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Author
Contributors
Last Commit
Oct. 18, 2017
Created
Sep. 15, 2017

ShuffleNet

An implementation of ShuffleNet introduced in in TensorFlow. According to the authors, ShuffleNet is a computationally efficient CNN architecture designed specifically for mobile devices with very limited computing power. It outperforms Google MobileNet by some error percentage at much lower FLOPs.

Link to the original paper: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

ShuffleNet Unit



Group Convolutions

The paper uses the group convolution operator. However, that operator is not implemented in TensorFlow backend. So, I implemented the operator using graph operations. Despite the fact that this is the same operator as the one stated in the paper, it lead to slower performance than the regular convolution. So, to get the same performance stated in the paper, CuDNN efficient implementation for the operator should be done. """CALL FOR CONTRIBUTION"""

This issue was discussed here: Support Channel groups in convolutional layers #10482

Channel Shuffling



Channel Shuffling can be achieved by applying three operations:

  1. Reshaping the input tensor from (N, H, W, C) into (N, H, W, G, C').

  2. Performing matrix transpose operation on the two dimensions (G, C').

  3. Reshaping the tensor back into (N, H, W, C).

    N: Batch size, H: Feature map height, W: Feature map width, C: Number of channels, G: Number of groups, C': Number of channels / Number of groups

    Note that: The number of channels should be divisible by the number of groups.

Usage

Main Dependencies

tensorflow 1.3.0
numpy 1.13.1
tqdm 4.15.0
bunch 1.0.1
matplotlib 2.0.2

Train and Test

  1. Prepare your data, and modify the data_loader.py/DataLoader/load_data() method.
  2. Modify the config/test.json to meet your needs.

Run

python main.py config/test.json

Results

The model have successfully overfitted TinyImageNet-200 that was presented in CS231n - Convolutional Neural Networks for Visual Recognition. I have no resources to train on actual ImageNet dataset.

Benchmarking

The paper has achieved 140 MFLOPs using the vanilla version. Using the group convolution operator implemented in TensorFlow, I have achieved approximately 277 MFLOPs.

To calculate the FLOPs in TensorFlow, make sure to set the batch size equal to 1, and execute the following line when the model is loaded into memory.

tf.profiler.profile(
        tf.get_default_graph(),
        options=tf.profiler.ProfileOptionBuilder.float_operation(), cmd='scope')

TODO (CALL FOR CONTRIBUTION)

  • Training on ImageNet dataset
  • Group Convolution in CuDNN
  • Benchmarking the complexity of the model.

Updates

  • Working on porting a pretrained ImageNet model to this TensorFlow architecture.
  • Bug fixes within days.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

Thanks for all who helped me in my work and special thanks for my colleagues: Mo'men Abdelrazek, and Mohamed Zahran.