def testDecodeImageLabels(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature(b'jpeg'),
'image/class/label': dataset_util.int64_list_feature([1, 2]),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_image_classes in tensor_dict)
self.assertAllEqual(
tensor_dict[fields.InputDataFields.groundtruth_image_classes],
np.array([1, 2]))
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/class/text':
dataset_util.bytes_list_feature([b'dog', b'cat']),
})).SerializeToString()
label_map_string = """
item {
id:3
name:'cat'
}
item {
id:1
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
sess.run(tf.tables_initializer())
tensor_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_image_classes in tensor_dict)
self.assertAllEqual(
tensor_dict[fields.InputDataFields.groundtruth_image_classes],
np.array([1, 3]))
def testDecodeObjectGroupOf(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_group_of = [0, 1]
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/object/group_of':
dataset_util.int64_list_feature(object_group_of),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_group_of].
get_shape().as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
[bool(item) for item in object_group_of],
tensor_dict[fields.InputDataFields.groundtruth_group_of])
def testDecodeAdditionalChannels(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
additional_channel_tensor = np.random.randint(
256, size=(4, 5, 1)).astype(np.uint8)
encoded_additional_channel = self._EncodeImage(
additional_channel_tensor)
decoded_additional_channel = self._DecodeImage(
encoded_additional_channel)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/additional_channels/encoded':
dataset_util.bytes_list_feature([encoded_additional_channel] *
2),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/source_id':
dataset_util.bytes_feature(b'image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
num_additional_channels=2)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
np.concatenate([decoded_additional_channel] * 2, axis=2),
tensor_dict[fields.InputDataFields.image_additional_channels])
def testDecodeEmptyPngInstanceMasks(self):
image_tensor = np.random.randint(256,
size=(10, 10, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
encoded_masks = []
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature(b'jpeg'),
'image/object/mask': dataset_util.bytes_list_feature(
encoded_masks),
'image/height': dataset_util.int64_feature(10),
'image/width': dataset_util.int64_feature(10),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True,
instance_mask_type=input_reader_pb2.PNG_MASKS)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
tensor_dict[
fields.InputDataFields.groundtruth_instance_masks].shape,
[0, 10, 10])
def testDecodePngInstanceMasks(self):
image_tensor = np.random.randint(256,
size=(10, 10, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
mask_1 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
mask_2 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
encoded_png_1 = self._EncodeImage(mask_1, encoding_type='png')
decoded_png_1 = np.squeeze(mask_1.astype(np.float32))
encoded_png_2 = self._EncodeImage(mask_2, encoding_type='png')
decoded_png_2 = np.squeeze(mask_2.astype(np.float32))
encoded_masks = [encoded_png_1, encoded_png_2]
decoded_masks = np.stack([decoded_png_1, decoded_png_2])
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature(b'jpeg'),
'image/object/mask': dataset_util.bytes_list_feature(
encoded_masks)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True,
instance_mask_type=input_reader_pb2.PNG_MASKS)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
decoded_masks,
tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
def testDecodeObjectWeight(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_weights = [0.75, 1.0]
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/object/weight':
dataset_util.float_list_feature(object_weights),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_weights].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
object_weights,
tensor_dict[fields.InputDataFields.groundtruth_weights])
def testDecodeJpegImage(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
decoded_jpeg = self._DecodeImage(encoded_jpeg)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature(b'jpeg'),
'image/source_id': dataset_util.bytes_feature(b'image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.image].get_shape().as_list()),
[None, None, 3])
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.original_image_spatial_shape].
get_shape().as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_jpeg,
tensor_dict[fields.InputDataFields.image])
self.assertAllEqual(
[4, 5],
tensor_dict[fields.InputDataFields.original_image_spatial_shape])
self.assertEqual(b'image_id',
tensor_dict[fields.InputDataFields.source_id])
def testDecodeInstanceSegmentation(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(256,
size=(image_height, image_width,
3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_masks = (np.random.randint(2,
size=(num_instances, image_height,
image_width)).astype(
np.float32))
instance_masks_flattened = np.reshape(instance_masks, [-1])
# Randomly generate class labels for each instance.
object_classes = np.random.randint(100, size=(num_instances)).astype(
np.int64)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/object/mask':
dataset_util.float_list_feature(instance_masks_flattened),
'image/object/class/label':
dataset_util.int64_list_feature(object_classes)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_instance_masks].
get_shape().as_list()), [4, 5, 3])
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
[4])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
instance_masks.astype(np.float32),
tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
self.assertAllEqual(
object_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeKeypoint(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
bbox_xmins = [1.0, 5.0]
bbox_ymaxs = [2.0, 6.0]
bbox_xmaxs = [3.0, 7.0]
keypoint_ys = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
keypoint_xs = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
'image/object/keypoint/y':
dataset_util.float_list_feature(keypoint_ys),
'image/object/keypoint/x':
dataset_util.float_list_feature(keypoint_xs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(num_keypoints=3)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_boxes].get_shape().as_list()),
[None, 4])
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_keypoints].
get_shape().as_list()), [2, 3, 2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
expected_boxes = np.vstack(
[bbox_ymins, bbox_xmins, bbox_ymaxs, bbox_xmaxs]).transpose()
self.assertAllEqual(
expected_boxes,
tensor_dict[fields.InputDataFields.groundtruth_boxes])
self.assertAllEqual(
2, tensor_dict[fields.InputDataFields.num_groundtruth_boxes])
expected_keypoints = (np.vstack([keypoint_ys,
keypoint_xs]).transpose().reshape(
(2, 3, 2)))
self.assertAllEqual(
expected_keypoints,
tensor_dict[fields.InputDataFields.groundtruth_keypoints])
def build(input_reader_config):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
if input_reader_config.WhichOneof(
'input_reader') == 'tf_record_input_reader':
config = input_reader_config.tf_record_input_reader
if not config.input_path:
raise ValueError('At least one input path must be specified in '
'`input_reader_config`.')
_, string_tensor = parallel_reader.parallel_read(
config.input_path[:], # Convert `RepeatedScalarContainer` to list.
reader_class=tf.TFRecordReader,
num_epochs=(input_reader_config.num_epochs
if input_reader_config.num_epochs else None),
num_readers=input_reader_config.num_readers,
shuffle=input_reader_config.shuffle,
dtypes=[tf.string, tf.string],
capacity=input_reader_config.queue_capacity,
min_after_dequeue=input_reader_config.min_after_dequeue)
label_map_proto_file = None
if input_reader_config.HasField('label_map_path'):
label_map_proto_file = input_reader_config.label_map_path
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file)
return decoder.decode(string_tensor)
raise ValueError('Unsupported input_reader_config.')
def testDecodeObjectLabelNoText(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes = [1, 2]
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/object/class/label':
dataset_util.int64_list_feature(bbox_classes),
})).SerializeToString()
label_map_string = """
item {
id:1
name:'cat'
}
item {
id:2
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
[None])
init = tf.tables_initializer()
with self.test_session() as sess:
sess.run(init)
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
bbox_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeImageKeyAndFilename(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/key/sha256': dataset_util.bytes_feature(b'abc'),
'image/filename': dataset_util.bytes_feature(b'filename')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertEqual(b'abc', tensor_dict[fields.InputDataFields.key])
self.assertEqual(b'filename',
tensor_dict[fields.InputDataFields.filename])
def testInstancesNotAvailableByDefault(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(256,
size=(image_height, image_width,
3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_masks = (np.random.randint(2,
size=(num_instances, image_height,
image_width)).astype(
np.float32))
instance_masks_flattened = np.reshape(instance_masks, [-1])
# Randomly generate class labels for each instance.
object_classes = np.random.randint(100, size=(num_instances)).astype(
np.int64)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/object/mask':
dataset_util.float_list_feature(instance_masks_flattened),
'image/object/class/label':
dataset_util.int64_list_feature(object_classes)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertTrue(fields.InputDataFields.groundtruth_instance_masks
not in tensor_dict)
def _predict_input_fn(params=None):
"""Decodes serialized tf.Examples and returns `ServingInputReceiver`.
Args:
params: Parameter dictionary passed from the estimator.
Returns:
`ServingInputReceiver`.
"""
del params
example = tf.placeholder(dtype=tf.string, shape=[], name='tf_example')
num_classes = config_util.get_number_of_classes(model_config)
model = model_builder.build(model_config, is_training=False)
image_resizer_config = config_util.get_image_resizer_config(model_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
transform_fn = functools.partial(
transform_input_data, model_preprocess_fn=model.preprocess,
image_resizer_fn=image_resizer_fn,
num_classes=num_classes,
data_augmentation_fn=None)
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=False,
num_additional_channels=predict_input_config.num_additional_channels)
input_dict = transform_fn(decoder.decode(example))
images = tf.to_float(input_dict[fields.InputDataFields.image])
images = tf.expand_dims(images, axis=0)
true_image_shape = tf.expand_dims(
input_dict[fields.InputDataFields.true_image_shape], axis=0)
return tf.estimator.export.ServingInputReceiver(
features={
fields.InputDataFields.image: images,
fields.InputDataFields.true_image_shape: true_image_shape},
receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})
def testDecodeDefaultGroundtruthWeights(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
bbox_xmins = [1.0, 5.0]
bbox_ymaxs = [2.0, 6.0]
bbox_xmaxs = [3.0, 7.0]
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(b'jpeg'),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_boxes].get_shape().as_list()),
[None, 4])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllClose(
tensor_dict[fields.InputDataFields.groundtruth_weights],
np.ones(2, dtype=np.float32))
def build(input_reader_config,
transform_input_data_fn=None,
batch_size=None,
max_num_boxes=None,
num_classes=None,
spatial_image_shape=None,
num_additional_channels=0):
"""Builds a tf.data.Dataset.
Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
records. Applies a padded batch to the resulting dataset.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
transform_input_data_fn: Function to apply to all records, or None if
no extra decoding is required.
batch_size: Batch size. If None, batching is not performed.
max_num_boxes: Max number of groundtruth boxes needed to compute shapes for
padding. If None, will use a dynamic shape.
num_classes: Number of classes in the dataset needed to compute shapes for
padding. If None, will use a dynamic shape.
spatial_image_shape: A list of two integers of the form [height, width]
containing expected spatial shape of the image after applying
transform_input_data_fn. If None, will use dynamic shapes.
num_additional_channels: Number of additional channels to use in the input.
Returns:
A tf.data.Dataset based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
config = input_reader_config.tf_record_input_reader
if not config.input_path:
raise ValueError('At least one input path must be specified in '
'`input_reader_config`.')
label_map_proto_file = None
if input_reader_config.HasField('label_map_path'):
label_map_proto_file = input_reader_config.label_map_path
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file,
use_display_name=input_reader_config.use_display_name,
num_additional_channels=num_additional_channels)
def process_fn(value):
processed = decoder.decode(value)
if transform_input_data_fn is not None:
return transform_input_data_fn(processed)
return processed
dataset = dataset_util.read_dataset(
functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000),
process_fn, config.input_path[:], input_reader_config)
if batch_size:
padding_shapes = _get_padding_shapes(dataset, max_num_boxes, num_classes,
spatial_image_shape)
dataset = dataset.apply(
tf.contrib.data.padded_batch_and_drop_remainder(batch_size,
padding_shapes))
return dataset
raise ValueError('Unsupported input_reader_config.')
def build(input_reader_config, batch_size=None, transform_input_data_fn=None):
"""Builds a tf.data.Dataset.
Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
records. Applies a padded batch to the resulting dataset.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
batch_size: Batch size. If batch size is None, no batching is performed.
transform_input_data_fn: Function to apply transformation to all records,
or None if no extra decoding is required.
Returns:
A tf.data.Dataset based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
if input_reader_config.WhichOneof(
'input_reader') == 'tf_record_input_reader':
config = input_reader_config.tf_record_input_reader
if not config.input_path:
raise ValueError('At least one input path must be specified in '
'`input_reader_config`.')
label_map_proto_file = None
if input_reader_config.HasField('label_map_path'):
label_map_proto_file = input_reader_config.label_map_path
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file,
use_display_name=input_reader_config.use_display_name,
num_additional_channels=input_reader_config.num_additional_channels
)
def process_fn(value):
"""Sets up tf graph that decodes, transforms and pads input data."""
processed_tensors = decoder.decode(value)
if transform_input_data_fn is not None:
processed_tensors = transform_input_data_fn(processed_tensors)
return processed_tensors
dataset = read_dataset(
functools.partial(tf.data.TFRecordDataset,
buffer_size=8 * 1000 * 1000),
config.input_path[:], input_reader_config)
if input_reader_config.sample_1_of_n_examples > 1:
dataset = dataset.shard(input_reader_config.sample_1_of_n_examples,
0)
# TODO(rathodv): make batch size a required argument once the old binaries
# are deleted.
if batch_size:
num_parallel_calls = batch_size * input_reader_config.num_parallel_batches
else:
num_parallel_calls = input_reader_config.num_parallel_map_calls
dataset = dataset.map(process_fn,
num_parallel_calls=num_parallel_calls)
if batch_size:
dataset = dataset.apply(
tf.contrib.data.batch_and_drop_remainder(batch_size))
dataset = dataset.prefetch(input_reader_config.num_prefetch_batches)
return dataset
raise ValueError('Unsupported input_reader_config.')
def decode(tf_example_string_tensor):
tensor_dict = tf_example_decoder.TfExampleDecoder().decode(
tf_example_string_tensor)
image_tensor = tensor_dict[fields.InputDataFields.image]
return image_tensor
