add ABC for descriptors

deepmd/descriptor/descriptor.py

@@ -0,0 +1,282 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+from deepmd.env import tf
+
+
+class Descriptor(ABC):
+    r"""The abstract class for descriptors. All specific descriptors should
+    be based on this class.
+
+    The descriptor :math:`\mathcal{D}` describes the environment of an atom,
+    which should be a function of coordinates and types of its neighbour atoms.
+
+    Notes
+    -----
+    Only methods and attributes defined in this class are generally public,
+    that can be called by other classes.
+    """
+
+    @abstractmethod
+    def get_rcut(self) -> float:
+        """
+        Returns the cut-off radius.
+
+        Returns
+        -------
+        float
+            the cut-off radius
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    @abstractmethod
+    def get_ntypes(self) -> int:
+        """
+        Returns the number of atom types.
+
+        Returns
+        -------
+        int
+            the number of atom types
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    @abstractmethod
+    def get_dim_out(self) -> int:
+        """
+        Returns the output dimension of this descriptor.
+
+        Returns
+        -------
+        int
+            the output dimension of this descriptor
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    def get_dim_rot_mat_1(self) -> int:
+        """
+        Returns the first dimension of the rotation matrix. The rotation is of shape
+        dim_1 x 3
+
+        Returns
+        -------
+        int
+            the first dimension of the rotation matrix
+        """
+        # TODO: I think this method should be implemented as it's called by dipole and
+        # polar fitting network. However, currently not all descriptors have this
+        # method.
+        raise NotImplementedError
+
+    def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
+        """
+        Returns neighbor information.
+
+        Returns
+        -------
+        nlist : tf.Tensor
+            Neighbor list
+        rij : tf.Tensor
+            The relative distance between the neighbor and the center atom.
+        sel_a : list[int]
+            The number of neighbors with full information
+        sel_r : list[int]
+            The number of neighbors with only radial information
+        """
+        # TODO: I think this method should be implemented as it's called by energy
+        # model. However, se_ar and hybrid doesn't have this method.
+        raise NotImplementedError
+
+    @abstractmethod
+    def compute_input_stats(self,
+                            data_coord: List[np.ndarray],
+                            data_box: List[np.ndarray],
+                            data_atype: List[np.ndarray],
+                            natoms_vec: List[np.ndarray],
+                            mesh: List[np.ndarray],
+                            input_dict: Dict[str, List[np.ndarray]]
+                            ) -> None:
+        """
+        Compute the statisitcs (avg and std) of the training data. The input will be
+        normalized by the statistics.
+
+        Parameters
+        ----------
+        data_coord : list[np.ndarray]
+            The coordinates. Can be generated by
+            :meth:`deepmd.model.model_stat.make_stat_input`
+        data_box : list[np.ndarray]
+            The box. Can be generated by
+            :meth:`deepmd.model.model_stat.make_stat_input`
+        data_atype : list[np.ndarray]
+            The atom types. Can be generated by :meth:`deepmd.model.model_stat.make_stat_input`
+        natoms_vec : list[np.ndarray]
+            The vector for the number of atoms of the system and different types of
+            atoms. Can be generated by :meth:`deepmd.model.model_stat.make_stat_input`
+        mesh : list[np.ndarray]
+            The mesh for neighbor searching. Can be generated by
+            :meth:`deepmd.model.model_stat.make_stat_input`
+        input_dict : dict[str, list[np.ndarray]]
+            Dictionary for additional input
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    @abstractmethod
+    def build(self,
+              coord_: tf.Tensor,
+              atype_: tf.Tensor,
+              natoms: tf.Tensor,
+              box_: tf.Tensor,
+              mesh: tf.Tensor,
+              input_dict: Dict[str, Any],
+              reuse: bool = None,
+              suffix: str = '',
+              ) -> tf.Tensor:
+        """
+        Build the computational graph for the descriptor.
+
+        Parameters
+        ----------
+        coord_ : tf.Tensor
+            The coordinate of atoms
+        atype_ : tf.Tensor
+            The type of atoms
+        natoms : tf.Tensor
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: number of local atoms
+            natoms[1]: total number of atoms held by this processor
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
+        box : tf.Tensor
+            The box of frames
+        mesh : tf.Tensor
+            For historical reasons, only the length of the Tensor matters.
+            if size of mesh == 6, pbc is assumed.
+            if size of mesh == 0, no-pbc is assumed.
+        input_dict : dict[str, Any]
+            Dictionary for additional inputs
+        reuse : bool, optional
+            The weights in the networks should be reused when get the variable.
+        suffix : str, optional
+            Name suffix to identify this descriptor
+
+        Returns
+        -------
+        descriptor: tf.Tensor
+            The output descriptor
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    def enable_compression(self,
+                           min_nbor_dist: float,
+                           model_file: str = 'frozon_model.pb',
+                           table_extrapolate: float = 5.,
+                           table_stride_1: float = 0.01,
+                           table_stride_2: float = 0.1,
+                           check_frequency: int = -1
+                           ) -> None:
+        """
+        Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the
+        training data.
+
+        Parameters
+        ----------
+        min_nbor_dist : float
+                The nearest distance between atoms
+        model_file : str, default: 'frozon_model.pb'
+                The original frozen model, which will be compressed by the program
+        table_extrapolate : float, default: 5.
+                The scale of model extrapolation
+        table_stride_1 : float, default: 0.01
+                The uniform stride of the first table
+        table_stride_2 : float, default: 0.1
+                The uniform stride of the second table
+        check_frequency : int, default: -1
+                The overflow check frequency
+
+        Notes
+        -----
+        This method is called by others when the descriptor supported compression.
+        """
+        raise NotImplementedError(
+            "Descriptor %s doesn't support compression!" % self.__name__)
+
+    @abstractmethod
+    def prod_force_virial(self,
+                          atom_ener: tf.Tensor,
+                          natoms: tf.Tensor
+                          ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
+        """
+        Compute force and virial.
+
+        Parameters
+        ----------
+        atom_ener : tf.Tensor
+            The atomic energy
+        natoms : tf.Tensor
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: number of local atoms
+            natoms[1]: total number of atoms held by this processor
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
+
+        Returns
+        -------
+        force : tf.Tensor
+            The force on atoms
+        virial : tf.Tensor
+            The total virial
+        atom_virial : tf.Tensor
+            The atomic virial
+        """
+
+    def get_feed_dict(self,
+                      coord_: tf.Tensor,
+                      atype_: tf.Tensor,
+                      natoms: tf.Tensor,
+                      box: tf.Tensor,
+                      mesh: tf.Tensor
+                      ) -> Dict[str, tf.Tensor]:
+        """
+        Generate the feed_dict for current descriptor
+
+        Parameters
+        ----------
+        coord_ : tf.Tensor
+            The coordinate of atoms
+        atype_ : tf.Tensor
+            The type of atoms
+        natoms : tf.Tensor
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: number of local atoms
+            natoms[1]: total number of atoms held by this processor
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
+        box : tf.Tensor
+            The box. Can be generated by deepmd.model.make_stat_input
+        mesh : tf.Tensor
+            For historical reasons, only the length of the Tensor matters.
+            if size of mesh == 6, pbc is assumed. 
+            if size of mesh == 0, no-pbc is assumed. 
+
+        Returns
+        -------
+        feed_dict : dict[str, tf.Tensor]
+            The output feed_dict of current descriptor
+        """
+        # TODO: currently only SeA has this method, but I think the method can be
+        # moved here as it doesn't contain anything related to a specific descriptor
+        raise NotImplementedError

deepmd/descriptor/hybrid.py

@@ -12,6 +12,7 @@
 # from deepmd.descriptor import DescrptSeAEbd
 # from deepmd.descriptor import DescrptSeAEf
 # from deepmd.descriptor import DescrptSeR
+from .descriptor import Descriptor
 from .se_a import DescrptSeA
 from .se_r import DescrptSeR
 from .se_ar import DescrptSeAR
@@ -20,7 +21,7 @@
 from .se_a_ef import DescrptSeAEf
 from .loc_frame import DescrptLocFrame
 
-class DescrptHybrid ():
+class DescrptHybrid (Descriptor):
     """Concate a list of descriptors to form a new descriptor.
 
     Parameters

deepmd/descriptor/loc_frame.py

@@ -7,8 +7,9 @@
 from deepmd.env import op_module
 from deepmd.env import default_tf_session_config
 from deepmd.utils.sess import run_sess
+from .descriptor import Descriptor
 
-class DescrptLocFrame () :
+class DescrptLocFrame (Descriptor) :
     """Defines a local frame at each atom, and the compute the descriptor as local
     coordinates under this frame.
 

deepmd/descriptor/se_a.py

@@ -14,8 +14,9 @@
 from deepmd.utils.type_embed import embed_atom_type
 from deepmd.utils.sess import run_sess
 from deepmd.utils.graph import load_graph_def, get_tensor_by_name_from_graph
+from .descriptor import Descriptor
 
-class DescrptSeA ():
+class DescrptSeA (Descriptor):
     r"""DeepPot-SE constructed from all information (both angular and radial) of
     atomic configurations. The embedding takes the distance between atoms as input.
 

deepmd/descriptor/se_a_ef.py

@@ -10,8 +10,9 @@
 from deepmd.env import op_module
 from deepmd.env import default_tf_session_config
 from .se_a import DescrptSeA
+from .descriptor import Descriptor
 
-class DescrptSeAEf ():
+class DescrptSeAEf (Descriptor):
     """
 
     Parameters

deepmd/descriptor/se_ar.py

@@ -5,8 +5,9 @@
 from .se_a import DescrptSeA
 from .se_r import DescrptSeR
 from deepmd.env import op_module
+from .descriptor import Descriptor
 
-class DescrptSeAR ():
+class DescrptSeAR (Descriptor):
     def __init__ (self, jdata):
         args = ClassArg()\
                .add('a',      dict,   must = True) \

deepmd/descriptor/se_r.py

@@ -10,8 +10,9 @@
 from deepmd.env import default_tf_session_config
 from deepmd.utils.network import embedding_net, embedding_net_rand_seed_shift
 from deepmd.utils.sess import run_sess
+from .descriptor import Descriptor
 
-class DescrptSeR ():
+class DescrptSeR (Descriptor):
     """DeepPot-SE constructed from radial information of atomic configurations.
 
     The embedding takes the distance between atoms as input.

deepmd/descriptor/se_t.py

@@ -10,8 +10,9 @@
 from deepmd.env import default_tf_session_config
 from deepmd.utils.network import embedding_net, embedding_net_rand_seed_shift
 from deepmd.utils.sess import run_sess
+from .descriptor import Descriptor
 
-class DescrptSeT ():
+class DescrptSeT (Descriptor):
     """DeepPot-SE constructed from all information (both angular and radial) of atomic
     configurations.