class DataConfig(cfg.DataConfig): """Input config for training.""" output_size: List[int] = dataclasses.field(default_factory=list) # If crop_size is specified, image will be resized first to # output_size, then crop of size crop_size will be cropped. crop_size: List[int] = dataclasses.field(default_factory=list) input_path: str = '' global_batch_size: int = 0 is_training: bool = True dtype: str = 'float32' shuffle_buffer_size: int = 1000 cycle_length: int = 10 # If resize_eval_groundtruth is set to False, original image sizes are used # for eval. In that case, groundtruth_padded_size has to be specified too to # allow for batching the variable input sizes of images. resize_eval_groundtruth: bool = True groundtruth_padded_size: List[int] = dataclasses.field( default_factory=list) aug_scale_min: float = 1.0 aug_scale_max: float = 1.0 aug_rand_hflip: bool = True aug_policy: Optional[str] = None drop_remainder: bool = True file_type: str = 'tfrecord' decoder: Optional[common.DataDecoder] = common.DataDecoder()
class DataConfig(cfg.DataConfig): """Input config for training.""" input_path: str = '' global_batch_size: int = 0 is_training: bool = False dtype: str = 'bfloat16' decoder: common.DataDecoder = common.DataDecoder() parser: Parser = Parser() shuffle_buffer_size: int = 10000 file_type: str = 'tfrecord'
class DataConfig(cfg.DataConfig): """Input config for training.""" input_path: str = '' global_batch_size: int = 0 is_training: bool = False dtype: str = 'bfloat16' decoder: common.DataDecoder = common.DataDecoder() parser: Parser = Parser() shuffle_buffer_size: int = 10000 file_type: str = 'tfrecord' drop_remainder: bool = True # Number of examples in the data set, it's used to create the annotation file. num_examples: int = -1
class DataConfig(cfg.DataConfig): """Input config for training.""" input_path: str = '' global_batch_size: int = 0 is_training: bool = True dtype: str = 'float32' shuffle_buffer_size: int = 10000 cycle_length: int = 10 is_multilabel: bool = False aug_rand_hflip: bool = True aug_type: Optional[ common.Augmentation] = None # Choose from AutoAugment and RandAugment. file_type: str = 'tfrecord' image_field_key: str = 'image/encoded' label_field_key: str = 'image/class/label' decode_jpeg_only: bool = True decoder: Optional[common.DataDecoder] = common.DataDecoder() # Keep for backward compatibility. aug_policy: Optional[str] = None # None, 'autoaug', or 'randaug'. randaug_magnitude: Optional[int] = 10