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GETTING_STARTED.md

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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
    └── FOOD101
        └── captions

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 -m paddle.distributed.launch --gpus={GPU RANKS} tools/train.py -c ${CONFIG_FILE} [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 -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/moco/moco_v2_r50.yaml

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 -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c ${CLS_CONFIG_FILE} --pretrained ${WEIGHT_FILE}
# Evaluation
python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/*clas_*.yaml --load ${CLS_WEIGHT_FILE} --evaluate-only

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

Extract student wights from distillation model

Checkpoints saved in distillation training include both Teacher's and Student's weights. You can extract the weights of Student by following command.

python tools/extract_weight.py ${DISTILLATION_WEIGHTS_FILE} --prefix Student --remove_prefix --output ${STUDENT_WEIGHTS_FILE}