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.cached_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 _create_tfrecord_dataset(tmpdir):
if not gfile.Exists(tmpdir):
gfile.MakeDirs(tmpdir)
data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1)
keys_to_features = {
'image/encoded':
parsing_ops.FixedLenFeature(shape=(),
dtype=dtypes.string,
default_value=''),
'image/format':
parsing_ops.FixedLenFeature(shape=(),
dtype=dtypes.string,
default_value='jpeg'),
'image/class/label':
parsing_ops.FixedLenFeature(shape=[1],
dtype=dtypes.int64,
default_value=array_ops.zeros(
[1], dtype=dtypes.int64))
}
items_to_handlers = {
'image': tfexample_decoder.Image(),
'label': tfexample_decoder.Tensor('image/class/label'),
}
decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
return dataset.Dataset(data_sources=data_sources,
reader=io_ops.TFRecordReader,
decoder=decoder,
num_samples=100,
items_to_descriptions=None)
def testDecodeExampleWithLookup(self):
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeature(np.array(['cat', 'dog', 'guinea pig'])),
}))
serialized_example = example.SerializeToString()
# 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
table = lookup_ops.index_table_from_tensor(
constant_op.constant(['dog', 'guinea pig', 'cat']))
with self.cached_session() as sess:
sess.run(lookup_ops.tables_initializer())
serialized_example = array_ops.reshape(serialized_example, shape=[])
keys_to_features = {
'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
}
items_to_handlers = {
'labels':
tfexample_decoder.LookupTensor('image/object/class/text', table),
}
decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
obtained_class_ids = decoder.decode(serialized_example)[0].eval()
self.assertAllClose([2, 0, 1], obtained_class_ids)
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.cached_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 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.cached_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 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.cached_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 testDecodeExampleWithBackupHandlerLookup(self):
example1 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeature(np.array(['cat', 'dog', 'guinea pig'])),
'image/object/class/label':
self._EncodedInt64Feature(np.array([42, 10, 900]))
}))
example2 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeature(np.array(['cat', 'dog', 'guinea pig'])),
}))
example3 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/label':
self._EncodedInt64Feature(np.array([42, 10, 901]))
}))
# 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
table = lookup_ops.index_table_from_tensor(
constant_op.constant(['dog', 'guinea pig', 'cat']))
keys_to_features = {
'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
'image/object/class/label': parsing_ops.VarLenFeature(dtypes.int64),
}
backup_handler = tfexample_decoder.BackupHandler(
handler=tfexample_decoder.Tensor('image/object/class/label'),
backup=tfexample_decoder.LookupTensor('image/object/class/text', table))
items_to_handlers = {
'labels': backup_handler,
}
decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
obtained_class_ids_each_example = []
with self.cached_session() as sess:
sess.run(lookup_ops.tables_initializer())
for example in [example1, example2, example3]:
serialized_example = array_ops.reshape(
example.SerializeToString(), shape=[])
obtained_class_ids_each_example.append(
decoder.decode(serialized_example)[0].eval())
self.assertAllClose([42, 10, 900], obtained_class_ids_each_example[0])
self.assertAllClose([2, 0, 1], obtained_class_ids_each_example[1])
self.assertAllClose([42, 10, 901], obtained_class_ids_each_example[2])
def testDecodeExampleWithBoundingBoxDense(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.cached_session():
serialized_example = array_ops.reshape(serialized_example, shape=[])
keys_to_features = {
'image/object/bbox/ymin':
parsing_ops.FixedLenSequenceFeature(
[], dtypes.float32, allow_missing=True),
'image/object/bbox/xmin':
parsing_ops.FixedLenSequenceFeature(
[], dtypes.float32, allow_missing=True),
'image/object/bbox/ymax':
parsing_ops.FixedLenSequenceFeature(
[], dtypes.float32, allow_missing=True),
'image/object/bbox/xmax':
parsing_ops.FixedLenSequenceFeature(
[], dtypes.float32, allow_missing=True),
}
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)
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.cached_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 testDecodeExampleWithRepeatedImages(self):
image_shape = (2, 3, 3)
image_format = 'png'
image, _ = self.GenerateImage(
image_format=image_format, image_shape=image_shape)
tf_encoded = self._Encoder(image, image_format)
with self.cached_session():
tf_string = tf_encoded.eval()
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/encoded':
feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(
value=[tf_string, tf_string])),
'image/format':
self._StringFeature(image_format),
}))
serialized_example = example.SerializeToString()
with self.cached_session():
serialized_example = array_ops.reshape(serialized_example, shape=[])
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features={
'image/encoded':
parsing_ops.FixedLenFeature((2,), dtypes.string),
'image/format':
parsing_ops.FixedLenFeature(
(), dtypes.string, default_value=image_format),
},
items_to_handlers={'image': tfexample_decoder.Image(repeated=True)})
[tf_image] = decoder.decode(serialized_example, ['image'])
output_image = tf_image.eval()
self.assertEqual(output_image.shape, (2, 2, 3, 3))
self.assertAllEqual(np.squeeze(output_image[0, :, :, :]), image)
self.assertAllEqual(np.squeeze(output_image[1, :, :, :]), image)
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.cached_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 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.cached_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 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
