Add a switch for POST_NMS per batch/image during training (#695)

maskrcnn_benchmark/config/defaults.py

@@ -165,6 +165,9 @@
 # all FPN levels
 _C.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN = 2000
 _C.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST = 2000
+# Apply the post NMS per batch (default) or per image during training
+# (default is True to be consistent with Detectron, see Issue #672)
+_C.MODEL.RPN.FPN_POST_NMS_PER_BATCH = True
 # Custom rpn head, empty to use default conv or separable conv
 _C.MODEL.RPN.RPN_HEAD = "SingleConvRPNHead"
 

maskrcnn_benchmark/modeling/rpn/inference.py

@@ -24,6 +24,7 @@ def __init__(
         min_size,
         box_coder=None,
         fpn_post_nms_top_n=None,
+        fpn_post_nms_per_batch=True,
     ):
         """
         Arguments:
@@ -47,6 +48,7 @@ def __init__(
         if fpn_post_nms_top_n is None:
             fpn_post_nms_top_n = post_nms_top_n
         self.fpn_post_nms_top_n = fpn_post_nms_top_n
+        self.fpn_post_nms_per_batch = fpn_post_nms_per_batch
 
     def add_gt_proposals(self, proposals, targets):
         """
@@ -154,9 +156,9 @@ def select_over_all_levels(self, boxlists):
         # different behavior during training and during testing:
         # during training, post_nms_top_n is over *all* the proposals combined, while
         # during testing, it is over the proposals for each image
-        # TODO resolve this difference and make it consistent. It should be per image,
-        # and not per batch
-        if self.training:
+        # NOTE: it should be per image, and not per batch. However, to be consistent 
+        # with Detectron, the default is per batch (see Issue #672)
+        if self.training and self.fpn_post_nms_per_batch:
             objectness = torch.cat(
                 [boxlist.get_field("objectness") for boxlist in boxlists], dim=0
             )
@@ -189,6 +191,7 @@ def make_rpn_postprocessor(config, rpn_box_coder, is_train):
     if not is_train:
         pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TEST
         post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TEST
+    fpn_post_nms_per_batch = config.MODEL.RPN.FPN_POST_NMS_PER_BATCH
     nms_thresh = config.MODEL.RPN.NMS_THRESH
     min_size = config.MODEL.RPN.MIN_SIZE
     box_selector = RPNPostProcessor(
@@ -198,5 +201,6 @@ def make_rpn_postprocessor(config, rpn_box_coder, is_train):
         min_size=min_size,
         box_coder=rpn_box_coder,
         fpn_post_nms_top_n=fpn_post_nms_top_n,
+        fpn_post_nms_per_batch=fpn_post_nms_per_batch,
     )
     return box_selector