Update bit_vision.py

fix the predict
Atashnezhad · Jun 22, 2023 · fcdd3a6 · fcdd3a6
1 parent 23ef848
commit fcdd3a6
Showing 1 changed file with 22 additions and 22 deletions.
diff --git a/neural_network_model/bit_vision.py b/neural_network_model/bit_vision.py
@@ -281,10 +281,10 @@ def train_model(
  callbacks=[self._check_points(model_save_address, model_name)],
  )
 
- self.model.save(
- model_save_address / model_name
- or SETTING.MODEL_SETTING.MODEL_PATH / SETTING.MODEL_SETTING.MODEL_NAME
- )
+ # self.model.save(
+ #  model_save_address / model_name
+ #  or SETTING.MODEL_SETTING.MODEL_PATH / SETTING.MODEL_SETTING.MODEL_NAME
+ # )
  logger.info(f"Model saved to {SETTING.MODEL_SETTING.MODEL_PATH}")
 
  def plot_history(self, *args, **kwargs):
@@ -351,11 +351,11 @@ def predict(self, *args, **kwargs):
  if model_path is None:
  logger.info(f"model_path from SETTING is was used - {model_path}")
 
- test_folder_dir = kwargs.get(
- "test_folder_dir", SETTING.DATA_ADDRESS_SETTING.TEST_DIR_ADDRESS
- )
- if test_folder_dir is None:
- raise ValueError("test_folder_address is None")
+ # test_folder_dir = kwargs.get(
+ #  "test_folder_dir", SETTING.DATA_ADDRESS_SETTING.TEST_DIR_ADDRESS
+ # )
+ # if test_folder_dir is None:
+ #  raise ValueError("test_folder_address is None")
 
  model = keras.models.load_model(model_path)
  logger.info(f"Model loaded from {model_path}")
@@ -365,20 +365,20 @@ def predict(self, *args, **kwargs):
  number_of_cols = SETTING.FIGURE_SETTING.NUM_COLS_IN_PRED_MODEL
  number_of_rows = SETTING.FIGURE_SETTING.NUM_ROWS_IN_PRED_MODEL
  number_of_test_to_pred = SETTING.MODEL_SETTING.NUMBER_OF_TEST_TO_PRED
- if test_folder_dir:
- train_test_val_dir = (
- test_folder_dir
- or SETTING.PREPROCESSING_SETTING.TRAIN_TEST_VAL_SPLIT_DIR_ADDRESS
- )
- else:
- train_test_val_dir = (
- self.train_test_val_dir
- or SETTING.PREPROCESSING_SETTING.TRAIN_TEST_VAL_SPLIT_DIR_ADDRESS
- )
+ # if test_folder_dir:
+ #  train_test_val_dir = (
+ #  test_folder_dir
+ #  or SETTING.PREPROCESSING_SETTING.TRAIN_TEST_VAL_SPLIT_DIR_ADDRESS
+ #  )
+ # else:
+ #  train_test_val_dir = (
+ #  self.train_test_val_dir
+ #  or SETTING.PREPROCESSING_SETTING.TRAIN_TEST_VAL_SPLIT_DIR_ADDRESS
+ #  )
 
  # get the list of test images
  test_images_list = os.listdir(
- train_test_val_dir
+ self.train_test_val_dir
  / SETTING.PREPROCESSING_SETTING.TRAIN_TEST_SPLIT_DIR_NAMES[1]
  / category
  )
@@ -387,7 +387,7 @@ def predict(self, *args, **kwargs):
 
  for i, img in enumerate(test_images_list[0:number_of_test_to_pred]):
  path_to_img = (
- train_test_val_dir
+ self.train_test_val_dir
  / SETTING.PREPROCESSING_SETTING.TRAIN_TEST_SPLIT_DIR_NAMES[1]
  / category
  / str(img)
@@ -432,7 +432,7 @@ def predict(self, *args, **kwargs):
 
  datagen = image.ImageDataGenerator(SETTING.DATA_GEN_SETTING.RESCALE)
  DoubleCheck_generator = datagen.flow_from_directory(
- directory=test_folder_dir / "test",
+ directory=self.train_test_val_dir / "test",
  target_size=SETTING.FLOW_FROM_DIRECTORY_SETTING.TARGET_SIZE,
  color_mode=SETTING.FLOW_FROM_DIRECTORY_SETTING.COLOR_MODE,
  classes=None,