deepmd.infer.deep_eval

deepmd.infer.deep_eval#

Classes#

`DeepEvalBackend`	Low-level Deep Evaluator interface.
`DeepEval`	High-level Deep Evaluator interface.

Module Contents#

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

Bases: abc.ABC

Low-level Deep Evaluator interface.

Backends should inherbit implement this interface. High-level interface will be built on top of this.

Parameters:

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: 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.

_OUTDEF_DP2BACKEND: ClassVar[dict][source]#

abstractmethod 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.

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

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

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

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

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

abstractmethod eval_descriptor(coords: numpy.ndarray, cells: numpy.ndarray | None, atom_types: numpy.ndarray, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, efield: numpy.ndarray | None = None, mixed_type: bool = False, **kwargs: Any) → numpy.ndarray[source]#

Evaluate descriptors 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
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.
efield: The external field on atoms. The array should be of size nframes x natoms x 3
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns:

descriptor: Descriptors.

abstractmethod 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], where ntypes is the number of types, and o_size is the number of nodes in the output layer.

Raises:

KeyError: If the model does not enable type embedding.

_check_mixed_types(atom_types: numpy.ndarray) → bool[source]#

Check if atom types of all frames are the same.

Traditional descriptors like se_e2_a requires all the frames to have the same atom types.

Parameters:

atom_typesnp.ndarray: The atom types of all frames, in shape nframes * natoms.

property model_type: type[DeepEval][source]#

Abstractmethod:

The the evaluator of the model type.

abstractmethod 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.

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

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

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

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

abstractmethod get_model_def_script() → dict[source]#: Get model definition script.

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

Bases: abc.ABC

High-level Deep Evaluator interface.

The specific DeepEval, such as DeepPot and DeepTensor, should inherit from this class. This class provides a high-level interface on the top of the low-level interface.

Parameters:

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: 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.

deep_eval[source]#

property output_def: deepmd.dpmodel.output_def.ModelOutputDef[source]#

Abstractmethod:

Returns the output variable definitions.

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_natoms_and_nframes(coords: numpy.ndarray, atom_types: numpy.ndarray, mixed_type: bool = False) → tuple[int, int][source]#

_expande_atype(atype: numpy.ndarray, nframes: int, mixed_type: bool)[source]#

eval_descriptor(coords: numpy.ndarray, cells: numpy.ndarray | None, atom_types: numpy.ndarray, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, mixed_type: bool = False, **kwargs: Any) → numpy.ndarray[source]#

Evaluate descriptors 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
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.
efield: The external field on atoms. The array should be of size nframes x natoms x 3
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns:

descriptor: Descriptors.

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], where ntypes is the number of types, and o_size is the number of nodes in the output layer.

Raises:

KeyError: If the model does not enable type embedding.