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model.py
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model.py
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import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import *
from tensorflow.keras import backend
from tensorflow.keras.activations import *
from tensorflow_addons.layers import AdaptiveAveragePooling2D
class Net(Model):
def __init__(self, config):
super(Net, self).__init__()
self.attention_filter = config.attention_filter
self.filter = config.filter
self.encoder_kernel = config.encoder_kernel
self.decoder_kernel = config.decoder_kernel
self.triple_pass_filter = config.triple_pass_filter
def attention_network(self, I_l, I_h):
h,w,c = list([I_l.shape[1], I_l.shape[2], I_l.shape[3]])
concat = tf.concat([I_l, I_h], axis=-1)
lay1 = Conv2D(self.attention_filter, self.encoder_kernel, padding='same', activation='relu')(concat)
lay1 = Conv2D(self.attention_filter, self.encoder_kernel, padding='same', activation='relu')(lay1)
out = Conv2D(c, self.encoder_kernel, padding='same', activation='sigmoid')(lay1)
return out
def CA(self, X, i):
c = list(X.shape)[-1]
if i == 1:
cc = c
else:
cc = c/8
gap = GlobalAveragePooling2D()(X)
d = tf.reshape(gap, shape=(-1,1,1,c))
d1 = ReLU()(Conv2D(filters=cc, kernel_size=(1,1), kernel_initializer = 'he_normal')(d))
d_bid = sigmoid(Conv2D(filters=c, kernel_size=(1,1), kernel_initializer = 'he_normal')(d1))
return X*d_bid
def SA(self, X):
gap = tf.reduce_max(X, axis=-1)
gap = tf.expand_dims(gap, axis=-1)
gmp = tf.reduce_mean(X, axis=-1)
gmp = tf.expand_dims(gmp, axis=-1)
ff = Concatenate(axis=-1)([gap, gmp])
f = Conv2D(1, kernel_size=(1,1), kernel_initializer = 'he_normal')(ff)
f = sigmoid(f)
return X * f
def dual_attention(self, X, i):
c = list(X.shape)[-1]
M = Conv2D(c, kernel_size=(3,3), padding='same', kernel_initializer = 'he_normal')(X)
M = ReLU()(M)
M = Conv2D(c, kernel_size=(3,3), padding='same', kernel_initializer = 'he_normal')(M)
ca = self.CA(M, i)
sa = self.SA(M)
# ca_X = tf.multiply(ca, X)
# sa_X = tf.multiply(sa, X)
concat = Concatenate(axis=-1)([ca, sa])
concat2 = Conv2D(c, kernel_size=(1,1), kernel_initializer = 'he_normal')(concat)
return Add()([X, concat2])
def attention_mask(self, I):
h,w,c = list([I.shape[1], I.shape[2], I.shape[3]])
lay1 = Conv2D(c, self.encoder_kernel, padding='same', activation='relu')(I)
lay2 = Conv2D(c, self.encoder_kernel, padding='same', activation='sigmoid')(lay1)
out = tf.math.multiply(I, lay2)
return out
def adaptive_interpolation(self, required_size, img):
h, w, c = list([img.shape[1], img.shape[2], img.shape[3]])
pool_size = (int(required_size[0]/img.shape[1]), int(required_size[1]/img.shape[2]))
img = UpSampling2D(size=pool_size)(img)
img = Conv2D(c, self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(img)
return img
def encoder_1_1(self, X, i=1):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
# X = MaxPooling2D()(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_1_2(self, X, i=2):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
X1 = MaxPooling2D()(X1)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_1_3(self, X, i=4):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
X1 = MaxPooling2D()(X1)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_2_1(self, X, i=1):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
# X = MaxPooling2D()(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_2_2(self, X, i=2):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
X1 = MaxPooling2D()(X1)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_2_3(self, X, i=4):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
X1 = MaxPooling2D()(X1)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_last_1(self, X, i=8):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
X1 = MaxPooling2D()(X1)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def encoder_last_2(self, X, i=8):
# X = self.attention_mask(X)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X)
X1 = MaxPooling2D()(X1)
X1 = Conv2D(int(self.filter*i), self.encoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(X1)
return X1
def decoder_1(self, X, i=4):
X = UpSampling2D(size= (2,2))(X)
X = Conv2D(int(self.filter*i), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
X = Conv2D(int(self.filter*i), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
return X
def decoder_2(self, X, i=2):
X = UpSampling2D(size= (2,2))(X)
X = Conv2D(int(self.filter*i), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
X = Conv2D(int(self.filter*i), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
return X
def decoder_3(self, X, i=1):
X = UpSampling2D(size= (2,2))(X)
X = Conv2D(int(self.filter*i), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
X = Conv2D(int(self.filter*i), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
return X
def decoder_last(self, X):
# X = UpSampling2D(size= (2,2))(X)
X = Conv2D(self.filter, self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
X = Conv2D(self.filter, self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(X)
X = LeakyReLU()(X)
return X
def triplepass(self, T0):
T1 = Conv2D(self.triple_pass_filter, kernel_size=(1,1), strides=(1,1), padding='same', kernel_initializer = 'he_normal')(T0)
T1 = ReLU()(T1)
T2 = Conv2D(self.triple_pass_filter, kernel_size=(3,3), strides=(1,1), padding='same', kernel_initializer = 'he_normal')(T0)
T2 = ReLU()(T2)
T3 = Conv2D(self.triple_pass_filter, kernel_size=(5,5), strides=(1,1), padding='same', kernel_initializer = 'he_normal')(T0)
T3 = ReLU()(T3)
T3_ = tf.concat([T1, T2, T3], axis=-1)
T4 = Conv2D(self.triple_pass_filter, kernel_size=(3,3), strides=(1,1), padding='same', kernel_initializer = 'he_normal')(T3_)
T4 = ReLU()(T4)
T5 = Add()([T4, T0])
T5 = Conv2D(self.triple_pass_filter, kernel_size=(3,3), strides=(1,1), padding='same', kernel_initializer = 'he_normal')(T5)
T5 = ReLU()(T5)
return T5
def global_non_local(self, X):
h, w , c = list(X.shape)[1], list(X.shape)[2], list(X.shape)[3]
theta = Conv2D(256, kernel_size=(1,1), padding='same', kernel_initializer = 'he_normal')(X)
theta = ReLU()(theta)
theta_rsh = Reshape((h*w, 256))(theta)
phi = Conv2D(256, kernel_size=(1,1), padding='same', kernel_initializer = 'he_normal')(X)
phi = ReLU()(phi)
phi_rsh = Reshape((256, h*w))(phi)
g = Conv2D(256, kernel_size=(1,1), padding='same', kernel_initializer = 'he_normal')(X)
g = ReLU()(g)
g_rsh = Reshape((h*w, 256))(g)
theta_phi = tf.matmul(theta_rsh, phi_rsh)
theta_phi = tf.keras.layers.Softmax()(theta_phi)
theta_phi_g = tf.matmul(theta_phi, g_rsh)
theta_phi_g = Reshape((h, w, 256))(theta_phi_g)
theta_phi_g = Conv2D(self.triple_pass_filter, kernel_size=(1,1), padding='same', kernel_initializer = 'he_normal')(theta_phi_g)
theta_phi_g = ReLU()(theta_phi_g)
out = Add()([theta_phi_g, X])
return out
def main_model(self, X):
## attention network
X_i = X[:,:,:,:3]
X_r = X[:,:,:,3:]
# X_i = Conv2D(self.filter, (1,1), padding='same', kernel_initializer='he_normal')(X_i)
# X_r = Conv2D(self.filter, (1,1), padding='same', kernel_initializer='he_normal')(X_r)
X_1_masked = self.dual_attention(X_i, i=1)
X_2_masked = self.dual_attention(X_r, i=1)
# mask1 = self.attention_network(X_i, X_r)
# mask2 = self.attention_network(X_r, X_i)
# X_1_masked = tf.math.multiply(mask1, X_i)
# X_2_masked = tf.math.multiply(mask2, X_r)
X_i_64 = self.encoder_1_1(X_1_masked)
X_r_64 = self.encoder_2_1(X_2_masked)
X_1_1_masked = self.dual_attention(X_i_64, i=2)
X_2_1_masked = self.dual_attention(X_r_64, i=2)
# mask1_1 = self.attention_network(X_i_64, X_r_64)
# mask2_1 = self.attention_network(X_r_64, X_i_64)
# X_1_1_masked = tf.math.multiply(mask1_1, X_i_64)
# X_2_1_masked = tf.math.multiply(mask2_1, X_r_64)
X_i_128 = self.encoder_1_2(X_1_1_masked)
X_r_128 = self.encoder_2_2(X_2_1_masked)
X_1_2_masked = self.dual_attention(X_i_128, i=2)
X_2_2_masked = self.dual_attention(X_r_128, i=2)
# mask1_2 = self.attention_network(X_i_128, X_r_128)
# mask2_2 = self.attention_network(X_r_128, X_i_128)
# X_1_2_masked = tf.math.multiply(mask1_2, X_i_128)
# X_2_2_masked = tf.math.multiply(mask2_2, X_r_128)
X_i_256 = self.encoder_1_3(X_1_2_masked)
X_r_256 = self.encoder_2_3(X_2_2_masked)
X_1_3_masked = self.dual_attention(X_i_256, i=2)
X_2_3_masked = self.dual_attention(X_r_256, i=2)
# mask1_3 = self.attention_network(X_i_256, X_r_256)
# mask2_3 = self.attention_network(X_r_256, X_i_256)
# X_1_3_masked = tf.math.multiply(mask1_3, X_i_256)
# X_2_3_masked = tf.math.multiply(mask2_3, X_r_256)
X_i_512 = self.encoder_last_1(X_1_3_masked)
X_r_512 = self.encoder_last_2(X_2_3_masked)
X_1_4_masked = self.dual_attention(X_i_512, i=2)
X_2_4_masked = self.dual_attention(X_r_512, i=2)
# mask1_4 = self.attention_network(X_i_512, X_r_512)
# mask2_4 = self.attention_network(X_r_512, X_i_512)
# X_1_4_masked = tf.math.multiply(mask1_4, X_i_512)
# X_2_4_masked = tf.math.multiply(mask2_4, X_r_512)
encoder_cat = tf.concat([X_1_4_masked, X_2_4_masked], axis=-1)
# encoder_cat = Dropout(0.2)(encoder_cat)
# encoder_last = tf.nn.depth_to_space(encoder_cat, 2)
encoder_last = Conv2D(self.triple_pass_filter, kernel_size=(3,3), padding='same', kernel_initializer = 'he_normal', activation='relu')(encoder_cat)
encoder_last = Conv2D(self.triple_pass_filter, kernel_size=(3,3), padding='same', kernel_initializer = 'he_normal', activation='relu')(encoder_last)
encoder_last = self.dual_attention(encoder_last, i=2)
## upper path ##
tpl_out = self.triplepass(encoder_last)
tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
# tpl_out = self.triplepass(tpl_out)
## lower path ##
glb_out = AdaptiveAveragePooling2D(output_size=(7,7))(encoder_last)
glb_out = self.global_non_local(glb_out)
required_size = [encoder_last.shape[1], encoder_last.shape[2]]
glb_out = self.adaptive_interpolation(required_size, glb_out)
# glb_out = self.adapmaxpooling(encoder_last, 16)
# glb_out = self.global_non_local(glb_out)
# required_size = [encoder_last.shape[1], encoder_last.shape[2]]
# glb_out = self.adapupsampling(required_size, glb_out)
## cat ##
merger = tf.concat([tpl_out, glb_out, X_1_4_masked, X_2_4_masked], axis=-1)
merger = Conv2D(512, self.decoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(merger)
merger = Conv2D(256, self.decoder_kernel, padding='same', kernel_initializer = 'he_normal', activation='relu')(merger)
# merger = Dropout(0.2)(merger)
O_256 = self.decoder_1(merger)
O_256 = tf.concat([X_1_3_masked, X_2_3_masked, O_256], axis=-1)
O_256 = Conv2D(int(self.filter*4), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(O_256)
O_256 = LeakyReLU()(O_256)
O_128 = self.decoder_2(O_256)
O_128 = tf.concat([X_1_2_masked, X_2_2_masked, O_128], axis=-1)
O_128 = Conv2D(int(self.filter*2), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(O_128)
O_128 = LeakyReLU()(O_128)
O_64 = self.decoder_3(O_128)
O_64 = tf.concat([X_1_1_masked, X_2_1_masked, O_64], axis=-1)
O_64 = Conv2D(int(self.filter*1), self.decoder_kernel, padding='same', kernel_initializer = 'he_normal')(O_64)
O_64 = LeakyReLU()(O_64)
O_64 = self.decoder_last(O_64)
O_3 = Conv2D(64, (3,3), padding='same', activation='relu', kernel_initializer = 'he_normal')(O_64)
O_3 = Conv2D(3, (3,3), padding='same', activation='relu', kernel_initializer = 'he_normal')(O_3)
O_9 = tf.concat([X_1_masked, X_2_masked, O_3], axis=-1)
O_10 = Conv2D(9, (3,3), padding='same', activation='relu', kernel_initializer = 'he_normal')(O_9)
out = Conv2D(3, (1,1), padding='same', activation='sigmoid', kernel_initializer = 'he_normal')(O_10)
return out