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train.py
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train.py
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# coding: utf-8
import threading
import argparse
import progressbar
import time
from progressbar import Bar, ETA, Percentage, ProgressBar, SimpleProgress
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from keras import backend as K
from dataset import Dataset
from ops import *
from model import create_netD, create_netG, my_init
np.random.seed(1234)
tf.set_random_seed(1234)
sess = tf.Session()
K.set_session(sess)
# Parameters
parser = argparse.ArgumentParser(description='Training Pix2Pix Model')
parser.add_argument('--dataset', '-d', default='facades', help='Select the datasets from facades')
parser.add_argument('--out', '-o', default='./output_imgs', help='Directory path for generated images')
parser.add_argument('--batchsize', '-b', type=int, default=1, help='Number of images in each mini-batch')
parser.add_argument('--learningrate', '-l', type=float, default=0.0002, help='Learning rate')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1; momentum')
parser.add_argument('--epoch', '-e', type=int, default=200, help='Epoch')
parser.add_argument('--thread', '-t', type=int, default=1, help='num of thread')
parser.add_argument('--filter', '-f', type=int, default=4, help='kernel/filter size')
args = parser.parse_args()
mkdir(args.out)
ngf = 64
ndf = 64
batch_size = args.batchsize
nb_epochs = args.epoch
data = Dataset(dataset=args.dataset, batch_size=batch_size, thread_num=args.thread)
train_X, train_y = data.get_inputs()
test_img, test_label = load_image('./datasets/%s/val/%d.jpg' % (args.dataset, 1))
test_img = img_shift(test_img)
test_label = img_shift(test_label)
img_shape, label_shape = data.get_shape()
image_width = img_shape[0]
image_height = img_shape[1]
input_channel = label_shape[2]
output_channel = img_shape[2]
##############################################
# Generator
# U-NET
#
# CD512-CD1024-CD1024-C1024-C1024-C512-C256-C128
#
##############################################
tmp_x = tf.placeholder(tf.float32, [batch_size, image_width, image_height, input_channel])
D = create_netD(image_width, image_height, input_channel+output_channel, ndf, args.filter)
dec_output, generated_img, encoder_decoder = create_netG(train_X, tmp_x, ngf, args.filter, image_width, image_height, input_channel, output_channel, batch_size)
# ## Objective function
loss_d = tf.reduce_mean(tf.log(D(concat(train_X, train_y)) + 1e-12)) + tf.reduce_mean(tf.log(1 - D(concat(train_X, dec_output)) + 1e-12))
loss_g_1 = tf.reduce_mean(tf.log(1 - D(concat(train_X, dec_output)) + 1e-12))
loss_g_2 = tf.reduce_mean(tf.abs(train_y - dec_output))
loss_g = loss_g_1 + 100. * loss_g_2
# ## Optimizer
train_d = tf.train.AdamOptimizer(0.0002, beta1=0.5).minimize(-loss_d, var_list=D.trainable_weights)
train_g = tf.train.AdamOptimizer(0.0002, beta1=0.5).minimize(loss_g, var_list=[op for l in map(lambda x: x.trainable_weights, encoder_decoder) for op in l])
# ## Initialize
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess=sess)
data.start_threads(sess)
saver = tf.train.Saver()
mkdir('./model')
# # Training
print 'start training'
widgets = ['Train: ', Percentage(), '(', SimpleProgress(), ') ',Bar(marker='#', left='[', right=']'), ' ', ETA()]
for i in range(nb_epochs):
ave_d = []
ave_g = []
pbar = ProgressBar(widgets=widgets, maxval=data.get_size() - 1 )
pbar.start()
for j in range(data.get_size() - 1):
sess.run(train_d, feed_dict={K.learning_phase(): 1})
sess.run(train_g, feed_dict={K.learning_phase(): 1})
loss_d_val = sess.run(loss_d, feed_dict={K.learning_phase(): 1})
ave_d.append(loss_d_val)
ave_g.append(sess.run(loss_g, feed_dict={K.learning_phase(): 1}))
time.sleep(0.001)
pbar.update(j)
pbar.finish()
print "Epoch %d/%d - dis_loss: %g - gen_loss: %g" % (i+1, nb_epochs, np.mean(ave_d), np.mean(ave_g))
generated_image = sess.run(generated_img, feed_dict={tmp_x: [test_label], K.learning_phase(): 1})
save_image(generated_image[0], args.out + '/' + args.dataset , i+1)
saver.save(sess, './model/{}/model.ckpt'.format(args.dataset), global_step=i+1)