Convert config.BACKBONE_SHAPES to a function.

config.py

@@ -169,12 +169,6 @@ class Config(object):
         self.IMAGE_SHAPE = np.array(
             [self.IMAGE_MAX_DIM, self.IMAGE_MAX_DIM, 3])
 
-        # Compute backbone size from input image size
-        self.BACKBONE_SHAPES = np.array(
-            [[int(math.ceil(self.IMAGE_SHAPE[0] / stride)),
-              int(math.ceil(self.IMAGE_SHAPE[1] / stride))]
-             for stride in self.BACKBONE_STRIDES])
-
         # Image meta data length
         # See compose_image_meta() for details
         self.IMAGE_META_SIZE = 1 + 3 + 3 + 4 + 1 + self.NUM_CLASSES

model.py

@@ -73,6 +73,20 @@ class BatchNorm(KL.BatchNormalization):
         return super(self.__class__, self).call(inputs, training=training)
 
 
+def compute_backbone_shapes(config, image_shape):
+    """Computes the width and height of each stage of the backbone network.
+    
+    Returns:
+        [N, (height, width)]. Where N is the number of stages
+    """
+    # Currently supports ResNet only
+    assert config.BACKBONE in ["resnet50", "resnet101"]
+    return np.array(
+        [[int(math.ceil(image_shape[0] / stride)),
+            int(math.ceil(image_shape[1] / stride))]
+            for stride in config.BACKBONE_STRIDES])
+
+
 ############################################################
 #  Resnet Graph
 ############################################################
@@ -1663,9 +1677,10 @@ def data_generator(dataset, config, shuffle=True, augment=False, augmentation=No
 
     # Anchors
     # [anchor_count, (y1, x1, y2, x2)]
+    backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE)
     anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
                                              config.RPN_ANCHOR_RATIOS,
-                                             config.BACKBONE_SHAPES,
+                                             backbone_shapes,
                                              config.BACKBONE_STRIDES,
                                              config.RPN_ANCHOR_STRIDE)
 
@@ -1897,9 +1912,10 @@ class MaskRCNN():
         mrcnn_feature_maps = [P2, P3, P4, P5]
 
         # Generate Anchors
+        backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE)
         self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
                                                       config.RPN_ANCHOR_RATIOS,
-                                                      config.BACKBONE_SHAPES,
+                                                      backbone_shapes,
                                                       config.BACKBONE_STRIDES,
                                                       config.RPN_ANCHOR_STRIDE)