dgs.models.dgs.dgs.DGSModule

class dgs.models.dgs.dgs.DGSModule(*args: Any, **kwargs: Any)

Torch module containing the code for the model called ‘dynamically gated similarities’.

Params:

• names (list[NodePath]) – The names or NodePath’s of the keys within the current configuration which contain all the SimilarityModule’s used in this module.

• combine (NodePath) – The name or NodePath of the key in the current configuration containing the parameters for the CombineSimilaritiesModule used to combine the similarities.

Optional Params:

new_track_weight (float, optional) – The weight of the new tracks as probability. “0.0” means, that existing tracks will always be preferred, while “1.0” means that new tracks are preferred. Default DEF_VAL.dgs.similarity_softmax.

Methods

__init__(config: dict[str, any], path: list[str])

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(ds: State, target: State, **kwargs) → torch.Tensor

Given a State containing the current detections and a target, compute the similarity between every pair.

Returns:

The combined similarity matrix as tensor of shape [nof_detections x (nof_tracks + nof_detections)].

terminate() → None

Terminate the DGS module and delete the torch modules.

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.
sim_mods
combine
new_track_weight