fix compute_output_shape behavior in normalizations.py

tensorflow_addons/layers/normalizations.py

@@ -155,9 +155,6 @@ def get_config(self):
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def compute_output_shape(self, input_shape):
-        return input_shape
-
     def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
 
         group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
@@ -447,9 +444,6 @@ def call(self, inputs):
         normalized_inputs = inputs * tf.math.rsqrt(nu2 + epsilon)
         return self.gamma * normalized_inputs + self.beta
 
-    def compute_output_shape(self, input_shape):
-        return input_shape
-
     def get_config(self):
         config = {
             "axis": self.axis,

tensorflow_addons/layers/tests/normalizations_test.py

@@ -346,6 +346,52 @@ def test_groupnorm_convnet_no_center_no_scale():
     )
 
 
+@pytest.mark.usefixtures("maybe_run_functions_eagerly")
+@pytest.mark.parametrize("center", [True, False])
+@pytest.mark.parametrize("scale", [True, False])
+def test_group_norm_compute_output_shape(center, scale):
+
+    target_variables_len = [center, scale].count(True)
+    target_trainable_variables_len = [center, scale].count(True)
+    layer1 = GroupNormalization(groups=2, center=center, scale=scale)
+    layer1.build(input_shape=[8, 28, 28, 16])  # build()
+    assert len(layer1.variables) == target_variables_len
+    assert len(layer1.trainable_variables) == target_trainable_variables_len
+
+    layer2 = GroupNormalization(groups=2, center=center, scale=scale)
+    layer2.compute_output_shape(input_shape=[8, 28, 28, 16])  # compute_output_shape()
+    assert len(layer2.variables) == target_variables_len
+    assert len(layer2.trainable_variables) == target_trainable_variables_len
+
+    layer3 = GroupNormalization(groups=2, center=center, scale=scale)
+    layer3(tf.random.normal(shape=[8, 28, 28, 16]))  # call()
+    assert len(layer3.variables) == target_variables_len
+    assert len(layer3.trainable_variables) == target_trainable_variables_len
+
+
+@pytest.mark.usefixtures("maybe_run_functions_eagerly")
+@pytest.mark.parametrize("center", [True, False])
+@pytest.mark.parametrize("scale", [True, False])
+def test_instance_norm_compute_output_shape(center, scale):
+
+    target_variables_len = [center, scale].count(True)
+    target_trainable_variables_len = [center, scale].count(True)
+    layer1 = InstanceNormalization(groups=2, center=center, scale=scale)
+    layer1.build(input_shape=[8, 28, 28, 16])  # build()
+    assert len(layer1.variables) == target_variables_len
+    assert len(layer1.trainable_variables) == target_trainable_variables_len
+
+    layer2 = InstanceNormalization(groups=2, center=center, scale=scale)
+    layer2.compute_output_shape(input_shape=[8, 28, 28, 16])  # compute_output_shape()
+    assert len(layer2.variables) == target_variables_len
+    assert len(layer2.trainable_variables) == target_trainable_variables_len
+
+    layer3 = InstanceNormalization(groups=2, center=center, scale=scale)
+    layer3(tf.random.normal(shape=[8, 28, 28, 16]))  # call()
+    assert len(layer3.variables) == target_variables_len
+    assert len(layer3.trainable_variables) == target_trainable_variables_len
+
+
 def calculate_frn(
     x, beta=0.2, gamma=1, eps=1e-6, learned_epsilon=False, dtype=np.float32
 ):
@@ -471,3 +517,23 @@ def test_filter_response_normalization_save(tmpdir):
     model.save(filepath, save_format="h5")
     filepath = str(tmpdir / "test")
     model.save(filepath, save_format="tf")
+
+
+@pytest.mark.usefixtures("maybe_run_functions_eagerly")
+def test_filter_response_norm_compute_output_shape():
+    target_variables_len = 2
+    target_trainable_variables_len = 2
+    layer1 = FilterResponseNormalization()
+    layer1.build(input_shape=[8, 28, 28, 16])  # build()
+    assert len(layer1.variables) == target_variables_len
+    assert len(layer1.trainable_variables) == target_trainable_variables_len
+
+    layer2 = FilterResponseNormalization()
+    layer2.compute_output_shape(input_shape=[8, 28, 28, 16])  # compute_output_shape()
+    assert len(layer2.variables) == target_variables_len
+    assert len(layer2.trainable_variables) == target_trainable_variables_len
+
+    layer3 = FilterResponseNormalization()
+    layer3(tf.random.normal(shape=[8, 28, 28, 16]))  # call()
+    assert len(layer3.variables) == target_variables_len
+    assert len(layer3.trainable_variables) == target_trainable_variables_len