dgs.models.embedding_generator.embedding_generator.EmbeddingGeneratorModule

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

Base class for handling modules of embedding generators.

Description

Given some model-specific data through the State, child models of this class will predict one embedding per single sample (detection) of data, describing it. The child models should also work for batched input data.

Params

embedding_size (int):

The size of the embedding. This size does not necessarily have to match other embedding sizes.

nof_classes (int):

The number of classes in the dataset. Used during training to predict the id.

Optional Params

embedding_key (str, optional):

The key to use to retrieve the embedding of the image. Default DEF_VAL.embed_gen.embedding_key.

save_embeddings (bool, optional):

Whether to save the computed embeddings in the given State. Default DEF_VAL.embed_gen.save_embeddings.

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

Methods

configure_torch_module(module[, train])	Set compute mode and send model to the device or multiple parallel devices if applicable.
embedding_key_exists(s)	Return whether the embedding_key of this model exists in a given state.
forward(ds)	Predict next outputs, given any data in a State object, using this Re-ID model.
terminate()	Terminate this module and all of its submodules.
validate_params(validations[, attrib_name])	Given per key validations, validate this module's parameters.

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.
embedding_size	The size of the embedding.
nof_classes	The number of classes in the dataset / embedding.