Make gradient norm clipping a config option.

config.py

@@ -87,7 +87,7 @@ class Config(object):
     USE_MINI_MASK = True
     MINI_MASK_SHAPE = (56, 56)  # (height, width) of the mini-mask
 
-    # Input image resing
+    # Input image resizing
     # Images are resized such that the smallest side is >= IMAGE_MIN_DIM and
     # the longest side is <= IMAGE_MAX_DIM. In case both conditions can't
     # be satisfied together the IMAGE_MAX_DIM is enforced.
@@ -148,6 +148,9 @@ class Config(object):
     # train the RPN.
     USE_RPN_ROIS = True
 
+    # Gradient norm clipping
+    GRADIENT_CLIP_NORM = 5.0
+
     def __init__(self):
         """Set values of computed attributes."""
         # Effective batch size

model.py

@@ -2063,7 +2063,7 @@ class MaskRCNN():
         """
         # Optimizer object
         optimizer = keras.optimizers.SGD(lr=learning_rate, momentum=momentum,
-                                         clipnorm=5.0)
+                                         clipnorm=self.config.GRADIENT_CLIP_NORM)
         # Add Losses
         # First, clear previously set losses to avoid duplication
         self.keras_model._losses = []