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# -*- coding: utf-8 -*-
# Copyright 2020 MINH ANH (@dathudeptrai)
# MIT License (https://opensource.org/licenses/MIT)
"""Tensorflow Layer modules complatible with pytorch."""
import tensorflow as tf
class TFReflectionPad1d(tf.keras.layers.Layer):
"""Tensorflow ReflectionPad1d module."""
def __init__(self, padding_size):
"""Initialize TFReflectionPad1d module.
Args:
padding_size (int): Padding size.
"""
super(TFReflectionPad1d, self).__init__()
self.padding_size = padding_size
@tf.function
def call(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input tensor (B, T, 1, C).
Returns:
Tensor: Padded tensor (B, T + 2 * padding_size, 1, C).
"""
return tf.pad(
x,
[[0, 0], [self.padding_size, self.padding_size], [0, 0], [0, 0]],
"REFLECT",
)
class TFConvTranspose1d(tf.keras.layers.Layer):
"""Tensorflow ConvTranspose1d module."""
def __init__(self, channels, kernel_size, stride, padding):
"""Initialize TFConvTranspose1d( module.
Args:
channels (int): Number of channels.
kernel_size (int): kernel size.
strides (int): Stride width.
padding (str): Padding type ("same" or "valid").
"""
super(TFConvTranspose1d, self).__init__()
self.conv1d_transpose = tf.keras.layers.Conv2DTranspose(
filters=channels,
kernel_size=(kernel_size, 1),
strides=(stride, 1),
padding=padding,
)
@tf.function
def call(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input tensor (B, T, 1, C).
Returns:
Tensors: Output tensor (B, T', 1, C').
"""
x = self.conv1d_transpose(x)
return x
class TFResidualStack(tf.keras.layers.Layer):
"""Tensorflow ResidualStack module."""
def __init__(
self,
kernel_size,
channels,
dilation,
bias,
nonlinear_activation,
nonlinear_activation_params,
padding,
):
"""Initialize TFResidualStack module.
Args:
kernel_size (int): Kernel size.
channles (int): Number of channels.
dilation (int): Dilation ine.
bias (bool): Whether to add bias parameter in convolution layers.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (dict): Hyperparameters for activation function.
padding (str): Padding type ("same" or "valid").
"""
super(TFResidualStack, self).__init__()
self.block = [
getattr(tf.keras.layers, nonlinear_activation)(
**nonlinear_activation_params
),
TFReflectionPad1d(dilation),
tf.keras.layers.Conv2D(
filters=channels,
kernel_size=(kernel_size, 1),
dilation_rate=(dilation, 1),
use_bias=bias,
padding="valid",
),
getattr(tf.keras.layers, nonlinear_activation)(
**nonlinear_activation_params
),
tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias),
]
self.shortcut = tf.keras.layers.Conv2D(
filters=channels, kernel_size=1, use_bias=bias
)
@tf.function
def call(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input tensor (B, T, 1, C).
Returns:
Tensor: Output tensor (B, T, 1, C).
"""
_x = tf.identity(x)
for i, layer in enumerate(self.block):
_x = layer(_x)
shortcut = self.shortcut(x)
return shortcut + _x
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