update tests: check for BN layers; some standard keras namings seems to have some incons. #62

test/test_model_modules/test_inception_model.py

@@ -44,6 +44,7 @@ class TestInceptionModelBase:
         tower = base.create_conv_tower(**opts)
         # check last element of tower (activation)
         assert base.part_of_block == 1
+        # assert tower.name == 'Block_0a_act_2_1/Relu:0'
         assert tower.name == 'Block_0a_act_2/Relu:0'
         act_layer = tower._keras_history[0]
         assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
@@ -74,6 +75,48 @@ class TestInceptionModelBase:
         assert conv_layer2.name == 'Block_0a_1x1'
         assert conv_layer2.input._keras_shape == (None, 32, 32, 3)
 
+    def test_create_conv_tower_3x3_batch_norm(self, base, input_x):
+        # import keras
+        opts = {'input_x': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3),
+                'padding': 'SymPad2D', 'batch_normalisation': True}
+        tower = base.create_conv_tower(**opts)
+        # check last element of tower (activation)
+        assert base.part_of_block == 1
+        # assert tower.name == 'Block_0a_act_2/Relu:0'
+        assert tower.name == 'Block_0a_act_2_1/Relu:0'
+        act_layer = tower._keras_history[0]
+        assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
+        assert act_layer.name == "Block_0a_act_2"
+        # check previous element of tower (batch_normal)
+        batch_layer = self.step_in(act_layer)
+        assert isinstance(batch_layer, keras.layers.BatchNormalization)
+        assert batch_layer.name == 'Block_0a_BN'
+        # check previous element of tower (conv2D)
+        conv_layer = self.step_in(batch_layer)
+        assert isinstance(conv_layer, keras.layers.Conv2D)
+        assert conv_layer.filters == 32
+        assert conv_layer.padding == 'valid'
+        assert conv_layer.kernel_size == (3, 3)
+        assert conv_layer.strides == (1, 1)
+        assert conv_layer.name == "Block_0a_3x3"
+        # check previous element of tower (padding)
+        pad_layer = self.step_in(conv_layer)
+        assert isinstance(pad_layer, SymmetricPadding2D)
+        assert pad_layer.padding == ((1, 1), (1, 1))
+        assert pad_layer.name == 'Block_0a_Pad'
+        # check previous element of tower (activation)
+        act_layer2 = self.step_in(pad_layer)
+        assert isinstance(act_layer2, keras.layers.advanced_activations.ReLU)
+        assert act_layer2.name == "Block_0a_act_1"
+        # check previous element of tower (conv2D)
+        conv_layer2 = self.step_in(act_layer2)
+        assert isinstance(conv_layer2, keras.layers.Conv2D)
+        assert conv_layer2.filters == 64
+        assert conv_layer2.kernel_size == (1, 1)
+        assert conv_layer2.padding == 'valid'
+        assert conv_layer2.name == 'Block_0a_1x1'
+        assert conv_layer2.input._keras_shape == (None, 32, 32, 3)
+
     def test_create_conv_tower_3x3_activation(self, base, input_x):
         # import keras
         opts = {'input_x': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3)}
@@ -96,7 +139,8 @@ class TestInceptionModelBase:
         tower = base.create_conv_tower(**opts)
         # check last element of tower (activation)
         assert base.part_of_block == 1
-        assert tower.name == 'Block_0a_act_1_1/Relu:0'
+        assert tower.name == 'Block_0a_act_1_2/Relu:0'
+        # assert tower.name == 'Block_0a_act_1_1/Relu:0'
         act_layer = tower._keras_history[0]
         assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
         assert act_layer.name == "Block_0a_act_1"
@@ -125,7 +169,7 @@ class TestInceptionModelBase:
         # check last element of tower (activation)
         assert base.part_of_block == 1
         # assert tower.name == 'Block_0a_act_1/Relu:0'
-        assert tower.name == 'Block_0a_act_1_3/Relu:0'
+        assert tower.name == 'Block_0a_act_1_4/Relu:0'
         act_layer = tower._keras_history[0]
         assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
         assert act_layer.name == "Block_0a_act_1"
@@ -218,6 +262,22 @@ class TestInceptionModelBase:
         assert self.step_in(block_pool._keras_history[0], depth=3).name == 'Block_2c_Pad'
         assert isinstance(self.step_in(block_pool._keras_history[0], depth=3), ReflectionPadding2D)
 
+    def test_inception_block_invalid_batchnorm(self, base, input_x):
+        conv = {'tower_1': {'reduction_filter': 64,
+                            'tower_kernel': (3, 3),
+                            'tower_filter': 64, },
+                'tower_2': {'reduction_filter': 64,
+                            'tower_kernel': (5, 5),
+                            'tower_filter': 64,
+                            'activation': 'tanh',
+                            'padding': 'SymPad2D', },
+                }
+        pool = {'pool_kernel': (3, 3), 'tower_filter': 64, 'padding': ReflectionPadding2D, 'max_pooling': 'yes'}
+        opts = {'input_x': input_x, 'tower_conv_parts': conv, 'tower_pool_parts': pool, }
+        with pytest.raises(AttributeError) as einfo:
+            block = base.inception_block(**opts)
+        assert "max_pooling has to be either a bool or empty. Given was: yes" in str(einfo.value)
+
     def test_batch_normalisation(self, base, input_x):
         # import keras
         base.part_of_block += 1