add pt2tf tool

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

@@ -0,0 +1,45 @@
+import tensorflow as tf
+
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+
+
+@onnx_op("SequenceAt")
+class SequenceAt(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 - 1], where n is the
+    number of tensors in 'input_sequence'.
+
+    :param input_seq: input sequence
+    :param pos: position of the output tensor
+
+    :return: True if position is in-bounds or input length is dynamic.
+    """
+    seq_length = input_seq.shape[0].value
+    if seq_length is None:
+      return True
+
+    seq_length = tf.cast(seq_length, pos.dtype)
+
+    cond1 = tf.greater_equal(pos, tf.negative(seq_length))
+    cond2 = tf.less_equal(pos, seq_length - 1)
+
+    # 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]]
+    position = tensor_dict[node.inputs[1]]
+
+    # 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]):
+      return [input_sequence[position]]