From e936d100b8f7cd5b38adc3f7697dd1b26b07257c Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Thu, 14 Nov 2019 10:22:12 +0100
Subject: [PATCH] implemented time tracking class, /close #22
---
src/helpers.py | 39 +++++++++++++++++++++++++++++
test/test_helpers.py | 59 +++++++++++++++++++++++++++++++++++++++++++-
2 files changed, 97 insertions(+), 1 deletion(-)
diff --git a/src/helpers.py b/src/helpers.py
index 3c422f1b..9c7ab255 100644
--- a/src/helpers.py
+++ b/src/helpers.py
@@ -9,6 +9,7 @@ import math
from typing import Union
import numpy as np
import os
+import time
def to_list(arg):
@@ -84,3 +85,41 @@ class LearningRateDecay(keras.callbacks.History):
self.lr['lr'].append(current_lr)
logging.info(f"Set learning rate to {current_lr}")
return K.get_value(self.model.optimizer.lr)
+
+
+class TimeTracking(object):
+ """
+ Track time to measure execution time. Time tracking automatically starts on initialisation and ends by calling stop
+ method. Duration can always be shown by printing the time tracking object or calling get_current_duration.
+ """
+
+ def __init__(self, start=True):
+ self.start = None
+ self.end = None
+ if start:
+ self._start()
+
+ def _start(self):
+ self.start = time.time()
+
+ def _end(self):
+ self.end = time.time()
+
+ def _duration(self):
+ if self.end:
+ return self.end - self.start
+ else:
+ return time.time() - self.start
+
+ def __repr__(self):
+ return f"{round(self._duration(), 2)}s"
+
+ def run(self):
+ self._start()
+
+ def stop(self):
+ self.end = time.time()
+ return self._duration()
+
+ def duration(self):
+ return self._duration()
diff --git a/test/test_helpers.py b/test/test_helpers.py
index ffda6ac4..aa80ec38 100644
--- a/test/test_helpers.py
+++ b/test/test_helpers.py
@@ -1,5 +1,5 @@
import pytest
-from src.helpers import to_list, check_path_and_create, l_p_loss, LearningRateDecay
+from src.helpers import *
import logging
import os
import keras
@@ -78,3 +78,60 @@ class TestLearningRateDecay:
model.compile(optimizer=keras.optimizers.Adam(), loss=l_p_loss(2))
model.fit(np.array([1, 0, 2, 0.5]), np.array([1, 1, 0, 0.5]), epochs=5, callbacks=[lr_decay])
assert lr_decay.lr['lr'] == [0.02, 0.02, 0.02*0.95, 0.02*0.95, 0.02*0.95*0.95]
+
+
+class TestTimeTracking:
+
+ def test_init(self):
+ t = TimeTracking()
+ assert t.start is not None
+ assert t.start < time.time()
+ assert t.end is None
+ t2 = TimeTracking(start=False)
+ assert t2.start is None
+
+ def test__start(self):
+ t = TimeTracking(start=False)
+ t._start()
+ assert t.start < time.time()
+
+ def test__end(self):
+ t = TimeTracking()
+ t._end()
+ assert t.end > t.start
+
+ def test__duration(self):
+ t = TimeTracking()
+ d1 = t._duration()
+ assert d1 > 0
+ d2 = t._duration()
+ assert d2 > d1
+ t._end()
+ d3 = t._duration()
+ assert d3 > d2
+ assert d3 == t._duration()
+
+ def test_repr(self):
+ t = TimeTracking()
+ t._end()
+ duration = t._duration()
+ assert t.__repr__().rstrip() == f"{round(duration, 2)}s".rstrip()
+
+ def test_run(self):
+ t = TimeTracking(start=False)
+ assert t.start is None
+ t.run()
+ assert t.start is not None
+
+ def test_stop(self):
+ t = TimeTracking()
+ assert t.end is None
+ duration = t.stop()
+ assert duration == t._duration()
+
+ def test_duration(self):
+ t = TimeTracking()
+ duration = t
+ assert duration is not None
+ duration = t.stop()
+ assert duration == t.duration()
--
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