Self-Attention-GAN-Tensorflow

Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)

Requirements

• Tensorflow 1.8
• Python 3.6

Summary

Framework

Code

    def attention(self, x, ch):
      f = conv(x, ch // 8, kernel=1, stride=1, sn=self.sn, scope='f_conv') # [bs, h, w, c']
      g = conv(x, ch // 8, kernel=1, stride=1, sn=self.sn, scope='g_conv') # [bs, h, w, c']
      h = conv(x, ch, kernel=1, stride=1, sn=self.sn, scope='h_conv') # [bs, h, w, c]

      # N = h * w
      s = tf.matmul(hw_flatten(g), hw_flatten(f), transpose_b=True) # # [bs, N, N]

      beta = tf.nn.softmax(s, axis=-1)  # attention map

      o = tf.matmul(beta, hw_flatten(h)) # [bs, N, C]
      gamma = tf.get_variable("gamma", [1], initializer=tf.constant_initializer(0.0))

      o = tf.reshape(o, shape=x.shape) # [bs, h, w, C]
      x = gamma * o + x

      return x

Usage

dataset

> python download.py celebA

• mnist and cifar10 are used inside keras
• For your dataset, put images like this:

├── dataset
   └── YOUR_DATASET_NAME
       ├── xxx.jpg (name, format doesn't matter)
       ├── yyy.png
       └── ...

train

• python main.py --phase train --dataset celebA --gan_type hinge

test

• python main.py --phase test --dataset celebA --gan_type hinge

Results

ImageNet

 

CelebA (100K iteration, hinge loss)

Author

Junho Kim