Fix multi-GPU training.

A previous fix to let validation run across more
than one batch caused an issue with multi-GPU
training. The issue seems to be in how Keras
averages loss and metric values, where it expects
them to be scalars rather than arrays. This fix
causes scalar outputs from a model to remain
scalar in multi-GPU training.

mrcnn/parallel_model.py

@@ -89,16 +89,18 @@ def make_parallel(self):
         with tf.device('/cpu:0'):
             merged = []
             for outputs, name in zip(outputs_all, output_names):
-                # If outputs are numbers without dimensions, add a batch dim.
-                def add_dim(tensor):
-                    """Add a dimension to tensors that don't have any."""
-                    if K.int_shape(tensor) == ():
-                        return KL.Lambda(lambda t: K.reshape(t, [1, 1]))(tensor)
-                    return tensor
-                outputs = list(map(add_dim, outputs))
-
-                # Concatenate
-                merged.append(KL.Concatenate(axis=0, name=name)(outputs))
+                # Concatenate or average outputs?
+                # Outputs usually have a batch dimension and we concatenate
+                # across it. If they don't, then the output is likely a loss
+                # or a metric value that gets averaged across the batch.
+                # Keras expects losses and metrics to be scalars.
+                if K.int_shape(outputs[0]) == ():
+                    # Average
+                    m = KL.Lambda(lambda o: tf.add_n(o) / len(outputs), name=name)(outputs)
+                else:
+                    # Concatenate
+                    m = KL.Concatenate(axis=0, name=name)(outputs)
+                merged.append(m)
         return merged