import keras as ks
from kgcnn.layers.scale import get as get_scaler
from ._model import model_disjoint_weighted, model_disjoint_behler, model_disjoint_atom_wise
from kgcnn.layers.modules import Input
from kgcnn.models.casting import (template_cast_output, template_cast_list_input,
template_cast_list_input_docs, template_cast_output_docs)
from kgcnn.models.utils import update_model_kwargs
from keras.backend import backend as backend_to_use
# To be updated if model is changed in a significant way.
__model_version__ = "2023-12-06"
# Supported backends
__kgcnn_model_backend_supported__ = ["tensorflow", "torch", "jax"]
if backend_to_use() not in __kgcnn_model_backend_supported__:
raise NotImplementedError("Backend '%s' for model 'HDNNP2nd' is not supported." % backend_to_use())
# Implementation of HDNNP in `keras` from paper:
# Atom-centered symmetry functions for constructing high-dimensional neural network potentials
# by Jörg Behler (2011)
# https://aip.scitation.org/doi/abs/10.1063/1.3553717
model_default_weighted = {
"name": "HDNNP2nd",
"inputs": [
{"shape": (None,), "name": "node_number", "dtype": "int64"},
{"shape": (None, 3), "name": "node_coordinates", "dtype": "float32"},
{"shape": (None, 2), "name": "edge_indices", "dtype": "int64"},
{"shape": (None, 3), "name": "angle_indices_nodes", "dtype": "int64"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"},
{"shape": (), "name": "total_angles", "dtype": "int64"}
],
"input_tensor_type": "padded",
"cast_disjoint_kwargs": {},
"w_acsf_ang_kwargs": {},
"w_acsf_rad_kwargs": {},
"normalize_kwargs": None,
"const_normalize_kwargs": None,
"mlp_kwargs": {"units": [64, 64, 64],
"num_relations": 96,
"activation": ["swish", "swish", "linear"]},
"node_pooling_args": {"pooling_method": "sum"},
"verbose": 10,
"output_embedding": "graph", "output_to_tensor": True,
"use_output_mlp": False,
"output_mlp": {"use_bias": [True, True], "units": [64, 1],
"activation": ["swish", "linear"]},
"output_tensor_type": "padded",
"output_scaling": None
}
[docs]@update_model_kwargs(model_default_weighted, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_model_weighted(inputs: list = None,
input_tensor_type: str = None,
cast_disjoint_kwargs: dict = None,
node_pooling_args: dict = None,
name: str = None,
verbose: int = None,
w_acsf_ang_kwargs: dict = None,
w_acsf_rad_kwargs: dict = None,
normalize_kwargs: dict = None,
const_normalize_kwargs: dict = None,
mlp_kwargs: dict = None,
output_embedding: str = None,
use_output_mlp: bool = None,
output_to_tensor: bool = None,
output_mlp: dict = None,
output_scaling: dict = None,
output_tensor_type: str = None
):
r"""Make 2nd generation `HDNNP <https://arxiv.org/abs/1706.08566>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.HDNNP2nd.model_default_weighted` .
Uses weighted `wACSF <https://arxiv.org/abs/1712.05861>`__ .
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[node_number, coordinates, edge_indices, angle_indices, ...]`
with '...' indicating mask or ID tensors following the template below.
Requires node number for atom-wise neural networks.
%s
**Model outputs**:
The standard output template:
%s
Args:
inputs (list): List of dictionaries unpacked in :obj:`Input`. Order must match model definition.
input_tensor_type (str): Input type of graph tensor. Default is "padded".
cast_disjoint_kwargs (dict): Dictionary of arguments for casting layer.
node_pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes` layers.
verbose (int): Level of verbosity.
name (str): Name of the model.
w_acsf_ang_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`wACSFAng` layer.
w_acsf_rad_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`wACSFRad` layer.
mlp_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`RelationalMLP` layer.
normalize_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`GraphBatchNormalization` layer.
const_normalize_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`ACSFConstNormalization` layer.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
use_output_mlp (bool): Whether to use the final output MLP. Possibility to skip final MLP.
output_to_tensor (bool): Deprecated in favour of `output_tensor_type` .
output_mlp (dict): Dictionary of layer arguments unpacked in the final classification :obj:`MLP` layer block.
Defines number of model outputs and activation.
output_scaling (dict): Dictionary of layer arguments unpacked in scaling layers. Default is None.
output_tensor_type (str): Output type of graph tensors such as nodes or edges. Default is "padded".
Returns:
:obj:`keras.models.Model`
"""
# Make input
model_inputs = [Input(**x) for x in inputs]
dj = template_cast_list_input(
model_inputs,
input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=[0, 0, 1, 2],
index_assignment=[None, None, 0, 0]
)
n, x, disjoint_indices, ang_ind, batch_id_node, batch_id_edge, batch_id_angles, node_id, edge_id, angle_id, count_nodes, count_edges, count_angle = dj
out = model_disjoint_weighted(
[n, x, disjoint_indices, ang_ind, batch_id_node, count_nodes],
node_pooling_args=node_pooling_args,
w_acsf_ang_kwargs=w_acsf_ang_kwargs,
w_acsf_rad_kwargs=w_acsf_rad_kwargs,
normalize_kwargs=normalize_kwargs,
const_normalize_kwargs=const_normalize_kwargs,
mlp_kwargs=mlp_kwargs,
output_embedding=output_embedding,
use_output_mlp=use_output_mlp,
output_mlp=output_mlp
)
if output_scaling is not None:
scaler = get_scaler(output_scaling["name"])(**output_scaling)
if scaler.extensive:
# Node information must be numbers, or we need an additional input.
out = scaler([out, n, batch_id_node])
else:
out = scaler(out)
# Output embedding choice
out = template_cast_output(
[out, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges],
output_embedding=output_embedding, output_tensor_type=output_tensor_type,
input_tensor_type=input_tensor_type, cast_disjoint_kwargs=cast_disjoint_kwargs,
)
model = ks.models.Model(inputs=model_inputs, outputs=out, name=name)
model.__kgcnn_model_version__ = __model_version__
if output_scaling is not None:
def set_scale(*args, **kwargs):
scaler.set_scale(*args, **kwargs)
setattr(model, "set_scale", set_scale)
return model
make_model_weighted.__doc__ = make_model_weighted.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)
model_default_behler = {
"name": "HDNNP2nd",
"inputs": [
{"shape": (None,), "name": "node_number", "dtype": "int64"},
{"shape": (None, 3), "name": "node_coordinates", "dtype": "float32"},
{"shape": (None, 2), "name": "edge_indices", "dtype": "int64"},
{"shape": (None, 3), "name": "angle_indices_nodes", "dtype": "int64"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"},
{"shape": (), "name": "total_angles", "dtype": "int64"}
],
"input_tensor_type": "padded",
"cast_disjoint_kwargs": {},
"g2_kwargs": {"eta": [0.0, 0.3], "rs": [0.0, 3.0], "rc": 10.0, "elements": [1, 6, 16]},
"g4_kwargs": {"eta": [0.0, 0.3], "lamda": [-1.0, 1.0], "rc": 6.0,
"zeta": [1.0, 8.0], "elements": [1, 6, 16], "multiplicity": 2.0},
"normalize_kwargs": {},
"const_normalize_kwargs": None,
"mlp_kwargs": {"units": [64, 64, 64],
"num_relations": 96,
"activation": ["swish", "swish", "linear"]},
"node_pooling_args": {"pooling_method": "sum"},
"verbose": 10,
"output_embedding": "graph", "output_to_tensor": True,
"use_output_mlp": False,
"output_mlp": {"use_bias": [True, True], "units": [64, 1],
"activation": ["swish", "linear"]},
"output_tensor_type": "padded",
"output_scaling": None
}
[docs]@update_model_kwargs(model_default_behler, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_model_behler(inputs: list = None,
input_tensor_type: str = None,
cast_disjoint_kwargs: dict = None,
node_pooling_args: dict = None,
name: str = None,
verbose: int = None, # noqa
normalize_kwargs: dict = None,
const_normalize_kwargs: dict = None,
g2_kwargs: dict = None,
g4_kwargs: dict = None,
mlp_kwargs: dict = None,
output_embedding: str = None,
use_output_mlp: bool = None,
output_to_tensor: bool = None, # noqa
output_mlp: dict = None,
output_scaling: dict = None,
output_tensor_type: str = None
):
r"""Make 2nd generation `HDNNP <https://arxiv.org/abs/1706.08566>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.HDNNP2nd.model_default_behler` .
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[node_number, coordinates, edge_indices, angle_indices, ...]`
with '...' indicating mask or ID tensors following the template below.
Requires node number for atom-wise neural networks.
%s
**Model outputs**:
The standard output template:
%s
Args:
inputs (list): List of dictionaries unpacked in :obj:`Input`. Order must match model definition.
input_tensor_type (str): Input type of graph tensor. Default is "padded".
cast_disjoint_kwargs (dict): Dictionary of arguments for casting layer.
node_pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes` layers.
verbose (int): Level of verbosity.
name (str): Name of the model.
g2_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`ACSFG2` layer.
g4_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`ACSFG4` layer.
normalize_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`GraphBatchNormalization` layer.
const_normalize_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`ACSFConstNormalization` layer.
mlp_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`RelationalMLP` layer.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
use_output_mlp (bool): Whether to use the final output MLP. Possibility to skip final MLP.
output_to_tensor (bool): Deprecated in favour of `output_tensor_type` .
output_mlp (dict): Dictionary of layer arguments unpacked in the final classification :obj:`MLP` layer block.
Defines number of model outputs and activation.
output_scaling (dict): Dictionary of layer arguments unpacked in scaling layers. Default is None.
output_tensor_type (str): Output type of graph tensors such as nodes or edges. Default is "padded".
Returns:
:obj:`keras.models.Model`
"""
# Make input
model_inputs = [Input(**x) for x in inputs]
dj = template_cast_list_input(
model_inputs,
input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=[0, 0, 1, 2],
index_assignment=[None, None, 0, 0]
)
n, x, disjoint_indices, ang_index, batch_id_node, batch_id_edge, batch_id_angles, node_id, edge_id, angle_id, count_nodes, count_edges, count_angle = dj
out = model_disjoint_behler(
[n, x, disjoint_indices, ang_index, batch_id_node, count_nodes],
node_pooling_args=node_pooling_args,
normalize_kwargs=normalize_kwargs,
const_normalize_kwargs=const_normalize_kwargs,
g2_kwargs=g2_kwargs,
g4_kwargs=g4_kwargs,
mlp_kwargs=mlp_kwargs,
output_embedding=output_embedding,
use_output_mlp=use_output_mlp,
output_mlp=output_mlp
)
if output_scaling is not None:
scaler = get_scaler(output_scaling["name"])(**output_scaling)
if scaler.extensive:
# Node information must be numbers, or we need an additional input.
out = scaler([out, n, batch_id_node])
else:
out = scaler(out)
# Output embedding choice
out = template_cast_output(
[out, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges],
output_embedding=output_embedding, output_tensor_type=output_tensor_type,
input_tensor_type=input_tensor_type, cast_disjoint_kwargs=cast_disjoint_kwargs,
)
model = ks.models.Model(inputs=model_inputs, outputs=out, name=name)
model.__kgcnn_model_version__ = __model_version__
if output_scaling is not None:
def set_scale(*args, **kwargs):
scaler.set_scale(*args, **kwargs)
setattr(model, "set_scale", set_scale)
return model
make_model_behler.__doc__ = make_model_behler.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)
model_default_atom_wise = {
"name": "HDNNP2nd",
"inputs": [
{"shape": (None,), "name": "node_number", "dtype": "int64"},
{"shape": (None, 3), "name": "node_representation", "dtype": "float32"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
],
"input_tensor_type": "padded",
"cast_disjoint_kwargs": {},
"mlp_kwargs": {"units": [64, 64, 64],
"num_relations": 96,
"activation": ["swish", "swish", "linear"]},
"node_pooling_args": {"pooling_method": "sum"},
"verbose": 10,
"output_embedding": "graph", "output_to_tensor": True,
"use_output_mlp": False,
"output_mlp": {"use_bias": [True, True], "units": [64, 1],
"activation": ["swish", "linear"]},
"output_tensor_type": "padded",
"output_scaling": None
}
[docs]@update_model_kwargs(model_default_atom_wise, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_model_atom_wise(inputs: list = None,
input_tensor_type: str = None,
cast_disjoint_kwargs: dict = None,
node_pooling_args: dict = None,
name: str = None,
verbose: int = None,
mlp_kwargs: dict = None,
output_embedding: str = None,
use_output_mlp: bool = None,
output_to_tensor: bool = None,
output_mlp: dict = None,
output_scaling: dict = None,
output_tensor_type: str = None
):
r"""Make 2nd generation `HDNNP <https://arxiv.org/abs/1706.08566>`__ network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.HDNNP2nd.model_default_atom_wise` .
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[node_number, node_representation, ...]`
with '...' indicating mask or ID tensors following the template below.
Requires node number for atom-wise neural networks.
The representation are given directly to the model as they are expected to be pre-computed.
%s
**Model outputs**:
The standard output template:
%s
Args:
inputs (list): List of dictionaries unpacked in :obj:`Input`. Order must match model definition.
input_tensor_type (str): Input type of graph tensor. Default is "padded".
cast_disjoint_kwargs (dict): Dictionary of arguments for casting layer.
node_pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes` layers.
verbose (int): Level of verbosity.
name (str): Name of the model.
mlp_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`RelationalMLP` layer.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
use_output_mlp (bool): Whether to use the final output MLP. Possibility to skip final MLP.
output_to_tensor (bool): Deprecated in favour of `output_tensor_type` .
output_mlp (dict): Dictionary of layer arguments unpacked in the final classification :obj:`MLP` layer block.
Defines number of model outputs and activation.
output_scaling (dict): Dictionary of layer arguments unpacked in scaling layers. Default is None.
output_tensor_type (str): Output type of graph tensors such as nodes or edges. Default is "padded".
Returns:
:obj:`keras.models.Model`
"""
# Make input
model_inputs = [Input(**x) for x in inputs]
dj = template_cast_list_input(
model_inputs,
input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=[0, 0],
index_assignment=[None, None]
)
n, x, batch_id_node, node_id, count_nodes = dj
batch_id_edge, edge_id, count_edges = None, None, None
out = model_disjoint_atom_wise(
[n, x, batch_id_node, count_nodes],
node_pooling_args=node_pooling_args,
mlp_kwargs=mlp_kwargs,
output_embedding=output_embedding,
use_output_mlp=use_output_mlp,
output_mlp=output_mlp
)
if output_scaling is not None:
scaler = get_scaler(output_scaling["name"])(**output_scaling)
if scaler.extensive:
# Node information must be numbers, or we need an additional input.
out = scaler([out, n, batch_id_node])
else:
out = scaler(out)
# Output embedding choice
out = template_cast_output(
[out, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges],
output_embedding=output_embedding, output_tensor_type=output_tensor_type,
input_tensor_type=input_tensor_type, cast_disjoint_kwargs=cast_disjoint_kwargs,
)
model = ks.models.Model(inputs=model_inputs, outputs=out, name=name)
model.__kgcnn_model_version__ = __model_version__
if output_scaling is not None:
def set_scale(*args, **kwargs):
scaler.set_scale(*args, **kwargs)
setattr(model, "set_scale", set_scale)
return model
make_model_atom_wise.__doc__ = make_model_atom_wise.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)
# For default, the weighted ACSF are used, since they do should in principle work for all elements.
make_model = make_model_weighted
model_default = model_default_weighted