/
quaternion_layers.py
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/
quaternion_layers.py
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#!/usr/bin/env python
import numpy as np
import numpy as np
import tensorflow as tf
import keras.backend as K
from keras.models import Model
from keras import backend as K
from keras import initializers
from keras.utils import conv_utils
from numpy.random import RandomState
from keras.layers import Layer, InputSpec
from keras.initializers import Initializer
from keras import activations, initializers, regularizers, constraints
from keras.layers import Lambda, Layer, InputSpec, Convolution1D, Convolution2D, add, multiply, Activation, Input, concatenate
class QuaternionConv(Layer):
def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(QuaternionConv, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = 'channels_last' if rank == 1 else K.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis] // 3
self.kernel_shape = self.kernel_size + (input_dim, self.filters)
kern_init = QuaternionInit(
kernel_size=self.kernel_size,
input_dim=input_dim,
weight_dim=self.rank,
nb_filters=self.filters,
)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=kern_init,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
trainable=True
)
if self.use_bias:
bias_shape = (3 * self.filters,)
self.bias = self.add_weight(
shape=bias_shape,
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True
)
else:
self.bias = None
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim * 3})
self.built = True
def call(self, inputs):
channel_axis = 1 if self.data_format == 'channels_first' else -1
input_dim = K.shape(inputs)[channel_axis] // 3
if self.rank == 1:
f_phase = self.kernel[:, :, :self.filters]
f_modulus = self.kernel[:, :, self.filters:]
elif self.rank == 2:
f_phase = self.kernel[:, :, :, :self.filters]
f_modulus = self.kernel[:, :, :, self.filters:]
elif self.rank == 3:
f_phase = self.kernel[:, :, :, :, :self.filters]
f_modulus = self.kernel[:, :, :, :, self.filters:]
f_phase1 = tf.cos(f_phase)
f_phase2 = tf.sin(f_phase) * (3 ** 0.5 / 3)
convArgs = {"strides": self.strides[0] if self.rank == 1 else self.strides,
"padding": self.padding,
"data_format": self.data_format,
"dilation_rate": self.dilation_rate[0] if self.rank == 1 else self.dilation_rate}
convFunc = {1: K.conv1d,
2: K.conv2d,
3: K.conv3d}[self.rank]
f1 = (K.pow(f_phase1, 2) - K.pow(f_phase2, 2)) * f_modulus
f2 = (2 * (K.pow(f_phase2, 2) - f_phase2 * f_phase1)) * f_modulus
f3 = (2 * (K.pow(f_phase2, 2) + f_phase2 * f_phase1)) * f_modulus
f4 = (2 * (K.pow(f_phase2, 2) + f_phase2 * f_phase1)) * f_modulus
f5 = (K.pow(f_phase1, 2) - K.pow(f_phase2, 2)) * f_modulus
f6 = (2 * (K.pow(f_phase2, 2) - f_phase2 * f_phase1)) * f_modulus
f7 = (2 * (K.pow(f_phase2, 2) - f_phase2 * f_phase1)) * f_modulus
f8 = (2 * (K.pow(f_phase2, 2) + f_phase2 * f_phase1)) * f_modulus
f9 = (K.pow(f_phase1, 2) - K.pow(f_phase2, 2)) * f_modulus
f1._keras_shape = self.kernel_shape
f2._keras_shape = self.kernel_shape
f3._keras_shape = self.kernel_shape
f4._keras_shape = self.kernel_shape
f5._keras_shape = self.kernel_shape
f6._keras_shape = self.kernel_shape
f7._keras_shape = self.kernel_shape
f8._keras_shape = self.kernel_shape
f9._keras_shape = self.kernel_shape
f_phase1._keras_shape = self.kernel_shape
f_phase2._keras_shape = self.kernel_shape
matrix1 = K.concatenate([f1, f2, f3], axis=-2)
matrix2 = K.concatenate([f4, f5, f6], axis=-2)
matrix3 = K.concatenate([f7, f8, f9], axis=-2)
matrix = K.concatenate([matrix1, matrix2, matrix3], axis=-1)
matrix._keras_shape = self.kernel_size + (3 * input_dim, 3 * self.filters)
output = convFunc(inputs, matrix, **convArgs)
if self.use_bias:
output = K.bias_add(
output,
self.bias,
data_format=self.data_format
)
if self.activation is not None:
output = self.activation(output)
return output
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (3 * self.filters,)
if self.data_format == 'channels_first':
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + (3 * self.filters,) + tuple(new_space)
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(QuaternionConv, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class QuaternionConv1D(QuaternionConv):
def __init__(self, filters,
kernel_size,
strides=1,
padding='valid',
dilation_rate=1,
activation=None,
use_bias=True,
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
seed=None,
**kwargs):
super(QuaternionConv1D, self).__init__(
rank=1,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format='channels_last',
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
def get_config(self):
config = super(QuaternionConv1D, self).get_config()
config.pop('rank')
config.pop('data_format')
return config
class QuaternionConv2D(QuaternionConv):
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
seed=None,
**kwargs):
super(QuaternionConv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
def get_config(self):
config = super(QuaternionConv2D, self).get_config()
config.pop('rank')
return config
class QuaternionConv3D(QuaternionConv):
def __init__(self, filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1, 1),
activation=None,
use_bias=True,
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
seed=None,
**kwargs):
super(QuaternionConv3D, self).__init__(
rank=3,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
def get_config(self):
config = super(QuaternionConv3D, self).get_config()
config.pop('rank')
return config
class QuaternionDense(Layer):
def __init__(self, units,
activation=None,
use_bias=True,
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(QuaternionDense, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=2)
self.supports_masking = True
def build(self, input_shape):
assert len(input_shape) == 2
assert input_shape[-1] % 2 == 0
input_dim = input_shape[-1] // 3
data_format = K.image_data_format()
kernel_shape = (input_dim, self.units)
fan_in, fan_out = initializers._compute_fans(
kernel_shape,
data_format=data_format
)
s = np.sqrt(1. / fan_in)
def init_phase(shape, dtype=None):
return np.random.normal(
size=kernel_shape,
loc=0,
scale=np.pi / 2,
)
def init_modulus(shape, dtype=None):
return np.random.normal(
size=kernel_shape,
loc=0,
scale=s
)
phase_init = init_phase
modulus_init = init_modulus
self.phase_kernel = self.add_weight(
shape=kernel_shape,
initializer=phase_init,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint
)
self.modulus_kernel = self.add_weight(
shape=kernel_shape,
initializer=modulus_init,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint
)
if self.use_bias:
self.bias = self.add_weight(
shape=(3 * self.units,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint
)
else:
self.bias = None
self.input_spec = InputSpec(ndim=2, axes={-1: 3 * input_dim})
self.built = True
def call(self, inputs):
input_shape = K.shape(inputs)
input_dim = input_shape[-1] // 3
phase_input = inputs[:, :input_dim]
modulus_input = inputs[:, input_dim:]
f_phase = self.phase_kernel
f_phase1 = tf.cos(f_phase)
f_phase2 = tf.sin(f_phase) * (3 ** 0.5 / 3)
f_modulus = self.modulus_kernel
f1 = (K.pow(f_phase1, 2) - K.pow(f_phase2, 2)) * f_modulus
f2 = (2 * (K.pow(f_phase2, 2) - f_phase2 * f_phase1)) * f_modulus
f3 = (2 * (K.pow(f_phase2, 2) + f_phase2 * f_phase1)) * f_modulus
f4 = (2 * (K.pow(f_phase2, 2) + f_phase2 * f_phase1)) * f_modulus
f5 = (K.pow(f_phase1, 2) - K.pow(f_phase2, 2)) * f_modulus
f6 = (2 * (K.pow(f_phase2, 2) - f_phase2 * f_phase1)) * f_modulus
f7 = (2 * (K.pow(f_phase2, 2) - f_phase2 * f_phase1)) * f_modulus
f8 = (2 * (K.pow(f_phase2, 2) + f_phase2 * f_phase1)) * f_modulus
f9 = (K.pow(f_phase1, 2) - K.pow(f_phase2, 2)) * f_modulus
matrix1 = K.concatenate([f1, f2, f3], axis=-1)
matrix2 = K.concatenate([f4, f5, f6], axis=-1)
matrix3 = K.concatenate([f7, f8, f9], axis=-1)
matrix = K.concatenate([matrix1, matrix2, matrix3], axis=0)
output = K.dot(inputs, matrix)
if self.use_bias:
output = K.bias_add(output, self.bias)
if self.activation is not None:
output = self.activation(output)
return output
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 2
assert input_shape[-1]
output_shape = list(input_shape)
output_shape[-1] = 3 * self.units
return tuple(output_shape)
def get_config(self):
config = {
'units': self.units,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
}
base_config = super(QuaternionDense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class QuaternionInit(Initializer):
def __init__(self, kernel_size, input_dim,
weight_dim, nb_filters=None,
criterion='he', seed=None):
assert len(kernel_size) == weight_dim and weight_dim in {0, 1, 2, 3}
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.input_dim = input_dim
self.weight_dim = weight_dim
self.criterion = criterion
def __call__(self, shape, dtype=None):
if self.nb_filters is not None:
kernel_shape = tuple(self.kernel_size) + (int(self.input_dim), self.nb_filters)
else:
kernel_shape = (int(self.input_dim), self.kernel_size[-1])
fan_in, fan_out = initializers._compute_fans(
tuple(self.kernel_size) + (self.input_dim, self.nb_filters)
)
if self.criterion == 'glorot':
s = 1. / (fan_in + fan_out)
elif self.criterion == 'he':
s = 1. / fan_in
else:
raise ValueError('Invalid criterion: ' + self.criterion)
rng = RandomState(1337)
modulus = rng.uniform(low=-np.sqrt(s) * np.sqrt(3), high=np.sqrt(s) * np.sqrt(3), size=kernel_shape)
phase = rng.uniform(low=-np.pi / 2, high=np.pi / 2, size=kernel_shape)
wm = modulus
wp = phase
weight = np.concatenate([wp, wm], axis=-1)
return weight