def _kgcnn_map_output_ragged(self, inputs, input_partition_type, output_tensor_type=None):
x = inputs
ragged_keys = ["ragged", "RaggedTensor"]
value_partition_keys = ["disjoint", "values_partition"]
if output_tensor_type is None:
output_tensor_type = self.output_tensor_type
elif isinstance(output_tensor_type, int):
output_tensor_type = self._tensor_input_type_found[output_tensor_type]
if isinstance(x, tf.RaggedTensor):
if output_tensor_type in ragged_keys:
return x
elif output_tensor_type in value_partition_keys:
return kgcnn_ops_dyn_cast(x, input_tensor_type="ragged",
output_tensor_type=output_tensor_type, partition_type=self.partition_type)
else:
raise TypeError("Error:", self.name, "output type for ragged-like input is not supported for", x)
elif isinstance(x, list):
if len(x) != 2:
print("Warning:", self.name, "output does not match rank=1 partition scheme for batch dimension.")
if output_tensor_type in ragged_keys:
return kgcnn_ops_dyn_cast(x, input_tensor_type="values_partition",
output_tensor_type=output_tensor_type, partition_type=input_partition_type)
elif output_tensor_type in value_partition_keys:
tens_part = kgcnn_ops_change_partition_type(x[1], input_partition_type, self.partition_type)
return [x[0], tens_part]
else:
raise TypeError("Error:", self.name, "output type for ragged-like input is not supported for", x)
else:
raise TypeError("Error:", self.name, "input type for ragged-like input is not supported for", x)
def call(self, inputs, **kwargs):
"""Forward pass.
Args:
inputs: [nodes, adjacency]
- nodes: Node features of shape (batch, [N], F)
- adjacency (tf.sparse): SparseTensor of the adjacency matrix of shape (batch*None,batch*None)
Returns:
features (tf.tensor): Pooled node features of shape (batch,F)
"""
adj = inputs[1]
found_node_type = kgcnn_ops_get_tensor_type(
inputs[0],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
node, node_part = kgcnn_ops_dyn_cast(
inputs[0],
input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
out = tf.sparse.sparse_dense_matmul(adj, node)
return kgcnn_ops_dyn_cast([out, node_part],
input_tensor_type="values_partition",
output_tensor_type=found_node_type,
partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs (list): of [node, edges, edge_index]
- nodes: Node features of shape (batch, [N], F)
- edges: Edge or message features of shape (batch, [N], F)
- edge_index: Edge indices of shape (batch, [N], 2)
Returns:
features: Pooled feature tensor of pooled edge features for each node.
"""
found_node_type = kgcnn_ops_get_tensor_type(inputs[0], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(inputs[1], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_index_type = kgcnn_ops_get_tensor_type(inputs[2], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
nod, node_part = kgcnn_ops_dyn_cast(inputs[0], input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge, _ = kgcnn_ops_dyn_cast(inputs[1], input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgeind, edge_part = kgcnn_ops_dyn_cast(inputs[2], input_tensor_type=found_index_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
shiftind = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(edgeind, node_part, edge_part,
partition_type_node=self.partition_type,
partition_type_edge=self.partition_type,
to_indexing='batch',
from_indexing=self.node_indexing)
nodind = shiftind[:, 0] # Pick first index eg. ingoing
dens = edge
if not self.is_sorted:
# Sort edgeindices
node_order = tf.argsort(nodind, axis=0, direction='ASCENDING', stable=True)
nodind = tf.gather(nodind, node_order, axis=0)
dens = tf.gather(dens, node_order, axis=0)
# Pooling via e.g. segment_sum
out = kgcnn_ops_segment_operation_by_name(self.pooling_method, dens, nodind)
if self.has_unconnected:
out = kgcnn_ops_scatter_segment_tensor_nd(out, nodind, tf.shape(nod))
return kgcnn_ops_dyn_cast([out, node_part], input_tensor_type="values_partition",
output_tensor_type=found_node_type, partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs (list): [nodes, edge_index]
- nodes: Node embeddings of shape (batch, [N], F)
- edge_index: Edge indices of shape (batch, [N], 2)
Returns:
embeddings: Gathered node embeddings that match the number of edges.
"""
found_node_type = kgcnn_ops_get_tensor_type(
inputs[0],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(
inputs[1],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
# We cast to values here
node, node_part = kgcnn_ops_dyn_cast(
inputs[0],
input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge_index, edge_part = kgcnn_ops_dyn_cast(
inputs[1],
input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
indexlist = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
edge_index,
node_part,
edge_part,
partition_type_node=self.partition_type,
partition_type_edge=self.partition_type,
to_indexing='batch',
from_indexing=self.node_indexing)
out = tf.gather(node, indexlist[:, 0], axis=0)
# For ragged tensor we can now also try:
# out = tf.gather(nod, edge_index[:, :, 0], batch_dims=1)
return kgcnn_ops_dyn_cast([out, edge_part],
input_tensor_type="values_partition",
output_tensor_type=found_edge_type,
partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs: [state, target]
- state: Graph specific embedding tensor. This is usually tensor of shape (batch, F.)
- target: Target to collect state for. This can be node or edge embeddings of shape (batch, [N], F)
Returns:
state: Graph embedding with repeated single state for each graph.
"""
found_state_type = kgcnn_ops_get_tensor_type(
inputs[0],
input_tensor_type="tensor",
node_indexing=self.node_indexing)
found_ref_type = kgcnn_ops_get_tensor_type(
inputs[1],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
# We cast to values here
env = kgcnn_ops_dyn_cast(inputs[0],
input_tensor_type=found_state_type,
output_tensor_type="tensor",
partition_type=self.partition_type)
_, target_part = kgcnn_ops_dyn_cast(
inputs[1],
input_tensor_type=found_ref_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
target_len = kgcnn_ops_change_partition_type(target_part,
self.partition_type,
"row_length")
out = tf.repeat(env, target_len, axis=0)
return kgcnn_ops_dyn_cast([out, target_part],
input_tensor_type="values_partition",
output_tensor_type=found_ref_type,
partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs: Edge features or message embeddings of shape (batch, [N], F)
Returns:
tf.tensor: Pooled edges feature list of shape (batch,F).
"""
found_edge_type = kgcnn_ops_get_tensor_type(inputs, input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
edge, edge_part = kgcnn_ops_dyn_cast(inputs, input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
batchi = kgcnn_ops_change_partition_type(edge_part, self.partition_type, "value_rowids")
out = kgcnn_ops_segment_operation_by_name(self.pooling_method, edge, batchi)
# Output already has correct shape and type
return out
def call(self, inputs, **kwargs):
"""Forward pass.
This can be either a tuple of (values, partition) tensors of shape (batch*None,F)
and a partition tensor of the type "row_length", "row_splits" or "value_rowids". This usually uses
disjoint indexing defined by 'node_indexing'. Or a tuple of (values, mask) tensors of shape
(batch, N, F) and mask (batch, N) or a single RaggedTensor of shape (batch,None,F)
or a singe tensor for equally sized graphs (batch,N,F).
Args:
inputs: Embeddings to be encoded of shape (batch, [N], F)
Returns:
q_star (tf.tensor): Pooled tensor of shape (batch,1,2*channels)
"""
found_node_type = kgcnn_ops_get_tensor_type(
inputs,
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
x, batch_part = kgcnn_ops_dyn_cast(
inputs,
input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
batch_num = kgcnn_ops_change_partition_type(batch_part,
self.partition_type,
"row_length")
batch_index = kgcnn_ops_change_partition_type(batch_part,
self.partition_type,
"value_rowids")
# Reading to memory removed here, is to be done by seperately
m = x # (batch*None,feat)
# Initialize q0 and r0
qstar = self.qstar0(m, batch_index, batch_num)
# start loop
for i in range(0, self.T):
q = self.lay_lstm(qstar) # (batch,feat)
qt = tf.repeat(q, batch_num, axis=0) # (batch*num,feat)
et = self.f_et(m, qt) # (batch*num,)
# get at = exp(et)/sum(et) with sum(et)
at = ksb.exp(et - self.get_scale_per_sample(
et, batch_index, batch_num)) # (batch*num,)
norm = self.get_norm(at, batch_index, batch_num) # (batch*num,)
at = norm * at # (batch*num,) x (batch*num,)
# calculate rt
at = ksb.expand_dims(at, axis=1)
rt = m * at # (batch*num,feat) x (batch*num,1)
rt = tf.math.segment_sum(rt, batch_index) # (batch,feat)
# qstar = [q,r]
qstar = ksb.concatenate([q, rt], axis=1) # (batch,2*feat)
qstar = ksb.expand_dims(qstar, axis=1) # (batch,1,2*feat)
return qstar
def call(self, inputs, **kwargs):
"""Forward pass.
Args:
inputs: Graph tensor-information.
Returns:
outputs: Changed tensor-information.
"""
return kgcnn_ops_dyn_cast(inputs,
input_tensor_type=self.input_tensor_type,
output_tensor_type=self.output_tensor_type,
partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge,
i.e. (batch, None, 2)
Args:
inputs (list): of [trafo, edges]
- trafo: Transformation by matrix multiplication for each message. Must be reshaped to (batch, [N], FxF).
- edges: Edge embeddings or messages (batch, [N], F)
Returns:
node_updates: Transformation of messages by matrix multiplication of shape (batch, [N], F)
"""
found_trafo_type = kgcnn_ops_get_tensor_type(inputs[0], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(inputs[1], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
dens_trafo, trafo_part = kgcnn_ops_dyn_cast(inputs[0], input_tensor_type=found_trafo_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
dens_e, epart = kgcnn_ops_dyn_cast(inputs[1], input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
dens_m = tf.reshape(dens_trafo,
(ks.backend.shape(dens_trafo)[0], self.target_shape, ks.backend.shape(dens_e)[-1]))
out = tf.keras.backend.batch_dot(dens_m, dens_e)
return kgcnn_ops_dyn_cast([out, epart], input_tensor_type="values_partition",
output_tensor_type=found_edge_type, partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs (list): of [node, weights]
- nodes: Node features of shape (batch, [N], F)
- weights: Edge or message weights. Most broadcast to nodes.
Returns:
nodes (tf.tensor): Pooled node features of shape (batch,F)
"""
found_node_type = kgcnn_ops_get_tensor_type(inputs[0], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_weight_type = kgcnn_ops_get_tensor_type(inputs[1], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
nod, node_part = kgcnn_ops_dyn_cast(inputs[0], input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
weights, _ = kgcnn_ops_dyn_cast(inputs[1], input_tensor_type=found_weight_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
batchi = kgcnn_ops_change_partition_type(node_part, self.partition_type, "value_rowids")
nod = tf.math.multiply(nod, weights)
out = kgcnn_ops_segment_operation_by_name(self.pooling_method, nod, batchi)
# Output should have correct shape
return out
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge,
i.e. (batch, None, 2)
Args:
inputs (list): of [nodes, updates]
- nodes (tf.tensor): Node embeddings of shape (batch, [N], F)
- updates (tf.tensor): Matching node updates of shape (batch, [N], F
Returns:
updated_nodes (tf.tensor): Updated nodes of shape (batch*None,F)
"""
found_node_type = kgcnn_ops_get_tensor_type(inputs[0], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_updates_type = kgcnn_ops_get_tensor_type(inputs[1], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
n, npart = kgcnn_ops_dyn_cast(inputs[0], input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
eu, _ = kgcnn_ops_dyn_cast(inputs[1], input_tensor_type=found_updates_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
out, _ = self.gru_cell(eu, [n], **kwargs)
return kgcnn_ops_dyn_cast([out, npart], input_tensor_type="values_partition",
output_tensor_type=found_node_type, partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
Inputs list of [nodes, edges, edge_index]
- nodes: Node feature tensor of shape (batch, [N], F)
- edges: Edge feature ragged tensor of shape (batch, [N], 1)
- edge_index: Ragged edge_indices of shape (batch, [N], 2)
Returns:
tf.sparse: Sparse disjoint matrix of shape (batch*None,batch*None)
"""
found_node_type = kgcnn_ops_get_tensor_type(
inputs[0],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(
inputs[1],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_index_type = kgcnn_ops_get_tensor_type(
inputs[2],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
nod, node_part = kgcnn_ops_dyn_cast(
inputs[0],
input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge, _ = kgcnn_ops_dyn_cast(inputs[1],
input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge_index, edge_part = kgcnn_ops_dyn_cast(
inputs[2],
input_tensor_type=found_index_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
# Cast to length tensor
node_len = kgcnn_ops_change_partition_type(node_part,
self.partition_type,
"row_length")
edge_len = kgcnn_ops_change_partition_type(edge_part,
self.partition_type,
"row_length")
# batch-wise indexing
edge_index = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
edge_index,
node_len,
edge_len,
partition_type_node="row_length",
partition_type_edge="row_length",
from_indexing=self.node_indexing,
to_indexing="batch")
indexlist = edge_index
valuelist = edge
if not self.is_sorted:
# Sort per outgoing
batch_order = tf.argsort(indexlist[:, 1],
axis=0,
direction='ASCENDING')
indexlist = tf.gather(indexlist, batch_order, axis=0)
valuelist = tf.gather(valuelist, batch_order, axis=0)
# Sort per ingoing node
node_order = tf.argsort(indexlist[:, 0],
axis=0,
direction='ASCENDING',
stable=True)
indexlist = tf.gather(indexlist, node_order, axis=0)
valuelist = tf.gather(valuelist, node_order, axis=0)
indexlist = tf.cast(indexlist, dtype=tf.int64)
dense_shape = tf.concat(
[tf.shape(nod)[0:1], tf.shape(nod)[0:1]], axis=0)
dense_shape = tf.cast(dense_shape, dtype=tf.int64)
out = tf.sparse.SparseTensor(indexlist, valuelist[:, 0], dense_shape)
return out
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs: [node, edges, attention, edge_indices]
- nodes: Node features of shape (batch, [N], F)
- edges: Edge or message features of shape (batch, [N], F)
- attention: Attention coefficients of shape (batch, [N], 1)
- edge_index: Edge indices of shape (batch, [N], F)
Returns:
embeddings: Feature tensor of pooled edge attentions for each node.
"""
found_node_type = kgcnn_ops_get_tensor_type(
inputs[0],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(
inputs[1],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_att_type = kgcnn_ops_get_tensor_type(
inputs[2],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_index_type = kgcnn_ops_get_tensor_type(
inputs[3],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
# We cast to values here
nod, node_part = kgcnn_ops_dyn_cast(
inputs[0],
input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge, _ = kgcnn_ops_dyn_cast(inputs[1],
input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
attention, _ = kgcnn_ops_dyn_cast(
inputs[2],
input_tensor_type=found_att_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgeind, edge_part = kgcnn_ops_dyn_cast(
inputs[3],
input_tensor_type=found_index_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
shiftind = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
edgeind,
node_part,
edge_part,
partition_type_node=self.partition_type,
partition_type_edge=self.partition_type,
to_indexing='batch',
from_indexing=self.node_indexing)
nodind = shiftind[:, 0] # Pick first index eg. ingoing
dens = edge
ats = attention
if not self.is_sorted:
# Sort edgeindices
node_order = tf.argsort(nodind,
axis=0,
direction='ASCENDING',
stable=True)
nodind = tf.gather(nodind, node_order, axis=0)
dens = tf.gather(dens, node_order, axis=0)
ats = tf.gather(ats, node_order, axis=0)
# Apply segmented softmax
ats = segment_softmax(ats, nodind)
get = dens * ats
get = tf.math.segment_sum(get, nodind)
if self.has_unconnected:
# Need to fill tensor since the maximum node may not be also in pooled
# Does not happen if all nodes are also connected
get = kgcnn_ops_scatter_segment_tensor_nd(get, nodind,
tf.shape(nod))
return kgcnn_ops_dyn_cast([get, node_part],
input_tensor_type="values_partition",
output_tensor_type=found_node_type,
partition_type=self.partition_type)
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs (list): of [nodes, node_partition, edges, edge_partition, edge_indices]
- nodes: Node embeddings of shape (batch, [N], F)
- edges: Edge embeddings of shape (batch, [N], F)
- edge_indices: Edge index list of shape of shape (batch, [N], 2)
Returns:
Tuple: [nodes, edges, edge_indices], [map_nodes, map_edges]
- nodes: Pooled node feature tensor
- edges: Pooled edge feature list
- edge_indices (tf.tensor): Pooled edge index list
- map_nodes (tf.tensor): Index map between original and pooled nodes
- map_edges (tf.tensor): Index map between original and pooled edges
"""
found_node_type = kgcnn_ops_get_tensor_type(
inputs[0],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(
inputs[1],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_index_type = kgcnn_ops_get_tensor_type(
inputs[2],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
node, node_part = kgcnn_ops_dyn_cast(
inputs[0],
input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgefeat, edge_part = kgcnn_ops_dyn_cast(
inputs[1],
input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgeindref, _ = kgcnn_ops_dyn_cast(
inputs[2],
input_tensor_type=found_index_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgelen = kgcnn_ops_change_partition_type(edge_part,
self.partition_type,
"row_length")
nodelen = kgcnn_ops_change_partition_type(node_part,
self.partition_type,
"row_length")
index_dtype = edgeindref.dtype
# Get node properties
nvalue = node
nrowlength = tf.cast(nodelen, dtype=index_dtype)
erowlength = tf.cast(edgelen, dtype=index_dtype)
nids = tf.repeat(tf.range(tf.shape(nrowlength)[0], dtype=index_dtype),
nrowlength)
# Use kernel p to get score
norm_p = ks.backend.sqrt(
ks.backend.sum(ks.backend.square(self.kernel_p),
axis=-1,
keepdims=True))
nscore = ks.backend.sum(nvalue * self.kernel_p / norm_p, axis=-1)
# Sort nodes according to score
# Then sort after former node ids -> stable = True keeps previous order
sort1 = tf.argsort(nscore, direction='ASCENDING', stable=False)
nids_sorted1 = tf.gather(nids, sort1)
sort2 = tf.argsort(nids_sorted1, direction='ASCENDING',
stable=True) # Must be stable=true here
sort12 = tf.gather(
sort1, sort2) # index goes from 0 to batch*N, no in batch indexing
nvalue_sorted = tf.gather(nvalue, sort12, axis=0)
nscore_sorted = tf.gather(nscore, sort12, axis=0)
# Make Mask
nremove = tf.cast(tf.math.round(
self.k * tf.cast(nrowlength, dtype=tf.keras.backend.floatx())),
dtype=index_dtype)
nkeep = nrowlength - nremove
n_remove_keep = ks.backend.flatten(
tf.concat([
ks.backend.expand_dims(nremove, axis=-1),
ks.backend.expand_dims(nkeep, axis=-1)
],
axis=-1))
mask_remove_keep = ks.backend.flatten(
tf.concat([
ks.backend.expand_dims(tf.zeros_like(nremove, dtype=tf.bool),
axis=-1),
ks.backend.expand_dims(tf.ones_like(nkeep, tf.bool), axis=-1)
],
axis=-1))
mask = tf.repeat(mask_remove_keep, n_remove_keep)
# Apply Mask to remove lower score nodes
pooled_n = nvalue_sorted[mask]
pooled_score = nscore_sorted[mask]
pooled_id = nids[mask] # nids should not have changed by final sorting
pooled_len = nkeep # shape=(batch,)
pooled_index = tf.cast(
sort12[mask],
dtype=index_dtype) # the index goes from 0 to N*batch
removed_index = tf.cast(
sort12[tf.math.logical_not(mask)],
dtype=index_dtype) # the index goes from 0 to N*batch
# Pass through gate
gated_n = pooled_n * ks.backend.expand_dims(
tf.keras.activations.sigmoid(pooled_score), axis=-1)
# Make index map for new nodes towards old index
index_new_nodes = tf.range(tf.shape(pooled_index)[0],
dtype=index_dtype)
old_shape = tf.cast(ks.backend.expand_dims(tf.shape(nvalue)[0]),
dtype=index_dtype)
map_index = tf.scatter_nd(
ks.backend.expand_dims(pooled_index, axis=-1), index_new_nodes,
old_shape)
# Shift index if necessary
edge_ids = tf.repeat(tf.range(tf.shape(edgelen)[0], dtype=index_dtype),
edgelen)
shiftind = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
edgeindref,
nrowlength,
edge_ids,
partition_type_node="row_length",
partition_type_edge="value_rowids",
from_indexing=self.node_indexing,
to_indexing="batch")
shiftind = tf.cast(
shiftind,
dtype=index_dtype) # already shifted by batch offset (subgraphs)
# Remove edges that were from filtered nodes via mask
mask_edge = ks.backend.expand_dims(
shiftind, axis=-1) == ks.backend.expand_dims(
ks.backend.expand_dims(removed_index, axis=0),
axis=0) # this creates large tensor (batch*#edges,2,remove)
mask_edge = tf.math.logical_not(
ks.backend.any(ks.backend.any(mask_edge, axis=-1), axis=-1))
clean_shiftind = shiftind[mask_edge]
clean_edge_ids = edge_ids[mask_edge]
# clean_edge_len = tf.math.segment_sum(tf.ones_like(clean_edge_ids), clean_edge_ids)
clean_edge_len = tf.scatter_nd(
tf.expand_dims(clean_edge_ids, axis=-1),
tf.ones_like(clean_edge_ids),
tf.cast(tf.shape(erowlength), dtype=index_dtype))
# Map edgeindex to new index
new_edge_index = tf.concat([
ks.backend.expand_dims(tf.gather(map_index, clean_shiftind[:, 0]),
axis=-1),
ks.backend.expand_dims(tf.gather(map_index, clean_shiftind[:, 1]),
axis=-1)
],
axis=-1)
batch_order = tf.argsort(new_edge_index[:, 0],
axis=0,
direction='ASCENDING',
stable=True)
new_edge_index_sorted = tf.gather(new_edge_index, batch_order, axis=0)
# Remove the batch offset from edge_indices again for indexing type
out_indexlist = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
new_edge_index_sorted,
pooled_len,
clean_edge_ids,
partition_type_node="row_length",
partition_type_edge="value_rowids",
from_indexing="batch",
to_indexing=self.node_indexing)
# Correct edge features the same way (remove and reorder)
edge_feat = edgefeat
clean_edge_feat = edge_feat[mask_edge]
clean_edge_feat_sorted = tf.gather(clean_edge_feat,
batch_order,
axis=0)
# Make edge feature map for new edge features
edge_position_old = tf.range(tf.shape(edgefeat)[0], dtype=index_dtype)
edge_position_new = edge_position_old[mask_edge]
edge_position_new = tf.gather(edge_position_new, batch_order, axis=0)
# Collect output tensors
out_node = gated_n
out_edge = clean_edge_feat_sorted
out_edge_index = out_indexlist
# Change length to partition required
out_np = kgcnn_ops_change_partition_type(pooled_len, "row_length",
self.partition_type)
out_ep = kgcnn_ops_change_partition_type(clean_edge_len, "row_length",
self.partition_type)
# Collect reverse pooling info
# Remove batch offset for old indicies -> but with new length
out_pool = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
pooled_index,
nrowlength,
pooled_len,
partition_type_node="row_length",
partition_type_edge="row_length",
from_indexing="batch",
to_indexing=self.node_indexing,
axis=0)
out_pool_edge = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
edge_position_new,
erowlength,
clean_edge_ids,
partition_type_node="row_length",
partition_type_edge="value_rowids",
from_indexing="batch",
to_indexing=self.node_indexing,
axis=0)
out = [
kgcnn_ops_dyn_cast([out_node, out_np],
input_tensor_type="values_partition",
output_tensor_type=found_node_type,
partition_type=self.partition_type),
kgcnn_ops_dyn_cast([out_edge, out_ep],
input_tensor_type="values_partition",
output_tensor_type=found_edge_type,
partition_type=self.partition_type),
kgcnn_ops_dyn_cast([out_edge_index, out_ep],
input_tensor_type="values_partition",
output_tensor_type=found_index_type,
partition_type=self.partition_type)
]
out_map = [
kgcnn_ops_dyn_cast([out_pool, out_np],
input_tensor_type="values_partition",
output_tensor_type=found_node_type,
partition_type=self.partition_type),
kgcnn_ops_dyn_cast([out_pool_edge, out_ep],
input_tensor_type="values_partition",
output_tensor_type=found_edge_type,
partition_type=self.partition_type)
]
return out, out_map
def call(self, inputs, **kwargs):
"""Forward pass.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge,
i.e. (batch, None, 2)
Args:
inputs (list): [node, edge, edge_indices, map_node, map_edge, node_pool, edge_pool, edge_indices_pool]
- node: Original node tensor
- edge: Original edge feature tensor
- edge_indices: Original index tensor
- map_node: Index map between original and pooled nodes
- map_edge: Index map between original and pooled edges
- node_pool: Pooled node tensor
- edge_pool: Pooled edge feature tensor
- edge_indices: Pooled index tensor
Returns:
List: [nodes, edges, edge_indices]
- nodes: Unpooled node feature tensor
- edges: Unpooled edge feature list
- edge_indices: Unpooled edge index
"""
found_input_type = [
kgcnn_ops_get_tensor_type(inputs[i],
input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
for i in range(8)
]
node_old, nodepart_old = kgcnn_ops_dyn_cast(
inputs[0],
input_tensor_type=found_input_type[0],
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge_old, edgepart_old = kgcnn_ops_dyn_cast(
inputs[1],
input_tensor_type=found_input_type[1],
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgeind_old, _ = kgcnn_ops_dyn_cast(
inputs[2],
input_tensor_type=found_input_type[2],
output_tensor_type="values_partition",
partition_type=self.partition_type)
map_node, _ = kgcnn_ops_dyn_cast(inputs[3],
input_tensor_type=found_input_type[3],
output_tensor_type="values_partition",
partition_type=self.partition_type)
map_edge, _ = kgcnn_ops_dyn_cast(inputs[4],
input_tensor_type=found_input_type[4],
output_tensor_type="values_partition",
partition_type=self.partition_type)
node_new, nodpart_new = kgcnn_ops_dyn_cast(
inputs[5],
input_tensor_type=found_input_type[5],
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge_new, edgepart_new = kgcnn_ops_dyn_cast(
inputs[6],
input_tensor_type=found_input_type[6],
output_tensor_type="values_partition",
partition_type=self.partition_type)
edgeind_new, _ = kgcnn_ops_dyn_cast(
inputs[7],
input_tensor_type=found_input_type[7],
output_tensor_type="values_partition",
partition_type=self.partition_type)
nrowlength = kgcnn_ops_change_partition_type(nodepart_old,
self.partition_type,
"row_length")
erowlength = kgcnn_ops_change_partition_type(edgepart_old,
self.partition_type,
"row_length")
pool_node_len = kgcnn_ops_change_partition_type(
nodpart_new, self.partition_type, "row_length")
pool_edge_id = kgcnn_ops_change_partition_type(edgepart_new,
self.partition_type,
"value_rowids")
# Correct map index for flatten batch offset
map_node = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
map_node,
nrowlength,
pool_node_len,
partition_type_node="row_length",
partition_type_edge="row_length",
from_indexing=self.node_indexing,
to_indexing="batch",
axis=0)
map_edge = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(
map_edge,
erowlength,
pool_edge_id,
partition_type_node="row_length",
partition_type_edge="value_rowids",
from_indexing=self.node_indexing,
to_indexing="batch",
axis=0)
index_dtype = map_node.dtype
node_shape = tf.stack([
tf.cast(tf.shape(node_old)[0], dtype=index_dtype),
tf.cast(tf.shape(node_new)[1], dtype=index_dtype)
])
out_node = tf.scatter_nd(ks.backend.expand_dims(map_node, axis=-1),
node_new, node_shape)
index_dtype = map_edge.dtype
edge_shape = tf.stack([
tf.cast(tf.shape(edge_old)[0], dtype=index_dtype),
tf.cast(tf.shape(edge_new)[1], dtype=index_dtype)
])
out_edge = tf.scatter_nd(ks.backend.expand_dims(map_edge, axis=-1),
edge_new, edge_shape)
outlist = [
kgcnn_ops_dyn_cast([out_node, nodepart_old],
input_tensor_type="values_partition",
output_tensor_type=found_input_type[0],
partition_type=self.partition_type),
kgcnn_ops_dyn_cast([out_edge, edgepart_old],
input_tensor_type="values_partition",
output_tensor_type=found_input_type[1],
partition_type=self.partition_type),
kgcnn_ops_dyn_cast([edgeind_old, edgepart_old],
input_tensor_type="values_partition",
output_tensor_type=found_input_type[2],
partition_type=self.partition_type)
]
return outlist
def call(self, inputs, **kwargs):
"""Forward path.
The tensor representation can be tf.RaggedTensor, tf.Tensor or a list of (values, partition).
The RaggedTensor has shape (batch, None, F) or in case of equal sized graphs (batch, N, F).
For disjoint representation (values, partition), the node embeddings are given by
a flatten value tensor of shape (batch*None, F) and a partition tensor of either "row_length",
"row_splits" or "value_rowids" that matches the tf.RaggedTensor partition information. In this case
the partition_type and node_indexing scheme, i.e. "batch", must be known by the layer.
For edge indices, the last dimension holds indices from outgoing to ingoing node (i,j) as a directed edge.
Args:
inputs (list): [nodes, edges, edge_indices]
- nodes: Node emebeddings of shape (batch, [N], F)
- edges: Adjacency entries of shape (batch, [N], 1)
- edge_indices: Index list of shape (batch, [N], 2)
Returns:
list: [edges, edge_indices]
- edges: Adjacency entries of shape (batch, [N], 1)
- edge_indices: Flatten index list of shape (batch, [N], 2)
"""
found_node_type = kgcnn_ops_get_tensor_type(inputs[0], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_edge_type = kgcnn_ops_get_tensor_type(inputs[1], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
found_index_type = kgcnn_ops_get_tensor_type(inputs[2], input_tensor_type=self.input_tensor_type,
node_indexing=self.node_indexing)
nod, node_part = kgcnn_ops_dyn_cast(inputs[0], input_tensor_type=found_node_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge, _ = kgcnn_ops_dyn_cast(inputs[1], input_tensor_type=found_edge_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
edge_index, edge_part = kgcnn_ops_dyn_cast(inputs[2], input_tensor_type=found_index_type,
output_tensor_type="values_partition",
partition_type=self.partition_type)
# Cast to length tensor
node_len = kgcnn_ops_change_partition_type(node_part, self.partition_type, "row_length")
edge_len = kgcnn_ops_change_partition_type(edge_part, self.partition_type, "row_length")
# batch-wise indexing
edge_index = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(edge_index,
node_len, edge_len,
partition_type_node="row_length",
partition_type_edge="row_length",
from_indexing=self.node_indexing,
to_indexing="sample")
ind_batch = tf.cast(tf.expand_dims(tf.repeat(tf.range(tf.shape(edge_len)[0]), edge_len), axis=-1),
dtype=edge_index.dtype)
ind_all = tf.concat([ind_batch, edge_index], axis=-1)
ind_all = tf.cast(ind_all, dtype=tf.int64)
max_index = tf.reduce_max(node_len)
dense_shape = tf.stack([tf.cast(tf.shape(node_len)[0], dtype=max_index.dtype), max_index, max_index])
adj = tf.zeros(dense_shape, dtype=edge.dtype)
ind_flat = tf.range(tf.cast(tf.shape(node_len)[0], dtype=max_index.dtype) * max_index * max_index)
adj = tf.expand_dims(adj, axis=-1)
adj = tf.tensor_scatter_nd_update(adj, ind_all, edge[:, 0:1])
adj = tf.squeeze(adj, axis=-1)
out0 = adj
out = adj
for i in range(self.n - 1):
out = tf.linalg.matmul(out, out0)
# debug_result = out
# sparsify
mask = out > tf.keras.backend.epsilon()
mask = tf.reshape(mask, (-1,))
out = tf.reshape(out, (-1,))
new_edge = out[mask]
new_edge = tf.expand_dims(new_edge, axis=-1)
new_indices = tf.unravel_index(ind_flat[mask], dims=dense_shape)
new_egde_ids = new_indices[0]
new_edge_index = tf.concat([tf.expand_dims(new_indices[1], axis=-1), tf.expand_dims(new_indices[2], axis=-1)],
axis=-1)
new_edge_len = tf.tensor_scatter_nd_add(tf.zeros_like(node_len), tf.expand_dims(new_egde_ids, axis=-1),
tf.ones_like(new_egde_ids))
# Outpartition
new_edge_part = kgcnn_ops_change_partition_type(new_edge_len, "row_length", self.partition_type)
# batchwise indexing
new_edge_index = kgcnn_ops_change_edge_tensor_indexing_by_row_partition(new_edge_index,
node_len, new_edge_len,
partition_type_node="row_length",
partition_type_edge="row_length",
from_indexing="sample",
to_indexing=self.node_indexing)
outlist = [kgcnn_ops_dyn_cast([new_edge, new_edge_part], input_tensor_type="values_partition",
output_tensor_type=found_edge_type, partition_type=self.partition_type),
kgcnn_ops_dyn_cast([new_edge_index, new_edge_part], input_tensor_type="values_partition",
output_tensor_type=found_index_type, partition_type=self.partition_type)
]
return outlist
