[MOT] add enhance fairmot (#4251)

* add sge attention on fpn

* add iou head

* add config, fix head

* fix iou offset weight

* fix fairmot centerhead

configs/mot/README.md

@@ -140,14 +140,17 @@ If you use a stronger detection model, you can get better results. Each txt is t
 ### Results on MOT-16 Test Set
 | backbone       | input shape |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  download | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_30e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_30e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### Results on MOT-17 Test Set
 | backbone       | input shape |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  download  | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_30e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_30e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
-**注意:**
+**Notes:**
+ FairMOT enhance DLA-34 used 8 GPUs for training and mini-batch size as 16 on each GPU，and trained for 60 epoches. The crowdhuman dataset is added to the train-set during training.
  FairMOT enhance HarDNet-85 used 8 GPUs for training and mini-batch size as 10 on each GPU，and trained for 30 epoches. The crowdhuman dataset is added to the train-set during training.
 
 

configs/mot/README_cn.md

@@ -140,14 +140,17 @@ wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 ### 在MOT-16 Test Set上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  下载链接 | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### 在MOT-17 Test Set上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  下载链接  | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 **注意:**
+ FairMOT enhance DLA-34使用8个GPU进行训练，每个GPU上batch size为16，训练60个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
  FairMOT enhance HarDNet-85 使用8个GPU进行训练，每个GPU上batch size为10，训练30个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
 
 

configs/mot/fairmot/README.md

@@ -41,14 +41,17 @@ English | [简体中文](README_cn.md)
 ### Results on MOT-16 Test Set
 | backbone       | input shape |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  download | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_60e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### Results on MOT-17 Test Set
 | backbone       | input shape |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  download  | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_60e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
-**注意:**
+**Notes:**
+ FairMOT enhance DLA-34 used 8 GPUs for training and mini-batch size as 16 on each GPU，and trained for 60 epoches. The crowdhuman dataset is added to the train-set during training.
  FairMOT enhance HarDNet-85 used 8 GPUs for training and mini-batch size as 10 on each GPU，and trained for 30 epoches. The crowdhuman dataset is added to the train-set during training.
 
 
@@ -64,7 +67,7 @@ English | [简体中文](README_cn.md)
 | HRNetV2-W18   | 1088x608 |  70.7  |  65.7  |  4281  |  22485  | 138468 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.pdparams) | [config](./fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml) |
 
 **Notes:**
- FairMOT HRNetV2-W18 used 8 GPUs for training and mini-batch size as 6 on each GPU, and trained for 30 epoches. Only ImageNet pre-train model is used, and the optimizer adopts Momentum. The crowdhuman dataset is added to the train-set during training.
+ FairMOT HRNetV2-W18 used 8 GPUs for training and mini-batch size as 4 on each GPU, and trained for 30 epoches. Only ImageNet pre-train model is used, and the optimizer adopts Momentum. The crowdhuman dataset is added to the train-set during training.
 
 
 ## Getting Start

configs/mot/fairmot/README_cn.md

@@ -40,14 +40,17 @@
 ### 在MOT-16 Test Set上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  下载链接 | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### 在MOT-17 Test Set上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  下载链接  | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
+| DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
 | HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 **注意:**
+ FairMOT enhance DLA-34使用8个GPU进行训练，每个GPU上batch size为16，训练60个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
  FairMOT enhance HarDNet-85 使用8个GPU进行训练，每个GPU上batch size为10，训练30个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
 
 

configs/mot/fairmot/_base_/fairmot_dla34.yml

@@ -14,8 +14,31 @@ CenterNet:
   head: CenterNetHead
   post_process: CenterNetPostProcess
 
+CenterNetDLAFPN:
+  down_ratio: 4
+  last_level: 5
+  out_channel: 0
+  dcn_v2: True
+  with_sge: False
+
+CenterNetHead:
+  head_planes: 256
+  heatmap_weight: 1
+  regress_ltrb: True
+  size_weight: 0.1
+  size_loss: 'L1'
+  offset_weight: 1
+  iou_weight: 0
+
+FairMOTEmbeddingHead:
+  ch_head: 256
+  ch_emb: 128
+  num_identifiers: 14455 # for mix dataset (Caltech, CityPersons, CUHK-SYSU, PRW, ETHZ and MOT16)
+
 CenterNetPostProcess:
   max_per_img: 500
+  down_ratio: 4
+  regress_ltrb: True
 
 JDETracker:
   conf_thres: 0.4

configs/mot/fairmot/fairmot_enhance_dla34_60e_1088x608.yml

+_BASE_: [
+  '../../datasets/mot.yml',
+  '../../runtime.yml',
+  '_base_/optimizer_30e.yml',
+  '_base_/fairmot_dla34.yml',
+  '_base_/fairmot_reader_1088x608.yml',
+]
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+
+worker_num: 4
+TrainReader:
+  inputs_def:
+    image_shape: [3, 608, 1088]
+  sample_transforms:
+    - Decode: {}
+    - RGBReverse: {}
+    - AugmentHSV: {}
+    - LetterBoxResize: {target_size: [608, 1088]}
+    - MOTRandomAffine: {reject_outside: False}
+    - RandomFlip: {}
+    - BboxXYXY2XYWH: {}
+    - NormalizeBox: {}
+    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1]}
+    - RGBReverse: {}
+    - Permute: {}
+  batch_transforms:
+    - Gt2FairMOTTarget: {}
+  batch_size: 16
+  shuffle: True
+  drop_last: True
+  use_shared_memory: True
+
+epoch: 60
+LearningRate:
+  base_lr: 0.0005
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [40,]
+    use_warmup: False
+
+OptimizerBuilder:
+  optimizer:
+    type: Adam
+  regularizer: NULL
+
+weights: output/fairmot_enhance_dla34_60e_1088x608/model_final

configs/mot/fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml

@@ -2,7 +2,7 @@ _BASE_: [
   '../../datasets/mot.yml',
   '../../runtime.yml',
   '_base_/optimizer_30e.yml',
-  '_base_/fairmot_enhance_hardnet85.yml',
+  '_base_/fairmot_hardnet85.yml',
   '_base_/fairmot_reader_1088x608.yml',
 ]
 norm_type: sync_bn

ppdet/modeling/architectures/fairmot.py

@@ -70,8 +70,8 @@ class FairMOT(BaseArch):
     def _forward(self):
         loss = dict()
         # det_outs keys:
-        # train: det_loss, heatmap_loss, size_loss, offset_loss, neck_feat
-        # eval/infer: bbox, bbox_inds, neck_feat
+        # train: neck_feat, det_loss, heatmap_loss, size_loss, offset_loss (optional: iou_loss)
+        # eval/infer: neck_feat, bbox, bbox_inds
         det_outs = self.detector(self.inputs)
         neck_feat = det_outs['neck_feat']
         if self.training:
@@ -79,12 +79,11 @@ class FairMOT(BaseArch):
 
             det_loss = det_outs['det_loss']
             loss = self.loss(det_loss, reid_loss)
-            loss.update({
-                'heatmap_loss': det_outs['heatmap_loss'],
-                'size_loss': det_outs['size_loss'],
-                'offset_loss': det_outs['offset_loss'],
-                'reid_loss': reid_loss
-            })
+            for k, v in det_outs.items():
+                if 'loss' not in k:
+                    continue
+                loss.update({k: v})
+            loss.update({'reid_loss': reid_loss})
             return loss
         else:
             embedding = self.reid(neck_feat, self.inputs)

ppdet/modeling/heads/centernet_head.py

@@ -18,7 +18,7 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle.nn.initializer import Constant, Uniform
 from ppdet.core.workspace import register
-from ppdet.modeling.losses import CTFocalLoss
+from ppdet.modeling.losses import CTFocalLoss, GIoULoss
 
 
 class ConvLayer(nn.Layer):
@@ -51,7 +51,6 @@ class ConvLayer(nn.Layer):
 
     def forward(self, inputs):
         out = self.conv(inputs)
-
         return out
 
 
@@ -66,8 +65,9 @@ class CenterNetHead(nn.Layer):
         regress_ltrb (bool): whether to regress left/top/right/bottom or
             width/height for a box, true by default
         size_weight (float): the weight of box size loss, 0.1 by default.
+        size_loss (): the type of size regression loss, 'L1 loss' by default.
         offset_weight (float): the weight of center offset loss, 1 by default.
-
+        iou_weight (float): the weight of iou head loss, 0 by default.
     """
 
     __shared__ = ['num_classes']
@@ -79,13 +79,18 @@ class CenterNetHead(nn.Layer):
                  heatmap_weight=1,
                  regress_ltrb=True,
                  size_weight=0.1,
-                 offset_weight=1):
+                 size_loss='L1',
+                 offset_weight=1,
+                 iou_weight=0):
         super(CenterNetHead, self).__init__()
         self.weights = {
             'heatmap': heatmap_weight,
             'size': size_weight,
-            'offset': offset_weight
+            'offset': offset_weight,
+            'iou': iou_weight
         }
+
+        # heatmap head
         self.heatmap = nn.Sequential(
             ConvLayer(
                 in_channels, head_planes, kernel_size=3, padding=1, bias=True),
@@ -99,6 +104,8 @@ class CenterNetHead(nn.Layer):
                 bias=True))
         with paddle.no_grad():
             self.heatmap[2].conv.bias[:] = -2.19
+
+        # size(ltrb or wh) head
         self.size = nn.Sequential(
             ConvLayer(
                 in_channels, head_planes, kernel_size=3, padding=1, bias=True),
@@ -110,13 +117,33 @@ class CenterNetHead(nn.Layer):
                 stride=1,
                 padding=0,
                 bias=True))
+        self.size_loss = size_loss
+
+        # offset head
         self.offset = nn.Sequential(
             ConvLayer(
                 in_channels, head_planes, kernel_size=3, padding=1, bias=True),
             nn.ReLU(),
             ConvLayer(
                 head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
-        self.focal_loss = CTFocalLoss()
+
+        # iou head (optinal)
+        if iou_weight > 0:
+            self.iou = nn.Sequential(
+                ConvLayer(
+                    in_channels,
+                    head_planes,
+                    kernel_size=3,
+                    padding=1,
+                    bias=True),
+                nn.ReLU(),
+                ConvLayer(
+                    head_planes,
+                    4 if regress_ltrb else 2,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    bias=True))
 
     @classmethod
     def from_config(cls, cfg, input_shape):
@@ -128,22 +155,29 @@ class CenterNetHead(nn.Layer):
         heatmap = self.heatmap(feat)
         size = self.size(feat)
         offset = self.offset(feat)
+        iou = self.iou(feat) if hasattr(self, 'iou_weight') else None
+
         if self.training:
-            loss = self.get_loss(heatmap, size, offset, self.weights, inputs)
+            loss = self.get_loss(
+                inputs, self.weights, heatmap, size, offset, iou=iou)
             return loss
         else:
             heatmap = F.sigmoid(heatmap)
-            return {'heatmap': heatmap, 'size': size, 'offset': offset}
+            head_outs = {'heatmap': heatmap, 'size': size, 'offset': offset}
+            if iou is not None:
+                head_outs.update({'iou': iou})
+            return head_outs
 
-    def get_loss(self, heatmap, size, offset, weights, inputs):
+    def get_loss(self, inputs, weights, heatmap, size, offset, iou=None):
+        # heatmap head loss: CTFocalLoss
         heatmap_target = inputs['heatmap']
-        size_target = inputs['size']
-        offset_target = inputs['offset']
-        index = inputs['index']
-        mask = inputs['index_mask']
         heatmap = paddle.clip(F.sigmoid(heatmap), 1e-4, 1 - 1e-4)
-        heatmap_loss = self.focal_loss(heatmap, heatmap_target)
+        ctfocal_loss = CTFocalLoss()
+        heatmap_loss = ctfocal_loss(heatmap, heatmap_target)
 
+        # size head loss: L1 loss or GIoU loss
+        index = inputs['index']
+        mask = inputs['index_mask']
         size = paddle.transpose(size, perm=[0, 2, 3, 1])
         size_n, size_h, size_w, size_c = size.shape
         size = paddle.reshape(size, shape=[size_n, -1, size_c])
@@ -161,11 +195,32 @@ class CenterNetHead(nn.Layer):
         size_mask = paddle.cast(size_mask, dtype=pos_size.dtype)
         pos_num = size_mask.sum()
         size_mask.stop_gradient = True
+        if self.size_loss == 'L1':
+            size_target = inputs['size']
             size_target.stop_gradient = True
             size_loss = F.l1_loss(
                 pos_size * size_mask, size_target * size_mask, reduction='sum')
             size_loss = size_loss / (pos_num + 1e-4)
+        elif self.size_loss == 'giou':
+            size_target = inputs['bbox_xys']
+            size_target.stop_gradient = True
+            centers_x = (size_target[:, :, 0:1] + size_target[:, :, 2:3]) / 2.0
+            centers_y = (size_target[:, :, 1:2] + size_target[:, :, 3:4]) / 2.0
+            x1 = centers_x - pos_size[:, :, 0:1]
+            y1 = centers_y - pos_size[:, :, 1:2]
+            x2 = centers_x + pos_size[:, :, 2:3]
+            y2 = centers_y + pos_size[:, :, 3:4]
+            pred_boxes = paddle.concat([x1, y1, x2, y2], axis=-1)
+            giou_loss = GIoULoss(reduction='sum')
+            size_loss = giou_loss(
+                pred_boxes * size_mask,
+                size_target * size_mask,
+                iou_weight=size_mask,
+                loc_reweight=None)
+            size_loss = size_loss / (pos_num + 1e-4)
 
+        # offset head loss: L1 loss
+        offset_target = inputs['offset']
         offset = paddle.transpose(offset, perm=[0, 2, 3, 1])
         offset_n, offset_h, offset_w, offset_c = offset.shape
         offset = paddle.reshape(offset, shape=[offset_n, -1, offset_c])
@@ -181,12 +236,43 @@ class CenterNetHead(nn.Layer):
             reduction='sum')
         offset_loss = offset_loss / (pos_num + 1e-4)
 
-        det_loss = weights['heatmap'] * heatmap_loss + weights[
-            'size'] * size_loss + weights['offset'] * offset_loss
+        # iou head loss: GIoU loss
+        if iou is not None:
+            iou = paddle.transpose(iou, perm=[0, 2, 3, 1])
+            iou_n, iou_h, iou_w, iou_c = iou.shape
+            iou = paddle.reshape(iou, shape=[iou_n, -1, iou_c])
+            pos_iou = paddle.gather_nd(iou, index=index)
+            iou_mask = paddle.expand_as(mask, pos_iou)
+            iou_mask = paddle.cast(iou_mask, dtype=pos_iou.dtype)
+            pos_num = iou_mask.sum()
+            iou_mask.stop_gradient = True
+            gt_bbox_xys = inputs['bbox_xys']
+            gt_bbox_xys.stop_gradient = True
+            centers_x = (gt_bbox_xys[:, :, 0:1] + gt_bbox_xys[:, :, 2:3]) / 2.0
+            centers_y = (gt_bbox_xys[:, :, 1:2] + gt_bbox_xys[:, :, 3:4]) / 2.0
+            x1 = centers_x - pos_size[:, :, 0:1]
+            y1 = centers_y - pos_size[:, :, 1:2]
+            x2 = centers_x + pos_size[:, :, 2:3]
+            y2 = centers_y + pos_size[:, :, 3:4]
+            pred_boxes = paddle.concat([x1, y1, x2, y2], axis=-1)
+            giou_loss = GIoULoss(reduction='sum')
+            iou_loss = giou_loss(
+                pred_boxes * iou_mask,
+                gt_bbox_xys * iou_mask,
+                iou_weight=iou_mask,
+                loc_reweight=None)
+            iou_loss = iou_loss / (pos_num + 1e-4)
 
-        return {
-            'det_loss': det_loss,
+        losses = {
             'heatmap_loss': heatmap_loss,
             'size_loss': size_loss,
-            'offset_loss': offset_loss
+            'offset_loss': offset_loss,
         }
+        det_loss = weights['heatmap'] * heatmap_loss + weights[
+            'size'] * size_loss + weights['offset'] * offset_loss
+
+        if iou is not None:
+            losses.update({'iou_loss': iou_loss})
+            det_loss = det_loss + weights['iou'] * iou_loss
+        losses.update({'det_loss': det_loss})
+        return losses

ppdet/modeling/necks/centernet_fpn.py

@@ -27,6 +27,74 @@ from ..shape_spec import ShapeSpec
 __all__ = ['CenterNetDLAFPN', 'CenterNetHarDNetFPN']
 
 
+# SGE attention
+class BasicConv(nn.Layer):
+    def __init__(self,
+                 in_planes,
+                 out_planes,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 relu=True,
+                 bn=True,
+                 bias_attr=False):
+        super(BasicConv, self).__init__()
+        self.out_channels = out_planes
+        self.conv = nn.Conv2D(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias_attr=bias_attr)
+        self.bn = nn.BatchNorm2D(
+            out_planes,
+            epsilon=1e-5,
+            momentum=0.01,
+            weight_attr=False,
+            bias_attr=False) if bn else None
+        self.relu = nn.ReLU() if relu else None
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        if self.relu is not None:
+            x = self.relu(x)
+        return x
+
+
+class ChannelPool(nn.Layer):
+    def forward(self, x):
+        return paddle.concat(
+            (paddle.max(x, 1).unsqueeze(1), paddle.mean(x, 1).unsqueeze(1)),
+            axis=1)
+
+
+class SpatialGate(nn.Layer):
+    def __init__(self):
+        super(SpatialGate, self).__init__()
+        kernel_size = 7
+        self.compress = ChannelPool()
+        self.spatial = BasicConv(
+            2,
+            1,
+            kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            relu=False)
+
+    def forward(self, x):
+        x_compress = self.compress(x)
+        x_out = self.spatial(x_compress)
+        scale = F.sigmoid(x_out)  # broadcasting
+        return x * scale
+
+
 def fill_up_weights(up):
     weight = up.weight.numpy()
     f = math.ceil(weight.shape[2] / 2)
@@ -145,10 +213,10 @@ class CenterNetDLAFPN(nn.Layer):
         last_level (int): the last level of input feature fed into the upsamplng block
         out_channel (int): the channel of the output feature, 0 by default means
             the channel of the input feature whose down ratio is `down_ratio`
-        dcn_v2 (bool): whether use the DCNv2, true by default
-        first_level (int|None): the first level of input feature fed into the upsamplng block.
+        first_level (None): the first level of input feature fed into the upsamplng block.
             if None, the first level stands for logs(down_ratio)
-        
+        dcn_v2 (bool): whether use the DCNv2, True by default
+        with_sge (bool): whether use SGE attention, False by default
     """
 
     def __init__(self,
@@ -156,8 +224,9 @@ class CenterNetDLAFPN(nn.Layer):
                  down_ratio=4,
                  last_level=5,
                  out_channel=0,
+                 first_level=None,
                  dcn_v2=True,
-                 first_level=None):
+                 with_sge=False):
         super(CenterNetDLAFPN, self).__init__()
         self.first_level = int(np.log2(
             down_ratio)) if first_level is None else first_level
@@ -180,6 +249,10 @@ class CenterNetDLAFPN(nn.Layer):
             [2**i for i in range(self.last_level - self.first_level)],
             dcn_v2=dcn_v2)
 
+        self.with_sge = with_sge
+        if self.with_sge:
+            self.sge_attention = SpatialGate()
+
     @classmethod
     def from_config(cls, cfg, input_shape):
         return {'in_channels': [i.channels for i in input_shape]}
@@ -194,7 +267,10 @@ class CenterNetDLAFPN(nn.Layer):
 
         self.ida_up(ida_up_feats, 0, len(ida_up_feats))
 
-        return ida_up_feats[-1]
+        feat = ida_up_feats[-1]
+        if self.with_sge:
+            feat = self.sge_attention(feat)
+        return feat
 
     @property
     def out_shape(self):