README.md

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Contributions

In now, this repo contains general architectures and functions that are useful for the GAN.

I will continue to add useful things to other areas.

Also, your pull requests and issues are always welcome.

And write what you want to implement on the issue. I'll implement it.

How to use

Import

ops.py
- operations
- from ops import *
utils.py
- image processing
- from utils import *

Network template

def network(x, is_training=True, reuse=False, scope="network"):
    with tf.variable_scope(scope, reuse=reuse):
        x = conv(...)
        
        ...
        
        return logit

Insert data to network using DatasetAPI

Image_Data_Class = ImageData(img_size, img_ch, augment_flag)
trainA = trainA.map(Image_Data_Class.image_processing, num_parallel_calls=16)
trainA = trainA.shuffle(buffer_size=10000).prefetch(buffer_size=batch_size).batch(batch_size).repeat()

trainA_iterator = trainA.make_one_shot_iterator()
data_A = trainA_iterator.get_next()
logit = network(data_A)

See this for more information.

Option

padding='SAME'
- pad = ceil[ (kernel - stride) / 2 ]
pad_type
- 'zero' or 'reflect'
sn
- use spectral_normalization or not
Ra
- use relativistic gan or not
loss_func
- gan
- lsgan
- hinge
- wgan
- wgan-gp
- dragan

Caution

If you don't want to share variable, set all scope names differently.

Weight

weight_init = tf.truncated_normal_initializer(mean=0.0, stddev=0.02)
weight_regularizer = tf.contrib.layers.l2_regularizer(0.0001)
weight_regularizer_fully = tf.contrib.layers.l2_regularizer(0.0001)

Initialization

Xavier : tf.contrib.layers.xavier_initializer()
He : tf.contrib.layers.variance_scaling_initializer()
Normal : tf.random_normal_initializer(mean=0.0, stddev=0.02)
Truncated_normal : tf.truncated_normal_initializer(mean=0.0, stddev=0.02)
Orthogonal : tf.orthogonal_initializer(1.0) / # if relu = sqrt(2), the others = 1.0

Regularization

l2_decay : tf.contrib.layers.l2_regularizer(0.0001)
orthogonal_regularizer : orthogonal_regularizer(0.0001) & orthogonal_regularizer_fully(0.0001)

Convolution

basic conv

x = conv(x, channels=64, kernel=3, stride=2, pad=1, pad_type='reflect', use_bias=True, sn=True, scope='conv')

partial conv (NVIDIA Partial Convolution)

x = partial_conv(x, channels=64, kernel=3, stride=2, use_bias=True, padding='SAME', sn=True, scope='partial_conv')

dilated conv

x = dilate_conv(x, channels=64, kernel=3, rate=2, use_bias=True, padding='SAME', sn=True, scope='dilate_conv')

Deconvolution

basic deconv

x = deconv(x, channels=64, kernel=3, stride=2, padding='SAME', use_bias=True, sn=True, scope='deconv')

Fully-connected

x = fully_conneted(x, units=64, use_bias=True, sn=True, scope='fully_connected')

Pixel shuffle

x = conv_pixel_shuffle_down(x, scale_factor=2, use_bias=True, sn=True, scope='pixel_shuffle_down')
x = conv_pixel_shuffle_up(x, scale_factor=2, use_bias=True, sn=True, scope='pixel_shuffle_up')

down ===> [height, width] -> [height // scale_factor, width // scale_factor]
up ===> [height, width] -> [height * scale_factor, width * scale_factor]

Block

residual block

x = resblock(x, channels=64, is_training=is_training, use_bias=True, sn=True, scope='residual_block')
x = resblock_down(x, channels=64, is_training=is_training, use_bias=True, sn=True, scope='residual_block_down')
x = resblock_up(x, channels=64, is_training=is_training, use_bias=True, sn=True, scope='residual_block_up')

down ===> [height, width] -> [height // 2, width // 2]
up ===> [height, width] -> [height * 2, width * 2]

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attention block

x = self_attention(x, channels=64, use_bias=True, sn=True, scope='self_attention')
x = self_attention_with_pooling(x, channels=64, use_bias=True, sn=True, scope='self_attention_version_2')

x = squeeze_excitation(x, channels=64, ratio=16, use_bias=True, sn=True, scope='squeeze_excitation')

x = convolution_block_attention(x, channels=64, ratio=16, use_bias=True, sn=True, scope='convolution_block_attention')

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Normalization

x = batch_norm(x, is_training=is_training, scope='batch_norm')
x = instance_norm(x, scope='instance_norm')
x = layer_norm(x, scope='layer_norm')
x = group_norm(x, groups=32, scope='group_norm')

x = pixel_norm(x)

x = batch_instance_norm(x, scope='batch_instance_norm')

x = condition_batch_norm(x, z, is_training=is_training, scope='condition_batch_norm'):

x = adaptive_instance_norm(x, gamma, beta):

See this for how to use condition_batch_norm
See this for how to use adaptive_instance_norm

Activation

x = relu(x)
x = lrelu(x, alpha=0.2)
x = tanh(x)
x = sigmoid(x)
x = swish(x)

Pooling & Resize

x = up_sample(x, scale_factor=2)

x = max_pooling(x, pool_size=2)
x = avg_pooling(x, pool_size=2)

x = global_max_pooling(x)
x = global_avg_pooling(x)

x = flatten(x)
x = hw_flatten(x)

Loss

classification loss

loss, accuracy = classification_loss(logit, label)

pixel loss

loss = L1_loss(x, y)
loss = L2_loss(x, y)
loss = huber_loss(x, y)
loss = histogram_loss(x, y)

histogram_loss means the difference in the color distribution of the image pixel values.

gan loss

d_loss = discriminator_loss(Ra=True, loss_func='wgan-gp', real=real_logit, fake=fake_logit)
g_loss = generator_loss(Ra=True, loss_func='wgan_gp', real=real_logit, fake=fake_logit)

See this for how to use gradient_penalty

kl-divergence (z ~ N(0, 1))

loss = kl_loss(mean, logvar)

Author

Junho Kim

GitHub - taki0112/Tensorflow-Cookbook: Simple Tensorflow Cookbook for easy-to-us...