def _streaming_internal_state(self, inputs):
outputs = super(Conv1DTranspose, self).call(inputs)
if self.overlap == 0:
if self.crop_output:
return tf.identity(outputs[:, 0:self.output_time_dim, :])
else:
return tf.identity(outputs)
output_shape = outputs.shape.as_list()
# need to add remainder state to a specific region of output as below:
# outputs[:,0:self.overlap,:] = outputs[:,0:self.overlap,:] + self.states
# but 'Tensor' object does not support item assignment,
# so doing it through full summation below
output_shape[1] -= self.state_shape[1]
padded_remainder = tf.concat(
[self.states, tf.zeros(output_shape, tf.float32)], 1)
outputs = outputs + padded_remainder
# extract remainder state and substruct bias if it is used:
# bias will be added in the next iteration again and remainder
# should have only convolution part, so that bias is not added twice
if self.use_bias:
new_state = outputs[:, -self.overlap:, :] - self.bias
else:
new_state = outputs[:, -self.overlap:, :]
assign_states = self.states.assign(new_state)
with tf.control_dependencies([assign_states]):
if self.crop_output:
return tf.identity(outputs[:, 0:self.output_time_dim, :])
else:
return tf.identity(outputs)
def _streaming_internal_state(self, inputs):
outputs = super(Conv1DTranspose, self).call(inputs)
if self.overlap == 0:
if self.crop_output:
return tf.identity(outputs[:, 0:self.output_time_dim, :])
else:
return tf.identity(outputs)
output_shape = outputs.shape.as_list()
# need to add remainder state to a specific region of output as below:
# outputs[:,0:self.overlap,:] = outputs[:,0:self.overlap,:] + self.states
# but 'Tensor' object does not support item assignment,
# so doing it through full summation below
output_shape[1] -= self.state_shape[1]
padded_remainder = tf.concat(
[self.states, tf.zeros(output_shape, tf.float32)], 1)
outputs = outputs + padded_remainder
new_state = outputs[:, -self.overlap:, :]
assign_states = self.states.assign(new_state)
with tf.control_dependencies([assign_states]):
if self.crop_output:
return tf.identity(outputs[:, 0:self.output_time_dim, :])
else:
return tf.identity(outputs)
def _streaming_internal_state(self, inputs):
inversed_frames, new_states = self._streaming_external_state(
inputs, self.states)
assign_states = self.states.assign(new_states)
with tf.control_dependencies([assign_states]):
# use tf.identity to ensure that assign_states is executed
return tf.identity(inversed_frames)
def _streaming_internal_state(self, inputs):
memory = tf.keras.backend.concatenate([self.states, inputs], 1)
outputs = memory[:, 0:inputs.shape.as_list()[1], :]
new_memory = memory[:, -self.delay:, :]
assign_states = self.states.assign(new_memory)
with tf.control_dependencies([assign_states]):
return tf.identity(outputs)
def _streaming_internal_state(self, inputs):
if isinstance(self.get_core_layer(), tf.keras.layers.Conv2DTranspose):
outputs = self.cell(inputs)
if self.ring_buffer_size_in_time_dim == 0:
if self.transposed_conv_crop_output:
outputs = outputs[:, 0:self.output_time_dim]
return outputs
output_shape = outputs.shape.as_list()
# need to add remainder state to a specific region of output as below:
# outputs[:,0:self.ring_buffer_size_in_time_dim,:] =
# outputs[:,0:self.ring_buffer_size_in_time_dim,:] + self.states
# but 'Tensor' object does not support item assignment,
# so doing it through full summation below
output_shape[1] -= self.state_shape[1]
padded_remainder = tf.concat(
[self.states, tf.zeros(output_shape, tf.float32)], 1)
outputs = outputs + padded_remainder
# extract remainder state and subtract bias if it is used:
# bias will be added in the next iteration again and remainder
# should have only convolution part, so that bias is not added twice
if self.get_core_layer().get_config()['use_bias']:
# need to access bias of the cell layer,
# where cell can be wrapped by wrapper layer
bias = self.get_core_layer().bias
new_state = outputs[:, -self.ring_buffer_size_in_time_dim:, :] - bias # pylint: disable=invalid-unary-operand-type
else:
new_state = outputs[:, -self.ring_buffer_size_in_time_dim:, :] # pylint: disable=invalid-unary-operand-type
assign_states = self.states.assign(new_state)
with tf.control_dependencies([assign_states]):
if self.transposed_conv_crop_output:
return tf.identity(outputs[:, 0:self.output_time_dim, :])
else:
return tf.identity(outputs)
else:
if self.use_one_step:
# The time dimenstion always has to equal 1 in streaming mode.
if inputs.shape[1] != 1:
raise ValueError('inputs.shape[1]: %d must be 1 ' % inputs.shape[1])
# remove latest row [batch_size, (memory_size-1), feature_dim, channel]
memory = self.states[:, 1:self.ring_buffer_size_in_time_dim, :]
# add new row [batch_size, memory_size, feature_dim, channel]
memory = tf.keras.backend.concatenate([memory, inputs], 1)
assign_states = self.states.assign(memory)
with tf.control_dependencies([assign_states]):
return self.cell(memory)
else:
# add new row [batch_size, memory_size, feature_dim, channel]
if self.ring_buffer_size_in_time_dim:
memory = tf.keras.backend.concatenate([self.states, inputs], 1)
state_update = memory[:, -self.ring_buffer_size_in_time_dim:, :] # pylint: disable=invalid-unary-operand-type
assign_states = self.states.assign(state_update)
with tf.control_dependencies([assign_states]):
return self.cell(memory)
else:
return self.cell(inputs)