lingorx / explainer Goto Github PK

For installation and data preparation, please refer to the guidelines in MMClassification v0.18.0 and MMSegmentation v0.20.2.

All studies are conducted on CUDA 11.4 and pytorch 1.8.1

pip install torchvision==0.9.1
pip install timm==0.3.2
pip install mmcv-full==1.4.1
pip install opencv-python==4.5.1.48
cd DNC_classification && pip install -e . --user

We also provide the conda environment file. You can directly activate it without complex installation via

export PATH="$(pwd)/cellp/bin:$PATH"
export LD_LIBRARY_PATH="$(pwd)/cellp/lib:$LD_LIBRARY_PATH"

For the training of EXPLAINER with ResNet-50 as the backbone, you can run it on Cifar-100 by simply applying the following command:

sh env_run.sh

For other setups, please replace the config file specified in env_run.sh with your customized file. For example, train on ImageNet-1K with swin-small as the backbone, you can apply

./tools/dist_train.sh configs/_pattern_/swin-small_8xb128_in1k.py 4

For the testing of EXPLAINER, please specify the config and checkpoint file in the following command

python tools/test.py configs/_pattern_/resnet50_EXPLAINER_4xb32_cifar100.py model.pth --metrics accuracy