assert class ids not out of bounds (#1336)

examples/FasterRCNN/data.py

@@ -48,6 +48,8 @@ def print_class_histogram(roidbs):
         # filter crowd?
         gt_inds = np.where((entry["class"] > 0) & (entry["is_crowd"] == 0))[0]
         gt_classes = entry["class"][gt_inds]
+        if len(gt_classes):
+            assert gt_classes.max() <= len(class_names) - 1
         gt_hist += np.histogram(gt_classes, bins=hist_bins)[0]
     data = list(itertools.chain(*[[class_names[i + 1], v] for i, v in enumerate(gt_hist[1:])]))
     COL = min(6, len(data))
@@ -97,6 +99,7 @@ class TrainingDataPreprocessor:
         points = tfms.apply_coords(points)
         boxes = point8_to_box(points)
         if len(boxes):
+            assert klass.max() <= cfg.DATA.NUM_CATEGORY, "Invalid category {}!".format(klass.max())
             assert np.min(np_area(boxes)) > 0, "Some boxes have zero area!"
 
         ret = {"image": im}

examples/FasterRCNN/modeling/model_mrcnn.py

@@ -23,12 +23,12 @@ def maskrcnn_loss(mask_logits, fg_labels, fg_target_masks):
     if get_tf_version_tuple() >= (1, 14):
         mask_logits = tf.gather(
             mask_logits, tf.reshape(fg_labels - 1, [-1, 1]), batch_dims=1)
+        mask_logits = tf.squeeze(mask_logits, axis=1)
     else:
         indices = tf.stack([tf.range(tf.size(fg_labels, out_type=tf.int64)),
                             fg_labels - 1], axis=1)  # #fgx2
         mask_logits = tf.gather_nd(mask_logits, indices)  # #fg x h x w
 
-    mask_logits = tf.squeeze(mask_logits, axis=1)
     mask_probs = tf.sigmoid(mask_logits)
 
     # add some training visualizations to tensorboard