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_schnet_conv.py
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_schnet_conv.py
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import tensorflow as tf
from kgcnn.layers.base import GraphBaseLayer
from kgcnn.layers.modules import LazyMultiply, Dense, LazyAdd
from kgcnn.layers.aggr import AggregateLocalEdges
from kgcnn.layers.gather import GatherNodesOutgoing
@tf.keras.utils.register_keras_serializable(package='kgcnn', name='SchNetCFconv')
class SchNetCFconv(GraphBaseLayer):
r"""Continuous filter convolution of `SchNet <https://aip.scitation.org/doi/pdf/10.1063/1.5019779>`__ .
Edges are processed by 2 :obj:`Dense` layers, multiplied on outgoing node features and pooled for receiving node.
Args:
units (int): Units for Dense layer.
cfconv_pool (str): Pooling method. Default is 'segment_sum'.
use_bias (bool): Use bias. Default is True.
activation (str): Activation function. Default is 'kgcnn>shifted_softplus'.
kernel_regularizer: Kernel regularization. Default is None.
bias_regularizer: Bias regularization. Default is None.
activity_regularizer: Activity regularization. Default is None.
kernel_constraint: Kernel constrains. Default is None.
bias_constraint: Bias constrains. Default is None.
kernel_initializer: Initializer for kernels. Default is 'glorot_uniform'.
bias_initializer: Initializer for bias. Default is 'zeros'.
"""
def __init__(self, units,
cfconv_pool='segment_sum',
use_bias=True,
activation='kgcnn>shifted_softplus',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
**kwargs):
"""Initialize Layer."""
super(SchNetCFconv, self).__init__(**kwargs)
self.cfconv_pool = cfconv_pool
self.units = units
self.use_bias = use_bias
kernel_args = {"kernel_regularizer": kernel_regularizer, "activity_regularizer": activity_regularizer,
"bias_regularizer": bias_regularizer, "kernel_constraint": kernel_constraint,
"bias_constraint": bias_constraint, "kernel_initializer": kernel_initializer,
"bias_initializer": bias_initializer}
# Layer
self.lay_dense1 = Dense(units=self.units, activation=activation, use_bias=self.use_bias, **kernel_args)
self.lay_dense2 = Dense(units=self.units, activation='linear', use_bias=self.use_bias, **kernel_args)
self.lay_sum = AggregateLocalEdges(pooling_method=cfconv_pool)
self.gather_n = GatherNodesOutgoing()
self.lay_mult = LazyMultiply()
def build(self, input_shape):
"""Build layer."""
super(SchNetCFconv, self).build(input_shape)
def call(self, inputs, **kwargs):
"""Forward pass. Calculate edge update.
Args:
inputs: [nodes, edges, edge_index]
- nodes (tf.RaggedTensor): Node embeddings of shape (batch, [N], F)
- edges (tf.RaggedTensor): Edge or message embeddings of shape (batch, [N], F)
- edge_index (tf.RaggedTensor): Edge indices referring to nodes of shape (batch, [N], 2)
Returns:
tf.RaggedTensor: Updated node features.
"""
node, edge, indexlist = inputs
x = self.lay_dense1(edge, **kwargs)
x = self.lay_dense2(x, **kwargs)
node2exp = self.gather_n([node, indexlist], **kwargs) # message
x = self.lay_mult([node2exp, x], **kwargs)# message
x = self.lay_sum([node, x, indexlist], **kwargs)
return x
def get_config(self):
"""Update layer config."""
config = super(SchNetCFconv, self).get_config()
config.update({"cfconv_pool": self.cfconv_pool, "units": self.units})
config_dense = self.lay_dense1.get_config()
for x in ["kernel_regularizer", "activity_regularizer", "bias_regularizer", "kernel_constraint",
"bias_constraint", "kernel_initializer", "bias_initializer", "activation", "use_bias"]:
config.update({x: config_dense[x]})
return config
@tf.keras.utils.register_keras_serializable(package='kgcnn', name='SchNetInteraction')
class SchNetInteraction(GraphBaseLayer):
r"""`SchNet <https://aip.scitation.org/doi/pdf/10.1063/1.5019779>`_ interaction block,
which uses the continuous filter convolution from :obj:`SchNetCFconv`.
Args:
units (int): Dimension of node embedding. Default is 128.
cfconv_pool (str): Pooling method information for SchNetCFconv layer. Default is'segment_sum'.
use_bias (bool): Use bias in last layers. Default is True.
activation (str): Activation function. Default is 'kgcnn>shifted_softplus'.
kernel_regularizer: Kernel regularization. Default is None.
bias_regularizer: Bias regularization. Default is None.
activity_regularizer: Activity regularization. Default is None.
kernel_constraint: Kernel constrains. Default is None.
bias_constraint: Bias constrains. Default is None.
kernel_initializer: Initializer for kernels. Default is 'glorot_uniform'.
bias_initializer: Initializer for bias. Default is 'zeros'.
"""
def __init__(self,
units=128,
cfconv_pool='sum',
use_bias=True,
activation='kgcnn>shifted_softplus',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
**kwargs):
"""Initialize Layer."""
super(SchNetInteraction, self).__init__(**kwargs)
self.cfconv_pool = cfconv_pool
self.use_bias = use_bias
self.units = units
kernel_args = {"kernel_regularizer": kernel_regularizer, "activity_regularizer": activity_regularizer,
"bias_regularizer": bias_regularizer, "kernel_constraint": kernel_constraint,
"bias_constraint": bias_constraint, "kernel_initializer": kernel_initializer,
"bias_initializer": bias_initializer}
conv_args = {"units": self.units, "use_bias": use_bias, "activation": activation, "cfconv_pool": cfconv_pool}
# Layers
self.lay_cfconv = SchNetCFconv(**conv_args, **kernel_args)
self.lay_dense1 = Dense(units=self.units, activation='linear', use_bias=False, **kernel_args)
self.lay_dense2 = Dense(units=self.units, activation=activation, use_bias=self.use_bias, **kernel_args)
self.lay_dense3 = Dense(units=self.units, activation='linear', use_bias=self.use_bias, **kernel_args)
self.lay_add = LazyAdd()
def build(self, input_shape):
"""Build layer."""
super(SchNetInteraction, self).build(input_shape)
def call(self, inputs, **kwargs):
"""Forward pass. Calculate node update.
Args:
inputs: [nodes, edges, tensor_index]
- nodes (tf.RaggedTensor): Node embeddings of shape (batch, [N], F)
- edges (tf.RaggedTensor): Edge or message embeddings of shape (batch, [N], F)
- tensor_index (tf.RaggedTensor): Edge indices referring to nodes of shape (batch, [N], 2)
Returns:
tf.RaggedTensor: Updated node embeddings of shape (batch, [N], F).
"""
node, edge, indexlist = inputs
x = self.lay_dense1(node, **kwargs)
x = self.lay_cfconv([x, edge, indexlist], **kwargs)
x = self.lay_dense2(x, **kwargs)
x = self.lay_dense3(x, **kwargs)
out = self.lay_add([node, x], **kwargs)
return out
def get_config(self):
config = super(SchNetInteraction, self).get_config()
config.update({"cfconv_pool": self.cfconv_pool, "units": self.units, "use_bias": self.use_bias})
conf_dense = self.lay_dense2.get_config()
for x in ["activation", "kernel_regularizer", "bias_regularizer", "activity_regularizer",
"kernel_constraint", "bias_constraint", "kernel_initializer", "bias_initializer"]:
config.update({x: conf_dense[x]})
return config