def test_global_step_name(self):
with ops.Graph().as_default() as g, session_lib.Session() as sess:
with variable_scope.variable_scope('bar'):
foo_step = variable_scope.get_variable(
'foo',
initializer=0,
trainable=False,
collections=[
ops.GraphKeys.GLOBAL_STEP,
ops.GraphKeys.GLOBAL_VARIABLES
])
train_op = state_ops.assign_add(foo_step, 1)
summary_writer = fake_summary_writer.FakeSummaryWriter(
self.log_dir, g)
hook = basic_session_run_hooks.StepCounterHook(
summary_writer=summary_writer,
every_n_steps=1,
every_n_secs=None)
hook.begin()
sess.run(variables_lib.global_variables_initializer())
mon_sess = monitored_session._HookedSession(sess, [hook])
mon_sess.run(train_op)
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
self.assertTrue(summary_writer.summaries,
'No summaries were created.')
self.assertItemsEqual([2], summary_writer.summaries.keys())
summary_value = summary_writer.summaries[2][0].value[0]
self.assertEqual('bar/foo/sec', summary_value.tag)
def test_step_counter_every_n_secs(self):
with ops.Graph().as_default() as g, session_lib.Session() as sess:
global_step = variables.get_or_create_global_step()
train_op = state_ops.assign_add(global_step, 1)
summary_writer = fake_summary_writer.FakeSummaryWriter(
self.log_dir, g)
hook = basic_session_run_hooks.StepCounterHook(
summary_writer=summary_writer,
every_n_steps=None,
every_n_secs=0.1)
hook.begin()
sess.run(variables_lib.global_variables_initializer())
mon_sess = monitored_session._HookedSession(sess, [hook])
mon_sess.run(train_op)
time.sleep(0.2)
mon_sess.run(train_op)
time.sleep(0.2)
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
self.assertTrue(summary_writer.summaries,
'No summaries were created.')
self.assertItemsEqual([2, 3], summary_writer.summaries.keys())
for summary in summary_writer.summaries.values():
summary_value = summary[0].value[0]
self.assertEqual('global_step/sec', summary_value.tag)
self.assertGreater(summary_value.simple_value, 0)
def test_step_counter_every_n_steps(self):
with ops.Graph().as_default() as g, session_lib.Session() as sess:
variables.get_or_create_global_step()
train_op = training_util._increment_global_step(1)
summary_writer = fake_summary_writer.FakeSummaryWriter(self.log_dir, g)
hook = basic_session_run_hooks.StepCounterHook(
summary_writer=summary_writer, every_n_steps=10)
hook.begin()
sess.run(variables_lib.global_variables_initializer())
mon_sess = monitored_session._HookedSession(sess, [hook])
with test.mock.patch.object(tf_logging, 'warning') as mock_log:
for _ in range(30):
time.sleep(0.01)
mon_sess.run(train_op)
# logging.warning should not be called.
self.assertIsNone(mock_log.call_args)
hook.end(sess)
summary_writer.assert_summaries(
test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
self.assertItemsEqual([11, 21], summary_writer.summaries.keys())
for step in [11, 21]:
summary_value = summary_writer.summaries[step][0].value[0]
self.assertEqual('global_step/sec', summary_value.tag)
self.assertGreater(summary_value.simple_value, 0)
def setUp(self):
test.TestCase.setUp(self)
self.log_dir = 'log/dir'
self.summary_writer = fake_summary_writer.FakeSummaryWriter(self.log_dir)
var = variable_scope.get_variable('var', initializer=0.0, use_resource=True)
tensor = state_ops.assign_add(var, 1.0)
self.summary_op = summary_lib.scalar('my_summary', tensor)
with variable_scope.variable_scope('foo', use_resource=True):
global_step = variables.get_or_create_global_step()
self.train_op = state_ops.assign_add(global_step, 1)
def setUp(self):
test.TestCase.setUp(self)
self.log_dir = 'log/dir'
self.summary_writer = fake_summary_writer.FakeSummaryWriter(self.log_dir)
var = variables_lib.Variable(0.0)
tensor = state_ops.assign_add(var, 1.0)
tensor2 = tensor * 2
self.summary_op = summary_lib.scalar('my_summary', tensor)
self.summary_op2 = summary_lib.scalar('my_summary2', tensor2)
global_step = variables.get_or_create_global_step()
self.train_op = state_ops.assign_add(global_step, 1)
