https://github.com/mingtaoguo/sngan_projection_tensorflow
Simply implement the paper: cGANs with Projection Discriminator by TensorFlow
https://github.com/mingtaoguo/sngan_projection_tensorflow
gan sngan tensorflow
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Simply implement the paper: cGANs with Projection Discriminator by TensorFlow
- Host: GitHub
- URL: https://github.com/mingtaoguo/sngan_projection_tensorflow
- Owner: MingtaoGuo
- License: mit
- Created: 2018-11-07T11:46:56.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-04-19T03:17:05.000Z (about 7 years ago)
- Last Synced: 2025-04-05T13:11:18.574Z (about 1 year ago)
- Topics: gan, sngan, tensorflow
- Language: Python
- Homepage:
- Size: 797 KB
- Stars: 40
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# sngan_projection_TensorFlow
Simply implementing the paper: cGANs with Projection Discriminator
## Introduction
SNGAN with projection discriminator implemented by TensorFlow. The paper [cGANs with Projection Discriminator](https://arxiv.org/pdf/1802.05637v1.pdf)
## Results
### More results are under training ......
### Generate cifar-10
As shown in below is trained about 10000 iterations with batch size of 64.
#### Consecutive category morphing with fixed z:
|cat2human|cat2human|zi2zi|zi2zi|
|-|-|-|-|
|||||
#### What is conditional batch normalization?
More details about 'condition', please see this repository: [Conditional Instance Normalization for n style transfer](https://github.com/MingtaoGuo/Conditional-Instance-Norm-for-n-Style-Transfer)
``` python
def conditional_batchnorm(x, train_phase, scope_bn, y=None, nums_class=10):
#Batch Normalization
#Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[J]. 2015:448-456.
with tf.variable_scope(scope_bn):
if y == None:
beta = tf.get_variable(name=scope_bn + 'beta', shape=[x.shape[-1]],
initializer=tf.constant_initializer([0.]), trainable=True) # label_nums x C
gamma = tf.get_variable(name=scope_bn + 'gamma', shape=[x.shape[-1]],
initializer=tf.constant_initializer([1.]), trainable=True) # label_nums x C
else:
beta = tf.get_variable(name=scope_bn + 'beta', shape=[nums_class, x.shape[-1]],
initializer=tf.constant_initializer([0.]), trainable=True) # label_nums x C
gamma = tf.get_variable(name=scope_bn + 'gamma', shape=[nums_class, x.shape[-1]],
initializer=tf.constant_initializer([1.]), trainable=True) # label_nums x C
beta, gamma = tf.nn.embedding_lookup(beta, y), tf.nn.embedding_lookup(gamma, y)
beta = tf.reshape(beta, [-1, 1, 1, x.shape[-1]])
gamma = tf.reshape(gamma, [-1, 1, 1, x.shape[-1]])
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments', keep_dims=True)
ema = tf.train.ExponentialMovingAverage(decay=0.5)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(train_phase, mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
```
#### What is projection?
``` python
def Inner_product(global_pooled, y):
#global_pooled: B x D, embeded_y: B x Num_label
H = y.shape[-1]
W = global_pooled.shape[-1]
V = tf.get_variable("V", [H, W], initializer=tf.truncated_normal_initializer(stddev=0.02))
V = spectral_normalization("embed", V)
temp = tf.matmul(y, V)
temp = tf.reduce_sum(temp * global_pooled, axis=1)
return temp
```
## How to use
1. Download the [ImageNet_64x64](https://patrykchrabaszcz.github.io/Imagenet32/) dataset.
*PS.* If necessary, you can contact me, E-mail: gmt798714378@hotmail.com, i will send a part of dataset to you :stuck_out_tongue_winking_eye:.
2. Put the imagenet dataset into the folder 'dataset'
```
├── dataset
├── 1
├── 0.jpg
├── 1.jpg
...
├── 2
├── 0.jpg
├── 1.jpg
...
├── 3
├── 0.jpg
├── 1.jpg
...
...
├── 1000
├── 0.jpg
├── 1.jpg
...
├── save_para
├── save_img
├── results
├── main.py
├── networks.py
├── ops.py
├── Train.py
├── utils.py
```
3. Execute main.py
## Requirements
- python3.5
- tensorflow1.4.0
- numpy
- scipy
- pillow
### Acknowledgement
[Author's chainer code](https://github.com/pfnet-research/sngan_projection)
### Reference
[1]. Miyato T, Koyama M. cGANs with Projection Discriminator[J]. 2018.