add pt2tf tool

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

@@ -0,0 +1,52 @@
+import tensorflow as tf
+
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+
+
+@onnx_op("SequenceInsert")
+class SequenceInsert(BackendHandler):
+
+  @classmethod
+  def chk_pos_in_bounds(cls, input_seq, pos):
+    """ 
+    Check the position is in-bounds with respect to the sequence.
+    Accepted range for 'position' is in [-n, n], where n is the 
+    number of tensors in 'input_sequence'. 
+
+    :param input_seq: input sequence
+    :param pos: position to insert the tensor
+
+    :return: True if position is in-bounds.
+    """
+    seq_length = tf.shape(input_seq.to_sparse(), out_type=pos.dtype)[0]
+
+    cond1 = tf.greater_equal(pos, tf.negative(seq_length))
+    cond2 = tf.less_equal(pos, seq_length)
+
+    # pos >= -n and pos <= n
+    return tf.reduce_all(tf.logical_and(cond1, cond2))
+
+  @classmethod
+  def version_11(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+    input_sequence = tensor_dict[node.inputs[0]]
+    input_tensor = tensor_dict[node.inputs[1]]
+
+    position = tensor_dict[node.inputs[2]] if len(
+        node.inputs) > 2 else tf.shape(input_sequence.to_sparse())[0]
+
+    # check whether position is in-bounds and assert if not
+    result = cls.chk_pos_in_bounds(input_sequence, position)
+    assert_pos = tf.Assert(tf.equal(result, True), [result])
+
+    with tf.control_dependencies([assert_pos]):
+      input_tensor = tf.expand_dims(input_tensor, 0)
+      if input_sequence.shape[0] == 0:
+        output_seq = tf.RaggedTensor.from_tensor(input_tensor)
+      else:
+        s1 = input_sequence[:position]
+        s2 = input_sequence[position:]
+        output_seq = tf.concat([s1, input_tensor, s2], axis=0)
+
+      return [output_seq]