# Copyright 2016 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 slim.data.tfexample_decoder.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.slim.python.slim.data import tfexample_decoder from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import image_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import test class TFExampleDecoderTest(test.TestCase): def _EncodedFloatFeature(self, ndarray): return feature_pb2.Feature(float_list=feature_pb2.FloatList( value=ndarray.flatten().tolist())) def _EncodedInt64Feature(self, ndarray): return feature_pb2.Feature(int64_list=feature_pb2.Int64List( value=ndarray.flatten().tolist())) def _EncodedBytesFeature(self, tf_encoded): with self.test_session(): encoded = tf_encoded.eval() def BytesList(value): return feature_pb2.BytesList(value=[value]) return feature_pb2.Feature(bytes_list=BytesList(encoded)) def _BytesFeature(self, ndarray): values = ndarray.flatten().tolist() for i in range(len(values)): values[i] = values[i].encode('utf-8') return feature_pb2.Feature(bytes_list=feature_pb2.BytesList(value=values)) def _StringFeature(self, value): value = value.encode('utf-8') return feature_pb2.Feature(bytes_list=feature_pb2.BytesList(value=[value])) def _Encoder(self, image, image_format): assert image_format in ['jpeg', 'JPEG', 'png', 'PNG', 'raw', 'RAW'] if image_format in ['jpeg', 'JPEG']: tf_image = constant_op.constant(image, dtype=dtypes.uint8) return image_ops.encode_jpeg(tf_image) if image_format in ['png', 'PNG']: tf_image = constant_op.constant(image, dtype=dtypes.uint8) return image_ops.encode_png(tf_image) if image_format in ['raw', 'RAW']: return constant_op.constant(image.tostring(), dtype=dtypes.string) def GenerateImage(self, image_format, image_shape): """Generates an image and an example containing the encoded image. Args: image_format: the encoding format of the image. image_shape: the shape of the image to generate. Returns: image: the generated image. example: a TF-example with a feature key 'image/encoded' set to the serialized image and a feature key 'image/format' set to the image encoding format ['jpeg', 'JPEG', 'png', 'PNG', 'raw']. """ num_pixels = image_shape[0] * image_shape[1] * image_shape[2] image = np.linspace( 0, num_pixels - 1, num=num_pixels).reshape(image_shape).astype(np.uint8) tf_encoded = self._Encoder(image, image_format) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/encoded': self._EncodedBytesFeature(tf_encoded), 'image/format': self._StringFeature(image_format) })) return image, example.SerializeToString() def DecodeExample(self, serialized_example, item_handler, image_format): """Decodes the given serialized example with the specified item handler. Args: serialized_example: a serialized TF example string. item_handler: the item handler used to decode the image. image_format: the image format being decoded. Returns: the decoded image found in the serialized Example. """ serialized_example = array_ops.reshape(serialized_example, shape=[]) decoder = tfexample_decoder.TFExampleDecoder( keys_to_features={ 'image/encoded': parsing_ops.FixedLenFeature( (), dtypes.string, default_value=''), 'image/format': parsing_ops.FixedLenFeature( (), dtypes.string, default_value=image_format), }, items_to_handlers={'image': item_handler}) [tf_image] = decoder.decode(serialized_example, ['image']) return tf_image def RunDecodeExample(self, serialized_example, item_handler, image_format): tf_image = self.DecodeExample(serialized_example, item_handler, image_format) with self.test_session(): decoded_image = tf_image.eval() # We need to recast them here to avoid some issues with uint8. return decoded_image.astype(np.float32) def testDecodeExampleWithJpegEncoding(self): image_shape = (2, 3, 3) image, serialized_example = self.GenerateImage( image_format='jpeg', image_shape=image_shape) decoded_image = self.RunDecodeExample( serialized_example, tfexample_decoder.Image(), image_format='jpeg') # Need to use a tolerance of 1 because of noise in the jpeg encode/decode self.assertAllClose(image, decoded_image, atol=1.001) def testDecodeExampleWithJPEGEncoding(self): test_image_channels = [1, 3] for channels in test_image_channels: image_shape = (2, 3, channels) image, serialized_example = self.GenerateImage( image_format='JPEG', image_shape=image_shape) decoded_image = self.RunDecodeExample( serialized_example, tfexample_decoder.Image(channels=channels), image_format='JPEG') # Need to use a tolerance of 1 because of noise in the jpeg encode/decode self.assertAllClose(image, decoded_image, atol=1.001) def testDecodeExampleWithNoShapeInfo(self): test_image_channels = [1, 3] for channels in test_image_channels: image_shape = (2, 3, channels) _, serialized_example = self.GenerateImage( image_format='jpeg', image_shape=image_shape) tf_decoded_image = self.DecodeExample( serialized_example, tfexample_decoder.Image( shape=None, channels=channels), image_format='jpeg') self.assertEqual(tf_decoded_image.get_shape().ndims, 3) def testDecodeExampleWithPngEncoding(self): test_image_channels = [1, 3, 4] for channels in test_image_channels: image_shape = (2, 3, channels) image, serialized_example = self.GenerateImage( image_format='png', image_shape=image_shape) decoded_image = self.RunDecodeExample( serialized_example, tfexample_decoder.Image(channels=channels), image_format='png') self.assertAllClose(image, decoded_image, atol=0) def testDecodeExampleWithPNGEncoding(self): test_image_channels = [1, 3, 4] for channels in test_image_channels: image_shape = (2, 3, channels) image, serialized_example = self.GenerateImage( image_format='PNG', image_shape=image_shape) decoded_image = self.RunDecodeExample( serialized_example, tfexample_decoder.Image(channels=channels), image_format='PNG') self.assertAllClose(image, decoded_image, atol=0) def testDecodeExampleWithRawEncoding(self): image_shape = (2, 3, 3) image, serialized_example = self.GenerateImage( image_format='raw', image_shape=image_shape) decoded_image = self.RunDecodeExample( serialized_example, tfexample_decoder.Image(shape=image_shape), image_format='raw') self.assertAllClose(image, decoded_image, atol=0) def testDecodeExampleWithRAWEncoding(self): image_shape = (2, 3, 3) image, serialized_example = self.GenerateImage( image_format='RAW', image_shape=image_shape) decoded_image = self.RunDecodeExample( serialized_example, tfexample_decoder.Image(shape=image_shape), image_format='RAW') self.assertAllClose(image, decoded_image, atol=0) def testDecodeExampleWithStringTensor(self): tensor_shape = (2, 3, 1) np_array = np.array([[['ab'], ['cd'], ['ef']], [['ghi'], ['jkl'], ['mnop']]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._BytesFeature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.FixedLenFeature( tensor_shape, dtypes.string, default_value=constant_op.constant( '', shape=tensor_shape, dtype=dtypes.string)) } items_to_handlers = {'labels': tfexample_decoder.Tensor('labels'),} decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() labels = labels.astype(np_array.dtype) self.assertTrue(np.array_equal(np_array, labels)) def testDecodeExampleWithFloatTensor(self): np_array = np.random.rand(2, 3, 1).astype('f') example = example_pb2.Example(features=feature_pb2.Features(feature={ 'array': self._EncodedFloatFeature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.float32) } items_to_handlers = {'array': tfexample_decoder.Tensor('array'),} decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_array] = decoder.decode(serialized_example, ['array']) self.assertAllEqual(tf_array.eval(), np_array) def testDecodeExampleWithInt64Tensor(self): np_array = np.random.randint(1, 10, size=(2, 3, 1)) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'array': self._EncodedInt64Feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.int64) } items_to_handlers = {'array': tfexample_decoder.Tensor('array'),} decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_array] = decoder.decode(serialized_example, ['array']) self.assertAllEqual(tf_array.eval(), np_array) def testDecodeExampleWithVarLenTensor(self): np_array = np.array([[[1], [2], [3]], [[4], [5], [6]]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._EncodedInt64Feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = {'labels': tfexample_decoder.Tensor('labels'),} decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels, np_array.flatten()) def testDecodeExampleWithFixLenTensorWithShape(self): np_array = np.array([[1, 2, 3], [4, 5, 6]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._EncodedInt64Feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.FixedLenFeature( np_array.shape, dtype=dtypes.int64), } items_to_handlers = { 'labels': tfexample_decoder.Tensor( 'labels', shape=np_array.shape), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels, np_array) def testDecodeExampleWithVarLenTensorToDense(self): np_array = np.array([[1, 2, 3], [4, 5, 6]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._EncodedInt64Feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = { 'labels': tfexample_decoder.Tensor( 'labels', shape=np_array.shape), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels, np_array) def testDecodeExampleShapeKeyTensor(self): np_image = np.random.rand(2, 3, 1).astype('f') np_labels = np.array([[[1], [2], [3]], [[4], [5], [6]]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image': self._EncodedFloatFeature(np_image), 'image/shape': self._EncodedInt64Feature(np.array(np_image.shape)), 'labels': self._EncodedInt64Feature(np_labels), 'labels/shape': self._EncodedInt64Feature(np.array(np_labels.shape)), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image': parsing_ops.VarLenFeature(dtype=dtypes.float32), 'image/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = { 'image': tfexample_decoder.Tensor( 'image', shape_keys='image/shape'), 'labels': tfexample_decoder.Tensor( 'labels', shape_keys='labels/shape'), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_image, tf_labels] = decoder.decode(serialized_example, ['image', 'labels']) self.assertAllEqual(tf_image.eval(), np_image) self.assertAllEqual(tf_labels.eval(), np_labels) def testDecodeExampleMultiShapeKeyTensor(self): np_image = np.random.rand(2, 3, 1).astype('f') np_labels = np.array([[[1], [2], [3]], [[4], [5], [6]]]) height, width, depth = np_labels.shape example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image': self._EncodedFloatFeature(np_image), 'image/shape': self._EncodedInt64Feature(np.array(np_image.shape)), 'labels': self._EncodedInt64Feature(np_labels), 'labels/height': self._EncodedInt64Feature(np.array([height])), 'labels/width': self._EncodedInt64Feature(np.array([width])), 'labels/depth': self._EncodedInt64Feature(np.array([depth])), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image': parsing_ops.VarLenFeature(dtype=dtypes.float32), 'image/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels/height': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels/width': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels/depth': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = { 'image': tfexample_decoder.Tensor( 'image', shape_keys='image/shape'), 'labels': tfexample_decoder.Tensor( 'labels', shape_keys=['labels/height', 'labels/width', 'labels/depth']), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_image, tf_labels] = decoder.decode(serialized_example, ['image', 'labels']) self.assertAllEqual(tf_image.eval(), np_image) self.assertAllEqual(tf_labels.eval(), np_labels) def testDecodeExampleWithSparseTensor(self): np_indices = np.array([[1], [2], [5]]) np_values = np.array([0.1, 0.2, 0.6]).astype('f') example = example_pb2.Example(features=feature_pb2.Features(feature={ 'indices': self._EncodedInt64Feature(np_indices), 'values': self._EncodedFloatFeature(np_values), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'values': parsing_ops.VarLenFeature(dtype=dtypes.float32), } items_to_handlers = {'labels': tfexample_decoder.SparseTensor(),} decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels.indices, np_indices) self.assertAllEqual(labels.values, np_values) self.assertAllEqual(labels.dense_shape, np_values.shape) def testDecodeExampleWithSparseTensorWithKeyShape(self): np_indices = np.array([[1], [2], [5]]) np_values = np.array([0.1, 0.2, 0.6]).astype('f') np_shape = np.array([6]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'indices': self._EncodedInt64Feature(np_indices), 'values': self._EncodedFloatFeature(np_values), 'shape': self._EncodedInt64Feature(np_shape), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'values': parsing_ops.VarLenFeature(dtype=dtypes.float32), 'shape': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = { 'labels': tfexample_decoder.SparseTensor(shape_key='shape'), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels.indices, np_indices) self.assertAllEqual(labels.values, np_values) self.assertAllEqual(labels.dense_shape, np_shape) def testDecodeExampleWithSparseTensorWithGivenShape(self): np_indices = np.array([[1], [2], [5]]) np_values = np.array([0.1, 0.2, 0.6]).astype('f') np_shape = np.array([6]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'indices': self._EncodedInt64Feature(np_indices), 'values': self._EncodedFloatFeature(np_values), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'values': parsing_ops.VarLenFeature(dtype=dtypes.float32), } items_to_handlers = { 'labels': tfexample_decoder.SparseTensor(shape=np_shape), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels.indices, np_indices) self.assertAllEqual(labels.values, np_values) self.assertAllEqual(labels.dense_shape, np_shape) def testDecodeExampleWithSparseTensorToDense(self): np_indices = np.array([1, 2, 5]) np_values = np.array([0.1, 0.2, 0.6]).astype('f') np_shape = np.array([6]) np_dense = np.array([0.0, 0.1, 0.2, 0.0, 0.0, 0.6]).astype('f') example = example_pb2.Example(features=feature_pb2.Features(feature={ 'indices': self._EncodedInt64Feature(np_indices), 'values': self._EncodedFloatFeature(np_values), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'values': parsing_ops.VarLenFeature(dtype=dtypes.float32), } items_to_handlers = { 'labels': tfexample_decoder.SparseTensor( shape=np_shape, densify=True), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllClose(labels, np_dense) def testDecodeExampleWithTensor(self): tensor_shape = (2, 3, 1) np_array = np.random.rand(2, 3, 1) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/depth_map': self._EncodedFloatFeature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/depth_map': parsing_ops.FixedLenFeature( tensor_shape, dtypes.float32, default_value=array_ops.zeros(tensor_shape)) } items_to_handlers = {'depth': tfexample_decoder.Tensor('image/depth_map')} decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_depth] = decoder.decode(serialized_example, ['depth']) depth = tf_depth.eval() self.assertAllClose(np_array, depth) def testDecodeExampleWithItemHandlerCallback(self): np.random.seed(0) tensor_shape = (2, 3, 1) np_array = np.random.rand(2, 3, 1) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/depth_map': self._EncodedFloatFeature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/depth_map': parsing_ops.FixedLenFeature( tensor_shape, dtypes.float32, default_value=array_ops.zeros(tensor_shape)) } def HandleDepth(keys_to_tensors): depth = list(keys_to_tensors.values())[0] depth += 1 return depth items_to_handlers = { 'depth': tfexample_decoder.ItemHandlerCallback('image/depth_map', HandleDepth) } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_depth] = decoder.decode(serialized_example, ['depth']) depth = tf_depth.eval() self.assertAllClose(np_array, depth - 1) def testDecodeImageWithItemHandlerCallback(self): image_shape = (2, 3, 3) for image_encoding in ['jpeg', 'png']: image, serialized_example = self.GenerateImage( image_format=image_encoding, image_shape=image_shape) with self.test_session(): def ConditionalDecoding(keys_to_tensors): """See base class.""" image_buffer = keys_to_tensors['image/encoded'] image_format = keys_to_tensors['image/format'] def DecodePng(): return image_ops.decode_png(image_buffer, 3) def DecodeJpg(): return image_ops.decode_jpeg(image_buffer, 3) image = control_flow_ops.case( { math_ops.equal(image_format, 'png'): DecodePng, }, default=DecodeJpg, exclusive=True) image = array_ops.reshape(image, image_shape) return image keys_to_features = { 'image/encoded': parsing_ops.FixedLenFeature( (), dtypes.string, default_value=''), 'image/format': parsing_ops.FixedLenFeature( (), dtypes.string, default_value='jpeg') } items_to_handlers = { 'image': tfexample_decoder.ItemHandlerCallback( ['image/encoded', 'image/format'], ConditionalDecoding) } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_image] = decoder.decode(serialized_example, ['image']) decoded_image = tf_image.eval() if image_encoding == 'jpeg': # For jenkins: image = image.astype(np.float32) decoded_image = decoded_image.astype(np.float32) self.assertAllClose(image, decoded_image, rtol=.5, atol=1.001) else: self.assertAllClose(image, decoded_image, atol=0) def testDecodeExampleWithBoundingBox(self): num_bboxes = 10 np_ymin = np.random.rand(num_bboxes, 1) np_xmin = np.random.rand(num_bboxes, 1) np_ymax = np.random.rand(num_bboxes, 1) np_xmax = np.random.rand(num_bboxes, 1) np_bboxes = np.hstack([np_ymin, np_xmin, np_ymax, np_xmax]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/object/bbox/ymin': self._EncodedFloatFeature(np_ymin), 'image/object/bbox/xmin': self._EncodedFloatFeature(np_xmin), 'image/object/bbox/ymax': self._EncodedFloatFeature(np_ymax), 'image/object/bbox/xmax': self._EncodedFloatFeature(np_xmax), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/object/bbox/ymin': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/xmin': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/ymax': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/xmax': parsing_ops.VarLenFeature(dtypes.float32), } items_to_handlers = { 'object/bbox': tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) [tf_bboxes] = decoder.decode(serialized_example, ['object/bbox']) bboxes = tf_bboxes.eval() self.assertAllClose(np_bboxes, bboxes) if __name__ == '__main__': test.main()