tfm.vision.configs.maskrcnn.Losses

Inherits From: Config, ParamsDict

tfm.vision.configs.maskrcnn.Losses(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    loss_weight: float = 1.0,
    rpn_huber_loss_delta: float = (1.0 / 9.0),
    frcnn_huber_loss_delta: float = 1.0,
    frcnn_class_use_binary_cross_entropy: bool = False,
    frcnn_class_loss_top_k_percent: float = 1.0,
    l2_weight_decay: float = 0.0,
    rpn_score_weight: float = 1.0,
    rpn_box_weight: float = 1.0,
    frcnn_class_weight: float = 1.0,
    frcnn_box_weight: float = 1.0,
    mask_weight: float = 1.0,
    class_weights: Optional[List[float]] = None
)

BUILDER

default_params Dataclass field restrictions Dataclass field loss_weight Dataclass field rpn_huber_loss_delta Dataclass field frcnn_huber_loss_delta Dataclass field frcnn_class_use_binary_cross_entropy Dataclass field frcnn_class_loss_top_k_percent Dataclass field l2_weight_decay Dataclass field rpn_score_weight Dataclass field rpn_box_weight Dataclass field frcnn_class_weight Dataclass field frcnn_box_weight Dataclass field mask_weight Dataclass field class_weights Dataclass field

Methods

as_dict

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as_dict()

Returns a dict representation of params_dict.ParamsDict.

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

from_args

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@classmethod
from_args(
    *args, **kwargs
)

Builds a config from the given list of arguments.

from_json

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@classmethod
from_json(
    file_path: str
)

Wrapper for from_yaml.

from_yaml

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@classmethod
from_yaml(
    file_path: str
)

get

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get(
    key, value=None
)

Accesses through built-in dictionary get method.

lock

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lock()

Makes the ParamsDict immutable.

override

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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

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replace(
    **kwargs
)

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

validate

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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__

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__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'>) class_weights None default_params None frcnn_box_weight 1.0 frcnn_class_loss_top_k_percent 1.0 frcnn_class_use_binary_cross_entropy False frcnn_class_weight 1.0 frcnn_huber_loss_delta 1.0 l2_weight_decay 0.0 loss_weight 1.0 mask_weight 1.0 restrictions None rpn_box_weight 1.0 rpn_huber_loss_delta 0.1111111111111111 rpn_score_weight 1.0