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Getting Started

This page provides basic tutorials about the usage of PASSL. For installation instructions, please see INSTALL.md.

Data Preparation

  • Download the ImageNet dataset from the link.
  • Then, and move validation images to labeled subfolders, using the following shell script
  • Create a symlink under PASSL/data/ as the following examples: ln -s $YOUR_ILSVRC2012_PATH data/ILSVRC2012
  • At last, the folder looks like: 
    PASSL
├── configs
├── LICENSE
├── passl
├── tools
├── README.md
└── data
    └── ILSVRC2012
        ├── train
        └── val

Train existing methods

Note: The default learning rate in config files is for 8 GPUs. If using differnt number GPUs, the total batch size will change in proportion, you have to scale the learning rate following new_lr = old_lr * new_ngpus / old_ngpus.

Train with single/multiple GPUs

python tools/train.py -c ${CONFIG_FILE} --num-gpus {GPUS} [optional arguments]

Optional arguments are:

  • --resume ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.
  • --pretrained ${PRETRAIN_WEIGHTS}: Load pretrained weights for the backbone.

An example:

# checkpoints and logs saved in OUTPUTS=outputs/
python tools/train.py -c configs/moco/moco_v2_r50.yaml --num-gpus 8

ImageNet Linear Classification

First, extract backbone weights:

python tools/extract_weight.py ${CHECKPOINT} --output ${WEIGHT_FILE}

Arguments:

  • CHECKPOINTS: the checkpoint file of a PASSL method named as epoch_*.pdparams.
  • WEIGHT_FILE: the output backbone weights file, e.g., pretrains/moco_v2_r50.pdparams.

Next, train and test linear classification:

# Train
python tools/train.py -c ${CLS_CONFIG_FILE} --pretrained ${WEIGHT_FILE} --num-gpus 8
# Evaluation
python tools/train.py -c configs/*clas_*.yaml --load ${CLS_WEIGHT_FILE} --evaluate-only --num-gpus 8

Augments:

  • CLS_CONFIG_FILE: Use config files under "configs/benchmarks/*clas*/". Note that if you want to test benchmark MoCo v2, you have to use the config file named configs/moco/*_clas_*.yaml, e.g., configs/moco/moco_clas_r50.yaml.
  • Optional arguments include:
    • --resume ${CHECKPOINT_FILE}: Resume from a previous checkpoint file. The trained linear weights in conjuction with the backbone weights can be found at MoCo v1 linear and MoCo v2 linear