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tf_example_parser_test.py
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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 object_detection.data_decoders.tf_example_parser."""
import numpy as np
import numpy.testing as np_testing
import tensorflow as tf
from object_detection.core import standard_fields as fields
from object_detection.metrics import tf_example_parser
class TfExampleDecoderTest(tf.test.TestCase):
def _Int64Feature(self, value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _FloatFeature(self, value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _BytesFeature(self, value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def testParseDetectionsAndGT(self):
source_id = 'abc.jpg'
# y_min, x_min, y_max, x_max
object_bb = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6], [1.0, 0.6, 0.8],
[1.0, 0.6, 0.7]]).transpose()
detection_bb = np.array([[0.1, 0.2], [0.0, 0.8], [1.0, 0.6],
[1.0, 0.85]]).transpose()
object_class_label = [1, 1, 2]
object_difficult = [1, 0, 0]
object_group_of = [0, 0, 1]
detection_class_label = [2, 1]
detection_score = [0.5, 0.3]
features = {
fields.TfExampleFields.source_id:
self._BytesFeature(source_id),
fields.TfExampleFields.object_bbox_ymin:
self._FloatFeature(object_bb[:, 0].tolist()),
fields.TfExampleFields.object_bbox_xmin:
self._FloatFeature(object_bb[:, 1].tolist()),
fields.TfExampleFields.object_bbox_ymax:
self._FloatFeature(object_bb[:, 2].tolist()),
fields.TfExampleFields.object_bbox_xmax:
self._FloatFeature(object_bb[:, 3].tolist()),
fields.TfExampleFields.detection_bbox_ymin:
self._FloatFeature(detection_bb[:, 0].tolist()),
fields.TfExampleFields.detection_bbox_xmin:
self._FloatFeature(detection_bb[:, 1].tolist()),
fields.TfExampleFields.detection_bbox_ymax:
self._FloatFeature(detection_bb[:, 2].tolist()),
fields.TfExampleFields.detection_bbox_xmax:
self._FloatFeature(detection_bb[:, 3].tolist()),
fields.TfExampleFields.detection_class_label:
self._Int64Feature(detection_class_label),
fields.TfExampleFields.detection_score:
self._FloatFeature(detection_score),
}
example = tf.train.Example(features=tf.train.Features(feature=features))
parser = tf_example_parser.TfExampleDetectionAndGTParser()
results_dict = parser.parse(example)
self.assertIsNone(results_dict)
features[fields.TfExampleFields.object_class_label] = (
self._Int64Feature(object_class_label))
features[fields.TfExampleFields.object_difficult] = (
self._Int64Feature(object_difficult))
example = tf.train.Example(features=tf.train.Features(feature=features))
results_dict = parser.parse(example)
self.assertIsNotNone(results_dict)
self.assertEqual(source_id, results_dict[fields.DetectionResultFields.key])
np_testing.assert_almost_equal(
object_bb, results_dict[fields.InputDataFields.groundtruth_boxes])
np_testing.assert_almost_equal(
detection_bb,
results_dict[fields.DetectionResultFields.detection_boxes])
np_testing.assert_almost_equal(
detection_score,
results_dict[fields.DetectionResultFields.detection_scores])
np_testing.assert_almost_equal(
detection_class_label,
results_dict[fields.DetectionResultFields.detection_classes])
np_testing.assert_almost_equal(
object_difficult,
results_dict[fields.InputDataFields.groundtruth_difficult])
np_testing.assert_almost_equal(
object_class_label,
results_dict[fields.InputDataFields.groundtruth_classes])
parser = tf_example_parser.TfExampleDetectionAndGTParser()
features[fields.TfExampleFields.object_group_of] = (
self._Int64Feature(object_group_of))
example = tf.train.Example(features=tf.train.Features(feature=features))
results_dict = parser.parse(example)
self.assertIsNotNone(results_dict)
np_testing.assert_almost_equal(
object_group_of,
results_dict[fields.InputDataFields.groundtruth_group_of])
def testParseString(self):
string_val = 'abc'
features = {'string': self._BytesFeature(string_val)}
example = tf.train.Example(features=tf.train.Features(feature=features))
parser = tf_example_parser.StringParser('string')
result = parser.parse(example)
self.assertIsNotNone(result)
self.assertEqual(result, string_val)
parser = tf_example_parser.StringParser('another_string')
result = parser.parse(example)
self.assertIsNone(result)
def testParseFloat(self):
float_array_val = [1.5, 1.4, 2.0]
features = {'floats': self._FloatFeature(float_array_val)}
example = tf.train.Example(features=tf.train.Features(feature=features))
parser = tf_example_parser.FloatParser('floats')
result = parser.parse(example)
self.assertIsNotNone(result)
np_testing.assert_almost_equal(result, float_array_val)
parser = tf_example_parser.StringParser('another_floats')
result = parser.parse(example)
self.assertIsNone(result)
def testInt64Parser(self):
int_val = [1, 2, 3]
features = {'ints': self._Int64Feature(int_val)}
example = tf.train.Example(features=tf.train.Features(feature=features))
parser = tf_example_parser.Int64Parser('ints')
result = parser.parse(example)
self.assertIsNotNone(result)
np_testing.assert_almost_equal(result, int_val)
parser = tf_example_parser.Int64Parser('another_ints')
result = parser.parse(example)
self.assertIsNone(result)
def testBoundingBoxParser(self):
bounding_boxes = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6],
[1.0, 0.6, 0.8], [1.0, 0.6, 0.7]]).transpose()
features = {
'ymin': self._FloatFeature(bounding_boxes[:, 0]),
'xmin': self._FloatFeature(bounding_boxes[:, 1]),
'ymax': self._FloatFeature(bounding_boxes[:, 2]),
'xmax': self._FloatFeature(bounding_boxes[:, 3])
}
example = tf.train.Example(features=tf.train.Features(feature=features))
parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax', 'ymax')
result = parser.parse(example)
self.assertIsNotNone(result)
np_testing.assert_almost_equal(result, bounding_boxes)
parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax',
'another_ymax')
result = parser.parse(example)
self.assertIsNone(result)
if __name__ == '__main__':
tf.test.main()