def tf_signature(self):
"""
Adjacency matrix has shape [batch, n_nodes, n_nodes]
Node features have shape [batch, n_nodes, n_node_features]
Edge features have shape [batch, n_nodes, n_nodes, n_edge_features]
Labels have shape [batch, n_labels]
"""
signature = self.signature
for k in signature:
signature[k]["shape"] = prepend_none(signature[k]["shape"])
if "x" in signature and self.mask:
# In case we have a mask, the mask is concatenated to the features
signature["x"]["shape"] = signature["x"]["shape"][:-1] + (
signature["x"]["shape"][-1] + 1, )
if "a" in signature:
# Adjacency matrix in batch mode is dense
signature["a"]["spec"] = tf.TensorSpec
if "e" in signature:
# Edge attributes have an extra None dimension in batch mode
signature["e"]["shape"] = prepend_none(signature["e"]["shape"])
if "y" in signature and self.node_level:
# Node labels have an extra None dimension
signature["y"]["shape"] = prepend_none(signature["y"]["shape"])
return to_tf_signature(signature)
def tf_signature(self):
"""
Adjacency matrix has shape [n_nodes, n_nodes]
Node features have shape [n_nodes, n_node_features]
Edge features have shape [n_edges, n_node_features]
Targets have shape [..., n_labels]
"""
signature = self.dataset.signature
return to_tf_signature(signature)
def tf_signature(self):
"""
Returns the signature of the collated batches using the tf.TypeSpec system.
By default, the signature is that of the dataset (`dataset.signature`):
- Adjacency matrix has shape `[n_nodes, n_nodes]`
- Node features have shape `[n_nodes, n_node_features]`
- Edge features have shape `[n_edges, n_node_features]`
- Targets have shape `[..., n_labels]`
"""
signature = self.dataset.signature
return to_tf_signature(signature)
def tf_signature(self):
signature = self.dataset.signature
for k in signature:
signature[k]['shape'] = prepend_none(signature[k]['shape'])
if 'a' in signature:
# Adjacency matrix in batch mode is dense
signature['a']['spec'] = tf.TensorSpec
if 'e' in signature:
# Edge attributes have an extra None dimension in batch mode
signature['e']['shape'] = prepend_none(signature['e']['shape'])
return to_tf_signature(signature)
def tf_signature(self):
"""
Adjacency matrix has shape [n_nodes, n_nodes]
Node features have shape [batch, n_nodes, n_node_features]
Edge features have shape [batch, n_edges, n_edge_features]
Targets have shape [batch, ..., n_labels]
"""
signature = self.dataset.signature
for k in ["x", "e", "y"]:
if k in signature:
signature[k]["shape"] = prepend_none(signature[k]["shape"])
return to_tf_signature(signature)
def tf_signature(self):
signature = self.dataset.signature
if 'y' in signature:
if not self.node_level:
signature['y']['shape'] = prepend_none(signature['y']['shape'])
if 'a' in signature:
signature['a']['spec'] = tf.SparseTensorSpec
signature['i'] = dict()
signature['i']['spec'] = tf.TensorSpec
signature['i']['shape'] = (None,)
signature['i']['dtype'] = tf.as_dtype(tf.int64)
return to_tf_signature(signature)
def tf_signature(self):
"""
Adjacency matrix has shape [batch, n_nodes, n_nodes]
Node features have shape [batch, n_nodes, n_node_features]
Edge features have shape [batch, n_nodes, n_nodes, n_edge_features]
Targets have shape [batch, ..., n_labels]
"""
signature = self.dataset.signature
for k in signature:
signature[k]["shape"] = prepend_none(signature[k]["shape"])
if "a" in signature:
# Adjacency matrix in batch mode is dense
signature["a"]["spec"] = tf.TensorSpec
if "e" in signature:
# Edge attributes have an extra None dimension in batch mode
signature["e"]["shape"] = prepend_none(signature["e"]["shape"])
return to_tf_signature(signature)
def tf_signature(self):
"""
Adjacency matrix has shape [n_nodes, n_nodes]
Node features have shape [batch, n_nodes, n_node_features]
Edge features have shape [batch, n_edges, n_edge_features]
Targets have shape [batch, ..., n_labels]
"""
signature = self.dataset.signature
for k in ["x", "e", "y"]:
if k in signature:
signature[k]["shape"] = prepend_none(signature[k]["shape"])
signature["a"] = dict()
signature["a"]["spec"] = get_spec(self.dataset.a)
signature["a"]["shape"] = (None, None)
signature["a"]["dtype"] = tf.as_dtype(self.dataset.a.dtype)
return to_tf_signature(signature)
def tf_signature(self):
"""
Adjacency matrix has shape [n_nodes, n_nodes]
Node features have shape [n_nodes, n_node_features]
Edge features have shape [n_edges, n_edge_features]
Targets have shape [..., n_labels]
"""
signature = self.dataset.signature
if "y" in signature:
signature["y"]["shape"] = prepend_none(signature["y"]["shape"])
if "a" in signature:
signature["a"]["spec"] = tf.SparseTensorSpec
signature["i"] = dict()
signature["i"]["spec"] = tf.TensorSpec
signature["i"]["shape"] = (None, )
signature["i"]["dtype"] = tf.as_dtype(tf.int64)
return to_tf_signature(signature)
def tf_signature(self):
signature = self.dataset.signature
return to_tf_signature(signature)
