add pt2tf tool

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

@@ -0,0 +1,56 @@
+import copy
+
+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("Split")
+@tf_func(tf.split)
+class Split(BackendHandler):
+
+  @classmethod
+  def args_check(cls, node, **kwargs):
+    axis = node.attrs.get("axis", 0)
+    x_rank = len(kwargs["tensor_dict"][node.inputs[0]].get_shape().as_list())
+    if axis > x_rank - 1 or axis < -x_rank:
+      raise ValueError("Axis is out of bound")
+
+  @classmethod
+  def get_attrs_processor_param(cls):
+    return {"default": {"axis": 0}}
+
+  @classmethod
+  def _common(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+    x_shape = tensor_dict[node.inputs[0]].get_shape().as_list()
+    attrs = copy.deepcopy(node.attrs)
+    axis = attrs.get("axis", 0)
+    axis = axis if axis >= 0 else len(x_shape) + axis
+    if "split" in node.attrs:
+      split = attrs["split"]
+    elif len(node.inputs) == 2:  # since version 1
+      split = tensor_dict[node.inputs[1]]
+    else:
+      per_part = x_shape[axis] / len(node.outputs)
+      if int(per_part) != per_part:
+        raise ValueError("Split can not be evenly divided.")
+      split = [int(per_part)] * len(node.outputs)
+    attrs["num_or_size_splits"] = split
+    return list(
+        cls.make_tensor_from_onnx_node(
+            node, inputs=[tensor_dict[node.inputs[0]]], attrs=attrs, **kwargs))
+
+  @classmethod
+  def version_1(cls, node, **kwargs):
+    return cls._common(node, **kwargs)
+
+  @classmethod
+  def version_2(cls, node, **kwargs):
+    return cls._common(node, **kwargs)
+
+  @classmethod
+  def version_11(cls, node, **kwargs):
+    return cls._common(node, **kwargs)