def test_basic_logging(self):
v = ScheduledVariable('v', 1.)
logger = MetricLogger()
self.assertEqual(logger.format_logs(), '')
with self.test_session() as sess:
ensure_variables_initialized()
logger.collect_metrics(dict(loss=v))
logger.collect_metrics(dict(loss=2., valid_loss=3., valid_timer=0.1))
logger.collect_metrics(dict(loss=4., valid_acc=5., train_time=0.2))
logger.collect_metrics(dict(loss=6., valid_acc=7., train_time=0.3))
logger.collect_metrics(dict(other_metric=5.))
self.assertEqual(
logger.format_logs(),
'train time: 0.25s (±0.05s); '
'valid timer: 0.1s; '
'other metric: 5; '
'loss: 3.25 (±1.92029); '
'valid loss: 3; '
'valid acc: 6 (±1)'
)
logger.clear()
self.assertEqual(logger.format_logs(), '')
logger.collect_metrics({'loss': 1.})
self.assertEqual(logger.format_logs(), 'loss: 1')
def test_summary_writer(self):
with TemporaryDirectory() as tempdir:
# generate the metric summary
with contextlib.closing(tf.summary.FileWriter(tempdir)) as sw:
logger = MetricLogger(sw,
summary_skip_pattern=r'.*(time|timer)$',
summary_commit_freqs={'every_two': 2})
step = 0
for epoch in range(1, 3):
for data in range(10):
step += 1
logger.collect_metrics({'acc': step * 100 + data},
step)
logger.collect_metrics({'time': epoch}, step)
logger.collect_metrics({'every_two': step * 2}, step)
with self.test_session(use_gpu=False):
logger.collect_metrics({'valid_loss': -epoch},
tf.constant(step))
# read the metric summary
acc_steps = []
acc_values = []
valid_loss_steps = []
valid_loss_values = []
every_two_steps = []
every_two_values = []
tags = set()
event_file_path = os.path.join(tempdir, os.listdir(tempdir)[0])
for e in tf.train.summary_iterator(event_file_path):
for v in e.summary.value:
tags.add(v.tag)
if v.tag == 'acc':
acc_steps.append(e.step)
acc_values.append(v.simple_value)
elif v.tag == 'valid_loss':
valid_loss_steps.append(e.step)
valid_loss_values.append(v.simple_value)
elif v.tag == 'every_two':
every_two_steps.append(e.step)
every_two_values.append(v.simple_value)
self.assertEqual(sorted(tags), ['acc', 'every_two', 'valid_loss'])
np.testing.assert_equal(acc_steps, np.arange(1, 21))
np.testing.assert_almost_equal(
acc_values,
np.arange(1, 21) * 100 +
np.concatenate([np.arange(10), np.arange(10)]))
np.testing.assert_equal(every_two_steps, np.arange(1, 21, 2))
np.testing.assert_almost_equal(every_two_values,
np.arange(1, 21, 2) * 2)
np.testing.assert_equal(valid_loss_steps, [10, 20])
np.testing.assert_almost_equal(valid_loss_values, [-1, -2])
def test_basic_logging(self):
logger = MetricLogger()
self.assertEqual(logger.format_logs(), '')
logger.collect_metrics(dict(loss=SimpleDynamicValue(1.)))
logger.collect_metrics(dict(loss=2., valid_loss=3., valid_timer=0.1))
logger.collect_metrics(dict(loss=4., valid_acc=5., train_time=0.2))
logger.collect_metrics(dict(loss=6., valid_acc=7., train_time=0.3))
logger.collect_metrics(dict(other_metric=5.))
self.assertEqual(
logger.format_logs(), 'train time: 0.25 sec (±0.05 sec); '
'valid timer: 0.1 sec; '
'loss: 3.25 (±1.92029); '
'valid loss: 3; '
'valid acc: 6 (±1); '
'other metric: 5')
logger.clear()
self.assertEqual(logger.format_logs(), '')
logger.collect_metrics({'loss': 1.})
self.assertEqual(logger.format_logs(), 'loss: 1')