B-DCGAN : Binary-Deep Convolutional & Conditional Generative Adversarial Networks

Hideo Terada, Hayaru Shouno

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This codes are described in the article B-DCGAN:Evaluation of Binarized DCGAN for FPGA.

Setup

Environment

• Liunx PC(cf. Ubuntu) with NVIDIA Cuda GPU (use used TITAN-X)
• python 2.7
• Theano 1.0
• Lasagne 0.2

Prepare the Dataset

• Place the MNIST datasets into a directory
  • default directory: ~/datasets ; it is defined in file 'b-dcgan/training/dcgan_code/lib/config.py').
• Each file must be unzipped.
• File List: (filename must be set as below)
  • t10k-images.idx3-ubyte
  • t10k-labels.idx1-ubyte
  • train-images.idx3-ubyte
  • train-labels.idx1-ubyte

Run Training

$cd b-dcgan/training
$python bdcgan_mnist_train.py

Scenario setting

The parameters of scenario can be modified in source code directly:

• in b-dcgan/training/binary_mnist.py -- The property variables of class BinaryMnist

Result of Training

• Model parameters are in sub directory 'models'.
• Generated images are in sub directory 'samples'

Convert Trained Model to C++ header

Because of historical reason, there are 2 kinds of trained model files which concern with the B-DCGAN generator. One is called 'gen_params' (generator params), The other is called 'gen_sp_params' (generator special params).

• The 'gen_params' includes Weights of FullConnect layers and Deconvolution layers, and also includes 'beta' and 'gamma' of BatchNormalization.
• The 'gen_sp_params' includes 'mean' and 'inv_std' of BatchNormalization.
• Practically, these files are named as below:
  • <N>_gen_params.jl
  • <N>_gen_params.jl
• <N> is the number of training iteration of 4-digits.

To convert these params to C++ header, do like this:

$cd b-dcgan/training
$python model_to_ch.py 0082_gen_params.jl 0082_gen_sp_params.jl

CAUTION: The <N> value of two files must be same value.

The results are:

$ls *.h
model_deconv_W_0.h
model_deconv_W_1.h
model_dense_W_0.h
model_dense_W_1.h
model_dense_W_2.h
model_dense_W_3.h
model_sp_BinBN_tau_0.h
model_sp_BinBN_tau_1.h
model_sp_BinBN_tau_2.h
model_sp_BinBN_tau_3.h
model_sp_BinBN_tau_4.h

Setting of 'model_to_ch.py'

The 'model_to_ch.py' runs by refering python object of class 'BinaryMnist' in 'binary_mnist.py' module which is used in training-time. So you should let the object's property(flags) 'as-is' when it was used at training.