deepmd.pd.model.model.frozen

deepmd.pd.model.model.frozen#

Classes#

FrozenModel

Load model from a frozen model, which cannot be trained.

Module Contents#

class deepmd.pd.model.model.frozen.FrozenModel(model_file: str, **kwargs)[source]#

Bases: deepmd.pd.model.model.model.BaseModel

Load model from a frozen model, which cannot be trained.

Parameters:

model_filestr: The path to the frozen model

model_file[source]#

fitting_output_def() → deepmd.dpmodel.output_def.FittingOutputDef[source]#: Get the output def of developer implemented atomic models.

get_rcut() → float[source]#: Get the cut-off radius.

get_type_map() → list[str][source]#: Get the type map.

get_sel() → list[int][source]#: Returns the number of selected atoms for each type.

get_dim_fparam() → int[source]#: Get the number (dimension) of frame parameters of this atomic model.

get_dim_aparam() → int[source]#: Get the number (dimension) of atomic parameters of this atomic model.

get_sel_type() → list[int][source]#

Get the selected atom types of this model.

Only atoms with selected atom types have atomic contribution to the result of the model. If returning an empty list, all atom types are selected.

is_aparam_nall() → bool[source]#

Check whether the shape of atomic parameters is (nframes, nall, ndim).

If False, the shape is (nframes, nloc, ndim).

mixed_types() → bool[source]#

If true, the model 1. assumes total number of atoms aligned across frames; 2. uses a neighbor list that does not distinguish different atomic types.

If false, the model 1. assumes total number of atoms of each atom type aligned across frames; 2. uses a neighbor list that distinguishes different atomic types.

has_message_passing() → bool[source]#: Returns whether the descriptor has message passing.

need_sorted_nlist_for_lower() → bool[source]#: Returns whether the model needs sorted nlist when using forward_lower.

forward(coord, atype, box: paddle.Tensor | None = None, fparam: paddle.Tensor | None = None, aparam: paddle.Tensor | None = None, do_atomic_virial: bool = False) → dict[str, paddle.Tensor][source]#

get_model_def_script() → str[source]#: Get the model definition script.

get_min_nbor_dist() → float | None[source]#: Get the minimum neighbor distance.

serialize() → dict[source]#

Serialize the model.

Returns:

dict: The serialized data

classmethod deserialize(data: dict)[source]#

Deserialize the model.

Parameters:

datadict: The serialized data

Returns:

BaseModel: The deserialized model

get_nnei() → int[source]#: Returns the total number of selected neighboring atoms in the cut-off radius.

get_nsel() → int[source]#: Returns the total number of selected neighboring atoms in the cut-off radius.

classmethod update_sel(train_data: deepmd.utils.data_system.DeepmdDataSystem, type_map: list[str] | None, local_jdata: dict) → tuple[dict, float | None][source]#

Update the selection and perform neighbor statistics.

Parameters:

train_dataDeepmdDataSystem: data used to do neighbor statistics
type_maplist[str], optional: The name of each type of atoms
local_jdatadict: The local data refer to the current class

Returns:

dict: The updated local data
float: The minimum distance between two atoms

model_output_type() → str[source]#: Get the output type for the model.