.py | # python scripts/uncertainty/temp_scaling/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',
... | 1,316 | 32.769231 | 77 | py |
null | OpenOOD-main/tools/plot/tsne_tools.py | # srun -p dsta --mpi=pmi2 --cpus-per-task=1
# --kill-on-bad-exit=1 --job-name=tsne -w SG-IDC1-10-51-2-73
# python compute_tsne.py
import os
import time
import numpy as np
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
l2_normalize = lambda x: x / np.linalg.norm(x, axis=1, keepdims=True)
de... | 2,194 | 34.403226 | 78 | py |
null | OpenOOD-main/tools/sweep/hyperparam.py | 0 | 0 | 0 | py | |
ILA | ILA-master/README.md |
# [ICCV'2023] Implicit Temporal Modeling with Learnable Alignment for Video Recognition
This is an official implementation of [ILA](https://arxiv.org/abs/2304.10465), a new temporal modeling method for video action recognition.
> [**Implicit Temporal Modeling with Learnable Alignment for Video Recognition**](https:/... | 6,068 | 48.341463 | 359 | md |
ILA | ILA-master/main.py | import os
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import argparse
import datetime
import shutil
from pathlib import Path
from PIL import Image
from einops import rearrange
from utils.config import get_config
from utils.optimizer import build_optimizer, b... | 11,613 | 38.104377 | 146 | py |
ILA | ILA-master/clip/__init__.py | from .clip import *
| 23 | 3.8 | 19 | py |
ILA | ILA-master/clip/clip.py | import hashlib
import os
import urllib
import warnings
from typing import Union, List
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
# from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenize... | 7,595 | 37.363636 | 154 | py |
ILA | ILA-master/clip/model.py | import copy
from collections import OrderedDict
from typing import Tuple, Union
from timm.models.layers import trunc_normal_
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from torch.utils.checkpoint import checkpoint_sequential
import math
import clip
... | 9,197 | 38.646552 | 178 | py |
ILA | ILA-master/clip/model_zoo.py | import os
def get_model_path(ckpt):
if os.path.isfile(ckpt):
return ckpt
else:
print('not found pretrained model in {}'.format(ckpt))
raise FileNotFoundError
| 190 | 22.875 | 62 | py |
ILA | ILA-master/clip/simple_tokenizer.py | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... | 4,628 | 33.804511 | 144 | py |
ILA | ILA-master/configs/k400/14_16_336.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: kinetics400
NUM_FRAMES: 16
NUM_CLASSES: 400
LABEL_LIST: 'labels/kinetics_400_labels.csv'
INPUT_SIZE: 336
MODEL:
ARCH: ViT-L/14@336px
TRAIN:
BATCH_SIZE: 8
... | 344 | 23.642857 | 48 | yaml |
ILA | ILA-master/configs/k400/14_8.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: kinetics400
NUM_FRAMES: 8
NUM_CLASSES: 400
LABEL_LIST: 'labels/kinetics_400_labels.csv'
MODEL:
ARCH: ViT-L/14
TRAIN:
BATCH_SIZE: 8
ACCUMULATION_STEPS: 4 | 317 | 23.461538 | 48 | yaml |
ILA | ILA-master/configs/k400/16_16.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: kinetics400
NUM_FRAMES: 16
NUM_CLASSES: 400
LABEL_LIST: 'labels/kinetics_400_labels.csv'
MODEL:
ARCH: ViT-B/32
TRAIN:
BATCH_SIZE: 8
ACCUMULATION_STEPS: 4 | 318 | 23.538462 | 48 | yaml |
ILA | ILA-master/configs/k400/16_8.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: kinetics400
NUM_FRAMES: 8
NUM_CLASSES: 400
LABEL_LIST: 'labels/kinetics_400_labels.csv'
MODEL:
ARCH: ViT-B/32
TRAIN:
BATCH_SIZE: 8
ACCUMULATION_STEPS: 4 | 317 | 23.461538 | 48 | yaml |
ILA | ILA-master/configs/k400/32_16.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: kinetics400
NUM_FRAMES: 16
NUM_CLASSES: 400
LABEL_LIST: 'labels/kinetics_400_labels.csv'
MODEL:
ARCH: ViT-B/32
TRAIN:
BATCH_SIZE: 8
ACCUMULATION_STEPS: 4 | 318 | 23.538462 | 48 | yaml |
ILA | ILA-master/configs/k400/32_8.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: kinetics400
NUM_FRAMES: 8
NUM_CLASSES: 400
LABEL_LIST: 'labels/kinetics_400_labels.csv'
MODEL:
ARCH: ViT-B/32
TRAIN:
BATCH_SIZE: 8
ACCUMULATION_STEPS: 4 | 317 | 23.461538 | 48 | yaml |
ILA | ILA-master/configs/ssv2/14_16_336.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: something-somethingv2
NUM_FRAMES: 16
NUM_CLASSES: 174
LABEL_LIST: 'labels/something-something-v2-labels.csv'
INPUT_SIZE: 336
MODEL:
ARCH: ViT-L/14@336px
TRAIN:... | 365 | 25.142857 | 58 | yaml |
ILA | ILA-master/configs/ssv2/14_8.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: something-somethingv2
NUM_FRAMES: 8
NUM_CLASSES: 174
LABEL_LIST: 'labels/something-something-v2-labels.csv'
MODEL:
ARCH: ViT-L/14
TRAIN:
BATCH_SIZE: 4
ACCU... | 337 | 25 | 58 | yaml |
ILA | ILA-master/configs/ssv2/16_16.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: something-somethingv2
NUM_FRAMES: 16
NUM_CLASSES: 174
LABEL_LIST: 'labels/something-something-v2-labels.csv'
MODEL:
ARCH: ViT-B/16
TRAIN:
BATCH_SIZE: 4
ACC... | 338 | 25.076923 | 58 | yaml |
ILA | ILA-master/configs/ssv2/16_32.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: something-somethingv2
NUM_FRAMES: 32
NUM_CLASSES: 174
LABEL_LIST: 'labels/something-something-v2-labels.csv'
MODEL:
ARCH: ViT-B/16
TRAIN:
BATCH_SIZE: 4
ACC... | 338 | 25.076923 | 58 | yaml |
ILA | ILA-master/configs/ssv2/16_8.yaml | DATA:
ROOT: '/PATH/TO/videos'
TRAIN_FILE: '/PATH/TO/train_list_videos.txt'
VAL_FILE: '/PATH/TO/val_list_videos.txt'
DATASET: something-somethingv2
NUM_FRAMES: 8
NUM_CLASSES: 174
LABEL_LIST: 'labels/something-something-v2-labels.csv'
MODEL:
ARCH: ViT-B/16
TRAIN:
BATCH_SIZE: 8
ACCU... | 337 | 25 | 58 | yaml |
ILA | ILA-master/datasets/__init__.py | 0 | 0 | 0 | py | |
ILA | ILA-master/datasets/blending.py | from abc import ABCMeta, abstractmethod
import torch
import torch.nn.functional as F
from torch.distributions.beta import Beta
import numpy as np
def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
x = x.long().view(-1, 1)
return torch.full((x.size()[0], num_classes), off_value, device=dev... | 8,103 | 36.693023 | 139 | py |
ILA | ILA-master/datasets/build.py | from logging import Logger
from torch.utils.data import DataLoader
import torch.distributed as dist
import torch
import numpy as np
from functools import partial
import random
import io
import os
import os.path as osp
import shutil
import warnings
from collections.abc import Mapping, Sequence
from mmcv.utils import Re... | 12,997 | 35.105556 | 153 | py |
ILA | ILA-master/datasets/pipeline.py | import io
import os
import os.path as osp
import shutil
import warnings
from collections.abc import Sequence
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import Dataset
import copy
import os.path as osp
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict,... | 90,339 | 37.344652 | 143 | py |
ILA | ILA-master/datasets/rand_augment.py | """
This implementation is based on
https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/auto_augment.py
pulished under an Apache License 2.0.
COMMENT FROM ORIGINAL:
AutoAugment, RandAugment, and AugMix for PyTorch
This code implements the searched ImageNet policies with various tweaks and
improveme... | 16,174 | 29.347092 | 119 | py |
ILA | ILA-master/models/align.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from typing import Optional
import numpy as np
def aligned_mask_generation(point, resolution):
L = resolution
shape = point.size()[:-1]
point = point.reshape(-1, 1, 2)
N = point.size()[0]
element = torc... | 8,866 | 40.629108 | 142 | py |
ILA | ILA-master/models/mat.py | from collections import OrderedDict
from typing import Tuple
from einops import rearrange, reduce, repeat
from timm.models.layers import trunc_normal_
import torch
from torch import nn
import numpy as np
from torch.utils.checkpoint import checkpoint_sequential
import sys
from models.align import ILA
from models.metric... | 11,947 | 38.17377 | 173 | py |
ILA | ILA-master/models/metrics.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
def timewise_cos(x, y):
l, b, t, c = x.size()
x = rearrange(x, "l b t c -> b t l c", b=b, t=t, l=l, c=c)
y = rearrange(y, "l b t c -> b t l c", b=b, t=t, l=l, c=c)
x = x.squeeze()
y = ... | 1,207 | 28.463415 | 64 | py |
ILA | ILA-master/models/mit.py | import torch
from torch import nn
from collections import OrderedDict
from timm.models.layers import trunc_normal_
import sys
sys.path.append("../")
from clip.model import QuickGELU
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
supe... | 2,814 | 32.915663 | 133 | py |
ILA | ILA-master/models/prompt.py | from timm.models.layers import trunc_normal_
import torch
from torch import nn
import sys
sys.path.append("../")
from clip.model import QuickGELU
class MulitHeadAttention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
... | 3,565 | 32.641509 | 109 | py |
ILA | ILA-master/models/temporal_shift.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from einops import rearrange, reduce, repeat
import torchvision
from models.mat import MultiAxisTransformer
class TemporalShift(nn.Module):
def __init__(self, net, n_segment=3, n_div=8, inplace=False):
super(TemporalShi... | 5,893 | 34.506024 | 105 | py |
ILA | ILA-master/models/xclip.py | import os
from collections import OrderedDict
from typing import Tuple, Union
import torch
from torch import nn
import numpy as np
from .mat import MultiAxisTransformer
from .mit import MultiframeIntegrationTransformer
from .prompt import VideoSpecificPrompt
import sys
import warnings
sys.path.append("../")
from clip.... | 12,571 | 41.187919 | 207 | py |
ILA | ILA-master/utils/__init__.py | 0 | 0 | 0 | py | |
ILA | ILA-master/utils/config.py | import os
import yaml
from yacs.config import CfgNode as CN
_C = CN()
# Base config files
_C.BASE = ['']
_C.DATA = CN()
_C.DATA.ROOT = ''
_C.DATA.TRAIN_FILE = ''
_C.DATA.VAL_FILE = ''
_C.DATA.DATASET = 'kinetics400'
_C.DATA.INPUT_SIZE = 224
_C.DATA.NUM_FRAMES = 8
_C.DATA.NUM_CLASSES = 400
_C.DATA.LABEL_LIST = 'labe... | 2,596 | 22.1875 | 68 | py |
ILA | ILA-master/utils/helper.py | import numpy
import torch.distributed as dist
import torch
import clip
import os
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | 3,045 | 31.404255 | 108 | py |
ILA | ILA-master/utils/logger.py | import os
import sys
import logging
import functools
from termcolor import colored
@functools.lru_cache()
def create_logger(output_dir, dist_rank=0, name=''):
# create logger
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
logger.propagate = False
# create formatter
fmt = '[%(... | 1,203 | 33.4 | 102 | py |
ILA | ILA-master/utils/optimizer.py | import copy
import torch.optim as optim
from timm.scheduler.cosine_lr import CosineLRScheduler
import torch.distributed as dist
def is_main_process():
return dist.get_rank() == 0
def check_keywords_in_name(name, keywords=()):
isin = False
for keyword in keywords:
if keyword in name:
is... | 3,646 | 33.733333 | 123 | py |
null | Multi-domain-learning-FAS-main/README.md | # SiW-Mv2 Dataset and Multi-domain FAS
<p align="center">
<img src="https://github.com/CHELSEA234/Multi-domain-learning-FAS/blob/main/source_SiW_Mv2/figures/dataset_gallery.png" alt="drawing" width="1000"/>
</p>
This project page contains **S**poof **i**n **W**ild with **M**ultiple Attacks **V**ersion 2 (SiW-Mv2) dat... | 8,062 | 82.123711 | 685 | md |
null | Multi-domain-learning-FAS-main/source_multi_domain/utils.py | from skimage.draw import line_aa
import cv2
import tensorflow as tf
import sys
import glob
import random
import numpy as np
import math as m
import tensorflow.keras.layers as layers
import matplotlib.tri as mtri
from scipy import ndimage, misc
from PIL import Image, ImageDraw
class Logging(object):
def __init__(sel... | 10,107 | 35.359712 | 123 | py |
null | Multi-domain-learning-FAS-main/source_multi_domain/model.py | import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow.keras import layers
from warp import tf_batch_map_offsets
class Conv(layers.Layer):
def __init__(self, ch=32, ksize=3, stride=1, norm='batch', nl=True, dropout=False, name=None):
super(Conv, self).__init__()
self.norm = norm
... | 9,164 | 37.029046 | 105 | py |
null | Multi-domain-learning-FAS-main/source_multi_domain/dataset.py | # Copyright 2022
#
# Authors: Xiao Guo, Yaojie Liu, Anil Jain, and Xiaoming Liu.
#
# All Rights Reserved.s
#
# This research is based upon work supported by the Office of the Director of
# National Intelligence (ODNI), Intelligence Advanced Research Projects Activity
# (IARPA), via IARPA R&D Contract No. 2017-17020... | 12,987 | 46.229091 | 122 | py |
null | Multi-domain-learning-FAS-main/source_multi_domain/train_architecture.py | # -*- coding: utf-8 -*-
# Copyright 2022
#
# Authors: Xiao Guo, Yaojie Liu, Anil Jain, and Xiaoming Liu.
#
# All Rights Reserved.s
#
# This research is based upon work supported by the Office of the Director of
# National Intelligence (ODNI), Intelligence Advanced Research Projects Activity
# (IARPA), via IARPA R&D... | 13,810 | 43.551613 | 106 | py |