deepmd.utils.argcheck

deepmd.utils.argcheck#

Classes#

ArgsPlugin

Functions#

`list_to_doc`(xx)
`make_link`(→ str)
`deprecate_argument_extra_check`(→ Callable[[dict], bool])	Generate an extra check to deprecate an argument in sub fields.
`type_embedding_args`()
`spin_args`()
`descrpt_local_frame_args`()
`descrpt_se_a_args`()
`descrpt_se_t_args`()
`descrpt_se_a_tpe_args`()
`descrpt_se_r_args`()
`descrpt_hybrid_args`()
`descrpt_se_atten_common_args`()
`descrpt_se_atten_args`()
`descrpt_se_e3_tebd_args`()
`descrpt_se_atten_v2_args`()
`descrpt_dpa2_args`()
`dpa2_repinit_args`()
`dpa2_repformer_args`()
`descrpt_se_a_ebd_v2_args`()
`descrpt_se_a_mask_args`()
`descrpt_variant_type_args`(→ dargs.Variant)
`fitting_ener`()
`fitting_dos`()
`fitting_property`()
`fitting_polar`()
`fitting_dipole`()
`fitting_variant_type_args`()
`modifier_dipole_charge`()
`modifier_variant_type_args`()
`model_compression`()
`model_compression_type_args`()
`model_args`([exclude_hybrid])
`standard_model_args`(→ dargs.Argument)
`pairwise_dprc`(→ dargs.Argument)
`frozen_model_args`(→ dargs.Argument)
`pairtab_model_args`(→ dargs.Argument)
`linear_ener_model_args`(→ dargs.Argument)
`learning_rate_exp`()
`learning_rate_variant_type_args`()
`learning_rate_args`(→ dargs.Argument)
`start_pref`(→ str)
`limit_pref`(→ str)
`loss_ener`()
`loss_ener_spin`()
`loss_dos`()
`loss_property`()
`loss_tensor`()
`loss_variant_type_args`()
`loss_args`()
`training_data_args`()
`validation_data_args`()
`mixed_precision_args`()
`training_args`([multi_task])
`multi_model_args`()
`multi_loss_args`()
`make_index`(keys)
`gen_doc`(→ str)
`gen_json`(→ str)
`gen_args`(→ list[dargs.Argument])
`gen_args_multi_task`(→ dargs.Argument)	Generate multi-task arguments.
`gen_json_schema`(→ str)	Generate JSON schema.
`normalize`(data[, multi_task])

Module Contents#

deepmd.utils.argcheck.log[source]#

deepmd.utils.argcheck.ACTIVATION_FN_DICT[source]#

deepmd.utils.argcheck.PRECISION_DICT[source]#

deepmd.utils.argcheck.doc_only_tf_supported = '(Supported Backend: TensorFlow) '[source]#

deepmd.utils.argcheck.doc_only_pt_supported = '(Supported Backend: PyTorch) '[source]#

deepmd.utils.argcheck.doc_loc_frame = 'Defines a local frame at each atom, and the compute the descriptor as local coordinates under...[source]#

deepmd.utils.argcheck.doc_se_e2_a = 'Used by the smooth edition of Deep Potential. The full relative coordinates are used to...[source]#

deepmd.utils.argcheck.doc_se_e2_r = 'Used by the smooth edition of Deep Potential. Only the distance between atoms is used to...[source]#

deepmd.utils.argcheck.doc_se_e3 = 'Used by the smooth edition of Deep Potential. The full relative coordinates are used to...[source]#

deepmd.utils.argcheck.doc_se_a_tpe = 'Used by the smooth edition of Deep Potential. The full relative coordinates are used to...[source]#

deepmd.utils.argcheck.doc_se_atten = 'Used by the smooth edition of Deep Potential. The full relative coordinates are used to...[source]#

deepmd.utils.argcheck.doc_se_atten_v2 = 'Used by the smooth edition of Deep Potential. The full relative coordinates are used to...[source]#

deepmd.utils.argcheck.doc_se_a_mask = 'Used by the smooth edition of Deep Potential. It can accept a variable number of atoms in a...[source]#

deepmd.utils.argcheck.doc_hybrid = 'Concatenate of a list of descriptors as a new descriptor.'[source]#

deepmd.utils.argcheck.doc_ener = 'Fit an energy model (potential energy surface).'[source]#

deepmd.utils.argcheck.doc_dos = 'Fit a density of states model. The total density of states / site-projected density of states...[source]#

deepmd.utils.argcheck.doc_dipole = 'Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected...[source]#

deepmd.utils.argcheck.doc_polar = 'Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability...[source]#

deepmd.utils.argcheck.list_to_doc(xx)[source]#

deepmd.utils.argcheck.make_link(content, ref_key) → str[source]#

deepmd.utils.argcheck.deprecate_argument_extra_check(key: str) → Callable[[dict], bool][source]#

Generate an extra check to deprecate an argument in sub fields.

Parameters:

keystr: The name of the deprecated argument.

deepmd.utils.argcheck.type_embedding_args()[source]#

deepmd.utils.argcheck.spin_args()[source]#

class deepmd.utils.argcheck.ArgsPlugin[source]#

__plugin[source]#

register(name: str, alias: list[str] | None = None, doc: str = '') → Callable[[Callable[[], dargs.Argument] | Callable[[], list[dargs.Argument]]], Callable[[], dargs.Argument] | Callable[[], list[dargs.Argument]]][source]#

Register a descriptor argument plugin.

Parameters:

namestr: the name of a descriptor
aliaslist[str], optional: the list of aliases of this descriptor

Returns:

Callable[[Union[Callable[[], Argument], Callable[[], list[Argument]]]], Union[Callable[[], Argument], Callable[[], list[Argument]]]]: decorator to return the registered descriptor argument method

Examples

>>> some_plugin = ArgsPlugin()
>>> @some_plugin.register("some_descrpt")
    def descrpt_some_descrpt_args():
        return []

get_all_argument(exclude_hybrid: bool = False) → list[dargs.Argument][source]#

Get all arguments.

Parameters:

exclude_hybridbool: exclude hybrid descriptor to prevent circular calls

Returns:

list[Argument]: all arguments

deepmd.utils.argcheck.descrpt_args_plugin[source]#

deepmd.utils.argcheck.descrpt_local_frame_args()[source]#

deepmd.utils.argcheck.descrpt_se_a_args()[source]#

deepmd.utils.argcheck.descrpt_se_t_args()[source]#

deepmd.utils.argcheck.descrpt_se_a_tpe_args()[source]#

deepmd.utils.argcheck.descrpt_se_r_args()[source]#

deepmd.utils.argcheck.descrpt_hybrid_args()[source]#

deepmd.utils.argcheck.descrpt_se_atten_common_args()[source]#

deepmd.utils.argcheck.descrpt_se_atten_args()[source]#

deepmd.utils.argcheck.descrpt_se_e3_tebd_args()[source]#

deepmd.utils.argcheck.descrpt_se_atten_v2_args()[source]#

deepmd.utils.argcheck.descrpt_dpa2_args()[source]#

deepmd.utils.argcheck.dpa2_repinit_args()[source]#

deepmd.utils.argcheck.dpa2_repformer_args()[source]#

deepmd.utils.argcheck.descrpt_se_a_ebd_v2_args()[source]#

deepmd.utils.argcheck.descrpt_se_a_mask_args()[source]#

deepmd.utils.argcheck.descrpt_variant_type_args(exclude_hybrid: bool = False) → dargs.Variant[source]#

deepmd.utils.argcheck.fitting_args_plugin[source]#

deepmd.utils.argcheck.fitting_ener()[source]#

deepmd.utils.argcheck.fitting_dos()[source]#

deepmd.utils.argcheck.fitting_property()[source]#

deepmd.utils.argcheck.fitting_polar()[source]#

deepmd.utils.argcheck.fitting_dipole()[source]#

deepmd.utils.argcheck.fitting_variant_type_args()[source]#

deepmd.utils.argcheck.modifier_dipole_charge()[source]#

deepmd.utils.argcheck.modifier_variant_type_args()[source]#

deepmd.utils.argcheck.model_compression()[source]#

deepmd.utils.argcheck.model_compression_type_args()[source]#

deepmd.utils.argcheck.model_args_plugin[source]#

deepmd.utils.argcheck.hybrid_model_args_plugin[source]#

deepmd.utils.argcheck.model_args(exclude_hybrid=False)[source]#

deepmd.utils.argcheck.standard_model_args() → dargs.Argument[source]#

deepmd.utils.argcheck.pairwise_dprc() → dargs.Argument[source]#

deepmd.utils.argcheck.frozen_model_args() → dargs.Argument[source]#

deepmd.utils.argcheck.pairtab_model_args() → dargs.Argument[source]#

deepmd.utils.argcheck.linear_ener_model_args() → dargs.Argument[source]#

deepmd.utils.argcheck.learning_rate_exp()[source]#

deepmd.utils.argcheck.learning_rate_variant_type_args()[source]#

deepmd.utils.argcheck.learning_rate_args(fold_subdoc: bool = False) → dargs.Argument[source]#

deepmd.utils.argcheck.start_pref(item, label=None, abbr=None) → str[source]#

deepmd.utils.argcheck.limit_pref(item) → str[source]#

deepmd.utils.argcheck.loss_args_plugin[source]#

deepmd.utils.argcheck.loss_ener()[source]#

deepmd.utils.argcheck.loss_ener_spin()[source]#

deepmd.utils.argcheck.loss_dos()[source]#

deepmd.utils.argcheck.loss_property()[source]#

deepmd.utils.argcheck.loss_tensor()[source]#

deepmd.utils.argcheck.loss_variant_type_args()[source]#

deepmd.utils.argcheck.loss_args()[source]#

deepmd.utils.argcheck.training_data_args()[source]#

deepmd.utils.argcheck.validation_data_args()[source]#

deepmd.utils.argcheck.mixed_precision_args()[source]#

deepmd.utils.argcheck.training_args(multi_task=False)[source]#

deepmd.utils.argcheck.multi_model_args()[source]#

deepmd.utils.argcheck.multi_loss_args()[source]#

deepmd.utils.argcheck.make_index(keys)[source]#

deepmd.utils.argcheck.gen_doc(*, make_anchor=True, make_link=True, multi_task=False, **kwargs) → str[source]#

deepmd.utils.argcheck.gen_json(multi_task: bool = False, **kwargs) → str[source]#

deepmd.utils.argcheck.gen_args(multi_task: bool = False) → list[dargs.Argument][source]#

deepmd.utils.argcheck.gen_args_multi_task() → dargs.Argument[source]#: Generate multi-task arguments.

deepmd.utils.argcheck.gen_json_schema(multi_task: bool = False) → str[source]#

Generate JSON schema.

Returns:

str: JSON schema.

deepmd.utils.argcheck.normalize(data, multi_task: bool = False)[source]#

`log`
`ACTIVATION_FN_DICT`
`PRECISION_DICT`
`doc_only_tf_supported`
`doc_only_pt_supported`
`doc_loc_frame`
`doc_se_e2_a`
`doc_se_e2_r`
`doc_se_e3`
`doc_se_a_tpe`
`doc_se_atten`
`doc_se_atten_v2`
`doc_se_a_mask`
`doc_hybrid`
`doc_ener`
`doc_dos`
`doc_dipole`
`doc_polar`
`descrpt_args_plugin`
`fitting_args_plugin`
`model_args_plugin`
`hybrid_model_args_plugin`
`loss_args_plugin`

deepmd.utils.argcheck

Contents

deepmd.utils.argcheck#

Attributes#

Classes#

Functions#

Module Contents#