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# Copyright 2018 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.
# ==============================================================================
"""Large tests for metric_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib.metrics.python.ops import metric_ops
from tensorflow.python.framework import dtypes as dtypes_lib
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class StreamingPrecisionRecallAtEqualThresholdsLargeTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def testLargeCase(self):
shape = [32, 512, 256, 1]
predictions = random_ops.random_uniform(
shape, 0.0, 1.0, dtype=dtypes_lib.float32)
labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5)
result, update_op = metric_ops.precision_recall_at_equal_thresholds(
labels=labels, predictions=predictions, num_thresholds=201)
# Run many updates, enough to cause highly inaccurate values if the
# code used float32 for accumulation.
num_updates = 71
with self.cached_session() as sess:
sess.run(variables.local_variables_initializer())
for _ in xrange(num_updates):
sess.run(update_op)
prdata = sess.run(result)
# Since we use random values, we won't know the tp/fp/tn/fn values, but
# tp and fp at threshold 0 should be the total number of positive and
# negative labels, hence their sum should be total number of pixels.
expected_value = 1.0 * np.product(shape) * num_updates
got_value = prdata.tp[0] + prdata.fp[0]
# They should be at least within 1.
self.assertNear(got_value, expected_value, 1.0)
if __name__ == '__main__':
test.main()
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