dgs.models.combine.static.StaticAlphaCombine

class dgs.models.combine.static.StaticAlphaCombine(*args: Any, **kwargs: Any)

Weight two or more similarity matrices using constant (float) values for alpha.

Params:

alpha (list[float]) – A list containing the constant weights for the different similarities. The weights should be probabilities and therefore sum to one and lie within [0..1].

Methods

__init__(*args, **kwargs)

configure_torch_module(module: torch.nn.Module, train: bool | None = None) → torch.nn.Module

Set compute mode and send model to the device or multiple parallel devices if applicable.

Parameters:

• module – The torch module instance to configure.

• train – Whether to train or eval this module, defaults to the value set in the base config.

Returns:

The module on the specified device or in parallel.

forward(*tensors, **_kwargs) → torch.Tensor

Given alpha from the configuration file and args of the same length, multiply each alpha with each matrix and compute the sum.

Parameters:

tensors (tuple[torch.Tensor, ...]) – A number of similarity tensors. Should have the same length as alpha. All the tensors should have the same size.

Returns:

The weighted similarity matrix as FloatTensor.

Raises:

• ValueError – If the tensors argument has the wrong shape

• TypeError – If the tensors argument contains an object that is not a torch.tensor.

terminate() → None

Terminate this module and all of its submodules.

If nothing has to be done, just pass. Is used for terminating parallel execution and threads in specific models.

validate_params(validations: dict[str, list[str | type | tuple[str, any] | Callable[[any, any], bool]]], attrib_name: str = 'params') → None

Given per key validations, validate this module’s parameters.

Throws exceptions on invalid or nonexistent params.

Parameters:

• attrib_name – name of the attribute to validate, should be “params” and only for base class “config”

• validations –

  Dictionary with the name of the parameter as key and a list of validations as value. Every validation in this list has to be true for the validation to be successful.

  The value for the validation can have multiple types:

  • A lambda function or other type of callable

  • A string as reference to a predefined validation function with one argument

  • None for existence

  • A tuple with a string as reference to a predefined validation function with one additional argument

  • It is possible to write nested validations, but then every nested validation has to be a tuple, or a tuple of tuples. For convenience, there are implementations for “any”, “all”, “not”, “eq”, “neq”, and “xor”. Those can have data which is a tuple containing other tuples or validations, or a single validation.

  • Lists and other iterables can be validated using “forall” running the given validations for every item in the input. A single validation or a tuple of (nested) validations is accepted as data.

Example

This example is an excerpt of the validation for the BaseModule-configuration.

>>> validations = {
    "device": [
            str,
            ("any",
                [
                    ("in", ["cuda", "cpu"]),
                    ("instance", torch.device)
                ]
            )
        ],
        "print_prio": [("in", PRINT_PRIORITY)],
        "callable": (lambda value: value == 1),
    }

And within the class __init__() call:

>>> self.validate_params()

Raises:

• InvalidParameterException – If one of the parameters is invalid.

• ValidationException – If the validation list is invalid or contains an unknown validation.

Attributes

device	Get the device of this module.
is_training	Get whether this module is set to training-mode.
module_name	Get the name of the module.
module_type
name	Get the name of the module.
name_safe	Get the escaped name of the module usable in filepaths by replacing spaces and underscores.
precision	Get the (floating point) precision used in multiple parts of this module.
softmax