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_make.py
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_make.py
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import keras as ks
from typing import Union
from kgcnn.layers.scale import get as get_scaler
from kgcnn.models.utils import update_model_kwargs
from kgcnn.models.casting import (template_cast_output, template_cast_list_input,
template_cast_list_input_docs, template_cast_output_docs)
from keras.backend import backend as backend_to_use
from kgcnn.layers.modules import Input
from ._model import model_disjoint
# Keep track of model version from commit date in literature.
# To be updated if model is changed in a significant way.
__model_version__ = "2023-10-30"
# 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 'CMPNN' is not supported." % backend_to_use())
# Implementation of CMPNN in `keras` from paper:
# Communicative Representation Learning on Attributed Molecular Graphs
# Ying Song, Shuangjia Zheng, Zhangming Niu, Zhang-Hua Fu, Yutong Lu and Yuedong Yang
# https://www.ijcai.org/proceedings/2020/0392.pdf
model_default = {
"name": "CMPNN",
"inputs": [
{"shape": (None,), "name": "node_number", "dtype": "int64"},
{"shape": (None,), "name": "edge_number", "dtype": "int64"},
{"shape": (None, 2), "name": "edge_indices", "dtype": "int64"},
{"shape": (None, 1), "name": "edge_indices_reverse", "dtype": "int64"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"},
{"shape": (), "name": "total_reverse", "dtype": "int64"}
],
"input_tensor_type": "padded",
"cast_disjoint_kwargs": {},
'input_embedding': None, # deprecated
"input_node_embedding": {"input_dim": 95, "output_dim": 64},
"input_edge_embedding": {"input_dim": 20, "output_dim": 64},
"node_initialize": {"units": 300, "activation": "relu"},
"edge_initialize": {"units": 300, "activation": "relu"},
"edge_dense": {"units": 300, "activation": "linear"},
"node_dense": {"units": 300, "activation": "linear"},
"edge_activation": {"activation": "relu"},
"verbose": 10,
"depth": 5,
"dropout": {"rate": 0.1},
"use_final_gru": True,
"pooling_gru": {"units": 300},
"pooling_kwargs": {"pooling_method": "sum"},
"output_embedding": "graph",
"output_scaling": None,
"output_tensor_type": "padded",
"output_to_tensor": None, # deprecated
"output_mlp": {"use_bias": [True, True, False], "units": [300, 100, 1],
"activation": ["relu", "relu", "linear"]}
}
@update_model_kwargs(model_default, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_model(name: str = None,
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,
edge_initialize: dict = None,
node_initialize: dict = None,
edge_dense: dict = None,
node_dense: dict = None,
edge_activation: dict = None,
depth: int = None,
dropout: Union[dict, None] = None,
verbose: int = None,
use_final_gru: bool = True,
pooling_gru: dict = None,
pooling_kwargs: dict = None,
output_embedding: str = None,
output_to_tensor: bool = None,
output_tensor_type: str = None,
output_mlp: dict = None,
output_scaling: dict = None
):
r"""Make `CMPNN <https://www.ijcai.org/proceedings/2020/0392.pdf>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.CMPNN.model_default` .
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[nodes, edges, edge_indices, reverse_indices, ...]`
with '...' indicating mask or id tensors following the template below.
Here, reverse indices are in place of angle indices and refer to edges.
%s
**Model outputs**:
The standard output template:
%s
Args:
name (str): Name of the model. Should be "CMPNN".
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 layers if used.
input_embedding (dict): Deprecated in favour of input_node_embedding etc.
input_node_embedding (dict): Dictionary of arguments for nodes unpacked in :obj:`Embedding` layers.
input_edge_embedding (dict): Dictionary of arguments for edge unpacked in :obj:`Embedding` layers.
edge_initialize (dict): Dictionary of layer arguments unpacked in :obj:`Dense` layer for first edge embedding.
node_initialize (dict): Dictionary of layer arguments unpacked in :obj:`Dense` layer for first node embedding.
edge_dense (dict): Dictionary of layer arguments unpacked in :obj:`Dense` layer for edge communicate.
node_dense (dict): Dictionary of layer arguments unpacked in :obj:`Dense` layer for node communicate.
edge_activation (dict): Dictionary of layer arguments unpacked in :obj:`Activation` layer for edge communicate.
depth (int): Number of graph embedding units or depth of the network.
verbose (int): Level for print information.
dropout (dict): Dictionary of layer arguments unpacked in :obj:`Dropout`.
pooling_kwargs (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodes`,
:obj:`AggregateLocalEdges` layers.
use_final_gru (bool): Whether to use GRU for final readout.
pooling_gru (dict): Dictionary of layer arguments unpacked in :obj:`PoolingNodesGRU`.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
output_to_tensor (bool): WDeprecated in favour of `output_tensor_type` .
output_tensor_type (str): Output type of graph tensors such as nodes or edges. Default is "padded".
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): Kwargs for scaling layer, if scaling layer is to be used.
Returns:
:obj:`keras.models.Model`
"""
# Make input
model_inputs = [Input(**x) for x in inputs]
di = template_cast_list_input(
model_inputs,
input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=[0, 1, 1, 2],
index_assignment=[None, None, 0, 2]
)
n, ed, edi, e_pairs, batch_id_node, batch_id_edge, _, node_id, edge_id, _, count_nodes, count_edges, _ = di
# Wrapping disjoint model.
out = model_disjoint(
[n, ed, edi, e_pairs, batch_id_node, node_id, count_nodes],
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
use_edge_embedding=("int" in inputs[1]['dtype']) if input_edge_embedding is not None else False,
input_node_embedding=input_node_embedding,
input_edge_embedding=input_edge_embedding,
node_initialize=node_initialize,
edge_initialize=edge_initialize,
depth=depth,
pooling_kwargs=pooling_kwargs,
edge_dense=edge_dense,
edge_activation=edge_activation,
dropout=dropout,
node_dense=node_dense,
output_embedding=output_embedding,
use_final_gru=use_final_gru,
output_mlp=output_mlp,
pooling_gru=pooling_gru
)
if output_scaling is not None:
scaler = get_scaler(output_scaling["name"])(**output_scaling)
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)