import keras as ks
from kgcnn.layers.modules import Input
from kgcnn.models.utils import update_model_kwargs
from keras.backend import backend as backend_to_use
from kgcnn.layers.scale import get as get_scaler
from kgcnn.models.casting import (template_cast_output, template_cast_list_input,
template_cast_list_input_docs, template_cast_output_docs)
from ._model import model_disjoint, model_disjoint_crystal
# To be updated if model is changed in a significant way.
__model_version__ = "2023-10-04"
# 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 'PAiNN' is not supported." % backend_to_use())
# Implementation of PAiNN in `keras` from paper:
# Equivariant message passing for the prediction of tensorial properties and molecular spectra
# Kristof T. Schuett, Oliver T. Unke and Michael Gastegger
# https://arxiv.org/pdf/2102.03150.pdf
model_default = {
"name": "PAiNN",
"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": (), "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": 128},
"has_equivariant_input": False,
"equiv_initialize_kwargs": {"dim": 3, "method": "zeros", "units": 128},
"bessel_basis": {"num_radial": 20, "cutoff": 5.0, "envelope_exponent": 5},
"pooling_args": {"pooling_method": "scatter_sum"},
"conv_args": {"units": 128, "cutoff": None, "conv_pool": "scatter_sum"},
"update_args": {"units": 128},
"equiv_normalization": False, "node_normalization": False,
"depth": 3,
"verbose": 10,
"output_embedding": "graph",
"output_to_tensor": None, # deprecated
"output_tensor_type": "padded",
"output_scaling": None,
"output_mlp": {"use_bias": [True, True], "units": [128, 1], "activation": ["swish", "linear"]}
}
[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,
has_equivariant_input: bool = None,
equiv_initialize_kwargs: dict = None,
bessel_basis: dict = None,
depth: int = None,
pooling_args: dict = None,
conv_args: dict = None,
update_args: dict = None,
equiv_normalization: bool = None,
node_normalization: bool = None,
name: str = None,
verbose: int = None, # noqa
output_embedding: str = None,
output_to_tensor: bool = None, # noqa
output_mlp: dict = None,
output_scaling: dict = None,
output_tensor_type: str = None
):
r"""Make `PAiNN <https://arxiv.org/pdf/2102.03150.pdf>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.PAiNN.model_default`.
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[nodes, coordinates, edge_indices, ...]`
with '...' indicating mask or ID tensors following the template below.
If equivariant input is used via `has_equivariant_input` then input is extended to
:obj:`[equiv, nodes, coordinates, edge_indices, ...]`
%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".
input_embedding (dict): Deprecated in favour of input_node_embedding etc.
cast_disjoint_kwargs (dict): Dictionary of arguments for casting layers.
input_node_embedding (dict): Dictionary of embedding arguments for nodes unpacked in :obj:`Embedding` layers.
equiv_initialize_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`EquivariantInitialize` layer.
bessel_basis (dict): Dictionary of layer arguments unpacked in final :obj:`BesselBasisLayer` layer.
depth (int): Number of graph embedding units or depth of the network.
has_equivariant_input (bool): Whether the first equivariant node embedding is passed to the model.
pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes` layer.
conv_args (dict): Dictionary of layer arguments unpacked in :obj:`PAiNNconv` layer.
update_args (dict): Dictionary of layer arguments unpacked in :obj:`PAiNNUpdate` layer.
equiv_normalization (bool): Whether to apply :obj:`GraphLayerNormalization` to equivariant tensor update.
node_normalization (bool): Whether to apply :obj:`GraphBatchNormalization` to node tensor update.
verbose (int): Level of verbosity.
name (str): Name of the model.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
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]
disjoint_inputs = template_cast_list_input(
model_inputs, input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=([0] if has_equivariant_input else []) + [0, 0, 1],
index_assignment=([None] if has_equivariant_input else []) + [None, None, 0 + int(has_equivariant_input)]
)
if not has_equivariant_input:
z, x, edi, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = disjoint_inputs
v = None
else:
v, z, x, edi, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = disjoint_inputs
# Wrapping disjoint model.
out = model_disjoint(
[z, x, edi, batch_id_node, batch_id_edge, count_nodes, count_edges, v],
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
input_node_embedding=input_node_embedding,
equiv_initialize_kwargs=equiv_initialize_kwargs,
bessel_basis=bessel_basis, depth=depth, pooling_args=pooling_args, conv_args=conv_args,
update_args=update_args, equiv_normalization=equiv_normalization, node_normalization=node_normalization,
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, z, 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": "PAiNN",
"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': "edge_image", 'dtype': 'int64'},
{'shape': (3, 3), 'name': "graph_lattice", 'dtype': 'float32'},
{"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": 128},
"has_equivariant_input": False,
"equiv_initialize_kwargs": {"dim": 3, "method": "zeros"},
"bessel_basis": {"num_radial": 20, "cutoff": 5.0, "envelope_exponent": 5},
"pooling_args": {"pooling_method": "scatter_sum"},
"conv_args": {"units": 128, "cutoff": None, "conv_pool": "scatter_sum"},
"update_args": {"units": 128},
"equiv_normalization": False,
"node_normalization": False,
"depth": 3,
"verbose": 10,
"output_embedding": "graph",
"output_to_tensor": None, # deprecated
"output_scaling": None,
"output_tensor_type": "padded",
"output_mlp": {"use_bias": [True, True], "units": [128, 1], "activation": ["swish", "linear"]}
}
[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,
input_embedding: dict = None, # noqa
cast_disjoint_kwargs: dict = None,
has_equivariant_input: bool = None,
input_node_embedding: dict = None,
equiv_initialize_kwargs: dict = None,
bessel_basis: dict = None,
depth: int = None,
pooling_args: dict = None,
conv_args: dict = None,
update_args: dict = None,
equiv_normalization: bool = None,
node_normalization: bool = None,
name: str = None,
verbose: int = None, # noqa
output_embedding: str = None,
output_to_tensor: bool = None, # noqa
output_mlp: dict = None,
output_scaling: dict = None,
output_tensor_type: str = None
):
r"""Make `PAiNN <https://arxiv.org/pdf/2102.03150.pdf>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.PAiNN.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, image_translation, lattice, ...]`
with '...' indicating mask or ID tensors following the template below.
If equivariant input is used via `has_equivariant_input` then input is extended to
:obj:`[equiv, nodes, coordinates, edge_indices, image_translation, lattice, ...]`
%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".
input_embedding (dict): Deprecated in favour of input_node_embedding etc.
cast_disjoint_kwargs (dict): Dictionary of arguments for casting layers.
input_node_embedding (dict): Dictionary of embedding arguments for nodes unpacked in :obj:`Embedding` layers.
bessel_basis (dict): Dictionary of layer arguments unpacked in final :obj:`BesselBasisLayer` layer.
equiv_initialize_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`EquivariantInitialize` layer.
depth (int): Number of graph embedding units or depth of the network.
pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes` layer.
has_equivariant_input (bool): Whether the first equivariant node embedding is passed to the model.
conv_args (dict): Dictionary of layer arguments unpacked in :obj:`PAiNNconv` layer.
update_args (dict): Dictionary of layer arguments unpacked in :obj:`PAiNNUpdate` layer.
equiv_normalization (bool): Whether to apply :obj:`GraphLayerNormalization` to equivariant tensor update.
node_normalization (bool): Whether to apply :obj:`GraphBatchNormalization` to node tensor update.
verbose (int): Level of verbosity.
name (str): Name of the model.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
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_inputs = template_cast_list_input(
model_inputs, input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=([0] if has_equivariant_input else []) + [
0, 0, 1, 1, None],
index_assignment=([0] if has_equivariant_input else []) + [
None, None, 0 + int(has_equivariant_input), None, None]
)
if not has_equivariant_input:
z, x, edi, img, lattice, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = dj_inputs
v = None
else:
v, z, x, edi, img, lattice, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = dj_inputs
# Wrapping disjoint model.
out = model_disjoint_crystal(
[z, x, edi, img, lattice, batch_id_node, batch_id_edge, count_nodes, count_edges, v],
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
input_node_embedding=input_node_embedding, equiv_initialize_kwargs=equiv_initialize_kwargs,
bessel_basis=bessel_basis, depth=depth, pooling_args=pooling_args, conv_args=conv_args,
update_args=update_args, equiv_normalization=equiv_normalization, node_normalization=node_normalization,
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, z, 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)
model.__kgcnn_model_version__ = __model_version__
return model
make_crystal_model.__doc__ = make_crystal_model.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)