[nGraph] Reorder nGraph LSTMSequence inputs and outputs dimensions (#560)

* Reorder nGraph LSTMSequence input/outpt dimensions

* Update nGraph pythonAPI for LSTMSequence

* Reorder axes in ONNX importer LSTM

* Tests update

* Fix clang warning

* Use opset3 namespace

* Style apply

* Tests update

* Use opset1  namespace

* Remove usage of  GetOutputElement in ONNX importer LSTM

* Remove opset0 header

* Use Node::output()

ngraph/python/src/ngraph/ops.py

@@ -472,11 +472,11 @@ def lstm_sequence(
 ) -> Node:
     """Return a node which performs LSTMSequence operation.
 
-    :param X: The input tensor. Shape: [seq_length, batch_size, input_size].
+    :param X: The input tensor. Shape: [batch_size, seq_length, input_size].
     :param initial_hidden_state:    The hidden state tensor.
-                                    Shape: [num_directions, batch_size, hidden_size].
+                                    Shape: [batch_size, num_directions, hidden_size].
     :param initial_cell_state:      The cell state tensor.
-                                    Shape: [num_directions, batch_size, hidden_size].
+                                    Shape: [batch_size, num_directions, hidden_size].
     :param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                     Shape: [batch_size]. Integer type.
     :param W: Tensor with weights for matrix multiplication operation with input portion of data.

ngraph/python/test/ngraph/test_create_op.py

@@ -258,9 +258,9 @@ def test_lstm_sequence_operator_bidirectional(dtype):
     num_directions = 2
     seq_length = 2
 
-    X_shape = [seq_length, batch_size, input_size]
-    H_t_shape = [num_directions, batch_size, hidden_size]
-    C_t_shape = [num_directions, batch_size, hidden_size]
+    X_shape = [batch_size, seq_length, input_size]
+    H_t_shape = [batch_size, num_directions, hidden_size]
+    C_t_shape = [batch_size, num_directions, hidden_size]
     seq_len_shape = [batch_size]
     W_shape = [num_directions, 4 * hidden_size, input_size]
     R_shape = [num_directions, 4 * hidden_size, hidden_size]
@@ -323,9 +323,9 @@ def test_lstm_sequence_operator_reverse(dtype):
     num_directions = 1
     seq_length = 2
 
-    X_shape = [seq_length, batch_size, input_size]
-    H_t_shape = [num_directions, batch_size, hidden_size]
-    C_t_shape = [num_directions, batch_size, hidden_size]
+    X_shape = [batch_size, seq_length, input_size]
+    H_t_shape = [batch_size, num_directions, hidden_size]
+    C_t_shape = [batch_size, num_directions, hidden_size]
     seq_len_shape = [batch_size]
     W_shape = [num_directions, 4 * hidden_size, input_size]
     R_shape = [num_directions, 4 * hidden_size, hidden_size]
@@ -389,9 +389,9 @@ def test_lstm_sequence_operator_forward(dtype):
     num_directions = 1
     seq_length = 2
 
-    X_shape = [seq_length, batch_size, input_size]
-    H_t_shape = [num_directions, batch_size, hidden_size]
-    C_t_shape = [num_directions, batch_size, hidden_size]
+    X_shape = [batch_size, seq_length, input_size]
+    H_t_shape = [batch_size, num_directions, hidden_size]
+    C_t_shape = [batch_size, num_directions, hidden_size]
     seq_len_shape = [batch_size]
     W_shape = [num_directions, 4 * hidden_size, input_size]
     R_shape = [num_directions, 4 * hidden_size, hidden_size]