import keras as ks
from kgcnn.layers.scale import get as get_scaler
from ._model import model_disjoint, model_disjoint_crystal
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__ = "2023-12-05"
# 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 'Megnet' is not supported." % backend_to_use())
# Implementation of Megnet in `tf.keras` from paper:
# Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
# by Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong*
# https://github.com/materialsvirtuallab/megnet
# https://pubs.acs.org/doi/10.1021/acs.chemmater.9b01294
model_default = {
"name": "Megnet",
"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': [1], 'name': "charge", 'dtype': 'float32'}, # graph state
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"}
],
"input_tensor_type": "padded",
"input_embedding": None, # deprecated
"cast_disjoint_kwargs": {},
"input_node_embedding": {"input_dim": 95, "output_dim": 64},
"input_graph_embedding": {"input_dim": 100, "output_dim": 64},
"make_distance": True, "expand_distance": True,
"gauss_args": {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4},
"meg_block_args": {"node_embed": [64, 32, 32], "edge_embed": [64, 32, 32],
"env_embed": [64, 32, 32], "activation": "kgcnn>softplus2"},
"set2set_args": {"channels": 16, "T": 3, "pooling_method": "sum", "init_qstar": "0"},
"node_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"},
"edge_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"},
"state_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"},
"nblocks": 3, "has_ff": True, "dropout": None, "use_set2set": True,
"verbose": 10,
"output_embedding": "graph",
"output_mlp": {"use_bias": [True, True, True], "units": [32, 16, 1],
"activation": ["kgcnn>softplus2", "kgcnn>softplus2", "linear"]},
"output_scaling": None
}
[docs]@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, # noqa
input_node_embedding: dict = None,
input_graph_embedding: dict = None,
expand_distance: bool = None,
make_distance: bool = None,
gauss_args: dict = None,
meg_block_args: dict = None,
set2set_args: dict = None,
node_ff_args: dict = None,
edge_ff_args: dict = None,
state_ff_args: dict = None,
use_set2set: bool = None,
nblocks: int = None,
has_ff: bool = None,
dropout: float = None,
name: str = None,
verbose: int = None, # noqa
output_embedding: str = None,
output_mlp: dict = None,
output_tensor_type: str = None,
output_scaling: dict = None
):
r"""Make `MegNet <https://pubs.acs.org/doi/10.1021/acs.chemmater.9b01294>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.Megnet.model_default`.
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[nodes, coordinates, edge_indices, graph_state, ...]` with `make_distance` and
with '...' indicating mask or ID tensors following the template below.
Note that you could also supply edge features with `make_distance` to False, which would make the input
:obj:`[nodes, edges, edge_indices, graph_state...]` .
%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.
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_graph_embedding (dict): Dictionary of embedding arguments for graph unpacked in :obj:`Embedding` layers.
make_distance (bool): Whether input is distance or coordinates at in place of edges.
expand_distance (bool): If the edge input are actual edges or node coordinates instead that are expanded to
form edges with a gauss distance basis given edge indices. Expansion uses `gauss_args`.
gauss_args (dict): Dictionary of layer arguments unpacked in :obj:`GaussBasisLayer` layer.
meg_block_args (dict): Dictionary of layer arguments unpacked in :obj:`MEGnetBlock` layer.
set2set_args (dict): Dictionary of layer arguments unpacked in `:obj:PoolingSet2SetEncoder` layer.
node_ff_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` feed-forward layer.
edge_ff_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` feed-forward layer.
state_ff_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` feed-forward layer.
use_set2set (bool): Whether to use :obj:`PoolingSet2SetEncoder` layer.
nblocks (int): Number of graph embedding blocks or depth of the network.
has_ff (bool): Use feed-forward MLP in each block.
dropout (int): Dropout to use. Default is None.
name (str): Name of the model.
verbose (int): Verbosity level of print.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
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 if make_distance else 1, 1, None],
index_assignment=[None, None, 0, None]
)
n, x, disjoint_indices, gs, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = dj
out = model_disjoint(
[n, x, disjoint_indices, gs, batch_id_node, batch_id_edge, count_nodes, count_edges],
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
use_graph_embedding=("int" in inputs[3]['dtype']) if input_graph_embedding is not None else False,
input_node_embedding=input_node_embedding,
input_graph_embedding=input_graph_embedding,
expand_distance=expand_distance,
make_distance=make_distance,
gauss_args=gauss_args,
meg_block_args=meg_block_args,
set2set_args=set2set_args,
node_ff_args=node_ff_args,
edge_ff_args=edge_ff_args,
state_ff_args=state_ff_args,
use_set2set=use_set2set,
nblocks=nblocks,
has_ff=has_ff,
dropout=dropout,
output_embedding=output_embedding,
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)
model_crystal_default = {
'name': "Megnet",
'inputs': [
{'shape': (None,), 'name': "node_attributes", 'dtype': 'float32', 'ragged': True},
{'shape': (None, 3), 'name': "node_coordinates", 'dtype': 'float32', 'ragged': True},
{'shape': (None, 2), 'name': "edge_indices", 'dtype': 'int64', 'ragged': True},
{'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False},
{'shape': (None, 3), 'name': "edge_image", 'dtype': 'int64', 'ragged': True},
{'shape': (3, 3), 'name': "graph_lattice", 'dtype': 'float32', 'ragged': False}
],
"input_tensor_type": "ragged",
"input_embedding": None, # deprecated
"cast_disjoint_kwargs": {},
"input_node_embedding": {"input_dim": 95, "output_dim": 64},
"input_graph_embedding": {"input_dim": 100, "output_dim": 64},
"make_distance": True, "expand_distance": True,
'gauss_args': {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4},
'meg_block_args': {'node_embed': [64, 32, 32], 'edge_embed': [64, 32, 32],
'env_embed': [64, 32, 32], 'activation': 'kgcnn>softplus2'},
'set2set_args': {'channels': 16, 'T': 3, "pooling_method": "sum", "init_qstar": "0"},
'node_ff_args': {"units": [64, 32], "activation": "kgcnn>softplus2"},
'edge_ff_args': {"units": [64, 32], "activation": "kgcnn>softplus2"},
'state_ff_args': {"units": [64, 32], "activation": "kgcnn>softplus2"},
'nblocks': 3, 'has_ff': True, 'dropout': None, 'use_set2set': True,
'verbose': 10,
'output_embedding': 'graph',
'output_mlp': {"use_bias": [True, True, True], "units": [32, 16, 1],
"activation": ['kgcnn>softplus2', 'kgcnn>softplus2', 'linear']},
"output_scaling": None
}
[docs]@update_model_kwargs(model_crystal_default, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_crystal_model(inputs: list = None,
input_tensor_type: str = None,
cast_disjoint_kwargs: dict = None,
input_embedding: dict = None, # noqa
input_node_embedding: dict = None,
input_graph_embedding: dict = None,
expand_distance: bool = None,
make_distance: bool = None,
gauss_args: dict = None,
meg_block_args: dict = None,
set2set_args: dict = None,
node_ff_args: dict = None,
edge_ff_args: dict = None,
state_ff_args: dict = None,
use_set2set: bool = None,
nblocks: int = None,
has_ff: bool = None,
dropout: float = None,
name: str = None,
verbose: int = None, # noqa
output_embedding: str = None,
output_mlp: dict = None,
output_tensor_type: str = None,
output_scaling: dict = None
):
r"""Make `MegNet <https://pubs.acs.org/doi/10.1021/acs.chemmater.9b01294>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.Megnet.model_crystal_default`.
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[nodes, coordinates, edge_indices, graph_state, image_translation, lattice, ...]`
with '...' indicating mask or ID tensors following the template below.
%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.
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_graph_embedding (dict): Dictionary of embedding arguments for graph unpacked in :obj:`Embedding` layers.
make_distance (bool): Whether input is distance or coordinates at in place of edges.
expand_distance (bool): If the edge input are actual edges or node coordinates instead that are expanded to
form edges with a gauss distance basis given edge indices. Expansion uses `gauss_args`.
gauss_args (dict): Dictionary of layer arguments unpacked in :obj:`GaussBasisLayer` layer.
meg_block_args (dict): Dictionary of layer arguments unpacked in :obj:`MEGnetBlock` layer.
set2set_args (dict): Dictionary of layer arguments unpacked in `:obj:PoolingSet2SetEncoder` layer.
node_ff_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` feed-forward layer.
edge_ff_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` feed-forward layer.
state_ff_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` feed-forward layer.
use_set2set (bool): Whether to use :obj:`PoolingSet2SetEncoder` layer.
nblocks (int): Number of graph embedding blocks or depth of the network.
has_ff (bool): Use feed-forward MLP in each block.
dropout (int): Dropout to use. Default is None.
name (str): Name of the model.
verbose (int): Verbosity level of print.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
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 if make_distance else 1, 1, None, 1, None],
index_assignment=[None, None, 0, None, None, None]
)
n, x, djx, gs, img, lattice, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = dj
# Wrapp disjoint model
out = model_disjoint_crystal(
[n, x, djx, gs, img, lattice, batch_id_node, batch_id_edge, count_nodes, count_edges],
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
use_graph_embedding=("int" in inputs[3]['dtype']) if input_graph_embedding is not None else False,
input_node_embedding=input_node_embedding,
input_graph_embedding=input_graph_embedding,
expand_distance=expand_distance,
make_distance=make_distance,
gauss_args=gauss_args,
meg_block_args=meg_block_args,
set2set_args=set2set_args,
node_ff_args=node_ff_args,
edge_ff_args=edge_ff_args,
state_ff_args=state_ff_args,
use_set2set=use_set2set,
nblocks=nblocks,
has_ff=has_ff,
dropout=dropout,
output_embedding=output_embedding,
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_crystal_model.__doc__ = make_crystal_model.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)