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Tutorial 6: Run Benchmarks

In MMSelfSup, we provide many benchmarks, thus the models can be evaluated on different downstream tasks. Here are comprehensive tutorials and examples to explain how to run all benchmarks with MMSelfSup.

First, you are supposed to extract your backbone weights by tools/model_converters/extract_backbone_weights.py

python ./tools/model_converters/extract_backbone_weights.py {CHECKPOINT} {MODEL_FILE}

Arguments:

  • CHECKPOINT: the checkpoint file of a selfsup method named as epoch_*.pth.

  • MODEL_FILE: the output backbone weights file. If not mentioned, the PRETRAIN below uses this extracted model file.

Classification

As for classification, we provide scripts in folder tools/benchmarks/classification/, which has 4 .sh files, 1 folder for VOC SVM related classification task and 1 folder for ImageNet nearest-neighbor classification task.

VOC SVM / Low-shot SVM

To run these benchmarks, you should first prepare your VOC datasets. Please refer to prepare_data.md for the details of data preparation.

To evaluate the pre-trained models, you can run command below.

# distributed version
bash tools/benchmarks/classification/svm_voc07/dist_test_svm_pretrain.sh ${SELFSUP_CONFIG} ${GPUS} ${PRETRAIN} ${FEATURE_LIST}

# slurm version
bash tools/benchmarks/classification/svm_voc07/slurm_test_svm_pretrain.sh ${PARTITION} ${JOB_NAME} ${SELFSUP_CONFIG} ${PRETRAIN} ${FEATURE_LIST}

Besides, if you want to evaluate the ckpt files saved by runner, you can run command below.

# distributed version
bash tools/benchmarks/classification/svm_voc07/dist_test_svm_epoch.sh ${SELFSUP_CONFIG} ${EPOCH} ${FEATURE_LIST}

# slurm version
bash tools/benchmarks/classification/svm_voc07/slurm_test_svm_epoch.sh ${PARTITION} ${JOB_NAME} ${SELFSUP_CONFIG} ${EPOCH} ${FEATURE_LIST}

To test with ckpt, the code uses the epoch_*.pth file, there is no need to extract weights.

Remarks:

  • ${SELFSUP_CONFIG} is the config file of the self-supervised experiment.

  • ${FEATURE_LIST} is a string to specify features from layer1 to layer5 to evaluate; e.g., if you want to evaluate layer5 only, then FEATURE_LIST is “feat5”, if you want to evaluate all features, then FEATURE_LIST is “feat1 feat2 feat3 feat4 feat5” (separated by space). If left empty, the default FEATURE_LIST is “feat5”.

  • PRETRAIN: the pre-trained model file.

  • if you want to change GPU numbers, you could add GPUS_PER_NODE=4 GPUS=4 at the beginning of the command.

  • EPOCH is the epoch number of the ckpt that you want to test

Linear Evaluation

The linear evaluation is one of the most general benchmarks, we integrate several papers’ config settings, also including multi-head linear evaluation. We write classification model in our own codebase for the multi-head function, thus, to run linear evaluation, we still use .sh script to launch training. The supported datasets are ImageNet, Places205 and iNaturalist18.

# distributed version
bash tools/benchmarks/classification/dist_train_linear.sh ${CONFIG} ${PRETRAIN}

# slurm version
bash tools/benchmarks/classification/slurm_train_linear.sh ${PARTITION} ${JOB_NAME} ${CONFIG} ${PRETRAIN}

Remarks:

  • The default GPU number is 8. When changing GPUS, please also change samples_per_gpu in the config file accordingly to ensure the total batch size is 256.

  • CONFIG: Use config files under configs/benchmarks/classification/. Specifically, imagenet (excluding imagenet_*percent folders), places205 and inaturalist2018.

  • PRETRAIN: the pre-trained model file.

ImageNet Semi-Supervised Classification

To run ImageNet semi-supervised classification, we still use .sh script to launch training.

# distributed version
bash tools/benchmarks/classification/dist_train_semi.sh ${CONFIG} ${PRETRAIN}

# slurm version
bash tools/benchmarks/classification/slurm_train_semi.sh ${PARTITION} ${JOB_NAME} ${CONFIG} ${PRETRAIN}

Remarks:

  • The default GPU number is 4.

  • CONFIG: Use config files under configs/benchmarks/classification/imagenet/, named imagenet_*percent folders.

  • PRETRAIN: the pre-trained model file.

ImageNet Nearest-Neighbor Classification

To evaluate the pre-trained models using the nearest-neighbor benchmark, you can run command below.

# distributed version
bash tools/benchmarks/classification/knn_imagenet/dist_test_knn_pretrain.sh ${SELFSUP_CONFIG} ${PRETRAIN}

# slurm version
bash tools/benchmarks/classification/knn_imagenet/slurm_test_knn_pretrain.sh ${PARTITION} ${JOB_NAME} ${SELFSUP_CONFIG} ${PRETRAIN}

Besides, if you want to evaluate the ckpt files saved by runner, you can run command below.

# distributed version
bash tools/benchmarks/classification/knn_imagenet/dist_test_knn_epoch.sh ${SELFSUP_CONFIG} ${EPOCH}

# slurm version
bash tools/benchmarks/classification/knn_imagenet/slurm_test_knn_epoch.sh ${PARTITION} ${JOB_NAME} ${SELFSUP_CONFIG} ${EPOCH}

To test with ckpt, the code uses the epoch_*.pth file, there is no need to extract weights.

Remarks:

  • ${SELFSUP_CONFIG} is the config file of the self-supervised experiment.

  • PRETRAIN: the pre-trained model file.

  • if you want to change GPU numbers, you could add GPUS_PER_NODE=4 GPUS=4 at the beginning of the command.

  • EPOCH is the epoch number of the ckpt that you want to test

Detection

Here, we prefer to use MMDetection to do the detection task. First, make sure you have installed MIM, which is also a project of OpenMMLab.

pip install openmim

It is very easy to install the package.

Besides, please refer to MMDet for installation and data preparation

After installation, you can run MMDet with simple command.

# distributed version
bash tools/benchmarks/mmdetection/mim_dist_train.sh ${CONFIG} ${PRETRAIN} ${GPUS}

# slurm version
bash tools/benchmarks/mmdetection/mim_slurm_train.sh ${PARTITION} ${CONFIG} ${PRETRAIN}

Remarks:

  • CONFIG: Use config files under configs/benchmarks/mmdetection/ or write your own config files

  • PRETRAIN: the pre-trained model file.

Or if you want to do detection task with detectron2, we also provides some config files. Please refer to INSTALL.md for installation and follow the directory structure to prepare your datasets required by detectron2.

conda activate detectron2 # use detectron2 environment here, otherwise use open-mmlab environment
cd benchmarks/detection
python convert-pretrain-to-detectron2.py ${WEIGHT_FILE} ${OUTPUT_FILE} # must use .pkl as the output extension.
bash run.sh ${DET_CFG} ${OUTPUT_FILE}

Segmentation

For semantic segmentation task, we use MMSegmentation. First, make sure you have installed MIM, which is also a project of OpenMMLab.

pip install openmim

It is very easy to install the package.

Besides, please refer to MMSeg for installation and data preparation.

After installation, you can run MMSeg with simple command.

# distributed version
bash tools/benchmarks/mmsegmentation/mim_dist_train.sh ${CONFIG} ${PRETRAIN} ${GPUS}

# slurm version
bash tools/benchmarks/mmsegmentation/mim_slurm_train.sh ${PARTITION} ${CONFIG} ${PRETRAIN}

Remarks:

  • CONFIG: Use config files under configs/benchmarks/mmsegmentation/ or write your own config files

  • PRETRAIN: the pre-trained model file.

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