Add compiling packnet model, refactor modules to not duplicate loaders and trainers

losses.py

@@ -6,15 +6,15 @@ def dense_depth_loss_function(y, y_pred):
     Implementation of the loss from the dense depth paper https://arxiv.org/pdf/1812.11941.pdf
     """
     # Point-wise L1 loss
-    l_depth = tf.reduce_mean(tf.math.abs(y_pred - y), axis=-1)
+    l1_depth = tf.reduce_mean(tf.math.abs(y_pred - y), axis=-1)
 
     # L1 loss over image gradients
     dy, dx = tf.image.image_gradients(y)
     dy_pred, dx_pred = tf.image.image_gradients(y_pred)
-    l_grad = tf.reduce_mean(tf.math.abs(dy_pred - dy) +
-                            tf.math.abs(dx_pred - dx), axis=-1)
+    gradient = tf.reduce_mean(tf.math.abs(dy_pred - dy) +
+                              tf.math.abs(dx_pred - dx), axis=-1)
 
     #  Structural Similarity (SSIM)
-    l_ssim = (1 - tf.image.ssim(y, y_pred, 500)) / 2
+    ssim = (1 - tf.image.ssim(y, y_pred, 500)) / 2
 
-    return 0.1 * tf.reduce_mean(l_depth) + tf.reduce_mean(l_grad) + l_ssim
+    return 0.1 * tf.reduce_mean(l1_depth) + tf.reduce_mean(gradient) + ssim