tfm.vision.configs.semantic_segmentation.DataConfig

Input config for training.

Inherits From: DataConfig, Config, ParamsDict

tfm.vision.configs.semantic_segmentation.DataConfig(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    input_path: Union[Sequence[str], str, tfm.hyperparams.Config] = None,
    tfds_name: Union[str, tfm.hyperparams.Config] = '',
    tfds_split: str = '',
    global_batch_size: int = 0,
    is_training: bool = True,
    drop_remainder: bool = True,
    shuffle_buffer_size: int = 1000,
    cache: bool = False,
    cycle_length: int = 10,
    block_length: int = 1,
    deterministic: Optional[bool] = None,
    sharding: bool = True,
    enable_tf_data_service: bool = False,
    tf_data_service_address: Optional[str] = None,
    tf_data_service_job_name: Optional[str] = None,
    tfds_data_dir: str = '',
    tfds_as_supervised: bool = False,
    tfds_skip_decoding_feature: str = '',
    enable_shared_tf_data_service_between_parallel_trainers: bool = False,
    apply_tf_data_service_before_batching: bool = False,
    trainer_id: Optional[str] = None,
    seed: Optional[int] = None,
    prefetch_buffer_size: Optional[int] = None,
    autotune_algorithm: Optional[str] = None,
    image_feature: tfm.vision.configs.semantic_segmentation.DenseFeatureConfig = dataclasses.field(default_factory=DenseFeatureConfig),
    output_size: List[int] = dataclasses.field(default_factory=list),
    crop_size: List[int] = dataclasses.field(default_factory=list),
    weights: Optional[tfm.hyperparams.Config] = None,
    dtype: str = 'float32',
    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,
    preserve_aspect_ratio: bool = True,
    aug_policy: Optional[str] = None,
    file_type: str = 'tfrecord',
    decoder: Optional[tfm.vision.configs.common.DataDecoder] = dataclasses.field(default_factory=common.DataDecoder),
    additional_dense_features: List[tfm.vision.configs.semantic_segmentation.DenseFeatureConfig] = dataclasses.field(default_factory=list)
)

BUILDER

default_params Dataclass field
restrictions Dataclass field
input_path Dataclass field
tfds_name Dataclass field
tfds_split Dataclass field
global_batch_size Dataclass field
is_training Dataclass field
drop_remainder Dataclass field
shuffle_buffer_size Dataclass field
cache Dataclass field
cycle_length Dataclass field
block_length Dataclass field
deterministic Dataclass field
sharding Dataclass field
enable_tf_data_service Dataclass field
tf_data_service_address Dataclass field
tf_data_service_job_name Dataclass field
tfds_data_dir Dataclass field
tfds_as_supervised Dataclass field
tfds_skip_decoding_feature Dataclass field
enable_shared_tf_data_service_between_parallel_trainers Dataclass field
apply_tf_data_service_before_batching Dataclass field
trainer_id Dataclass field
seed Dataclass field
prefetch_buffer_size Dataclass field
autotune_algorithm Dataclass field
image_feature Dataclass field
output_size Dataclass field
crop_size Dataclass field
weights Dataclass field
dtype Dataclass field
resize_eval_groundtruth Dataclass field
groundtruth_padded_size Dataclass field
aug_scale_min Dataclass field
aug_scale_max Dataclass field
aug_rand_hflip Dataclass field
preserve_aspect_ratio Dataclass field
aug_policy Dataclass field
file_type Dataclass field
decoder Dataclass field
additional_dense_features Dataclass field

Methods

as_dict

View source

as_dict()

Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.

from_args

View source

@classmethod
from_args(
    *args, **kwargs
)

Builds a config from the given list of arguments.

from_json

View source

@classmethod
from_json(
    file_path: str
)

Wrapper for from_yaml.

from_yaml

View source

@classmethod
from_yaml(
    file_path: str
)

get

View source

get(
    key, value=None
)

Accesses through built-in dictionary get method.

lock

View source

lock()

Makes the ParamsDict immutable.

override

View source

override(
    override_params, is_strict=True
)

Override the ParamsDict with a set of given params.

Args
override_params a dict or a ParamsDict specifying the parameters to be overridden.
is_strict a boolean specifying whether override is strict or not. If True, keys in override_params must be present in the ParamsDict. If False, keys in override_params can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys.

replace

View source

replace(
    **kwargs
)

Overrides/returns a unlocked copy with the current config unchanged.

validate

View source

validate()

Validate the parameters consistency based on the restrictions.

This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.

For example, for a ParamsDict like the following

a:
  a1: 1
  a2: 2
b:
  bb:
    bb1: 10
    bb2: 20
  ccc:
    a1: 1
    a3: 3

one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']

What it enforces are

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= b.bb.bb2 = 20

Raises
KeyError if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found.
ValueError if the restriction defined in the string is not supported.

__contains__

View source

__contains__(
    key
)

Implements the membership test operator.

__eq__

__eq__(
    other
)

IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>)
RESERVED_ATTR ['_locked', '_restrictions']
SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>)
apply_tf_data_service_before_batching False
aug_policy None
aug_rand_hflip True
aug_scale_max 1.0
aug_scale_min 1.0
autotune_algorithm None
block_length 1
cache False
cycle_length 10
default_params None
deterministic None
drop_remainder True
dtype 'float32'
enable_shared_tf_data_service_between_parallel_trainers False
enable_tf_data_service False
file_type 'tfrecord'
global_batch_size 0
input_path None
is_training True
prefetch_buffer_size None
preserve_aspect_ratio True
resize_eval_groundtruth True
restrictions None
seed None
sharding True
shuffle_buffer_size 1000
tf_data_service_address None
tf_data_service_job_name None
tfds_as_supervised False
tfds_data_dir ''
tfds_name ''
tfds_skip_decoding_feature ''
tfds_split ''
trainer_id None
weights None