deepmd-kit/deepmd/dpmodel/model/dos_model.py at 53a7e7f199b6786a55af0b10524b64b90d7d60e5 · wanghan-iapcm/deepmd-kit · GitHub

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# SPDX-License-Identifier: LGPL-3.0-or-later
from typing import (
    Any,
)

from deepmd.dpmodel.array_api import (
    Array,
)
from deepmd.dpmodel.atomic_model import (
    DPDOSAtomicModel,
)
from deepmd.dpmodel.model.base_model import (
    BaseModel,
)

from .dp_model import (
    DPModelCommon,
)
from .make_model import (
    make_model,
)

DPDOSModel_ = make_model(DPDOSAtomicModel)


@BaseModel.register("dos")
class DOSModel(DPModelCommon, DPDOSModel_):
    model_type = "dos"

    def __init__(
        self,
        *args: Any,
        **kwargs: Any,
    ) -> None:
        DPModelCommon.__init__(self)
        DPDOSModel_.__init__(self, *args, **kwargs)

    def translated_output_def(self) -> dict[str, Any]:
        out_def_data = self.model_output_def().get_data()
        output_def = {
            "atom_dos": out_def_data["dos"],
            "dos": out_def_data["dos_redu"],
        }
        if "mask" in out_def_data:
            output_def["mask"] = out_def_data["mask"]
        return output_def

    def call(
        self,
        coord: Array,
        atype: Array,
        box: Array | None = None,
        fparam: Array | None = None,
        aparam: Array | None = None,
        do_atomic_virial: bool = False,
    ) -> dict[str, Array]:
        model_ret = self.call_common(
            coord,
            atype,
            box,
            fparam=fparam,
            aparam=aparam,
            do_atomic_virial=do_atomic_virial,
        )
        if self.get_fitting_net() is not None:
            model_predict = {}
            model_predict["atom_dos"] = model_ret["dos"]
            model_predict["dos"] = model_ret["dos_redu"]
            if "mask" in model_ret:
                model_predict["mask"] = model_ret["mask"]
        else:
            model_predict = model_ret
            model_predict["updated_coord"] += coord
        return model_predict

    def call_lower(
        self,
        extended_coord: Array,
        extended_atype: Array,
        nlist: Array,
        mapping: Array | None = None,
        fparam: Array | None = None,
        aparam: Array | None = None,
        do_atomic_virial: bool = False,
    ) -> dict[str, Array]:
        model_ret = self.call_common_lower(
            extended_coord,
            extended_atype,
            nlist,
            mapping,
            fparam=fparam,
            aparam=aparam,
            do_atomic_virial=do_atomic_virial,
        )
        if self.get_fitting_net() is not None:
            model_predict = {}
            model_predict["atom_dos"] = model_ret["dos"]
            model_predict["dos"] = model_ret["dos_redu"]
        else:
            model_predict = model_ret
        return model_predict

    forward_lower = call_lower