Update tf.fill() to use dynamic size instead of const.

Fix MatrixBandPart to avoid dangling input references

Author: jignparm

tf2onnx/graph.py

@@ -1067,7 +1067,9 @@ def insert_new_node_on_input(self, node, op_type, input_name, name=None, domain=
         Args:
             node: we want to replace the input for this node
             op_type: type for new operation
-            input_name: the names of the outputs above us
+            input_name: the name(s) of the outputs above us
+                if scalar, new node placed above input_name
+                if list, new node placed above input_name[0]. list is inputs into new node
             name: the name of the new op
             kwargs: attributes of the new node
 
@@ -1077,9 +1079,12 @@ def insert_new_node_on_input(self, node, op_type, input_name, name=None, domain=
         if name is None:
             name = utils.make_name(node.name)
         new_output = port_name(name)
-        new_node = self.make_node(op_type, [input_name], attr=kwargs, outputs=[new_output], name=name, domain=domain)
+        if type(input_name) is not list:
+            input_name = [input_name]
+
+        new_node = self.make_node(op_type, input_name, attr=kwargs, outputs=[new_output], name=name, domain=domain)
         for i, n in enumerate(node.input):
-            if n == input_name:
+            if n == input_name[0]:
                 node.input[i] = new_output
                 break
         return new_node

tf2onnx/onnx_opset/generator.py

@@ -16,9 +16,9 @@
 from tf2onnx import utils
 from tf2onnx.handler import tf_op
 
-
 logger = logging.getLogger(__name__)
 
+
 # pylint: disable=unused-argument,missing-docstring
 
 @tf_op(["Const", "ConstV2"])
@@ -102,6 +102,15 @@ def version_9(cls, ctx, node, **kwargs):
         node.set_attr("value", value_proto)
         del node.input[1]
 
+    @classmethod
+    def version_11(cls, ctx, node, **kwargs):
+        # cls.version_7(ctx, node, **kwargs)
+        node.type = "Expand"
+        node.input = [node.input[1], node.input[0]]
+        # cast shape to int64 if needed
+        if ctx.get_dtype(node.input[1]) != onnx_pb.TensorProto.INT64:
+            ctx.insert_new_node_on_input(node, "Cast", node.input[1], to=onnx_pb.TensorProto.INT64)
+
 
 @tf_op("Multinomial")
 class Multinomial:

tf2onnx/onnx_opset/nn.py

@@ -452,9 +452,8 @@ def version_11(cls, ctx, node, **kwargs):
         if ctx.get_dtype(node.input[1]) != onnx_pb.TensorProto.INT64:
             ctx.insert_new_node_on_input(node, "Cast", node.input[1], to=onnx_pb.TensorProto.INT64)
         ctx.insert_new_node_on_input(node, "Transpose", node.input[1])
-        reshape = ctx.insert_new_node_on_input(node, "Reshape", node.input[1])
         shape_const = ctx.make_const(utils.make_name(node.name), np.array([-1]).astype(np.int64))
-        reshape.input = [reshape.input[0], shape_const.name]
+        ctx.insert_new_node_on_input(node, "Reshape", [node.input[1], shape_const.name])
 
         origin_dtype = ctx.get_dtype(node.output[0])
         if origin_dtype not in [TensorProto.FLOAT, TensorProto.DOUBLE,
@@ -715,8 +714,12 @@ def version_7(cls, ctx, node, **kwargs):
         # 2: "loop" to generate mask matrix: generate col or row of matrix one by one
         g = ctx.create_new_graph_with_same_config()
         node_name = utils.make_name("const_zero_bool")
-        const_zero_bool = ctx.make_const(name=node_name, np_val=np.array([[0]]).astype(np.bool))
-        ctx.set_dtype(const_zero_bool.output[0], onnx_pb.TensorProto.BOOL)
+        const_zero_bool = g.make_const(name=node_name, np_val=np.array([[0]]).astype(np.bool))
+        g.set_dtype(const_zero_bool.output[0], onnx_pb.TensorProto.BOOL)
+
+        g.add_graph_input("trip", onnx_pb.TensorProto.INT64, [])
+        g.add_graph_input("cond", onnx_pb.TensorProto.BOOL, [])
+        g.add_graph_input("line", onnx_pb.TensorProto.BOOL, [-1, -1])
 
         # shift right the line and add zero at the left.
         new_line = g.make_node(op_type="Concat", inputs=[const_zero_bool.output[0], "line"],
@@ -730,10 +733,6 @@ def version_7(cls, ctx, node, **kwargs):
         g.make_node("Identity", ["line"], outputs=["res"])
         g.make_node("Identity", [slice_node], outputs=["line_out"])
 
-        g.add_graph_input("trip", onnx_pb.TensorProto.INT64, [])
-        g.add_graph_input("cond", onnx_pb.TensorProto.BOOL, [])
-        g.add_graph_input("line", onnx_pb.TensorProto.BOOL, [-1, -1])
-
         g.add_graph_output("cond_out", onnx_pb.TensorProto.BOOL, [])
         g.add_graph_output("line_out", onnx_pb.TensorProto.BOOL, [-1, -1])
         g.add_graph_output("res", onnx_pb.TensorProto.BOOL, [-1, -1])