Parcollet Titouan / Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition

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complexnn/utils.py 3.08 KB
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f2d3bd141   Parcollet Titouan   Initial commit wi... 
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  #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  # Authors: Titouan Parcollet
  
  
  import keras.backend as K
  from keras.layers import Layer, Lambda
  
  ######
  # Need to rewrite this part to have only one getter for each part
  #
  ######################
  #  Quaternions TIMIT #
  ######################
  
  def get_rpart_first(x):
  	ndim = K.ndim(x)
  	input_shape = K.shape(x)
  	data_format = 'channels_first'
  	if (data_format == 'channels_first' and ndim != 3) or ndim == 2:
  		input_dim = input_shape[1] // 4
  		return x[:, :input_dim]
  
  	input_dim = input_shape[-1] // 4
  	if ndim == 3:
  		return x[:, :, :input_dim]
  	elif ndim == 4:
  		return x[:, :, :, :input_dim]
  	elif ndim == 5:
  		return x[:, :, :, :, :input_dim]
  
  def get_ipart_first(x):
  	ndim = K.ndim(x)
  	input_shape = K.shape(x)
  	data_format = 'channels_first'
  	if (data_format == 'channels_first' and ndim != 3) or ndim == 2:
  		input_dim = input_shape[1] // 4
  		return x[:, input_dim:input_dim*2]
  
  	input_dim = input_shape[-1] // 4
  	if ndim == 3:
  		return x[:, :, input_dim:input_dim*2]
  	elif ndim == 4:
  		return x[:, :, :, input_dim:input_dim*2]
  	elif ndim == 5:
  		return x[:, :, :, :, input_dim:input_dim*2]
  
  def get_jpart_first(x):
  	ndim = K.ndim(x)
  	input_shape = K.shape(x)
  	data_format = 'channels_first'	
  	if (data_format == 'channels_first' and ndim != 3) or ndim == 2:
  		input_dim = input_shape[1] // 4
  		return x[:, input_dim*2:input_dim*3]
  
  	input_dim = input_shape[-1] // 4
  	if ndim == 3:
  		return x[:, :, input_dim*2:input_dim*3]
  	elif ndim == 4:
  		return x[:, :, :, input_dim*2:input_dim*3]
  	elif ndim == 5:
  		return x[:, :, :, :, input_dim*2:input_dim*3]
  
  def get_kpart_first(x):
  	ndim = K.ndim(x)
  	input_shape = K.shape(x)
  	data_format = 'channels_first'
  	if (data_format == 'channels_first' and ndim != 3) or ndim == 2:
  		input_dim = input_shape[1] // 4
  		return x[:, input_dim*3:]
  
  	input_dim = input_shape[-1] // 4
  	if ndim == 3:
  		return x[:, :, input_dim*3:]
  	elif ndim == 4:
  		return x[:, :, :, input_dim*3:]
  	elif ndim == 5:
  		return x[:, :, :, :, input_dim*3:]
  
  class GetRFirst(Layer):
  	def call(self, inputs):
  		return get_rpart_first(inputs)
  	def compute_output_shape(self, input_shape):
  		return getpart_quaternion_output_shape_first(input_shape)
  class GetIFirst(Layer):
  	def call(self, inputs):
  		return get_ipart_first(inputs)
  	def compute_output_shape(self, input_shape):
  		return getpart_quaternion_output_shape_first(input_shape)
  class GetJFirst(Layer):
  	def call(self, inputs):
  		return get_jpart_first(inputs)
  	def compute_output_shape(self, input_shape):
  		return getpart_quaternion_output_shape_first(input_shape)
  class GetKFirst(Layer):
  	def call(self, inputs):
  		return get_kpart_first(inputs)
  	def compute_output_shape(self, input_shape):
  		return getpart_quaternion_output_shape_first(input_shape)
  
  def getpart_quaternion_output_shape_first(input_shape):
  	returned_shape = list(input_shape[:])
  	image_format = K.image_data_format()
  	ndim = len(returned_shape)
  	
  	data_format = 'channels_first'
  	if (data_format == 'channels_first' and ndim != 3) or ndim == 2:
  		axis = 1
  	else:
  		axis = -1
  
  	returned_shape[axis] = returned_shape[axis] // 4
  
  	return tuple(returned_shape)