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module.py
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module.py
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import tensorflow as tf
def gated_linear_layer(inputs, gates, name = None):
activation = tf.multiply(x = inputs, y = tf.sigmoid(gates), name = name)
return activation
def instance_norm_layer(
inputs,
epsilon = 1e-06,
activation_fn = None,
name = None):
instance_norm_layer = tf.contrib.layers.instance_norm(
inputs = inputs,
epsilon = epsilon,
activation_fn = activation_fn)
return instance_norm_layer
def conv1d_layer(
inputs,
filters,
kernel_size,
strides = 1,
padding = 'same',
activation = None,
kernel_initializer = None,
name = None):
conv_layer = tf.layers.conv1d(
inputs = inputs,
filters = filters,
kernel_size = kernel_size,
strides = strides,
padding = padding,
activation = activation,
kernel_initializer = kernel_initializer,
name = name)
return conv_layer
def conv2d_layer(
inputs,
filters,
kernel_size,
strides,
padding = 'same',
activation = None,
kernel_initializer = None,
name = None):
conv_layer = tf.layers.conv2d(
inputs = inputs,
filters = filters,
kernel_size = kernel_size,
strides = strides,
padding = padding,
activation = activation,
kernel_initializer = kernel_initializer,
name = name)
return conv_layer
def residual1d_block(
inputs,
filters = 1024,
kernel_size = 3,
strides = 1,
name_prefix = 'residule_block_'):
h1 = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
h1_norm = instance_norm_layer(inputs = h1, activation_fn = None, name = name_prefix + 'h1_norm')
h1_gates = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
h1_norm_gates = instance_norm_layer(inputs = h1_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
h2 = conv1d_layer(inputs = h1_glu, filters = filters // 2, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h2_conv')
h2_norm = instance_norm_layer(inputs = h2, activation_fn = None, name = name_prefix + 'h2_norm')
h3 = inputs + h2_norm
return h3
def downsample1d_block(
inputs,
filters,
kernel_size,
strides,
name_prefix = 'downsample1d_block_'):
h1 = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
h1_norm = instance_norm_layer(inputs = h1, activation_fn = None, name = name_prefix + 'h1_norm')
h1_gates = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
h1_norm_gates = instance_norm_layer(inputs = h1_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
return h1_glu
def downsample2d_block(
inputs,
filters,
kernel_size,
strides,
name_prefix = 'downsample2d_block_'):
h1 = conv2d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
h1_norm = instance_norm_layer(inputs = h1, activation_fn = None, name = name_prefix + 'h1_norm')
h1_gates = conv2d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
h1_norm_gates = instance_norm_layer(inputs = h1_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
return h1_glu
def upsample1d_block(
inputs,
filters,
kernel_size,
strides,
shuffle_size = 2,
name_prefix = 'upsample1d_block_'):
h1 = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
h1_shuffle = pixel_shuffler(inputs = h1, shuffle_size = shuffle_size, name = name_prefix + 'h1_shuffle')
h1_norm = instance_norm_layer(inputs = h1_shuffle, activation_fn = None, name = name_prefix + 'h1_norm')
h1_gates = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
h1_shuffle_gates = pixel_shuffler(inputs = h1_gates, shuffle_size = shuffle_size, name = name_prefix + 'h1_shuffle_gates')
h1_norm_gates = instance_norm_layer(inputs = h1_shuffle_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
return h1_glu
def pixel_shuffler(inputs, shuffle_size = 2, name = None):
n = tf.shape(inputs)[0]
w = tf.shape(inputs)[1]
c = inputs.get_shape().as_list()[2]
oc = c // shuffle_size
ow = w * shuffle_size
outputs = tf.reshape(tensor = inputs, shape = [n, ow, oc], name = name)
return outputs
def generator_gatedcnn(inputs, reuse = False, scope_name = 'generator_gatedcnn'):
# inputs has shape [batch_size, num_features, time]
# we need to convert it to [batch_size, time, num_features] for 1D convolution
inputs = tf.transpose(inputs, perm = [0, 2, 1], name = 'input_transpose')
with tf.variable_scope(scope_name) as scope:
# Discriminator would be reused in CycleGAN
if reuse:
scope.reuse_variables()
else:
assert scope.reuse is False
h1 = conv1d_layer(inputs = inputs, filters = 128, kernel_size = 15, strides = 1, activation = None, name = 'h1_conv')
h1_gates = conv1d_layer(inputs = inputs, filters = 128, kernel_size = 15, strides = 1, activation = None, name = 'h1_conv_gates')
h1_glu = gated_linear_layer(inputs = h1, gates = h1_gates, name = 'h1_glu')
# Downsample
d1 = downsample1d_block(inputs = h1_glu, filters = 256, kernel_size = 5, strides = 2, name_prefix = 'downsample1d_block1_')
d2 = downsample1d_block(inputs = d1, filters = 512, kernel_size = 5, strides = 2, name_prefix = 'downsample1d_block2_')
# Residual blocks
r1 = residual1d_block(inputs = d2, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block1_')
r2 = residual1d_block(inputs = r1, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block2_')
r3 = residual1d_block(inputs = r2, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block3_')
r4 = residual1d_block(inputs = r3, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block4_')
r5 = residual1d_block(inputs = r4, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block5_')
r6 = residual1d_block(inputs = r5, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block6_')
# Upsample
u1 = upsample1d_block(inputs = r6, filters = 1024, kernel_size = 5, strides = 1, shuffle_size = 2, name_prefix = 'upsample1d_block1_')
u2 = upsample1d_block(inputs = u1, filters = 512, kernel_size = 5, strides = 1, shuffle_size = 2, name_prefix = 'upsample1d_block2_')
# Output
o1 = conv1d_layer(inputs = u2, filters = 24, kernel_size = 15, strides = 1, activation = None, name = 'o1_conv')
o2 = tf.transpose(o1, perm = [0, 2, 1], name = 'output_transpose')
return o2
def discriminator(inputs, reuse = False, scope_name = 'discriminator'):
# inputs has shape [batch_size, num_features, time]
# we need to add channel for 2D convolution [batch_size, num_features, time, 1]
inputs = tf.expand_dims(inputs, -1)
with tf.variable_scope(scope_name) as scope:
# Discriminator would be reused in CycleGAN
if reuse:
scope.reuse_variables()
else:
assert scope.reuse is False
h1 = conv2d_layer(inputs = inputs, filters = 128, kernel_size = [3, 3], strides = [1, 2], activation = None, name = 'h1_conv')
h1_gates = conv2d_layer(inputs = inputs, filters = 128, kernel_size = [3, 3], strides = [1, 2], activation = None, name = 'h1_conv_gates')
h1_glu = gated_linear_layer(inputs = h1, gates = h1_gates, name = 'h1_glu')
# Downsample
d1 = downsample2d_block(inputs = h1_glu, filters = 256, kernel_size = [3, 3], strides = [2, 2], name_prefix = 'downsample2d_block1_')
d2 = downsample2d_block(inputs = d1, filters = 512, kernel_size = [3, 3], strides = [2, 2], name_prefix = 'downsample2d_block2_')
d3 = downsample2d_block(inputs = d2, filters = 1024, kernel_size = [6, 3], strides = [1, 2], name_prefix = 'downsample2d_block3_')
# Output
o1 = tf.layers.dense(inputs = d3, units = 1, activation = tf.nn.sigmoid)
return o1