tfm.vision.configs.semantic_segmentation.SegmentationHead

Segmentation head config.

Inherits From: Config, ParamsDict

tfm.vision.configs.semantic_segmentation.SegmentationHead( default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None, restrictions: dataclasses.InitVar[Optional[List[str]]] = None, level: int = 3, num_convs: int = 2, num_filters: int = 256, use_depthwise_convolution: bool = False, prediction_kernel_size: int = 1, upsample_factor: int = 1, logit_activation: Optional[str] = None, feature_fusion: Optional[str] = None, low_level: Union[int, str] = 2, low_level_num_filters: int = 48, decoder_min_level: Optional[Union[int, str]] = None, decoder_max_level: Optional[Union[int, str]] = None )

BUILDER

default_params Dataclass field
restrictions Dataclass field
level Dataclass field
num_convs Dataclass field
num_filters Dataclass field
use_depthwise_convolution Dataclass field
prediction_kernel_size Dataclass field
upsample_factor Dataclass field
logit_activation Dataclass field
feature_fusion Dataclass field
low_level Dataclass field
low_level_num_filters Dataclass field
decoder_min_level Dataclass field
decoder_max_level 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'>)
decoder_max_level None
decoder_min_level None
default_params None
feature_fusion None
level 3
logit_activation None
low_level 2
low_level_num_filters 48
num_convs 2
num_filters 256
prediction_kernel_size 1
restrictions None
upsample_factor 1
use_depthwise_convolution False