Removed tta argument from common;

Fix issue with save-masks arg in argparse;
Added metric saving to cvs file.

config/dataset_config.py

@@ -9,7 +9,6 @@ class DatasetCommonConfig(BaseModel):
     """
     seed: Optional[int] = 0         # Seed for splitting if data is not pre-split (and all random operations)
     device: str = "cuda:0"          # Device used for training/testing (e.g., 'cpu' or 'cuda')
-    use_tta: bool = False           # Flag to use Test-Time Augmentation (TTA)
     use_amp: bool = False           # Flag to use Automatic Mixed Precision (AMP)
     masks_subdir: str = ""          # Subdirectory where the required masks are located, e.g. 'masks/cars'
     predictions_dir: str = "."      # Directory to save predictions

core/segmentator.py

@@ -1,4 +1,3 @@
-import time
 import random
 import numpy as np
 from numba import njit, prange
@@ -26,7 +25,9 @@ import matplotlib.colors as mcolors
 
 import os
 import glob
+import csv
 import copy
+import time
 import tifffile as tiff
 
 from pprint import pformat
@@ -374,6 +375,10 @@ class CellSegmentator:
             test_metrics = self.__run_epoch("test", save_results=save_results, only_masks=only_masks)
             self.__print_with_logging(test_metrics, 0)
             
+            save_path = self._dataset_setup.common.predictions_dir
+            os.makedirs(save_path, exist_ok=True)
+            self.__save_metrics_to_csv(test_metrics, os.path.join(save_path, 'metrics.csv'))
+
   
     def evaluate(self, save_results: bool = True, only_masks: bool = False) -> None:
         """
@@ -387,6 +392,10 @@ class CellSegmentator:
         test_metrics = self.__run_epoch("test", save_results=save_results, only_masks=only_masks)
         self.__print_with_logging(test_metrics, 0)
         
+        save_path = self._dataset_setup.common.predictions_dir
+        os.makedirs(save_path, exist_ok=True)
+        self.__save_metrics_to_csv(test_metrics, os.path.join(save_path, 'metrics.csv'))
+
  
     def predict(self, only_masks: bool = False) -> None:
         """
@@ -446,6 +455,12 @@ class CellSegmentator:
         """
         start_time = time.time()
         
+        logger.info(
+            f"Masks saving: {'enabled' if save_results else 'disabled'}; "
+            f"Additional visualizations: "
+            f"{'enabled' if save_results and not only_masks else 'disabled'}"
+        )
+
         # 1) TRAINING PATH
         if self._dataset_setup.is_training:
             # Launch the full training loop (with validation, scheduler steps, etc.)
@@ -778,17 +793,17 @@ class CellSegmentator:
         return Dataset(data, transforms)
     
     
-    def __print_with_logging(self, results: Dict[str, Union[float, np.ndarray]], step: int) -> None:
+    def __print_with_logging(self, metrics: Dict[str, Union[float, np.ndarray]], step: int) -> None:
         """
         Print metrics in a tabular format and log to W&B.
 
         Args:
-            results (Dict[str, Union[float, np.ndarray]]): Mapping from metric names 
+            metrics (Dict[str, Union[float, np.ndarray]]): Mapping from metric names 
                 to either a float or a ND numpy array.
             step (int): epoch index.
         """
         rows: list[tuple[str, str]] = []
-        for key, val in results.items():
+        for key, val in metrics.items():
             if isinstance(val, np.ndarray):
                 # Convert array to string, e.g. '[0.2, 0.8, 0.5]'
                 val_str = np.array2string(val, separator=', ')
@@ -808,7 +823,7 @@ class CellSegmentator:
         if self._wandb_config.use_wandb:
             # Keep only scalar values
             scalar_results: dict[str, float] = {}
-            for key, val in results.items():
+            for key, val in metrics.items():
                 if isinstance(val, np.ndarray):
                     continue
                 # Ensure float type
@@ -816,6 +831,34 @@ class CellSegmentator:
             wandb.log(scalar_results, step=step)
     
     
+    def __save_metrics_to_csv(
+        self,
+        metrics: Dict[str, Union[float, np.ndarray]],
+        output_path: str
+    ) -> None:
+        """
+        Saves a dictionary of metrics to a CSV file with columns 'Metric' and 'Value'.
+
+        Args:
+            metrics (Dict[str, Union[float, np.ndarray]]):
+                Mapping from metric names to scalar values or numpy arrays.
+            output_path (str):
+                Path to the output CSV file.
+        """
+        with open(output_path, mode='w', newline='') as csv_file:
+            writer = csv.writer(csv_file)
+            writer.writerow(['Metric', 'Value'])
+            for name, value in metrics.items():
+                # Convert numpy arrays to string representation
+                if isinstance(value, np.ndarray):
+                    # Flatten and join with commas
+                    flat = value.flatten()
+                    val_str = ','.join([f"{v}" for v in flat])
+                else:
+                    val_str = f"{value}"
+                writer.writerow([name, val_str])
+                
+    
     def __run_epoch(self,
         mode: Literal["train", "valid", "test"],
         epoch: Optional[int] = None,
@@ -900,7 +943,7 @@ class CellSegmentator:
                             predicted_masks=preds,
                             ground_truth_masks=labels_post, # type: ignore
                             iou_threshold=0.5,
-                            return_error_masks=(mode == "test")
+                            return_error_masks=(mode == "test") and save_results is True
                         )
                         all_tp.append(tp)
                         all_fp.append(fp)

main.py

@@ -24,17 +24,16 @@ def main():
         help='Run mode: train, test or predict'
     )
     parser.add_argument(
-        '-s', '--save-masks',
-        action='store_true',
-        default=True,
-        help='If set to False, do not save predicted masks; by default, saving is enabled'
+        '--no-save-masks',
+        action='store_false',
+        dest='save_masks',
+        help='If set, do NOT save predicted masks (saving is enabled by default)'
     )
     parser.add_argument(
         '--only-masks',
         action='store_true',
-        default=False,
         help=('If set and save-masks set, save only the raw predicted'
-              ' masks without additional visualizations or metrics')
+              ' masks without additional visualizations')
     )
 
     args = parser.parse_args()