code refactor according to review comments

Author: zhijxu-MS

tests/run_pretrained_models.py

@@ -23,7 +23,9 @@
 import tensorflow as tf
 from tensorflow.core.framework import graph_pb2
 from tensorflow.python.framework.graph_util import convert_variables_to_constants
-from tensorflow.contrib.rnn import GRUBlockCell  # pylint: disable=unused-import
+# contrib ops are registered only when the module is imported, the following import statement is needed,
+# otherwise tf runtime error will show up when the tf model is restored from pb file because of un-registered ops.
+import tensorflow.contrib.rnn  # pylint: disable=unused-import
 import yaml
 import PIL.Image
 

tests/test_gru.py

@@ -312,7 +312,6 @@ def test_dynamic_bigru(self):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell1 = rnn.GRUCell(
@@ -326,7 +325,6 @@ def test_dynamic_bigru(self):
                 cell2,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
         _ = tf.identity(cell_state, name="cell_state")
@@ -345,7 +343,6 @@ def test_dynamic_bigru_output_consumed_only(self):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell1 = rnn.GRUCell(
@@ -359,7 +356,6 @@ def test_dynamic_bigru_output_consumed_only(self):
                 cell2,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
 
@@ -377,7 +373,6 @@ def test_dynamic_bigru_state_consumed_only(self):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell1 = rnn.GRUCell(
@@ -386,12 +381,11 @@ def test_dynamic_bigru_state_consumed_only(self):
             cell2 = rnn.GRUCell(
                 units,
                 kernel_initializer=initializer)
-            outputs, cell_state = tf.nn.bidirectional_dynamic_rnn(
+            _, cell_state = tf.nn.bidirectional_dynamic_rnn(
                 cell1,
                 cell2,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
             _ = tf.identity(cell_state, name="cell_state")
 
@@ -409,7 +403,6 @@ def test_dynamic_bidirectional_but_one_gru(self):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell = rnn.GRUCell(
@@ -420,7 +413,6 @@ def test_dynamic_bidirectional_but_one_gru(self):
                 cell,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
         _ = tf.identity(cell_state, name="cell_state")
@@ -438,7 +430,6 @@ def test_dynamic_bidirectional_but_one_gru_and_output_consumed_only(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell = rnn.GRUCell(
@@ -448,7 +439,6 @@ def test_dynamic_bidirectional_but_one_gru_and_output_consumed_only(self):
                 cell,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
 
@@ -465,17 +455,15 @@ def test_dynamic_bidirectional_but_one_gru_and_state_consumed_only(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell = rnn.GRUCell(
                 units)
-            outputs, cell_state = tf.nn.bidirectional_dynamic_rnn(
+            _, cell_state = tf.nn.bidirectional_dynamic_rnn(
                 cell,
                 cell,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(cell_state, name="cell_state")
 

tests/test_grublock.py

@@ -290,7 +290,6 @@ def test_dynamic_bigru(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell1 = rnn.GRUBlockCell(
@@ -302,7 +301,6 @@ def test_dynamic_bigru(self):
                 cell2,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
         _ = tf.identity(cell_state, name="cell_state")
@@ -320,7 +318,6 @@ def test_dynamic_bigru_output_consumed_only(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell1 = rnn.GRUBlockCell(
@@ -332,7 +329,6 @@ def test_dynamic_bigru_output_consumed_only(self):
                 cell2,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
 
@@ -349,7 +345,6 @@ def test_dynamic_bigru_state_consumed_only(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell1 = rnn.GRUBlockCell(
@@ -361,7 +356,6 @@ def test_dynamic_bigru_state_consumed_only(self):
                 cell2,
                 x,
                 dtype=tf.float32)
-            gru_list.append(cell_state)
 
         _ = tf.identity(cell_state, name="cell_state")
 
@@ -378,7 +372,6 @@ def test_dynamic_bidirectional_but_one_gru(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell = rnn.GRUBlockCell(
@@ -388,7 +381,6 @@ def test_dynamic_bidirectional_but_one_gru(self):
                 cell,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
         _ = tf.identity(cell_state, name="cell_state")
@@ -406,7 +398,6 @@ def test_dynamic_bidirectional_but_one_gru_and_output_consumed_only(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell = rnn.GRUBlockCell(
@@ -416,7 +407,6 @@ def test_dynamic_bidirectional_but_one_gru_and_output_consumed_only(self):
                 cell,
                 x,
                 dtype=tf.float32)
-            gru_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
 
@@ -433,7 +423,6 @@ def test_dynamic_bidirectional_but_one_gru_and_state_consumed_only(self):
 
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
 
-        gru_list = []
         if True:
             # bigru, no scope
             cell = rnn.GRUBlockCell(
@@ -443,7 +432,6 @@ def test_dynamic_bidirectional_but_one_gru_and_state_consumed_only(self):
                 cell,
                 x,
                 dtype=tf.float32)
-            gru_list.append(cell_state)
 
         _ = tf.identity(cell_state, name="cell_state")
 

tests/test_lstm.py

@@ -396,7 +396,6 @@ def internal_test_dynamic_bilstm_with_parameters(self, state_is_tuple):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        lstm_list = []
         if True:
             # bilstm, no scope
             cell1 = rnn.LSTMCell(
@@ -412,7 +411,6 @@ def internal_test_dynamic_bilstm_with_parameters(self, state_is_tuple):
                 cell2,
                 x,
                 dtype=tf.float32)
-            lstm_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
         _ = tf.identity(cell_state, name="cell_state")
@@ -431,7 +429,6 @@ def test_dynamic_bilstm_output_consumed_only(self, state_is_tuple=True):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        lstm_list = []
         if True:
             # bilstm, no scope
             cell1 = rnn.LSTMCell(
@@ -447,7 +444,6 @@ def test_dynamic_bilstm_output_consumed_only(self, state_is_tuple=True):
                 cell2,
                 x,
                 dtype=tf.float32)
-            lstm_list.append(outputs)
 
         _ = tf.identity(outputs, name="output")
 
@@ -465,7 +461,6 @@ def test_dynamic_bilstm_state_consumed_only(self, state_is_tuple=True):
         x = tf.placeholder(tf.float32, x_val.shape, name="input_1")
         initializer = init_ops.constant_initializer(0.5)
 
-        lstm_list = []
         if True:
             # bilstm, no scope
             cell1 = rnn.LSTMCell(
@@ -476,12 +471,11 @@ def test_dynamic_bilstm_state_consumed_only(self, state_is_tuple=True):
                 units,
                 initializer=initializer,
                 state_is_tuple=state_is_tuple)
-            outputs, cell_state = tf.nn.bidirectional_dynamic_rnn(
+            _, cell_state = tf.nn.bidirectional_dynamic_rnn(
                 cell1,
                 cell2,
                 x,
                 dtype=tf.float32)
-            lstm_list.append(outputs)
 
         _ = tf.identity(cell_state, name="cell_state")