Awesome

Prune Your Model Before Distill It

Welcome

This is an PyTorch implement of the paper ``Prune Your Model Before Distill It''.

Table of Contents

1 How to run

2 ResNet Optimization

<a name=how_to_run></a>1 How to run

1.1 Full framework

python main.py --pre_train --pruning --kd

1. code progress

• Train the model. (default: vgg-19 on cifar100)

• Proceed with pruning the previously trained model. (default: lr-rewinding, 0.79 pruning ratio)

• KD the model with previously pruned model. (default: vanilla KD|vgg19-rwd-st79|cifar100)

• To test the full framework: python main.py --pre_train --pruning --kd

• You can check all argument using 'help' command and change them to .json file. (json file path should be experiments/hyperparam)

• Default setting is vgg19 (79% weight pruned) teacher and vgg19-rwd-st79 student.

• All training result will be stored in result/data.

• If you want to run training | pruning | kd separately, you can refer to 1.2/1.3/1.4 below.

• In this version, we provide the student model used in the experiment. (Structured pruning applied)

• If you want to use the end-to-end student model, use --student_model vgg-custom. This creates a VGG student model according to the teacher model (only VGG is currently available and ResNet version will be updated)

1.2 Train Model

The following command will train the model.

python main.py --pre_train

• Run model training only.

1.3 Prune the Teacher

python main.py --pruning

• Run model pruning only.
• Put the trained model to be pruned as .pth in the experiments/trained_model folder. Put the name of the model in the --pre_model_name argument. The model name is separated by _.
• If the name of the trained model is vgg19_trainedmodel.pth then you can directly edit the prune_default.json in experiments/hyperparam or enter python commands to run it. ex) python main.py --pruning --pre_model_name vgg19_traindmodel

1.4 Distill the Teacher to Student

python main.py --kd

• Run KD only
• Put the teacher model to be pruned as .pth in the experiments/teacher_model folder. Put the name of the model in the --teacher_model_name argument. The model name is separated by _.
• If the name of the teacher model is vgg19_pruned.pth then you can directly edit the kd_default.json in experiments/hyperparam or enter python commands to run it. ex) python main.py --kd --teacher_model_name vgg19_pruned
• If you want to use the end-to-end student model, use --student_model vgg-custom. This creates a VGG student model according to the teacher model (only VGG is currently available and ResNet version will be updated)

1.5 Available models

• CIFAR100

    cifar100_models = {
        'vgg11',
        'vgg19',
        'vgg19-rwd-cl1',
        'vgg19-rwd-cl2',
        'vgg19-rwd-st36',
        'vgg19-rwd-st59',
        'vgg19-rwd-st79',
        'vgg19dbl',
        'vgg19dbl-rwd-st36',
        'vgg19dbl-rwd-st59',
        'vgg19dbl-rwd-st79',
        'vgg-custom'
    }

• Tiny-ImageNet

    tiny_imagenet_models = {
        'vgg16',
        'resnet18',
        'resnet18-rwd-st36',
        'resnet18-rwd-st59',
        'resnet18-rwd-st79',
        'resnet18dbl',
        'resnet18dbl-rwd-st36',
        'resnet18dbl-rwd-st59',
        'resnet18dbl-rwd-st79',
        'resnet50',
        'resnet50-rwd-st36',
        'resnet50-rwd-st59',
        'resnet50-rwd-st79',        
        'mobilenet-v2'
    }

<a name=resnet_opt></a>2 ResNet Optimization

• We experimented in different ways in ResNet optimization for Tiny-ImageNet.

If the maxpooling layer is used in conv1, a larger batche_size and learning rate should be used, and the accuracy gain obtained by using the pruned teacher may slightly decrease.