def __init__(self, flags):
#Initialize:
self.flags = flags
image_size = int(self.flags.image_size)
num_class = int(self.flags.num_class)
# Place holder
self.keep_probability = tf.placeholder(tf.float32,
name="keep_probabilty")
self.image = tf.placeholder(tf.float32,
shape=[None, image_size, image_size, 3],
name="input_image")
self.phase_train = tf.placeholder(tf.bool, name='phase_train')
self.pred_annotation, self.image_segment_logits, self.reconstruct_image = \
self.inference(self.image, self.keep_probability, self.phase_train, self.flags)
self.colorized_pred_annotation = utils.batch_colorize(
self.pred_annotation, 0, num_class, self.flags.cmap)
self.loss = tf.reduce_mean(
tf.reshape((self.image - self.reconstruct_image)**2, shape=[-1]))
# Train var and op
trainable_var = tf.trainable_variables()
if self.flags.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
self.learning_rate, self.train_op = self.train(self.loss,
trainable_var,
self.flags)
self.learning_rate_summary = tf.summary.scalar("learning_rate",
self.learning_rate)
# Summary
print("Setting up summary op...")
tf.summary.image("input_image", self.image, max_outputs=2)
tf.summary.image("reconstruct_image",
self.reconstruct_image,
max_outputs=2)
tf.summary.image("pred_annotation",
self.colorized_pred_annotation,
max_outputs=2)
self.loss_summary = tf.summary.scalar("total_loss", self.loss)
self.summary_op = tf.summary.merge_all()
# Session ,saver, and writer
print("Setting up Session and Saver...")
self.sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True))
self.saver = tf.train.Saver(max_to_keep=2)
self.train_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'train'), self.sess.graph)
self.validation_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'validation'))
print("Initialize tf variables")
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.flags.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
print("Model restored...")
return
def __init__(self, flags):
"""
Initialize:
placeholder,
train_op,
summary,
session,
saver and file_writer
"""
self.flags = flags
image_size = int(self.flags.image_size)
num_class = int(self.flags.num_class)
# Place holder
self.keep_probability = tf.placeholder(tf.float32,
name="keep_probabilty")
self.image = tf.placeholder(tf.float32,
shape=[None, image_size, image_size, 3],
name="input_image")
self.phase_train = tf.placeholder(tf.bool, name='phase_train')
self.neighbor_indeces = tf.placeholder(tf.int64,
name="neighbor_indeces")
self.neighbor_vals = tf.placeholder(tf.float32, name="neighbor_vals")
self.neighbor_shape = tf.placeholder(tf.int64, name="neighbor_shape")
neighbor_filter = (self.neighbor_indeces, self.neighbor_vals,
self.neighbor_shape)
_image_weights = brightness_weight(self.image,
neighbor_filter,
sigma_I=0.05)
image_weights = convert_to_batchTensor(*_image_weights)
# Prediction and loss
self.pred_annotation, self.image_segment_logits, self.reconstruct_image = \
self.inference(self.image, self.keep_probability, self.phase_train, self.flags)
image_segment = tf.nn.softmax(self.image_segment_logits)
self.colorized_pred_annotation = utils.batch_colorize(
self.pred_annotation, 0, num_class, self.flags.cmap)
self.reconstruct_loss = tf.reduce_mean(
tf.reshape((self.image - self.reconstruct_image)**2, shape=[-1]))
batch_soft_ncut = soft_ncut(self.image, image_segment, image_weights)
self.soft_ncut = tf.reduce_mean(batch_soft_ncut)
self.loss = self.reconstruct_loss + self.soft_ncut
# Train var and op
trainable_var = tf.trainable_variables()
encode_trainable_var = tf.trainable_variables("infer_encode")
if self.flags.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
self.reconst_learning_rate, self.train_reconst_op = \
self.train(self.reconstruct_loss, trainable_var, self.flags)
self.softNcut_learning_rate, self.train_softNcut_op = \
self.train(self.soft_ncut, encode_trainable_var, self.flags)
self.reconst_learning_rate_summary = tf.summary.scalar(
"reconst_learning_rate", self.reconst_learning_rate)
self.softNcut_learning_rate_summary = tf.summary.scalar(
"softNcut_learning_rate", self.softNcut_learning_rate)
# Summary
tf.summary.image("input_image", self.image, max_outputs=2)
tf.summary.image("reconstruct_image",
self.reconstruct_image,
max_outputs=2)
tf.summary.image("pred_annotation",
self.colorized_pred_annotation,
max_outputs=2)
reconstLoss_summary = tf.summary.scalar("reconstruct_loss",
self.reconstruct_loss)
softNcutLoss_summary = tf.summary.scalar("soft_ncut_loss",
self.soft_ncut)
totLoss_summary = tf.summary.scalar("total_loss", self.loss)
self.loss_summary = tf.summary.merge(
[reconstLoss_summary, softNcutLoss_summary, totLoss_summary])
self.summary_op = tf.summary.merge_all()
# Session ,saver, and writer
print("Setting up Session and Saver...")
self.sess = tf.Session()
self.saver = tf.train.Saver(max_to_keep=2)
# create two summary writers to show training loss and validation loss in the same graph
# need to create two folders 'train' and 'validation' inside FLAGS.logs_dir
self.train_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'train'), self.sess.graph)
self.validation_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'validation'))
print("Initialize tf variables")
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.flags.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
print("Model restored...")
return
def __init__(self, flags):
"""
Initialize:
placeholder,
train_op,
summary,
session,
saver and file_writer
"""
self.flags = flags
image_size = int(self.flags.image_size)
num_class = int(self.flags.num_class)
# Place holder
self.image = tf.placeholder(tf.float32,
shape=[None, image_size, image_size, 3],
name="input_image")
self.annotation = tf.placeholder(
tf.int32,
shape=[None, image_size, image_size, num_class],
name="annotation")
self.phase_train = tf.placeholder(tf.bool, name='phase_train')
self.keep_probability = tf.placeholder(tf.float32,
name="keep_probabilty")
# Prediction and loss
self.pred_annotation, self.image_segment_logits = \
self.inference(self.image, self.keep_probability, self.phase_train, self.flags)
image_segment = tf.nn.softmax(self.image_segment_logits)
colorized_annotation = tf.argmax(self.annotation, axis=3)
colorized_annotation = tf.expand_dims(colorized_annotation, dim=3)
self.colorized_annotation = utils.batch_colorize(
colorized_annotation, 0, num_class, self.flags.cmap)
self.colorized_pred_annotation = utils.batch_colorize(
self.pred_annotation, 0, num_class, self.flags.cmap)
self.loss = tf.reduce_mean((tf.nn.softmax_cross_entropy_with_logits(
logits=self.image_segment_logits,
labels=self.annotation,
name="entropy")))
# Train var and op
trainable_var = tf.trainable_variables()
if self.flags.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
self.learning_rate, self.train_op = self.train(self.loss,
trainable_var,
self.flags)
self.learning_rate_summary = tf.summary.scalar("learning_rate",
self.learning_rate)
# Summary
print("Setting up summary op...")
tf.summary.image("input_image", self.image, max_outputs=2)
tf.summary.image("annotation",
self.colorized_annotation,
max_outputs=2)
tf.summary.image("pred_annotation",
self.colorized_pred_annotation,
max_outputs=2)
self.loss_summary = tf.summary.scalar("total_loss", self.loss)
self.summary_op = tf.summary.merge_all()
# Session ,saver, and writer
print("Setting up Session and Saver...")
self.sess = tf.Session()
self.saver = tf.train.Saver(max_to_keep=2)
# create two summary writers to show training loss and validation loss in the same graph
# need to create two folders 'train' and 'validation' inside FLAGS.logs_dir
self.train_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'train'), self.sess.graph)
self.validation_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'validation'))
print("Initialize tf variables")
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.flags.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
print("Model restored...")
return
def __init__(self, flags):
"""
Initialize:
placeholder,
train_op,
summary,
session,
saver and file_writer
"""
self.flags = flags
self.use_soft_ncut = self.flags.soft_ncut
image_size = int(self.flags.image_size)
num_class = int(self.flags.num_class)
# Place holder
self.keep_probability = tf.placeholder(tf.float32,
name="keep_probabilty")
self.image = tf.placeholder(tf.float32,
shape=[None, image_size, image_size, 4],
name="input_image")
self.vis = self.image[..., 0:3]
self.ice = tf.expand_dims(self.image[..., 3], axis=-1)
self.annotation = tf.placeholder(tf.float32,
shape=[None, image_size, image_size],
name="annotation")
self.phase_train = tf.placeholder(tf.bool, name='phase_train')
# Prediction and loss
self.pred_annotation, self.image_segment_logits, self.reconstruct_ice = \
self.inference(self.image, self.keep_probability, self.phase_train, self.flags)
image_segment = tf.nn.softmax(self.image_segment_logits)
self.colorized_pred_annotation = utils.batch_colorize(
self.pred_annotation, 0, num_class, self.flags.cmap)
self.reconstruct_loss = tf.reduce_mean(
tf.reshape(((self.ice - self.reconstruct_ice) / 255)**2,
shape=[-1]))
if self.use_soft_ncut:
# TODO: USE ICE ACTUALLY so that it's unsupervised
# batch_soft_ncut = global_soft_ncut(self.annotation, image_segment)
batch_soft_ncut = soft_n_cut_loss(self.annotation, image_segment, \
num_class, self.flags.image_size, self.flags.image_size)
self.soft_ncut = tf.reduce_mean(batch_soft_ncut)
self.loss = self.reconstruct_loss + self.soft_ncut
else:
self.loss = self.reconstruct_loss
# Train var and op
trainable_var = tf.trainable_variables()
encode_trainable_var = tf.trainable_variables("infer_encode")
if self.flags.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
self.reconst_learning_rate, self.train_reconst_op = \
self.train(self.reconstruct_loss, trainable_var, self.flags)
if self.use_soft_ncut:
self.softNcut_learning_rate, self.train_softNcut_op = \
self.train(self.soft_ncut, encode_trainable_var, self.flags)
self.reconst_learning_rate_summary = tf.summary.scalar(
"reconst_learning_rate", self.reconst_learning_rate)
if self.use_soft_ncut:
self.softNcut_learning_rate_summary = tf.summary.scalar(
"softNcut_learning_rate", self.softNcut_learning_rate)
# Summary
tf.summary.image("input_vis", self.vis, max_outputs=2)
tf.summary.image("input_ice", self.ice, max_outputs=2)
tf.summary.image("reconstruct_ice",
self.reconstruct_ice,
max_outputs=2)
tf.summary.image("pred_annotation",
self.colorized_pred_annotation,
max_outputs=2)
reconstLoss_summary = tf.summary.scalar("reconstruct_loss",
self.reconstruct_loss)
if self.use_soft_ncut:
softNcutLoss_summary = tf.summary.scalar("soft_ncut_loss",
self.soft_ncut)
totLoss_summary = tf.summary.scalar("total_loss", self.loss)
self.loss_summary = tf.summary.merge(
[reconstLoss_summary, softNcutLoss_summary, totLoss_summary])
else:
self.loss_summary = reconstLoss_summary
self.summary_op = tf.summary.merge_all()
# Session ,saver, and writer
print("Setting up Session and Saver...")
self.sess = tf.Session()
self.saver = tf.train.Saver(max_to_keep=2)
# create two summary writers to show training loss and validation loss in the same graph
# need to create two folders 'train' and 'validation' inside FLAGS.logs_dir
self.train_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'train'), self.sess.graph)
self.validation_writer = tf.summary.FileWriter(
os.path.join(self.flags.logs_dir, 'validation'))
print("Initialize tf variables")
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.flags.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
print("Model restored...")
return
