add pt2tf tool

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

@@ -0,0 +1,45 @@
+import numpy as np
+import tensorflow as tf
+
+
+class BroadcastMixin(object):
+
+  @classmethod
+  def explicit_broadcast(cls, inputs, axis=None, tensor_dict=None):
+    x = inputs[0] if isinstance(inputs[0],
+                                tf.Tensor) else tensor_dict[inputs[0]]
+    y = inputs[1] if isinstance(inputs[1],
+                                tf.Tensor) else tensor_dict[inputs[1]]
+
+    if np.prod(y.shape) == 1:
+      return y
+
+    if not isinstance(x, tf.Tensor) or not isinstance(y, tf.Tensor):
+      raise ValueError("Targets for explicit broadcasting need to be Tensor.")
+
+    if axis is None:
+      return y
+
+    total_num_dim = len(x.get_shape())
+    if axis < 0:
+      axis += total_num_dim
+
+    if axis + len(y.get_shape()) == total_num_dim:
+      return y
+
+    dims = [axis + i for i in range(len(y.get_shape()))]
+    new_y = y
+    for i in range(total_num_dim):
+      if i not in dims:
+        new_y = tf.expand_dims(new_y, i)
+    return new_y
+
+  @classmethod
+  def limited_broadcast(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+    x = tensor_dict[node.inputs[0]]
+    y = tensor_dict[node.inputs[1]]
+    if node.attrs.get("broadcast") == 1:
+      y = cls.explicit_broadcast([x, y], node.attrs.get("axis", None))
+      return [cls.make_tensor_from_onnx_node(node, inputs=[x, y], **kwargs)]
+    return [cls.make_tensor_from_onnx_node(node, **kwargs)]