add test_dynamic_decode_normal_stop

Author: nbcsm

tests/test_seq2seq.py

@@ -12,7 +12,7 @@
 from tensorflow.contrib import rnn
 from tensorflow.python.ops import init_ops
 from backend_test_base import Tf2OnnxBackendTestBase
-from common import check_opset_min_version, unittest_main
+from common import unittest_main
 
 
 # pylint: disable=missing-docstring
@@ -23,8 +23,8 @@ def test_dynamic_decode_maximum_iterations(self):
         num_units = 4
         vocab_size = 5
         embedding_size = 3
-        GO_SYMBOL = 0
-        END_SYMBOL = 1
+        go_token = 0
+        end_token = 1
 
         embedding = tf.constant(np.ones([vocab_size, embedding_size], dtype=np.float32))
         state_val = np.reshape([np.ones([num_units], dtype=np.float32) * i for i in range(batch_size)],
@@ -38,8 +38,8 @@ def test_dynamic_decode_maximum_iterations(self):
 
         helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
             embedding=embedding,
-            start_tokens=tf.tile([GO_SYMBOL], [batch_size]),
-            end_token=END_SYMBOL)
+            start_tokens=tf.tile([go_token], [batch_size]),
+            end_token=end_token)
 
         output_layer = tf.layers.Dense(vocab_size, kernel_initializer=initializer)
         decoder = tf.contrib.seq2seq.BasicDecoder(
@@ -65,6 +65,88 @@ def test_dynamic_decode_maximum_iterations(self):
 
         self.run_test_case({}, [], output_names_with_port, atol=1e-06, rtol=1e-6)
 
+    def test_dynamic_decode_normal_stop(self):
+        batch_size = 2
+        num_units = 4
+        vocab_size = 5
+        embedding_size = 3
+        go_token = 0
+        end_token = 1
+
+        embedding = tf.constant(np.ones([vocab_size, embedding_size], dtype=np.float32))
+        state_val = np.reshape([np.ones([num_units], dtype=np.float32) * i for i in range(batch_size)],
+                               [batch_size, num_units])
+        encoder_state = tf.nn.rnn_cell.LSTMStateTuple(state_val, state_val)
+
+        cell_initializer = init_ops.constant_initializer(
+            np.array([[-0.9592235, 0.42451382, 0.7437744, -0.54485345, -0.80763197,
+                       0.19663906, -0.22738314, 0.7762785, 0.7464578, 0.27227187,
+                       0.7661047, 0.3596425, -0.8528242, -0.89316916, -0.48946142,
+                       0.87882376],
+                      [0.86586094, -0.75018406, 0.25992537, -0.69368935, 0.2515502,
+                       -0.26379275, 0.8954313, 0.5759742, -0.7753072, -0.4388857,
+                       0.95751476, -0.82085776, -0.9467752, -0.37055635, -0.18570113,
+                       -0.86504984],
+                      [0.02305841, 0.3850248, 0.893692, -0.6866486, -0.83703446,
+                       -0.9828961, 0.3989377, -0.59993076, 0.5330808, 0.6916566,
+                       0.98468065, -0.6047034, 0.10823512, 0.34599304, -0.7834821,
+                       -0.7852347],
+                      [0.81643987, 0.31507468, -0.51369476, -0.12273741, 0.9701307,
+                       -0.79669356, -0.34496522, -0.88750815, -0.17995334, 0.34707904,
+                       -0.09201193, 0.5363934, -0.87229705, -0.5073328, -0.95894027,
+                       0.5481839],
+                      [-0.84093595, -0.2341497, -0.86047816, 0.43370056, -0.39073753,
+                       0.37730122, 0.48026466, 0.3004985, -0.60727096, 0.9043884,
+                       -0.37619448, 0.22490788, -0.03739262, 0.61672115, 0.478899,
+                       -0.40780973],
+                      [0.31202435, -0.22045255, -0.6087918, 0.95115066, 0.00199413,
+                       -0.688287, -0.1103518, 0.4169519, 0.7913246, -0.9844644,
+                       -0.6193857, 0.38659644, -0.4726901, -0.44781208, -0.5174744,
+                       -0.605911],
+                      [0.66771054, 0.34912825, 0.22297978, -0.4990945, 0.24057317,
+                       -0.5540829, 0.92277217, 0.74939895, -0.35278273, -0.21587133,
+                       -0.28613377, -0.8794241, -0.40119147, 0.67175174, -0.22741508,
+                       0.37898326]], dtype=np.float32))
+        dense_initializer = init_ops.constant_initializer(
+            np.array([[0.56177187, -0.6233454, 0.73997784, 0.35032558, 0.6479795],
+                      [0.6831174, -0.34233975, 0.39330363, 0.45177555, -0.49649096],
+                      [-0.98890066, 0.6175642, 0.09800482, -0.6721206, 0.48805737],
+                      [0.19671416, 0.2623148, 0.742548, 0.13555217, 0.56009054]], dtype=np.float32))
+
+        cell = rnn.LSTMCell(
+            num_units=num_units,
+            initializer=cell_initializer,
+            state_is_tuple=True)
+
+        helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
+            embedding=embedding,
+            start_tokens=tf.tile([go_token], [batch_size]),
+            end_token=end_token)
+
+        output_layer = tf.layers.Dense(vocab_size, kernel_initializer=dense_initializer)
+        decoder = tf.contrib.seq2seq.BasicDecoder(
+            cell=cell,
+            helper=helper,
+            initial_state=encoder_state,
+            output_layer=output_layer)
+
+        outputs, state, sequence_lengths = tf.contrib.seq2seq.dynamic_decode(
+            decoder=decoder,
+            maximum_iterations=6)
+
+        _ = tf.identity(outputs.rnn_output, name="rnn_output")
+        _ = tf.identity(outputs.sample_id, name="sample_id")
+        _ = tf.identity(state, name="state")
+        _ = tf.identity(sequence_lengths, name="sequence_lengths")
+
+        output_names_with_port = [
+            "rnn_output:0",
+            # "sample_id:0",  # incomplete type support for Transpose on onnxruntime 0.2.1
+            "state:0",
+        ]
+
+        self.run_test_case({}, [], output_names_with_port, atol=1e-06, rtol=1e-6)
+
 
 if __name__ == '__main__':
     unittest_main()