def test_netout_multiple_device(self):
n_device = 4
collector = OutputsCollector(n_devices=n_device)
for idx in range(n_device):
with tf.name_scope('worker_%d' % idx):
image = tf.ones([2, 32, 32, 32, 1])
foo = tf.zeros([2, 2])
bar = tf.zeros([42])
collector.add_to_collection(name='image',
var=image,
collection=NETWORK_OUTPUT,
average_over_devices=False)
collector.add_to_collection(name='foo',
var=foo,
collection=NETWORK_OUTPUT,
average_over_devices=False)
collector.add_to_collection(name='bar',
var=bar,
collection=NETWORK_OUTPUT,
average_over_devices=True)
self.assertEqual(
set(collector.variables(NETWORK_OUTPUT)),
{'image_1', 'image_3', 'image_2',
'image', 'foo_1', 'foo_2', 'foo_3', 'foo', 'bar'})
self.assertEqual(len(collector.variables(NETWORK_OUTPUT)['bar']),
n_device)
collector.finalise_output_op()
self.assertIsInstance(collector.variables(NETWORK_OUTPUT)['bar'],
tf.Tensor)
def test_add_to_single_device(self):
n_device = 1
collector = OutputsCollector(n_devices=n_device)
for idx in range(n_device):
with tf.name_scope('worker_%d' % idx):
image = tf.ones([2, 32, 32, 32, 1])
foo = tf.zeros([2, 2])
bar = tf.zeros([42])
collector.add_to_collection(name='image',
var=image,
average_over_devices=False)
collector.add_to_collection(name='foo',
var=foo,
average_over_devices=False)
collector.add_to_collection(name='bar',
var=bar,
collection=NETWORK_OUTPUT,
average_over_devices=False)
self.assertDictEqual(collector.variables(collection=CONSOLE),
{'image': image, 'foo': foo})
self.assertDictEqual(collector.variables(collection=NETWORK_OUTPUT),
{'bar': bar})
class ApplicationDriver(object):
"""
This class initialises an application by building a TF graph,
and maintaining a session and coordinator. It controls the
starting/stopping of an application. Applications should be
implemented by inheriting niftynet.application.base_application
to be compatible with this driver.
"""
# pylint: disable=too-many-instance-attributes
def __init__(self):
self.app = None
self.graph = tf.Graph()
self.saver = None
self.is_training = True
self.num_threads = 0
self.num_gpus = 0
self.model_dir = None
self.summary_dir = None
self.session_prefix = None
self.max_checkpoints = 20
self.save_every_n = 10
self.tensorboard_every_n = 20
self.initial_iter = 0
self.final_iter = 0
self._coord = None
self._init_op = None
self.outputs_collector = None
self.gradients_collector = None
def initialise_application(self, workflow_param, data_param):
"""
This function receives all parameters from user config file,
create an instance of application.
:param workflow_param: a dictionary of user parameters,
keys correspond to sections in the config file
:param data_param: a dictionary of input image parameters,
keys correspond to data properties to be used by image_reader
:return:
"""
try:
system_param = workflow_param.get('SYSTEM', None)
net_param = workflow_param.get('NETWORK', None)
train_param = workflow_param.get('TRAINING', None)
infer_param = workflow_param.get('INFERENCE', None)
app_param = workflow_param.get('CUSTOM', None)
except AttributeError:
tf.logging.fatal('parameters should be dictionaries')
raise
assert os.path.exists(system_param.model_dir), \
'Model folder not exists {}'.format(system_param.model_dir)
self.is_training = (system_param.action == "train")
# hardware-related parameters
self.num_threads = max(system_param.num_threads, 1) \
if self.is_training else 1
self.num_gpus = system_param.num_gpus \
if self.is_training else min(system_param.num_gpus, 1)
set_cuda_device(system_param.cuda_devices)
# set output TF model folders
self.model_dir = touch_folder(
os.path.join(system_param.model_dir, 'models'))
self.session_prefix = os.path.join(self.model_dir, FILE_PREFIX)
if self.is_training:
assert train_param, 'training parameters not specified'
summary_root = os.path.join(system_param.model_dir, 'logs')
self.summary_dir = get_latest_subfolder(
summary_root, create_new=train_param.starting_iter == 0)
# training iterations-related parameters
self.initial_iter = train_param.starting_iter
self.final_iter = train_param.max_iter
self.save_every_n = train_param.save_every_n
self.tensorboard_every_n = train_param.tensorboard_every_n
self.max_checkpoints = train_param.max_checkpoints
self.gradients_collector = GradientsCollector(
n_devices=max(self.num_gpus, 1))
action_param = train_param
else:
assert infer_param, 'inference parameters not specified'
self.initial_iter = infer_param.inference_iter
action_param = infer_param
self.outputs_collector = OutputsCollector(
n_devices=max(self.num_gpus, 1))
# create an application instance
assert app_param, 'application specific param. not specified'
app_module = ApplicationDriver._create_app(app_param.name)
self.app = app_module(net_param, action_param, self.is_training)
# initialise data input
self.app.initialise_dataset_loader(data_param, app_param)
# pylint: disable=not-context-manager
with self.graph.as_default(), tf.name_scope('Sampler'):
self.app.initialise_sampler()
def run_application(self):
"""
Initialise a TF graph, connect data sampler and network within
the graph context, run training loops or inference loops.
The training loop terminates when self.final_iter reached.
The inference loop terminates when there is no more
image sample to be processed from image reader.
:return:
"""
config = ApplicationDriver._tf_config()
with tf.Session(config=config, graph=self.graph) as session:
tf.logging.info('Filling queues (this can take a few minutes)')
self._coord = tf.train.Coordinator()
# start samplers' threads
try:
samplers = self.app.get_sampler()
if samplers is not None:
for sampler in samplers:
sampler.run_threads(session, self._coord,
self.num_threads)
except (TypeError, AttributeError, IndexError):
tf.logging.fatal(
"samplers not running, pop_batch_op operations "
"are blocked.")
raise
self.graph = self._create_graph(self.graph)
self.app.check_initialisations()
# initialise network
# fill variables with random values or values from file
tf.logging.info('starting from iter %d', self.initial_iter)
self._rand_init_or_restore_vars(session)
start_time = time.time()
loop_status = {}
try:
# iteratively run the graph
if self.is_training:
loop_status['current_iter'] = self.initial_iter
self._training_loop(session, loop_status)
else:
loop_status['all_saved_flag'] = False
self._inference_loop(session, loop_status)
except KeyboardInterrupt:
tf.logging.warning('User cancelled application')
except tf.errors.OutOfRangeError:
pass
except RuntimeError:
import sys
import traceback
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type,
exc_value,
exc_traceback,
file=sys.stdout)
finally:
tf.logging.info('Cleaning up...')
if self.is_training and loop_status.get('current_iter', None):
self._save_model(session, loop_status['current_iter'])
elif not loop_status.get('all_saved_flag', None):
tf.logging.warning('stopped early, incomplete loops')
tf.logging.info('stopping sampling threads')
self.app.stop()
tf.logging.info("%s stopped (time in second %.2f).",
type(self.app).__name__,
(time.time() - start_time))
# pylint: disable=not-context-manager
def _create_graph(self, graph=tf.Graph()):
"""
tensorflow graph is only created within this function
"""
assert isinstance(graph, tf.Graph)
main_device = self._device_string(0, is_worker=False)
# start constructing the graph, handling training and inference cases
with graph.as_default(), tf.device(main_device):
# initialise network, these are connected in
# the context of multiple gpus
self.app.initialise_network()
# for data parallelism --
# defining and collecting variables from multiple devices
bn_ops = None
for gpu_id in range(0, max(self.num_gpus, 1)):
worker_device = self._device_string(gpu_id, is_worker=True)
scope_string = 'worker_{}'.format(gpu_id)
with tf.name_scope(scope_string) as scope:
with tf.device(worker_device):
# setup network for each of the multiple devices
self.app.connect_data_and_network(
self.outputs_collector, self.gradients_collector)
if self.is_training:
# batch norm statistics from the last device
bn_ops = tf.get_collection(BN_COLLECTION, scope)
# assemble all training operations
if self.is_training and self.gradients_collector:
updates_op = []
# batch normalisation moving averages operation
if bn_ops:
updates_op.extend(bn_ops)
# combine them with model parameter updating operation
with tf.name_scope('ApplyGradients'):
with graph.control_dependencies(updates_op):
self.app.set_network_update_op(
self.gradients_collector.gradients)
# initialisation operation
with tf.name_scope('Initialization'):
self._init_op = global_vars_init_or_restore()
with tf.name_scope('MergedOutputs'):
self.outputs_collector.finalise_output_op()
# saving operation
self.saver = tf.train.Saver(max_to_keep=self.max_checkpoints)
# no more operation definitions after this point
tf.Graph.finalize(graph)
return graph
def _rand_init_or_restore_vars(self, sess):
"""
Randomly initialising all trainable variables defined in session,
or loading checkpoint files as variable initialisations
"""
if self.is_training and self.initial_iter == 0:
sess.run(self._init_op)
tf.logging.info('Parameters from random initialisations ...')
return
# check model's folder
assert os.path.exists(self.model_dir), \
"Model folder not found {}, please check" \
"config parameter: model_dir".format(self.model_dir)
# check model's file
ckpt_state = tf.train.get_checkpoint_state(self.model_dir)
if ckpt_state is None:
tf.logging.fatal(
"%s/checkpoint not found, please check"
"config parameter: model_dir", self.model_dir)
if self.initial_iter > 0:
checkpoint = '{}-{}'.format(self.session_prefix, self.initial_iter)
else:
try:
checkpoint = ckpt_state.model_checkpoint_path
assert checkpoint, 'checkpoint path not found ' \
'in {}/checkpoints'.format(self.model_dir)
self.initial_iter = int(checkpoint.rsplit('-')[-1])
tf.logging.info(
'set initial_iter to %d based '
'on checkpoints', self.initial_iter)
except (ValueError, AttributeError):
tf.logging.fatal(
'failed to get iteration number'
'from checkpoint path, please set'
'inference_iter or starting_iter to a positive integer')
raise
# restore session
tf.logging.info('Accessing %s ...', checkpoint)
try:
self.saver.restore(sess, checkpoint)
except tf.errors.NotFoundError:
tf.logging.fatal(
'checkpoint %s not found or variables to restore do not '
'match the current application graph', checkpoint)
raise
def _training_loop(self, sess, loop_status):
"""
Training loop is running through the training_ops generator
defined for each application (the application can specify
training ops based on the current iteration number, this allows
for complex optimisation schedules).
At every iteration it also evaluates all variables returned by
the output_collector.
"""
writer = tf.summary.FileWriter(self.summary_dir, sess.graph)
# running through training_op from application
for iter_ops in self.app.training_ops(self.initial_iter,
self.final_iter):
if len(iter_ops) == 3:
iter_i, train_op, data_dict = iter_ops
else:
iter_i, train_op = iter_ops
data_dict = None
loop_status['current_iter'] = iter_i
local_time = time.time()
if self._coord.should_stop():
break
# variables to the graph
vars_to_run = dict(train_op=train_op)
vars_to_run[CONSOLE], vars_to_run[NETWORK_OUTPUT] = \
self.outputs_collector.variables(CONSOLE), \
self.outputs_collector.variables(NETWORK_OUTPUT)
if self.tensorboard_every_n > 0 and \
(iter_i % self.tensorboard_every_n == 0):
# adding tensorboard summary
vars_to_run[TF_SUMMARIES] = \
self.outputs_collector.variables(collection=TF_SUMMARIES)
# run all variables in one go
if data_dict:
graph_output = sess.run(vars_to_run, feed_dict=data_dict)
else:
graph_output = sess.run(vars_to_run)
# process graph outputs
self.app.interpret_output(graph_output[NETWORK_OUTPUT])
console_str = self._console_vars_to_str(graph_output[CONSOLE])
summary = graph_output.get(TF_SUMMARIES, {})
if summary:
writer.add_summary(summary, iter_i)
# save current model
if (self.save_every_n > 0) and (iter_i % self.save_every_n == 0):
self._save_model(sess, iter_i)
tf.logging.info('iter %d, %s (%.3fs)', iter_i, console_str,
time.time() - local_time)
def _inference_loop(self, sess, loop_status):
"""
Runs all variables returned by outputs_collector,
this loop stops when the return value of
application.interpret_output is False.
"""
loop_status['all_saved_flag'] = False
while True:
local_time = time.time()
if self._coord.should_stop():
break
# build variables to run
vars_to_run = dict()
vars_to_run[NETWORK_OUTPUT], vars_to_run[CONSOLE] = \
self.outputs_collector.variables(NETWORK_OUTPUT), \
self.outputs_collector.variables(CONSOLE)
# evaluate the graph variables
graph_output = sess.run(vars_to_run)
# process the graph outputs
if not self.app.interpret_output(graph_output[NETWORK_OUTPUT]):
tf.logging.info('processed all batches.')
loop_status['all_saved_flag'] = True
break
console_str = self._console_vars_to_str(graph_output[CONSOLE])
tf.logging.info('%s (%.3fs)', console_str,
time.time() - local_time)
def _save_model(self, session, iter_i):
"""
save session parameters to the hard drive
"""
if iter_i <= 0:
return
self.saver.save(sess=session,
save_path=self.session_prefix,
global_step=iter_i)
tf.logging.info('iter %d saved: %s', iter_i, self.session_prefix)
def _device_string(self, device_id=0, is_worker=True):
"""
assigning CPU/GPU based on user specifications
"""
# pylint: disable=no-name-in-module
from tensorflow.python.client import device_lib
devices = device_lib.list_local_devices()
n_local_gpus = sum([x.device_type == 'GPU' for x in devices])
if self.num_gpus <= 0: # user specified no gpu at all
return '/cpu:{}'.format(device_id)
if self.is_training:
# in training: use gpu only for workers whenever n_local_gpus
device = 'gpu' if (is_worker and n_local_gpus > 0) else 'cpu'
if device == 'gpu' and device_id >= n_local_gpus:
tf.logging.fatal(
'trying to use gpu id %s, but only has %s GPU(s), '
'please set num_gpus to %s at most', device_id,
n_local_gpus, n_local_gpus)
raise ValueError
return '/{}:{}'.format(device, device_id)
# in inference: use gpu for everything whenever n_local_gpus
return '/gpu:0' if n_local_gpus > 0 else '/cpu:0'
@staticmethod
def _console_vars_to_str(console_dict):
"""
Printing values of variable evaluations to command line output
"""
if not console_dict:
return ''
console_str = ', '.join('{}={}'.format(key, val)
for (key, val) in console_dict.items())
return console_str
@staticmethod
def _create_app(app_type_string):
"""
Import the application module
"""
return ApplicationFactory.create(app_type_string)
@staticmethod
def _tf_config():
"""
tensorflow system configurations
"""
config = tf.ConfigProto()
config.log_device_placement = False
config.allow_soft_placement = True
return config
class ApplicationDriver(object):
"""
This class initialises an application by building a TF graph,
and maintaining a session and coordinator. It controls the
starting/stopping of an application. Applications should be
implemented by inheriting ``niftynet.application.base_application``
to be compatible with this driver.
"""
# pylint: disable=too-many-instance-attributes
pre_train_iter = signal('pre_train_iter')
post_train_iter = signal('post_train_iter')
pre_validation_iter = signal('pre_validation_iter')
post_validation_iter = signal('post_validation_iter')
pre_infer_iter = signal('pre_infer_iter')
post_infer_iter = signal('post_infer_iter')
post_training = signal('post_training')
def __init__(self):
self.app = None
self.graph = tf.Graph()
self.saver = None
self.is_training = True
self.num_threads = 0
self.num_gpus = 0
self.model_dir = None
self.summary_dir = None
self.session_prefix = None
self.max_checkpoints = 2
self.save_every_n = 0
self.tensorboard_every_n = -1
self.validation_every_n = -1
self.validation_max_iter = 1
self.initial_iter = 0
self.final_iter = 0
self._coord = tf.train.Coordinator()
self._init_op = None
self._data_partitioner = None
self.outputs_collector = None
self.gradients_collector = None
self.console = None
self.tensorboard = None
self.model_saver = None
def initialise_application(self, workflow_param, data_param):
"""
This function receives all parameters from user config file,
create an instance of application.
:param workflow_param: a dictionary of user parameters,
keys correspond to sections in the config file
:param data_param: a dictionary of input image parameters,
keys correspond to data properties to be used by image_reader
:return:
"""
try:
system_param = workflow_param.get('SYSTEM', None)
net_param = workflow_param.get('NETWORK', None)
train_param = workflow_param.get('TRAINING', None)
infer_param = workflow_param.get('INFERENCE', None)
app_param = workflow_param.get('CUSTOM', None)
except AttributeError:
tf.logging.fatal('parameters should be dictionaries')
raise
assert os.path.exists(system_param.model_dir), \
'Model folder not exists {}'.format(system_param.model_dir)
self.is_training = (system_param.action == "train")
# hardware-related parameters
self.num_threads = max(system_param.num_threads, 1) \
if self.is_training else 1
self.num_gpus = system_param.num_gpus \
if self.is_training else min(system_param.num_gpus, 1)
set_cuda_device(system_param.cuda_devices)
# set output TF model folders
self.model_dir = touch_folder(
os.path.join(system_param.model_dir, 'models'))
self.session_prefix = os.path.join(self.model_dir, FILE_PREFIX)
# set training params.
if self.is_training:
assert train_param, 'training parameters not specified'
summary_root = os.path.join(system_param.model_dir, 'logs')
self.summary_dir = get_latest_subfolder(
summary_root,
create_new=train_param.starting_iter == 0)
self.initial_iter = train_param.starting_iter
self.final_iter = max(train_param.max_iter, self.initial_iter)
self.save_every_n = train_param.save_every_n
self.tensorboard_every_n = train_param.tensorboard_every_n
self.max_checkpoints = \
max(train_param.max_checkpoints, self.max_checkpoints)
self.gradients_collector = GradientsCollector(
n_devices=max(self.num_gpus, 1))
self.validation_every_n = train_param.validation_every_n
if self.validation_every_n > 0:
self.validation_max_iter = max(self.validation_max_iter,
train_param.validation_max_iter)
action_param = train_param
else: # set inference params.
assert infer_param, 'inference parameters not specified'
self.initial_iter = infer_param.inference_iter
action_param = infer_param
self.outputs_collector = OutputsCollector(
n_devices=max(self.num_gpus, 1))
# create an application instance
assert app_param, 'application specific param. not specified'
app_module = ApplicationDriver._create_app(app_param.name)
self.app = app_module(net_param, action_param, system_param.action)
# initialise data input
data_partitioner = ImageSetsPartitioner()
# clear the cached file lists
data_partitioner.reset()
do_new_partition = \
self.is_training and self.initial_iter == 0 and \
(not os.path.isfile(system_param.dataset_split_file)) and \
(train_param.exclude_fraction_for_validation > 0 or
train_param.exclude_fraction_for_inference > 0)
data_fractions = None
if do_new_partition:
assert train_param.exclude_fraction_for_validation > 0 or \
self.validation_every_n <= 0, \
'validation_every_n is set to {}, ' \
'but train/validation splitting not available,\nplease ' \
'check "exclude_fraction_for_validation" in the config ' \
'file (current config value: {}).'.format(
self.validation_every_n,
train_param.exclude_fraction_for_validation)
data_fractions = (train_param.exclude_fraction_for_validation,
train_param.exclude_fraction_for_inference)
if data_param:
data_partitioner.initialise(
data_param=data_param,
new_partition=do_new_partition,
ratios=data_fractions,
data_split_file=system_param.dataset_split_file)
if data_param and self.is_training and self.validation_every_n > 0:
assert data_partitioner.has_validation, \
'validation_every_n is set to {}, ' \
'but train/validation splitting not available.\nPlease ' \
'check dataset partition list {} ' \
'(remove file to generate a new dataset partition). ' \
'Or set validation_every_n to -1.'.format(
self.validation_every_n, system_param.dataset_split_file)
# initialise readers
self.app.initialise_dataset_loader(
data_param, app_param, data_partitioner)
self._data_partitioner = data_partitioner
# pylint: disable=not-context-manager
with self.graph.as_default(), tf.name_scope('Sampler'):
self.app.initialise_sampler()
def _run_sampler_threads(self, session=None):
"""
Get samplers from application and try to run sampler threads.
Note: Overriding app.get_sampler() method by returning None to bypass
this step.
:param session: TF session used for fill
tf.placeholders with sampled data
:return:
"""
if session is None:
return
if self._coord is None:
return
if self.num_threads <= 0:
return
try:
samplers = self.app.get_sampler()
for sampler in traverse_nested(samplers):
if sampler is None:
continue
sampler.run_threads(session, self._coord, self.num_threads)
tf.logging.info('Filling queues (this can take a few minutes)')
except (NameError, TypeError, AttributeError, IndexError):
tf.logging.fatal(
"samplers not running, pop_batch_op operations "
"are blocked.")
raise
def run_application(self):
"""
Initialise a TF graph, connect data sampler and network within
the graph context, run training loops or inference loops.
The training loop terminates when ``self.final_iter`` reached.
The inference loop terminates when there is no more
image sample to be processed from image reader.
:return:
"""
config = ApplicationDriver._tf_config()
with tf.Session(config=config, graph=self.graph) as session:
# start samplers' threads
self._run_sampler_threads(session=session)
self.graph = self._create_graph(self.graph)
# check app variables initialised and ready for starts
self.app.check_initialisations()
# initialise network trainable parameters
self._rand_init_or_restore_vars(session)
start_time = time.time()
loop_status = {}
try:
# iteratively run the graph
if self.is_training:
self.model_saver = ModelSaver(session, self.saver,
self.save_every_n,
self.session_prefix)
loop_status['current_iter'] = self.initial_iter
self._training_loop(session, loop_status)
else:
loop_status['all_saved_flag'] = False
self._inference_loop(session, loop_status)
except KeyboardInterrupt:
tf.logging.warning('User cancelled application')
except tf.errors.OutOfRangeError:
if loop_status.get('all_saved_flag', None) is not None:
# reached the end of inference Dataset
loop_status['all_saved_flag'] = True
except RuntimeError:
import sys
import traceback
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(
exc_type, exc_value, exc_traceback, file=sys.stdout)
finally:
tf.logging.info('Cleaning up...')
if self.is_training:
# saving model at the last iteration
iter_msg = IterationMessage()
iter_msg.current_iter = loop_status.get('current_iter', -1)
self.post_training.send(iter_msg)
elif not loop_status.get('all_saved_flag', None):
tf.logging.warning('stopped early, incomplete loops')
tf.logging.info('stopping sampling threads')
self.app.stop()
tf.logging.info(
"%s stopped (time in second %.2f).",
type(self.app).__name__, (time.time() - start_time))
# pylint: disable=not-context-manager
def _create_graph(self, graph=tf.Graph()):
"""
TensorFlow graph is only created within this function.
"""
assert isinstance(graph, tf.Graph)
main_device = self._device_string(0, is_worker=False)
# start constructing the graph, handling training and inference cases
with graph.as_default(), tf.device(main_device):
# initialise network, these are connected in
# the context of multiple gpus
self.app.initialise_network()
self.app.add_validation_flag()
# for data parallelism --
# defining and collecting variables from multiple devices
bn_ops = None
for gpu_id in range(0, max(self.num_gpus, 1)):
worker_device = self._device_string(gpu_id, is_worker=True)
scope_string = 'worker_{}'.format(gpu_id)
with tf.name_scope(scope_string) as scope:
with tf.device(worker_device):
# setup network for each of the multiple devices
self.app.connect_data_and_network(
self.outputs_collector,
self.gradients_collector)
if self.is_training:
# batch norm statistics from the last device
bn_ops = tf.get_collection(BN_COLLECTION, scope)
# assemble all training operations
if self.is_training and self.gradients_collector:
updates_op = []
# batch normalisation moving averages operation
if bn_ops:
updates_op.extend(bn_ops)
# combine them with model parameter updating operation
with tf.name_scope('ApplyGradients'):
with graph.control_dependencies(updates_op):
self.app.set_network_gradient_op(
self.gradients_collector.gradients)
# initialisation operation
with tf.name_scope('Initialization'):
self._init_op = global_vars_init_or_restore()
with tf.name_scope('MergedOutputs'):
self.outputs_collector.finalise_output_op()
# saving operation
self.saver = tf.train.Saver(max_to_keep=self.max_checkpoints,
save_relative_paths=True)
# no more operation definitions after this point
tf.Graph.finalize(graph)
return graph
def _rand_init_or_restore_vars(self, sess):
"""
Randomly initialising all trainable variables defined in session,
or loading checkpoint files as variable initialisations.
"""
tf.logging.info('starting from iter %d', self.initial_iter)
if self.is_training and self.initial_iter == 0:
sess.run(self._init_op)
tf.logging.info('Parameters from random initialisations ...')
return
# check model's folder
assert os.path.exists(self.model_dir), \
"Model folder not found {}, please check" \
"config parameter: model_dir".format(self.model_dir)
# check model's file
ckpt_state = tf.train.get_checkpoint_state(self.model_dir)
if ckpt_state is None:
tf.logging.warning(
"%s/checkpoint not found, please check "
"config parameter: model_dir", self.model_dir)
if self.initial_iter > 0:
checkpoint = '{}-{}'.format(self.session_prefix, self.initial_iter)
else:
try:
checkpoint = ckpt_state.model_checkpoint_path
assert checkpoint, 'checkpoint path not found ' \
'in {}/checkpoints'.format(self.model_dir)
self.initial_iter = int(checkpoint.rsplit('-')[-1])
tf.logging.info('set initial_iter to %d based '
'on checkpoints', self.initial_iter)
except (ValueError, AttributeError):
tf.logging.fatal(
'failed to get iteration number '
'from checkpoint path, please set '
'inference_iter or starting_iter to a positive integer')
raise
# restore session
tf.logging.info('Accessing %s ...', checkpoint)
try:
self.saver.restore(sess, checkpoint)
except tf.errors.NotFoundError:
tf.logging.fatal(
'checkpoint %s not found or variables to restore do not '
'match the current application graph', checkpoint)
raise
def interleaved_iteration_generator(self):
""" This generator yields a sequence of training and validation
iterations """
train_iters = iter_generator(range(self.initial_iter + 1,
self.final_iter + 1), TRAIN)
for train_iter_msg in train_iters:
self.app.set_iteration_update(train_iter_msg)
yield train_iter_msg
if train_iter_msg.current_iter > 0 and\
self.validation_every_n > 0 and \
(train_iter_msg.current_iter % self.validation_every_n == 0):
val_iters = [train_iter_msg.current_iter]
val_iters = val_iters * self.validation_max_iter
valid_iters = iter_generator(val_iters, VALID)
for valid_iter_msg in valid_iters:
self.app.set_iteration_update(valid_iter_msg)
yield valid_iter_msg
def _loop(self, iteration_generator, sess, loop_status):
for iter_msg in iteration_generator:
if self._coord.should_stop():
break
if iter_msg.should_stop:
break
loop_status['current_iter'] = iter_msg.current_iter
iter_msg.pre_iter.send(iter_msg)
iter_msg.ops_to_run[NETWORK_OUTPUT] = \
self.outputs_collector.variables(NETWORK_OUTPUT)
graph_output = sess.run(iter_msg.ops_to_run,
feed_dict=iter_msg.data_feed_dict)
iter_msg.current_iter_output = graph_output
iter_msg.status = self.app.interpret_output(
iter_msg.current_iter_output[NETWORK_OUTPUT])
iter_msg.post_iter.send(iter_msg)
if iter_msg.should_stop:
break
def _training_loop(self, sess, loop_status):
"""
The training loop iterates through training (and validation) iterations
Each iteration is represented as an ``IterationMessage`` object, whose
ops_to_run are populated with the ops to run in each iteration (by the
training loop or by objects watching for iteration events), fed into
into `session.run()` and then passed to the app (and observers) for
interpretation.
"""
# Add observers for tensorboard, and console output (move to io?)
self.tensorboard = TensorBoardLogger(self.outputs_collector,
self.summary_dir, sess.graph,
self.tensorboard_every_n)
self.console = ConsoleLogger(self.outputs_collector)
# Core training loop handling
def add_gradient(iter_msg):
""" Event handler to add the backpropagation update.
iter_msg is an IterationMessage object """
iter_msg.ops_to_run['gradients'] = self.app.gradient_op
self.pre_train_iter.connect(add_gradient)
self._loop(self.interleaved_iteration_generator(), sess, loop_status)
def _inference_loop(self, sess, loop_status):
"""
Runs all variables returned by outputs_collector,
this loop stops when the return value of
application.interpret_output is False.
"""
loop_status['all_saved_flag'] = False
self.console = ConsoleLogger(self.outputs_collector)
def is_complete(iter_msg):
""" Event handler to trigger the completion message.
iter_msg is an IterationMessage object """
if not iter_msg.status:
tf.logging.info('processed all batches.')
loop_status['all_saved_flag'] = True
iter_msg.should_stop = True
self.post_infer_iter.connect(is_complete)
self._loop(iter_generator(itertools.count(), INFER), sess, loop_status)
def _device_string(self, device_id=0, is_worker=True):
"""
assigning CPU/GPU based on user specifications
"""
# pylint: disable=no-name-in-module
from tensorflow.python.client import device_lib
devices = device_lib.list_local_devices()
n_local_gpus = sum([x.device_type == 'GPU' for x in devices])
if self.num_gpus <= 0: # user specified no gpu at all
return '/cpu:{}'.format(device_id)
if self.is_training:
# in training: use gpu only for workers whenever n_local_gpus
device = 'gpu' if (is_worker and n_local_gpus > 0) else 'cpu'
if device == 'gpu' and device_id >= n_local_gpus:
tf.logging.warning(
'trying to use gpu id %s, but only has %s GPU(s), '
'please set num_gpus to %s at most',
device_id, n_local_gpus, n_local_gpus)
#raise ValueError
return '/{}:{}'.format(device, device_id)
# in inference: use gpu for everything whenever n_local_gpus
return '/gpu:0' if n_local_gpus > 0 else '/cpu:0'
@staticmethod
def _create_app(app_type_string):
"""
Import the application module
"""
app_class = ApplicationFactory.create(app_type_string)
app_class.clear()
return app_class
@staticmethod
def _tf_config():
"""
tensorflow system configurations
"""
config = tf.ConfigProto()
config.log_device_placement = False
config.allow_soft_placement = True
return config
class ApplicationDriver(object):
"""
This class initialises an application by building a TF graph,
and maintaining a session and coordinator. It controls the
starting/stopping of an application. Applications should be
implemented by inheriting ``niftynet.application.base_application``
to be compatible with this driver.
"""
# pylint: disable=too-many-instance-attributes
pre_train_iter = signal('pre_train_iter')
post_train_iter = signal('post_train_iter')
pre_validation_iter = signal('pre_validation_iter')
post_validation_iter = signal('post_validation_iter')
pre_infer_iter = signal('pre_infer_iter')
post_infer_iter = signal('post_infer_iter')
post_training = signal('post_training')
def __init__(self):
self.app = None
self.graph = tf.Graph()
self.saver = None
self.is_training = True
self.num_threads = 0
self.num_gpus = 0
self.model_dir = None
self.summary_dir = None
self.session_prefix = None
self.max_checkpoints = 2
self.save_every_n = 0
self.tensorboard_every_n = -1
self.validation_every_n = -1
self.validation_max_iter = 1
self.initial_iter = 0
self.final_iter = 0
self._coord = tf.train.Coordinator()
self._init_op = None
self._data_partitioner = None
self.outputs_collector = None
self.gradients_collector = None
self.console = None
self.tensorboard = None
self.model_saver = None
def initialise_application(self, workflow_param, data_param):
"""
This function receives all parameters from user config file,
create an instance of application.
:param workflow_param: a dictionary of user parameters,
keys correspond to sections in the config file
:param data_param: a dictionary of input image parameters,
keys correspond to data properties to be used by image_reader
:return:
"""
try:
system_param = workflow_param.get('SYSTEM', None)
net_param = workflow_param.get('NETWORK', None)
train_param = workflow_param.get('TRAINING', None)
infer_param = workflow_param.get('INFERENCE', None)
app_param = workflow_param.get('CUSTOM', None)
except AttributeError:
tf.logging.fatal('parameters should be dictionaries')
raise
assert os.path.exists(system_param.model_dir), \
'Model folder not exists {}'.format(system_param.model_dir)
self.is_training = (system_param.action == "train")
# hardware-related parameters
self.num_threads = max(system_param.num_threads, 1) \
if self.is_training else 1
self.num_gpus = system_param.num_gpus \
if self.is_training else min(system_param.num_gpus, 1)
set_cuda_device(system_param.cuda_devices)
# set output TF model folders
self.model_dir = touch_folder(
os.path.join(system_param.model_dir, 'models'))
self.session_prefix = os.path.join(self.model_dir, FILE_PREFIX)
# set training params.
if self.is_training:
assert train_param, 'training parameters not specified'
summary_root = os.path.join(system_param.model_dir, 'logs')
self.summary_dir = get_latest_subfolder(
summary_root, create_new=train_param.starting_iter == 0)
self.initial_iter = train_param.starting_iter
self.final_iter = max(train_param.max_iter, self.initial_iter)
self.save_every_n = train_param.save_every_n
self.tensorboard_every_n = train_param.tensorboard_every_n
self.max_checkpoints = \
max(train_param.max_checkpoints, self.max_checkpoints)
self.gradients_collector = GradientsCollector(
n_devices=max(self.num_gpus, 1))
self.validation_every_n = train_param.validation_every_n
if self.validation_every_n > 0:
self.validation_max_iter = max(self.validation_max_iter,
train_param.validation_max_iter)
action_param = train_param
else: # set inference params.
assert infer_param, 'inference parameters not specified'
self.initial_iter = infer_param.inference_iter
action_param = infer_param
self.outputs_collector = OutputsCollector(
n_devices=max(self.num_gpus, 1))
# create an application instance
assert app_param, 'application specific param. not specified'
app_module = ApplicationDriver._create_app(app_param.name)
self.app = app_module(net_param, action_param, system_param.action)
# initialise data input
data_partitioner = ImageSetsPartitioner()
# clear the cached file lists
data_partitioner.reset()
do_new_partition = \
self.is_training and self.initial_iter == 0 and \
(not os.path.isfile(system_param.dataset_split_file)) and \
(train_param.exclude_fraction_for_validation > 0 or
train_param.exclude_fraction_for_inference > 0)
data_fractions = None
if do_new_partition:
assert train_param.exclude_fraction_for_validation > 0 or \
self.validation_every_n <= 0, \
'validation_every_n is set to {}, ' \
'but train/validation splitting not available,\nplease ' \
'check "exclude_fraction_for_validation" in the config ' \
'file (current config value: {}).'.format(
self.validation_every_n,
train_param.exclude_fraction_for_validation)
data_fractions = (train_param.exclude_fraction_for_validation,
train_param.exclude_fraction_for_inference)
if data_param:
data_partitioner.initialise(
data_param=data_param,
new_partition=do_new_partition,
ratios=data_fractions,
data_split_file=system_param.dataset_split_file)
if data_param and self.is_training and self.validation_every_n > 0:
assert data_partitioner.has_validation, \
'validation_every_n is set to {}, ' \
'but train/validation splitting not available.\nPlease ' \
'check dataset partition list {} ' \
'(remove file to generate a new dataset partition). ' \
'Or set validation_every_n to -1.'.format(
self.validation_every_n, system_param.dataset_split_file)
# initialise readers
self.app.initialise_dataset_loader(data_param, app_param,
data_partitioner)
self._data_partitioner = data_partitioner
# pylint: disable=not-context-manager
with self.graph.as_default(), tf.name_scope('Sampler'):
self.app.initialise_sampler()
def _run_sampler_threads(self, session=None):
"""
Get samplers from application and try to run sampler threads.
Note: Overriding app.get_sampler() method by returning None to bypass
this step.
:param session: TF session used for fill
tf.placeholders with sampled data
:return:
"""
if session is None:
return
if self._coord is None:
return
if self.num_threads <= 0:
return
try:
samplers = self.app.get_sampler()
for sampler in traverse_nested(samplers):
if sampler is None:
continue
sampler.run_threads(session, self._coord, self.num_threads)
tf.logging.info('Filling queues (this can take a few minutes)')
except (NameError, TypeError, AttributeError, IndexError):
tf.logging.fatal("samplers not running, pop_batch_op operations "
"are blocked.")
raise
def run_application(self):
"""
Initialise a TF graph, connect data sampler and network within
the graph context, run training loops or inference loops.
The training loop terminates when ``self.final_iter`` reached.
The inference loop terminates when there is no more
image sample to be processed from image reader.
:return:
"""
config = ApplicationDriver._tf_config()
with tf.Session(config=config, graph=self.graph) as session:
# start samplers' threads
self._run_sampler_threads(session=session)
self.graph = self._create_graph(self.graph)
# check app variables initialised and ready for starts
self.app.check_initialisations()
# initialise network trainable parameters
self._rand_init_or_restore_vars(session)
start_time = time.time()
loop_status = {}
try:
# iteratively run the graph
if self.is_training:
self.model_saver = ModelSaver(session, self.saver,
self.save_every_n,
self.session_prefix)
loop_status['current_iter'] = self.initial_iter
self._training_loop(session, loop_status)
else:
loop_status['all_saved_flag'] = False
self._inference_loop(session, loop_status)
except KeyboardInterrupt:
tf.logging.warning('User cancelled application')
except tf.errors.OutOfRangeError:
if loop_status.get('all_saved_flag', None) is not None:
# reached the end of inference Dataset
loop_status['all_saved_flag'] = True
except RuntimeError:
import sys
import traceback
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type,
exc_value,
exc_traceback,
file=sys.stdout)
finally:
tf.logging.info('Cleaning up...')
if self.is_training:
# saving model at the last iteration
iter_msg = IterationMessage()
iter_msg.current_iter = loop_status.get('current_iter', -1)
self.post_training.send(iter_msg)
elif not loop_status.get('all_saved_flag', None):
tf.logging.warning('stopped early, incomplete loops')
tf.logging.info('stopping sampling threads')
self.app.stop()
tf.logging.info("%s stopped (time in second %.2f).",
type(self.app).__name__,
(time.time() - start_time))
# pylint: disable=not-context-manager
def _create_graph(self, graph=tf.Graph()):
"""
TensorFlow graph is only created within this function.
"""
assert isinstance(graph, tf.Graph)
main_device = self._device_string(0, is_worker=False)
# start constructing the graph, handling training and inference cases
with graph.as_default(), tf.device(main_device):
# initialise network, these are connected in
# the context of multiple gpus
self.app.initialise_network()
self.app.add_validation_flag()
# for data parallelism --
# defining and collecting variables from multiple devices
bn_ops = None
for gpu_id in range(0, max(self.num_gpus, 1)):
worker_device = self._device_string(gpu_id, is_worker=True)
scope_string = 'worker_{}'.format(gpu_id)
with tf.name_scope(scope_string) as scope:
with tf.device(worker_device):
# setup network for each of the multiple devices
self.app.connect_data_and_network(
self.outputs_collector, self.gradients_collector)
if self.is_training:
# batch norm statistics from the last device
bn_ops = tf.get_collection(BN_COLLECTION, scope)
# assemble all training operations
if self.is_training and self.gradients_collector:
updates_op = []
# batch normalisation moving averages operation
if bn_ops:
updates_op.extend(bn_ops)
# combine them with model parameter updating operation
with tf.name_scope('ApplyGradients'):
with graph.control_dependencies(updates_op):
self.app.set_network_gradient_op(
self.gradients_collector.gradients)
# initialisation operation
with tf.name_scope('Initialization'):
self._init_op = global_vars_init_or_restore()
with tf.name_scope('MergedOutputs'):
self.outputs_collector.finalise_output_op()
# saving operation
self.saver = tf.train.Saver(max_to_keep=self.max_checkpoints,
save_relative_paths=True)
# no more operation definitions after this point
tf.Graph.finalize(graph)
return graph
def _rand_init_or_restore_vars(self, sess):
"""
Randomly initialising all trainable variables defined in session,
or loading checkpoint files as variable initialisations.
"""
tf.logging.info('starting from iter %d', self.initial_iter)
if self.is_training and self.initial_iter == 0:
sess.run(self._init_op)
tf.logging.info('Parameters from random initialisations ...')
return
# check model's folder
assert os.path.exists(self.model_dir), \
"Model folder not found {}, please check" \
"config parameter: model_dir".format(self.model_dir)
# check model's file
ckpt_state = tf.train.get_checkpoint_state(self.model_dir)
if ckpt_state is None:
tf.logging.warning(
"%s/checkpoint not found, please check "
"config parameter: model_dir", self.model_dir)
if self.initial_iter > 0:
checkpoint = '{}-{}'.format(self.session_prefix, self.initial_iter)
else:
try:
checkpoint = ckpt_state.model_checkpoint_path
assert checkpoint, 'checkpoint path not found ' \
'in {}/checkpoints'.format(self.model_dir)
self.initial_iter = int(checkpoint.rsplit('-')[-1])
tf.logging.info(
'set initial_iter to %d based '
'on checkpoints', self.initial_iter)
except (ValueError, AttributeError):
tf.logging.fatal(
'failed to get iteration number '
'from checkpoint path, please set '
'inference_iter or starting_iter to a positive integer')
raise
# restore session
tf.logging.info('Accessing %s ...', checkpoint)
try:
self.saver.restore(sess, checkpoint)
except tf.errors.NotFoundError:
tf.logging.fatal(
'checkpoint %s not found or variables to restore do not '
'match the current application graph', checkpoint)
raise
def interleaved_iteration_generator(self):
""" This generator yields a sequence of training and validation
iterations """
train_iters = iter_generator(
range(self.initial_iter + 1, self.final_iter + 1), TRAIN)
for train_iter_msg in train_iters:
self.app.set_iteration_update(train_iter_msg)
yield train_iter_msg
if train_iter_msg.current_iter > 0 and\
self.validation_every_n > 0 and \
(train_iter_msg.current_iter % self.validation_every_n == 0):
val_iters = [train_iter_msg.current_iter]
val_iters = val_iters * self.validation_max_iter
valid_iters = iter_generator(val_iters, VALID)
for valid_iter_msg in valid_iters:
self.app.set_iteration_update(valid_iter_msg)
yield valid_iter_msg
def _loop(self, iteration_generator, sess, loop_status):
for iter_msg in iteration_generator:
if self._coord.should_stop():
break
if iter_msg.should_stop:
break
loop_status['current_iter'] = iter_msg.current_iter
iter_msg.pre_iter.send(iter_msg)
iter_msg.ops_to_run[NETWORK_OUTPUT] = \
self.outputs_collector.variables(NETWORK_OUTPUT)
graph_output = sess.run(iter_msg.ops_to_run,
feed_dict=iter_msg.data_feed_dict)
iter_msg.current_iter_output = graph_output
iter_msg.status = self.app.interpret_output(
iter_msg.current_iter_output[NETWORK_OUTPUT])
iter_msg.post_iter.send(iter_msg)
if iter_msg.should_stop:
break
def _training_loop(self, sess, loop_status):
"""
The training loop iterates through training (and validation) iterations
Each iteration is represented as an ``IterationMessage`` object, whose
ops_to_run are populated with the ops to run in each iteration (by the
training loop or by objects watching for iteration events), fed into
into `session.run()` and then passed to the app (and observers) for
interpretation.
"""
# Add observers for tensorboard, and console output (move to io?)
self.tensorboard = TensorBoardLogger(self.outputs_collector,
self.summary_dir, sess.graph,
self.tensorboard_every_n)
self.console = ConsoleLogger(self.outputs_collector)
# Core training loop handling
def add_gradient(iter_msg):
""" Event handler to add the backpropagation update.
iter_msg is an IterationMessage object """
iter_msg.ops_to_run['gradients'] = self.app.gradient_op
self.pre_train_iter.connect(add_gradient)
self._loop(self.interleaved_iteration_generator(), sess, loop_status)
def _inference_loop(self, sess, loop_status):
"""
Runs all variables returned by outputs_collector,
this loop stops when the return value of
application.interpret_output is False.
"""
loop_status['all_saved_flag'] = False
self.console = ConsoleLogger(self.outputs_collector)
def is_complete(iter_msg):
""" Event handler to trigger the completion message.
iter_msg is an IterationMessage object """
if not iter_msg.status:
tf.logging.info('processed all batches.')
loop_status['all_saved_flag'] = True
iter_msg.should_stop = True
self.post_infer_iter.connect(is_complete)
self._loop(iter_generator(itertools.count(), INFER), sess, loop_status)
def _device_string(self, device_id=0, is_worker=True):
"""
assigning CPU/GPU based on user specifications
"""
# pylint: disable=no-name-in-module
from tensorflow.python.client import device_lib
devices = device_lib.list_local_devices()
n_local_gpus = sum([x.device_type == 'GPU' for x in devices])
if self.num_gpus <= 0: # user specified no gpu at all
return '/cpu:{}'.format(device_id)
if self.is_training:
# in training: use gpu only for workers whenever n_local_gpus
device = 'gpu' if (is_worker and n_local_gpus > 0) else 'cpu'
if device == 'gpu' and device_id >= n_local_gpus:
tf.logging.warning(
'trying to use gpu id %s, but only has %s GPU(s), '
'please set num_gpus to %s at most', device_id,
n_local_gpus, n_local_gpus)
#raise ValueError
return '/{}:{}'.format(device, device_id)
# in inference: use gpu for everything whenever n_local_gpus
return '/gpu:0' if n_local_gpus > 0 else '/cpu:0'
@staticmethod
def _create_app(app_type_string):
"""
Import the application module
"""
app_class = ApplicationFactory.create(app_type_string)
app_class.clear()
return app_class
@staticmethod
def _tf_config():
"""
tensorflow system configurations
"""
config = tf.ConfigProto()
config.log_device_placement = False
config.allow_soft_placement = True
return config