@@ -3,7 +3,7 @@ _base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
 model = dict(
     roi_head=dict(
         bbox_roi_extractor=dict(
-            type='SumGenericRoiExtractor',
+            type='GenericRoIExtractor',
             roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
             out_channels=256,
             featmap_strides=[4, 8, 16, 32],
_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
# model settings
model = dict(
    roi_head=dict(
        bbox_roi_extractor=dict(
            type='SumGenericRoiExtractor',
            roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
            pre_cfg=dict(
                type='ConvModule',
                in_channels=256,
                out_channels=256,
                kernel_size=5,
                padding=2,
                inplace=False,
            ),
            post_cfg=dict(
                type='GeneralizedAttention',
                in_channels=256,
                spatial_range=-1,
                num_heads=6,
                attention_type='0100',
                kv_stride=2)),
        mask_roi_extractor=dict(
            type='SumGenericRoiExtractor',
            roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
            pre_cfg=dict(
                type='ConvModule',
                in_channels=256,
                out_channels=256,
                kernel_size=5,
                padding=2,
                inplace=False,
            ),
            post_cfg=dict(
                type='GeneralizedAttention',
                in_channels=256,
                spatial_range=-1,
                num_heads=6,
                attention_type='0100',
                kv_stride=2))))

You may also add the argument `aggregation`.