batchnorm fixes + disabling batchnorm in DDPG (#353)

Co-authored-by: James Casbon <casbon+gh@gmail.com>

rl_coach/tests/architectures/tensorflow_components/embedders/test_image_embedder.py

@@ -25,17 +25,20 @@ def test_embedder(reset):
     with pytest.raises(ValueError):
         embedder = ImageEmbedder(np.array([10, 100, 100, 100]), name="test")
 
-    # creating a simple image embedder
-    embedder = ImageEmbedder(np.array([100, 100, 10]), name="test")
 
-    # make sure the ops where not created yet
-    assert len(tf.get_default_graph().get_operations()) == 0
+    is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
+    pre_ops = len(tf.get_default_graph().get_operations())
+    # creating a simple image embedder
+    embedder = ImageEmbedder(np.array([100, 100, 10]), name="test", is_training=is_training)
+
+    # make sure the only the is_training op is creates
+    assert len(tf.get_default_graph().get_operations()) == pre_ops
 
     # call the embedder
     input_ph, output_ph = embedder()
 
     # make sure that now the ops were created
-    assert len(tf.get_default_graph().get_operations()) > 0
+    assert len(tf.get_default_graph().get_operations()) > pre_ops
 
     # try feeding a batch of one example
     input = np.random.rand(1, 100, 100, 10)
@@ -55,7 +58,9 @@ def test_embedder(reset):
 @pytest.mark.unit_test
 def test_complex_embedder(reset):
     # creating a deep vector embedder
-    embedder = ImageEmbedder(np.array([100, 100, 10]), name="test", scheme=EmbedderScheme.Deep)
+    is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
+    embedder = ImageEmbedder(np.array([100, 100, 10]), name="test", scheme=EmbedderScheme.Deep, 
+        is_training=is_training)
 
     # call the embedder
     embedder()
@@ -71,8 +76,9 @@ def test_complex_embedder(reset):
 @pytest.mark.unit_test
 def test_activation_function(reset):
     # creating a deep image embedder with relu
+    is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
     embedder = ImageEmbedder(np.array([100, 100, 10]), name="relu", scheme=EmbedderScheme.Deep,
-                             activation_function=tf.nn.relu)
+                             activation_function=tf.nn.relu, is_training=is_training)
 
     # call the embedder
     embedder()
@@ -86,7 +92,7 @@ def test_activation_function(reset):
 
     # creating a deep image embedder with tanh
     embedder_tanh = ImageEmbedder(np.array([100, 100, 10]), name="tanh", scheme=EmbedderScheme.Deep,
-                                  activation_function=tf.nn.tanh)
+                                  activation_function=tf.nn.tanh, is_training=is_training)
 
     # call the embedder
     embedder_tanh()

rl_coach/tests/architectures/tensorflow_components/embedders/test_vector_embedder.py

@@ -22,16 +22,19 @@ def test_embedder(reset):
         embedder = VectorEmbedder(np.array([10, 10]), name="test")
 
     # creating a simple vector embedder
-    embedder = VectorEmbedder(np.array([10]), name="test")
+    is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
+    pre_ops = len(tf.get_default_graph().get_operations())
+
+    embedder = VectorEmbedder(np.array([10]), name="test", is_training=is_training)
 
     # make sure the ops where not created yet
-    assert len(tf.get_default_graph().get_operations()) == 0
+    assert len(tf.get_default_graph().get_operations()) == pre_ops
 
     # call the embedder
     input_ph, output_ph = embedder()
 
     # make sure that now the ops were created
-    assert len(tf.get_default_graph().get_operations()) > 0
+    assert len(tf.get_default_graph().get_operations()) > pre_ops
 
     # try feeding a batch of one example
     input = np.random.rand(1, 10)
@@ -51,7 +54,8 @@ def test_embedder(reset):
 @pytest.mark.unit_test
 def test_complex_embedder(reset):
     # creating a deep vector embedder
-    embedder = VectorEmbedder(np.array([10]), name="test", scheme=EmbedderScheme.Deep)
+    is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
+    embedder = VectorEmbedder(np.array([10]), name="test", scheme=EmbedderScheme.Deep, is_training=is_training)
 
     # call the embedder
     embedder()
@@ -67,8 +71,9 @@ def test_complex_embedder(reset):
 @pytest.mark.unit_test
 def test_activation_function(reset):
     # creating a deep vector embedder with relu
+    is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
     embedder = VectorEmbedder(np.array([10]), name="relu", scheme=EmbedderScheme.Deep,
-                              activation_function=tf.nn.relu)
+                              activation_function=tf.nn.relu, is_training=is_training)
 
     # call the embedder
     embedder()
@@ -82,7 +87,7 @@ def test_activation_function(reset):
 
     # creating a deep vector embedder with tanh
     embedder_tanh = VectorEmbedder(np.array([10]), name="tanh", scheme=EmbedderScheme.Deep,
-                                   activation_function=tf.nn.tanh)
+                                   activation_function=tf.nn.tanh, is_training=is_training)
 
     # call the embedder
     embedder_tanh()