edit test

tests/test_run.py

@@ -9,86 +9,57 @@
 main_dir = current_dir.parent
 # Add the main directory to the Python path
 sys.path.append(str(main_dir))
-# skip this test TODO: fix this test later
-import pytest
 
+import pytest
 from neural_network_model.bit_vision import BitVision
 from neural_network_model.process_data import Preprocessing
 
 
-@pytest.mark.skip(reason="no way of currently testing this")
+# @pytest.mark.skip(reason="no way of currently testing this")
 def test_run():
- # check if a dir name test_resources exists if not create one
- if not os.path.exists(Path(__file__).parent / "test_resources"):
- os.mkdir(Path(__file__).parent / "test_resources")
-
  # download the images
- obj = Preprocessing(
- dataset_address=Path(__file__).parent / "test_resources" / "dataset"
- )
+ obj = Preprocessing(dataset_address=Path(__file__).parent / "dataset")
  obj.download_images(limit=10)
  print(obj.image_dict)
  obj.augment_data(
  number_of_images_tobe_gen=10,
- augment_data_address=Path(__file__).parent
- / "test_resources"
- / "augmented_dataset",
+ augment_data_address=Path(__file__).parent / "augmented_dataset"
  )
  obj.train_test_split(
- augmented_data_address=Path(__file__).parent
- / "test_resources"
- / "augmented_dataset",
- train_test_val_split_dir_address=Path(__file__).parent
- / "test_resources"
- / "dataset_train_test_val",
+ augmented_data_address=Path(__file__).parent / "augmented_dataset",
+ train_test_val_split_dir_address=Path(__file__).parent / "dataset_train_test_val"
  )
 
- obj = BitVision(
- train_test_val_dir=Path(__file__).parent
- / "test_resources"
- / "dataset_train_test_val"
- )
+ obj = BitVision(train_test_val_dir=Path(__file__).parent / "dataset_train_test_val")
  print(obj.categories)
  print(obj.data_details)
  obj.plot_image_category()
  obj.compile_model()
-
+ #
  model_name = "model_epoch_{epoch:02d}_loss_{loss:.2f}_acc_{accuracy:.2f}_val_acc_{val_accuracy:.2f}_.h5"
  obj.train_model(
- model_save_address=Path(__file__).parent / "test_resources" / "deep_model",
- model_name=model_name,
- epochs=10,
- )
- obj.plot_history(
- fig_folder_address=Path(__file__).parent / "test_resources" / "figures"
+ epochs=8,
+ model_save_address=Path(__file__).parent / "deep_model",
+ model_name=model_name
  )
+ obj.plot_history(fig_folder_address=Path(__file__).parent / "figures")
+
+ best_model = obj.return_best_model_name(directory="deep_model")
 
  obj.predict(
- fig_save_address=Path(__file__).parent / "test_resources" / "figures",
- model_path=Path(__file__).parent / "test_resources" / "deep_model" / model_name,
- test_folder_address=Path(__file__).parent
- / "test_resources"
- / "dataset_train_test_val"
- / "test",
+ fig_save_address=Path(__file__).parent / "figures",
+ model_path=Path(__file__).parent / "deep_model" / best_model,
+ test_folder_address=Path(__file__).parent / "dataset_train_test_val" / "test"
  )
 
  # find list of images in the Path(__file__).parent / "dataset_train_test_val" / "test" / "pdc_bit"
- directory_path = (
- Path(__file__).parent
- / "test_resources"
- / "dataset_train_test_val"
- / "test"
- / "pdc_bit"
- )
+ directory_path = Path(__file__).parent / "dataset_train_test_val" / "test" / "pdc_bit"
  list_of_images = [str(x) for x in directory_path.glob("*.jpeg")]
+
  obj.grad_cam_viz(
- model_path=Path(__file__).parent / "test_resources" / "deep_model" / model_name,
- fig_to_save_address=Path(__file__).parent / "test_resources" / "figures",
- img_to_be_applied_path=Path(__file__).parent
- / "test_resources"
- / "dataset_train_test_val"
- / "test"
- / "pdc_bit"
- / list_of_images[0],
- output_gradcam_fig_name="gradcam.png",
+ model_path=Path(__file__).parent / "deep_model" / best_model,
+ fig_to_save_address=Path(__file__).parent / "figures",
+ img_to_be_applied_path=Path(__file__).parent / "dataset_train_test_val" / "test" / "pdc_bit" / list_of_images[
+ 0],
+ output_gradcam_fig_name="gradcam.png"
  )