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model.py
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model.py
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# Basic Code is taken from https://github.com/ckmarkoh/GAN-tensorflow
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from scipy.misc import imsave
import os
import shutil
from PIL import Image
import time
import random
from layers import *
img_height = 256
img_width = 256
img_layer = 3
img_size = img_height * img_width
batch_size = 1
pool_size = 50
ngf = 32
ndf = 64
def build_resnet_block(inputres, dim, name="resnet"):
with tf.variable_scope(name):
out_res = tf.pad(inputres, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
out_res = general_conv2d(out_res, dim, 3, 3, 1, 1, 0.02, "VALID","c1")
out_res = tf.pad(out_res, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
out_res = general_conv2d(out_res, dim, 3, 3, 1, 1, 0.02, "VALID","c2",do_relu=False)
return tf.nn.relu(out_res + inputres)
def build_generator_resnet_6blocks(inputgen, name="generator"):
with tf.variable_scope(name):
f = 7
ks = 3
pad_input = tf.pad(inputgen,[[0, 0], [ks, ks], [ks, ks], [0, 0]], "REFLECT")
o_c1 = general_conv2d(pad_input, ngf, f, f, 1, 1, 0.02,name="c1")
o_c2 = general_conv2d(o_c1, ngf*2, ks, ks, 2, 2, 0.02,"SAME","c2")
o_c3 = general_conv2d(o_c2, ngf*4, ks, ks, 2, 2, 0.02,"SAME","c3")
o_r1 = build_resnet_block(o_c3, ngf*4, "r1")
o_r2 = build_resnet_block(o_r1, ngf*4, "r2")
o_r3 = build_resnet_block(o_r2, ngf*4, "r3")
o_r4 = build_resnet_block(o_r3, ngf*4, "r4")
o_r5 = build_resnet_block(o_r4, ngf*4, "r5")
o_r6 = build_resnet_block(o_r5, ngf*4, "r6")
o_c4 = general_deconv2d(o_r6, [batch_size,64,64,ngf*2], ngf*2, ks, ks, 2, 2, 0.02,"SAME","c4")
o_c5 = general_deconv2d(o_c4, [batch_size,128,128,ngf], ngf, ks, ks, 2, 2, 0.02,"SAME","c5")
o_c5_pad = tf.pad(o_c5,[[0, 0], [ks, ks], [ks, ks], [0, 0]], "REFLECT")
o_c6 = general_conv2d(o_c5_pad, img_layer, f, f, 1, 1, 0.02,"VALID","c6",do_relu=False)
# Adding the tanh layer
out_gen = tf.nn.tanh(o_c6,"t1")
return out_gen
def build_generator_resnet_9blocks(inputgen, name="generator"):
with tf.variable_scope(name):
f = 7
ks = 3
pad_input = tf.pad(inputgen,[[0, 0], [ks, ks], [ks, ks], [0, 0]], "REFLECT")
o_c1 = general_conv2d(pad_input, ngf, f, f, 1, 1, 0.02,name="c1")
o_c2 = general_conv2d(o_c1, ngf*2, ks, ks, 2, 2, 0.02,"SAME","c2")
o_c3 = general_conv2d(o_c2, ngf*4, ks, ks, 2, 2, 0.02,"SAME","c3")
o_r1 = build_resnet_block(o_c3, ngf*4, "r1")
o_r2 = build_resnet_block(o_r1, ngf*4, "r2")
o_r3 = build_resnet_block(o_r2, ngf*4, "r3")
o_r4 = build_resnet_block(o_r3, ngf*4, "r4")
o_r5 = build_resnet_block(o_r4, ngf*4, "r5")
o_r6 = build_resnet_block(o_r5, ngf*4, "r6")
o_r7 = build_resnet_block(o_r6, ngf*4, "r7")
o_r8 = build_resnet_block(o_r7, ngf*4, "r8")
o_r9 = build_resnet_block(o_r8, ngf*4, "r9")
o_c4 = general_deconv2d(o_r9, [batch_size,128,128,ngf*2], ngf*2, ks, ks, 2, 2, 0.02,"SAME","c4")
o_c5 = general_deconv2d(o_c4, [batch_size,256,256,ngf], ngf, ks, ks, 2, 2, 0.02,"SAME","c5")
o_c6 = general_conv2d(o_c5, img_layer, f, f, 1, 1, 0.02,"SAME","c6",do_relu=False)
# Adding the tanh layer
out_gen = tf.nn.tanh(o_c6,"t1")
return out_gen
def build_gen_discriminator(inputdisc, name="discriminator"):
with tf.variable_scope(name):
f = 4
o_c1 = general_conv2d(inputdisc, ndf, f, f, 2, 2, 0.02, "SAME", "c1", do_norm=False, relufactor=0.2)
o_c2 = general_conv2d(o_c1, ndf*2, f, f, 2, 2, 0.02, "SAME", "c2", relufactor=0.2)
o_c3 = general_conv2d(o_c2, ndf*4, f, f, 2, 2, 0.02, "SAME", "c3", relufactor=0.2)
o_c4 = general_conv2d(o_c3, ndf*8, f, f, 1, 1, 0.02, "SAME", "c4",relufactor=0.2)
o_c5 = general_conv2d(o_c4, 1, f, f, 1, 1, 0.02, "SAME", "c5",do_norm=False,do_relu=False)
return o_c5
def patch_discriminator(inputdisc, name="discriminator"):
with tf.variable_scope(name):
f= 4
patch_input = tf.random_crop(inputdisc,[1,70,70,3])
o_c1 = general_conv2d(patch_input, ndf, f, f, 2, 2, 0.02, "SAME", "c1", do_norm="False", relufactor=0.2)
o_c2 = general_conv2d(o_c1, ndf*2, f, f, 2, 2, 0.02, "SAME", "c2", relufactor=0.2)
o_c3 = general_conv2d(o_c2, ndf*4, f, f, 2, 2, 0.02, "SAME", "c3", relufactor=0.2)
o_c4 = general_conv2d(o_c3, ndf*8, f, f, 2, 2, 0.02, "SAME", "c4", relufactor=0.2)
o_c5 = general_conv2d(o_c4, 1, f, f, 1, 1, 0.02, "SAME", "c5",do_norm=False,do_relu=False)
return o_c5