add pt2tf tool

pt2tf/onnx-tensorflow/onnx_tf/handlers/backend/constant_fill.py

@@ -0,0 +1,44 @@
+import tensorflow as tf
+
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+from onnx_tf.handlers.handler import tf_func
+
+
+@onnx_op("ConstantFill")
+@tf_func(tf.fill)
+class ConstantFill(BackendHandler):
+
+  @classmethod
+  def args_check(cls, node, **kwargs):
+    if node.inputs and "shape" in node.attrs:
+      raise ValueError(
+          "Cannot set the shape argument and pass in an input at the same time."
+      )
+    if not node.inputs and "extra_shape" in node.attrs:
+      raise ValueError("Cannot set extra_shape when there is no input.")
+
+  @classmethod
+  def get_attrs_processor_param(cls):
+    return {"default": {"value": 0.}}
+
+  @classmethod
+  def version_1(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+
+    if "shape" in node.attrs:
+      shape = node.attrs["shape"]
+    else:
+      shape = tensor_dict[
+          node.inputs[0]].get_shape().as_list() if node.attrs.get(
+              "input_as_shape", 0) == 0 else tensor_dict[node.inputs[0]]
+
+    if "extra_shape" in node.attrs:
+      shape = tf.concat([shape, node.attrs["extra_shape"]], 0)
+
+    value = node.attrs.get("value", 0.)
+
+    if "dtype" in node.attrs:
+      return [tf.cast(tf.fill(shape, value), dtype=node.attrs["dtype"])]
+
+    return [cls.make_tensor_from_onnx_node(node, inputs=[shape], **kwargs)]