def main(args=None):
print(args)
tf.reset_default_graph()
"""
Dataset Parser
"""
# Parse Dataset
aaai_parser = dataset_parser.AAAIParser('./dataset/AAAI',
target_height=FLAGS.image_height,
target_width=FLAGS.image_width)
aaai_parser.load_mat_train_paths()
"""
Pre-trained Model
"""
model_vgg19 = VGG19(weights='imagenet', include_top=False)
model_vgg16_pool5 = model_vgg16.get_layer('block5_pool').output
model_vgg16_pool4 = model_vgg16.get_layer('block4_pool').output
inputs = model_vgg16.input
model_vgg16_pool4 = Model(inputs=inputs, outputs=outputs)
batch, batch_size = 0, 1
x_batch, y_batch = aaai_parser.load_mat_train_datum_batch(
batch * batch_size, (batch + 1) * batch_size)
x_batch = np.array(x_batch, dtype=np.float32)
# y_batch = np.array(y_batch, dtype=np.int32)
x = keras.applications.vgg16.preprocess_input(x_batch[:, :, :, :3])
block4_pool_features = model_vgg16_pool4.predict(x)
print('hi')
def main(args=None):
print(args)
tf.reset_default_graph()
"""
Dataset Parser
"""
# Parse Dataset
aaai_parser = dataset_parser.AAAIParser('./dataset/AAAI',
target_height=FLAGS.image_height, target_width=FLAGS.image_width)
aaai_parser.load_mat_train_dd_paths()
# Hyper-parameters
epochs, batch_size = FLAGS.epochs, FLAGS.batch_size
data_len = len(aaai_parser.mat_train_paths)
print(data_len)
batches = data_len // batch_size
"""
Build Graph
"""
global_step = tf.Variable(0, trainable=False)
# Placeholder
learning_rate = tf.placeholder(tf.float32)
is_training = tf.placeholder(tf.bool)
drop_probability = tf.placeholder(tf.float32, name="drop_probability")
data_x = tf.placeholder(tf.float32, shape=[None, None, None, FLAGS.num_of_feature],
name="data_x")
data_y = tf.placeholder(tf.int32, shape=[None, None, None],
name="data_y")
"""
Network
"""
logits = simple_ae(x=data_x, drop_probability=drop_probability, is_training=is_training)
# Loss
loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=data_y, name="entropy")))
"""
Optimizer
"""
trainable_var = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='simple_ae')
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = tf.train.AdamOptimizer(learning_rate).minimize(
loss=loss, global_step=global_step, var_list=trainable_var)
"""
Graph Logs
"""
tf.summary.scalar("entropy", loss)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=2)
"""
Launch Session
"""
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/events', sess.graph)
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir + '/model')
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored: {}".format(ckpt.model_checkpoint_path))
else:
print("No Model found.")
if FLAGS.mode == 'train':
cur_learning_rate = FLAGS.learning_rate
for epoch in range(0, epochs):
np.random.shuffle(aaai_parser.mat_train_paths)
for batch in range(0, batches):
x_batch, y_batch = aaai_parser.load_mat_train_datum_batch(batch*batch_size, (batch+1)*batch_size)
x_batch = np.array(x_batch, dtype=np.float32)[:, :, :, :4]
y_batch = np.array(y_batch, dtype=np.int32)
feed_dict = {data_x: x_batch, data_y: y_batch,
drop_probability: 0.2, is_training: True, learning_rate: cur_learning_rate}
_, loss_sess, global_step_sess = sess.run([train_op, loss, global_step], feed_dict=feed_dict)
print('global_setp: {:d}, epoch: [{:d}/{:d}], batch: [{:d}/{:d}], data: {:d}-{:d}, loss: {:f}'
.format(global_step_sess, epoch, epochs, batch, batches,
batch*batch_size, (batch+1)*batch_size, loss_sess))
if global_step_sess % 10 == 1:
summary_str = sess.run(summary_op, feed_dict={
data_x: x_batch, data_y: y_batch, drop_probability: 0.0, is_training: False})
summary_writer.add_summary(summary_str, global_step_sess)
if global_step_sess % 5000 == 1:
logits_sess = sess.run(logits, feed_dict={
data_x: x_batch, drop_probability: 0.0, is_training: False})
print('Logging images..')
for batch_idx, mat_train_paths in \
enumerate(aaai_parser.mat_train_paths[batch*batch_size:(batch+1)*batch_size]):
name = mat_train_paths.split('/')[-1].split('.')[0]
scipy.misc.imsave('{}/images/{:d}_{}_0_rgb.png'.format(
FLAGS.logs_dir, global_step_sess, name), x_batch[batch_idx, :, :, :3])
scipy.misc.imsave('{}/images/{:d}_{}_1_s.png'.format(
FLAGS.logs_dir, global_step_sess, name), x_batch[batch_idx, :, :, 3])
# scipy.misc.imsave('{}/images/{:d}_{}_2_d.png'.format(
# FLAGS.logs_dir, global_step_sess, name), x_batch[batch_idx, :, :, 4])
scipy.misc.imsave('{}/images/{:d}_{}_3_gt.png'.format(
FLAGS.logs_dir, global_step_sess, name), y_batch[batch_idx])
scipy.misc.imsave('{}/images/{:d}_{}_4_pred.png'.format(
FLAGS.logs_dir, global_step_sess, name), np.argmax(logits_sess[batch_idx], axis=2))
if global_step_sess % 1500 == 0:
print('Saving model...')
saver.save(sess, FLAGS.logs_dir + "/model/model.ckpt", global_step=global_step_sess)
elif FLAGS.mode == 'test':
print(aaai_parser.test_name)
aaai_parser.load_mat_test_paths()
for idx, mat_valid_path in enumerate(aaai_parser.mat_test_paths):
mat_contents = sio.loadmat(mat_valid_path)
x = mat_contents['sample'][0][0]['RGBSD']
x_batch = np.array([x], dtype=np.float32)[:, :, :, :4]
feed_dict = {data_x: x_batch, drop_probability: 0.0, is_training: False}
logits_sess = sess.run(logits, feed_dict=feed_dict)
print('[{:d}/{:d}]'.format(idx, len(aaai_parser.mat_test_paths)))
name = mat_valid_path.split('/')[-1].split('.')[0]
scipy.misc.imsave('{}/test/{:d}_{}_0_rgb.png'.format(
FLAGS.logs_dir, idx, name), x_batch[0, :, :, :3])
scipy.misc.imsave('{}/test/{:d}_{}_1_s.png'.format(
FLAGS.logs_dir, idx, name), x_batch[0, :, :, 3])
# scipy.misc.imsave('{}/test/{:d}_{}_2_d.png'.format(
# FLAGS.logs_dir, idx, name), x_batch[0, :, :, 4])
scipy.misc.imsave('{}/test/{:d}_{}_4_pred.png'.format(FLAGS.logs_dir, idx, name),
np.argmax(logits_sess[0], axis=2))
mat_contents['pred'] = logits_sess
sio.savemat('./dataset/AAAI/{}_wo/{}'.format(
aaai_parser.test_name, name), {'a_dict': mat_contents})
import dataset_parser
import numpy as np
import scipy.io as sio
aaai_parser = dataset_parser.AAAIParser('./dataset/AAAI',
target_height=256,
target_width=256)
aaai_parser.load_mat_valid_paths()
mat_contents = sio.loadmat(aaai_parser.mat_valid_paths[0])
mat_contents['pred'] = np.zeros((256, 256), dtype=np.uint8)
name = aaai_parser.mat_valid_paths[0].split('/')[-1]
sio.savemat(name, {'a_dict': mat_contents})
def main(args=None):
print(args)
tf.reset_default_graph()
"""
Dataset Parser
"""
# Parse Dataset
aaai_parser = dataset_parser.AAAIParser('./dataset/AAAI',
target_height=FLAGS.image_height,
target_width=FLAGS.image_width)
aaai_parser.load_mat_train_paths()
# Hyper-parameters
epochs, batch_size = FLAGS.epochs, FLAGS.batch_size
data_len = len(aaai_parser.mat_train_paths)
print(data_len)
batches = data_len // batch_size
"""
Build Graph
"""
# TODO: learning rate decay
global_step = tf.Variable(0, trainable=False)
# Placeholder
with tf.variable_scope("placeholder"):
learning_rate = tf.placeholder(tf.float32)
is_training = tf.placeholder(tf.bool)
drop_probability = tf.placeholder(tf.float32, name="drop_probability")
data_x = tf.placeholder(tf.float32,
shape=[
None, FLAGS.image_height,
FLAGS.image_width, FLAGS.num_of_feature
],
name="data_x")
data_y = tf.placeholder(
tf.int32,
shape=[None, FLAGS.image_height, FLAGS.image_width],
name="data_y")
"""
Pre-trained Model
"""
with tf.variable_scope("VGG19"):
data_preprocess_x = data_x[:, :, :, :3] - np.array(
[103.939, 116.779, 123.68])
model_vgg19 = VGG19(weights='imagenet',
include_top=False,
input_tensor=data_preprocess_x)
model_vgg16_pool5 = model_vgg19.get_layer('block5_pool').output
model_vgg16_pool4 = model_vgg19.get_layer('block4_pool').output
model_vgg16_pool3 = model_vgg19.get_layer('block3_pool').output
model_vgg16_pool2 = model_vgg19.get_layer('block2_pool').output
model_vgg16_pool1 = model_vgg19.get_layer('block1_pool').output
with tf.variable_scope("network"):
logits = deconv_with_vgg19(x=data_x,
drop_probability=drop_probability,
is_training=is_training,
vgg_pool5=model_vgg16_pool5,
vgg_pool4=model_vgg16_pool4,
vgg_pool3=model_vgg16_pool3,
vgg_pool2=model_vgg16_pool2,
vgg_pool1=model_vgg16_pool1)
# Loss
with tf.variable_scope("loss"):
loss = tf.reduce_mean(
(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=data_y,
name="entropy")))
"""
Optimizer
"""
trainable_var = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='network/deconv_with_vgg19')
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = tf.train.AdamOptimizer(learning_rate).minimize(
loss=loss, global_step=global_step, var_list=trainable_var)
"""
Graph Logs
"""
tf.summary.scalar("entropy", loss)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=2)
"""
Launch Session
"""
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, sess.graph)
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored!")
else:
print("No Model found.")
cur_learning_rate = FLAGS.learning_rate
for epoch in range(0, epochs):
np.random.shuffle(aaai_parser.mat_train_paths)
for batch in range(0, batches):
x_batch, y_batch = aaai_parser.load_mat_train_datum_batch(
batch * batch_size, (batch + 1) * batch_size)
x_batch = np.array(x_batch, dtype=np.float32)
y_batch = np.array(y_batch, dtype=np.int32)
feed_dict = {
data_x: x_batch,
data_y: y_batch,
drop_probability: 0.2,
is_training: True,
learning_rate: cur_learning_rate
}
_, loss_sess, global_step_sess = sess.run(
[train_op, loss, global_step], feed_dict=feed_dict)
print(
'global_setp: {:d}, epoch: [{:d}/{:d}], batch: [{:d}/{:d}], data: {:d}-{:d}, loss: {:f}'
.format(global_step_sess, epoch, epochs, batch, batches,
batch * batch_size, (batch + 1) * batch_size,
loss_sess))
if global_step_sess % 10 == 1:
summary_str = sess.run(summary_op,
feed_dict={
data_x: x_batch,
data_y: y_batch,
drop_probability: 0.0,
is_training: False
})
summary_writer.add_summary(summary_str, global_step_sess)
if global_step_sess % 150 == 1:
logits_sess = sess.run(logits,
feed_dict={
data_x: x_batch,
drop_probability: 0.0,
is_training: False
})
print('Logging images..')
for batch_idx, mat_train_paths in \
enumerate(aaai_parser.mat_train_paths[batch*batch_size:(batch+1)*batch_size]):
name = mat_train_paths.split('/')[-1].split('.')[0]
scipy.misc.imsave(
'{}/images/{:d}_{}_0_rgb.png'.format(
FLAGS.logs_dir, global_step_sess, name),
x_batch[batch_idx, :, :, :3])
scipy.misc.imsave(
'{}/images/{:d}_{}_1_s.png'.format(
FLAGS.logs_dir, global_step_sess, name),
x_batch[batch_idx, :, :, 3])
scipy.misc.imsave(
'{}/images/{:d}_{}_2_d.png'.format(
FLAGS.logs_dir, global_step_sess, name),
x_batch[batch_idx, :, :, 4])
scipy.misc.imsave(
'{}/images/{:d}_{}_3_gt.png'.format(
FLAGS.logs_dir, global_step_sess, name),
y_batch[batch_idx])
scipy.misc.imsave(
'{}/images/{:d}_{}_4_pred.png'.format(
FLAGS.logs_dir, global_step_sess, name),
np.argmax(logits_sess[batch_idx], axis=2))
if global_step_sess % 500 == 0:
print('Saving model...')
saver.save(sess,
FLAGS.logs_dir + "/model.ckpt",
global_step=global_step_sess)
