added function for sampling y and y_hat images

utils/data_logging_utils.py

@@ -18,6 +18,7 @@ import matplotlib.pyplot as plt
 import shutil
 import logging
 import numpy as np
+import re
 
 # The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.
 # More here: https://tensorboardx.readthedocs.io/en/latest/tensorboard.html
@@ -77,6 +78,23 @@ class LogWriter():
         file_handler = logging.FileHandler("{}/{}.log".format(os.path.join(logs_directory, experiment_name), "console_logs"))
         self.logger.addHandler(file_handler)
 
+    def labels_generator(self, labels):
+        """ Label Generator Function
+
+        This function processess an input array of labels.
+
+        Args:
+            labels (arr): Vector/Array of labels (if applicable)
+
+        Returns:
+            label_classes (list): List of processed labels
+
+        Raises:
+            None
+        """
+    
+        return pass
+
     def log(self, message):
         """Log function
 
@@ -201,17 +219,45 @@ class LogWriter():
         else:
             self.log_writer[phase].add_figure(plot_name + '/' + phase, figure)
 
-    def plot_evaluation_box(self):
-        pass
+    # Currently, also no need for an evaluation box plot
 
-    def sample_image_per_epoch(self):
-        pass
+    def sample_image_per_epoch(self, prediction, ground_truth, phase, epoch):
+        """Function plotting mirrored images
+
+        This function plots a predicted and a grond truth images side-by-side.
+
+        Args:
+            prediction (torch.tensor): Predicted image after passing throught the network
+            ground_truth (torch.tensor): Labelled ground truth image
+            phase (str): Current run mode or phase
+            epoch (int): Current epoch value
+
+        Returns:
+            None
+
+        Raises
+            None
+        """
+
+        print("Sample Image is being loaded...", end='', flush= True)
+        figure, ax = plt.subplots(nrows = len(prediction), ncols = 2)
+
+        for i in range(len(prediction)):
+            ax[i][0].imshow(prediction[i])
+            ax[i][0].set_title("Predicted Image")
+            ax[i][0].axis('off')
+
+            ax[i][1].imshow(ground_truth[i])
+            ax[i][1].set_title('Ground Truth Image')
+            ax[i][1].axis('off')
+
+        figure.set_tight_layout()
+        self.log_writer[phase].add_figure('sample_prediction/'+phase, figure, epoch)
+
+        print("Sample Image successfully loaded!")
 
     def graph(self):
         pass
 
     def close(self):
         pass
-
-    def labels_generator(self):
-        return pass
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