def rebuild_graph(sess, checkpoint_dir, input_image, batch_size, feature_dim):
checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
num_initial_blocks = 1
skip_connections = False
stage_two_repeat = 2
with slim.arg_scope(ENet_arg_scope()):
_, _ = ENet(input_image,
num_classes=12,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
graph = tf.get_default_graph()
last_prelu = graph.get_tensor_by_name('ENet/bottleneck5_1_last_prelu:0')
logits = slim.conv2d_transpose(last_prelu, feature_dim, [2,2], stride=2,
scope='Instance/transfer_layer/conv2d_transpose')
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, checkpoint)
return logits
def MENet_Model(self):
with slim.arg_scope(ENet_arg_scope(weight_decay=self.opt.weight_decay)):
# Define the shared encoder
Encoder = ENetEncoder( self.batch_images,
batch_size=self.opt.batch_size,
is_training=True,
reuse=None,
num_initial_blocks=self.opt.num_initial_blocks,
stage_two_repeat=self.opt.stage_two_repeat,
skip_connections=self.opt.skip_connections)
# Collect tensors that are useful later (e.g. tf summary)
self.predictions = {}
# Define the decoder(s)
for task in self.Tasks:
if (task == 'segmentation'):
logits, probabilities = ENetSegDecoder( Encoder,
self.opt.num_classes,
is_training=True,
reuse=None,
stage_two_repeat=self.opt.stage_two_repeat,
skip_connections=self.opt.skip_connections)
self.probabilities = probabilities
self.predictions[task] = tf.identity(logits, name=task + '_pred')
elif (task == 'depth'):
disparity = ENetDepthDecoder( Encoder,
skip_connections=self.opt.skip_connections,
is_training=True,
reuse=None)
self.predictions[task] = tf.identity(disparity, name=task + '_pred')
def load_enet(sess, checkpoint_dir, input_image, batch_size, num_classes):
checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
num_initial_blocks = 1
skip_connections = False
stage_two_repeat = 2
with slim.arg_scope(ENet_arg_scope()):
logits, _ = ENet(input_image,
num_classes=12,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, checkpoint)
graph = tf.get_default_graph()
last_prelu = graph.get_tensor_by_name('ENet/bottleneck5_1_last_prelu:0')
output = slim.conv2d_transpose(
last_prelu,
num_classes, [2, 2],
stride=2,
weights_initializer=initializers.xavier_initializer(),
scope='Semantic/transfer_layer/conv2d_transpose')
probabilities = tf.nn.softmax(
output, name='Semantic/transfer_layer/logits_to_softmax')
with tf.variable_scope('', reuse=True):
weight = tf.get_variable(
'Semantic/transfer_layer/conv2d_transpose/weights')
bias = tf.get_variable(
'Semantic/transfer_layer/conv2d_transpose/biases')
sess.run([weight.initializer, bias.initializer])
return output, probabilities
def load_enet(sess, checkpoint_dir, input_image, batch_size):
checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
num_initial_blocks = 1
skip_connections = False
stage_two_repeat = 2
with slim.arg_scope(ENet_arg_scope()):
_, _ = ENet(input_image,
num_classes=12,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, checkpoint)
graph = tf.get_default_graph()
last_prelu = graph.get_tensor_by_name('ENet/bottleneck5_1_last_prelu:0')
return last_prelu
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TEST BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
input_queue = tf.train.slice_input_producer([images], shuffle=False)
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
preprocessed_image = preprocess(image, None, image_height, image_width)
images = tf.train.batch([preprocessed_image],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
#Create the global step and an increment op for monitoring
global_step = get_or_create_global_step()
global_step_op = tf.assign(
global_step, global_step + 1
) #no apply_gradient method so manually increasing the global_step
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session
with sv.managed_session() as sess:
#Save the images
if save_images:
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
for step in range(int(num_steps_per_epoch)):
# Compute summaries every 10 steps and continue evaluating
time_run = time.time()
predictions_val = sess.run([predictions])
time_run_end = time.time()
predictions_val_tuple = predictions_val[0]
print('totally cost (second)', time_run_end - time_run)
for i in range(predictions_val_tuple.shape[0]):
predicted_annotation = predictions_val_tuple[i]
# plt.subplot(1, 2, 1)
plt.imshow(predicted_annotation)
# plt.subplot(1, 2, 2)
# plt.imshow(img)
plt.savefig(photo_dir + "/image_" +
str(image_files[step * num_epochs +
i])[15:])
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TRAINING BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
annotations = tf.convert_to_tensor(annotation_files)
input_queue = tf.train.slice_input_producer(
[images,
annotations]) #Slice_input producer shuffles the data by default.
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
annotation = tf.read_file(input_queue[1])
annotation = tf.image.decode_image(annotation)
#preprocess and batch up the image and annotation
preprocessed_image, preprocessed_annotation = preprocess(
image, annotation, image_height, image_width)
images, annotations = tf.train.batch(
[preprocessed_image, preprocessed_annotation],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope(weight_decay=weight_decay)):
logits, probabilities = ENet(images,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
#perform one-hot-encoding on the ground truth annotation to get same shape as the logits
annotations = tf.reshape(annotations,
shape=[batch_size, image_height, image_width])
annotations_ohe = tf.one_hot(annotations, num_classes, axis=-1)
#Actually compute the loss
loss = weighted_cross_entropy(logits=logits,
onehot_labels=annotations_ohe,
class_weights=class_weights)
total_loss = tf.losses.get_total_loss()
#Create the global step for monitoring the learning_rate and training.
global_step = get_or_create_global_step()
#Define your exponentially decaying learning rate
lr = tf.train.exponential_decay(learning_rate=initial_learning_rate,
global_step=global_step,
decay_steps=decay_steps,
decay_rate=learning_rate_decay_factor,
staircase=True)
#Now we can define the optimizer that takes on the learning rate
optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=epsilon)
#Create the train_op.
train_op = slim.learning.create_train_op(total_loss, optimizer)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
accuracy, accuracy_update = tf.contrib.metrics.streaming_accuracy(
predictions, annotations)
mean_IOU, mean_IOU_update = tf.contrib.metrics.streaming_mean_iou(
predictions=predictions,
labels=annotations,
num_classes=num_classes)
metrics_op = tf.group(accuracy_update, mean_IOU_update)
#Now we need to create a training step function that runs both the train_op, metrics_op and updates the global_step concurrently.
def train_step(sess, train_op, global_step, metrics_op):
'''
Simply runs a session for the three arguments provided and gives a logging on the time elapsed for each global step
'''
#Check the time for each sess run
start_time = time.time()
total_loss, global_step_count, accuracy_val, mean_IOU_val, _ = sess.run(
[train_op, global_step, accuracy, mean_IOU, metrics_op])
time_elapsed = time.time() - start_time
#Run the logging to show some results
logging.info(
'global step %s: loss: %.4f (%.2f sec/step) Current Streaming Accuracy: %.4f Current Mean IOU: %.4f',
global_step_count, total_loss, time_elapsed, accuracy_val,
mean_IOU_val)
return total_loss, accuracy_val, mean_IOU_val
#================VALIDATION BRANCH========================
#Load the files into one input queue
images_val = tf.convert_to_tensor(image_val_files)
annotations_val = tf.convert_to_tensor(annotation_val_files)
input_queue_val = tf.train.slice_input_producer(
[images_val, annotations_val])
#Decode the image and annotation raw content
image_val = tf.read_file(input_queue_val[0])
image_val = tf.image.decode_jpeg(image_val, channels=3)
annotation_val = tf.read_file(input_queue_val[1])
annotation_val = tf.image.decode_png(annotation_val)
#preprocess and batch up the image and annotation
preprocessed_image_val, preprocessed_annotation_val = preprocess(
image_val, annotation_val, image_height, image_width)
images_val, annotations_val = tf.train.batch(
[preprocessed_image_val, preprocessed_annotation_val],
batch_size=eval_batch_size,
allow_smaller_final_batch=True)
with slim.arg_scope(ENet_arg_scope(weight_decay=weight_decay)):
logits_val, probabilities_val = ENet(
images_val,
num_classes,
batch_size=eval_batch_size,
is_training=True,
reuse=True,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
#perform one-hot-encoding on the ground truth annotation to get same shape as the logits
annotations_val = tf.reshape(
annotations_val,
shape=[eval_batch_size, image_height, image_width])
annotations_ohe_val = tf.one_hot(annotations_val, num_classes, axis=-1)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded. ----> Should we use OHE instead?
predictions_val = tf.argmax(probabilities_val, -1)
accuracy_val, accuracy_val_update = tf.contrib.metrics.streaming_accuracy(
predictions_val, annotations_val)
mean_IOU_val, mean_IOU_val_update = tf.contrib.metrics.streaming_mean_iou(
predictions=predictions_val,
labels=annotations_val,
num_classes=num_classes)
metrics_op_val = tf.group(accuracy_val_update, mean_IOU_val_update)
#Create an output for showing the segmentation output of validation images
segmentation_output_val = tf.cast(predictions_val, dtype=tf.float32)
segmentation_output_val = tf.reshape(
segmentation_output_val, shape=[-1, image_height, image_width, 1])
segmentation_ground_truth_val = tf.cast(annotations_val,
dtype=tf.float32)
segmentation_ground_truth_val = tf.reshape(
segmentation_ground_truth_val,
shape=[-1, image_height, image_width, 1])
def eval_step(sess, metrics_op):
'''
Simply takes in a session, runs the metrics op and some logging information.
'''
start_time = time.time()
_, accuracy_value, mean_IOU_value = sess.run(
[metrics_op, accuracy_val, mean_IOU_val])
time_elapsed = time.time() - start_time
#Log some information
logging.info(
'---VALIDATION--- Validation Accuracy: %.4f Validation Mean IOU: %.4f (%.2f sec/step)',
accuracy_value, mean_IOU_value, time_elapsed)
return accuracy_value, mean_IOU_value
#=====================================================
#Now finally create all the summaries you need to monitor and group them into one summary op.
tf.summary.scalar('Monitor/Total_Loss', total_loss)
tf.summary.scalar('Monitor/validation_accuracy', accuracy_val)
tf.summary.scalar('Monitor/training_accuracy', accuracy)
tf.summary.scalar('Monitor/validation_mean_IOU', mean_IOU_val)
tf.summary.scalar('Monitor/training_mean_IOU', mean_IOU)
tf.summary.scalar('Monitor/learning_rate', lr)
tf.summary.image('Images/Validation_original_image',
images_val,
max_outputs=1)
tf.summary.image('Images/Validation_segmentation_output',
segmentation_output_val,
max_outputs=1)
tf.summary.image('Images/Validation_segmentation_ground_truth',
segmentation_ground_truth_val,
max_outputs=1)
my_summary_op = tf.summary.merge_all()
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir, summary_op=None, init_fn=None)
# Run the managed session
with sv.managed_session() as sess:
for step in xrange(int(num_steps_per_epoch * num_epochs)):
#At the start of every epoch, show the vital information:
if step % num_batches_per_epoch == 0:
logging.info('Epoch %s/%s',
step / num_batches_per_epoch + 1, num_epochs)
learning_rate_value = sess.run([lr])
logging.info('Current Learning Rate: %s',
learning_rate_value)
#Log the summaries every 10 steps or every end of epoch, which ever lower.
if step % min(num_steps_per_epoch, 10) == 0:
loss, training_accuracy, training_mean_IOU = train_step(
sess, train_op, sv.global_step, metrics_op=metrics_op)
#Check the validation data only at every third of an epoch
if step % (num_steps_per_epoch / 3) == 0:
for i in xrange(
len(image_val_files) / eval_batch_size):
validation_accuracy, validation_mean_IOU = eval_step(
sess, metrics_op_val)
summaries = sess.run(my_summary_op)
sv.summary_computed(sess, summaries)
#If not, simply run the training step
else:
loss, training_accuracy, training_mean_IOU = train_step(
sess, train_op, sv.global_step, metrics_op=metrics_op)
#We log the final training loss
logging.info('Final Loss: %s', loss)
logging.info('Final Training Accuracy: %s', training_accuracy)
logging.info('Final Training Mean IOU: %s', training_mean_IOU)
logging.info('Final Validation Accuracy: %s', validation_accuracy)
logging.info('Final Validation Mean IOU: %s', validation_mean_IOU)
#Once all the training has been done, save the log files and checkpoint model
logging.info('Finished training! Saving model to disk now.')
sv.saver.save(sess, sv.save_path, global_step=sv.global_step)
if save_images:
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
#Plot the predictions - check validation images only
logging.info('Saving the images now...')
predictions_value, annotations_value = sess.run(
[predictions_val, annotations_val])
for i in xrange(eval_batch_size):
predicted_annotation = predictions_value[i]
annotation = annotations_value[i]
plt.subplot(1, 2, 1)
plt.imshow(predicted_annotation)
plt.subplot(1, 2, 2)
plt.imshow(annotation)
plt.savefig(photo_dir + "/image_" + str(i))
with tf.Graph().as_default() as graph:
images_tensor = tf.train.string_input_producer(images_list, shuffle=False)
reader = tf.WholeFileReader()
key, image_tensor = reader.read(images_tensor)
image = tf.image.decode_png(image_tensor, channels=3)
# image = tf.image.resize_image_with_crop_or_pad(image, 360, 480)
# image = tf.cast(image, tf.float32)
image = preprocess(image)
images = tf.train.batch([image],
batch_size=10,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images,
num_classes=12,
batch_size=10,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint)
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TEST BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
annotations = tf.convert_to_tensor(annotation_files)
input_queue = tf.train.slice_input_producer([images, annotations])
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
annotation = tf.read_file(input_queue[1])
annotation = tf.image.decode_image(annotation)
#preprocess and batch up the image and annotation
preprocessed_image, preprocessed_annotation = preprocess(
image, annotation, image_height, image_width)
images, annotations = tf.train.batch(
[preprocessed_image, preprocessed_annotation],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#perform one-hot-encoding on the ground truth annotation to get same shape as the logits
annotations = tf.reshape(annotations,
shape=[batch_size, image_height, image_width])
annotations_ohe = tf.one_hot(annotations, num_classes, axis=-1)
annotations = tf.cast(annotations, tf.int64)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
accuracy, accuracy_update = tf.contrib.metrics.streaming_accuracy(
predictions, annotations)
mean_IOU, mean_IOU_update = tf.contrib.metrics.streaming_mean_iou(
predictions=predictions,
labels=annotations,
num_classes=num_classes)
per_class_accuracy, per_class_accuracy_update = tf.metrics.mean_per_class_accuracy(
labels=annotations,
predictions=predictions,
num_classes=num_classes)
metrics_op = tf.group(accuracy_update, mean_IOU_update,
per_class_accuracy_update)
#Create the global step and an increment op for monitoring
global_step = get_or_create_global_step()
global_step_op = tf.assign(
global_step, global_step + 1
) #no apply_gradient method so manually increasing the global_step
#Create a evaluation step function
def eval_step(sess, metrics_op, global_step):
'''
Simply takes in a session, runs the metrics op and some logging information.
'''
start_time = time.time()
_, global_step_count, accuracy_value, mean_IOU_value, per_class_accuracy_value = sess.run(
[
metrics_op, global_step_op, accuracy, mean_IOU,
per_class_accuracy
])
time_elapsed = time.time() - start_time
#Log some information
logging.info(
'Global Step %s: Streaming Accuracy: %.4f Streaming Mean IOU: %.4f Per-class Accuracy: %.4f (%.2f sec/step)',
global_step_count, accuracy_value, mean_IOU_value,
per_class_accuracy_value, time_elapsed)
return accuracy_value, mean_IOU_value, per_class_accuracy_value
#Create your summaries
tf.summary.scalar('Monitor/test_accuracy', accuracy)
tf.summary.scalar('Monitor/test_mean_per_class_accuracy',
per_class_accuracy)
tf.summary.scalar('Monitor/test_mean_IOU', mean_IOU)
my_summary_op = tf.summary.merge_all()
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session
with sv.managed_session() as sess:
for step in range(int(num_steps_per_epoch * num_epochs)):
#print vital information every start of the epoch as always
if step % num_batches_per_epoch == 0:
accuracy_value, mean_IOU_value = sess.run(
[accuracy, mean_IOU])
logging.info('Epoch: %s/%s',
step / num_batches_per_epoch + 1, num_epochs)
logging.info('Current Streaming Accuracy: %.4f',
accuracy_value)
logging.info('Current Streaming Mean IOU: %.4f',
mean_IOU_value)
#Compute summaries every 10 steps and continue evaluating
if step % 10 == 0:
test_accuracy, test_mean_IOU, test_per_class_accuracy = eval_step(
sess,
metrics_op=metrics_op,
global_step=sv.global_step)
summaries = sess.run(my_summary_op)
sv.summary_computed(sess, summaries)
#Otherwise just run as per normal
else:
test_accuracy, test_mean_IOU, test_per_class_accuracy = eval_step(
sess,
metrics_op=metrics_op,
global_step=sv.global_step)
#At the end of all the evaluation, show the final accuracy
logging.info('Final Streaming Accuracy: %.4f', test_accuracy)
logging.info('Final Mean IOU: %.4f', test_mean_IOU)
logging.info('Final Per Class Accuracy %.4f',
test_per_class_accuracy)
#Show end of evaluation
logging.info('Finished evaluating!')
#Save the images
if save_images:
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
#Save the image visualizations for the first 10 images.
logging.info('Saving the images now...')
predictions_val, annotations_val = sess.run(
[predictions, annotations])
for i in range(10):
predicted_annotation = predictions_val[i]
annotation = annotations_val[i]
plt.subplot(1, 2, 1)
plt.imshow(predicted_annotation)
plt.subplot(1, 2, 2)
plt.imshow(annotation)
plt.savefig(photo_dir + "/image_" + str(i))
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TEST BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
input_queue = tf.train.slice_input_producer([images], shuffle=False)
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
preprocessed_image = preprocess(image, None, image_height, image_width)
images = tf.train.batch([preprocessed_image],
batch_size=batch_size,
allow_smaller_final_batch=True)
images_placeholder = tf.placeholder(
tf.float32, [None, image_height, image_width, 3], name='rgb_image')
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images_placeholder,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session sv.managed_session() ENet
with sv.managed_session() as sess:
#Save the images
if save_images:
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
# while True:
# image_files = sorted(
# [os.path.join(dataset_dir, 'test', file) for file in os.listdir(dataset_dir + "/test") if
# file.endswith('.png')])
# if len(image_files) > 0:
# # Load the files into one input queue
#
# for image_name in image_files:
# time_run = time.time()
# image = cv2.imread(image_name)
# image_resize = cv2.resize(image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)
# image_resize_float32 = image_resize.astype('float32')
# batch_x = np.zeros([batch_size, image_height, image_width, 3])
# batch_x_float32 = batch_x.astype('float32')
# batch_x_float32[0] = image_resize_float32
# feed_dict = {images_placeholder: batch_x_float32}
# probabilities_numpy = sess.run(probabilities, feed_dict=feed_dict)
# predictions_val = np.argmax(probabilities_numpy, -1)
#
# time_run_end = time.time()
# print('totally cost (second)', time_run_end - time_run)
#
# for i in range(batch_size): # predictions_val_tuple.shape[0]
# predicted_image = predictions_val[i]
# plt.imshow(predicted_image)
# plt.savefig(photo_dir + "/image_" + str(image_name)[15:])
# # cv2.imwrite(photo_dir + "/image_" + str(image_files[step * num_epochs + i])[15:], predicted_image)
# #delete images_files
#
# else:
# continue
# images = np.zeros([1, 360, 480, 3], dtype=np.float32)
for step in range(int(num_steps_per_epoch)):
# Compute summaries every 10 steps and continue evaluating
time_run = time.time()
image_numpy = images.eval(session=sess)
feed_dict = {images_placeholder: image_numpy}
probabilities_numpy = sess.run(probabilities,
feed_dict=feed_dict)
predictions_val = np.argmax(probabilities_numpy, -1)
time_run_end = time.time()
print('totally cost (second)', time_run_end - time_run)
for i in range(
batch_size): #predictions_val_tuple.shape[0]
predicted_image = predictions_val[i]
# plt.imshow(predicted_image)
plt.subplot(1, 2, 1)
plt.imshow(predicted_image)
plt.subplot(1, 2, 2)
original_image = cv2.imread(image_files[step])
plt.imshow(original_image)
plt.savefig(photo_dir + "/image_" +
str(image_files[step])[15:])
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TEST BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
annotations = tf.convert_to_tensor(annotation_files)
input_queue = tf.train.slice_input_producer([images, annotations])
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
annotation = tf.read_file(input_queue[1])
annotation = tf.image.decode_image(annotation)
#preprocess and batch up the image and annotation
preprocessed_image, preprocessed_annotation = preprocess(
image, annotation, image_height, image_width)
images, annotations = tf.train.batch(
[preprocessed_image, preprocessed_annotation],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
if (network == 'ENet'):
print('Building the network: ', network)
logits, probabilities = ENet(
images,
num_classes,
batch_size=batch_size,
is_training=is_training,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
if (network == 'ENet_Small'):
print('Building the network: ', network)
logits, probabilities = ENet_Small(
images,
num_classes,
batch_size=batch_size,
is_training=is_training,
reuse=None,
num_initial_blocks=num_initial_blocks,
skip_connections=skip_connections)
if (network == 'ErfNet'):
print('Building the network: ', network)
logits, probabilities = ErfNet(images,
num_classes,
batch_size=batch_size,
is_training=is_training,
reuse=None)
if (network == 'ErfNet_Small'):
print('Building the network: ', network)
logits, probabilities = ErfNet_Small(images,
num_classes,
batch_size=batch_size,
is_training=is_training,
reuse=None)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#perform one-hot-encoding on the ground truth annotation to get same shape as the logits
annotations = tf.reshape(annotations,
shape=[batch_size, image_height, image_width])
annotations_ohe = tf.one_hot(annotations, num_classes, axis=-1)
annotations = tf.cast(annotations, tf.int64)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
accuracy, accuracy_update = tf.contrib.metrics.streaming_accuracy(
predictions, annotations)
mean_IOU, mean_IOU_update = tf.contrib.metrics.streaming_mean_iou(
predictions=predictions,
labels=annotations,
num_classes=num_classes)
per_class_accuracy, per_class_accuracy_update = tf.metrics.mean_per_class_accuracy(
labels=annotations,
predictions=predictions,
num_classes=num_classes)
metrics_op = tf.group(accuracy_update, mean_IOU_update,
per_class_accuracy_update)
#Create the global step and an increment op for monitoring
global_step = get_or_create_global_step()
global_step_op = tf.assign(
global_step, global_step + 1
) #no apply_gradient method so manually increasing the global_step
#Create a evaluation step function
def eval_step(sess, metrics_op, global_step):
'''
Simply takes in a session, runs the metrics op and some logging information.
'''
_, global_step_count, accuracy_value, mean_IOU_value, per_class_accuracy_value = sess.run(
[
metrics_op, global_step_op, accuracy, mean_IOU,
per_class_accuracy
])
#Log some information
logging.info(
'Global Step %s: Streaming Accuracy: %.4f Streaming Mean IOU: %.4f Per-class Accuracy: %.4f (%.2f sec/step)',
global_step_count, accuracy_value, mean_IOU_value,
per_class_accuracy_value)
return accuracy_value, mean_IOU_value, per_class_accuracy_value
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session
with sv.managed_session() as sess:
start_time = time.time()
for step in range(int(num_steps_per_epoch * num_epochs)):
_, global_step_count, test_accuracy, test_mean_IOU, test_per_class_accuracy = sess.run(
[
metrics_op, global_step_op, accuracy, mean_IOU,
per_class_accuracy
])
time_elapsed = time.time() - start_time
#At the end of all the evaluation, show the final accuracy
logging.info('Final Streaming Accuracy: %.4f', test_accuracy)
logging.info('Final Mean IOU: %.4f', test_mean_IOU)
logging.info('Final Per Class Accuracy %.4f',
test_per_class_accuracy)
logging.info('Time Elapsed %.4f', time_elapsed)
logging.info('FPS %.4f',
(num_steps_per_epoch * num_epochs) / time_elapsed)
#Show end of evaluation
logging.info('Finished evaluating!')
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TEST BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
annotations = tf.convert_to_tensor(annotation_files)
input_queue = tf.train.slice_input_producer([images, annotations],
shuffle=False)
#Decode the image and annotation raw content
filename = input_queue[0]
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
annotation = tf.read_file(input_queue[1])
annotation = tf.image.decode_image(annotation)
#preprocess and batch up the image and annotation
preprocessed_image, preprocessed_annotation = preprocess_ori(
image, annotation, image_height, image_width)
images, annotations, filenames = tf.train.batch(
[preprocessed_image, preprocessed_annotation, filename],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#perform one-hot-encoding on the ground truth annotation to get same shape as the logits
annotations = tf.reshape(annotations,
shape=[batch_size, image_height, image_width])
annotations_ohe = one_hot(annotations, batch_size, dataset)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
accuracy, accuracy_update = tf.contrib.metrics.streaming_accuracy(
predictions, annotations)
mean_IOU, mean_IOU_update = tf.contrib.metrics.streaming_mean_iou(
predictions=predictions,
labels=annotations,
num_classes=num_classes)
per_class_accuracy, per_class_accuracy_update = tf.metrics.mean_per_class_accuracy(
labels=annotations,
predictions=predictions,
num_classes=num_classes)
metrics_op = tf.group(accuracy_update, mean_IOU_update,
per_class_accuracy_update)
#Create the global step and an increment op for monitoring
global_step = get_or_create_global_step()
global_step_op = tf.assign(
global_step, global_step + 1
) #no apply_gradient method so manually increasing the global_step
#Create a evaluation step function
def eval_step(sess, metrics_op, global_step):
'''
Simply takes in a session, runs the metrics op and some logging information.
'''
_, global_step_count, accuracy_value, mean_IOU_value, per_class_accuracy_value = sess.run(
[
metrics_op, global_step_op, accuracy, mean_IOU,
per_class_accuracy
])
start_time = time.time()
predictions_val, filename_val = sess.run([predictions, filenames])
time_elapsed = time.time() - start_time
#Log some information
logging.info(
'Global Step %s: Streaming Accuracy: %.4f Streaming Mean IOU: %.4f Per-class Accuracy: %.4f %.2f(sec/step) %.2f (fps)',
global_step_count, accuracy_value, mean_IOU_value,
per_class_accuracy_value, time_elapsed / batch_size,
batch_size / time_elapsed)
#Save the images
if save_images:
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
#Segmentation
for i in xrange(batch_size):
segmentation = produce_color_segmentation(
predictions_val[i], image_height, image_width, dataset)
filename = filename_val[i].split('/')
filename = filename[len(filename) - 1]
filename = photo_dir + "/trainResult_" + filename
cv2.imwrite(filename, segmentation)
return accuracy_value, mean_IOU_value, per_class_accuracy_value, time_elapsed
#Create your summaries
tf.summary.scalar('Monitor/test_accuracy', accuracy)
tf.summary.scalar('Monitor/test_mean_per_class_accuracy',
per_class_accuracy)
tf.summary.scalar('Monitor/test_mean_IOU', mean_IOU)
my_summary_op = tf.summary.merge_all()
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session
with sv.managed_session() as sess:
total_time = 0
for step in range(int(num_steps_per_epoch)):
#Compute summaries every 10 steps and continue evaluating
if step % 10 == 0:
test_accuracy, test_mean_IOU, test_per_class_accuracy, time_elapsed = eval_step(
sess,
metrics_op=metrics_op,
global_step=sv.global_step)
summaries = sess.run(my_summary_op)
sv.summary_computed(sess, summaries)
#Otherwise just run as per normal
else:
test_accuracy, test_mean_IOU, test_per_class_accuracy, time_elapsed = eval_step(
sess,
metrics_op=metrics_op,
global_step=sv.global_step)
total_time = total_time + time_elapsed
#At the end of all the evaluation, show the final accuracy
logging.info('Final Streaming Accuracy: %.4f', test_accuracy)
logging.info('Final Mean IOU: %.4f', test_mean_IOU)
logging.info('Final Per Class Accuracy: %.4f',
test_per_class_accuracy)
logging.info('Average Speed: %.4f fps',
batch_size * (num_steps_per_epoch - 1) / total_time)
#Show end of evaluation
logging.info('Finished evaluating!')
def run():
with tf.Graph().as_default() as graph:
image_name = sys.argv[1]
images = tf.convert_to_tensor([image_name])
input_queue = tf.train.slice_input_producer([images])
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
#Create the model inference
image = preprocess(image, None, image_height, image_width)
image = tf.train.batch([image],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(image,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=logdir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session
with sv.managed_session() as sess:
logging.info('Saving the images now...')
predictions_val = sess.run(predictions)
img = predictions_val[0]
plt.imshow(img)
plt.axis('off'), plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.subplots_adjust(left=0,
bottom=0,
right=1,
top=1,
hspace=0,
wspace=0)
out_file_name = "./output/result" + sys.argv[1].split('.')[0]
plt.savefig(out_file_name, bbox_inces='tight', pad_inches=0)
print(out_file_name)
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
images_placeholder = tf.placeholder(tf.float32, [batch_size, image_height, image_width, 3], name='rgb_image')
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images_placeholder,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir = logdir, summary_op = None, init_fn=restore_fn)
#Run the managed session sv.managed_session() ENet
with sv.managed_session() as sess:
#Save the images our_video
if save_images:
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
kernel = np.ones((10, 10), np.uint8)
while True:
image_files = sorted(
[os.path.join(dataset_dir, 'our_video', file) for file in os.listdir(dataset_dir + "/our_video") if
file.endswith('.jpg')])
if len(image_files) > 0:
# Load the files into one input queue
time_all_image_start = time.time()
for image_name in image_files:
time_run = time.time()
image = cv2.imread(image_name)
image_resize = cv2.resize(image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)
image_resize_float32 = image_resize.astype('float32')
image_resize_float32 = image_resize_float32[:, :, ::-1]
image_resize_float32 = image_resize_float32 / 255.0
batch_x = np.zeros([batch_size, image_height, image_width, 3])
batch_x_float32 = batch_x.astype('float32')
batch_x_float32[0] = image_resize_float32
feed_dict = {images_placeholder: batch_x_float32}
probabilities_numpy = sess.run(probabilities, feed_dict=feed_dict)
predictions_val = np.argmax(probabilities_numpy, -1)
time_Preprocessing_and_Predict_end = time.time()
print('One image Preprocessing_and_Predict cost (second)', time_Preprocessing_and_Predict_end - time_run)
for i in range(batch_size): # predictions_val_tuple.shape[0]
predicted_image = predictions_val[i]
# plt.imshow(predicted_image)
time_morphologyEx_start = time.time()
predicted_image_uint8 = predicted_image.astype('uint8')
predicted_image_closing = cv2.morphologyEx(predicted_image_uint8, cv2.MORPH_OPEN, kernel)
color_mask = np.ones(image_resize.shape, np.float)
time_morphologyEx_end = time.time()
print('morphologyEx cost (second)', time_morphologyEx_end - time_morphologyEx_start)
time_color_mask_start = time.time()
# for x in range(predicted_image.shape[0]):
# for y in range(predicted_image.shape[1]):
# color_mask[x, y, :] = gray_convert_color(predicted_image_closing[x, y])
# for label in range(11):
for label in np.unique(predicted_image_closing):
xy = np.where(predicted_image_closing == label)
color_mask[xy[0], xy[1], :] = gray_convert_color(label)
color_mask_uint8 = (color_mask * 255).astype('uint8')
time_color_mask_end = time.time()
print('color_mask cost (second)', time_color_mask_end - time_color_mask_start)
# ###开操作去噪声
# plt.subplot(1, 2, 1)
# plt.imshow(predicted_image)
# plt.subplot(1, 2, 2)
# # original_image = cv2.imread(image_name)
# plt.imshow(predicted_image_closing)
time_addWeighted_start = time.time()
overlapping = cv2.addWeighted(image_resize, 0.2, color_mask_uint8, 0.8, 0)
time_addWeighted_end = time.time()
print('addWeighted cost (second)', time_addWeighted_end - time_addWeighted_start)
# plt.imshow(overlapping)
time_result_save_start = time.time()
# cv2.imwrite(photo_dir + "/image_" + str(image_name)[10:], overlapping) #str(image_name)[15:]
cv2.imwrite(photo_dir + "/" + str(image_name)[20:], overlapping)
time_result_save_end = time.time()
print('result_save cost (second)', time_result_save_end - time_result_save_start)
# plt.savefig(photo_dir + "/image_" + str(image_name)[15:])
# cv2.imwrite(photo_dir + "/image_" + str(image_files[step * num_epochs + i])[15:], predicted_image)
time_run_end = time.time()
print('One image cost (second)', time_run_end - time_run)
time_all_image_end = time.time()
logging.info('There are %.4f image in all', len(image_files))
print('totally cost (second)', time_all_image_end - time_all_image_start)
#delete images_files
# python删除文件夹下所有文件
else:
print('There is no new images to deal with')
continue
def run():
with tf.Graph().as_default() as graph:
tf.logging.set_verbosity(tf.logging.INFO)
#===================TEST BRANCH=======================
#Load the files into one input queue
images = tf.convert_to_tensor(image_files)
input_queue = tf.train.slice_input_producer([images])
#Decode the image and annotation raw content
filename = input_queue[0]
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
#preprocess and batch up the image and annotation
preprocessed_image = preprocess_ori(image, None, image_height,
image_width)
images, filenames = tf.train.batch([preprocessed_image, filename],
batch_size=batch_size,
allow_smaller_final_batch=True)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images,
num_classes,
batch_size=batch_size,
is_training=True,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
# Set up the variables to restore and restoring function from a saver.
exclude = []
variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint_file)
#State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.
predictions = tf.argmax(probabilities, -1)
#Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory
sv = tf.train.Supervisor(logdir=photo_dir,
summary_op=None,
init_fn=restore_fn)
#Run the managed session
with sv.managed_session() as sess:
#Save the images
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
#Segmentation
total_time = 0
logging.info('Total Steps: %d', int(num_steps_per_epoch))
for step in range(int(num_steps_per_epoch)):
start_time = time.time()
predictions_val, filename_val = sess.run(
[predictions, filenames])
time_elapsed = time.time() - start_time
logging.info('step %d %.2f(sec/step) %.2f (fps)', step,
time_elapsed / batch_size,
batch_size / time_elapsed)
total_time = total_time + time_elapsed
if save_images:
for i in xrange(batch_size):
segmentation = produce_color_segmentation(
predictions_val[i], image_height, image_width,
color)
filename = filename_val[i].split('/')
filename = filename[len(filename) - 1]
filename = photo_dir + "/trainResult_" + filename
cv2.imwrite(filename, segmentation)
logging.info('Average speed: %.2f fps',
len(image_files) / total_time)
