Simplify casting base layer. Inputs are ignored for ragged inputs.

Author: PatReis

kgcnn/layers/casting.py

@@ -16,8 +16,12 @@ def _cat_one(t):
 
 class _CastBatchedDisjointBase(Layer):
 
-    def __init__(self, reverse_indices: bool = False, dtype_batch: str = "int64", dtype_index=None,
-                 padded_disjoint: bool = False, uses_mask: bool = False,
+    def __init__(self,
+                 reverse_indices: bool = False,
+                 dtype_batch: str = "int64",
+                 dtype_index=None,
+                 padded_disjoint: bool = False,
+                 uses_mask: bool = False,
                  static_batched_node_output_shape: tuple = None,
                  static_batched_edge_output_shape: tuple = None,
                  remove_padded_disjoint_from_batched_output: bool = True,
@@ -29,20 +33,26 @@ def __init__(self, reverse_indices: bool = False, dtype_batch: str = "int64", dt
             dtype_batch (str): Dtype for batch ID tensor. Default is 'int64'.
             dtype_index (str): Dtype for index tensor. Default is None.
             padded_disjoint (bool): Whether to keep padding in disjoint representation. Default is False.
+                Not used for ragged arguments.
             uses_mask (bool): Whether the padding is marked by a boolean mask or by a length tensor, counting the
                 non-padded nodes from index 0. Default is False.
+                Not used for ragged arguments.
             static_batched_node_output_shape (tuple): Statical output shape of nodes. Default is None.
+                Not used for ragged arguments.
             static_batched_edge_output_shape (tuple): Statical output shape of edges. Default is None.
+                Not used for ragged arguments.
             remove_padded_disjoint_from_batched_output (bool): Whether to remove the first element on batched output
                 in case of padding.
+                Not used for ragged arguments.
         """
         super(_CastBatchedDisjointBase, self).__init__(**kwargs)
         self.reverse_indices = reverse_indices
         self.dtype_index = dtype_index
         self.dtype_batch = dtype_batch
         self.uses_mask = uses_mask
         self.padded_disjoint = padded_disjoint
-        self.supports_jit = padded_disjoint
+        if padded_disjoint:
+            self.supports_jit = True
         self.static_batched_node_output_shape = static_batched_node_output_shape
         self.static_batched_edge_output_shape = static_batched_edge_output_shape
         self.remove_padded_disjoint_from_batched_output = remove_padded_disjoint_from_batched_output
@@ -536,31 +546,7 @@ def call(self, inputs: list, **kwargs):
 CastBatchedGraphStateToDisjoint.__init__.__doc__ = _CastBatchedDisjointBase.__init__.__doc__
 
 
-class _CastRaggedToDisjointBase(Layer):
-
-    def __init__(self, reverse_indices: bool = False, dtype_batch: str = "int64", dtype_index=None, **kwargs):
-        r"""Initialize layer.
-
-        Args:
-            reverse_indices (bool): Whether to reverse index order. Default is False.
-            dtype_batch (str): Dtype for batch ID tensor. Default is 'int64'.
-            dtype_index (str): Dtype for index tensor. Default is None.
-        """
-        super(_CastRaggedToDisjointBase, self).__init__(**kwargs)
-        self.reverse_indices = reverse_indices
-        self.dtype_index = dtype_index
-        self.dtype_batch = dtype_batch
-        # self.supports_jit = False
-
-    def get_config(self):
-        """Get config dictionary for this layer."""
-        config = super(_CastRaggedToDisjointBase, self).get_config()
-        config.update({"reverse_indices": self.reverse_indices, "dtype_batch": self.dtype_batch,
-                       "dtype_index": self.dtype_index})
-        return config
-
-
-class CastRaggedAttributesToDisjoint(_CastRaggedToDisjointBase):
+class CastRaggedAttributesToDisjoint(_CastBatchedDisjointBase):
 
     def __init__(self, **kwargs):
         super(CastRaggedAttributesToDisjoint, self).__init__(**kwargs)
@@ -598,10 +584,10 @@ def call(self, inputs, **kwargs):
         return decompose_ragged_tensor(inputs, batch_dtype=self.dtype_batch)
 
 
-CastRaggedAttributesToDisjoint.__init__.__doc__ = _CastRaggedToDisjointBase.__init__.__doc__
+CastRaggedAttributesToDisjoint.__init__.__doc__ = _CastBatchedDisjointBase.__init__.__doc__
 
 
-class CastRaggedIndicesToDisjoint(_CastRaggedToDisjointBase):
+class CastRaggedIndicesToDisjoint(_CastBatchedDisjointBase):
 
     def __init__(self, **kwargs):
         super(CastRaggedIndicesToDisjoint, self).__init__(**kwargs)
@@ -685,10 +671,10 @@ def call(self, inputs, **kwargs):
         return [nodes_flatten, disjoint_indices, graph_id_node, graph_id_edge, node_id, edge_id, node_len, edge_len]
 
 
-CastRaggedIndicesToDisjoint.__init__.__doc__ = _CastRaggedToDisjointBase.__init__.__doc__
+CastRaggedIndicesToDisjoint.__init__.__doc__ = _CastBatchedDisjointBase.__init__.__doc__
 
 
-class CastDisjointToRaggedAttributes(_CastRaggedToDisjointBase):
+class CastDisjointToRaggedAttributes(_CastBatchedDisjointBase):
 
     def __init__(self, **kwargs):
         super(CastDisjointToRaggedAttributes, self).__init__(**kwargs)
@@ -713,4 +699,4 @@ def call(self, inputs, **kwargs):
         raise NotImplementedError()
 
 
-CastDisjointToRaggedAttributes.__init__.__doc__ = CastDisjointToRaggedAttributes.__init__.__doc__
+CastDisjointToRaggedAttributes.__init__.__doc__ = _CastBatchedDisjointBase.__init__.__doc__