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)
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)
def get_split(split_name, dataset_dir, file_pattern=None, reader=None): """ :param split_name: :param dataset_dir: :param file_pattern: :param reader: :return: """ if split_name not in ['train', 'validation']: raise ValueError('illegal split name %s ' % split_name) num_classes = dataset_utils.get_number_of_classes_by_labels(dataset_dir) if not num_classes: raise FileNotFoundError('Dataset in %s not Found' % dataset_dir) if not file_pattern: file_pattern = _FILE_PATTERN file_pattern = os.path.join(dataset_dir, file_pattern % split_name) print("FILE PATTERN: %s" % file_pattern) # 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) bot_id = dirs.get_bot_id_from_dir(dataset_dir) training_data_dir = dirs.get_training_data_dir(bot_id) print("READING TRAINING DATA FROM: %s" % training_data_dir) if not bot_id: raise ValueError('bot id not recognized from dataset_dir %s' % dataset_dir) split_size = dataset_utils.get_split_size(training_data_dir, split_name, _SPLIT_FRAC) print("SPLIT SIZE: %s" % split_size) return slim.dataset.Dataset(data_sources=file_pattern, reader=reader, decoder=decoder, num_samples=split_size, items_to_descriptions=_ITEMS_TO_DESCRIPTIONS, num_classes=num_classes, labels_to_names=labels_to_names)
def get_split(split_name, dataset_dir, file_pattern=None, reader=None): """Gets a dataset tuple with instructions for reading ImageNet. 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='jpeg'), 'image/class/label': tf.FixedLenFeature([], dtype=tf.int64, default_value=-1), 'image/class/text': tf.FixedLenFeature([], dtype=tf.string, default_value=''), 'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32), 'image/object/class/label': tf.VarLenFeature(dtype=tf.int64), } items_to_handlers = { 'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'), 'label': slim.tfexample_decoder.Tensor('image/class/label'), 'label_text': slim.tfexample_decoder.Tensor('image/class/text'), 'object/bbox': slim.tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'), 'object/label': slim.tfexample_decoder.Tensor('image/object/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) else: labels_to_names = create_readable_names_for_imagenet_labels() dataset_utils.write_label_file(labels_to_names, 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)