class ResidualBlock(Layer):
def __init__(self,
dilation_rate: int,
nb_filters: int,
kernel_size: int,
padding: str,
activation: str = 'relu',
dropout_rate: float = 0,
kernel_initializer: str = 'he_normal',
use_batch_norm: bool = False,
use_layer_norm: bool = False,
use_weight_norm: bool = False,
**kwargs):
"""Defines the residual block for the WaveNet TCN
Args:
x: The previous layer in the model
training: boolean indicating whether the layer should behave in training mode or in inference mode
dilation_rate: The dilation power of 2 we are using for this residual block
nb_filters: The number of convolutional filters to use in this block
kernel_size: The size of the convolutional kernel
padding: The padding used in the convolutional layers, 'same' or 'causal'.
activation: The final activation used in o = Activation(x + F(x))
dropout_rate: Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer: Initializer for the kernel weights matrix (Conv1D).
use_batch_norm: Whether to use batch normalization in the residual layers or not.
use_layer_norm: Whether to use layer normalization in the residual layers or not.
use_weight_norm: Whether to use weight normalization in the residual layers or not.
kwargs: Any initializers for Layer class.
"""
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.use_weight_norm = use_weight_norm
self.kernel_initializer = kernel_initializer
self.layers = []
self.layers_outputs = []
self.shape_match_conv = None
self.res_output_shape = None
self.final_activation = None
super(ResidualBlock, self).__init__(**kwargs)
def _build_layer(self, layer):
"""Helper function for building layer
Args:
layer: Appends layer to internal layer list and builds it based on the current output
shape of ResidualBlocK. Updates current output shape.
"""
self.layers.append(layer)
self.layers[-1].build(self.res_output_shape)
self.res_output_shape = self.layers[-1].compute_output_shape(
self.res_output_shape)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.layers = []
self.res_output_shape = input_shape
for k in range(2):
name = 'conv1D_{}'.format(k)
with K.name_scope(
name
): # name scope used to make sure weights get unique names
conv = Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name=name,
kernel_initializer=self.kernel_initializer)
if self.use_weight_norm:
from tensorflow_addons.layers import WeightNormalization
# wrap it. WeightNormalization API is different than BatchNormalization or LayerNormalization.
with K.name_scope('norm_{}'.format(k)):
conv = WeightNormalization(conv)
self._build_layer(conv)
with K.name_scope('norm_{}'.format(k)):
if self.use_batch_norm:
self._build_layer(BatchNormalization())
elif self.use_layer_norm:
self._build_layer(LayerNormalization())
elif self.use_weight_norm:
pass # done above.
self._build_layer(Activation(self.activation))
self._build_layer(SpatialDropout1D(rate=self.dropout_rate))
if self.nb_filters != input_shape[-1]:
# 1x1 conv to match the shapes (channel dimension).
name = 'matching_conv1D'
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(
filters=self.nb_filters,
kernel_size=1,
padding='same',
name=name,
kernel_initializer=self.kernel_initializer)
else:
name = 'matching_identity'
self.shape_match_conv = Lambda(lambda x: x, name=name)
with K.name_scope(name):
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(
input_shape)
self._build_layer(Activation(self.activation))
self.final_activation = Activation(self.activation)
self.final_activation.build(
self.res_output_shape) # probably isn't necessary
# this is done to force Keras to add the layers in the list to self._layers
for layer in self.layers:
self.__setattr__(layer.name, layer)
self.__setattr__(self.shape_match_conv.name, self.shape_match_conv)
self.__setattr__(self.final_activation.name, self.final_activation)
super(ResidualBlock, self).build(
input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None):
"""
Returns: A tuple where the first element is the residual model tensor, and the second
is the skip connection tensor.
"""
x = inputs
self.layers_outputs = [x]
for layer in self.layers:
training_flag = 'training' in dict(
inspect.signature(layer.call).parameters)
x = layer(x, training=training) if training_flag else layer(x)
self.layers_outputs.append(x)
x2 = self.shape_match_conv(inputs)
self.layers_outputs.append(x2)
res_x = layers.add([x2, x])
self.layers_outputs.append(res_x)
res_act_x = self.final_activation(res_x)
self.layers_outputs.append(res_act_x)
return [res_act_x, x]
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
class ResidualBlock(Layer):
def __init__(self,
dilation_rate, # dilation power of 2
nb_filters, # number of conv filters
kernel_size, # conv kernel size
padding, # valid(no padding), same(inputlen=outputlen), causal
activation='relu', # o = Activation(x + F(x))
dropout_rate=0, # [0, 1]
kernel_initializer='he_normal',
use_batch_norm=True,
use_layer_norm=False,
last_block=True,
**kwargs): # key, value
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.kernel_initializer = kernel_initializer
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.last_block = last_block
self.layers = []
self.layers_outputs = []
self.shape_match_conv = None
self.res_output_shape = None
self.final_activation = None
super(ResidualBlock, self).__init__(**kwargs)
def _add_and_activate_layer(self, layer): # append layer to internal layer list
self.layers.append(layer)
self.layers[-1].build(self.res_output_shape)
self.res_output_shape = self.layers[-1].compute_output_shape(self.res_output_shape)
def build(self, input_shape):
with K.name_scope(self.name): # name scope used to make sure weights get unique names
self.layers = []
self.res_output_shape = input_shape
for k in range(2):
with K.name_scope('conv1D_{}'.format(k)): # conv1D_0 or conv1D_1
self._add_and_activate_layer(Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name='conv1D_{}'.format(k),
kernel_initializer=self.kernel_initializer))
#self._add_and_activate_layer(MaxPooling1D(pool_size=3))
with K.name_scope('norm_{}'.format(k)): # norm_0 or norm_1
if self.use_batch_norm:
self._add_and_activate_layer(BatchNormalization())
print('use batch_norm')
elif self.use_layer_norm:
self._add_and_activate_layer(LayerNormalization())
print('use layer_norm')
self._add_and_activate_layer(Activation('relu'))
self._add_and_activate_layer(SpatialDropout1D(rate=self.dropout_rate))
if not self.last_block:
# 1x1 conv to match the shapes (channel dimension).
name = 'conv1D_{}'.format(k + 1)
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(filters=self.nb_filters,
kernel_size=1,
padding='same', # len(input) == len(output)
name=name,
kernel_initializer=self.kernel_initializer)
else:
self.shape_match_conv = Lambda(lambda x: x, name='identity') # Lambda : Layer로 감싸줌
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(input_shape)
self.final_activation = Activation(self.activation)
self.final_activation.build(self.res_output_shape) # probably isn't necessary
# this is done to force Keras to add the layers in the list to self._layers
for layer in self.layers:
self.__setattr__(layer.name, layer)
super(ResidualBlock, self).build(input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None): # training : boolean whether it's training mode or not
x = inputs
self.layers_outputs = [x]
for layer in self.layers:
training_flag = 'training' in dict(inspect.signature(layer.call).parameters)
x = layer(x, training=training) if training_flag else layer(x)
# training_flag == False -> x = layer(x)
self.layers_outputs.append(x)
x2 = self.shape_match_conv(inputs)
self.layers_outputs.append(x2)
res_x = layers.add([x2, x])
self.layers_outputs.append(res_x)
res_act_x = self.final_activation(res_x)
self.layers_outputs.append(res_act_x)
return [res_act_x, x] # residual model tensor, skip connection tensor
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
class ResidualBlock(Layer):
def __init__(self,
dilation_rate: int,
nb_filters: int,
kernel_size: int,
padding: str,
activation: str = 'relu',
dropout_rate: float = 0,
kernel_initializer: str = 'he_normal',
use_batch_norm: bool = False,
use_layer_norm: bool = False,
use_weight_norm: bool = False,
**kwargs):
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.use_weight_norm = use_weight_norm
self.kernel_initializer = kernel_initializer
self.layers = []
self.layers_outputs = []
self.shape_match_conv = None
self.res_output_shape = None
self.final_activation = None
super(ResidualBlock, self).__init__(**kwargs)
def _build_layer(self, layer):
self.layers.append(layer)
self.layers[-1].build(self.res_output_shape)
self.res_output_shape = self.layers[-1].compute_output_shape(
self.res_output_shape)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.layers = []
self.res_output_shape = input_shape
for k in range(2):
name = 'conv1D_{}'.format(k)
with K.name_scope(
name
): # name scope used to make sure weights get unique names
conv = Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name=name,
kernel_initializer=self.kernel_initializer)
if self.use_weight_norm:
from tensorflow_addons.layers import WeightNormalization
# wrap it. WeightNormalization API is different than BatchNormalization or LayerNormalization.
with K.name_scope('norm_{}'.format(k)):
conv = WeightNormalization(conv)
self._build_layer(conv)
with K.name_scope('norm_{}'.format(k)):
if self.use_batch_norm:
self._build_layer(BatchNormalization())
elif self.use_layer_norm:
self._build_layer(LayerNormalization())
elif self.use_weight_norm:
pass # done above.
self._build_layer(Activation(self.activation))
self._build_layer(SpatialDropout1D(rate=self.dropout_rate))
if self.nb_filters != input_shape[-1]:
# 1x1 conv to match the shapes (channel dimension).
name = 'matching_conv1D'
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(
filters=self.nb_filters,
kernel_size=1,
padding='same',
name=name,
kernel_initializer=self.kernel_initializer)
else:
name = 'matching_identity'
self.shape_match_conv = Lambda(lambda x: x, name=name)
with K.name_scope(name):
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(
input_shape)
self._build_layer(Activation(self.activation))
self.final_activation = Activation(self.activation)
self.final_activation.build(
self.res_output_shape) # probably isn't necessary
# this is done to force Keras to add the layers in the list to self._layers
for layer in self.layers:
self.__setattr__(layer.name, layer)
self.__setattr__(self.shape_match_conv.name, self.shape_match_conv)
self.__setattr__(self.final_activation.name, self.final_activation)
super(ResidualBlock, self).build(
input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None):
x = inputs
self.layers_outputs = [x]
for layer in self.layers:
training_flag = 'training' in dict(
inspect.signature(layer.call).parameters)
x = layer(x, training=training) if training_flag else layer(x)
self.layers_outputs.append(x)
x2 = self.shape_match_conv(inputs)
self.layers_outputs.append(x2)
res_x = layers.add([x2, x])
self.layers_outputs.append(res_x)
res_act_x = self.final_activation(res_x)
self.layers_outputs.append(res_act_x)
return [res_act_x, x]
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
class ResidualBlock(Layer):
def __init__(self,
dilation_rate,
nb_filters,
kernel_size,
padding,
activation='relu',
dropout_rate=0,
kernel_initializer='he_normal',
use_batch_norm=False,
use_layer_norm=False,
last_block=True,
**kwargs):
# type: (int, int, int, str, str, float, str, bool, bool, dict) -> None
"""Defines the residual block for the WaveNet TCN
Args:
x: The previous layer in the model
training: boolean indicating whether the layer should behave in training mode or in inference mode
dilation_rate: The dilation power of 2 we are using for this residual block
nb_filters: The number of convolutional filters to use in this block
kernel_size: The size of the convolutional kernel
padding: The padding used in the convolutional layers, 'same' or 'causal'.
activation: The final activation used in o = Activation(x + F(x))
dropout_rate: Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer: Initializer for the kernel weights matrix (Conv1D).
use_batch_norm: Whether to use batch normalization in the residual layers or not.
use_layer_norm: Whether to use layer normalization in the residual layers or not.
kwargs: Any initializers for Layer class.
"""
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.kernel_initializer = kernel_initializer
self.last_block = last_block
super(ResidualBlock, self).__init__(**kwargs)
def _add_and_activate_layer(self, layer):
"""Helper function for building layer
Args:
layer: Appends layer to internal layer list and builds it based on the current output
shape of ResidualBlocK. Updates current output shape.
"""
self.residual_layers.append(layer)
self.residual_layers[-1].build(self.res_output_shape)
self.res_output_shape = self.residual_layers[-1].compute_output_shape(
self.res_output_shape)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.residual_layers = list()
self.res_output_shape = input_shape
for k in range(2):
name = 'conv1D_{}'.format(k)
with K.name_scope(
name
): # name scope used to make sure weights get unique names
self._add_and_activate_layer(
Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name=name,
kernel_initializer=self.kernel_initializer))
if self.use_batch_norm:
self._add_and_activate_layer(BatchNormalization())
elif self.use_layer_norm:
self._add_and_activate_layer(LayerNormalization())
self._add_and_activate_layer(Activation('relu'))
self._add_and_activate_layer(
SpatialDropout1D(rate=self.dropout_rate))
if not self.last_block:
# 1x1 conv to match the shapes (channel dimension).
name = 'conv1D_{}'.format(k + 1)
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(
filters=self.nb_filters,
kernel_size=1,
padding='same',
name=name,
kernel_initializer=self.kernel_initializer)
else:
self.shape_match_conv = Lambda(lambda x: x, name='identity')
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(
input_shape)
self.final_activation = Activation(self.activation)
self.final_activation.build(
self.res_output_shape) # probably isn't necessary
# this is done to force keras to add the layers in the list to self._layers
for layer in self.residual_layers:
self.__setattr__(layer.name, layer)
super(ResidualBlock, self).build(
input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None):
"""
Returns: A tuple where the first element is the residual model tensor, and the second
is the skip connection tensor.
"""
x = inputs
for layer in self.residual_layers:
if isinstance(layer, SpatialDropout1D):
x = layer(x, training=training)
else:
x = layer(x)
x2 = self.shape_match_conv(inputs)
res_x = layers.add([x2, x])
return [self.final_activation(res_x), x]
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
