[MaskRCNN] use L1

examples/FasterRCNN/config.py

@@ -178,7 +178,7 @@ _C.RPN.TEST_PER_LEVEL_NMS_TOPK = 1000
 
 # fastrcnn training ---------------------
 _C.FRCNN.BATCH_PER_IM = 512
-_C.FRCNN.BBOX_REG_WEIGHTS = [20., 20., 10., 10.]  # Detectron: 10, 10, 5, 5
+_C.FRCNN.BBOX_REG_WEIGHTS = [10., 10., 5., 5.]  # Slightly better setting: 20, 20, 10, 10
 _C.FRCNN.FG_THRESH = 0.5
 _C.FRCNN.FG_RATIO = 0.25  # fg ratio in a ROI batch
 

examples/FasterRCNN/modeling/model_frcnn.py

 # -*- coding: utf-8 -*-
-# File: model.py
+# File: model_frcnn.py
 
 import tensorflow as tf
 
@@ -164,8 +164,7 @@ def fastrcnn_losses(labels, label_logits, fg_boxes, fg_box_logits):
         fg_accuracy = tf.where(
             empty_fg, 0., tf.reduce_mean(tf.gather(correct, fg_inds)), name='fg_accuracy')
 
-    box_loss = tf.losses.huber_loss(
-        fg_boxes, fg_box_logits, reduction=tf.losses.Reduction.SUM)
+    box_loss = tf.reduce_sum(tf.abs(fg_boxes - fg_box_logits))
     box_loss = tf.truediv(
         box_loss, tf.cast(tf.shape(labels)[0], tf.float32), name='box_loss')