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GAN.py
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import os
import time
import tensorflow as tf
import numpy as np
from glob import glob
import datetime
import random
from PIL import Image
import matplotlib.pyplot as plt
def generator(z, output_channel_dim, training):
with tf.variable_scope("generator", reuse= not training):
# 8x8x1024
fully_connected = tf.layers.dense(z, 8*8*1024)
fully_connected = tf.reshape(fully_connected, (-1, 8, 8, 1024))
fully_connected = tf.nn.leaky_relu(fully_connected)
# 8x8x1024 -> 16x16x512
trans_conv1 = tf.layers.conv2d_transpose(inputs=fully_connected,
filters=512,
kernel_size=[5,5],
strides=[2,2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name="trans_conv1")
batch_trans_conv1 = tf.layers.batch_normalization(inputs = trans_conv1,
training=training,
epsilon=EPSILON,
name="batch_trans_conv1")
trans_conv1_out = tf.nn.leaky_relu(batch_trans_conv1,
name="trans_conv1_out")
# 16x16x512 -> 32x32x256
trans_conv2 = tf.layers.conv2d_transpose(inputs=trans_conv1_out,
filters=256,
kernel_size=[5,5],
strides=[2,2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name="trans_conv2")
batch_trans_conv2 = tf.layers.batch_normalization(inputs = trans_conv2,
training=training,
epsilon=EPSILON,
name="batch_trans_conv2")
trans_conv2_out = tf.nn.leaky_relu(batch_trans_conv2,
name="trans_conv2_out")
# 32x32x256 -> 64x64x128
trans_conv3 = tf.layers.conv2d_transpose(inputs=trans_conv2_out,
filters=128,
kernel_size=[5,5],
strides=[2,2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name="trans_conv3")
batch_trans_conv3 = tf.layers.batch_normalization(inputs = trans_conv3,
training=training,
epsilon=EPSILON,
name="batch_trans_conv3")
trans_conv3_out = tf.nn.leaky_relu(batch_trans_conv3,
name="trans_conv3_out")
# 64x64x128 -> 128x128x64
trans_conv4 = tf.layers.conv2d_transpose(inputs=trans_conv3_out,
filters=64,
kernel_size=[5,5],
strides=[2,2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name="trans_conv4")
batch_trans_conv4 = tf.layers.batch_normalization(inputs = trans_conv4,
training=training,
epsilon=EPSILON,
name="batch_trans_conv4")
trans_conv4_out = tf.nn.leaky_relu(batch_trans_conv4,
name="trans_conv4_out")
# 128x128x64 -> 128x128x3
logits = tf.layers.conv2d_transpose(inputs=trans_conv4_out,
filters=3,
kernel_size=[5,5],
strides=[1,1],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name="logits")
out = tf.tanh(logits, name="out")
return out
def discriminator(x, reuse):
with tf.variable_scope("discriminator", reuse=reuse):
# 128*128*3 -> 64x64x64
conv1 = tf.layers.conv2d(inputs=x,
filters=64,
kernel_size=[5,5],
strides=[2,2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name='conv1')
batch_norm1 = tf.layers.batch_normalization(conv1,
training=True,
epsilon=EPSILON,
name='batch_norm1')
conv1_out = tf.nn.leaky_relu(batch_norm1,
name="conv1_out")
# 64x64x64-> 32x32x128
conv2 = tf.layers.conv2d(inputs=conv1_out,
filters=128,
kernel_size=[5, 5],
strides=[2, 2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name='conv2')
batch_norm2 = tf.layers.batch_normalization(conv2,
training=True,
epsilon=EPSILON,
name='batch_norm2')
conv2_out = tf.nn.leaky_relu(batch_norm2,
name="conv2_out")
# 32x32x128 -> 16x16x256
conv3 = tf.layers.conv2d(inputs=conv2_out,
filters=256,
kernel_size=[5, 5],
strides=[2, 2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name='conv3')
batch_norm3 = tf.layers.batch_normalization(conv3,
training=True,
epsilon=EPSILON,
name='batch_norm3')
conv3_out = tf.nn.leaky_relu(batch_norm3,
name="conv3_out")
# 16x16x256 -> 16x16x512
conv4 = tf.layers.conv2d(inputs=conv3_out,
filters=512,
kernel_size=[5, 5],
strides=[1, 1],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name='conv4')
batch_norm4 = tf.layers.batch_normalization(conv4,
training=True,
epsilon=EPSILON,
name='batch_norm4')
conv4_out = tf.nn.leaky_relu(batch_norm4,
name="conv4_out")
# 16x16x512 -> 8x8x1024
conv5 = tf.layers.conv2d(inputs=conv4_out,
filters=1024,
kernel_size=[5, 5],
strides=[2, 2],
padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
name='conv5')
batch_norm5 = tf.layers.batch_normalization(conv5,
training=True,
epsilon=EPSILON,
name='batch_norm5')
conv5_out = tf.nn.leaky_relu(batch_norm5,
name="conv5_out")
flatten = tf.reshape(conv5_out, (-1, 8*8*1024))
logits = tf.layers.dense(inputs=flatten,
units=1,
activation=None)
out = tf.sigmoid(logits)
return out, logits
def model_loss(input_real, input_z, output_channel_dim):
g_model = generator(input_z, output_channel_dim, True)
noisy_input_real = input_real + tf.random_normal(shape=tf.shape(input_real),
mean=0.0,
stddev=random.uniform(0.0, 0.1),
dtype=tf.float32)
d_model_real, d_logits_real = discriminator(noisy_input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_model_real)*random.uniform(0.9, 1.0)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_model_fake)))
d_loss = tf.reduce_mean(0.5 * (d_loss_real + d_loss_fake))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_model_fake)))
return d_loss, g_loss
def model_optimizers(d_loss, g_loss):
t_vars = tf.trainable_variables()
g_vars = [var for var in t_vars if var.name.startswith("generator")]
d_vars = [var for var in t_vars if var.name.startswith("discriminator")]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
gen_updates = [op for op in update_ops if op.name.startswith('generator')]
with tf.control_dependencies(gen_updates):
d_train_opt = tf.train.AdamOptimizer(learning_rate=LR_D, beta1=BETA1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate=LR_G, beta1=BETA1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
def model_inputs(real_dim, z_dim):
inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='inputs_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
learning_rate_G = tf.placeholder(tf.float32, name="lr_g")
learning_rate_D = tf.placeholder(tf.float32, name="lr_d")
return inputs_real, inputs_z, learning_rate_G, learning_rate_D
def show_samples(sample_images, name, epoch):
figure, axes = plt.subplots(1, len(sample_images), figsize = (IMAGE_SIZE, IMAGE_SIZE))
for index, axis in enumerate(axes):
axis.axis('off')
image_array = sample_images[index]
axis.imshow(image_array)
image = Image.fromarray(image_array)
image.save(name+"_"+str(epoch)+"_"+str(index)+".png")
plt.savefig(name+"_"+str(epoch)+".png", bbox_inches='tight', pad_inches=0)
def test(sess, input_z, out_channel_dim, epoch):
example_z = np.random.uniform(-1, 1, size=[SAMPLES_TO_SHOW, input_z.get_shape().as_list()[-1]])
samples = sess.run(generator(input_z, out_channel_dim, False), feed_dict={input_z: example_z})
sample_images = [((sample + 1.0) * 127.5).astype(np.uint8) for sample in samples]
show_samples(sample_images, OUTPUT_DIR + "samples", epoch)
def summarize_epoch(epoch, duration, sess, d_losses, g_losses, input_z, data_shape):
minibatch_size = int(data_shape[0]//BATCH_SIZE)
print("Epoch {}/{}".format(epoch, EPOCHS),
"\nDuration: {:.5f}".format(duration),
"\nD Loss: {:.5f}".format(np.mean(d_losses[-minibatch_size:])),
"\nG Loss: {:.5f}".format(np.mean(g_losses[-minibatch_size:])))
fig, ax = plt.subplots()
plt.plot(d_losses, label='Discriminator', alpha=0.6)
plt.plot(g_losses, label='Generator', alpha=0.6)
plt.title("Losses")
plt.legend()
plt.savefig(OUTPUT_DIR + "losses_" + str(epoch) + ".png")
test(sess, input_z, data_shape[3], epoch)
plt.close('all')
def get_batches(data):
batches = []
for i in range(int(data.shape[0]//BATCH_SIZE)):
batch = data[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
augmented_images = []
for img in batch:
image = Image.fromarray(img)
if random.choice([True, False]):
image = image.transpose(Image.FLIP_LEFT_RIGHT)
augmented_images.append(np.asarray(image))
batch = np.asarray(augmented_images)
normalized_batch = (batch / 127.5) - 1.0
batches.append(normalized_batch)
return batches
def train(get_batches, data_shape, checkpoint_to_load=None):
input_images, input_z, lr_G, lr_D = model_inputs(data_shape[1:], NOISE_SIZE)
d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
d_opt, g_opt = model_optimizers(d_loss, g_loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
epoch = 0
iteration = 0
d_losses = []
g_losses = []
for epoch in range(EPOCHS):
epoch += 1
start_time = time.time()
for batch_images in get_batches:
iteration += 1
batch_z = np.random.uniform(-1, 1, size=(BATCH_SIZE, NOISE_SIZE))
_ = sess.run(d_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr_D: LR_D})
_ = sess.run(g_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr_G: LR_G})
d_losses.append(d_loss.eval({input_z: batch_z, input_images: batch_images}))
g_losses.append(g_loss.eval({input_z: batch_z}))
summarize_epoch(epoch, time.time()-start_time, sess, d_losses, g_losses, input_z, data_shape)
# Paths
INPUT_DATA_DIR = r"D:\ML_Datasets\Anime_Faces_Dataset\data" # Path to the folder with input images. For more info check simspons_dataset.txt
OUTPUT_DIR = "C:/Users/Harry/source/repos/PracticeGit/output/"
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
# Hyperparameters
IMAGE_SIZE = 128
NOISE_SIZE = 100
LR_D = 0.00004
LR_G = 0.0004
BATCH_SIZE = 64
EPOCHS = 10000 # For better results increase this value
BETA1 = 0.5
WEIGHT_INIT_STDDEV = 0.02
EPSILON = 0.00005
SAMPLES_TO_SHOW = 5
images = []
for root, dirs, files in os.walk(INPUT_DATA_DIR):
for file in files:
images.append(os.path.join(root, file))
# Training
input_images = np.asarray([np.asarray(Image.open(file).resize([IMAGE_SIZE, IMAGE_SIZE])) for file in images])
print ("Input: " + str(input_images.shape))
np.random.shuffle(input_images)
sample_images = random.sample(list(input_images), SAMPLES_TO_SHOW)
show_samples(sample_images, OUTPUT_DIR + "inputs", 0)
with tf.Graph().as_default():
train(get_batches(input_images), input_images.shape)