def _call_single(self, X, A):
# Reshape kernels for efficient message-passing
kernel = tf.reshape(self.kernel, (-1, self.attn_heads * self.channels))
attn_kernel_self = ops.transpose(self.attn_kernel_self, (2, 1, 0))
attn_kernel_neighs = ops.transpose(self.attn_kernel_neighs, (2, 1, 0))
# Prepare message-passing
indices = A.indices
N = tf.shape(X, out_type=indices.dtype)[0]
indices = ops.sparse_add_self_loops(indices, N)
targets, sources = indices[:, -2], indices[:, -1]
# Update node features
X = ops.dot(X, kernel)
X = tf.reshape(X, (-1, self.attn_heads, self.channels))
# Compute attention
attn_for_self = tf.reduce_sum(X * attn_kernel_self, -1)
attn_for_self = tf.gather(attn_for_self, targets)
attn_for_neighs = tf.reduce_sum(X * attn_kernel_neighs, -1)
attn_for_neighs = tf.gather(attn_for_neighs, sources)
attn_coef = attn_for_self + attn_for_neighs
attn_coef = tf.nn.leaky_relu(attn_coef, alpha=0.2)
attn_coef = ops.unsorted_segment_softmax(attn_coef, targets, N)
attn_coef = self.dropout(attn_coef)
attn_coef = attn_coef[..., None]
# Update representation
output = attn_coef * tf.gather(X, sources)
output = ops.scatter_sum(output, targets, N)
return output, attn_coef
def call(self, inputs, mask=None):
x, a, e = inputs
# Parameters
N = tf.shape(x)[-2]
F = tf.shape(x)[-1]
F_ = self.channels
# Filter network
kernel_network = e
for layer in self.kernel_network_layers:
kernel_network = layer(kernel_network)
# Convolution
mode = ops.autodetect_mode(x, a)
if mode == modes.BATCH:
kernel = K.reshape(kernel_network, (-1, N, N, F_, F))
output = kernel * a[..., None, None]
output = tf.einsum("abcde,ace->abd", output, x)
else:
# Enforce sparse representation
if not K.is_sparse(a):
warnings.warn("Casting dense adjacency matrix to SparseTensor."
"This can be an expensive operation. ")
a = tf.sparse.from_dense(a)
target_shape = (-1, F, F_)
if mode == modes.MIXED:
target_shape = (tf.shape(x)[0], ) + target_shape
kernel = tf.reshape(kernel_network, target_shape)
index_i = a.indices[:, 1]
index_j = a.indices[:, 0]
messages = tf.gather(x, index_j, axis=-2)
messages = tf.einsum("...ab,...abc->...ac", messages, kernel)
output = ops.scatter_sum(messages, index_i, N)
if self.root:
output += K.dot(x, self.root_kernel)
if self.use_bias:
output = K.bias_add(output, self.bias)
if mask is not None:
output *= mask[0]
output = self.activation(output)
return output
def call(self, inputs):
features = inputs[0]
fltr = inputs[1]
# Enforce sparse representation
if not K.is_sparse(fltr):
fltr = ops.dense_to_sparse(fltr)
# Propagation
targets = fltr.indices[:, -2]
sources = fltr.indices[:, -1]
messages = tf.gather(features, sources)
aggregated = ops.scatter_sum(targets, messages, N=tf.shape(features)[0])
hidden = (1.0 + self.eps) * features + aggregated
# MLP
output = self.mlp(hidden)
return output
def _call_single(self, inputs):
X = inputs[0] # (N, F)
A = inputs[1] # (N, N)
E = inputs[2] # (n_edges, S)
assert K.ndim(
E) == 2, 'In single mode, E must have shape (n_edges, S).'
# Enforce sparse representation
if not K.is_sparse(A):
A = ops.dense_to_sparse(A)
# Parameters
N = tf.shape(X)[-2]
F = K.int_shape(X)[-1]
F_ = self.channels
# Filter network
kernel_network = E
for l in self.kernel_network_layers:
kernel_network = l(kernel_network) # (n_edges, F * F_)
target_shape = (-1, F, F_)
kernel = tf.reshape(kernel_network, target_shape)
# Propagation
index_i = A.indices[:, -2]
index_j = A.indices[:, -1]
messages = tf.gather(X, index_j)
messages = ops.dot(messages[:, None, :], kernel)[:, 0, :]
aggregated = ops.scatter_sum(messages, index_i, N)
# Update
output = aggregated
if self.root:
output += ops.dot(X, self.root_kernel)
if self.use_bias:
output = K.bias_add(output, self.bias)
if self.activation is not None:
output = self.activation(output)
return output
def _call_single(self, inputs):
x, a, e = inputs
if K.ndim(e) != 2:
raise ValueError('In single mode, E must have shape '
'(n_edges, n_edge_features).')
# Enforce sparse representation
if not K.is_sparse(a):
a = ops.dense_to_sparse(a)
# Parameters
N = tf.shape(x)[-2]
F = K.int_shape(x)[-1]
F_ = self.channels
# Filter network
kernel_network = e
for layer in self.kernel_network_layers:
kernel_network = layer(kernel_network) # (n_edges, F * F_)
target_shape = (-1, F, F_)
kernel = tf.reshape(kernel_network, target_shape)
# Propagation
index_i = a.indices[:, -2]
index_j = a.indices[:, -1]
messages = tf.gather(x, index_j)
messages = ops.dot(messages[:, None, :], kernel)[:, 0, :]
aggregated = ops.scatter_sum(messages, index_i, N)
# Update
output = aggregated
if self.root:
output += ops.dot(x, self.root_kernel)
if self.use_bias:
output = K.bias_add(output, self.bias)
if self.activation is not None:
output = self.activation(output)
return output
