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model_mosaic.py Normal file
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from layer import *
class Model:
def __init__(self, x, mosaic, mask, local_x, global_completion, local_completion, is_training, batch_size):
self.batch_size = batch_size
self.merged = x * (1 - mask) + mosaic * (mask)
self.imitation = self.generator(self.merged, is_training)
self.completion = self.imitation * mask + x * (1 - mask)
self.real = self.discriminator(x, local_x, reuse=False)
self.fake = self.discriminator(global_completion, local_completion, reuse=True)
self.g_loss = self.calc_g_loss(x, self.completion)
self.d_loss = self.calc_d_loss(self.real, self.fake)
self.g_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
self.d_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
def generator(self, x, is_training):
with tf.variable_scope('generator'):
with tf.variable_scope('conv1'):
x = conv_layer(x, [5, 5, 3, 64], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv2'):
x = conv_layer(x, [3, 3, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv3'):
x = conv_layer(x, [3, 3, 128, 128], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv4'):
x = conv_layer(x, [3, 3, 128, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv5'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv6'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated1'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated2'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 4)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated3'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 8)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated4'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 16)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv7'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv8'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('deconv1'):
x = deconv_layer(x, [4, 4, 128, 256], [self.batch_size, 64, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv9'):
x = conv_layer(x, [3, 3, 128, 128], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('deconv2'):
x = deconv_layer(x, [4, 4, 64, 128], [self.batch_size, 128, 128, 64], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv10'):
x = conv_layer(x, [3, 3, 64, 32], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv11'):
x = conv_layer(x, [3, 3, 32, 3], 1)
x = tf.nn.tanh(x)
return x
def discriminator(self, global_x, local_x, reuse):
def global_discriminator(x):
is_training = tf.constant(True)
with tf.variable_scope('global'):
with tf.variable_scope('conv1'):
x = conv_layer(x, [5, 5, 3, 64], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv2'):
x = conv_layer(x, [5, 5, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv3'):
x = conv_layer(x, [5, 5, 128, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv4'):
x = conv_layer(x, [5, 5, 256, 512], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv5'):
x = conv_layer(x, [5, 5, 512, 512], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('fc'):
x = flatten_layer(x)
x = full_connection_layer(x, 1024)
return x
def local_discriminator(x):
is_training = tf.constant(True)
with tf.variable_scope('local'):
with tf.variable_scope('conv1'):
x = conv_layer(x, [5, 5, 3, 64], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv2'):
x = conv_layer(x, [5, 5, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv3'):
x = conv_layer(x, [5, 5, 128, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv4'):
x = conv_layer(x, [5, 5, 256, 512], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('fc'):
x = flatten_layer(x)
x = full_connection_layer(x, 1024)
return x
with tf.variable_scope('discriminator', reuse=reuse):
global_output = global_discriminator(global_x)
local_output = local_discriminator(local_x)
with tf.variable_scope('concatenation'):
output = tf.concat((global_output, local_output), 1)
output = full_connection_layer(output, 1)
return output
def calc_g_loss(self, x, completion):
loss = tf.nn.l2_loss(x - completion)
return tf.reduce_mean(loss)
def calc_d_loss(self, real, fake):
alpha = 4e-4
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real, labels=tf.ones_like(real)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake, labels=tf.zeros_like(fake)))
return tf.add(d_loss_real, d_loss_fake) * alpha

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import numpy as np
import tensorflow as tf
from PIL import Image, ImageFilter
import tqdm
from model_mosaic import Model
import load
IMAGE_SIZE = 128
LOCAL_SIZE = 64
HOLE_MIN = 24
HOLE_MAX = 48
MOSAIC_MIN = 8 #Minimum number of mosaic squares across image
MOSAIC_MAX = 32 #Maximum number of mosaic squares across image
MOSAIC_GAUSSIAN_P = 0.5 #represent images that have been compressed post-mosaic
MOSAIC_GAUSSIAN_MIN = 0.2
MOSAIC_GAUSSIAN_MAX = 1.2
LEARNING_RATE = 1e-3
BATCH_SIZE = 16
PRETRAIN_EPOCH = 100
def train():
x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
mosaic = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
is_training = tf.placeholder(tf.bool, [])
model = Model(x, mosaic, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
sess = tf.Session()
global_step = tf.Variable(0, name='global_step', trainable=False)
epoch = tf.Variable(0, name='epoch', trainable=False)
opt = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
g_train_op = opt.minimize(model.g_loss, global_step=global_step, var_list=model.g_variables)
d_train_op = opt.minimize(model.d_loss, global_step=global_step, var_list=model.d_variables)
init_op = tf.global_variables_initializer()
sess.run(init_op)
if tf.train.get_checkpoint_state('./models'):
saver = tf.train.Saver()
saver.restore(sess, './models/latest')
x_train, x_test = load.load()
x_train = np.array([a / 127.5 - 1 for a in x_train])
x_test = np.array([a / 127.5 - 1 for a in x_test])
step_num = int(len(x_train) / BATCH_SIZE)
while True:
sess.run(tf.assign(epoch, tf.add(epoch, 1)))
print('epoch: {}'.format(sess.run(epoch)))
np.random.shuffle(x_train)
# Completion
if sess.run(epoch) <= PRETRAIN_EPOCH:
g_loss_value = 0
for i in tqdm.tqdm(range(step_num)):
x_batch = x_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
points_batch, mask_batch = get_points()
mosaic_batch = get_mosaic(x_batch)
_, g_loss = sess.run([g_train_op, model.g_loss], feed_dict={x: x_batch, mask: mask_batch, mosaic: mosaic_batch, is_training: True})
g_loss_value += g_loss
print('Completion loss: {}'.format(g_loss_value))
f = open("loss.csv","a+")
f.write(str(sess.run(epoch)) + "," + str(g_loss_value) + "," + "0" + "\n")
f.close()
np.random.shuffle(x_test)
x_batch = x_test[:BATCH_SIZE]
mosaic_batch = get_mosaic(x_batch)
merged, completion = sess.run([model.merged, model.completion], feed_dict={x: x_batch, mask: mask_batch, mosaic: mosaic_batch, is_training: False})
sample = np.array((merged[0] + 1) * 127.5, dtype=np.uint8)
result = Image.fromarray(sample)
result.save('./training_output_images/{}_0.png'.format("{0:06d}".format(sess.run(epoch))))
sample = np.array((completion[0] + 1) * 127.5, dtype=np.uint8)
result = Image.fromarray(sample)
result.save('./training_output_images/{}_1.png'.format("{0:06d}".format(sess.run(epoch))))
saver = tf.train.Saver()
saver.save(sess, './models/latest', write_meta_graph=False)
if sess.run(epoch) == PRETRAIN_EPOCH:
saver.save(sess, './models/pretrained', write_meta_graph=False)
# Discrimitation
else:
g_loss_value = 0
d_loss_value = 0
for i in tqdm.tqdm(range(step_num)):
x_batch = x_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
points_batch, mask_batch = get_points()
mosaic_batch = get_mosaic(x_batch)
_, g_loss, completion = sess.run([g_train_op, model.g_loss, model.completion], feed_dict={x: x_batch, mask: mask_batch, mosaic: mosaic_batch, is_training: True})
g_loss_value += g_loss
local_x_batch = []
local_completion_batch = []
for i in range(BATCH_SIZE):
x1, y1, x2, y2 = points_batch[i]
local_x_batch.append(x_batch[i][y1:y2, x1:x2, :])
local_completion_batch.append(completion[i][y1:y2, x1:x2, :])
local_x_batch = np.array(local_x_batch)
local_completion_batch = np.array(local_completion_batch)
_, d_loss = sess.run(
[d_train_op, model.d_loss],
feed_dict={x: x_batch, mask: mask_batch, local_x: local_x_batch, global_completion: completion, local_completion: local_completion_batch, is_training: True})
d_loss_value += d_loss
print('Completion loss: {}'.format(g_loss_value))
print('Discriminator loss: {}'.format(d_loss_value))
np.random.shuffle(x_test)
x_batch = x_test[:BATCH_SIZE]
mosaic_batch = get_mosaic(x_batch)
merged, completion = sess.run([model.merged, model.completion], feed_dict={x: x_batch, mask: mask_batch, mosaic: mosaic_batch, is_training: False})
sample = np.array((merged[0] + 1) * 127.5, dtype=np.uint8)
result = Image.fromarray(sample)
result.save('./training_output_images/{}_0.png'.format("{0:06d}".format(sess.run(epoch))))
sample = np.array((completion[0] + 1) * 127.5, dtype=np.uint8)
result = Image.fromarray(sample)
result.save('./training_output_images/{}_1.png'.format("{0:06d}".format(sess.run(epoch))))
saver = tf.train.Saver()
saver.save(sess, './models/latest', write_meta_graph=False)
def get_points():
points = []
mask = []
for i in range(BATCH_SIZE):
x1, y1 = np.random.randint(0, IMAGE_SIZE - LOCAL_SIZE + 1, 2)
x2, y2 = np.array([x1, y1]) + LOCAL_SIZE
points.append([x1, y1, x2, y2])
w, h = np.random.randint(HOLE_MIN, HOLE_MAX + 1, 2)
p1 = x1 + np.random.randint(0, LOCAL_SIZE - w)
q1 = y1 + np.random.randint(0, LOCAL_SIZE - h)
p2 = p1 + w
q2 = q1 + h
m = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 1), dtype=np.uint8)
m[q1:q2 + 1, p1:p2 + 1] = 1
mask.append(m)
return np.array(points), np.array(mask)
def get_mosaic(x_batch):
mosaic = []
for i in range(BATCH_SIZE):
im = np.array((x_batch[i] + 1) * 127.5, dtype=np.uint8)
im = Image.fromarray(im)
size = np.random.randint(MOSAIC_MIN, MOSAIC_MAX)
im = im.resize((size,size),Image.LANCZOS)
im = im.resize((IMAGE_SIZE,IMAGE_SIZE),Image.NEAREST)
if np.random.rand() < MOSAIC_GAUSSIAN_P:
im = im.filter(ImageFilter.GaussianBlur(np.random.uniform(MOSAIC_GAUSSIAN_MIN, MOSAIC_GAUSSIAN_MAX)))
mosaic.append(np.array(im))
mosaic = np.array([a / 127.5 - 1 for a in mosaic])
return mosaic
if __name__ == '__main__':
train()