From d3f00294e3b0c67afe261c36e2ded07c93aec944 Mon Sep 17 00:00:00 2001
From: cnn <liuhui29@baidu.com>
Date: Thu, 5 Aug 2021 15:41:44 +0800
Subject: [PATCH] [dev] updata document and config (#3879)

* updata document, test=document_fix

* use 8 card for training as default, test=document_fix
---
 configs/dota/README.md                        | 31 +++++++------------
 configs/dota/_base_/s2anet_optimizer_1x.yml   |  2 +-
 configs/dota/_base_/s2anet_optimizer_2x.yml   |  2 +-
 configs/dota/s2anet_1x_spine.yml              |  4 +--
 ...nv_1x_dota.yml => s2anet_conv_2x_dota.yml} | 11 ++-----
 5 files changed, 18 insertions(+), 32 deletions(-)
 rename configs/dota/{s2anet_conv_1x_dota.yml => s2anet_conv_2x_dota.yml} (73%)

diff --git a/configs/dota/README.md b/configs/dota/README.md
index 809e7f8f0..f1512932f 100644
--- a/configs/dota/README.md
+++ b/configs/dota/README.md
@@ -9,7 +9,7 @@
 
 ## 简介
 
-[S2ANet](https://arxiv.org/pdf/2008.09397.pdf)是用于检测旋转框的模型，要求使用PaddlePaddle 2.0.1(可使用pip安装) 或适当的[develop版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-release)。
+[S2ANet](https://arxiv.org/pdf/2008.09397.pdf)是用于检测旋转框的模型，要求使用PaddlePaddle 2.1.1(可使用pip安装) 或适当的[develop版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-release)。
 
 
 ## 准备数据
@@ -53,11 +53,11 @@ DOTA数据集中总共有2806张图像，其中1411张图像作为训练集，45
 - PaddlePaddle >= 2.1.1
 - GCC == 8.2
 
-推荐使用docker镜像[paddle:2.0.1-gpu-cuda10.1-cudnn7](registry.baidubce.com/paddlepaddle/paddle:2.1.1-gpu-cuda10.2-cudnn7)。
+推荐使用docker镜像[paddle:2.1.1-gpu-cuda10.1-cudnn7](registry.baidubce.com/paddlepaddle/paddle:2.1.1-gpu-cuda10.1-cudnn7)。
 
 执行如下命令下载镜像并启动容器：
 ```
-sudo nvidia-docker run -it --name paddle_s2anet -v $PWD:/paddle --network=host registry.baidubce.com/paddlepaddle/paddle:2.1.1-gpu-cuda10.2-cudnn7 /bin/bash
+sudo nvidia-docker run -it --name paddle_s2anet -v $PWD:/paddle --network=host registry.baidubce.com/paddlepaddle/paddle:2.1.1-gpu-cuda10.1-cudnn7 /bin/bash
 ```
 
 镜像中paddle已安装好，进入python3.7，执行如下代码检查paddle安装是否正常：
@@ -90,15 +90,7 @@ python3.7 test.py
 
 ### 2. 训练
 **注意：**
-配置文件中学习率是按照4卡GPU训练设置的，如果使用单卡GPU训练，请将学习率设置为原来的1/4。
-
-准备数据
-```bash
-cd dataset/spine_coco
-wget https://paddledet.bj.bcebos.com/data/spine_coco.tar
-tar -xvf spine_coco.tar
-cd ../../
-```
+配置文件中学习率是按照8卡GPU训练设置的，如果使用单卡GPU训练，请将学习率设置为原来的1/8。
 
 GPU单卡训练
 ```bash
@@ -108,25 +100,26 @@ python3.7 tools/train.py -c configs/dota/s2anet_1x_spine.yml
 
 GPU多卡训练
 ```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-python3.7 -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/dota/s2anet_1x_spine.yml
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+python3.7 -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/dota/s2anet_1x_spine.yml
 ```
 
 可以通过`--eval`开启边训练边测试。
 
-### 2. 评估
+### 3. 评估
 ```bash
 python3.7 tools/eval.py -c configs/dota/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams
 ```
+** 注意：**  dota数据集中是train和val数据作为训练集一起训练的，对dota数据集进行评估时需要自定义设置评估数据集配置。
 
-### 3. 预测
-执行如下命令，会将图像预测结果保存到`output_dir`文件夹下。
+### 4. 预测
+执行如下命令，会将图像预测结果保存到`output`文件夹下。
 ```bash
 python3.7 tools/infer.py -c configs/dota/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams --infer_img=demo/39006.jpg
 ```
 
-### 4. DOTA数据评估
-执行如下命令，会在`output_dir`文件夹下将每个图像预测结果保存到同文件夹名的txt文本中。
+### 5. DOTA数据评估
+执行如下命令，会在`output`文件夹下将每个图像预测结果保存到同文件夹名的txt文本中。
 ```
 python3.7 tools/infer.py -c configs/dota/s2anet_1x_dota.yml -o weights=./weights/s2anet_1x_dota.pdparams  --infer_dir=dota_test_images --draw_threshold=0.05 --save_txt=True --output_dir=output
 ```
diff --git a/configs/dota/_base_/s2anet_optimizer_1x.yml b/configs/dota/_base_/s2anet_optimizer_1x.yml
index 91069a3bb..65f794dc3 100644
--- a/configs/dota/_base_/s2anet_optimizer_1x.yml
+++ b/configs/dota/_base_/s2anet_optimizer_1x.yml
@@ -1,7 +1,7 @@
 epoch: 12
 
 LearningRate:
-  base_lr: 0.005
+  base_lr: 0.01
   schedulers:
   - !PiecewiseDecay
     gamma: 0.1
diff --git a/configs/dota/_base_/s2anet_optimizer_2x.yml b/configs/dota/_base_/s2anet_optimizer_2x.yml
index c96027a61..54e73ce64 100644
--- a/configs/dota/_base_/s2anet_optimizer_2x.yml
+++ b/configs/dota/_base_/s2anet_optimizer_2x.yml
@@ -1,7 +1,7 @@
 epoch: 24
 
 LearningRate:
-  base_lr: 0.005
+  base_lr: 0.01
   schedulers:
   - !PiecewiseDecay
     gamma: 0.1
diff --git a/configs/dota/s2anet_1x_spine.yml b/configs/dota/s2anet_1x_spine.yml
index 440ac143a..5cf215b54 100644
--- a/configs/dota/s2anet_1x_spine.yml
+++ b/configs/dota/s2anet_1x_spine.yml
@@ -8,9 +8,9 @@ _BASE_: [
 
 weights: output/s2anet_1x_spine/model_final
 
-# for 4 card
+# for 8 card
 LearningRate:
-  base_lr: 0.005
+  base_lr: 0.01
 
 S2ANetHead:
   anchor_strides: [8, 16, 32, 64, 128]
diff --git a/configs/dota/s2anet_conv_1x_dota.yml b/configs/dota/s2anet_conv_2x_dota.yml
similarity index 73%
rename from configs/dota/s2anet_conv_1x_dota.yml
rename to configs/dota/s2anet_conv_2x_dota.yml
index a81c76f3f..3d95c566e 100644
--- a/configs/dota/s2anet_conv_1x_dota.yml
+++ b/configs/dota/s2anet_conv_2x_dota.yml
@@ -1,18 +1,11 @@
 _BASE_: [
   '../datasets/dota.yml',
   '../runtime.yml',
-  '_base_/s2anet_optimizer_1x.yml',
+  '_base_/s2anet_optimizer_2x.yml',
   '_base_/s2anet.yml',
   '_base_/s2anet_reader.yml',
 ]
-weights: output/s2anet_1x_dota/model_final
-
-ResNet:
-  depth: 50
-  variant: b
-  norm_type: bn
-  return_idx: [1,2,3]
-  num_stages: 4
+weights: output/s2anet_conv_1x_dota/model_final
 
 S2ANetHead:
   anchor_strides: [8, 16, 32, 64, 128]
-- 
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