def loop_step(application, iteration_message):
"""
Calling ``tf.session.run`` with parameters encapsulated in
iteration message as an iteration.
Broadcasting ITER_* events before and afterward.
:param application:
:param iteration_message: an ``engine.IterationMessage`` instances
:return:
"""
# broadcasting event of starting an iteration
ITER_STARTED.send(application, iter_msg=iteration_message)
# ``iter_msg.ops_to_run`` are populated with the ops to run in
# each iteration, fed into ``session.run()`` and then
# passed to the application (and observers) for interpretation.
sess = tf.get_default_session()
assert sess, 'method should be called within a TF session context.'
iteration_message.current_iter_output = sess.run(
iteration_message.ops_to_run,
feed_dict=iteration_message.data_feed_dict)
# broadcasting event of finishing an iteration
ITER_FINISHED.send(application, iter_msg=iteration_message)
def test_run_vars(self):
app_driver = get_initialised_driver()
test_graph = app_driver.create_graph(app_driver.app, 1, True)
test_tensor = test_graph.get_tensor_by_name("G/conv_bn_selu/conv_/w:0")
train_eval_msgs = []
test_vals = []
def get_iter_msgs(_sender, **msg):
"""" Captures iter_msg and model values for testing"""
train_eval_msgs.append(msg['iter_msg'])
test_vals.append(sess.run(test_tensor))
print(msg['iter_msg'].to_console_string())
ITER_FINISHED.connect(get_iter_msgs)
with self.test_session(graph=test_graph) as sess:
GRAPH_CREATED.send(app_driver.app, iter_msg=None)
SESS_STARTED.send(app_driver.app, iter_msg=None)
iterations = IterationMessageGenerator(initial_iter=0,
final_iter=3,
validation_every_n=2,
validation_max_iter=1,
is_training_action=True)
app_driver.loop(app_driver.app, iterations())
# Check sequence of iterations
self.assertRegexpMatches(train_eval_msgs[0].to_console_string(),
'training')
self.assertRegexpMatches(train_eval_msgs[1].to_console_string(),
'training')
self.assertRegexpMatches(train_eval_msgs[2].to_console_string(),
'validation')
self.assertRegexpMatches(train_eval_msgs[3].to_console_string(),
'training')
# Check durations
for iter_msg in train_eval_msgs:
self.assertGreater(iter_msg.iter_duration, 0.0)
# Check training changes test tensor
self.assertNotAlmostEqual(
np.mean(np.abs(test_vals[0] - test_vals[1])), 0.0)
self.assertNotAlmostEqual(
np.mean(np.abs(test_vals[2] - test_vals[3])), 0.0)
# Check validation doesn't change test tensor
self.assertAlmostEqual(
np.mean(np.abs(test_vals[1] - test_vals[2])), 0.0)
app_driver.app.stop()
ITER_FINISHED.disconnect(get_iter_msgs)
def __init__(self,
model_dir=None,
initial_iter=0,
tensorboard_every_n=0,
**_unused):
self.tensorboard_every_n = tensorboard_every_n
# creating new summary subfolder if it's not finetuning
self.summary_dir = get_latest_subfolder(
os.path.join(model_dir, 'logs'), create_new=initial_iter == 0)
self.writer_train = None
self.writer_valid = None
GRAPH_CREATED.connect(self.init_writer)
ITER_STARTED.connect(self.read_tensorboard_op)
ITER_FINISHED.connect(self.write_tensorboard)
def test_init(self):
ITER_FINISHED.connect(self.iteration_listener)
app_driver = get_initialised_driver()
app_driver.load_event_handlers([
'niftynet.engine.handler_model.ModelRestorer',
'niftynet.engine.handler_console.ConsoleLogger',
'niftynet.engine.handler_sampler.SamplerThreading'
])
graph = app_driver.create_graph(app_driver.app, 1, True)
with self.cached_session(graph=graph) as sess:
SESS_STARTED.send(app_driver.app, iter_msg=None)
msg = IterationMessage()
msg.current_iter = 1
app_driver.loop(app_driver.app, [msg])
app_driver.app.stop()
ITER_FINISHED.disconnect(self.iteration_listener)
def __init__(self,
model_dir,
save_every_n=0,
max_checkpoints=1,
is_training_action=True,
**_unused):
self.save_every_n = save_every_n
self.max_checkpoints = max_checkpoints
self.file_name_prefix = make_model_name(model_dir)
self.saver = None
# initialise the saver after the graph finalised
SESS_STARTED.connect(self.init_saver)
# save the training model at a positive frequency
if self.save_every_n > 0:
ITER_FINISHED.connect(self.save_model_interval)
# always save the final training model before exiting
if is_training_action:
SESS_FINISHED.connect(self.save_model)
def __init__(self, **_unused):
ITER_STARTED.connect(self.read_console_vars)
ITER_FINISHED.connect(self.print_console_vars)
def __init__(self, **_unused):
ITER_FINISHED.connect(self.check_criteria)
def __init__(self, **_unused):
ITER_FINISHED.connect(self.update_performance_history)
