deepmd.pd.model.atomic_model.dp_atomic_model

deepmd.pd.model.atomic_model.dp_atomic_model#

Attributes#

log

Classes#

DPAtomicModel

Model give atomic prediction of some physical property.

Module Contents#

deepmd.pd.model.atomic_model.dp_atomic_model.log[source]#

class deepmd.pd.model.atomic_model.dp_atomic_model.DPAtomicModel(descriptor: deepmd.pd.model.descriptor.base_descriptor.BaseDescriptor, fitting: deepmd.pd.model.task.base_fitting.BaseFitting, type_map: list[str], **kwargs: Any)[source]#

Bases: deepmd.pd.model.atomic_model.base_atomic_model.BaseAtomicModel

Model give atomic prediction of some physical property.

Parameters:

descriptor: Descriptor
fitting_net: Fitting net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.

type_map[source]#

ntypes[source]#

descriptor[source]#

rcut[source]#

sel[source]#

fitting_net[source]#

add_chg_spin_ebd: bool[source]#

enable_eval_descriptor_hook = False[source]#

enable_eval_fitting_last_layer_hook = False[source]#

eval_descriptor_list = [][source]#

eval_fitting_last_layer_list = [][source]#

set_eval_descriptor_hook(enable: bool) → None[source]#: Set the hook for evaluating descriptor and clear the cache for descriptor list.

eval_descriptor() → paddle.Tensor[source]#: Evaluate the descriptor.

set_eval_fitting_last_layer_hook(enable: bool) → None[source]#: Set the hook for evaluating fitting last layer output and clear the cache for fitting last layer output list.

eval_fitting_last_layer() → paddle.Tensor[source]#: Evaluate the fitting last layer output.

fitting_output_def() → deepmd.dpmodel.FittingOutputDef[source]#: Get the output def of the fitting net.

get_rcut() → float[source]#: Get the cut-off radius.

get_sel() → list[int][source]#: Get the neighbor selection.

get_buffer_type_map() → paddle.Tensor[source]#

Return the type map as a buffer-style Tensor for JIT saving.

The original type map (e.g., [‘Ni’, ‘O’]) is first joined into a single space-separated string (e.g., “Ni O”). Each character in this string is then converted to its ASCII code using ord(), and the resulting integer sequence is stored as a 1D paddle.Tensor of dtype int.

This format allows the type map to be serialized as a raw byte buffer during JIT model saving.

get_buffer_rcut() → paddle.Tensor[source]#: Get the cut-off radius as a buffer-style Tensor.

get_buffer_sel() → paddle.Tensor[source]#: Get the neighbor selection as a buffer-style Tensor.

set_case_embd(case_idx: int) → None[source]#: Set the case embedding of this atomic model by the given case_idx, typically concatenated with the output of the descriptor and fed into the fitting net.

mixed_types() → bool[source]#

If true, the model 1. assumes total number of atoms aligned across frames; 2. uses a neighbor list that does not distinguish different atomic types.

If false, the model 1. assumes total number of atoms of each atom type aligned across frames; 2. uses a neighbor list that distinguishes different atomic types.

change_type_map(type_map: list[str], model_with_new_type_stat: DPAtomicModel | None = None) → None[source]#: Change the type related params to new ones, according to type_map and the original one in the model. If there are new types in type_map, statistics will be updated accordingly to model_with_new_type_stat for these new types.

has_message_passing() → bool[source]#: Returns whether the atomic model has message passing.

need_sorted_nlist_for_lower() → bool[source]#: Returns whether the atomic model needs sorted nlist when using forward_lower.

serialize() → dict[source]#

classmethod deserialize(data: dict) → DPAtomicModel[source]#

enable_compression(min_nbor_dist: float, table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = -1) → None[source]#

Call descriptor enable_compression().

Parameters:

min_nbor_dist: The nearest distance between atoms
table_extrapolate: The scale of model extrapolation
table_stride_1: The uniform stride of the first table
table_stride_2: The uniform stride of the second table
check_frequency: The overflow check frequency

forward_atomic(extended_coord: paddle.Tensor, extended_atype: paddle.Tensor, nlist: paddle.Tensor, mapping: paddle.Tensor | None = None, fparam: paddle.Tensor | None = None, aparam: paddle.Tensor | None = None, comm_dict: dict[str, paddle.Tensor] | None = None) → dict[str, paddle.Tensor][source]#

Return atomic prediction.

Parameters:

extended_coord: coordinates in extended region
extended_atype: atomic type in extended region
nlist: neighbor list. nf x nloc x nsel
mapping: mapps the extended indices to local indices
fparam: frame parameter. nf x ndf
aparam: atomic parameter. nf x nloc x nda

Returns:

result_dict: the result dict, defined by the FittingOutputDef.

get_out_bias() → paddle.Tensor[source]#

compute_or_load_stat(sampled_func: collections.abc.Callable[[], list[dict]], stat_file_path: deepmd.utils.path.DPPath | None = None, compute_or_load_out_stat: bool = True) → None[source]#

Compute or load the statistics parameters of the model, such as mean and standard deviation of descriptors or the energy bias of the fitting net. When sampled is provided, all the statistics parameters will be calculated (or re-calculated for update), and saved in the stat_file_path`(s). When `sampled is not provided, it will check the existence of `stat_file_path`(s) and load the calculated statistics parameters.

Parameters:

sampled_func: The lazy sampled function to get data frames from different data systems.
stat_file_path: The dictionary of paths to the statistics files.
compute_or_load_out_statbool: Whether to compute the output statistics. If False, it will only compute the input statistics (e.g. mean and standard deviation of descriptors).

compute_fitting_input_stat(sample_merged: collections.abc.Callable[[], list[dict]] | list[dict], stat_file_path: deepmd.utils.path.DPPath | None = None) → None[source]#

Compute the input statistics (e.g. mean and stddev) for the fittings from packed data.

Parameters:

sample_mergedUnion[Callable[[], list[dict]], list[dict]]

list[dict]: A list of data samples from various data systems.
Each element, merged[i], is a data dictionary containing keys: paddle.Tensor originating from the i-th data system.
Callable[[], list[dict]]: A lazy function that returns data samples in the above format
only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once.

stat_file_pathOptional[DPPath]

The dictionary of paths to the statistics files.

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

get_buffer_dim_fparam() → paddle.Tensor[source]#: Get the number (dimension) of frame parameters of this atomic model as a buffer-style Tensor.

has_default_fparam() → bool[source]#: Check if the model has default frame parameters.

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

get_buffer_dim_aparam() → paddle.Tensor[source]#: Get the number (dimension) of atomic parameters of this atomic model as a buffer-style Tensor.

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.

is_aparam_nall() → bool[source]#

Check whether the shape of atomic parameters is (nframes, nall, ndim).

If False, the shape is (nframes, nloc, ndim).