Fix casting when computing negative ROI count.

model.py

@@ -528,7 +528,8 @@ def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config)
     positive_indices = tf.random_shuffle(positive_indices)[:positive_count]
     positive_count = tf.shape(positive_indices)[0]
     # Negative ROIs. Add enough to maintain positive:negative ratio.
-    negative_count = int((positive_count / config.ROI_POSITIVE_RATIO) - positive_count)
+    r = 1.0 / config.ROI_POSITIVE_RATIO
+    negative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count
     negative_indices = tf.random_shuffle(negative_indices)[:negative_count]
     # Gather selected ROIs
     positive_rois = tf.gather(proposals, positive_indices)
@@ -878,7 +879,6 @@ def fpn_classifier_graph(rois, feature_maps,
                            name="mrcnn_class_conv1")(x)
     x = KL.TimeDistributed(BatchNorm(axis=3), name='mrcnn_class_bn1')(x)
     x = KL.Activation('relu')(x)
-    # x = KL.Dropout(0.5)(x)
     x = KL.TimeDistributed(KL.Conv2D(1024, (1, 1)),
                            name="mrcnn_class_conv2")(x)
     x = KL.TimeDistributed(BatchNorm(axis=3),