def run_train_loop(self):
params = init.TrainingParamInitialization()
self.gan = CloudGAN(params)
self.train_img, self.train_alpha, self.train_reflectance =\
self.data_prepare.preprocess(self.gan.G_sample, self.gan.G_alpha, self.gan.G_relectance)
self.validate_img, self.validate_alpha, self.validate_reflectance = \
self.data_prepare.preprocess(self.gan.G_sample, self.gan.G_alpha,self.gan.G_relectance)
print('build matting net')
with tf.name_scope('train'):
train_op = self.train_op(self.train_img, self.train_alpha,
self.train_reflectance)
with tf.name_scope('validate'):
validate_op = self.validate_op(self.validate_img,
self.validate_alpha,
self.validate_reflectance,
reuse=True)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
self.load_checkpoints(sess)
step = 0
log_step = 50
saver = tf.train.Saver()
merge_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(self.logdir, sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists('rst'):
os.mkdir('rst')
while step < self.iter_step:
feed_dict = None
self.gan.run_train(sess)
X_mb = utils.get_batch(self.gan.data, self.batch_size,
'cloudimage', self.img_size)
Z_mb = utils.get_batch(self.gan.data, self.batch_size,
'cloudimage', self.img_size)
BG_mb = utils.get_batch(self.gan.data, self.batch_size,
'background', self.img_size)
feed_dict = {
self.gan.X: X_mb,
self.gan.Z: Z_mb,
self.gan.BG: BG_mb
}
[_, train_loss, step] = sess.run(train_op, feed_dict=feed_dict)
if np.mod(step, log_step) == 1:
X_mb = utils.get_batch(self.gan.data, self.batch_size,
'cloudimage', self.img_size)
Z_mb = utils.get_batch(self.gan.data, self.batch_size,
'cloudimage', self.img_size)
BG_mb = utils.get_batch(self.gan.data, self.batch_size,
'background', self.img_size)
feed_dict = {
self.gan.X: X_mb,
self.gan.Z: Z_mb,
self.gan.BG: BG_mb
}
merges = [merge_op] + validate_op
summary, image, alpha_image, reflectance_image, validate_loss = sess.run(
merges, feed_dict=feed_dict)
summary_writer.add_summary(summary, step)
print("step:%d,loss:%f,validate_loss:%f" %
(step, train_loss, validate_loss))
misc.imsave('rst/' + str(step) + '_image.png', image)
misc.imsave('rst/' + str(step) + '_alpha.png', alpha_image)
misc.imsave('rst/' + str(step) + '_foreground.png',
reflectance_image)
if np.mod(step, 500) == 1:
saver.save(sess, os.path.join(self.model_path, 'model'), step)
With this project, you can train a model to solve the following
inverse problems:
- on MNIST and CIFAR-10 datasets for separating superimposed images.
- image denoising on MNIST
- remove speckle and streak noise in CAPTCHAs
All the above tasks are trained w/ or w/o the help of pair-wise supervision.
"""
import tensorflow as tf
import tensorflow.contrib.slim as slim
import initializer as init
# All parameters used in this file
Params = init.TrainingParamInitialization()
def generator(Y, is_training):
# define the number of filters in each conv layer
mm = 64
if Params.task_name in ['unmixing_mnist_mnist', 'denoising', 'captcha']:
n_channels = 1
else:
n_channels = 3
G_input_size = Params.G_input_size
with tf.variable_scope('G_scope'):