test: expect new class RuntimeStopSignal, integrate with termination …

…conditions & update regressions

tests/conftest.py

@@ -1,8 +1,12 @@
 import os
 import typing as t
+from pathlib import Path
 
 import pytest
 
+# runtime directory of the `tests` directory
+tests_root: Path = Path(__file__).parent
+
 
 @pytest.fixture
 def test_suite():
@@ -12,6 +16,20 @@ def test_suite():
     return os.path.dirname(os.path.realpath(__file__))
 
 
+@pytest.fixture
+def test_root() -> Path:
+    """Root directory Path of the test suite; ie /projects/my-project/tests/"""
+    return tests_root
+
+
+@pytest.fixture
+def get_test_data_image() -> t.Callable[[str], Path]:
+    """Get the path of an image file in the test data directory."""
+    def get_image_file_path(file_name: str):
+        return tests_root / "data" / file_name
+    return get_image_file_path
+
+
 @pytest.fixture
 def test_image(test_suite):
     import os
@@ -86,13 +104,15 @@ def termination_condition_module():
         TerminationConditionFacility,
         TerminationConditionInterface,
         TimeLimit,
+        RuntimeStopSignal,
     )
 
-    # all tests require that the Facility already contains some implementations of TerminationCondition
+    # all tests require that the Facility already contains all implementations of TerminationCondition
     assert TerminationConditionFacility.class_registry.subclasses == {
         "max-iterations": MaxIterations,
         "time-limit": TimeLimit,
         "convergence": Convergence,
+        "stop-signal": RuntimeStopSignal
     }
     return type(
         "M",
@@ -230,9 +250,6 @@ def vgg_layers():
     return tuple((layer_id for _, layer_id in VGG_LAYERS))
 
 
-PRODUCTION_IMAGE_MODEL = os.environ.get("AA_VGG_19", "PRETRAINED_MODEL_NOT_FOUND")
-
-
 @pytest.fixture
 def pre_trained_models_1(vgg_layers, toy_model_data, toy_network_design):
     import typing as t
@@ -303,11 +320,6 @@ def model_routines(self):
 
 @pytest.fixture
 def model(pre_trained_models_1):
-    import os
-
-    print(f"\n -- PROD IM MODEL: {PRODUCTION_IMAGE_MODEL}")
-    print(f"Selected Prod?: {os.path.isfile(PRODUCTION_IMAGE_MODEL)}")
-
     return pre_trained_models_1["toy"]
     # return {
     #     True: pre_trained_models_1['vgg'],

tests/test_algorithm.py

@@ -44,29 +44,23 @@ def create_max_iterations_termination_condition_adapter(iterations):
 
 
 @pytest.fixture
-def algorithm_parameters_class():
+def algorithm():
     from artificial_artwork.algorithm import AlogirthmParameters
-
-    return AlogirthmParameters
-
-
-@pytest.fixture
-def algorithm(algorithm_parameters_class):
     from artificial_artwork.algorithm import NSTAlgorithm
 
     def _create_algorithm(*parameters):
-        return NSTAlgorithm(algorithm_parameters_class(*parameters))
+        return NSTAlgorithm(AlogirthmParameters(*parameters))
 
     return _create_algorithm
 
 
 @pytest.fixture
 def create_algorithm(algorithm, tmpdir):
-    def _create_algorithm(image_manager, termination_condition_adapter):
+    def _create_algorithm(image_manager, termination_condition_adapters):
         return algorithm(
             image_manager.content_image,
             image_manager.style_image,
-            termination_condition_adapter,
+            termination_condition_adapters,
             tmpdir,
         )
 
@@ -82,13 +76,13 @@ def create_production_algorithm_runner():
 
     noisy_ratio = 0.6
 
-    def _create_production_algorithm_runner(termination_condition_adapter, max_iterations):
+    def _create_production_algorithm_runner(termination_condition_adapters, max_iterations):
         algorithm_runner = NSTAlgorithmRunner.default(
             lambda matrix: noisy(matrix, noisy_ratio),
         )
 
         algorithm_runner.progress_subject.add(
-            termination_condition_adapter,
+            *termination_condition_adapters
         )
         algorithm_runner.persistance_subject.add(
             StylingObserver(Disk.save_image, convert_to_uint8, max_iterations)
@@ -100,9 +94,9 @@ def _create_production_algorithm_runner(termination_condition_adapter, max_itera
 
 @pytest.fixture
 def get_algorithm_runner(create_production_algorithm_runner):
-    def _get_algorithm_runner(termination_condition_adapter, max_iterations):
+    def _get_algorithm_runner(termination_condition_adapters, max_iterations):
         algorithm_runner = create_production_algorithm_runner(
-            termination_condition_adapter,
+            termination_condition_adapters,
             max_iterations,
         )
         return algorithm_runner
@@ -128,7 +122,7 @@ def test_nst_runner(
     max_iterations_adapter_factory_method,
     image_manager,
     test_image,
-    model,
+    model,  # Tiny Neural Network with 1 Layer
     tmpdir,
 ):
     """Test nst algorithm runner.
@@ -146,9 +140,11 @@ def test_nst_runner(
 
     termination_condition_adapter = max_iterations_adapter_factory_method(ITERATIONS)
 
-    algorithm_runner = get_algorithm_runner(termination_condition_adapter, ITERATIONS)
+    termination_conditions = [termination_condition_adapter]  # they implement listener interface
+
+    algorithm_runner = get_algorithm_runner(termination_conditions, ITERATIONS)
 
-    algorithm = create_algorithm(image_manager, termination_condition_adapter)
+    algorithm = create_algorithm(image_manager, termination_conditions)
 
     model_design = get_model_design(
         model.pretrained_model.handler,

tests/test_algorithm_params.py

@@ -8,13 +8,13 @@ def algorithm_parameters():
     return AlogirthmParameters(
         "content_image",
         "style_image",
-        "termination_condition",
+        [],  # some list of termination conditions
         "output_path",
     )
 
 
 def test_algorithm_parameters(algorithm_parameters):
     assert hasattr(algorithm_parameters, "content_image")
     assert hasattr(algorithm_parameters, "style_image")
-    assert hasattr(algorithm_parameters, "termination_condition")
+    assert hasattr(algorithm_parameters, "termination_conditions")
     assert hasattr(algorithm_parameters, "output_path")

tests/test_cv_model.py

@@ -9,7 +9,7 @@
 from artificial_artwork.production_networks import NetworkDesign
 
 
-class TestPreTrainedModel(t.Protocol):
+class PreTrainedModelForTesting(t.Protocol):
     # property/attribute for doing assertions
     expected_layers: t.Tuple[str]  # ie (conv1_1, conv1_2, .., avgpool5)
 
@@ -18,7 +18,7 @@ class TestPreTrainedModel(t.Protocol):
 
 
 class NSTTestingModelProtocol(t.Protocol):
-    pretrained_model: TestPreTrainedModel
+    pretrained_model: PreTrainedModelForTesting
     network_design: NetworkDesign
 
 

tests/test_image/test_image_processing.py

@@ -113,11 +113,22 @@ def test_uint8_convertion(test_image, expected_image, image_operations):
     assert np.nanmax(runtime_image) < np.power(2.0, 8)
     assert runtime_image.tolist() == expected_image
 
-
 @pytest.mark.parametrize(
     "test_image",
     [
         ([[np.nan, np.nan], [np.nan, np.nan]],),
+    ],
+)
+def test_all_nan_image_raises_error(test_image, image_operations):
+    with pytest.warns(RuntimeWarning, match=r"All-NaN slice encountered"):
+        with pytest.raises(ValueError, match=r"Minimum image value is not finite"):
+            runtime_image = image_operations.convert_to_uint8(
+                np.array([[np.nan, np.nan], [np.nan, np.nan]], dtype=np.float32)
+            )
+
+@pytest.mark.parametrize(
+    "test_image",
+    [
         ([[1, -float("inf")], [2, 3]],),
     ],
 )
@@ -127,7 +138,6 @@ def test_non_finite_minimum_value(test_image, image_operations):
             np.array(test_image, dtype=np.float32)
         )
 
-
 @pytest.mark.parametrize(
     "test_image",
     [