def train(trainSetFileNames, testSetFileNames):
with open(trainSetFileNames) as f:
trainSet = f.read().splitlines()
with open(testSetFileNames) as f:
testSet = f.read().splitlines()
with tf.Graph().as_default():
# Global step variable for tracking processes.
global_step = tf.Variable(0, trainable=False)
# Train and Test Set feeds.
trainSet_batch, trainLabel_batch = input_pipeline(trainSet, FLAGS.batch_size)
testSet_batch, testLabel_batch = input_pipeline(testSet, FLAGS.batch_size)
# Placeholder to switch between train and test sets.
dataShape = [FLAGS.batch_size, FLAGS.input_size, FLAGS.input_size, FLAGS.input_depth]
labelShape = [FLAGS.batch_size, LSPGlobals.TotalLabels]
example_batch = tf.placeholder(tf.float32, shape=dataShape)
label_batch = tf.placeholder(tf.float32, shape=labelShape)
keepProb = tf.placeholder(tf.float32)
# Build a Graph that computes the logits predictions from the inference model.
logits = LSPModels.inference(example_batch, FLAGS.batch_size, keepProb=keepProb)
# Calculate loss.
loss, meanPixelError = LSPModels.loss(logits, label_batch)
# Build a Graph that trains the model with one batch of examples and updates the model parameters.
train_op = LSPModels.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(init)
stepinit = 0
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
stepinit = sess.run(global_step)
else:
print("No checkpoint found...")
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, graph=sess.graph)
for step in range(stepinit, FLAGS.max_steps):
start_time = time.time()
train_examplebatch, train_labelbatch = sess.run([trainSet_batch, trainLabel_batch])
feeddict = {example_batch: train_examplebatch,
label_batch: train_labelbatch,
keepProb: 0.75}
_, PxErrValue = sess.run([train_op, meanPixelError], feed_dict=feeddict)
duration = time.time() - start_time
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, MeanPixelError = %.1f pixels (%.1f examples/sec; %.3f sec/batch)')
print (format_str % (datetime.now(), step, PxErrValue,
examples_per_sec, sec_per_batch))
if (step % 50 == 0) and (step != 0):
summary_str = sess.run(summary_op, feed_dict=feeddict)
summary_writer.add_summary(summary_str, step)
if (step % 100 == 0) and (step != 0):
test_examplebatch, test_labelbatch = sess.run([testSet_batch, testLabel_batch])
producedlabels, PxErrValue_Test = sess.run([logits,meanPixelError],
feed_dict={example_batch: test_examplebatch,
label_batch: test_labelbatch,
keepProb: 1})
draw(test_examplebatch[0,...], producedlabels[0,...], FLAGS.drawing_dir, step/100)
print('Test Set MeanPixelError: %.1f pixels' %PxErrValue_Test)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
def train():
with tf.Graph().as_default():
# Global step variable for tracking processes.
global_step = tf.Variable(0, trainable=False)
# Prepare data batches
train_set_batch, train_label_batch = inputs(is_train=True)
validation_set_batch, validation_label_batch = inputs(is_train=False)
# Placeholder to switch between train and test sets.
image_batch = tf.placeholder(tf.float32,
shape=[
FLAGS.batch_size, FLAGS.input_size,
FLAGS.input_size, FLAGS.input_depth
])
label_batch = tf.placeholder(
tf.int32, shape=[FLAGS.batch_size, LSPGlobals.TotalLabels])
keep_probability = tf.placeholder(tf.float32)
# Build a Graph that computes the logits predictions from the inference model.
logits = LSPModels.inference(image_batch, keep_prob=keep_probability)
# Calculate loss.
loss, mean_pixel_error = LSPModels.loss(logits, label_batch)
# Build a Graph that trains the model with one batch of examples and updates the model parameters.
train_op = LSPModels.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(init)
step_init = 0
checkpoint = tf.train.get_checkpoint_state(FLAGS.train_dir)
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(sess, checkpoint.model_checkpoint_path)
step_init = sess.run(global_step)
else:
print("No checkpoint found...")
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
graph=sess.graph)
for step in range(step_init, FLAGS.max_steps):
start_time = time.time()
images, labels = sess.run([train_set_batch, train_label_batch])
feed_dict = {
image_batch: images,
label_batch: labels,
keep_probability: 0.6
}
_, pixel_error_value = sess.run([train_op, mean_pixel_error],
feed_dict=feed_dict)
duration = time.time() - start_time
if not step == 0:
# Print current results.
if step % 50 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = '%s: step %d, MeanPixelError = %.1f pixels (%.1f examples/sec; %.3f sec/batch)'
print(format_str %
(datetime.now(), step, pixel_error_value,
examples_per_sec, sec_per_batch))
# Check results for validation set
if step % 500 == 0:
images, labels = sess.run(
[validation_set_batch, validation_label_batch])
feed_dict = {
image_batch: images,
label_batch: labels,
keep_probability: 1
}
produced_labels, pixel_error_value = sess.run(
[logits, mean_pixel_error], feed_dict=feed_dict)
draw(images[0, ...], produced_labels[0, ...],
FLAGS.drawing_dir, step / 500)
print('Test Set MeanPixelError: %.1f pixels' %
pixel_error_value)
# Add summary to summary writer
if step % 1000 == 0:
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 5000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
print('Model checkpoint saved for step %d' % step)
coord.request_stop()
coord.join(threads)