[ENH] Test coverage for FCNNetwork Improved

aeon/networks/tests/test_fcn.py

@@ -0,0 +1,196 @@
+"""Test for the FCNNetwork class."""
+
+import pytest
+
+from aeon.networks import FCNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+def test_fcnnetwork_valid():
+    """Test FCNNetwork with valid configurations."""
+    input_shape = (100, 5)
+    model = FCNNetwork(n_layers=3)
+    input_layer, output_layer = model.build_network(input_shape)
+
+    assert hasattr(input_layer, "shape")
+    assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "activation, should_raise",
+    [
+        (["relu", "sigmoid", "tanh"], False),
+        (["relu", "sigmoid"], True),
+        (
+            ["relu", "sigmoid", "tanh", "softmax"],
+            True,
+        ),
+        ("relu", False),
+        ("sigmoid", False),
+        ("tanh", False),
+        ("softmax", False),
+    ],
+)
+def test_fcnnetwork_activation(activation, should_raise):
+    """Test FCNNetwork with valid and invalid activation configurations."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            model = FCNNetwork(activation=activation)
+            model.build_network(input_shape)
+    else:
+        model = FCNNetwork(activation=activation)
+        input_layer, output_layer = model.build_network(input_shape)
+
+        assert hasattr(input_layer, "shape")
+
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "kernel_size, should_raise",
+    [
+        ([3, 1, 2], False),
+        ([1, 3], True),
+        ([3, 1, 1, 3], True),
+        (3, False),
+    ],
+)
+def test_fcnnetwork_kernel_size(kernel_size, should_raise):
+    """Test FCNNetwork with valid and invalid kernel_size configurations."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            model = FCNNetwork(kernel_size=kernel_size, n_layers=3)
+            model.build_network(input_shape)
+    else:
+        model = FCNNetwork(kernel_size=kernel_size, n_layers=3)
+        input_layer, output_layer = model.build_network(input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "dilation_rate, should_raise",
+    [
+        ([1, 2, 1], False),
+        ([1, 4], True),
+        ([1, 2, 4, 1], True),
+        (1, False),
+    ],
+)
+def test_fcnnetwork_dilation_rate(dilation_rate, should_raise):
+    """Test FCNNetwork with valid and invalid dilation_rate configurations."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            model = FCNNetwork(dilation_rate=dilation_rate, n_layers=3)
+            model.build_network(input_shape)
+    else:
+        model = FCNNetwork(dilation_rate=dilation_rate, n_layers=3)
+        input_layer, output_layer = model.build_network(input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "strides, should_raise",
+    [
+        ([1, 2, 3], False),
+        ([1, 1], True),
+        ([1, 2, 2, 1], True),
+        (1, False),
+    ],
+)
+def test_fcnnetwork_strides(strides, should_raise):
+    """Test FCNNetwork with valid and invalid strides configurations."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            model = FCNNetwork(strides=strides, n_layers=3)
+            model.build_network(input_shape)
+    else:
+        model = FCNNetwork(strides=strides, n_layers=3)
+        input_layer, output_layer = model.build_network(input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "padding, should_raise",
+    [
+        (["same", "same", "valid"], False),
+        (["valid", "same"], True),
+        (["same", "valid", "same", "valid"], True),
+        ("same", False),
+        ("valid", False),
+    ],
+)
+def test_fcnnetwork_padding(padding, should_raise):
+    """Test FCNNetwork with valid and invalid padding configurations."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            model = FCNNetwork(padding=padding, n_layers=3)
+            model.build_network(input_shape)
+    else:
+        model = FCNNetwork(padding=padding, n_layers=3)
+        input_layer, output_layer = model.build_network(input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "n_filters, should_raise",
+    [
+        ([32, 64, 128], False),  # Valid case with a list of filters
+        ([32, 64], True),  # Invalid case with fewer filters than layers
+        ([32, 64, 128, 256], True),  # Invalid case with more filters than layers
+        (32, False),  # Valid case with a single filter value
+    ],
+)
+def test_fcnnetwork_n_filters(n_filters, should_raise):
+    """Test FCNNetwork with valid and invalid n_filters configurations."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            model = FCNNetwork(n_filters=n_filters, n_layers=3)
+            model.build_network(input_shape)
+    else:
+        model = FCNNetwork(n_filters=n_filters, n_layers=3)
+        input_layer, output_layer = model.build_network(input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")