Added function to retrieve SSD model

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

src/twomartens/masterthesis/ssd.py

@@ -152,6 +152,72 @@ class DropoutSSD:
         return self.model(inputs)
 
 
+def get_model(use_dropout: bool,
+              dropout_model: callable, vanilla_model: callable,
+              image_size: int, nr_classes: int, mode: str,
+              iou_threshold: float, dropout_rate: float, top_k: int,
+              pre_trained_weights_file: Optional[str] = None) -> Tuple[tf.keras.models.Model, np.ndarray]:
+    """
+    Returns the correct SSD model and the corresponding predictor sizes.
+    
+    Args:
+        use_dropout: True if dropout variant should be used, False otherwise
+        dropout_model: function to build dropout SSD model
+        vanilla_model: function to build vanilla SSD model
+        image_size: size of the resized images
+        nr_classes: number of classes
+        mode: one of "training", "inference", "inference_fast"
+        iou_threshold: all boxes with higher iou to local maximum box are suppressed
+        dropout_rate: rate for dropout layers (only applies if dropout is used)
+        top_k: number of highest scoring predictions kept for each batch item
+        pre_trained_weights_file: path to h5 file with pre-trained weights
+
+    Returns:
+        SSD model, predictor_sizes
+    """
+    
+    image_size = (image_size, image_size, 3)
+    scales = [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]
+    if use_dropout:
+        model, predictor_sizes = dropout_model(
+            image_size=image_size,
+            n_classes=nr_classes,
+            mode=mode,
+            iou_threshold=iou_threshold,
+            dropout_rate=dropout_rate,
+            top_k=top_k,
+            scales=scales,
+            return_predictor_sizes=True
+        )
+    else:
+        model, predictor_sizes = vanilla_model(
+            image_size=image_size,
+            n_classes=nr_classes,
+            mode=mode,
+            iou_threshold=iou_threshold,
+            top_k=top_k,
+            scales=scales,
+            return_predictor_sizes=True
+        )
+    
+    if mode == "training":
+        # set non-classifier layers to non-trainable
+        classifier_names = ['conv4_3_norm_mbox_conf',
+                            'fc7_mbox_conf',
+                            'conv6_2_mbox_conf',
+                            'conv7_2_mbox_conf',
+                            'conv8_2_mbox_conf',
+                            'conv9_2_mbox_conf']
+        for layer in model.layers:
+            if layer.name not in classifier_names:
+                layer.trainable = False
+    
+    if pre_trained_weights_file is not None:
+        model.load_weights(pre_trained_weights_file, by_name=True)
+    
+    return model, predictor_sizes
+
+
 def predict_keras(generator: callable,
                   steps_per_epoch: int,
                   ssd_model: tf.keras.models.Model,