|
| 1 | +# SPDX-License-Identifier: LGPL-3.0-or-later |
| 2 | +import numpy as np |
| 3 | + |
| 4 | +try: |
| 5 | + from deepmd_utils._version import version as __version__ |
| 6 | +except ImportError: |
| 7 | + __version__ = "unknown" |
| 8 | + |
| 9 | +from typing import ( |
| 10 | + Any, |
| 11 | + List, |
| 12 | + Optional, |
| 13 | +) |
| 14 | + |
| 15 | +from .common import ( |
| 16 | + DEFAULT_PRECISION, |
| 17 | + NativeOP, |
| 18 | +) |
| 19 | +from .env_mat import ( |
| 20 | + EnvMat, |
| 21 | +) |
| 22 | +from .network import ( |
| 23 | + EmbeddingNet, |
| 24 | +) |
| 25 | + |
| 26 | + |
| 27 | +class DescrptSeA(NativeOP): |
| 28 | + def __init__( |
| 29 | + self, |
| 30 | + rcut: float, |
| 31 | + rcut_smth: float, |
| 32 | + sel: List[str], |
| 33 | + neuron: List[int] = [24, 48, 96], |
| 34 | + axis_neuron: int = 8, |
| 35 | + resnet_dt: bool = False, |
| 36 | + trainable: bool = True, |
| 37 | + type_one_side: bool = True, |
| 38 | + exclude_types: List[List[int]] = [], |
| 39 | + set_davg_zero: bool = False, |
| 40 | + activation_function: str = "tanh", |
| 41 | + precision: str = DEFAULT_PRECISION, |
| 42 | + spin: Optional[Any] = None, |
| 43 | + stripped_type_embedding: bool = False, |
| 44 | + ) -> None: |
| 45 | + ## seed, uniform_seed, multi_task, not included. |
| 46 | + if not type_one_side: |
| 47 | + raise NotImplementedError("type_one_side == False not implemented") |
| 48 | + if stripped_type_embedding: |
| 49 | + raise NotImplementedError("stripped_type_embedding is not implemented") |
| 50 | + if exclude_types != []: |
| 51 | + raise NotImplementedError("exclude_types is not implemented") |
| 52 | + if spin is not None: |
| 53 | + raise NotImplementedError("spin is not implemented") |
| 54 | + |
| 55 | + self.rcut = rcut |
| 56 | + self.rcut_smth = rcut_smth |
| 57 | + self.sel = sel |
| 58 | + self.ntypes = len(self.sel) |
| 59 | + self.neuron = neuron |
| 60 | + self.axis_neuron = axis_neuron |
| 61 | + self.resnet_dt = resnet_dt |
| 62 | + self.trainable = trainable |
| 63 | + self.type_one_side = type_one_side |
| 64 | + self.exclude_types = exclude_types |
| 65 | + self.set_davg_zero = set_davg_zero |
| 66 | + self.activation_function = activation_function |
| 67 | + self.precision = precision |
| 68 | + self.spin = spin |
| 69 | + self.stripped_type_embedding = stripped_type_embedding |
| 70 | + |
| 71 | + in_dim = 1 # not considiering type embedding |
| 72 | + self.embeddings = [] |
| 73 | + for ii in range(self.ntypes): |
| 74 | + self.embeddings.append( |
| 75 | + EmbeddingNet( |
| 76 | + in_dim, |
| 77 | + self.neuron, |
| 78 | + self.activation_function, |
| 79 | + self.resnet_dt, |
| 80 | + self.precision, |
| 81 | + ) |
| 82 | + ) |
| 83 | + self.env_mat = EnvMat(self.rcut, self.rcut_smth) |
| 84 | + self.nnei = np.sum(self.sel) |
| 85 | + self.nneix4 = self.nnei * 4 |
| 86 | + self.davg = np.zeros([self.ntypes, self.nneix4]) |
| 87 | + self.dstd = np.ones([self.ntypes, self.nneix4]) |
| 88 | + self.orig_sel = self.sel |
| 89 | + |
| 90 | + def __setitem__(self, key, value): |
| 91 | + if key in ("avg", "data_avg", "davg"): |
| 92 | + self.davg = value |
| 93 | + elif key in ("std", "data_std", "dstd"): |
| 94 | + self.dstd = value |
| 95 | + else: |
| 96 | + raise KeyError(key) |
| 97 | + |
| 98 | + def __getitem__(self, key): |
| 99 | + if key in ("avg", "data_avg", "davg"): |
| 100 | + return self.davg |
| 101 | + elif key in ("std", "data_std", "dstd"): |
| 102 | + return self.dstd |
| 103 | + else: |
| 104 | + raise KeyError(key) |
| 105 | + |
| 106 | + def cal_g( |
| 107 | + self, |
| 108 | + ss, |
| 109 | + ll, |
| 110 | + ): |
| 111 | + nf, nloc, nnei = ss.shape[0:3] |
| 112 | + ss = ss.reshape(nf, nloc, nnei, 1) |
| 113 | + # nf x nloc x nnei x ng |
| 114 | + gg = self.embeddings[ll].call(ss) |
| 115 | + return gg |
| 116 | + |
| 117 | + def call( |
| 118 | + self, |
| 119 | + coord_ext, |
| 120 | + atype_ext, |
| 121 | + nlist, |
| 122 | + ): |
| 123 | + """Compute the environment matrix. |
| 124 | +
|
| 125 | + Parameters |
| 126 | + ---------- |
| 127 | + coord_ext |
| 128 | + The extended coordinates of atoms. shape: nf x (nallx3) |
| 129 | + atype_ext |
| 130 | + The extended aotm types. shape: nf x nall |
| 131 | + nlist |
| 132 | + The neighbor list. shape: nf x nloc x nnei |
| 133 | +
|
| 134 | + Returns |
| 135 | + ------- |
| 136 | + descriptor |
| 137 | + The descriptor. shape: nf x nloc x ng x axis_neuron |
| 138 | + """ |
| 139 | + # nf x nloc x nnei x 4 |
| 140 | + rr, ww = self.env_mat.call(nlist, coord_ext, atype_ext, self.davg, self.dstd) |
| 141 | + nf, nloc, nnei, _ = rr.shape |
| 142 | + sec = np.append([0], np.cumsum(self.sel)) |
| 143 | + |
| 144 | + ng = self.neuron[-1] |
| 145 | + gr = np.zeros([nf, nloc, ng, 4]) |
| 146 | + for tt in range(self.ntypes): |
| 147 | + tr = rr[:, :, sec[tt] : sec[tt + 1], :] |
| 148 | + ss = tr[..., 0:1] |
| 149 | + gg = self.cal_g(ss, tt) |
| 150 | + # nf x nloc x ng x 4 |
| 151 | + gr += np.einsum("flni,flnj->flij", gg, tr) |
| 152 | + gr /= self.nnei |
| 153 | + gr1 = gr[:, :, : self.axis_neuron, :] |
| 154 | + # nf x nloc x ng x ng1 |
| 155 | + grrg = np.einsum("flid,fljd->flij", gr, gr1) |
| 156 | + # nf x nloc x (ng x ng1) |
| 157 | + grrg = grrg.reshape(nf, nloc, ng * self.axis_neuron) |
| 158 | + return grrg |
| 159 | + |
| 160 | + def serialize(self) -> dict: |
| 161 | + return { |
| 162 | + "rcut": self.rcut, |
| 163 | + "rcut_smth": self.rcut_smth, |
| 164 | + "sel": self.sel, |
| 165 | + "neuron": self.neuron, |
| 166 | + "axis_neuron": self.axis_neuron, |
| 167 | + "resnet_dt": self.resnet_dt, |
| 168 | + "trainable": self.trainable, |
| 169 | + "type_one_side": self.type_one_side, |
| 170 | + "exclude_types": self.exclude_types, |
| 171 | + "set_davg_zero": self.set_davg_zero, |
| 172 | + "activation_function": self.activation_function, |
| 173 | + "precision": self.precision, |
| 174 | + "spin": self.spin, |
| 175 | + "stripped_type_embedding": self.stripped_type_embedding, |
| 176 | + "env_mat": self.env_mat.serialize(), |
| 177 | + "embeddings": [ii.serialize() for ii in self.embeddings], |
| 178 | + "@variables": { |
| 179 | + "davg": self.davg, |
| 180 | + "dstd": self.dstd, |
| 181 | + }, |
| 182 | + } |
| 183 | + |
| 184 | + @classmethod |
| 185 | + def deserialize(cls, data: dict) -> "DescrptSeA": |
| 186 | + variables = data.pop("@variables") |
| 187 | + embeddings = data.pop("embeddings") |
| 188 | + env_mat = data.pop("env_mat") |
| 189 | + obj = cls(**data) |
| 190 | + |
| 191 | + obj["davg"] = variables["davg"] |
| 192 | + obj["dstd"] = variables["dstd"] |
| 193 | + obj.embeddings = [EmbeddingNet.deserialize(dd) for dd in embeddings] |
| 194 | + obj.env_mat = EnvMat.deserialize(env_mat) |
| 195 | + return obj |
0 commit comments