built constructor, defined functions

utils/data_logging_utils.py

@@ -13,3 +13,98 @@ To use content from this folder, import the functions and instantiate them as yo
 """
 
 import os
+import matplotlib.pyplot as plt
+import shutil
+import logging
+
+# 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
+from tensorboardX import SummaryWriter
+
+plt.axis('scaled')
+
+class LogWriter():
+
+    """Log Writer class for the BrainMapper U-net.
+
+    This class contains the pytorch implementation of the several logging functions required for the BrainMapper project.
+    These functions are designed to keep track of progress during training, and also aid debugging.
+
+    Args:
+        number_of_classes (int): Number of classes
+        logs_directory (str): Directory for outputing training logs
+        experiment_name (str): Name of the experiment
+        use_last_checkpoint (bool): Flag for loading the previous checkpoint
+        labels (arr): Vector/Array of labels (if applicable)
+        confusion_matrix_cmap (class): Colour Map to be used for the Conusion Matrix
+
+    Returns:
+        None
+
+    Raises:
+        None
+    """
+
+    def __init__(self, number_of_classes, logs_directory, experiment_name, use_last_checkpoint=False, labels=None, confusion_matrix_cmap= plt.cm.Blues):
+        
+        self.number_of_classes = number_of_classes
+        training_logs_directory = os.path.join(logs_directory, experiment_name, "train")
+        testing_logs_directory = os.path.join(logs_directory, experiment_name, "test")
+
+        # If the logs directory exist, we clear their contents to allow new logs to be created
+        if not use_last_checkpoint:
+            if os.path.exists(training_logs_directory):
+                shutil.rmtree(training_logs_directory)
+            if os.path.exists(testing_logs_directory):
+                shutil.rmtree(testing_logs_directory)
+
+        self.log_writer = {
+            'train': SummaryWriter(logdir= training_logs_directory),
+            'test:': SummaryWriter(logdir= testing_logs_directory)
+        }
+
+        self.confusion_matrix_color_map = confusion_matrix_cmap
+
+        self.current_iteration = 1
+
+        self.labels = self.labels_generator(labels)
+
+        self.logger = logging.getLogger()
+        file_handler = logging.FileHandler("{}/{}.log".format(os.path.join(logs_directory, experiment_name), "console_logs"))
+        self.logger.addHandler(file_handler)
+
+    def log(self):
+        pass
+
+    def loss_per_iteration(self):
+        pass
+
+    def loss_per_epoch(self):
+        pass
+
+    def confusion_matrix_per_epoch(self):
+        pass
+
+    def plot_confusion_matrix(self):
+        pass
+
+    def dice_score_per_epoch(self):
+        pass
+
+    def plot_dice_score(self):
+        pass
+
+    def plot_evaluation_box(self):
+        pass
+
+    def sample_image_per_epoch(self):
+        pass
+
+    def graph(self):
+        pass
+
+    def close(self):
+        pass
+
+    def labels_generator(self):
+        pass
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