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changes in flenema
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import tensorflow as tf
class ReflectionPad1d(tf.keras.layers.Layer):
def __init__(self, padding):
super(ReflectionPad1d, self).__init__()
self.padding = padding
def call(self, x):
return tf.pad(x, [[0, 0], [self.padding, self.padding], [0, 0], [0, 0]], "REFLECT")
class ResidualStack(tf.keras.layers.Layer):
def __init__(self, channels, num_res_blocks, kernel_size, name):
super(ResidualStack, self).__init__(name=name)
assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
base_padding = (kernel_size - 1) // 2
self.blocks = []
num_layers = 2
for idx in range(num_res_blocks):
layer_kernel_size = kernel_size
layer_dilation = layer_kernel_size**idx
layer_padding = base_padding * layer_dilation
block = [
tf.keras.layers.LeakyReLU(0.2),
ReflectionPad1d(layer_padding),
tf.keras.layers.Conv2D(filters=channels,
kernel_size=(kernel_size, 1),
dilation_rate=(layer_dilation, 1),
use_bias=True,
padding='valid',
name=f'blocks.{idx}.{num_layers}'),
tf.keras.layers.LeakyReLU(0.2),
tf.keras.layers.Conv2D(filters=channels,
kernel_size=(1, 1),
use_bias=True,
name=f'blocks.{idx}.{num_layers + 2}')
]
self.blocks.append(block)
self.shortcuts = [
tf.keras.layers.Conv2D(channels,
kernel_size=1,
use_bias=True,
name=f'shortcuts.{i}')
for i in range(num_res_blocks)
]
def call(self, x):
for block, shortcut in zip(self.blocks, self.shortcuts):
res = shortcut(x)
for layer in block:
x = layer(x)
x += res
return x