[PPYOLOE] update doc, params, flops (#5543)

configs/ppyoloe/README.md

@@ -9,7 +9,7 @@ English | [简体中文](README_cn.md)
 - [Appendix](#Appendix)
 
 ## Introduction
-PP-YOLOE is an excellent single-stage anchor-free model based on PP-YOLOv2, surpassing a variety of popular yolo models. PP-YOLOE has a series of models, named s/m/l/x, which are configured through width multiplier and depth multiplier. PP-YOLOE avoids using special operators, such as deformable convolution or matrix nms, to be deployed friendly on various hardware. For more details, please refer to our report.
+PP-YOLOE is an excellent single-stage anchor-free model based on PP-YOLOv2, surpassing a variety of popular yolo models. PP-YOLOE has a series of models, named s/m/l/x, which are configured through width multiplier and depth multiplier. PP-YOLOE avoids using special operators, such as deformable convolution or matrix nms, to be deployed friendly on various hardware. For more details, please refer to our [report](https://arxiv.org/abs/2203.16250).
 
 <div align="center">
   <img src="../../docs/images/ppyoloe_map_fps.png" width=500 />
@@ -24,12 +24,12 @@ PP-YOLOE is composed of following methods:
 - [SiLU activation function](https://arxiv.org/abs/1710.05941)
 
 ## Model Zoo
-|          Model           | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
-|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
-| PP-YOLOE-s                  |     8      |     32     | cspresnet-s |     640     |       42.7        |        43.1         |      208.3      |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml)                   |
-| PP-YOLOE-m                  |     8      |     32     | cspresnet-m |     640     |       48.6        |        48.9         |      123.4      |          208.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml)                   |
-| PP-YOLOE-l                  |     8      |     24     | cspresnet-l |     640     |       50.9        |        51.4         |      78.1      |          149.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml)                   |
-| PP-YOLOE-x                  |     8      |     16     | cspresnet-x |     640     |       51.9        |        52.2         |      45.0      |          95.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml)                   |
+|          Model           | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
+|:------------------------:|:-------:|:----------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:| :------------: | :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  |     8      |     32     | cspresnet-s |     640     |       42.7        |        43.1         |   7.93    |  17.36   |      208.3      |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  |     8      |     28     | cspresnet-m |     640     |       48.6        |        48.9         |   23.43   |  49.91   |      123.4      |          208.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  |     8      |     20      | cspresnet-l |     640     |       50.9        |        51.4         |   52.20   |  110.07  |      78.1      |          149.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  |     8      |     16     | cspresnet-x |     640     |       51.9        |        52.2         |   98.42   |  206.59  |      45.0      |          95.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml)                   |
 
 **Notes:**
 

configs/ppyoloe/README_cn.md

@@ -9,7 +9,7 @@
 - [附录](#附录)
 
 ## 简介
-PP-YOLOE是基于PP-YOLOv2的卓越的单阶段Anchor-free模型，超越了多种流行的yolo模型。PP-YOLOE有一系列的模型，即s/m/l/x，可以通过width multiplier和depth multiplier配置。PP-YOLOE避免使用诸如deformable convolution或者matrix nms之类的特殊算子，以使其能轻松地部署在多种多样的硬件上。更多细节可以参考我们的report。
+PP-YOLOE是基于PP-YOLOv2的卓越的单阶段Anchor-free模型，超越了多种流行的yolo模型。PP-YOLOE有一系列的模型，即s/m/l/x，可以通过width multiplier和depth multiplier配置。PP-YOLOE避免使用诸如deformable convolution或者matrix nms之类的特殊算子，以使其能轻松地部署在多种多样的硬件上。更多细节可以参考我们的[report](https://arxiv.org/abs/2203.16250)。
 
 <div align="center">
   <img src="../../docs/images/ppyoloe_map_fps.png" width=500 />
@@ -24,12 +24,12 @@ PP-YOLOE由以下方法组成
 - [SiLU激活函数](https://arxiv.org/abs/1710.05941)
 
 ## 模型库
-|          模型           | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件  |
-|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
-| PP-YOLOE-s                  |     8      |     32     | cspresnet-s |     640     |       42.7        |        43.1         |      208.3      |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml)                   |
-| PP-YOLOE-m                  |     8      |     32     | cspresnet-m |     640     |       48.6        |        48.9         |      123.4      |          208.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml)                   |
-| PP-YOLOE-l                  |     8      |     24     | cspresnet-l |     640     |       50.9        |        51.4         |      78.1      |          149.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml)                   |
-| PP-YOLOE-x                  |     8      |     16     | cspresnet-x |     640     |       51.9        |        52.2         |      45.0      |          95.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml)                   |
+|          模型           | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件  |
+|:------------------------:|:-------:|:--------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:|:---------------:| :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  |     8      |    32    | cspresnet-s |     640     |       42.7        |        43.1         |   7.93    |  17.36   |       208.3 |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  |     8      |    28    | cspresnet-m |     640     |       48.6        |        48.9         |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  |     8      |    20    | cspresnet-l |     640     |       50.9        |        51.4         |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  |     8      |    16    | cspresnet-x |     640     |       51.9        |        52.2         |   98.42   |  206.59  |   45.0    |   95.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml)                   |
 
 **注意:**
 

ppdet/engine/trainer.py

@@ -373,7 +373,8 @@ class Trainer(object):
         # enabel auto mixed precision mode
         if self.cfg.get('amp', False):
             scaler = amp.GradScaler(
-                enable=self.cfg.use_gpu or self.cfg.use_npu, init_loss_scaling=1024)
+                enable=self.cfg.use_gpu or self.cfg.use_npu,
+                init_loss_scaling=1024)
 
         self.status.update({
             'epoch_id': self.start_epoch,

ppdet/modeling/backbones/cspresnet.py

@@ -96,6 +96,8 @@ class RepVggBlock(nn.Layer):
         kernel, bias = self.get_equivalent_kernel_bias()
         self.conv.weight.set_value(kernel)
         self.conv.bias.set_value(bias)
+        self.__delattr__('conv1')
+        self.__delattr__('conv2')
 
     def get_equivalent_kernel_bias(self):
         kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
@@ -176,7 +178,7 @@ class CSPResStage(nn.Layer):
             self.conv_down = None
         self.conv1 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
         self.conv2 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
-        self.blocks = nn.Sequential(* [
+        self.blocks = nn.Sequential(*[
             block_fn(
                 ch_mid // 2, ch_mid // 2, act=act, shortcut=True)
             for i in range(n)
@@ -252,9 +254,9 @@ class CSPResNet(nn.Layer):
                     act=act)))
 
         n = len(channels) - 1
-        self.stages = nn.Sequential(* [(str(i), CSPResStage(
+        self.stages = nn.Sequential(*[(str(i), CSPResStage(
             BasicBlock, channels[i], channels[i + 1], layers[i], 2, act=act))
-                                       for i in range(n)])
+                                      for i in range(n)])
 
         self._out_channels = channels[1:]
         self._out_strides = [4, 8, 16, 32]