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model.py
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model.py
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import tensorflow as tf
import numpy as np
import tensorflow.keras.backend as K
from tensorflow import keras
tf.random.set_seed(22)
np.random.seed(22)
class InstanceNormalization(keras.layers.Layer):
def __init__(self,
axis=None,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(InstanceNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = keras.initializers.get(beta_initializer)
self.gamma_initializer = keras.initializers.get(gamma_initializer)
self.beta_regularizer = keras.regularizers.get(beta_regularizer)
self.gamma_regularizer = keras.regularizers.get(gamma_regularizer)
self.beta_constraint = keras.constraints.get(beta_constraint)
self.gamma_constraint = keras.constraints.get(gamma_constraint)
def build(self, input_shape):
ndim = len(input_shape)
if self.axis == 0:
raise ValueError('Axis cannot be zero')
if (self.axis is not None) and (ndim == 2):
raise ValueError('Cannot specify axis for rank 1 tensor')
self.input_spec = keras.layers.InputSpec(ndim=ndim)
if self.axis is None:
shape = (1,)
else:
shape = (input_shape[self.axis],)
if self.scale:
self.gamma = self.add_weight(shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
if self.center:
self.beta = self.add_weight(shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
self.built = True
def call(self, inputs, training=None):
input_shape = K.int_shape(inputs)
reduction_axes = list(range(0, len(input_shape)))
if self.axis is not None:
del reduction_axes[self.axis]
del reduction_axes[0]
mean = K.mean(inputs, reduction_axes, keepdims=True)
stddev = K.std(inputs, reduction_axes, keepdims=True) + self.epsilon
normed = (inputs - mean) / stddev
broadcast_shape = [1] * len(input_shape)
if self.axis is not None:
broadcast_shape[self.axis] = input_shape[self.axis]
if self.scale:
broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
normed = normed * broadcast_gamma
if self.center:
broadcast_beta = K.reshape(self.beta, broadcast_shape)
normed = normed + broadcast_beta
return normed
def get_config(self):
config = {
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': keras.initializers.serialize(self.beta_initializer),
'gamma_initializer': keras.initializers.serialize(self.gamma_initializer),
'beta_regularizer': keras.regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': keras.regularizers.serialize(self.gamma_regularizer),
'beta_constraint': keras.constraints.serialize(self.beta_constraint),
'gamma_constraint': keras.constraints.serialize(self.gamma_constraint)
}
base_config = super(InstanceNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
""" Motion correction network for MC2-Net """
class Encoder(keras.Model):
def __init__(self, initial_filters=64):
super(Encoder, self).__init__()
self.filters = initial_filters
self.conv1 = keras.layers.Conv2D(self.filters, kernel_size=7, strides=1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.conv2 = keras.layers.Conv2D(self.filters*2, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.conv3 = keras.layers.Conv2D(self.filters*4, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.n1 = InstanceNormalization()
self.n2 = InstanceNormalization()
self.n3 = InstanceNormalization()
def call(self, x, training=True):
x = self.conv1(x)
x = self.n1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.n2(x, training=training)
x = tf.nn.relu(x)
x = self.conv3(x)
x = self.n3(x, training=training)
x = tf.nn.relu(x)
return x
class Residual(keras.Model):
def __init__(self, initial_filters=256):
super(Residual, self).__init__()
self.filters = initial_filters
self.conv1 = keras.layers.Conv2D(self.filters, kernel_size=3, strides=1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.conv2 = keras.layers.Conv2D(self.filters, kernel_size=3, strides=1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.in1 = InstanceNormalization()
self.in2 = InstanceNormalization()
def call(self, x, training=True):
inputs = x
x = self.conv1(x)
# x = self.in1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
# x = self.in2(x, training=training)
x = tf.nn.relu(x)
x = tf.add(x, inputs)
return x
class Decoder(keras.Model):
def __init__(self, initial_filters=128):
super(Decoder, self).__init__()
self.filters = initial_filters
self.conv1 = keras.layers.Conv2DTranspose(self.filters, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.conv2 = keras.layers.Conv2DTranspose(self.filters//2, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.conv3 = keras.layers.Conv2D(1, kernel_size=7, strides=1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.02))
self.in1 = InstanceNormalization()
self.in2 = InstanceNormalization()
self.in3 = InstanceNormalization()
def call(self, x, training=True):
x = self.conv1(x)
x = self.in1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.in2(x, training=training)
x = tf.nn.relu(x)
x = self.conv3(x)
x = self.in3(x, training=training)
x = tf.nn.relu(x)
return x
class MC_Net(keras.Model):
def __init__(self,
img_size=256,
num_filter=16,
num_contrast=3,
num_res_block=9):
super(MC_Net, self).__init__()
self.img_size = img_size
self.filters = num_filter
self.num_contrast = num_contrast
self.num_res_block = num_res_block
self.encoder_list = []
for _ in range(num_contrast):
self.encoder_list.append(Encoder(initial_filters=self.filters))
self.res_block_list = []
for _ in range(num_res_block):
self.res_block_list.append(Residual(initial_filters=self.filters*4*num_contrast))
self.decoder_list = []
for _ in range(num_contrast):
self.decoder_list.append(Decoder(initial_filters=self.filters*2))
def build(self, input_shape):
assert isinstance(input_shape, list)
super(MC_Net, self).build(input_shape)
def call(self, x, training=True):
x_list = []
for i in range(self.num_contrast):
x_list.append(self.encoder_list[i](x[i], training))
x = tf.concat(x_list, axis=-1)
for i in range(self.num_res_block):
x = (self.res_block_list[i](x, training))
y = tf.split(x, num_or_size_splits=self.num_contrast, axis=-1)
y_list = []
for i in range(self.num_contrast):
y_list.append(self.decoder_list[i](y[i], training))
return y_list
def ssim_loss(img1, img2):
return -tf.math.log((tf.image.ssim(img1, img2, max_val=1.0)+1)/2)
def vgg_layers(layer_names):
vgg = tf.keras.applications.vgg16.VGG16(include_top=False, weights='imagenet', input_shape=(256, 256, 3))
vgg.trainable = False
outputs = [vgg.get_layer(name).output for name in layer_names]
model = tf.keras.Model([vgg.input], outputs)
return model
def vgg_loss(img1, img2, loss_model):
img1 = tf.repeat(img1, 3, -1)
img2 = tf.repeat(img2, 3, -1)
return tf.reduce_mean(tf.square(loss_model(img1) - loss_model(img2)))
def make_custom_loss(l1, l2, loss_model):
def custom_loss(y_true, y_pred):
return l1*ssim_loss(y_true, y_pred) + l2*vgg_loss(y_true, y_pred, loss_model)
return custom_loss