Adjusted predict function to new checkpoint design

Signed-off-by: Jim Martens <github@2martens.de>

src/twomartens/masterthesis/ssd.py

@@ -171,15 +171,25 @@ def predict(dataset: tf.data.Dataset,
     if weights_path is None and checkpoint_path is None:
         raise ValueError("Either 'weights_path' or 'checkpoint_path' must be given.")
 
-    checkpointables = {}
+    # model
     if use_dropout:
-        checkpointables.update({
-            'ssd': DropoutSSD(mode='training', weights_path=weights_path)
-        })
+        ssd = DropoutSSD(mode='training', weights_path=weights_path)
     else:
-        checkpointables.update({
-            'ssd': SSD(mode='inference_fast', weights_path=weights_path)
-        })
+        ssd = SSD(mode='training', weights_path=weights_path)
+    
+    checkpointables = {
+        'ssd': ssd.model,
+        'learning_rate_var': K.variable(0),
+    }
+
+    checkpointables.update({
+        # optimizer
+        'ssd_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
+                                                beta1=0.5, beta2=0.999),
+        # global step counter
+        'global_step':   tf.train.get_or_create_global_step(),
+        'epoch_var':     K.variable(-1, dtype=tf.int64)
+    })
 
     if checkpoint_path is not None:
         # checkpoint
@@ -188,7 +198,7 @@ def predict(dataset: tf.data.Dataset,
         checkpoint.restore(latest_checkpoint)
 
     outputs = _predict_one_epoch(dataset, use_dropout, output_path, forward_passes_per_image,
-                                 nr_digits, **checkpointables)
+                                 nr_digits, checkpointables['ssd'])
     
     if verbose:
         print((