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cGanEncoderGeneratorDiskriminatorSequental.py
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cGanEncoderGeneratorDiskriminatorSequental.py
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import numpy as np
import pickle
import cv2
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten,BatchNormalization,LeakyReLU,Reshape,Conv2DTranspose,Dropout, Embedding, Concatenate, Activation, MaxPooling2D, ReLU
from tensorflow.keras import Input
from tensorflow.keras.models import Model
from tensorflow.keras.utils import plot_model
from tensorflow.keras.optimizers import RMSprop, Adam
import os
from tensorflow.keras.preprocessing import image
import random
IMAGE_DIR="result_cGAN"
INPUT_DIR="imdb_resized"
NUM_IMAGES = 0 #len(age_difference)
NUM_SHIFT = 0
LOAD_IMAGES = True
LOAD_WEIGHTS=True
INPUT_MODEL="gan48step_23500.h5"
LATENT_DIM=122
MODEL_SAVE="models_cGAN"
ITERATIONS=80000
BATCH_SIZE=32
HEIGHT=64
WIDTH=64
CHANNELS=3
LEARNING_RATE=0.0002
TRAIN_ADV=True
LEARNING_RELATION_ADV=1
TRAIN_DISC=True
LEARNING_RELATION_DISC=2
TRAIN_GEN=False
LEARNING_RELATION_GEN = 1
if LOAD_IMAGES:
data = []
for i in range(NUM_IMAGES):
img = image.load_img(os.path.join(INPUT_DIR,str(i+NUM_SHIFT) + ".jpg"))
img = image.img_to_array(img)/(255./2.)-1
if type(img) != np.ndarray:
print("wiki_resized\\" + str(i) + ".jpg")
print("is " + str(type(img)))
continue
data.append(img)
if (i+1)%10000==0:
print(str(i) + " images loaded")
def generator(latent_dim=122,y_dim=6,gen_dim=2):
input_latent = Input((latent_dim,))
input_label= Input((y_dim,))
input_gen = Input((gen_dim,))
gen = Concatenate()([input_latent, input_label,input_gen])
gen = Dense(1024*4*4)(gen)
gen = ReLU()(gen)
gen = Reshape((4,4,1024))(gen)
gen = Conv2DTranspose(512,4,strides=2,padding="same")(gen) #8,8
gen = ReLU()(gen)
gen = Conv2DTranspose(256,4,strides=2,padding="same")(gen) #8,8
#gen = BatchNormalization()(gen)
gen = ReLU()(gen)
gen = Conv2DTranspose(128,4,strides=2,padding="same")(gen) #8,8
gen = ReLU()(gen)
gen = Conv2DTranspose(CHANNELS,4,strides=2,padding="same")(gen) #8,8
gen = Activation("tanh")(gen)
gen = Model([input_latent,input_label,input_gen],gen)
#plot_model(gen, to_file="model.png")
return gen
def discriminator(height,width,channels,label_dim_1,label_dim_2,learning_rate):
input_image = Input((height,width,channels))
input_label = Input((label_dim_1,))
input_gen = Input((label_dim_2,))
label = Concatenate()([input_label, input_gen])
label = Dense(HEIGHT * WIDTH)(label)
label = LeakyReLU()(label)
label = Reshape((HEIGHT, WIDTH, 1))(label)
disc = Concatenate()([input_image,label])
disc = Conv2D(128, 4,strides=2,padding="same")(disc)
disc = LeakyReLU()(disc)
disc = Conv2D(256, 4,strides=2,padding="same")(disc)
disc = LeakyReLU()(disc)
disc = Conv2D(512, 4,strides=2,padding="same")(disc)
#disc = BatchNormalization()(disc)
disc = LeakyReLU()(disc)
disc = Conv2D(1024, 4,strides=2,padding="same")(disc)
disc = LeakyReLU()(disc)
disc = Flatten()(disc)
disc = Dropout(0.4)(disc)
disc = Dense(1, activation="sigmoid")(disc)
disc = Model([input_image,input_label,input_gen],disc)
discriminator_optimizer = Adam(lr=LEARNING_RATE,beta_1=0.5)
disc.compile(discriminator_optimizer, loss="binary_crossentropy",metrics=['binary_accuracy'])
return disc
def cGan(generator_model,discriminator_model,learning_rate):
discriminator_model.trainable=False
input_latent, input_label_1,input_label_2 = generator_model.input
print(input_latent.shape)
print(input_label_1.shape)
generator_output = generator_model([input_latent, input_label_1,input_label_2])
gan_output = discriminator_model([generator_output, input_label_1,input_label_2])
gan = Model([input_latent, input_label_1,input_label_2], gan_output)
gan_optimizer = Adam(lr=learning_rate * LEARNING_RELATION_ADV,beta_1=0.5)
gan.compile(optimizer=gan_optimizer, loss="binary_crossentropy")
return gan
def encoder(height,width,channels,label_dim,latent_dim,generator_model):
input_image = Input((height,width,channels))
enc = Conv2D(128, 3,padding="same")(input_image)
enc = BatchNormalization()(enc)
enc = LeakyReLU()(enc)
enc = Conv2D(256, 4, strides=2,padding="same")(enc)
enc = BatchNormalization()(enc)
enc = LeakyReLU()(enc)
enc = Conv2D(512, 4, strides=2,padding="same")(enc)
enc = BatchNormalization()(enc)
enc = LeakyReLU()(enc)
enc = Conv2D(128, 4, strides=2,padding="same")(enc)
enc = BatchNormalization()(enc)
enc = LeakyReLU()(enc)
enc = Flatten()(enc)
enc = Dense((latent_dim))(enc)
enc = Activation("tanh")(enc)
enc = Model(input_image,enc)
return enc
def fullEnkoder(encoder_model,generator_model,learning_rate):
input_latent, input_label = generator_model.input
input_image = encoder_model.input
print(input_image.shape)
generator_model.trainable = False
encoder_output=encoder_model(input_image)
output= generator_model([encoder_output, input_label])
enc_gen = Model([input_image, input_label], output)
generator_optimizer = Adam(lr=learning_rate)
enc_gen.compile(generator_optimizer, loss="binary_crossentropy")
return enc_gen
gen = generator()
disc = discriminator(HEIGHT,WIDTH,CHANNELS,6,2,LEARNING_RATE)
gan = cGan(gen,disc,LEARNING_RATE)
print(gan.summary())
print(gen.summary())
print(disc.summary())
start = 0
if LOAD_WEIGHTS:
gan.load_weights(MODEL_SAVE+"\\"+INPUT_MODEL)
age_difference=to_categorical(age_difference)
for step in range(ITERATIONS):
random_labels = np.random.randint(0,6,BATCH_SIZE)
random_labels = to_categorical(random_labels, 6)
stop=start+BATCH_SIZE
real_images=data[start:stop]
real_labels = age_difference[start:stop]
latent_space = np.random.normal(size=(BATCH_SIZE,LATENT_DIM))
generated_images=gen.predict([latent_space,random_labels])
combined_images=np.concatenate([generated_images,real_images])
combined_labels = np.concatenate([random_labels,real_labels])
labels=np.concatenate([np.ones((BATCH_SIZE))-0.05*np.random.random((BATCH_SIZE)),
np.zeros((BATCH_SIZE))+0.05*np.random.random((BATCH_SIZE))])
if TRAIN_GEN:
g_loss = enc_gen.train_on_batch([real_images,real_labels],real_images)
else:
g_loss = None
if TRAIN_DISC:
d_loss=disc.train_on_batch([combined_images,combined_labels],labels)
else:
d_loss=None
random_labels = np.random.randint(0, 6, (BATCH_SIZE))
random_labels = to_categorical(random_labels,6)
misleading_targets= np.zeros((BATCH_SIZE))
if TRAIN_ADV:
a_loss = gan.train_on_batch([latent_space,random_labels],misleading_targets)
else:
a_loss = None
start += BATCH_SIZE
if start > len(data) - BATCH_SIZE:
start=0
if step%10==0:
print("step: ", step)
print("discriminator loss:", d_loss)
print("adversial loss", a_loss)
print("generator_loss", g_loss)
if step%50 == 0:
if(step%500==0):
gan.save_weights(MODEL_SAVE+"\\gan" + str(len(os.listdir(MODEL_SAVE)))+ "step_" + str(step) + ".h5")
img = image.array_to_img((real_images[0] + 1) * (255. / 2), scale=False)
img.save(os.path.join(IMAGE_DIR,
"real" + str(len(os.listdir(IMAGE_DIR))) + "step_" + str(step) + "_age_" + str(
real_labels[0]) + ".jpg"))
print("--------------------------------------")
print("step: ",step)
print("discriminator loss:", d_loss)
print("adversial loss", a_loss)
print("generator_loss", g_loss)
img= image.array_to_img((generated_images[0]+1)*(255./2), scale=False)
img.save(os.path.join(IMAGE_DIR,"generated" + str(len(os.listdir(IMAGE_DIR))) + "step_" + str(step) + "_age_" + str(random_labels[0]) + ".jpg"))