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#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import (Input, Conv2d, BatchNorm2d, Elementwise, SubpixelConv2d, Flatten, Dense)
from tensorlayer.models import Model
def get_G(input_shape):
w_init = tf.random_normal_initializer(stddev=0.02)
g_init = tf.random_normal_initializer(1., 0.02)
nin = Input(input_shape)
n = Conv2d(64, (3, 3), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init)(nin)
temp = n
# B residual blocks
for i in range(16):
nn = Conv2d(64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
nn = BatchNorm2d(act=tf.nn.relu, gamma_init=g_init)(nn)
nn = Conv2d(64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(nn)
nn = BatchNorm2d(gamma_init=g_init)(nn)
nn = Elementwise(tf.add)([n, nn])
n = nn
n = Conv2d(64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(gamma_init=g_init)(n)
n = Elementwise(tf.add)([n, temp])
# B residual blacks end
n = Conv2d(256, (3, 3), (1, 1), padding='SAME', W_init=w_init)(n)
n = SubpixelConv2d(scale=2, n_out_channels=None, act=tf.nn.relu)(n)
n = Conv2d(256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init)(n)
n = SubpixelConv2d(scale=2, n_out_channels=None, act=tf.nn.relu)(n)
nn = Conv2d(3, (1, 1), (1, 1), act=tf.nn.tanh, padding='SAME', W_init=w_init)(n)
G = Model(inputs=nin, outputs=nn, name="generator")
return G
def get_D(input_shape):
w_init = tf.random_normal_initializer(stddev=0.02)
gamma_init = tf.random_normal_initializer(1., 0.02)
df_dim = 64
lrelu = lambda x: tl.act.lrelu(x, 0.2)
nin = Input(input_shape)
n = Conv2d(df_dim, (4, 4), (2, 2), act=lrelu, padding='SAME', W_init=w_init)(nin)
n = Conv2d(df_dim * 2, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 4, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 8, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 16, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 32, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 16, (1, 1), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 8, (1, 1), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
nn = BatchNorm2d(gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 2, (1, 1), (1, 1), padding='SAME', W_init=w_init, b_init=None)(nn)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 2, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
n = Conv2d(df_dim * 8, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
n = BatchNorm2d(gamma_init=gamma_init)(n)
n = Elementwise(combine_fn=tf.add, act=lrelu)([n, nn])
n = Flatten()(n)
no = Dense(n_units=1, W_init=w_init)(n)
D = Model(inputs=nin, outputs=no, name="discriminator")
return D
# def get_G2(input_shape):
# w_init = tf.random_normal_initializer(stddev=0.02)
# g_init = tf.random_normal_initializer(1., 0.02)
#
# n = InputLayer(t_image, name='in')
# n = Conv2d(n, 64, (3, 3), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init, name='n64s1/c')
# temp = n
#
# # B residual blocks
# for i in range(16):
# nn = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c1/%s' % i)
# nn = BatchNormLayer(nn, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='n64s1/b1/%s' % i)
# nn = Conv2d(nn, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c2/%s' % i)
# nn = BatchNormLayer(nn, is_train=is_train, gamma_init=g_init, name='n64s1/b2/%s' % i)
# nn = ElementwiseLayer([n, nn], tf.add, name='b_residual_add/%s' % i)
# n = nn
#
# n = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c/m')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s1/b/m')
# n = ElementwiseLayer([n, temp], tf.add, name='add3')
# # B residual blacks end
#
# # n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/1')
# # n = SubpixelConv2d(n, scale=2, n_out_channel=None, act=tf.nn.relu, name='pixelshufflerx2/1')
# #
# # n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/2')
# # n = SubpixelConv2d(n, scale=2, n_out_channel=None, act=tf.nn.relu, name='pixelshufflerx2/2')
#
# ## 0, 1, 2, 3 BILINEAR NEAREST BICUBIC AREA
# n = UpSampling2dLayer(n, size=[size[1] * 2, size[2] * 2], is_scale=False, method=1, align_corners=False, name='up1/upsample2d')
# n = Conv2d(n, 64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='up1/conv2d') # <-- may need to increase n_filter
# n = BatchNormLayer(n, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='up1/batch_norm')
#
# n = UpSampling2dLayer(n, size=[size[1] * 4, size[2] * 4], is_scale=False, method=1, align_corners=False, name='up2/upsample2d')
# n = Conv2d(n, 32, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='up2/conv2d') # <-- may need to increase n_filter
# n = BatchNormLayer(n, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='up2/batch_norm')
#
# n = Conv2d(n, 3, (1, 1), (1, 1), act=tf.nn.tanh, padding='SAME', W_init=w_init, name='out')
# return n
# def SRGAN_d2(t_image, is_train=False, reuse=False):
# """ Discriminator in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
# feature maps (n) and stride (s) feature maps (n) and stride (s)
# """
# w_init = tf.random_normal_initializer(stddev=0.02)
# b_init = None
# g_init = tf.random_normal_initializer(1., 0.02)
# lrelu = lambda x: tl.act.lrelu(x, 0.2)
# with tf.variable_scope("SRGAN_d", reuse=reuse) as vs:
# # tl.layers.set_name_reuse(reuse) # remove for TL 1.8.0+
# n = InputLayer(t_image, name='in')
# n = Conv2d(n, 64, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, name='n64s1/c')
#
# n = Conv2d(n, 64, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n64s2/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s2/b')
#
# n = Conv2d(n, 128, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n128s1/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n128s1/b')
#
# n = Conv2d(n, 128, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n128s2/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n128s2/b')
#
# n = Conv2d(n, 256, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n256s1/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n256s1/b')
#
# n = Conv2d(n, 256, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n256s2/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n256s2/b')
#
# n = Conv2d(n, 512, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n512s1/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n512s1/b')
#
# n = Conv2d(n, 512, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n512s2/c')
# n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n512s2/b')
#
# n = FlattenLayer(n, name='f')
# n = DenseLayer(n, n_units=1024, act=lrelu, name='d1024')
# n = DenseLayer(n, n_units=1, name='out')
#
# logits = n.outputs
# n.outputs = tf.nn.sigmoid(n.outputs)
#
# return n, logits
# def Vgg19_simple_api(rgb, reuse):
# """
# Build the VGG 19 Model
#
# Parameters
# -----------
# rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1]
# """
# VGG_MEAN = [103.939, 116.779, 123.68]
# with tf.variable_scope("VGG19", reuse=reuse) as vs:
# start_time = time.time()
# print("build model started")
# rgb_scaled = rgb * 255.0
# # Convert RGB to BGR
# if tf.__version__ <= '0.11':
# red, green, blue = tf.split(3, 3, rgb_scaled)
# else: # TF 1.0
# # print(rgb_scaled)
# red, green, blue = tf.split(rgb_scaled, 3, 3)
# assert red.get_shape().as_list()[1:] == [224, 224, 1]
# assert green.get_shape().as_list()[1:] == [224, 224, 1]
# assert blue.get_shape().as_list()[1:] == [224, 224, 1]
# if tf.__version__ <= '0.11':
# bgr = tf.concat(3, [
# blue - VGG_MEAN[0],
# green - VGG_MEAN[1],
# red - VGG_MEAN[2],
# ])
# else:
# bgr = tf.concat(
# [
# blue - VGG_MEAN[0],
# green - VGG_MEAN[1],
# red - VGG_MEAN[2],
# ], axis=3)
# assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
# """ input layer """
# net_in = InputLayer(bgr, name='input')
# """ conv1 """
# network = Conv2d(net_in, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_1')
# network = Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_2')
# network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')
# """ conv2 """
# network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_1')
# network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_2')
# network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')
# """ conv3 """
# network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_1')
# network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_2')
# network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_3')
# network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_4')
# network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool3')
# """ conv4 """
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_1')
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_2')
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_3')
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_4')
# network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool4') # (batch_size, 14, 14, 512)
# conv = network
# """ conv5 """
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_1')
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_2')
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_3')
# network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_4')
# network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool5') # (batch_size, 7, 7, 512)
# """ fc 6~8 """
# network = FlattenLayer(network, name='flatten')
# network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc6')
# network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc7')
# network = DenseLayer(network, n_units=1000, act=tf.identity, name='fc8')
# print("build model finished: %fs" % (time.time() - start_time))
# return network, conv
# def vgg16_cnn_emb(t_image, reuse=False):
# """ t_image = 244x244 [0~255] """
# with tf.variable_scope("vgg16_cnn", reuse=reuse) as vs:
# tl.layers.set_name_reuse(reuse)
#
# mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
# net_in = InputLayer(t_image - mean, name='vgg_input_im')
# """ conv1 """
# network = tl.layers.Conv2dLayer(net_in,
# act = tf.nn.relu,
# shape = [3, 3, 3, 64], # 64 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv1_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 64, 64], # 64 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv1_2')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool1')
# """ conv2 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 64, 128], # 128 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv2_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 128, 128], # 128 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv2_2')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool2')
# """ conv3 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 128, 256], # 256 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv3_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 256, 256], # 256 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv3_2')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 256, 256], # 256 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv3_3')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool3')
# """ conv4 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 256, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv4_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv4_2')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv4_3')
#
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool4')
# conv4 = network
#
# """ conv5 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv5_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv5_2')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv5_3')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool5')
#
# network = FlattenLayer(network, name='vgg_flatten')
#
# # # network = DropoutLayer(network, keep=0.6, is_fix=True, is_train=is_train, name='vgg_out/drop1')
# # new_network = tl.layers.DenseLayer(network, n_units=4096,
# # act = tf.nn.relu,
# # name = 'vgg_out/dense')
# #
# # # new_network = DropoutLayer(new_network, keep=0.8, is_fix=True, is_train=is_train, name='vgg_out/drop2')
# # new_network = DenseLayer(new_network, z_dim, #num_lstm_units,
# # b_init=None, name='vgg_out/out')
# return conv4, network
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