tfm.vision.configs.maskrcnn.MaskRCNN

Inherits From: Config, ParamsDict

tfm.vision.configs.maskrcnn.MaskRCNN( default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None, restrictions: dataclasses.InitVar[Optional[List[str]]] = None, num_classes: int = 0, input_size: List[int] = dataclasses.field(default_factory=list), min_level: int = 2, max_level: int = 6, anchor: tfm.vision.configs.maskrcnn.Anchor = dataclasses.field(default_factory=Anchor), include_mask: bool = True, outer_boxes_scale: float = 1.0, backbone: tfm.vision.configs.backbones.Backbone = dataclasses.field(default_factory=lambda : backbones.Backbone(type='resnet',\n resnet=backbones.ResNet())), decoder: decoders.Decoder = dataclasses.field(default_factory=lambda : decoders.Decoder(type='fpn', fpn\n =decoders.FPN())), rpn_head: tfm.vision.configs.maskrcnn.RPNHead = dataclasses.field(default_factory=RPNHead), detection_head: tfm.vision.configs.maskrcnn.DetectionHead = dataclasses.field(default_factory=DetectionHead), roi_generator: tfm.vision.configs.maskrcnn.ROIGenerator = dataclasses.field(default_factory=ROIGenerator), roi_sampler: tfm.vision.configs.maskrcnn.ROISampler = dataclasses.field(default_factory=ROISampler), roi_aligner: tfm.vision.configs.maskrcnn.ROIAligner = dataclasses.field(default_factory=ROIAligner), detection_generator: tfm.vision.configs.maskrcnn.DetectionGenerator = dataclasses.field(default_factory=DetectionGenerator), mask_head: Optional[tfm.vision.configs.maskrcnn.MaskHead] = dataclasses.field(default_factory=MaskHead), mask_sampler: Optional[tfm.vision.configs.maskrcnn.MaskSampler] = dataclasses.field(default_factory=MaskSampler), mask_roi_aligner: Optional[tfm.vision.configs.maskrcnn.MaskROIAligner] = dataclasses.field(default_factory=MaskROIAligner), norm_activation: tfm.vision.configs.common.NormActivation = dataclasses.field(default_factory=lambda : common.NormActivation(\n norm_momentum=0.997, norm_epsilon=0.0001, use_sync_bn=True)) )

BUILDER

default_params Dataclass field
restrictions Dataclass field
num_classes Dataclass field
input_size Dataclass field
min_level Dataclass field
max_level Dataclass field
anchor Dataclass field
include_mask Dataclass field
outer_boxes_scale Dataclass field
backbone Dataclass field
decoder Dataclass field
rpn_head Dataclass field
detection_head Dataclass field
roi_generator Dataclass field
roi_sampler Dataclass field
roi_aligner Dataclass field
detection_generator Dataclass field
mask_head Dataclass field
mask_sampler Dataclass field
mask_roi_aligner Dataclass field
norm_activation 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

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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'>)
default_params None
include_mask True
max_level 6
min_level 2
num_classes 0
outer_boxes_scale 1.0
restrictions None