def Discriminator(inputs,targets):
traindir = os.path.join(logdir, 'G2\\pix2pix_D')
if tf.gfile.Exists(traindir):
tf.gfile.DeleteRecursively(traindir)
tf.gfile.MakeDirs(traindir)
fiber_output,fiber_input = inputs
encoder, label = targets
with tf.variable_scope('Generator'):
with tf.variable_scope('G2'):
generated_data = pix2pix_G(fiber_input) * circle(FLAGS.input_size,FLAGS.input_size)
with tf.variable_scope('Discriminator',reuse=tf.AUTO_REUSE):
discriminator_gen_outputs = pix2pix_D(tf.concat((generated_data,fiber_input),-1))
discriminator_real_outputs = pix2pix_D(tf.concat((label, fiber_input), -1))
with tf.name_scope('Train_summary'):
reshaped_label = get_summary_image(label,FLAGS.grid_size)
reshaped_fiber_input = get_summary_image(fiber_input,FLAGS.grid_size)
reshaped_generated_data = get_summary_image(generated_data,FLAGS.grid_size)
tf.summary.image('Fiber_Label', reshaped_label)
tf.summary.image('Fiber_Input', reshaped_fiber_input)
tf.summary.image('Generated_Data', reshaped_generated_data)
with tf.name_scope('Train_Loss'):
predict_real = discriminator_real_outputs
predict_fake = discriminator_gen_outputs
discrim_real_loss = tf.reduce_mean(tf.abs(1-predict_real))
discrim_gen_loss = tf.reduce_mean(tf.abs(-1-predict_fake))
discrim_loss = discrim_real_loss + discrim_gen_loss
total_loss = discrim_loss + tf.losses.get_regularization_loss()
total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
tf.summary.scalar('Total_loss',total_loss)
tf.summary.scalar('discrim_loss', discrim_loss)
tf.summary.scalar('discrim_real_loss',discrim_real_loss)
tf.summary.scalar('discrim_gen_loss',discrim_gen_loss)
with tf.name_scope('Train_OP'):
tf.summary.scalar('predict_real', tf.reduce_mean(predict_real))
tf.summary.scalar('predict_fake', tf.reduce_mean(predict_fake))
tf.summary.scalar('discrim_lr', get_lr(FLAGS.discriminator_lr,decay_steps=5000))
train_op = slim.learning.create_train_op(total_loss,
get_optimizer(get_lr(FLAGS.discriminator_lr,decay_steps=5000)),
update_ops =tf.get_collection(tf.GraphKeys.UPDATE_OPS),
variables_to_train=
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Discriminator')
)
slim.learning.train(train_op, traindir,
number_of_steps =FLAGS.max_iter,
log_every_n_steps=FLAGS.log_n_steps,
init_fn=get_init_fn('E:\GitHub\MMFI\log\\G2\\pix2pix_G',
inclusion_scope=['Generator/G2']),
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs = FLAGS.save_interval_secs)
def Generator_2(inputs,targets):
traindir = os.path.join(logdir, 'G2\\pix2pix_G')
if tf.gfile.Exists(traindir):
tf.gfile.DeleteRecursively(traindir)
tf.gfile.MakeDirs(traindir)
fiber_output,fiber_input = inputs
encoder, label = targets
with tf.variable_scope('Generator'):
with tf.variable_scope('G2'):
generated_data = pix2pix_G(fiber_input) * circle(FLAGS.input_size,FLAGS.input_size)
with tf.name_scope('Train_summary'):
reshaped_fiber_input = get_summary_image(fiber_input,FLAGS.grid_size)
reshaped_label = get_summary_image(label,FLAGS.grid_size)
reshaped_generated_data = get_summary_image(generated_data,FLAGS.grid_size)
tf.summary.image('Fiber_Input', reshaped_fiber_input)
tf.summary.image('Fiber_Label', reshaped_label)
tf.summary.image('Generated_Data', reshaped_generated_data)
with tf.name_scope('g2_loss'):
G2_loss = combine_loss(generated_data, label, add_summary=True)
with tf.name_scope('Train_Loss'):
reg_loss = tf.losses.get_regularization_loss()
total_loss = G2_loss + reg_loss
total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
tf.summary.scalar('Regularization_loss',reg_loss)
tf.summary.scalar('G2_loss', G2_loss)
tf.summary.scalar('Total_loss',total_loss)
lr = get_lr(FLAGS.generator_lr)
optimizer = get_optimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = slim.learning.create_train_op(total_loss, optimizer, update_ops =update_ops,
variables_to_train=
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Generator/G2')
)
with tf.name_scope('Train_ops'):
psnr = tf.reduce_mean(tf.image.psnr(generated_data, label, max_val=1.0))
ssim = tf.reduce_mean(tf.image.ssim(generated_data, label, max_val=1.0))
corr = correlation(generated_data, label)
tf.summary.scalar('PSNR', psnr)
tf.summary.scalar('SSIM', ssim)
tf.summary.scalar('Relation', corr)
tf.summary.scalar('Learning_rate', lr)
slim.learning.train(train_op, traindir,
number_of_steps =FLAGS.max_iter,
log_every_n_steps=FLAGS.log_n_steps,
# init_fn=get_init_fn('E:\GitHub\MMFI\log\\G2\\pix2pix_G'),
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs = FLAGS.save_interval_secs)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
logdir = 'E:\GitHub\MMFI\log\GG12\\CNN'
evaldir = os.path.join(logdir, 'eval')
if not tf.gfile.Exists(evaldir):
# tf.gfile.DeleteRecursively(evaldir)
tf.gfile.MakeDirs(evaldir)
with tf.name_scope('inputs'):
fiber_output, fiber_input, encoder, label = data_loader.read_inputs('valid.txt', False)
with tf.variable_scope('Generator'):
with tf.variable_scope('G1'):
generated_input = pix2pix_G(fiber_output, is_training=False) \
* circle(FLAGS.input_size,FLAGS.input_size)
with tf.variable_scope('G2'):
generated_data = pix2pix_G(generated_input,is_training=False)\
* circle(FLAGS.input_size,FLAGS.input_size)
with tf.name_scope('Valid_summary'):
reshaped_fiber_input = get_summary_image(fiber_input, FLAGS.grid_size)
reshaped_label = get_summary_image(label, FLAGS.grid_size)
reshaped_generated_input = get_summary_image(generated_input, FLAGS.grid_size)
reshaped_generated_data = get_summary_image(generated_data, FLAGS.grid_size)
tf.summary.image('Input_Fiber', reshaped_fiber_input)
tf.summary.image('Input_Generator', reshaped_generated_input)
tf.summary.image('Data_Real', reshaped_label)
tf.summary.image('Data_Generator', reshaped_generated_data)
with tf.name_scope('Valid_op'):
psnr = tf.reduce_mean(tf.image.psnr(generated_data, label, max_val=1.0))
ssim = tf.reduce_mean(tf.image.ssim(generated_data, label, max_val=1.0))
corr = correlation(generated_data, label)
# inception_score = get_inception_score(generated_data)
tf.summary.scalar('PSNR', psnr)
tf.summary.scalar('SSIM', ssim)
tf.summary.scalar('Relation', corr)
grate = tf.ones([1,FLAGS.grid_size*FLAGS.input_size,10,1],dtype=tf.float32)
reshaped_images = tf.concat((reshaped_generated_input, grate,
reshaped_fiber_input, grate,
reshaped_label, grate,
reshaped_generated_data, grate), 2)
uint8_images = tf.cast(reshaped_images*255, tf.uint8)
image_write_ops = tf.write_file('%s/%s' % (evaldir, 'Generator_is_training_False.png'),
tf.image.encode_png(uint8_images[0]))
status_message = tf.string_join([' PSNR: ', tf.as_string(psnr), ' ',
' SSIM: ', tf.as_string(ssim), ' ',
' Correlation: ', tf.as_string(corr)],
name='status_message')
checkpoint_path = tf.train.latest_checkpoint(logdir)
tf.logging.info('Evaluating %s' % checkpoint_path)
tf.contrib.training.evaluate_once(
checkpoint_path,
hooks=[tf.contrib.training.SummaryAtEndHook(evaldir),
tf.contrib.training.StopAfterNEvalsHook(50),
tf.train.LoggingTensorHook([status_message],every_n_iter=5)],
eval_ops=image_write_ops)