tfm.vision.configs.backbones.VisionTransformer

VisionTransformer config.

Inherits From: Config, ParamsDict

tfm.vision.configs.backbones.VisionTransformer(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    model_name: str = 'vit-b16',
    pooler: str = 'token',
    representation_size: int = 0,
    hidden_size: int = 1,
    patch_size: int = 16,
    transformer: tfm.vision.configs.backbones.Transformer = dataclasses.field(default_factory=Transformer),
    init_stochastic_depth_rate: float = 0.0,
    original_init: bool = True,
    pos_embed_shape: Optional[Tuple[int, int]] = None,
    output_encoded_tokens: bool = True,
    output_2d_feature_maps: bool = False,
    layer_scale_init_value: float = 0.0,
    transformer_partition_dims: Optional[Tuple[int, int, int, int]] = None
)

BUILDER

default_params Dataclass field
restrictions Dataclass field
model_name Dataclass field
pooler Dataclass field
representation_size Dataclass field
hidden_size Dataclass field
patch_size Dataclass field
transformer Dataclass field
init_stochastic_depth_rate Dataclass field
original_init Dataclass field
pos_embed_shape Dataclass field
output_encoded_tokens Dataclass field
output_2d_feature_maps Dataclass field
layer_scale_init_value Dataclass field
transformer_partition_dims 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'>)
default_params None
hidden_size 1
init_stochastic_depth_rate 0.0
layer_scale_init_value 0.0
model_name 'vit-b16'
original_init True
output_2d_feature_maps False
output_encoded_tokens True
patch_size 16
pooler 'token'
pos_embed_shape None
representation_size 0
restrictions None
transformer_partition_dims None