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add Lightweight backbone of centernet (PaddlePaddle#4586)
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‎configs/centernet/README_cn.md‎

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2020
| DLA-34(paper) | 512x512 | 37.4 | - | - | - |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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## 引用
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```

‎configs/centernet/centernet_ghostnet_1_3x_140e_coco.yml‎

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_BASE_: [
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'centernet_r50_140e_coco.yml'
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/GhostNet_x1_3_ssld_pretrained.pdparams
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weights: output/centernet_ghostnet_1_3x_140e_coco/model_final
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CenterNet:
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backbone: GhostNet
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neck: CenterNetDLAFPN
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head: CenterNetHead
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post_process: CenterNetPostProcess
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GhostNet:
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scale: 1.3
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conv_decay: 0.00004
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feature_maps: [4, 6, 12, 17]
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TrainReader:
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batch_size: 32

‎configs/centernet/centernet_lcnet_1x_140e_coco.yml‎

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_BASE_: [
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'centernet_r50_140e_coco.yml'
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_0_pretrained.pdparams
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weights: output/centernet_lcnet_1x_140e_coco/model_final
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CenterNet:
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backbone: LCNet
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neck: CenterNetDLAFPN
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head: CenterNetHead
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post_process: CenterNetPostProcess
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LCNet:
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scale: 1.0
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feature_maps: [2, 3, 4, 5]
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TrainReader:
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batch_size: 32

‎configs/centernet/centernet_mbv1_1x_140e_coco.yml‎

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_BASE_: [
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'centernet_r50_140e_coco.yml'
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV1_pretrained.pdparams
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weights: output/centernet_mbv1_1x_140e_coco/model_final
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CenterNet:
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backbone: MobileNet
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neck: CenterNetDLAFPN
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head: CenterNetHead
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post_process: CenterNetPostProcess
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MobileNet:
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scale: 1.
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with_extra_blocks: false
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extra_block_filters: []
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feature_maps: [3, 5, 11, 13]
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TrainReader:
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batch_size: 32

‎configs/centernet/centernet_mbv3_large_1x_140e_coco.yml‎

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_BASE_: [
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'centernet_r50_140e_coco.yml'
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
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weights: output/centernet_mbv3_large_1x_140e_coco/model_final
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CenterNet:
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backbone: MobileNetV3
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neck: CenterNetDLAFPN
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head: CenterNetHead
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post_process: CenterNetPostProcess
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MobileNetV3:
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model_name: large
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scale: 1.
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with_extra_blocks: false
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extra_block_filters: []
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feature_maps: [4, 7, 13, 16]
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TrainReader:
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batch_size: 32

‎configs/centernet/centernet_shufflenetv2_1x_140e_coco.yml‎

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_BASE_: [
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'centernet_r50_140e_coco.yml'
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ShuffleNetV2_x1_0_pretrained.pdparams
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weights: output/centernet_shufflenetv2_1x_140e_coco/model_final
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CenterNet:
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backbone: ShuffleNetV2
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neck: CenterNetDLAFPN
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head: CenterNetHead
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post_process: CenterNetPostProcess
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ShuffleNetV2:
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scale: 1.0
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feature_maps: [5, 13, 17]
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act: leaky_relu
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CenterNetDLAFPN:
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first_level: 0
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last_level: 3
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down_ratio: 8
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dcn_v2: False
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TrainReader:
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batch_size: 32
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TestReader:
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sample_transforms:
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- Decode: {}
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- WarpAffine: {keep_res: False, input_h: 512, input_w: 512}
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- NormalizeImage: {mean: [0.40789655, 0.44719303, 0.47026116], std: [0.2886383 , 0.27408165, 0.27809834]}
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- Permute: {}

‎deploy/python/keypoint_preprocess.py‎

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

‎ppdet/modeling/backbones/mobilenet_v1.py‎

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norm_type=norm_type,
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name="conv2_1"))
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self.dwsl.append(dws21)
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self._update_out_channels(64, len(self.dwsl), feature_maps)
229+
self._update_out_channels(int(64 * scale), len(self.dwsl), feature_maps)
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dws22 = self.add_sublayer(
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"conv2_2",
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sublayer=DepthwiseSeparable(
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norm_type=norm_type,
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name="conv2_2"))
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self.dwsl.append(dws22)
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self._update_out_channels(128, len(self.dwsl), feature_maps)
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self._update_out_channels(int(128 * scale), len(self.dwsl), feature_maps)
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# 1/4
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dws31 = self.add_sublayer(
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"conv3_1",
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norm_type=norm_type,
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name="conv3_1"))
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self.dwsl.append(dws31)
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self._update_out_channels(128, len(self.dwsl), feature_maps)
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self._update_out_channels(int(128 * scale), len(self.dwsl), feature_maps)
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dws32 = self.add_sublayer(
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"conv3_2",
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sublayer=DepthwiseSeparable(
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norm_type=norm_type,
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name="conv3_2"))
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self.dwsl.append(dws32)
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self._update_out_channels(256, len(self.dwsl), feature_maps)
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self._update_out_channels(int(256 * scale), len(self.dwsl), feature_maps)
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# 1/8
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dws41 = self.add_sublayer(
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"conv4_1",
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norm_type=norm_type,
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name="conv4_1"))
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self.dwsl.append(dws41)
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self._update_out_channels(256, len(self.dwsl), feature_maps)
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self._update_out_channels(int(256 * scale), len(self.dwsl), feature_maps)
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dws42 = self.add_sublayer(
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"conv4_2",
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sublayer=DepthwiseSeparable(
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norm_type=norm_type,
309309
name="conv4_2"))
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self.dwsl.append(dws42)
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self._update_out_channels(512, len(self.dwsl), feature_maps)
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self._update_out_channels(int(512 * scale), len(self.dwsl), feature_maps)
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# 1/16
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for i in range(5):
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tmp = self.add_sublayer(
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"conv5_" + str(i + 1),
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sublayer=DepthwiseSeparable(
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in_channels=512,
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in_channels=int(512 * scale),
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out_channels1=512,
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out_channels2=512,
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num_groups=512,
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norm_type=norm_type,
327327
name="conv5_" + str(i + 1)))
328328
self.dwsl.append(tmp)
329-
self._update_out_channels(512, len(self.dwsl), feature_maps)
329+
self._update_out_channels(int(512 * scale), len(self.dwsl), feature_maps)
330330
dws56 = self.add_sublayer(
331331
"conv5_6",
332332
sublayer=DepthwiseSeparable(
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342342
norm_type=norm_type,
343343
name="conv5_6"))
344344
self.dwsl.append(dws56)
345-
self._update_out_channels(1024, len(self.dwsl), feature_maps)
345+
self._update_out_channels(int(1024 * scale), len(self.dwsl), feature_maps)
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# 1/32
347347
dws6 = self.add_sublayer(
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"conv6",
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norm_type=norm_type,
360360
name="conv6"))
361361
self.dwsl.append(dws6)
362-
self._update_out_channels(1024, len(self.dwsl), feature_maps)
362+
self._update_out_channels(int(1024 * scale), len(self.dwsl), feature_maps)
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364364
if self.with_extra_blocks:
365365
self.extra_blocks = []

‎ppdet/modeling/necks/centernet_fpn.py‎

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feat = ida_up_feats[-1]
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if self.with_sge:
272272
feat = self.sge_attention(feat)
273+
if self.down_ratio != 4:
274+
feat = F.interpolate(feat, scale_factor=self.down_ratio // 4, mode="bilinear", align_corners=True)
273275
return feat
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@property

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