SimplePruning

This repository provides a cnn channels pruning demo with tensorflow. You can pruning your own model(support conv2d,depthwise conv2d,pool,fc,concat, add ops and so on) defined in modelsets.py. Have a good time!

     

•  Author: Haibo Wang
  
•  Email: [email protected]
•  Home Page: dasuda.top

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Dependencies

• Tensorflow >= 1.10.0
• python >= 3.5
• opencv-python >= 4.1.0
• numpy >= 1.14.5
• matplotlib >= 3.0.3

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Getting Started

• Clone the repository

  $ git clone https://github.com/DasudaRunner/SimplePruning.git

• Downdload the Cifar10 dataset, and put into cifar-10-python/

  Url: http://www.cs.toronto.edu/~kriz/cifar.html

• (Optional) Define your model in modesets.py

  You must use add_layer() API defined in pruner.py to set up your model. More details to modelsets.py

• (Optional) Config params in utils/config.py

  e.g. model name, learning rate, pruning rate.

• Train a full model, .ckpt and .pb model file will be saved in ckpt_model/

  $ python full_train.py

• Channel pruning. .ckpt and .pb model file will be saved in channels_pruned_model/

  $ python channels_pruning.py

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Supported ops (Tensorflow)

• Conv2d
• FullyConnected
• MaxPooling, AveragePooling
• BatchNormalization
• Activation
• DepthwiseConv2d
• GlobalMaxPooling, GlobalAveragePooling
• Concat
• Add
• Flatten

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Evaluation on Cifar10 dataset

Model	Dataset	Pruning rate	Model size / MB	Inference time / ms*64pic
SimpleNet	cifar-10	0.5	8.7 -> 1.8	5.8 -> 2.7
VGG19	cifar-10	0.5	53.4 -> 13.5	28.62 -> 9.44
DenseNet40	cifar-10	0.5	4.3 -> 1.5	77.87 -> 39.97
MobileNet V1	cifar-10	0.5	6.6 -> 1.8	19.39 -> 8.01
OCR-Net	---	0.5	2426.2 -> 841.9	10.36->7.3

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Update logs

• 2019.07.24
  • Add support for Add op.
  • Add support for ResNet18/ResNet34 in modelsets.py.
• 2019.07.16
  • Add support for Concat op.
  • Add support for DenseNet40 in modelsets.py.
• 2019.07.14
  • Reconsitution SimplePruning.