Replace keep_dims with keepdims in TF calls.

TF replaced keep_dims with keepdims a while ago
and now shows a warning when using the old name.

mrcnn/model.py

@@ -1082,6 +1082,9 @@ def mrcnn_class_loss_graph(target_class_ids, pred_class_logits,
         classes that are in the dataset of the image, and 0
         for classes that are not in the dataset.
     """
+    # During model building, Keras calls this function with
+    # target_class_ids of type float32. Unclear why. Cast it
+    # to int to get around it.
     target_class_ids = tf.cast(target_class_ids, 'int64')
 
     # Find predictions of classes that are not in the dataset.
@@ -2137,7 +2140,7 @@ def compile(self, learning_rate, momentum):
             if layer.output in self.keras_model.losses:
                 continue
             loss = (
-                tf.reduce_mean(layer.output, keep_dims=True)
+                tf.reduce_mean(layer.output, keepdims=True)
                 * self.config.LOSS_WEIGHTS.get(name, 1.))
             self.keras_model.add_loss(loss)
 
@@ -2161,7 +2164,7 @@ def compile(self, learning_rate, momentum):
             layer = self.keras_model.get_layer(name)
             self.keras_model.metrics_names.append(name)
             loss = (
-                tf.reduce_mean(layer.output, keep_dims=True)
+                tf.reduce_mean(layer.output, keepdims=True)
                 * self.config.LOSS_WEIGHTS.get(name, 1.))
             self.keras_model.metrics_tensors.append(loss)
 

samples/coco/coco.py

@@ -14,8 +14,8 @@
     # Train a new model starting from pre-trained COCO weights
     python3 coco.py train --dataset=/path/to/coco/ --model=coco
 
-    # Train a new model starting from ImageNet weights
-    python3 coco.py train --dataset=/path/to/coco/ --model=imagenet
+    # Train a new model starting from ImageNet weights. Also auto download COCO dataset
+    python3 coco.py train --dataset=/path/to/coco/ --model=imagenet --download=True
 
     # Continue training a model that you had trained earlier
     python3 coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5