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Registry module

mixupvi_module_cti(granularity, device='cpu')

Factory function to create a MixupVIModule with predefined parameters.

Parameters:

Name Type Description Default
granularity Literal['1st_granularity', '2nd_granularity']

The granularity level for the MixupVIModule.

 required
device str

The device to load the model on, by default "cpu".

 'cpu'

Returns:

Type Description
Tuple[Module, dict]

A tuple containing the MixupVIModule instance and its parameters.
Source code in src/pydeconv/model/nn/registry.py 
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def mixupvi_module_cti(
    granularity: Literal["1st_granularity", "2nd_granularity"], device="cpu"
) -> Tuple[nn.Module, dict]:
    """Factory function to create a MixupVIModule with predefined parameters.
    Parameters
    ----------
    granularity : Literal["1st_granularity", "2nd_granularity"]
        The granularity level for the MixupVIModule.
    device : str, optional
        The device to load the model on, by default "cpu".
    Returns
    -------
    Tuple[nn.Module, dict]
        A tuple containing the MixupVIModule instance and its parameters.
    """
    path_parameters = download_file_to_cache(
        repo_url=REPO_URL,
        origin=ORIGIN,
        relative_path=f"mixupvi/cti_dirichlet_{granularity}/mixupvi_params.json",
    )
    with open(path_parameters) as f:
        params = json.load(f)

    model = MixupVIModule(
        input_dim=params["input"]["input_dim"],
        latent_dim=params["output"]["latent_dim"],
    )

    path_model = download_file_to_cache(
        repo_url=REPO_URL,
        origin=ORIGIN,
        relative_path=f"mixupvi/cti_dirichlet_{granularity}/mixupvi_weights.pt",
    )

    model_dict = torch.load(
        path_model,
        map_location=device,
        weights_only=False,
    )

    model.model.load_state_dict(model_dict["model_state_dict"])
    return model, params

scaden_module_cti(granularity, device='cpu')

Factory function to create a ScadenModule with predefined parameters.

Parameters:

Name Type Description Default
version

The version of the ScadenModule to create.

 required

Returns:

Type Description
Module

An instance of ScadenModule with the specified version.
Source code in src/pydeconv/model/nn/registry.py 
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def scaden_module_cti(
    granularity: Literal["1st_granularity", "2nd_granularity"], device="cpu"
) -> Tuple[nn.Module, dict]:
    """Factory function to create a ScadenModule with predefined parameters.

    Parameters
    ----------
    version :str
        The version of the ScadenModule to create.

    Returns
    ----------
    nn.Module
        An instance of ScadenModule with the specified version.
    """
    path_parameters = download_file_to_cache(
        repo_url=REPO_URL,
        origin=ORIGIN,
        relative_path=f"scaden/cti_dirichlet_{granularity}/scaden_params.json",
    )
    with open(path_parameters) as f:
        params = json.load(f)

    model = ScadenModule(
        input_dim=params["input"]["input_dim"],
        output_dim=params["output"]["output_dim"],
        model_params_dict=DEFAULT_SCADEN_ARCHITECTURES,
    )
    for shape in ["256", "512", "1024"]:
        path_model = download_file_to_cache(
            repo_url=REPO_URL,
            origin=ORIGIN,
            relative_path=f"scaden/cti_dirichlet_{granularity}/scaden_weights_{shape}.pth",
        )
        state_dict = torch.load(
            path_model,
            map_location=device,
            weights_only=True,
        )
        for key in list(state_dict.keys()):
            state_dict[key.replace("model.", "mlp.")] = state_dict.pop(key)

        getattr(model, f"model_{shape}").load_state_dict(state_dict)
    return model, params

tape_module_cti(granularity, adaptative, device='cpu')

Factory function to create a ScadenModule with predefined parameters.

Parameters:

Name Type Description Default
version

The version of the ScadenModule to create.

 required

Returns:

Type Description
nn.Module: An instance of ScadenModule with the specified version.
Source code in src/pydeconv/model/nn/registry.py 
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def tape_module_cti(
    granularity: Literal["1st_granularity", "2nd_granularity"], adaptative, device="cpu"
) -> Tuple[nn.Module, dict]:
    """Factory function to create a ScadenModule with predefined parameters.

    Parameters
    ----------
    version :str
        The version of the ScadenModule to create.

    Returns
    ----------
    nn.Module: An instance of ScadenModule with the specified version.
    """
    path_parameters = download_file_to_cache(
        repo_url=REPO_URL,
        origin=ORIGIN,
        relative_path=f"tape/cti_dirichlet_{granularity}/tape_params.json",
    )
    with open(path_parameters) as f:
        params = json.load(f)

    model = TapeModule(
        input_dim=params["input"]["input_dim"], output_dim=params["output"]["output_dim"], adaptative=adaptative
    )

    path_model = download_file_to_cache(
        repo_url=REPO_URL,
        origin=ORIGIN,
        relative_path=f"tape/cti_dirichlet_{granularity}/tape_weights.pth",
    )
    state_dict = torch.load(
        path_model,
        map_location=device,
        weights_only=True,
    )
    model.load_state_dict(state_dict)
    return model, params