def eval(testLabelsFileName):
with open(testLabelsFileName) as f:
testSet = f.read().splitlines()
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
testSet_batch, testLabel_batch = input_pipeline(testSet, 1, num_epochs=None)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = LSPModels.inference(testSet_batch, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(LSPGlobals.MOVING_AVERAGE_DECAY)
variables_to_restore = {}
for v in tf.all_variables():
if v in tf.trainable_variables():
restore_name = variable_averages.average_name(v)
else:
restore_name = v.op.name
variables_to_restore[restore_name] = v
saver = tf.train.Saver(variables_to_restore)
#while True:
eval_once(saver, logits, testLabel_batch, testSet_batch)
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 evalDeepPose(imagelist):
with tf.Graph().as_default():
# Placeholder to switch between train and test sets.
dataShape = [FLAGS.batch_size, FLAGS.input_size, FLAGS.input_size, FLAGS.input_depth]
input_images = tf.placeholder(tf.float32, shape=dataShape)
# Build a Graph that computes the logits predictions from the inference model.
logits = LSPModels.inference(input_images, FLAGS.batch_size, keep_prob=1)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# 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)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("No checkpoint found!")
return
numSteps = int(math.floor(len(imagelist)/FLAGS.batch_size)+0.1)
if (len(imagelist)%FLAGS.batch_size > 0):
numSteps = numSteps +1
resizeValues = np.empty([FLAGS.batch_size, 2], dtype=np.float32)
input_pack = np.empty(dataShape, dtype=np.float32)
for step in xrange(numSteps):
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
(imWidth, imHeight) = imgFile.size
imgFile = imgFile.resize((FLAGS.input_size,FLAGS.input_size), Image.ANTIALIAS)
resizeValues[i, 0] = float(imWidth)/FLAGS.input_size
resizeValues[i, 1] = float(imHeight)/FLAGS.input_size
input_pack[i,...] = np.asarray(imgFile)
start_time = time.time()
labels = sess.run([logits], feed_dict={input_images: input_pack})
duration = time.time() - start_time
labels_reshaped = np.asarray(labels).reshape([FLAGS.batch_size, FLAGS.label_count, FLAGS.label_size])
resizedLabels = resizeLabels(labels_reshaped, resizeValues)
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
img = np.asarray(imgFile).astype(np.uint8)
draw(img, resizedLabels[i,...], FLAGS.output_dir, 0, fname=b(imagelist[index]), reshaped=True)
examples_per_sec = FLAGS.batch_size / duration
format_str = ('%s: Done: %d, (FeedForward Time: %.1f examples/sec)')
print (format_str % (datetime.now(), (step+1)*FLAGS.batch_size, examples_per_sec))
coord.request_stop()
coord.join(threads)
print('Process Finished...')
def evalDeepPose(imagelist):
with tf.Graph().as_default():
# Placeholder to switch between train and test sets.
dataShape = [FLAGS.batch_size, FLAGS.input_size, FLAGS.input_size, FLAGS.input_depth]
input_images = tf.placeholder(tf.float32, shape=dataShape)
# Build a Graph that computes the logits predictions from the inference model.
logits = LSPModels.inference(input_images, FLAGS.batch_size, keepProb=1)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# 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)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("No checkpoint found!")
return
numSteps = int(math.floor(len(imagelist)/FLAGS.batch_size)+0.1)
if (len(imagelist)%FLAGS.batch_size > 0):
numSteps = numSteps +1
resizeValues = np.empty([FLAGS.batch_size, 2], dtype=np.float32)
input_pack = np.empty(dataShape, dtype=np.float32)
for step in xrange(numSteps):
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
(imWidth, imHeight) = imgFile.size
imgFile = imgFile.resize((FLAGS.input_size,FLAGS.input_size), Image.ANTIALIAS)
resizeValues[i, 0] = float(imWidth)/FLAGS.input_size
resizeValues[i, 1] = float(imHeight)/FLAGS.input_size
input_pack[i,...] = np.asarray(imgFile)
start_time = time.time()
labels = sess.run([logits], feed_dict={input_images: input_pack})
duration = time.time() - start_time
labels_reshaped = np.asarray(labels).reshape([FLAGS.batch_size, FLAGS.label_count, FLAGS.label_size])
resizedLabels = resizeLabels(labels_reshaped, resizeValues)
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
img = np.asarray(imgFile).astype(np.uint8)
draw(img, resizedLabels[i,...], FLAGS.output_dir, 0, fname=b(imagelist[index]), reshaped=True)
examples_per_sec = FLAGS.batch_size / duration
format_str = ('%s: Done: %d, (FeedForward Time: %.1f examples/sec)')
print (format_str % (datetime.now(), (step+1)*FLAGS.batch_size, examples_per_sec))
coord.request_stop()
coord.join(threads)
print('Process Finished...')
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)
