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