lczerolens.model#

Class for wrapping the LCZero models.

Attributes#

MISSING_HF_ERROR

Classes#

`LczeroModel`	Class for wrapping the LCZero models.
`ForceValue`	Class for forcing and isolating the value flow.
`Flow`	Base class for isolating a flow.
`PolicyFlow`	Class for isolating the policy flow.
`ValueFlow`	Class for isolating the value flow.
`WdlFlow`	Class for isolating the WDL flow.
`MlhFlow`	Class for isolating the MLH flow.

Module Contents#

lczerolens.model.MISSING_HF_ERROR = 'huggingface_hub is required to push or load the model from the Hugging Face Hub. Install it...[source]#

class lczerolens.model.LczeroModel(module, out_keys, **kwargs)[source]#

Bases: tensordict.nn.TensorDictModule

Class for wrapping the LCZero models.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

prepare_boards(*boards, input_encoding=InputEncoding.INPUT_CLASSICAL_112_PLANE)[source]#

Prepares the boards for the model.

Parameters:

*boards (LczeroBoard) – The boards to prepare.
input_encoding (InputEncoding, optional) – The encoding of the boards.

Returns:

The prepared boards.

Return type:

torch.Tensor

forward(inputs, prepare_kwargs=None, **kwargs)[source]#

Parameters:

inputs (Union[TensorDict, Iterable[LczeroBoard], torch.Tensor]) – The inputs to the model.
prepare_kwargs (Optional[Dict[str, Any]], optional) – Keyword arguments to pass to the prepare_boards method, by default None
**kwargs (Any) – Additional keyword arguments to pass to the super().forward method.

Returns:

The output of the model.

Return type:

TensorDict

_call_module(tensors, **kwargs)[source]#

Parameters:

tensors (Sequence[torch.Tensor])
kwargs (Any)

Return type:

Sequence[torch.Tensor]

classmethod from_model(model, **kwargs)[source]#

Creates a wrapper from a model.

Parameters:

model (nn.Module) – The model to wrap.
**kwargs (Any) – Additional keyword arguments to pass to the super().__init__ method.

Returns:

The wrapped model instance

Return type:

LczeroModel

classmethod from_path(model_path, **kwargs)[source]#

Creates a wrapper from a model path.

Parameters:: model_path (str) – Path to the model file (.onnx or .pt)
Returns:: The wrapped model instance
Return type:: LczeroModel
Raises:: NotImplementedError – If the model file extension is not supported

classmethod from_onnx_path(onnx_model_path, check=True, **kwargs)[source]#

Builds a model from an ONNX file path.

Parameters:

onnx_model_path (str) – Path to the ONNX model file
check (bool, optional) – Whether to perform shape inference check, by default True

Returns:

The wrapped model instance

Return type:

LczeroModel

Raises:

FileNotFoundError – If the model file does not exist
ValueError – If the model could not be loaded

classmethod from_torch_path(torch_model_path, weights_only=False, **kwargs)[source]#

Builds a model from a PyTorch file path.

Parameters:

torch_model_path (str) – Path to the PyTorch model file
weights_only (bool)

Returns:

The wrapped model instance

Return type:

LczeroModel

Raises:

FileNotFoundError – If the model file does not exist
ValueError – If the model could not be loaded or is not a valid model type

push_to_hf(repo_id, create_if_not_exists=True, create_kwargs=None, path_in_repo='model.pt', **kwargs)[source]#

Pushes the model to the Hugging Face Hub.

Parameters:

repo_id (str) – The repository id to push the model to.
create_if_not_exists (bool, optional) – Whether to create the repository if it does not exist, by default True
create_kwargs (Optional[Dict[str, Any]], optional) – Additional keyword arguments to pass to the create_repo method.
path_in_repo (str, optional) – The path in the repository to save the model to.
**kwargs (Any) – Additional keyword arguments to pass to the upload_file method.

Raises:

ImportError – If the huggingface_hub library is not installed.

classmethod from_hf(repo_id, filename='model.pt', hf_hub_kwargs=None, **kwargs)[source]#

Loads a model from the Hugging Face Hub.

Parameters:

repo_id (str) – The repository id to load the model from.
filename (str) – The filename of the model to load.
hf_hub_kwargs (Optional[Dict[str, Any]], optional) – Additional keyword arguments to pass to the hf_hub_download method.
**kwargs (Any) – Additional keyword arguments to pass to the from_path method.

Returns:

The loaded model instance

Return type:

LczeroModel

Raises:

ImportError – If the huggingface_hub library is not installed.

static _get_output_names(model)[source]#

Returns the output names of the model.

Parameters:: model (nn.Module) – The model to get the output names from.
Returns:: The output names of the model.
Return type:: List[str]

class lczerolens.model.ForceValue(module, out_keys, **kwargs)[source]#

Bases: LczeroModel

Class for forcing and isolating the value flow.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

_compute_value[source]#

_wdl_index[source]#

static _get_output_names(model)[source]#

Returns the output names of the model.

Parameters:: model (nn.Module) – The model to get the output names from.
Returns:: The output names of the model.
Return type:: List[str]

_call_module(tensors, **kwargs)[source]#

Parameters:

tensors (Sequence[torch.Tensor])
kwargs (Any)

Return type:

Sequence[torch.Tensor]

class lczerolens.model.Flow(module, out_keys, **kwargs)[source]#

Bases: LczeroModel

Base class for isolating a flow.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

property _flow_type: str[source]#

Abstractmethod:
Return type:: str

Returns the flow type.

class lczerolens.model.PolicyFlow(module, out_keys, **kwargs)[source]#

Bases: Flow

Class for isolating the policy flow.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

_flow_type = 'policy'[source]#: Returns the flow type.

class lczerolens.model.ValueFlow(module, out_keys, **kwargs)[source]#

Bases: Flow

Class for isolating the value flow.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

_flow_type = 'value'[source]#: Returns the flow type.

class lczerolens.model.WdlFlow(module, out_keys, **kwargs)[source]#

Bases: Flow

Class for isolating the WDL flow.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

_flow_type = 'wdl'[source]#: Returns the flow type.

class lczerolens.model.MlhFlow(module, out_keys, **kwargs)[source]#

Bases: Flow

Class for isolating the MLH flow.

Parameters:

module (torch.nn.Module)
out_keys (List[str])

_flow_type = 'mlh'[source]#: Returns the flow type.