grad cam ret

neural_network_model/transfer_learning.py

@@ -892,43 +892,16 @@ def grad_cam_viz(self, *args, **kwargs):
  num_rows = kwargs.get("num_rows", None)
  num_cols = kwargs.get("num_cols", None)
  last_conv_layer_name = kwargs.get("last_conv_layer_name", "Conv_1")
- img_size = kwargs.get("img_size", SETTING.FLOW_FROM_DIRECTORY_SETTING.TARGET_SIZE)
+ img_size = kwargs.get("img_size", (224, 224))
  gard_cam_image_name = kwargs.get("gard_cam_image_name", "transf_cam.jpg")
  figsize = kwargs.get("figsize", (8, 6))
  title_lable_size = kwargs.get("title_lable_size", 8)
- model_path = kwargs.get("model_path", None)
- test_dataset_address = kwargs.get("test_dataset_address", None)
- x_col = TRANSFER_LEARNING_SETTING.DF_X_COL_NAME,
- y_col = TRANSFER_LEARNING_SETTING.DF_Y_COL_NAME,
- save_path = kwargs.get("save_path", Path(__file__).parent / ".." / "figures" / "grad_cam.png", )
 
- if model_path:
- logger.info(f"Loading the model from {model_path}")
- self.model = tf.keras.models.load_model(model_path)
- else:
- logger.info(f"Using the self.model from memory")
  # Remove last layer's softmax
  self.model.layers[-1].activation = None
 
  # Display the part of the pictures used by the neural network to classify the pictures
- if test_dataset_address:
- # Get filepaths and labels
- filepaths = list(test_dataset_address.glob(r"**/*.png"))
- # add those with jpg extension
- filepaths.extend(list(test_dataset_address.glob(r"**/*.jpg")))
- # add those with jpeg extension
- filepaths.extend(list(test_dataset_address.glob(r"**/*.jpeg")))
- labels = [path.stem for path in filepaths]
-
- filepaths = pd.Series(filepaths, name=x_col).astype(str)
- labels = pd.Series(labels, name=y_col)
-
- # Concatenate filepaths and labels
- test_df = pd.concat([filepaths, labels], axis=1)
- # test_df, _ = train_test_split(test_df, train_size=1)
-
- else:
- _, test_df = self._train_test_split()
+ _, test_df = self._train_test_split()
 
  if not num_rows and not num_cols:
  # Get the number of rows and columns for subplots
@@ -977,7 +950,7 @@ def grad_cam_viz(self, *args, **kwargs):
  plt.tight_layout()
  # save the figure
  plt.savefig(
- save_path,
+ Path(__file__).parent / ".." / "figures" / "grad_cam.png",
  bbox_inches="tight",
  )
  plt.show()