Add dense-depth and experimental dense-net-nnconv5 models

Since dense-depth will use half labels by default, the nyu train/eval datasets can be loaded from here at half resolutions for labels

losses.py

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+import tensorflow as tf
+import tensorflow.keras.backend as K
+
+
+def dense_depth_loss_function(y_true, y_pred, theta=0.1, maxDepthVal=1000.0 / 10.0):
+    # Point-wise depth
+    l_depth = K.mean(K.abs(y_pred - y_true), axis=-1)
+
+    # Edges
+    dy_true, dx_true = tf.image.image_gradients(y_true)
+    dy_pred, dx_pred = tf.image.image_gradients(y_pred)
+    l_edges = K.mean(K.abs(dy_pred - dy_true) + K.abs(dx_pred - dx_true), axis=-1)
+
+    # Structural similarity (SSIM) index
+    l_ssim = K.clip((1 - tf.image.ssim(y_true, y_pred, maxDepthVal)) * 0.5, 0, 1)
+
+    # Weights
+    w1 = 1.0
+    w2 = 1.0
+    w3 = theta
+
+    return (w1 * l_ssim) + (w2 * K.mean(l_edges)) + (w3 * K.mean(l_depth))