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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Metrics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tempfile
from tensorflow.contrib.eager.python import metrics
from tensorflow.contrib.summary import summary_ops
from tensorflow.contrib.summary import summary_test_util
from tensorflow.python.eager import context
from tensorflow.python.eager import test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.training import training_util
class MetricsTest(test.TestCase):
def testMean(self):
m = metrics.Mean()
m([1, 10, 100])
m(1000)
m([10000.0, 100000.0])
self.assertEqual(111111.0/6, m.result().numpy())
self.assertEqual(dtypes.float64, m.dtype)
self.assertEqual(dtypes.float64, m.result().dtype)
def testVariableCollections(self):
with context.graph_mode(), ops.Graph().as_default():
m = metrics.Mean()
m(1000)
self.assertEqual(
set(m.variables),
set(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)))
self.assertEqual(
set(m.variables),
set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES)))
def testInitVariables(self):
m = metrics.Mean()
m([1, 10, 100, 1000])
m([10000.0, 100000.0])
self.assertEqual(111111.0/6, m.result().numpy())
m.init_variables()
m(7)
self.assertEqual(7.0, m.result().numpy())
def testWriteSummaries(self):
m = metrics.Mean()
m([1, 10, 100])
training_util.get_or_create_global_step()
logdir = tempfile.mkdtemp()
with summary_ops.create_summary_file_writer(
logdir, max_queue=0,
name="t0").as_default(), summary_ops.always_record_summaries():
m.result() # As a side-effect will write summaries.
events = summary_test_util.events_from_logdir(logdir)
self.assertEqual(len(events), 2)
self.assertEqual(events[1].summary.value[0].simple_value, 37.0)
def testWeightedMean(self):
m = metrics.Mean()
m([1, 100, 100000], weights=[1, 0.2, 0.3])
m([500000, 5000, 500]) # weights of 1 each
self.assertNear(535521/4.5, m.result().numpy(), 0.001)
def testMeanDtype(self):
# Can override default dtype of float64.
m = metrics.Mean(dtype=dtypes.float32)
m([0, 2])
self.assertEqual(1, m.result().numpy())
self.assertEqual(dtypes.float32, m.dtype)
self.assertEqual(dtypes.float32, m.result().dtype)
def testAccuracy(self):
m = metrics.Accuracy()
m([0, 1, 2, 3], [0, 0, 0, 0]) # 1 correct
m([4], [4]) # 1 correct
m([5], [0]) # 0 correct
m([6], [6]) # 1 correct
m([7], [2]) # 0 correct
self.assertEqual(3.0/8, m.result().numpy())
self.assertEqual(dtypes.float64, m.dtype)
self.assertEqual(dtypes.float64, m.result().dtype)
def testWeightedAccuracy(self):
m = metrics.Accuracy()
# 1 correct, total weight of 2
m([0, 1, 2, 3], [0, 0, 0, 0], weights=[1, 1, 0, 0])
m([4], [4], weights=[0.5]) # 1 correct with a weight of 0.5
m([5], [0], weights=[0.5]) # 0 correct, weight 0.5
m([6], [6]) # 1 correct, weight 1
m([7], [2]) # 0 correct, weight 1
self.assertEqual(2.5/5, m.result().numpy())
def testAccuracyDtype(self):
# Can override default dtype of float64.
m = metrics.Accuracy(dtype=dtypes.float32)
m([0, 0], [0, 1])
self.assertEqual(0.5, m.result().numpy())
self.assertEqual(dtypes.float32, m.dtype)
self.assertEqual(dtypes.float32, m.result().dtype)
def testTwoMeans(self):
# Verify two metrics with the same class and name don't
# accidentally share state.
m1 = metrics.Mean()
m1(0)
m2 = metrics.Mean()
m2(2)
self.assertAllEqual(0.0, m1.result())
self.assertAllEqual(2.0, m2.result())
def testNamesWithSpaces(self):
# Verify two metrics with the same class and name don't
# accidentally share state.
m1 = metrics.Mean("has space")
m1(0)
self.assertEqual(m1.name, "has space")
self.assertEqual(m1.numer.name, "has_space/numer:0")
def testGraph(self):
with context.graph_mode(), self.test_session() as sess:
m = metrics.Mean()
p = array_ops.placeholder(dtypes.float32)
accumulate = m(p)
init_op = m.init_variables()
init_op.run()
sess.run(accumulate, feed_dict={p: [1, 10, 100]})
sess.run(accumulate, feed_dict={p: 1000})
sess.run(accumulate, feed_dict={p: [10000, 100000]})
self.assertAllEqual(m.result().eval(), 111111.0/6)
# Second init resets all the variables.
init_op.run()
sess.run(accumulate, feed_dict={p: 7})
self.assertAllEqual(m.result().eval(), 7)
def testTwoMeansGraph(self):
# Verify two metrics with the same class and name don't
# accidentally share state.
with context.graph_mode():
m1 = metrics.Mean()
m1(0)
with self.assertRaises(ValueError):
m2 = metrics.Mean()
m2(2)
if __name__ == "__main__":
test.main()
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