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

load.py

@@ -1,6 +1,9 @@
-from util import crop_and_resize
-import tensorflow_datasets as tfds
 import tensorflow.keras as keras
+import tensorflow_datasets as tfds
+
+from losses import dense_depth_loss_function
+from metric import *
+from util import crop_and_resize
 
 
 def load_nyu(download_dir='../nyu', out_shape=(224, 224)):
@@ -31,3 +34,15 @@ def load_kitti(download_dir='../kitti', out_shape=(224, 224)):
     ds = tfds.builder('kitti_depth')
     ds.download_and_prepare(download_dir=download_dir)
     return ds.as_dataset(tfds.Split.TRAIN).batch(8).map(lambda x: crop_and_resize(x, out_shape))
+
+
+def load_model(file):
+    """
+    Load previously trained FastDepth model from disk. Will include relevant metrics (custom objects)
+    :param file: File/directory to load the model from
+    :return:
+    """
+    return keras.models.load_model(file, custom_objects={'delta1_metric': delta1_metric,
+                                                         'delta2_metric': delta2,
+                                                         'delta3_metric': delta3,
+                                                         'dense_depth_loss_function': dense_depth_loss_function})