Please use the following command to install the environment dependencies:
pip install -r requirements.txt
For CIFAR-10 or CIFAR-100, please download it to ~/data.
For Tiny-ImageNet, please download it to ~/tiny-imagenet-200.
python extract_features.py
python main.py --query-strategy e2oal_sampling --init-percent 1 --known-class 2 --query-batch 1500 --seed 1 --model resnet50 --dataset cifar10 --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 1 --known-class 3 --query-batch 1500 --seed 1 --model resnet50 --dataset cifar10 --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 1 --known-class 4 --query-batch 1500 --seed 1 --model resnet50 --dataset cifar10 --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 8 --known-class 20 --query-batch 1500 --seed 1 --model resnet50 --dataset cifar100 --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 8 --known-class 30 --query-batch 1500 --seed 1 --model resnet50 --dataset cifar100 --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 8 --known-class 40 --query-batch 1500 --seed 1 --model resnet50 --dataset cifar100 --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 8 --known-class 20 --query-batch 1500 --seed 1 --model resnet50 --dataset tinyimagenet --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 8 --known-class 30 --query-batch 1500 --seed 1 --model resnet50 --dataset tinyimagenet --max-query 11 --max-epoch 200 --gpu 0
python main.py --query-strategy e2oal_sampling --init-percent 8 --known-class 40 --query-batch 1500 --seed 1 --model resnet50 --dataset tinyimagenet --max-query 11 --max-epoch 200 --gpu 0
ValueError: too many values to unpack (expected 2)
You can resolve it with the following steps:
- Locate the source code of
torch.utils.data.DataLoader. The path is typically one of the following:anaconda3/lib/python3.x/site-packages/torch/utils/data/dataloader.pyanaconda3/envs/your_env_name/lib/python3.x/site-packages/torch/utils/data/dataloader.py
- Open the file
dataloader.py, and find the_SingleProcessDataLoaderIterclass. - Modify the
_next_datamethod by changing the return value from:return datatoreturn index, data