module.py · imayou/DeepCreamPy

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DeepCreamPy

module.py

module.py 11.96 KB

from __future__ import division
from ops import *
import tensorflow.contrib.layers as layers
import math

def conv_nn(input, dims1, dims2, size1, size2, k_size = 3):

pp = tf.pad(input, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 L1 = layers.conv2d(pp, dims1, [k_size, k_size], stride=[1, 1], padding='VALID', activation_fn=None)
 L1 = tf.nn.elu(L1)

pp = tf.pad(L1, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 L2 = layers.conv2d(pp, dims2, [k_size, k_size], stride=[1, 1], padding='VALID', activation_fn=None)
 L2 = tf.nn.elu(L2)
 L2 = tf.image.resize_nearest_neighbor(L2, (size1, size2))

return L2

def encoder(input, reuse, name):
 with tf.variable_scope(name):
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 CL1 = layers.conv2d(p, 32, [5, 5], stride=[1, 1], padding='VALID', activation_fn=None)
 CL1 = tf.nn.elu(CL1) # 256 256 32

p = tf.pad(CL1, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL2 = layers.conv2d(p, 64, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None)
 CL2 = tf.nn.elu(CL2) # 128 128 64

p = tf.pad(CL2, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL3 = layers.conv2d(p, 64, [3, 3], stride=[1, 1], padding='VALID', activation_fn=None)
 CL3 = tf.nn.elu(CL3) # 128 128 64

p = tf.pad(CL3, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL4 = layers.conv2d(p, 128, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None)
 CL4 = tf.nn.elu(CL4) # 64 64 128

p = tf.pad(CL4, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL5 = layers.conv2d(p, 128, [3, 3], stride=[1, 1], padding='VALID', activation_fn=None)
 CL5 = tf.nn.elu(CL5) # 64 64 128

p = tf.pad(CL5, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL6 = layers.conv2d(p, 256, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None)
 CL6 = tf.nn.elu(CL6) # 32 32 128

p = tf.pad(CL6, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 DCL1 = layers.conv2d(p, 256, [3, 3], rate=2, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL1 = tf.nn.elu(DCL1)
 p = tf.pad(DCL1, [[0, 0], [4, 4], [4, 4], [0, 0]], "REFLECT")
 DCL2 = layers.conv2d(p, 256, [3, 3], rate=4, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL2 = tf.nn.elu(DCL2)
 p = tf.pad(DCL2, [[0, 0], [8, 8], [8, 8], [0, 0]], "REFLECT")
 DCL3 = layers.conv2d(p, 256, [3, 3], rate=8, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL3 = tf.nn.elu(DCL3)
 p = tf.pad(DCL3, [[0, 0], [16, 16], [16, 16], [0, 0]], "REFLECT")
 DCL4 = layers.conv2d(p, 256, [3, 3], rate=16, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL4 = tf.nn.elu(DCL4) # 32 32 128

return DCL4

def decoder(input, size1, size2, reuse, name):
 with tf.variable_scope(name):
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

DL1 = conv_nn(input, 128, 128, int(size1/4), int(size2/4)) # 64 64 128

DL2 = conv_nn(DL1, 64, 64, int(size1/2), int(size2/2)) # 128 128 64

DL3 = conv_nn(DL2, 32, 32, int(size1), int(size2))

DL4 = conv_nn(DL3, 16, 16, int(size1), int(size2))

LL2 = layers.conv2d(DL4, 3, [3, 3], stride=[1, 1], padding='SAME', activation_fn=None) # 256 256 3
 LL2 = tf.clip_by_value(LL2, -1.0, 1.0)

return LL2

def discriminator_G(input, reuse, name):
 with tf.variable_scope(name):
 # image is 256 x 256 x input_c_dim
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L1 = layers.conv2d(p, 64, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L1 = instance_norm(L1, 'di1')
 L1 = tf.nn.leaky_relu(L1)

p = tf.pad(L1, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L2 = layers.conv2d(p, 128, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L2 = instance_norm(L2, 'di2')
 L2 = tf.nn.leaky_relu(L2)

p = tf.pad(L2, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L3 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L3 = instance_norm(L3, 'di3')
 L3 = tf.nn.leaky_relu(L3)

p = tf.pad(L3, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L4 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L4 = instance_norm(L4, 'di4')
 L4 = tf.nn.leaky_relu(L4)
 L4 = layers.flatten(L4)

L5 = tf.layers.dense(L4, 1)

return L5

def discriminator_L(input, reuse, name):
 with tf.variable_scope(name):
 # image is 256 x 256 x input_c_dim
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L1 = layers.conv2d(p, 64, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L1 = instance_norm(L1, 'di1l')
 L1 = tf.nn.leaky_relu(L1) # 32 32 64

p = tf.pad(L1, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L2 = layers.conv2d(p, 128, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L2 = instance_norm(L2, 'di2l')
 L2 = tf.nn.leaky_relu(L2) # 16 16 128

p = tf.pad(L2, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L3 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L3 = instance_norm(L3, 'di3l')
 L3 = tf.nn.leaky_relu(L3) # 8 8 256

p = tf.pad(L3, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L4 = layers.conv2d(p, 512, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L4 = instance_norm(L4, 'di4l')
 L4 = tf.nn.leaky_relu(L4) # 4 4 512
 L4 = layers.flatten(L4)

L5 = tf.layers.dense(L4, 1)

return L5

def discriminator_red(input, reuse, name):
 with tf.variable_scope(name):
 # image is 256 x 256 x input_c_dim
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

L1 = convolution_SN(input, 64, 5, 2, 'l1')
 # L1 = instance_norm(L1, 'di1')
 L1 = tf.nn.leaky_relu(L1)

L2 = convolution_SN(L1, 128, 5, 2, 'l2')
 # L2 = instance_norm(L2, 'di2')
 L2 = tf.nn.leaky_relu(L2)

L3 = convolution_SN(L2, 256, 5, 2, 'l3')
 # L3 = instance_norm(L3, 'di3')
 L3 = tf.nn.leaky_relu(L3)

L4 = convolution_SN(L3, 256, 5, 2, 'l4')
 # L4 = instance_norm(L4, 'di4')
 L4 = tf.nn.leaky_relu(L4)

L5 = convolution_SN(L4, 256, 5, 2, 'l5')
 # L5 = instance_norm(L5, 'di5')
 L5 = tf.nn.leaky_relu(L5)

L6 = convolution_SN(L5, 512, 5, 2, 'l6')
 # L6 = instance_norm(L6, 'di6')
 L6 = tf.nn.leaky_relu(L6)

L7 = dense_RED_SN(L6, 'l7')

return L7

def contextual_block(bg_in, fg_in, mask, k_size, lamda, name, stride=1):
 with tf.variable_scope(name):
 b, h, w, dims = [i.value for i in bg_in.get_shape()]
 temp = tf.image.resize_nearest_neighbor(mask, (h, w))
 temp = tf.expand_dims(temp[:, :, :, 0], 3) # b 128 128 1
 mask_r = tf.tile(temp, [1, 1, 1, dims]) # b 128 128 128
 bg = bg_in * mask_r

kn = int((k_size - 1) / 2)
 c = 0
 for p in range(kn, h - kn, stride):
 for q in range(kn, w - kn, stride):
 c += 1

patch1 = tf.extract_image_patches(bg, [1, k_size, k_size, 1], [1, stride, stride, 1], [1, 1, 1, 1], 'VALID')

patch1 = tf.reshape(patch1, (b, 1, c, k_size*k_size*dims))
 patch1 = tf.reshape(patch1, (b, 1, 1, c, k_size * k_size * dims))
 patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3])

patch2 = tf.extract_image_patches(fg_in, [1,k_size,k_size,1], [1,1,1,1], [1,1,1,1], 'SAME')
 ACL = []

for ib in range(b):

k1 = patch1[ib, :, :, :, :]
 k1d = tf.reduce_sum(tf.square(k1), axis=2)
 k2 = tf.reshape(k1, (k_size, k_size, dims, c))
 ww = patch2[ib, :, :, :]
 wwd = tf.reduce_sum(tf.square(ww), axis=2, keepdims=True)
 ft = tf.expand_dims(ww, 0)

CS = tf.nn.conv2d(ft, k1, strides=[1, 1, 1, 1], padding='SAME')

tt = k1d + wwd

DS1 = tf.expand_dims(tt, 0) - 2 * CS

DS2 = (DS1 - tf.reduce_mean(DS1, 3, True)) / reduce_std(DS1, 3, True)
 DS2 = -1 * tf.nn.tanh(DS2)

CA = softmax(lamda * DS2)

ACLt = tf.nn.conv2d_transpose(CA, k2, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME')
 ACLt = ACLt / (k_size ** 2)

if ib == 0:
 ACL = ACLt
 else:
 ACL = tf.concat((ACL, ACLt), 0)

ACL = bg + ACL * (1.0 - mask_r)

con1 = tf.concat([bg_in, ACL], 3)
 ACL2 = layers.conv2d(con1, dims, [1, 1], stride=[1, 1], padding='VALID', activation_fn=None, scope='ML')
 ACL2 = tf.nn.elu(ACL2)

return ACL2

def contextual_block_cs(bg_in, fg_in, mask, k_size, lamda, name, stride=1):
 with tf.variable_scope(name):
 b, h, w, dims = [i.value for i in bg_in.get_shape()]
 temp = tf.image.resize_nearest_neighbor(mask, (h, w))
 temp = tf.expand_dims(temp[:, :, :, 0], 3) # b 128 128 1
 mask_r = tf.tile(temp, [1, 1, 1, dims]) # b 128 128 128
 bg = bg_in * mask_r

kn = int((k_size - 1) / 2)
 c = 0
 for p in range(kn, h - kn, stride):
 for q in range(kn, w - kn, stride):
 c += 1

patch1 = tf.extract_image_patches(bg, [1, k_size, k_size, 1], [1, stride, stride, 1], [1, 1, 1, 1], 'VALID')

patch1 = tf.reshape(patch1, (b, 1, c, k_size*k_size*dims))
 patch1 = tf.reshape(patch1, (b, 1, 1, c, k_size * k_size * dims))
 patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3])

patch2 = tf.extract_image_patches(fg_in, [1,k_size,k_size,1], [1,1,1,1], [1,1,1,1], 'SAME')
 ACL = []

fuse_weight = tf.reshape(tf.eye(3), [3, 3, 1, 1])

for ib in range(b):

k1 = patch1[ib, :, :, :, :]
 k2 = k1 / tf.sqrt(tf.reduce_sum(tf.square(k1), axis=2, keepdims=True) + 1e-16)
 k1 = tf.reshape(k1, (k_size, k_size, dims, c))
 ww = patch2[ib, :, :, :]
 ft = ww / tf.sqrt(tf.reduce_sum(tf.square(ww), axis=2, keepdims=True) + 1e-16)
 ft = tf.expand_dims(ft, 0)

CA = tf.nn.conv2d(ft, k2, strides=[1, 1, 1, 1], padding='SAME')

CA = tf.reshape(CA, [1, h * w, c, 1])
 CA = tf.nn.conv2d(CA, fuse_weight, strides=[1, 1, 1, 1], padding='SAME')
 CA = tf.reshape(CA, [1, h, w, int(math.sqrt(c)), int(math.sqrt(c))])
 CA = tf.transpose(CA, [0, 2, 1, 4, 3])
 CA = tf.reshape(CA, [1, h * w, c, 1])
 CA = tf.nn.conv2d(CA, fuse_weight, strides=[1, 1, 1, 1], padding='SAME')
 CA = tf.reshape(CA, [1, h, w, int(math.sqrt(c)), int(math.sqrt(c))])
 CA = tf.transpose(CA, [0, 2, 1, 4, 3])
 CA = tf.reshape(CA, [1, h, w, c])

CA2 = softmax(lamda * CA)

ACLt = tf.nn.conv2d_transpose(CA2, k1, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME')
 ACLt = ACLt / (k_size ** 2)

if ib == 0:
 ACL = ACLt
 else:
 ACL = tf.concat((ACL, ACLt), 0)

ACL2 = bg + ACL * (1.0 - mask_r)

return ACL2

一键复制编辑原始数据按行查看历史

deeppomf 提交于 2019年08月07日 16:04 +08:00 . Add DCPv2 files

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from __future__ import division
from ops import *
import tensorflow.contrib.layers as layers
import math

def conv_nn(input, dims1, dims2, size1, size2, k_size = 3):

 pp = tf.pad(input, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 L1 = layers.conv2d(pp, dims1, [k_size, k_size], stride=[1, 1], padding='VALID', activation_fn=None)
 L1 = tf.nn.elu(L1)

 pp = tf.pad(L1, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 L2 = layers.conv2d(pp, dims2, [k_size, k_size], stride=[1, 1], padding='VALID', activation_fn=None)
 L2 = tf.nn.elu(L2)
 L2 = tf.image.resize_nearest_neighbor(L2, (size1, size2))

 return L2

def encoder(input, reuse, name):
 with tf.variable_scope(name):
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

 p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 CL1 = layers.conv2d(p, 32, [5, 5], stride=[1, 1], padding='VALID', activation_fn=None)
 CL1 = tf.nn.elu(CL1) # 256 256 32

 p = tf.pad(CL1, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL2 = layers.conv2d(p, 64, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None)
 CL2 = tf.nn.elu(CL2) # 128 128 64

 p = tf.pad(CL2, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL3 = layers.conv2d(p, 64, [3, 3], stride=[1, 1], padding='VALID', activation_fn=None)
 CL3 = tf.nn.elu(CL3) # 128 128 64

 p = tf.pad(CL3, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL4 = layers.conv2d(p, 128, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None)
 CL4 = tf.nn.elu(CL4) # 64 64 128

 p = tf.pad(CL4, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL5 = layers.conv2d(p, 128, [3, 3], stride=[1, 1], padding='VALID', activation_fn=None)
 CL5 = tf.nn.elu(CL5) # 64 64 128

 p = tf.pad(CL5, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
 CL6 = layers.conv2d(p, 256, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None)
 CL6 = tf.nn.elu(CL6) # 32 32 128

 p = tf.pad(CL6, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 DCL1 = layers.conv2d(p, 256, [3, 3], rate=2, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL1 = tf.nn.elu(DCL1)
 p = tf.pad(DCL1, [[0, 0], [4, 4], [4, 4], [0, 0]], "REFLECT")
 DCL2 = layers.conv2d(p, 256, [3, 3], rate=4, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL2 = tf.nn.elu(DCL2)
 p = tf.pad(DCL2, [[0, 0], [8, 8], [8, 8], [0, 0]], "REFLECT")
 DCL3 = layers.conv2d(p, 256, [3, 3], rate=8, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL3 = tf.nn.elu(DCL3)
 p = tf.pad(DCL3, [[0, 0], [16, 16], [16, 16], [0, 0]], "REFLECT")
 DCL4 = layers.conv2d(p, 256, [3, 3], rate=16, stride=[1, 1], padding='VALID', activation_fn=None)
 DCL4 = tf.nn.elu(DCL4) # 32 32 128

 return DCL4

def decoder(input, size1, size2, reuse, name):
 with tf.variable_scope(name):
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

 DL1 = conv_nn(input, 128, 128, int(size1/4), int(size2/4)) # 64 64 128

 DL2 = conv_nn(DL1, 64, 64, int(size1/2), int(size2/2)) # 128 128 64

 DL3 = conv_nn(DL2, 32, 32, int(size1), int(size2))

 DL4 = conv_nn(DL3, 16, 16, int(size1), int(size2))

 LL2 = layers.conv2d(DL4, 3, [3, 3], stride=[1, 1], padding='SAME', activation_fn=None) # 256 256 3
 LL2 = tf.clip_by_value(LL2, -1.0, 1.0)

 return LL2

def discriminator_G(input, reuse, name):
 with tf.variable_scope(name):
 # image is 256 x 256 x input_c_dim
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

 p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L1 = layers.conv2d(p, 64, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L1 = instance_norm(L1, 'di1')
 L1 = tf.nn.leaky_relu(L1)

 p = tf.pad(L1, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L2 = layers.conv2d(p, 128, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L2 = instance_norm(L2, 'di2')
 L2 = tf.nn.leaky_relu(L2)

 p = tf.pad(L2, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L3 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L3 = instance_norm(L3, 'di3')
 L3 = tf.nn.leaky_relu(L3)

 p = tf.pad(L3, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L4 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L4 = instance_norm(L4, 'di4')
 L4 = tf.nn.leaky_relu(L4)
 L4 = layers.flatten(L4)

 L5 = tf.layers.dense(L4, 1)

 return L5

def discriminator_L(input, reuse, name):
 with tf.variable_scope(name):
 # image is 256 x 256 x input_c_dim
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

 p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L1 = layers.conv2d(p, 64, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L1 = instance_norm(L1, 'di1l')
 L1 = tf.nn.leaky_relu(L1) # 32 32 64

 p = tf.pad(L1, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L2 = layers.conv2d(p, 128, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L2 = instance_norm(L2, 'di2l')
 L2 = tf.nn.leaky_relu(L2) # 16 16 128

 p = tf.pad(L2, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L3 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L3 = instance_norm(L3, 'di3l')
 L3 = tf.nn.leaky_relu(L3) # 8 8 256

 p = tf.pad(L3, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
 L4 = layers.conv2d(p, 512, [5, 5], stride=2, padding='VALID', activation_fn=None)
 #L4 = instance_norm(L4, 'di4l')
 L4 = tf.nn.leaky_relu(L4) # 4 4 512
 L4 = layers.flatten(L4)

 L5 = tf.layers.dense(L4, 1)

 return L5

def discriminator_red(input, reuse, name):
 with tf.variable_scope(name):
 # image is 256 x 256 x input_c_dim
 if reuse:
 tf.get_variable_scope().reuse_variables()
 else:
 assert tf.get_variable_scope().reuse is False

 L1 = convolution_SN(input, 64, 5, 2, 'l1')
 # L1 = instance_norm(L1, 'di1')
 L1 = tf.nn.leaky_relu(L1)

 L2 = convolution_SN(L1, 128, 5, 2, 'l2')
 # L2 = instance_norm(L2, 'di2')
 L2 = tf.nn.leaky_relu(L2)

 L3 = convolution_SN(L2, 256, 5, 2, 'l3')
 # L3 = instance_norm(L3, 'di3')
 L3 = tf.nn.leaky_relu(L3)

 L4 = convolution_SN(L3, 256, 5, 2, 'l4')
 # L4 = instance_norm(L4, 'di4')
 L4 = tf.nn.leaky_relu(L4)

 L5 = convolution_SN(L4, 256, 5, 2, 'l5')
 # L5 = instance_norm(L5, 'di5')
 L5 = tf.nn.leaky_relu(L5)

 L6 = convolution_SN(L5, 512, 5, 2, 'l6')
 # L6 = instance_norm(L6, 'di6')
 L6 = tf.nn.leaky_relu(L6)

 L7 = dense_RED_SN(L6, 'l7')

 return L7

def contextual_block(bg_in, fg_in, mask, k_size, lamda, name, stride=1):
 with tf.variable_scope(name):
 b, h, w, dims = [i.value for i in bg_in.get_shape()]
 temp = tf.image.resize_nearest_neighbor(mask, (h, w))
 temp = tf.expand_dims(temp[:, :, :, 0], 3) # b 128 128 1
 mask_r = tf.tile(temp, [1, 1, 1, dims]) # b 128 128 128
 bg = bg_in * mask_r

 kn = int((k_size - 1) / 2)
 c = 0
 for p in range(kn, h - kn, stride):
 for q in range(kn, w - kn, stride):
 c += 1

 patch1 = tf.extract_image_patches(bg, [1, k_size, k_size, 1], [1, stride, stride, 1], [1, 1, 1, 1], 'VALID')

 patch1 = tf.reshape(patch1, (b, 1, c, k_size*k_size*dims))
 patch1 = tf.reshape(patch1, (b, 1, 1, c, k_size * k_size * dims))
 patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3])

 patch2 = tf.extract_image_patches(fg_in, [1,k_size,k_size,1], [1,1,1,1], [1,1,1,1], 'SAME')
 ACL = []

 for ib in range(b):

 k1 = patch1[ib, :, :, :, :]
 k1d = tf.reduce_sum(tf.square(k1), axis=2)
 k2 = tf.reshape(k1, (k_size, k_size, dims, c))
 ww = patch2[ib, :, :, :]
 wwd = tf.reduce_sum(tf.square(ww), axis=2, keepdims=True)
 ft = tf.expand_dims(ww, 0)

 CS = tf.nn.conv2d(ft, k1, strides=[1, 1, 1, 1], padding='SAME')

 tt = k1d + wwd

 DS1 = tf.expand_dims(tt, 0) - 2 * CS

 DS2 = (DS1 - tf.reduce_mean(DS1, 3, True)) / reduce_std(DS1, 3, True)
 DS2 = -1 * tf.nn.tanh(DS2)

 CA = softmax(lamda * DS2)

 ACLt = tf.nn.conv2d_transpose(CA, k2, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME')
 ACLt = ACLt / (k_size ** 2)

 if ib == 0:
 ACL = ACLt
 else:
 ACL = tf.concat((ACL, ACLt), 0)

 ACL = bg + ACL * (1.0 - mask_r)

 con1 = tf.concat([bg_in, ACL], 3)
 ACL2 = layers.conv2d(con1, dims, [1, 1], stride=[1, 1], padding='VALID', activation_fn=None, scope='ML')
 ACL2 = tf.nn.elu(ACL2)

 return ACL2

def contextual_block_cs(bg_in, fg_in, mask, k_size, lamda, name, stride=1):
 with tf.variable_scope(name):
 b, h, w, dims = [i.value for i in bg_in.get_shape()]
 temp = tf.image.resize_nearest_neighbor(mask, (h, w))
 temp = tf.expand_dims(temp[:, :, :, 0], 3) # b 128 128 1
 mask_r = tf.tile(temp, [1, 1, 1, dims]) # b 128 128 128
 bg = bg_in * mask_r

 kn = int((k_size - 1) / 2)
 c = 0
 for p in range(kn, h - kn, stride):
 for q in range(kn, w - kn, stride):
 c += 1

 patch1 = tf.extract_image_patches(bg, [1, k_size, k_size, 1], [1, stride, stride, 1], [1, 1, 1, 1], 'VALID')

 patch1 = tf.reshape(patch1, (b, 1, c, k_size*k_size*dims))
 patch1 = tf.reshape(patch1, (b, 1, 1, c, k_size * k_size * dims))
 patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3])

 patch2 = tf.extract_image_patches(fg_in, [1,k_size,k_size,1], [1,1,1,1], [1,1,1,1], 'SAME')
 ACL = []

 fuse_weight = tf.reshape(tf.eye(3), [3, 3, 1, 1])

 for ib in range(b):

 k1 = patch1[ib, :, :, :, :]
 k2 = k1 / tf.sqrt(tf.reduce_sum(tf.square(k1), axis=2, keepdims=True) + 1e-16)
 k1 = tf.reshape(k1, (k_size, k_size, dims, c))
 ww = patch2[ib, :, :, :]
 ft = ww / tf.sqrt(tf.reduce_sum(tf.square(ww), axis=2, keepdims=True) + 1e-16)
 ft = tf.expand_dims(ft, 0)

 CA = tf.nn.conv2d(ft, k2, strides=[1, 1, 1, 1], padding='SAME')

 CA = tf.reshape(CA, [1, h * w, c, 1])
 CA = tf.nn.conv2d(CA, fuse_weight, strides=[1, 1, 1, 1], padding='SAME')
 CA = tf.reshape(CA, [1, h, w, int(math.sqrt(c)), int(math.sqrt(c))])
 CA = tf.transpose(CA, [0, 2, 1, 4, 3])
 CA = tf.reshape(CA, [1, h * w, c, 1])
 CA = tf.nn.conv2d(CA, fuse_weight, strides=[1, 1, 1, 1], padding='SAME')
 CA = tf.reshape(CA, [1, h, w, int(math.sqrt(c)), int(math.sqrt(c))])
 CA = tf.transpose(CA, [0, 2, 1, 4, 3])
 CA = tf.reshape(CA, [1, h, w, c])

 CA2 = softmax(lamda * CA)

 ACLt = tf.nn.conv2d_transpose(CA2, k1, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME')
 ACLt = ACLt / (k_size ** 2)

 if ib == 0:
 ACL = ACLt
 else:
 ACL = tf.concat((ACL, ACLt), 0)

 ACL2 = bg + ACL * (1.0 - mask_r)

 return ACL2