Refactor fast-depth

Addresses the following:
 - Rename nnconv5 block to nnconv5
 - Add skip connections directly to nnconv5 block
 - Allow custom metrics, loss and optimizer (keep defaults that reflect original paper) to train
 - Correctly use nyu evaluation dataset only when no dataset is provided

fast_depth_functional.py

@@ -29,14 +29,18 @@ def fix_windows_gpu():
             print(e)
 
 
-def FDDepthwiseBlock(inputs,
+def nnconv5(inputs,
-                     out_channels,
+            out_channels,
-                     block_id=1):
+            block_id=1,
+            skip_connection=None):
     x = keras.layers.DepthwiseConv2D(5, padding='same')(inputs)
     x = keras.layers.BatchNormalization()(x)
     x = keras.layers.ReLU(6.)(x)
     x = keras.layers.Conv2D(out_channels, 1, padding='same')(x)
     x = keras.layers.BatchNormalization()(x)
+    x = keras.layers.UpSampling2D()(x)
+    if skip_connection is not None:
+        x = keras.layers.Add()([x, skip_connection])
     return keras.layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
 
 
@@ -54,23 +58,14 @@ def mobilenet_nnconv5(weights=None, shape=(224, 224, 3)):
         layer.trainable = True
 
     # Fast depth decoder
-    x = FDDepthwiseBlock(mobilenet.output, 512, block_id=14)
+    x = nnconv5(mobilenet.output, 512, block_id=14)
-    # Nearest neighbour interpolation, used by fast depth paper
+    x = nnconv5(x, 256, block_id=15, skip_connection=mobilenet.get_layer(
-    x = keras.layers.UpSampling2D()(x)
+        name="conv_pw_5_relu").output)
-    x = FDDepthwiseBlock(x, 256, block_id=15)
+    x = nnconv5(x, 128, block_id=16, skip_connection=mobilenet.get_layer(
-    x = keras.layers.UpSampling2D()(x)
+        name="conv_pw_3_relu").output)
-    x = keras.layers.Add()(
+    x = nnconv5(x, 64, block_id=17, skip_connection=mobilenet.get_layer(
-        [x, mobilenet.get_layer(name="conv_pw_5_relu").output])
+        name="conv_pw_1_relu").output)
-    x = FDDepthwiseBlock(x, 128, block_id=16)
+    x = nnconv5(x, 32, block_id=18)
-    x = keras.layers.UpSampling2D()(x)
-    x = keras.layers.Add()(
-        [x, mobilenet.get_layer(name="conv_pw_3_relu").output])
-    x = FDDepthwiseBlock(x, 64, block_id=17)
-    x = keras.layers.UpSampling2D()(x)
-    x = keras.layers.Add()(
-        [x, mobilenet.get_layer(name="conv_pw_1_relu").output])
-    x = FDDepthwiseBlock(x, 32, block_id=18)
-    x = keras.layers.UpSampling2D()(x)
 
     x = keras.layers.Conv2D(1, 1, padding='same')(x)
     x = keras.layers.BatchNormalization()(x)
@@ -93,19 +88,21 @@ def delta3_metric(y_true, y_pred):
     return tf.nn.moments(tf.cast(maxRatio < tf.convert_to_tensor(1.25 ** 3), tf.float32), axes=None)[0]
 
 
-def compile(model):
+def compile(model, optimiser=keras.optimizers.SGD(), loss=keras.losses.MeanSquaredError(), custom_metrics=None):
     """
     Compile FastDepth model with relevant metrics
     :param model: Model to compile
+    :param optimiser: Custom optimiser to use
+    :param loss: Loss function to use
+    :param include_metrics: Whether to include metrics (RMSE, MSE, a1,2,3)
     """
-    # TODO: Learning rate (exponential decay)
+    model.compile(optimizer=optimiser,
-    model.compile(optimizer=keras.optimizers.SGD(momentum=0.9),
+                  loss=loss,
-                  loss=keras.losses.MeanSquaredError(),
                   metrics=[keras.metrics.RootMeanSquaredError(),
                            keras.metrics.MeanSquaredError(),
                            delta1_metric,
                            delta2_metric,
-                           delta3_metric])
+                           delta3_metric] if custom_metrics is None else custom_metrics)
 
 
 def train(existing_model=None, pretrained_weights='imagenet', epochs=4, save_file=None, dataset=None):
@@ -120,7 +117,7 @@ def train(existing_model=None, pretrained_weights='imagenet', epochs=4, save_fil
     """
     if not existing_model:
         existing_model = mobilenet_nnconv5(pretrained_weights)
-    compile(existing_model)
+        compile(existing_model)
     if not dataset:
         dataset = load_nyu()
     existing_model.fit(dataset, epochs=epochs)
@@ -137,7 +134,7 @@ def evaluate(compiled_model, dataset=None):
             where label width/height matches image width/height.
             Defaults to Tensorflow nyu_v2 evaluation split dataset (https://www.tensorflow.org/datasets/catalog/nyu_depth_v2)
     """
-    if not dataset:
+    if dataset is None:
         dataset = load_nyu_evaluate()
     compiled_model.evaluate(dataset, verbose=1)
 
@@ -200,3 +197,8 @@ def load_nyu_evaluate():
     builder = tfds.builder('nyu_depth_v2')
     builder.download_and_prepare(download_dir='../nyu')
     return builder.as_dataset(split='validation').batch(1).map(lambda x: crop_and_resize(x))
+
+
+if __name__ == '__main__':
+    model = mobilenet_nnconv5()
+    model.summary()