![]() def get_model ( img_size, num_classes ): inputs = keras. # Code adapted from: # (Apache 2.0 License: ) # A simple encoder-decoder model for semantic segmentation. batch ( batch_size, drop_remainder = True ) return ds. map ( preprocess_fn, num_parallel_calls = AUTOTUNE ) ds = ds. repeat ( num_epochs ) if shuffle : ds = ds. as_dataset ( read_config = read_config, split = split, shuffle_files = shuffle ) ds = ds. Returns: A `tf.data.Dataset` with the processed and batched features. shuffle_buffer_size: Number of examples in the shuffle buffer. shuffle: Whether to shuffle examples in memory. preprocess_fn: Callable for preprocessing. DatasetInfo ( builder = self, description = _DESCRIPTION, features = tfds. _init_ ( data_dir = data_dir, ** kwargs ) def _info ( self ) -> tfds. data_dir = data_dir or "/tmp/spacenet/tensorflow_datasets" super (). GeneratorBasedBuilder ): """Spacenet remote sensing dataset (Khartoum only).""" BUILDER_CONFIGS = def _init_ ( self, data_dir : Optional = None, ** kwargs ): # NOTE: use your GCS bucket path here to persist TFRecords across multiple # runs. max_val = _GLOBAL_MAX class Spacenet ( tfds. Args: **kwargs: keyword arguments forwarded to super. ![]() BuilderConfig ): """BuilderConfig for spacenet.""" def _init_ ( self, ** kwargs ): """Constructs a SpacenetConfig. array () IMAGE_HEIGHT, IMAGE_WIDTH = 650, 650 class SpacenetConfig ( tfds. # This can be pre-calculated in advance given access to all images or might not # be needed for your dataset at all. # Needed for Spacenet dataset to convert pixel values into range. _ROOT_DIR = "/tmp/spacenet/AOI_5_Khartoum_Train" # Min/Max RGB value ranges over data from Khartoum. _DESCRIPTION = "Spacenet (Khartoum only)" # The directory were the raw data lives.
0 Comments
Leave a Reply. |