from ._model import model_disjoint
from kgcnn.models.utils import update_model_kwargs
from kgcnn.layers.scale import get as get_scaler
from kgcnn.layers.modules import Input
from keras.backend import backend as backend_to_use
from kgcnn.models.casting import (template_cast_output, template_cast_list_input,
template_cast_list_input_docs, template_cast_output_docs)
from kgcnn.ops.activ import *
# Keep track of model version from commit date in literature.
# To be updated if model is changed in a significant way.
__model_version__ = "2023-12-08"
# 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 'INorp' is not supported." % backend_to_use())
# Implementation of INorp in `tf.keras` from paper:
# 'Interaction Networks for Learning about Objects, Relations and Physics'
# by Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu
# http://papers.nips.cc/paper/6417-interaction-networks-for-learning-about-objects-relations-and-physics
# https://arxiv.org/abs/1612.00222
# https://github.com/higgsfield/interaction_network_pytorch
model_default = {
'name': "INorp",
'inputs': [
{'shape': (None,), 'name': "node_number", 'dtype': 'int64'},
{'shape': (None,), 'name': "edge_number", 'dtype': 'int64'},
{'shape': (None, 2), 'name': "edge_indices", 'dtype': 'int64'},
{'shape': (64, ), 'name': "graph_attributes", 'dtype': 'float32'},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"}
],
'input_tensor_type': "padded",
'input_embedding': None,
"cast_disjoint_kwargs": {},
"input_node_embedding": {"input_dim": 95, "output_dim": 64},
"input_edge_embedding": {"input_dim": 5, "output_dim": 64},
"input_graph_embedding": {"input_dim": 100, "output_dim": 64},
"set2set_args": {
"channels": 32, "T": 3, "pooling_method": "mean",
"init_qstar": "mean"},
'node_mlp_args': {"units": [100, 50], "use_bias": True, "activation": ['relu', "linear"]},
'edge_mlp_args': {
"units": [100, 100, 100, 100, 50],
"activation": ['relu', 'relu', 'relu', 'relu', "linear"]},
'pooling_args': {'pooling_method': "mean"},
'depth': 3, 'use_set2set': False, 'verbose': 10,
'gather_args': {},
'output_embedding': 'graph',
"output_to_tensor": None, # deprecated
"output_tensor_type": "padded",
"output_scaling": None,
'output_mlp': {
"use_bias": [True, True, False], "units": [25, 10, 1],
"activation": ['relu', 'relu', 'sigmoid']}
}
[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_edge_embedding: dict = None,
input_graph_embedding: dict = None,
depth: int = None,
gather_args: dict = None,
edge_mlp_args: dict = None,
node_mlp_args: dict = None,
set2set_args: dict = None,
pooling_args: dict = None,
use_set2set: dict = 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 `INorp <https://arxiv.org/abs/1612.00222>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.INorp.model_default` .
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[nodes, edges, edge_indices, graph_state, ...]`
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:`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.
input_graph_embedding (dict): Dictionary of embedding arguments for graph unpacked in :obj:`Embedding` layers.
depth (int): Number of graph embedding units or depth of the network.
gather_args (dict): Dictionary of layer arguments unpacked in :obj:`GatherNodes` layer.
edge_mlp_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` layer for edge updates.
node_mlp_args (dict): Dictionary of layer arguments unpacked in :obj:`MLP` layer for node updates.
set2set_args (dict): Dictionary of layer arguments unpacked in :obj:`PoolingSet2SetEncoder` layer.
pooling_args (dict): Dictionary of layer arguments unpacked in :obj:`AggregateLocalEdges`, :obj:`PoolingNodes`
layer.
use_set2set (bool): Whether to use :obj:`PoolingSet2SetEncoder` layer.
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_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".
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.
Returns:
:obj:`keras.models.Model`
"""
# Make input
model_inputs = [Input(**x) for x in inputs]
di_inputs = template_cast_list_input(
model_inputs,
input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=[0, 1, 1, None],
index_assignment=[None, None, 0, None]
)
n, ed, disjoint_indices, gs, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = di_inputs
# Wrapping disjoint model.
out = model_disjoint(
[n, ed, 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_edge_embedding=("int" in inputs[1]['dtype']) if input_edge_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_edge_embedding=input_edge_embedding,
input_graph_embedding=input_graph_embedding,
gather_args=gather_args,
depth=depth,
edge_mlp_args=edge_mlp_args,
pooling_args=pooling_args,
node_mlp_args=node_mlp_args,
output_embedding=output_embedding,
use_set2set=use_set2set,
set2set_args=set2set_args,
output_mlp=output_mlp
)
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)