--- a
+++ b/lightning/logger.py
@@ -0,0 +1,61 @@
+import torch
+import torch.nn.functional as F
+
+from util.constants import IMAGENET_MEAN, IMAGENET_STD
+
+
+class TFLogger:
+    def log_images(self, images, tag, size=125):
+        """
+        Log images and optionally detection to tensorboard
+        :param logger: [Tensorboard Logger] Tensorboard logger object.
+        :param images: [tensor] batch of images indexed
+                    [batch, channel, size1, size2]
+        TODO: Include an argument for image labels;
+            Print the labels on the images.
+        """
+        images = prep_images_for_logging(images,
+                                         pretrained=self.args['pretrained'],
+                                         size=size)
+        self.logger.experiment.add_images(tag, images)
+
+
+def prep_images_for_logging(images, pretrained=False,
+                            size=125):
+    """
+    Prepare images to be logged
+    :param images: [tensor] batch of images indexed
+                   [channel, size1, size2]
+    :param mean: [list] mean values used to normalize images
+    :param std: [list] standard deviation values used to normalize images
+    :param size: [int] new size of the image to be rescaled
+    :return: images that are reversely normalized
+    """
+    if pretrained:
+        mean = IMAGENET_MEAN
+        std = IMAGENET_STD
+    else:
+        mean = [0, 0, 0]
+        std = [1, 1, 1]
+    images = normalize_inverse(images, mean, std)
+    images = F.interpolate(images, size=size,
+                           mode='bilinear', align_corners=True)
+    return images
+
+
+def normalize_inverse(images, mean=IMAGENET_MEAN, std=IMAGENET_STD):
+    """
+    Reverse Normalization of Pytorch Tensor
+    :param images: [tensor] batch of images indexed
+                   [batch, channel, size1, size2]
+    :param mean: [list] mean values used to normalize images
+    :param std: [list] standard deviation values used to normalize images
+    :return: images that are reversely normalized
+    """
+    mean_inv = torch.FloatTensor(
+        [-m/s for m, s in zip(mean, std)]).view(1, 3, 1, 1)
+    std_inv = torch.FloatTensor([1/s for s in std]).view(1, 3, 1, 1)
+    if torch.cuda.is_available():
+        mean_inv = mean_inv.cuda()
+        std_inv = std_inv.cuda()
+    return (images - mean_inv) / std_inv