WIP Refactor test

tests/helpers/config.py

@@ -0,0 +1,42 @@
+from pathlib import Path
+from typing import List, Optional, Union
+
+from omegaconf import DictConfig, ListConfig
+
+from anomalib.config import get_configurable_parameters
+
+from .dataset import get_dataset_path
+
+
+def get_test_configurable_parameters(
+    dataset_path: Optional[str] = None,
+    model_name: Optional[str] = None,
+    model_config_path: Optional[Union[Path, str]] = None,
+    weight_file: Optional[str] = None,
+    openvino: bool = False,
+    config_filename: Optional[str] = "config",
+    config_file_extension: Optional[str] = "yaml",
+) -> Union[DictConfig, ListConfig]:
+    """Get configurable parameters for testing.
+
+    Args:
+        datset_path: Optional[Path]: Path to dataset
+        model_name: Optional[str]:  (Default value = None)
+        model_config_path: Optional[Union[Path, str]]:  (Default value = None)
+        weight_file: Path to the weight file
+        openvino: Use OpenVINO
+        config_filename: Optional[str]:  (Default value = "config")
+        config_file_extension: Optional[str]:  (Default value = "yaml")
+
+    Returns:
+        Union[DictConfig, ListConfig]: Configurable parameters in DictConfig object.
+    """
+
+    config = get_configurable_parameters(
+        model_name, model_config_path, weight_file, openvino, config_filename, config_file_extension
+    )
+
+    # Update path to match the dataset path in the test image/runner
+    config.dataset.path = get_dataset_path() if dataset_path is None else dataset_path
+
+    return config

tests/pre_merge/datasets/test_dataset.py

@@ -5,14 +5,15 @@
 import numpy as np
 import pytest
 
-from anomalib.config import get_configurable_parameters, update_input_size_config
+from anomalib.config import update_input_size_config
 from anomalib.data import (
     BTechDataModule,
     FolderDataModule,
     MVTecDataModule,
     get_datamodule,
 )
 from anomalib.pre_processing.transforms import Denormalize, ToNumpy
+from tests.helpers.config import get_test_configurable_parameters
 from tests.helpers.dataset import TestDataset, get_dataset_path
 
 
@@ -202,11 +203,9 @@ class TestConfigToDataModule:
         ],
     )
     @TestDataset(num_train=20, num_test=10)
-    def test_image_size(self, input_size, effective_image_size, category="shapes", path=""):
+    def test_image_size(self, input_size, effective_image_size, category="shapes", path=None):
         """Test if the image size parameter works as expected."""
-        model_name = "stfpm"
-        configurable_parameters = get_configurable_parameters(model_name)
-        configurable_parameters.dataset.path = path
+        configurable_parameters = get_test_configurable_parameters(dataset_path=path, model_name="stfpm")
         configurable_parameters.dataset.category = category
         configurable_parameters.dataset.image_size = input_size
         configurable_parameters = update_input_size_config(configurable_parameters)

tests/pre_merge/pre_processing/transforms/test_transforms.py

@@ -8,7 +8,6 @@
 """
 
 import tempfile
-from pathlib import Path
 
 import albumentations as A
 import numpy as np

tests/pre_merge/utils/callbacks/openvino_callback/test_openvino.py

@@ -4,8 +4,8 @@
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 
-from anomalib.config import get_configurable_parameters
 from anomalib.utils.callbacks.openvino import OpenVINOCallback
+from tests.helpers.config import get_test_configurable_parameters
 from tests.pre_merge.utils.callbacks.openvino_callback.dummy_lightning_model import (
     DummyLightningModule,
     FakeDataModule,
@@ -15,7 +15,7 @@
 def test_openvino_model_callback():
     """Tests if an optimized model is created."""
 
-    config = get_configurable_parameters(
+    config = get_test_configurable_parameters(
         model_config_path="tests/pre_merge/utils/callbacks/openvino_callback/dummy_config.yml"
     )
 

tests/pre_merge/utils/metrics/test_adaptive_threshold.py

@@ -19,11 +19,11 @@
 import torch
 from pytorch_lightning import Trainer
 
-from anomalib.config import get_configurable_parameters
 from anomalib.data import get_datamodule
 from anomalib.models import get_model
 from anomalib.utils.callbacks import get_callbacks
 from anomalib.utils.metrics import AdaptiveThreshold
+from tests.helpers.config import get_test_configurable_parameters
 
 
 @pytest.mark.parametrize(
@@ -48,7 +48,7 @@ def test_non_adaptive_threshold():
     Test if the non-adaptive threshold gets used in the F1 score computation when
     adaptive thresholding is disabled and no normalization is used.
     """
-    config = get_configurable_parameters(model_config_path="anomalib/models/padim/config.yaml")
+    config = get_test_configurable_parameters(model_config_path="anomalib/models/padim/config.yaml")
 
     config.model.normalization_method = "none"
     config.model.threshold.adaptive = False