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complex_utils.py
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from math import pi
import numpy as np
import tensorflow as tf
def complex_conv(
tf_input, num_features, kernel_size, stride=1, data_format="channels_last", dilation_rate=(1, 1), use_bias=True,
kernel_initializer=None, kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True
):
# allocate half the features to real, half to imaginary
num_features = num_features // 2
tf_real = tf.real(tf_input)
tf_imag = tf.imag(tf_input)
with tf.variable_scope(None, default_name="complex_conv2d"):
tf_real_real = tf.layers.conv2d(
inputs=tf_real,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="real_conv",
)
tf_imag_real = tf.layers.conv2d(
tf_imag,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="real_conv",
reuse=True,
)
tf_real_imag = tf.layers.conv2d(
tf_real,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="imag_conv",
)
tf_imag_imag = tf.layers.conv2d(
tf_imag,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="imag_conv",
reuse=True,
)
real_out = tf_real_real - tf_imag_imag
imag_out = tf_imag_real + tf_real_imag
tf_output = tf.complex(real_out, imag_out)
return tf_output
def complex_conv_transpose(tf_input, num_features, kernel_size, stride, data_format="channels_last", use_bias=True,
kernel_initializer=None, kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True
):
# allocate half the features to real, half to imaginary
# num_features = num_features // 2
tf_real = tf.real(tf_input)
tf_imag = tf.imag(tf_input)
with tf.variable_scope(None, default_name="complex_conv2d"):
tf_real_real = tf.layers.conv2d_transpose(
inputs=tf_real,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="real_conv",
)
tf_imag_real = tf.layers.conv2d_transpose(
tf_imag,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="real_conv",
reuse=True,
)
tf_real_imag = tf.layers.conv2d_transpose(
tf_real,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="imag_conv",
)
tf_imag_imag = tf.layers.conv2d_transpose(
tf_imag,
filters=num_features,
kernel_size=kernel_size,
strides=[stride, stride],
padding="same",
data_format=data_format,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="imag_conv",
reuse=True,
)
real_out = tf_real_real - tf_imag_imag
imag_out = tf_imag_real + tf_real_imag
tf_output = tf.complex(real_out, imag_out)
return tf_output
def complex_conv1d(
tf_input, num_features, kernel_size, stride=1, data_format="channels_last", dilation_rate=(1), use_bias=True,
kernel_initializer=None, kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True
):
# allocate half the features to real, half to imaginary
num_features = num_features // 2
tf_real = tf.real(tf_input)
tf_imag = tf.imag(tf_input)
with tf.variable_scope(None, default_name="complex_conv1d"):
tf_real_real = tf.layers.conv1d(
inputs=tf_real,
filters=num_features,
kernel_size=kernel_size,
strides=stride,
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="real_conv",
)
tf_imag_real = tf.layers.conv1d(
tf_imag,
filters=num_features,
kernel_size=kernel_size,
strides=stride,
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="real_conv",
reuse=True,
)
tf_real_imag = tf.layers.conv1d(
tf_real,
filters=num_features,
kernel_size=kernel_size,
strides=stride,
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="imag_conv",
)
tf_imag_imag = tf.layers.conv1d(
tf_imag,
filters=num_features,
kernel_size=kernel_size,
strides=stride,
padding="same",
data_format=data_format,
dilation_rate=dilation_rate,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
name="imag_conv",
reuse=True,
)
real_out = tf_real_real - tf_imag_imag
imag_out = tf_imag_real + tf_real_imag
tf_output = tf.complex(real_out, imag_out)
return tf_output
def zrelu(x):
# x and tf_output are complex-valued
phase = tf.angle(x)
# Check whether phase <= pi/2
le = tf.less_equal(phase, pi / 2)
# if phase <= pi/2, keep it in comp
# if phase > pi/2, throw it away and set comp equal to 0
y = tf.zeros_like(x)
x = tf.where(le, x, y)
# Check whether phase >= 0
ge = tf.greater_equal(phase, 0)
# if phase >= 0, keep it
# if phase < 0, throw it away and set output equal to 0
output = tf.where(ge, x, y)
return output
def modrelu(x, data_format="channels_last"):
input_shape = tf.shape(x)
if data_format == "channels_last":
axis_z = 1
axis_y = 2
axis_c = 3
else:
axis_c = 1
axis_z = 2
axis_y = 3
# Channel size
shape_c = x.shape[axis_c]
with tf.name_scope("bias") as scope:
if data_format == "channels_last":
bias_shape = (1, 1, 1, shape_c)
else:
bias_shape = (1, shape_c, 1, 1)
bias = tf.get_variable(name=scope,
shape=bias_shape,
initializer=tf.constant_initializer(0.0),
trainable=True)
# relu(|z|+b) * (z / |z|)
norm = tf.abs(x)
scale = tf.nn.relu(norm + bias) / (norm + 1e-6)
output = tf.complex(tf.real(x) * scale,
tf.imag(x) * scale)
return output
def cardioid(x):
phase = tf.angle(x)
scale = 0.5 * (1 + tf.cos(phase))
output = tf.complex(tf.real(x) * scale, tf.imag(x) * scale)
# output = 0.5*(1+tf.cos(phase))*z
return output