本文整理汇总了Python中tensorflow.compat.v1.TFRecordReader方法的典型用法代码示例。如果您正苦于以下问题:Python v1.TFRecordReader方法的具体用法?Python v1.TFRecordReader怎么用?Python v1.TFRecordReader使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v1
的用法示例。
在下文中一共展示了v1.TFRecordReader方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: reader
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def reader(self):
return tf.TFRecordReader()
示例2: get_example
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def get_example(self, batch_size):
"""Get a single example from the tfrecord file.
Args:
batch_size: Int, minibatch size.
Returns:
tf.Example protobuf parsed from tfrecord.
"""
reader = tf.TFRecordReader()
num_epochs = None if self.is_training else 1
capacity = batch_size
path_queue = tf.train.input_producer(
[self.record_path],
num_epochs=num_epochs,
shuffle=self.is_training,
capacity=capacity)
unused_key, serialized_example = reader.read(path_queue)
features = {
"note_str": tf.FixedLenFeature([], dtype=tf.string),
"pitch": tf.FixedLenFeature([1], dtype=tf.int64),
"velocity": tf.FixedLenFeature([1], dtype=tf.int64),
"audio": tf.FixedLenFeature([64000], dtype=tf.float32),
"qualities": tf.FixedLenFeature([10], dtype=tf.int64),
"instrument_source": tf.FixedLenFeature([1], dtype=tf.int64),
"instrument_family": tf.FixedLenFeature([1], dtype=tf.int64),
}
example = tf.parse_single_example(serialized_example, features)
return example
示例3: reader
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def reader(self):
"""Return a reader for a single entry from the data set.
See io_ops.py for details of Reader class.
Returns:
Reader object that reads the data set.
"""
return tf.TFRecordReader()
示例4: build_input
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def build_input(tfrecord_paths):
"""Builds the graph's input.
Args:
tfrecord_paths: List of paths to the input TFRecords
Returns:
serialized_example_tensor: The next serialized example. String scalar Tensor
image_tensor: The decoded image of the example. Uint8 tensor,
shape=[1, None, None,3]
"""
filename_queue = tf.train.string_input_producer(
tfrecord_paths, shuffle=False, num_epochs=1)
tf_record_reader = tf.TFRecordReader()
_, serialized_example_tensor = tf_record_reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example_tensor,
features={
standard_fields.TfExampleFields.image_encoded:
tf.FixedLenFeature([], tf.string),
})
encoded_image = features[standard_fields.TfExampleFields.image_encoded]
image_tensor = tf.image.decode_image(encoded_image, channels=3)
image_tensor.set_shape([None, None, 3])
image_tensor = tf.expand_dims(image_tensor, 0)
return serialized_example_tensor, image_tensor
示例5: get_split
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading cifar10.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if not reader:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
'image/class/label': tf.FixedLenFeature(
[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image(shape=[32, 32, 3]),
'label': slim.tfexample_decoder.Tensor('image/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)
示例6: get_split
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading flowers.
Args:
split_name: A train/validation split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/validation split.
"""
if split_name not in SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if reader is None:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
'image/class/label': tf.FixedLenFeature(
[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image(),
'label': slim.tfexample_decoder.Tensor('image/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)
示例7: get_split
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading MNIST.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in _SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if reader is None:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='raw'),
'image/class/label': tf.FixedLenFeature(
[1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image(shape=[28, 28, 1], channels=1),
'label': slim.tfexample_decoder.Tensor('image/class/label', shape=[]),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=_SPLITS_TO_SIZES[split_name],
num_classes=_NUM_CLASSES,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
labels_to_names=labels_to_names)
示例8: build
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import TFRecordReader [as 别名]
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
input_type = input_reader_config.input_type
if input_type == input_reader_pb2.InputType.Value('TF_EXAMPLE'):
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,
load_context_features=input_reader_config.load_context_features)
return decoder.decode(string_tensor)
elif input_type == input_reader_pb2.InputType.Value('TF_SEQUENCE_EXAMPLE'):
decoder = tf_sequence_example_decoder.TfSequenceExampleDecoder(
label_map_proto_file=label_map_proto_file,
load_context_features=input_reader_config.load_context_features)
return decoder.decode(string_tensor)
raise ValueError('Unsupported input_type.')
raise ValueError('Unsupported input_reader_config.')