`deepmd.dpmodel.infer.deep_eval`

Module Contents

NumPy backend implementaion of DeepEval.

class deepmd.dpmodel.infer.deep_eval.DeepEval(model_file: str, output_def: deepmd.dpmodel.output_def.ModelOutputDef, *args: List[Any], auto_batch_size: bool | int | deepmd.dpmodel.utils.batch_size.AutoBatchSize = True, neighbor_list: ase.neighborlist.NewPrimitiveNeighborList | None = None, **kwargs: Dict[str, Any])[source]

NumPy backend implementaion of DeepEval.

Parameters:

model_filePath: The name of the frozen model file.
output_defModelOutputDef: The output definition of the model.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutomaticBatchSize, default: False: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
neighbor_listase.neighborlist.NewPrimitiveNeighborList, optional: The ASE neighbor list class to produce the neighbor list. If None, the neighbor list will be built natively in the model.
**kwargsdict: Keyword arguments.

property model_type: Type[deepmd.infer.deep_eval.DeepEval][source]: The the evaluator of the model type.

get_type_map() → List[str][source]: Get the type map (element name of the atom types) of this model.

get_dim_fparam() → int[source]: Get the number (dimension) of frame parameters of this DP.

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

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.

get_ntypes_spin()[source]: Get the number of spin atom types of this model.

eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: numpy.ndarray, atomic: bool = False, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, **kwargs: Dict[str, Any]) → Dict[str, numpy.ndarray][source]

Evaluate the energy, force and virial by using this DP.

Parameters:

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: Calculate the atomic energy and virial
fparam: The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam: The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
**kwargs: Other parameters

Returns:

output_dictdict: The output of the evaluation. The keys are the names of the output variables, and the values are the corresponding output arrays.

Get the requested output definitions.

When atomic is True, all output_def are requested. When atomic is False, only energy (tensor), force, and virial are requested.

Parameters:

Returns:

_eval_func(inner_func: Callable, numb_test: int, natoms: int) → Callable[source]

Wrapper method with auto batch size.

Parameters:

Returns:

_get_natoms_and_nframes(coords: numpy.ndarray, atom_types: numpy.ndarray, mixed_type: bool = False) → Tuple[int, int][source]

_eval_model(coords: numpy.ndarray, cells: numpy.ndarray | None, atom_types: numpy.ndarray, request_defs: List[deepmd.dpmodel.output_def.OutputVariableDef])[source]