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model.py
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model.py
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#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
# from tensorflow.python.ops import variable_scope as vs
# from tensorflow.python.ops import math_ops, init_ops, array_ops, nn
# from tensorflow.python.util import nest
# from tensorflow.contrib.rnn.python.ops import core_rnn_cell
# https://github.com/david-gpu/srez/blob/master/srez_model.py
## new generator- denser skip
def SRGAN_g(t_image, is_train=False, reuse=False):
""" Generator 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=0.01)
# w_init = tf.contrib.layers.variance_scaling_initializer()
# w_init = tf.contrib.layers.xavier_initializer()
# b_init = tf.constant_initializer(value=0.0)
b_init = None
# g_init = tf.random_normal_initializer(1., 0.01)
g_init = tf.ones_initializer()
lrelu = lambda x : tl.act.lrelu(x, 0.2)
with tf.variable_scope("SRGAN_g", reuse=reuse) as vss:
tl.layers.set_name_reuse(reuse)
n = InputLayer(t_image, name='in')
n = Conv2d(n, 128, (9, 9), (1, 1), act=None, padding='SAME', W_init=w_init, name='n64s1/c')
temp = []
# B residual blocks
for i in range(16):
nn = Conv2d(n, 128, (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=lrelu, is_train=is_train, gamma_init=g_init, name='n64s1/b1/%s' % i)
nn = Conv2d(nn, 128, (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, 'b_residual_add/%s' % i)
n = nn
t = Conv2d(nn, 128, (1, 1), (1, 1), act=None, padding='SAME', W_init=w_init,b_init=b_init, name='n64s1/c3/%s' % i)
temp.append(t)
# 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[0],temp[1],temp[2],temp[3],
temp[4],temp[5],temp[6],temp[7],
temp[8],temp[9],temp[10],temp[11],
temp[12],temp[13],temp[14]], tf.add, 'add3')
# B residual blacks end
n = Conv2d(n, 512, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/1')
n = SubpixelConv2d(n, scale=2, act=lrelu, n_out_channel=None, name='pixelshufflerx2/1')
n = Conv2d(n, 512, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init ,name='n256s1/2')
n = SubpixelConv2d(n, scale=2, act=lrelu, n_out_channel=None, name='pixelshufflerx2/2')
n = Conv2d(n, 3, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='out')
return n
def SRGAN_d(t_image, is_train=True, 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.truncated_normal_initializer(stddev=0.01)
w_init = tf.contrib.layers.variance_scaling_initializer()
b_init = tf.constant_initializer(value=0.0)
# g_init = tf.random_normal_initializer(1., 0.02)
g_init = tf.ones_initializer()
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)
n = InputLayer(t_image, name='in')
n = Conv2d(n, 64, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init,b_init=b_init, name='n64s1/c')
n = Conv2d(n, 64, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n64s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n64s2/b')
n = Conv2d(n, 128, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n128s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n128s1/b')
n = Conv2d(n, 128, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n128s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n128s2/b')
n = Conv2d(n, 256, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n256s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n256s1/b')
n = Conv2d(n, 256, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n256s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n256s2/b')
n = Conv2d(n, 512, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n512s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n512s1/b')
n = Conv2d(n, 512, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n512s2/c')
n = BatchNormLayer(n, act=lrelu, 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 SRGAN_d2(input_images, is_train=True, reuse=False):
# w_init = tf.random_normal_initializer(stddev=0.01)
w_init = tf.contrib.layers.variance_scaling_initializer()
b_init = None # tf.constant_initializer(value=0.0)
# gamma_init=tf.random_normal_initializer(1., 0.02)
gamma_init = tf.ones_initializer()
df_dim = 64
lrelu = lambda x: tl.act.lrelu(x, 0.2)
with tf.variable_scope("SRGAN_d", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_in = InputLayer(input_images, name='input/images')
net_h0 = Conv2d(net_in, df_dim, (4, 4), (2, 2), act=lrelu,
padding='SAME', W_init=w_init, name='h0/c')
net_h1 = Conv2d(net_h0, df_dim*2, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h1/c')
net_h1 = BatchNormLayer(net_h1, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h1/bn')
net_h2 = Conv2d(net_h1, df_dim*4, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h2/c')
net_h2 = BatchNormLayer(net_h2, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h2/bn')
net_h3 = Conv2d(net_h2, df_dim*8, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h3/c')
net_h3 = BatchNormLayer(net_h3, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h3/bn')
net_h4 = Conv2d(net_h3, df_dim*16, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h4/c')
net_h4 = BatchNormLayer(net_h4, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h4/bn')
net_h5 = Conv2d(net_h4, df_dim*32, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h5/c')
net_h5 = BatchNormLayer(net_h5, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h5/bn')
net_h6 = Conv2d(net_h5, df_dim*16, (1, 1), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h6/c')
net_h6 = BatchNormLayer(net_h6, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h6/bn')
net_h7 = Conv2d(net_h6, df_dim*8, (1, 1), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h7/c')
net_h7 = BatchNormLayer(net_h7, is_train=is_train,
gamma_init=gamma_init, name='h7/bn')
net = Conv2d(net_h7, df_dim*2, (1, 1), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='res/c')
net = BatchNormLayer(net, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='res/bn')
net = Conv2d(net, df_dim*2, (3, 3), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='res/c2')
net = BatchNormLayer(net, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='res/bn2')
net = Conv2d(net, df_dim*8, (3, 3), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='res/c3')
net = BatchNormLayer(net, is_train=is_train,
gamma_init=gamma_init, name='res/bn3')
net_h8 = ElementwiseLayer(layer=[net_h7, net],
combine_fn=tf.add, name='res/add')
net_h8.outputs = tl.act.lrelu(net_h8.outputs, 0.2)
net_ho = FlattenLayer(net_h8, name='ho/flatten')
net_ho = DenseLayer(net_ho, n_units=1, act=tf.identity,
W_init = w_init, name='ho/dense')
logits = net_ho.outputs
net_ho.outputs = tf.nn.sigmoid(net_ho.outputs)
return net_ho, logits
def SRGAN_vgg_d2(t_image, is_train=True, reuse=False):
# w_init = tf.truncated_normal_initializer(stddev=0.01)
w_init = tf.contrib.layers.variance_scaling_initializer()
b_init = tf.constant_initializer(value=0.0)
# g_init = tf.random_normal_initializer(1., 0.02)
g_init = tf.ones_initializer()
feat_dim = 64
lrelu = lambda x : tl.act.lrelu(x, 0.2)
with tf.variable_scope("SRGAN_vgg_d", reuse=reuse) as vs:
tl.layers.set_name_reuse(reuse)
n = InputLayer(t_image, name='in')
n = Conv2d(n, feat_dim, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init,b_init=b_init, name='n64s1/c')
n = Conv2d(n, feat_dim, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n64s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n64s2/b')
n = Conv2d(n, feat_dim*2, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n128s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n128s1/b')
n = Conv2d(n, feat_dim*2, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n128s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n128s2/b')
n = Conv2d(n, feat_dim*4, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n256s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n256s1/b')
n = Conv2d(n, feat_dim*4, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n256s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n256s2/b')
n = Conv2d(n, feat_dim*8, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n512s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n512s1/b')
n = Conv2d(n, feat_dim*8, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n512s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n512s2/b')
# network = Conv2d(network, n_filter=1024, filter_size=(3, 3),
# strides=(1,1), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init,name='conv6')
# network = Conv2d(network, n_filter=1024, filter_size=(3, 3),
# strides=(1,1), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init,name='conv7')
# network = Conv2d(network, n_filter=1, filter_size=(3, 3),
# strides=(1,1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='out')
# logits = network.outputs
# network.outputs = tf.nn.sigmoid(network.outputs)
n = Conv2d(n, 1024, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='d1024')
n = Conv2d(n, 1, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='out')
# 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 SRGAN_vgg_d(t_image, is_train=True, reuse=False):
# w_init = tf.truncated_normal_initializer(stddev=0.01)
w_init = tf.contrib.layers.variance_scaling_initializer()
b_init = tf.constant_initializer(value=0.0)
# g_init = tf.random_normal_initializer(1., 0.02)
g_init = tf.ones_initializer()
feat_dim = 64
lrelu = lambda x : tl.act.lrelu(x, 0.2)
with tf.variable_scope("SRGAN_vgg_d", reuse=reuse) as vs:
tl.layers.set_name_reuse(reuse)
n = InputLayer(t_image, name='in')
n = Conv2d(n, feat_dim, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init,b_init=b_init, name='n64s1/c')
n = Conv2d(n, feat_dim, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n64s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n64s2/b')
n = Conv2d(n, feat_dim*2, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n128s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n128s1/b')
n = Conv2d(n, feat_dim*2, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n128s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n128s2/b')
n = Conv2d(n, feat_dim*4, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n256s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n256s1/b')
n = Conv2d(n, feat_dim*4, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n256s2/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n256s2/b')
n = Conv2d(n, feat_dim*8, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=b_init, name='n512s1/c')
n = BatchNormLayer(n, act=lrelu, is_train=is_train, gamma_init=g_init, name='n512s1/b')
n = Conv2d(n, feat_dim*8, (3, 3), (2, 2), padding='SAME', W_init=w_init, b_init=b_init, name='n512s2/c')
n = BatchNormLayer(n, act=lrelu, 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 = tf.maximum(0.0,tf.minimum(rgb,1.0))
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')
conv2 = network
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')
conv3 = network
""" 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')
conv4 = network
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
padding='SAME', name='pool4') # (batch_size, 14, 14, 512)
""" 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')
conv5 = network
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, conv5