Fix for multi-GPU training.

And cleaner path split in nucleus sample.

mrcnn/model.py

@@ -1910,7 +1910,7 @@ class MaskRCNN():
             # TODO: can this be optimized to avoid duplicating the anchors?
             anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)
             # A hack to get around Keras's bad support for constants
-            anchors = KL.Lambda(lambda x: tf.constant(anchors), name="anchors")(input_image)
+            anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)
         else:
             anchors = input_anchors
 

samples/nucleus/nucleus.py

@@ -226,7 +226,8 @@ class NucleusDataset(utils.Dataset):
         """
         info = self.image_info[image_id]
         # Get mask directory from image path
-        mask_dir = os.path.join(info["path"].split("/images/")[0], "masks")
+        mask_dir = os.path.join(os.path.dirname(os.path.dirname(path)), "masks")
+
         # Read mask files from .png image
         mask = []
         for f in next(os.walk(mask_dir))[2]: