Merge pull request fizyr#705 from fizyr/fix-config-parsing

Fix config parsing bug.

keras_retinanet/bin/train.py

@@ -100,21 +100,25 @@ def create_models(backbone_retinanet, num_classes, weights, multi_gpu=0, freeze_
 
     modifier = freeze_model if freeze_backbone else None
 
+    # load anchor parameters, or pass None (so that defaults will be used)
+    anchor_params = None
+    num_anchors   = None
+    if config and 'anchor_parameters' in config:
+        anchor_params = parse_anchor_parameters(config)
+        num_anchors   = anchor_params.num_anchors()
+
     # Keras recommends initialising a multi-gpu model on the CPU to ease weight sharing, and to prevent OOM errors.
     # optionally wrap in a parallel model
     if multi_gpu > 1:
         from keras.utils import multi_gpu_model
         with tf.device('/cpu:0'):
-            model = model_with_weights(backbone_retinanet(num_classes, modifier=modifier), weights=weights, skip_mismatch=True)
+            model = model_with_weights(backbone_retinanet(num_classes, num_anchors=num_anchors, modifier=modifier), weights=weights, skip_mismatch=True)
         training_model = multi_gpu_model(model, gpus=multi_gpu)
     else:
-        model          = model_with_weights(backbone_retinanet(num_classes, modifier=modifier), weights=weights, skip_mismatch=True)
+        model          = model_with_weights(backbone_retinanet(num_classes, num_anchors=num_anchors, modifier=modifier), weights=weights, skip_mismatch=True)
         training_model = model
 
     # make prediction model
-    anchor_params = None
-    if config and 'anchor_parameters' in config:
-        anchor_params = parse_anchor_parameters(config)
     prediction_model = retinanet_bbox(model=model, anchor_params=anchor_params)
 
     # compile model

keras_retinanet/layers/_misc.py

@@ -31,20 +31,20 @@ def __init__(self, size, stride, ratios=None, scales=None, *args, **kwargs):
         Args
             size: The base size of the anchors to generate.
             stride: The stride of the anchors to generate.
-            ratios: The ratios of the anchors to generate (defaults to [0.5, 1, 2]).
-            scales: The scales of the anchors to generate (defaults to [2^0, 2^(1/3), 2^(2/3)]).
+            ratios: The ratios of the anchors to generate (defaults to AnchorParameters.default.ratios).
+            scales: The scales of the anchors to generate (defaults to AnchorParameters.default.scales).
         """
         self.size   = size
         self.stride = stride
         self.ratios = ratios
         self.scales = scales
 
         if ratios is None:
-            self.ratios  = np.array([0.5, 1, 2], keras.backend.floatx()),
+            self.ratios  = utils_anchors.AnchorParameters.default.ratios
         elif isinstance(ratios, list):
             self.ratios  = np.array(ratios)
         if scales is None:
-            self.scales  = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)], keras.backend.floatx()),
+            self.scales  = utils_anchors.AnchorParameters.default.scales
         elif isinstance(scales, list):
             self.scales  = np.array(scales)
 

keras_retinanet/models/retinanet.py

@@ -237,7 +237,7 @@ def retinanet(
     inputs,
     backbone_layers,
     num_classes,
-    num_anchors             = 9,
+    num_anchors             = None,
     create_pyramid_features = __create_pyramid_features,
     submodels               = None,
     name                    = 'retinanet'
@@ -264,6 +264,10 @@ def retinanet(
         ]
         ```
     """
+
+    if num_anchors is None:
+        num_anchors = AnchorParameters.default.num_anchors()
+
     if submodels is None:
         submodels = default_submodels(num_classes, num_anchors)