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 __init__(self):
self.items_to_handlers = {
fields.DetectionResultFields.key:
StringParser(fields.TfExampleFields.source_id),
# Object detections.
fields.DetectionResultFields.detection_boxes:
(tf_example_parser.BoundingBoxParser(
fields.TfExampleFields.detection_bbox_xmin,
fields.TfExampleFields.detection_bbox_ymin,
fields.TfExampleFields.detection_bbox_xmax,
fields.TfExampleFields.detection_bbox_ymax)),
fields.DetectionResultFields.detection_classes:
(tf_example_parser.Int64Parser(
fields.TfExampleFields.detection_class_label)),
fields.DetectionResultFields.detection_scores:
(tf_example_parser.FloatParser(
fields.TfExampleFields.detection_score)),
}
self.optional_items_to_handlers = {}
