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train_dfc_vae.py
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train_dfc_vae.py
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import dfc_vae_model as dfc
import tensorflow as tf
import numpy as np
import util
import matplotlib.pyplot as plt
import os
import sys
import imageio
############# Hyper-Parameters ################
### Adjust parameters in this part
BATCH_SIZE = 32
NUM_EPOCH = 10
VGG_LAYERS = ['conv1_1','conv2_1','conv3_1']
ALPHA = 1
BETA = 8e-6
LEARNING_RATE = 0.0001
IMG_HEIGHT = 64
IMG_WIDTH = 64
TRAINING_DATA = 'celeb_data_tfrecord'
IMG_MEAN = np.array([134.10714722, 102.52040863, 87.15436554])
IMG_STDDEV = np.sqrt(np.array([3941.30175781, 2856.94287109, 2519.35791016]))
###############################################
### restore checkpoint from "Checkpoint" folder
def _restore_checkpoint(saver, sess):
ckpt_path = os.path.dirname(os.path.join(os.getcwd(),'checkpoint/'))
ckpt = tf.train.get_checkpoint_state(ckpt_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("get checkpoint")
return ckpt_path
### create dataset and return iterator and dataset
def _get_data(training_data_tfrecord, batch_size):
dataset = util.read_tfrecord(training_data_tfrecord)
dataset = dataset.batch(batch_size)
iterator = dataset.make_initializable_iterator()
return iterator, dataset
def train_dfc_vae():
### setup hyper-parameter
batch_size = BATCH_SIZE
epoch = NUM_EPOCH
vgg_layers = VGG_LAYERS
alpha = ALPHA
beta = BETA
learning_rate = LEARNING_RATE
img_height = IMG_HEIGHT
img_width = IMG_WIDTH
training_data_tfrecord = TRAINING_DATA
### get training data
iterator, _ = _get_data(training_data_tfrecord, batch_size)
### create iterator's initializer for training data
iterator_init = iterator.initializer
data = iterator.get_next()
### define input data
img_input = (tf.reshape(data, shape=[-1, img_height, img_width, 3])-IMG_MEAN) / IMG_STDDEV
### build model graph
model = dfc.dfc_vae_model([img_height,img_width], img_input, alpha, beta, vgg_layers, learning_rate)
model.build_model(tf.AUTO_REUSE)
### create saver for restoring and saving variables
saver = tf.train.Saver()
with tf.Session() as sess:
### initialize global variable
sess.run(tf.global_variables_initializer())
### restore checkpoint
ckpt_path = _restore_checkpoint(saver,sess)
### load pre-trained vgg weights
model.load_vgg_weight('vgg16_weights.npz', sess)
### lists of losses, used for tracking
kl_loss = []
pct_loss = []
total_loss = []
iteration = []
### count how many training iteration (different from epoch)
iteration_count = 0
for i in range(epoch):
### initialize iterator
sess.run(iterator_init)
try:
while True:
sys.stdout.write('\r' + 'Iteration: ' + str(iteration_count))
sys.stdout.flush()
### every 100 iteration, print losses
if iteration_count % 100 == 0:
pct, kl, loss, tempmean, tempstd = sess.run([model.pct_loss, model.kl_loss, model.loss, model.mean, model.std])
pct = pct * beta
print("\nperceptual loss: {}, kl loss: {}, total loss: {}".format(pct,kl,loss))
print(tempmean[0,0:5])
print(tempstd[0,0:5])
iteration.append(iteration_count)
kl_loss.append(kl)
pct_loss.append(pct)
total_loss.append(loss)
### every 500 iteration, save images
if iteration_count % 500 == 0:
### get images from the dfc_vae_model
original_img, gen_img, ran_img = sess.run([model.img_input,model.gen_img,model.ran_img])
### denomalize
original_img = original_img*IMG_STDDEV + IMG_MEAN
gen_img = gen_img*IMG_STDDEV + IMG_MEAN
ran_img = ran_img*IMG_STDDEV + IMG_MEAN
### clip values to be in RGB range and transform to 0..1 float
original_img = np.clip(original_img,0.,255.).astype('float32')/255.
gen_img = np.clip(gen_img,0.,255.).astype('float32')/255.
ran_img = np.clip(ran_img,0.,255.).astype('float32')/255.
### save images in 5x5 grid
util.save_grid_img(original_img, os.path.join(os.getcwd(), 'outputs', str(i) + '_' + str(iteration_count) + '_orginal' + '.png'), img_height, img_width, 5, 5)
util.save_grid_img(gen_img, os.path.join(os.getcwd(), 'outputs', str(i) + '_' + str(iteration_count) + '_generated' + '.png'), img_height, img_width, 5, 5)
util.save_grid_img(ran_img, os.path.join(os.getcwd(), 'outputs', str(i) + '_' + str(iteration_count) + '_random' + '.png'), img_height, img_width, 5, 5)
### plot losses
plt.figure()
plt.plot(iteration, kl_loss)
plt.plot(iteration, pct_loss)
plt.plot(iteration, total_loss)
plt.legend(['kl loss', 'perceptual loss', 'total loss'], bbox_to_anchor=(1.05, 1), loc=2)
plt.title('Loss per iteration')
plt.show()
### run optimizer
sess.run(model.optimizer)
iteration_count += 1
except tf.errors.OutOfRangeError:
pass
### save session for each epoch
### recommend to change to save encoder, decoder, VGG's variables separately.
print("\nepoch: {}, loss: {}".format(i, loss))
saver.save(sess, os.path.join(ckpt_path,"Face_Vae"), global_step = iteration_count + model.gstep)
print("checkpoint saved")
def test_gen_img(model, sess, i):
real_img, gen_img, ran_img = sess.run([model.img_input, model.gen_img, model.ran_img])
ran_img = ran_img*IMG_STDDEV + IMG_MEAN
real_img = real_img*IMG_STDDEV + IMG_MEAN
gen_img = gen_img*IMG_STDDEV + IMG_MEAN
ran_img = ran_img/255.
real_img = real_img/255.
gen_img = gen_img/255.
util.save_grid_img(ran_img, os.path.join(os.getcwd(), 'outputs', 'test' , 'random-' + str(i) + '.png'), 64,64,8,8)
util.save_grid_img(real_img, os.path.join(os.getcwd(), 'outputs', 'test' , 'real-' + str(i) + '.png'), 64,64,8,8)
util.save_grid_img(gen_img, os.path.join(os.getcwd(), 'outputs', 'test' , 'gen-' + str(i) + '.png'), 64,64,8,8)
def test_interpolation(model, sess, i):
z1 = sess.run(model.z)
z2 = sess.run(model.z)
print(z1.shape)
print(z2.shape)
print(z1[0,:5])
print(z2[0,:5])
z = tf.Variable(np.zeros(z1.shape).astype(np.float32))
gen_img = model.decoder(z, tf.AUTO_REUSE)
interpolated_img_list = []
for j in range(31):
interpolated_z = z1 * (30-j)/30. + z2 * j/30.
sess.run(z.assign(interpolated_z))
interpolated_img = sess.run(gen_img)
interpolated_img = interpolated_img*IMG_STDDEV + IMG_MEAN
interpolated_img = interpolated_img/255.
interpolated_img = util.build_grid_img(interpolated_img, interpolated_img.shape[1], interpolated_img.shape[2],8,8)
interpolated_img_list.append(interpolated_img)
for j in range(31):
imageio.mimsave(os.path.join(os.getcwd(), 'outputs', 'test_interpolated' , 'interpolate' + str(i) + '.gif'), interpolated_img_list)
return interpolated_img_list
def test():
### setup hyper-parameter
num_test_set = 2
batch_size = 64
vgg_layers = VGG_LAYERS
img_height = IMG_HEIGHT
img_width = IMG_WIDTH
training_data_tfrecord = TRAINING_DATA
### get training data
iterator, _ = _get_data(training_data_tfrecord, batch_size)
### create iterator's initializer for training data
iterator_init = iterator.initializer
data = iterator.get_next()
### define input data
img_input = (tf.reshape(data, shape=[-1, img_height, img_width, 3]) - IMG_MEAN)/IMG_STDDEV
### build model graph
model = dfc.dfc_vae_model([img_height,img_width], img_input)
model.build_model(tf.AUTO_REUSE)
### create saver for restoring and saving variables
saver = tf.train.Saver()
with tf.Session() as sess:
### initialize global variable
sess.run(tf.global_variables_initializer())
### restore checkpoint
ckpt_path = _restore_checkpoint(saver,sess)
sess.run(iterator_init)
for i in range(num_test_set):
test_gen_img(model, sess, i)
x = test_interpolation(model, sess, i)
if __name__ == '__main__':
tf.reset_default_graph()
train_dfc_vae()
test()