Fix dense depth loss and model, fix metric naming

dense_depth_functional.py

@@ -1,5 +1,4 @@
 import tensorflow.keras as keras
-import tensorflow_datasets as tfds
 
 import fast_depth_functional as fd
 
@@ -12,12 +11,23 @@ def dense_upsample_block(input, out_channels, skip_connection):
     x = keras.layers.Concatenate()([x, skip_connection])
     x = keras.layers.Conv2D(filters=out_channels,
                             kernel_size=3, strides=1, padding='same')(x)
+    x = keras.layers.LeakyReLU(alpha=0.2)(x)
     x = keras.layers.Conv2D(filters=out_channels,
                             kernel_size=3, strides=1, padding='same')(x)
     return keras.layers.LeakyReLU(alpha=0.2)(x)
 
 
 def dense_depth(size, weights=None, shape=(224, 224, 3)):
+    """
+    Make the dense depth network graph using keras functional api.
+
+    Note that you should use the dense depth loss function, and use Adam as the optimiser with a learning rate of 0.0001
+    (default learning rate of Adam is 0.001).
+    :param size:
+    :param weights:
+    :param shape:
+    :return:
+    """
     input = keras.layers.Input(shape=shape)
     densenet = dense_net(input, size, weights, shape)
 
@@ -37,6 +47,8 @@ def dense_depth(size, weights=None, shape=(224, 224, 3)):
     decoder = dense_upsample_block(
         decoder, densenet_output_channels // 16, densenet.get_layer('conv1/relu').output)
 
+    decoder = dense_upsample_block(decoder, int(densenet_output_channels / 32), input)
+
     conv3 = keras.layers.Conv2D(
         filters=1, kernel_size=3, strides=1, padding='same', name='conv3')(decoder)
     return keras.Model(inputs=input, outputs=conv3, name='dense_depth')