deepmd.pt.infer.deep_eval

deepmd.pt.infer.deep_eval#

Classes#

DeepEval

PyTorch backend implementation of DeepEval.

Module Contents#

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

Bases: deepmd.infer.deep_eval.DeepEvalBackend

PyTorch backend implementation 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.

output_def[source]#

model_path[source]#

rcut[source]#

type_map[source]#

_has_spin[source]#

get_rcut() → float[source]#: Get the cutoff radius of this model.

get_ntypes() → int[source]#: Get the number of atom types of this model.

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_intensive() → bool[source]#

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

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_numb_dos() → int[source]#: Get the number of DOS.

get_task_dim() → int[source]#: Get the output dimension.

get_has_efield() → bool[source]#: Check if the model has efield.

get_ntypes_spin() → int[source]#: Get the number of spin atom types of this model. Only used in old implement.

get_has_spin()[source]#: Check if the model has spin atom types.

eval(coords: numpy.ndarray, cells: numpy.ndarray | None, atom_types: numpy.ndarray, atomic: bool = False, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, **kwargs: 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_request_defs(atomic: bool) → list[deepmd.dpmodel.output_def.OutputVariableDef][source]#

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:

atomicbool: Whether to request the atomic output.

Returns:

list[OutputVariableDef]: The requested output definitions.

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

Wrapper method with auto batch size.

Parameters:

inner_funcCallable: the method to be wrapped
numb_testint: number of tests
natomsint: number of atoms

Returns:

Callable: the wrapper

_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, fparam: numpy.ndarray | None, aparam: numpy.ndarray | None, request_defs: list[deepmd.dpmodel.output_def.OutputVariableDef])[source]#

_eval_model_spin(coords: numpy.ndarray, cells: numpy.ndarray | None, atom_types: numpy.ndarray, spins: numpy.ndarray, fparam: numpy.ndarray | None, aparam: numpy.ndarray | None, request_defs: list[deepmd.dpmodel.output_def.OutputVariableDef])[source]#

_get_output_shape(odef, nframes, natoms)[source]#

eval_typeebd() → numpy.ndarray[source]#

Evaluate output of type embedding network by using this model.

Returns:

np.ndarray: The output of type embedding network. The shape is [ntypes, o_size] or [ntypes + 1, o_size], where ntypes is the number of types, and o_size is the number of nodes in the output layer. If there are multiple type embedding networks, these outputs will be concatenated along the second axis.

Raises:

KeyError: If the model does not enable type embedding.