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Tensorflow 2 Cookbook

Contributions

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

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_dataset = ['./dataset/cat/trainA/a.jpg', 
                  './dataset/cat/trainA/b.png', 
                  './dataset/cat/trainA/c.jpeg', 
                  ...]
trainA = tf.data.Dataset.from_tensor_slices(trainA_dataset)
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

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()

  USE """tf.contrib.layers.variance_scaling_initializer()"""
  
  if uniform :
    factor = gain * gain
    mode = 'FAN_AVG'
  else :
    factor = (gain * gain) / 1.3
    mode = 'FAN_AVG'

He : tf.contrib.layers.variance_scaling_initializer()

  if uniform :
    factor = gain * gain
    mode = 'FAN_IN'
  else :
    factor = (gain * gain) / 1.3
    mode = 'FAN_OUT'

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='VALID', sn=True, scope='dilate_conv')

Deconvolution

basic deconv

x = deconv(x, channels=64, kernel=3, stride=1, padding='SAME', use_bias=True, sn=True, scope='deconv')

Fully-connected

x = fully_connected(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]

dense block

x = denseblock(x, channels=64, n_db=6, is_training=is_training, use_bias=True, sn=True, scope='denseblock')

n_db ===> The number of dense-block

residual-dense block

x = res_denseblock(x, channels=64, n_rdb=20, n_rdb_conv=6, is_training=is_training, use_bias=True, sn=True, scope='res_denseblock')

n_rdb ===> The number of RDB
n_rdb_conv ===> per RDB conv layer

attention block

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

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

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

x = global_context_block(x, use_bias=True, sn=True, scope='gc_block')

x = srm_block(x, use_bias=False, is_training=is_training, scope='srm_block')

Normalization

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

x = pixel_norm(x)

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

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

x = adaptive_instance_norm(x, gamma, beta)
x = adaptive_layer_instance_norm(x, gamma, beta, smoothing=True, scope='adaLIN')

See this for how to use condition_batch_norm
See this for how to use adaptive_instance_norm
See this for how to use adaptive_layer_instance_norm & layer_instance_norm

Activation

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

Pooling & Resize

x = nearest_up_sample(x, scale_factor=2)
x = bilinear_up_sample(x, scale_factor=2)
x = nearest_down_sample(x, scale_factor=2)
x = bilinear_down_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)

loss = dice_loss(n_classes=10, logit, label)

regularization loss

g_reg_loss = regularization_loss('generator')
d_reg_loss = regularization_loss('discriminator')

If you want to use regularizer, then you should write it

pixel loss

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

loss = gram_style_loss(x, y)

loss = color_consistency_loss(x, y)

histogram_loss means the difference in the color distribution of the image pixel values.
gram_style_loss means the difference between the styles using gram matrix.
color_consistency_loss means the color difference between the generated image and the input image.

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)

Ra
- use relativistic gan or not
loss_func
- gan
- lsgan
- hinge
- wgan-gp
- dragan
See this for how to use gradient_penalty

vdb loss

d_bottleneck_loss = vdb_loss(real_mu, real_logvar, i_c) + vdb_loss(fake_mu, fake_logvar, i_c)

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

loss = kl_loss(mean, logvar)

Author

Junho Kim

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