deepmd.pt.model.model.frozen

Module Contents

Classes

FrozenModel

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

class deepmd.pt.model.model.frozen.FrozenModel(model_file: str, **kwargs)

Bases: deepmd.pt.model.model.model.BaseModel

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

Parameters:

model_filestr

The path to the frozen model

fitting_output_def() → deepmd.dpmodel.output_def.FittingOutputDef

Get the output def of developer implemented atomic models.

get_rcut() → float

Get the cut-off radius.

get_type_map() → List[str]

Get the type map.

get_sel() → List[int]

Returns the number of selected atoms for each type.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this atomic model.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this atomic model.

get_sel_type() → List[int]

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

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

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

mixed_types() → bool

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.

forward(coord, atype, box: torch.Tensor | None = None, fparam: torch.Tensor | None = None, aparam: torch.Tensor | None = None, do_atomic_virial: bool = False) → Dict[str, torch.Tensor]

get_model_def_script() → str

Get the model definition script.

serialize() → dict

Serialize the model.

Returns:

dict

The serialized data

classmethod deserialize(data: dict)

Deserialize the model.

Parameters:

datadict

The serialized data

Returns:

BaseModel

The deserialized model

get_nnei() → int

Returns the total number of selected neighboring atoms in the cut-off radius.

get_nsel() → int

Returns the total number of selected neighboring atoms in the cut-off radius.

classmethod update_sel(global_jdata: dict, local_jdata: dict)

Update the selection and perform neighbor statistics.

Parameters:

global_jdatadict

The global data, containing the training section

local_jdatadict

The local data refer to the current class

model_output_type() → str

Get the output type for the model.