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https://github.com/taki0112/Self-Attention-GAN-Tensorflow
Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)
https://github.com/taki0112/Self-Attention-GAN-Tensorflow
Last synced: 3 months ago
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Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)
- Host: GitHub
- URL: https://github.com/taki0112/Self-Attention-GAN-Tensorflow
- Owner: taki0112
- License: mit
- Created: 2018-06-01T07:43:36.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-07-17T00:39:17.000Z (almost 5 years ago)
- Last Synced: 2024-01-16T22:04:30.278Z (5 months ago)
- Language: Python
- Size: 20.9 MB
- Stars: 545
- Watchers: 16
- Forks: 151
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-stars - taki0112/Self-Attention-GAN-Tensorflow - Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN) (Python)
README
# Self-Attention-GAN-Tensorflow
Simple Tensorflow implementation of ["Self-Attention Generative Adversarial Networks" (SAGAN)](https://arxiv.org/pdf/1805.08318.pdf)
## Requirements
* Tensorflow 1.8
* Python 3.6
## Related works
* [BigGAN-Tensorflow](https://github.com/taki0112/BigGAN-Tensorflow)
## Summary
### Framework
### Code
```python
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) # 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
```
### Code2 (Google Brain)
```python
def attention_2(self, x, ch):
batch_size, height, width, num_channels = x.get_shape().as_list()
f = conv(x, ch // 8, kernel=1, stride=1, sn=self.sn, scope='f_conv') # [bs, h, w, c']
f = max_pooling(f)
g = conv(x, ch // 8, kernel=1, stride=1, sn=self.sn, scope='g_conv') # [bs, h, w, c']
h = conv(x, ch // 2, kernel=1, stride=1, sn=self.sn, scope='h_conv') # [bs, h, w, c]
h = max_pooling(h)
# N = h * w
s = tf.matmul(hw_flatten(g), hw_flatten(f), transpose_b=True) # # [bs, N, N]
beta = tf.nn.softmax(s) # 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=[batch_size, height, width, num_channels // 2]) # [bs, h, w, C]
o = conv(o, ch, kernel=1, stride=1, sn=self.sn, scope='attn_conv')
x = gamma * o + x
return x
```
## Usage
### dataset
```python
> 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