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_make.py
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import keras as ks
from kgcnn.layers.scale import get as get_scaler
from ._model import model_disjoint
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__ = "2023-12-09"
# 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 'MXMNet' is not supported." % backend_to_use())
# Implementation of MXMNet in `tf.keras` from paper:
# Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
# by Shuo Zhang, Yang Liu, Lei Xie (2020)
# https://arxiv.org/abs/2011.07457
# https://github.com/zetayue/MXMNet
model_default = {
"name": "MXMNet",
"inputs": [
{"shape": (None, ), "name": "node_number", "dtype": "float32"},
{"shape": (None, 3), "name": "node_coordinates", "dtype": "float32"},
{"shape": (None, 64), "name": "edge_attributes", "dtype": "float32"},
{"shape": (None, 2), "name": "edge_indices", "dtype": "int64"},
{"shape": (None, 2), "name": "range_indices", "dtype": "int64"},
{"shape": [None, 2], "name": "angle_indices_1", "dtype": "int64"},
{"shape": [None, 2], "name": "angle_indices_2", "dtype": "int64"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"},
{"shape": (), "name": "total_ranges", "dtype": "int64"},
{"shape": (), "name": "total_angles_1", "dtype": "int64"},
{"shape": (), "name": "total_angles_2", "dtype": "int64"}
],
"input_tensor_type": "padded",
"input_embedding": None, # deprecated
"cast_disjoint_kwargs": {},
"input_node_embedding": {
"input_dim": 95, "output_dim": 32,
"embeddings_initializer": {
"class_name": "RandomUniform",
"config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}
},
"input_edge_embedding": {"input_dim": 32, "output_dim": 32},
"bessel_basis_local": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5},
"bessel_basis_global": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, # Should match range_indices
"spherical_basis_local": {"num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5},
"mlp_rbf_kwargs": {"units": 32, "activation": "swish"},
"mlp_sbf_kwargs": {"units": 32, "activation": "swish"},
"global_mp_kwargs": {"units": 32},
"local_mp_kwargs": {"units": 32, "output_units": 1, "output_kernel_initializer": "zeros"},
"use_edge_attributes": False,
"depth": 3,
"verbose": 10,
"node_pooling_args": {"pooling_method": "sum"},
"output_embedding": "graph",
"use_output_mlp": True,
"output_mlp": {"use_bias": [True], "units": [1],
"activation": ["linear"]},
"output_tensor_type": "padded",
"output_scaling": None,
"output_to_tensor": None # deprecated
}
@update_model_kwargs(model_default, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_model(inputs: list = None,
input_tensor_type: str = None,
cast_disjoint_kwargs: dict = None,
input_embedding: dict = None,
input_node_embedding: dict = None,
input_edge_embedding: dict = None,
depth: int = None,
name: str = None,
bessel_basis_local: dict = None,
bessel_basis_global: dict = None,
spherical_basis_local: dict = None,
use_edge_attributes: bool = None,
mlp_rbf_kwargs: dict = None,
mlp_sbf_kwargs: dict = None,
global_mp_kwargs: dict = None,
local_mp_kwargs: dict = None,
verbose: int = None,
output_embedding: str = None,
use_output_mlp: bool = None,
node_pooling_args: dict = None,
output_to_tensor: bool = None,
output_mlp: dict = None,
output_scaling: dict = None,
output_tensor_type: str = None,
):
r"""Make `MXMNet <https://arxiv.org/abs/2011.07457>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.MXMNet.model_default` .
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are
:obj:`[nodes, coordinates, edge_attributes, edge_indices, range_indices, angle_indices_1, angle_indices_2, ...]`
with '...' indicating mask or ID tensors following the template below.
Note that you must supply angle indices as index pairs that refer to two edges or two range connections.
%s
**Model outputs**:
The standard output template:
%s
Args:
inputs (list): List of dictionaries unpacked in :obj:`tf.keras.layers.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.
input_embedding (dict): Deprecated in favour of input_node_embedding etc.
input_node_embedding (dict): Dictionary of embedding arguments for nodes unpacked in :obj:`Embedding` layers.
input_edge_embedding (dict): Dictionary of embedding arguments for nodes unpacked in :obj:`Embedding` layers.
depth (int): Number of graph embedding units or depth of the network.
verbose (int): Level of verbosity.
name (str): Name of the model.
bessel_basis_local: Dictionary of layer arguments unpacked in local `:obj:BesselBasisLayer` layer.
bessel_basis_global: Dictionary of layer arguments unpacked in global `:obj:BesselBasisLayer` layer.
spherical_basis_local: Dictionary of layer arguments unpacked in `:obj:SphericalBasisLayer` layer.
use_edge_attributes: Whether to add edge attributes. Default is False.
mlp_rbf_kwargs: Dictionary of layer arguments unpacked in `:obj:MLP` layer for RBF feed-forward.
mlp_sbf_kwargs: Dictionary of layer arguments unpacked in `:obj:MLP` layer for SBF feed-forward.
global_mp_kwargs: Dictionary of layer arguments unpacked in `:obj:MXMGlobalMP` layer.
local_mp_kwargs: Dictionary of layer arguments unpacked in `:obj:MXMLocalMP` layer.
node_pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes` layers.
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): Whether to cast model output to :obj:`tf.Tensor` .
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, 1, 2, 3, 4],
index_assignment=[None, None, None, 0, 0, 3, 3]
)
n, x, ed, edi, rgi, adi1, adi2 = dj[:7]
batch_id_node, batch_id_edge, batch_id_ranges, batch_id_angles_1, batch_id_angles_2 = dj[7:12]
node_id, edge_id, range_id, angle_id1, angle_id2 = dj[12:17]
count_nodes, count_edges, count_ranges, count_angles1, count_angles2 = dj[17:]
out = model_disjoint(
dj,
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
use_edge_embedding=("int" in inputs[2]['dtype']) if input_edge_embedding is not None else False,
input_node_embedding=input_node_embedding,
input_edge_embedding=input_edge_embedding,
bessel_basis_local=bessel_basis_local,
spherical_basis_local=spherical_basis_local,
bessel_basis_global=bessel_basis_global,
use_edge_attributes=use_edge_attributes,
mlp_rbf_kwargs=mlp_rbf_kwargs,
mlp_sbf_kwargs=mlp_sbf_kwargs,
depth=depth,
global_mp_kwargs=global_mp_kwargs,
local_mp_kwargs=local_mp_kwargs,
node_pooling_args=node_pooling_args,
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.__doc__ = make_model.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)