black

neural_network_model/bit_vision.py

@@ -13,11 +13,22 @@
 import tensorflow as tf
 from keras import Sequential
 from keras.callbacks import ModelCheckpoint
-from keras.layers import BatchNormalization, Conv2D, Dense, Dropout, Flatten, MaxPooling2D
+from keras.layers import (
+    BatchNormalization,
+    Conv2D,
+    Dense,
+    Dropout,
+    Flatten,
+    MaxPooling2D,
+)
 from tensorflow import keras
 from tensorflow.keras.applications.resnet50 import decode_predictions, preprocess_input
 from tensorflow.keras.preprocessing import image
-from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
+from tensorflow.keras.preprocessing.image import (
+    ImageDataGenerator,
+    img_to_array,
+    load_img,
+)
 
 from neural_network_model.model import SETTING
 

neural_network_model/process_data.py

@@ -8,7 +8,11 @@
 
 from bing_image_downloader import downloader
 from tensorflow.keras.preprocessing import image
-from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
+from tensorflow.keras.preprocessing.image import (
+    ImageDataGenerator,
+    img_to_array,
+    load_img,
+)
 from tqdm import tqdm
 
 from neural_network_model.model import SETTING
@@ -289,8 +293,8 @@ def _populated_augmented_images_into_train_test_val_dirs(self, *arges, **kwargs)
 
             # Create three new lists containing 70%, 20%, and 10% of the original list
             train_list = selected_items[:num_train]
-            test_list = selected_items[num_train: num_train + num_test]
-            val_list = selected_items[num_train + num_test:]
+            test_list = selected_items[num_train : num_train + num_test]  # noqa: E203
+            val_list = selected_items[num_train + num_test :]  # noqa: E203
             # copy the images from the dataset_augmented pdc_bit folder to the train, test and validation folders
             self._copy_images(
                 train_list,

neural_network_model/script_run_all.py

@@ -5,7 +5,6 @@
 
 
 def main():
-
     parent_dir = Path(__file__).resolve().parent.parent
     # download the images
     obj = Preprocessing(dataset_address=parent_dir / "dataset")
@@ -17,7 +16,7 @@ def main():
     )
     obj.train_test_split(
         augmented_data_address=parent_dir / "dataset_augmented",
-        train_test_val_split_dir_address=parent_dir / "dataset_train_test_val"
+        train_test_val_split_dir_address=parent_dir / "dataset_train_test_val",
     )
 
     obj = BitVision(train_test_val_dir=parent_dir / "dataset_train_test_val")
@@ -28,9 +27,7 @@ def main():
     #
     model_name = "model_epoch_{epoch:02d}_loss_{loss:.2f}_acc_{accuracy:.2f}_val_acc_{val_accuracy:.2f}_.h5"
     obj.train_model(
-        epochs=8,
-        model_save_address=parent_dir / "deep_model",
-        model_name=model_name
+        epochs=8, model_save_address=parent_dir / "deep_model", model_name=model_name
     )
     obj.plot_history(fig_folder_address=parent_dir / "figures")
 
@@ -39,7 +36,7 @@ def main():
     obj.predict(
         fig_save_address=parent_dir / "figures",
         model_path=parent_dir / "deep_model" / best_model,
-        test_folder_address=parent_dir / "dataset_train_test_val" / "test"
+        test_folder_address=parent_dir / "dataset_train_test_val" / "test",
     )
 
     # find list of images in the parent_dir / "dataset_train_test_val" / "test" / "pdc_bit"
@@ -49,9 +46,12 @@ def main():
     obj.grad_cam_viz(
         model_path=parent_dir / "deep_model" / best_model,
         fig_to_save_address=parent_dir / "figures",
-        img_to_be_applied_path=parent_dir / "dataset_train_test_val" / "test" / "pdc_bit" / list_of_images[
-            0],
-        output_gradcam_fig_name="test.png"
+        img_to_be_applied_path=parent_dir
+        / "dataset_train_test_val"
+        / "test"
+        / "pdc_bit"
+        / list_of_images[0],
+        output_gradcam_fig_name="test.png",
     )
 
 

tests/test_run.py

@@ -11,6 +11,8 @@
 
 
 def test_run_all():
-    script_path = Path(__file__).parent / ".." / "neural_network_model" / "script_run_all.py"
-    os.system(f'python {script_path}')
+    script_path = (
+        Path(__file__).parent / ".." / "neural_network_model" / "script_run_all.py"
+    )
+    os.system(f"python {script_path}")
     assert True