add Lightweight backbone of centernet (#4586)

configs/centernet/README_cn.md

@@ -20,7 +20,11 @@
 | DLA-34(paper)  | 512x512 |  37.4  |     -   |    -   |   -    |
 | DLA-34         | 512x512 |  37.6  |     -   | [下载链接](https://bj.bcebos.com/v1/paddledet/models/centernet_dla34_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_dla34_140e_coco.yml) |
 | ResNet50 + DLAUp  | 512x512 |  38.9  |     -   | [下载链接](https://bj.bcebos.com/v1/paddledet/models/centernet_r50_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_r50_140e_coco.yml) |
-
+| GhostNet_x1_3 + DLAUp  | 512x512 | 28.9  |     -   | [下载链接](https://paddledet.bj.bcebos.com/models/centernet_ghostnet_1_3x_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_ghostnet_1_3x_140e_coco.yml) |
+| PP-LCNet_x1_0 + DLAUp  | 512x512 | 26.9  |     -   | [下载链接](https://paddledet.bj.bcebos.com/models/centernet_lcnet_1x_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_lcnet_1x_140e_coco.yml) |
+| MobileNetV1_1x + DLAUp  | 512x512 |  28.2  |     -   | [下载链接](https://paddledet.bj.bcebos.com/models/centernet_mbv1_1x_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_mbv1_1x_140e_coco.yml) |
+| MobileNetV3_large_x1_0 + DLAUp  | 512x512 |  27.1  |     -   | [下载链接](https://paddledet.bj.bcebos.com/models/centernet_mbv3_large_1x_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_mbv3_large_1x_140e_coco.yml) |
+| ShuffleNetV2_x1_0 + DLAUp  | 512x512 | 23.8  |     -   | [下载链接](https://paddledet.bj.bcebos.com/models/centernet_shufflenetv2_1x_140e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/centernet/centernet_shufflenetv2_1x_140e_coco.yml) |
 
 ## 引用
 ```

configs/centernet/centernet_ghostnet_1_3x_140e_coco.yml

+_BASE_: [
+  'centernet_r50_140e_coco.yml'
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/GhostNet_x1_3_ssld_pretrained.pdparams
+weights: output/centernet_ghostnet_1_3x_140e_coco/model_final
+
+CenterNet:
+  backbone: GhostNet
+  neck: CenterNetDLAFPN
+  head: CenterNetHead
+  post_process: CenterNetPostProcess
+
+GhostNet:
+  scale: 1.3
+  conv_decay: 0.00004
+  feature_maps: [4, 6, 12, 17]
+
+TrainReader:
+  batch_size: 32
\ No newline at end of file

configs/centernet/centernet_lcnet_1x_140e_coco.yml

+_BASE_: [
+  'centernet_r50_140e_coco.yml'
+]
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_0_pretrained.pdparams
+weights: output/centernet_lcnet_1x_140e_coco/model_final
+
+CenterNet:
+  backbone: LCNet
+  neck: CenterNetDLAFPN
+  head: CenterNetHead
+  post_process: CenterNetPostProcess
+
+LCNet:
+  scale: 1.0
+  feature_maps: [2, 3, 4, 5]
+
+TrainReader:
+  batch_size: 32
\ No newline at end of file

configs/centernet/centernet_mbv1_1x_140e_coco.yml

+_BASE_: [
+  'centernet_r50_140e_coco.yml'
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV1_pretrained.pdparams
+weights: output/centernet_mbv1_1x_140e_coco/model_final
+
+CenterNet:
+  backbone: MobileNet
+  neck: CenterNetDLAFPN
+  head: CenterNetHead
+  post_process: CenterNetPostProcess
+
+MobileNet:
+  scale: 1.
+  with_extra_blocks: false
+  extra_block_filters: []
+  feature_maps: [3, 5, 11, 13]
+
+TrainReader:
+  batch_size: 32
\ No newline at end of file

configs/centernet/centernet_mbv3_large_1x_140e_coco.yml

+_BASE_: [
+  'centernet_r50_140e_coco.yml'
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
+weights: output/centernet_mbv3_large_1x_140e_coco/model_final
+
+CenterNet:
+  backbone: MobileNetV3
+  neck: CenterNetDLAFPN
+  head: CenterNetHead
+  post_process: CenterNetPostProcess
+
+MobileNetV3:
+  model_name: large
+  scale: 1.
+  with_extra_blocks: false
+  extra_block_filters: []
+  feature_maps: [4, 7, 13, 16]
+
+TrainReader:
+  batch_size: 32
\ No newline at end of file

configs/centernet/centernet_shufflenetv2_1x_140e_coco.yml

+_BASE_: [
+  'centernet_r50_140e_coco.yml'
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ShuffleNetV2_x1_0_pretrained.pdparams
+weights: output/centernet_shufflenetv2_1x_140e_coco/model_final
+
+CenterNet:
+  backbone: ShuffleNetV2
+  neck: CenterNetDLAFPN
+  head: CenterNetHead
+  post_process: CenterNetPostProcess
+
+ShuffleNetV2:
+  scale: 1.0
+  feature_maps: [5, 13, 17]
+  act: leaky_relu
+
+CenterNetDLAFPN:
+  first_level: 0
+  last_level: 3
+  down_ratio: 8
+  dcn_v2: False
+
+TrainReader:
+  batch_size: 32
+
+TestReader:
+  sample_transforms:
+    - Decode: {}
+    - WarpAffine: {keep_res: False, input_h: 512, input_w: 512}
+    - NormalizeImage: {mean: [0.40789655, 0.44719303, 0.47026116], std: [0.2886383 , 0.27408165, 0.27809834]}
+    - Permute: {}
\ No newline at end of file

deploy/python/keypoint_preprocess.py

@@ -77,10 +77,10 @@ def get_affine_transform(center,
         np.ndarray: The transform matrix.
     """
     assert len(center) == 2
-    assert len(input_size) == 2
     assert len(output_size) == 2
     assert len(shift) == 2
-
+    if not isinstance(input_size, (np.ndarray, list)):
+        input_size = np.array([input_size, input_size], dtype=np.float32)
     scale_tmp = input_size
 
     shift = np.array(shift)

ppdet/modeling/backbones/mobilenet_v1.py

@@ -226,7 +226,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv2_1"))
         self.dwsl.append(dws21)
-        self._update_out_channels(64, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(64 * scale), len(self.dwsl), feature_maps)
         dws22 = self.add_sublayer(
             "conv2_2",
             sublayer=DepthwiseSeparable(
@@ -242,7 +242,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv2_2"))
         self.dwsl.append(dws22)
-        self._update_out_channels(128, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(128 * scale), len(self.dwsl), feature_maps)
         # 1/4
         dws31 = self.add_sublayer(
             "conv3_1",
@@ -259,7 +259,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv3_1"))
         self.dwsl.append(dws31)
-        self._update_out_channels(128, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(128 * scale), len(self.dwsl), feature_maps)
         dws32 = self.add_sublayer(
             "conv3_2",
             sublayer=DepthwiseSeparable(
@@ -275,7 +275,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv3_2"))
         self.dwsl.append(dws32)
-        self._update_out_channels(256, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(256 * scale), len(self.dwsl), feature_maps)
         # 1/8
         dws41 = self.add_sublayer(
             "conv4_1",
@@ -292,7 +292,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv4_1"))
         self.dwsl.append(dws41)
-        self._update_out_channels(256, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(256 * scale), len(self.dwsl), feature_maps)
         dws42 = self.add_sublayer(
             "conv4_2",
             sublayer=DepthwiseSeparable(
@@ -308,13 +308,13 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv4_2"))
         self.dwsl.append(dws42)
-        self._update_out_channels(512, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(512 * scale), len(self.dwsl), feature_maps)
         # 1/16
         for i in range(5):
             tmp = self.add_sublayer(
                 "conv5_" + str(i + 1),
                 sublayer=DepthwiseSeparable(
-                    in_channels=512,
+                    in_channels=int(512 * scale),
                     out_channels1=512,
                     out_channels2=512,
                     num_groups=512,
@@ -326,7 +326,7 @@ class MobileNet(nn.Layer):
                     norm_type=norm_type,
                     name="conv5_" + str(i + 1)))
             self.dwsl.append(tmp)
-            self._update_out_channels(512, len(self.dwsl), feature_maps)
+            self._update_out_channels(int(512 * scale), len(self.dwsl), feature_maps)
         dws56 = self.add_sublayer(
             "conv5_6",
             sublayer=DepthwiseSeparable(
@@ -342,7 +342,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv5_6"))
         self.dwsl.append(dws56)
-        self._update_out_channels(1024, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(1024 * scale), len(self.dwsl), feature_maps)
         # 1/32
         dws6 = self.add_sublayer(
             "conv6",
@@ -359,7 +359,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv6"))
         self.dwsl.append(dws6)
-        self._update_out_channels(1024, len(self.dwsl), feature_maps)
+        self._update_out_channels(int(1024 * scale), len(self.dwsl), feature_maps)
 
         if self.with_extra_blocks:
             self.extra_blocks = []

ppdet/modeling/necks/centernet_fpn.py

@@ -270,6 +270,8 @@ class CenterNetDLAFPN(nn.Layer):
         feat = ida_up_feats[-1]
         if self.with_sge:
             feat = self.sge_attention(feat)
+        if self.down_ratio != 4:
+            feat = F.interpolate(feat, scale_factor=self.down_ratio // 4, mode="bilinear", align_corners=True)
         return feat
 
     @property