fix some bugs and polish docs (#6755)

* fix some bugs and polish docs

* polish docs

* modify docs, test=document_fix

* modify docs, test=document_fix

configs/rotate/README.md

@@ -15,11 +15,12 @@
 
 | 模型 | mAP | 学习率策略 | 角度表示 | 数据增广 | GPU数目 | 每GPU图片数目 | 模型下载 | 配置文件 |
 |:---:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:|
-| [S2ANet](./s2anet/README.md) | 74.0 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_alignconv_2x_dota.yml) |
+| [S2ANet](./s2anet/README.md) | 73.84 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml) |
 
 **注意:**
 
 - 如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
+- 模型库中的模型默认使用单尺度训练单尺度测试。如果数据增广一栏标明MS，意味着使用多尺度训练和多尺度测试。如果数据增广一栏标明RR，意味着使用RandomRotate数据增广进行训练。
 
 ## 数据准备
 ### DOTA数据准备
@@ -36,8 +37,15 @@ ${DOTA_ROOT}
     └── labelTxt
 ```
 
-DOTA数据集分辨率较高，因此一般在训练和测试之前对图像进行切图，使用单尺度进行切图可以使用以下命令：
+对于有标注的数据，每一张图片会对应一个同名的txt文件，文件中每一行为一个旋转框的标注，其格式如下：
 ```
+x1 y1 x2 y2 x3 y3 x4 y4 class_name difficult
+```
+
+### 单尺度切图
+DOTA数据集分辨率较高，因此一般在训练和测试之前对图像进行离线切图，使用单尺度进行切图可以使用以下命令：
+``` bash
+# 对于有标注的数据进行切图
 python configs/rotate/tools/prepare_data.py \
     --input_dirs ${DOTA_ROOT}/train/ ${DOTA_ROOT}/val/ \
     --output_dir ${OUTPUT_DIR}/trainval1024/ \
@@ -45,26 +53,39 @@ python configs/rotate/tools/prepare_data.py \
     --subsize 1024 \
     --gap 200 \
     --rates 1.0
+
+# 对于无标注的数据进行切图需要设置--image_only
+python configs/rotate/tools/prepare_data.py \
+    --input_dirs ${DOTA_ROOT}/test/ \
+    --output_dir ${OUTPUT_DIR}/test1024/ \
+    --coco_json_file DOTA_test1024.json \
+    --subsize 1024 \
+    --gap 200 \
+    --rates 1.0 \
+    --image_only
+
 ```
+
+### 多尺度切图
 使用多尺度进行切图可以使用以下命令：
-```
+``` bash
+# 对于有标注的数据进行切图
 python configs/rotate/tools/prepare_data.py \
     --input_dirs ${DOTA_ROOT}/train/ ${DOTA_ROOT}/val/ \
     --output_dir ${OUTPUT_DIR}/trainval/ \
     --coco_json_file DOTA_trainval1024.json \
     --subsize 1024 \
     --gap 500 \
-    --rates 0.5 1.0 1.5 \
-```
-对于无标注的数据可以设置`--image_only`进行切图，如下所示：
-```
+    --rates 0.5 1.0 1.5
+
+# 对于无标注的数据进行切图需要设置--image_only
 python configs/rotate/tools/prepare_data.py \
     --input_dirs ${DOTA_ROOT}/test/ \
     --output_dir ${OUTPUT_DIR}/test1024/ \
     --coco_json_file DOTA_test1024.json \
     --subsize 1024 \
-    --gap 200 \
-    --rates 1.0 \
+    --gap 500 \
+    --rates 0.5 1.0 1.5 \
     --image_only
 ```
 

configs/rotate/README_en.md

@@ -14,11 +14,12 @@ Rotated object detection is used to detect rectangular bounding boxes with angle
 ## Model Zoo
 | Model | mAP | Lr Scheduler | Angle | Aug | GPU Number | images/GPU | download | config |
 |:---:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:|
-| [S2ANet](./s2anet/README.md) | 74.0 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_alignconv_2x_dota.yml) |
+| [S2ANet](./s2anet/README_en.md) | 73.84 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml) |
 
 **Notes:**
 
 - if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
+- Models in model zoo is trained and tested with single scale by default. If `MS` is indicated in the data augmentation column, it means that multi-scale training and multi-scale testing are used. If `RR` is indicated in the data augmentation column, it means that RandomRotate data augmentation is used for training.
 
 ## Data Preparation
 ### DOTA Dataset preparation
@@ -35,8 +36,16 @@ ${DOTA_ROOT}
     └── labelTxt
 ```
 
-The image resolution of DOTA dataset is relatively high, so we usually slice the images before training and testing. To slice the images with a single scale, you can use the command below
+For labeled data, each image corresponds to a txt file with the same name, and each row in the txt file represent a rotated bouding box. The format is as follows:
+
+```
+x1 y1 x2 y2 x3 y3 x4 y4 class_name difficult
 ```
+
+### Slicing data with single scale
+The image resolution of DOTA dataset is relatively high, so we usually slice the images before training and testing. To slice the images with a single scale, you can use the command below
+``` bash
+# slicing labeled data
 python configs/rotate/tools/prepare_data.py \
     --input_dirs ${DOTA_ROOT}/train/ ${DOTA_ROOT}/val/ \
     --output_dir ${OUTPUT_DIR}/trainval1024/ \
@@ -44,26 +53,37 @@ python configs/rotate/tools/prepare_data.py \
     --subsize 1024 \
     --gap 200 \
     --rates 1.0
+# slicing unlabeled data by setting --image_only
+python configs/rotate/tools/prepare_data.py \
+    --input_dirs ${DOTA_ROOT}/test/ \
+    --output_dir ${OUTPUT_DIR}/test1024/ \
+    --coco_json_file DOTA_test1024.json \
+    --subsize 1024 \
+    --gap 200 \
+    --rates 1.0 \
+    --image_only
+
 ```
+
+### Slicing data with multi scale
 To slice the images with multiple scales, you can use the command below
-```
+``` bash
+# slicing labeled data
 python configs/rotate/tools/prepare_data.py \
     --input_dirs ${DOTA_ROOT}/train/ ${DOTA_ROOT}/val/ \
     --output_dir ${OUTPUT_DIR}/trainval/ \
     --coco_json_file DOTA_trainval1024.json \
     --subsize 1024 \
     --gap 500 \
-    --rates 0.5 1.0 1.5 \
-```
-For data without annotations, you should set `--image_only` as follows
-```
+    --rates 0.5 1.0 1.5
+# slicing unlabeled data by setting --image_only
 python configs/rotate/tools/prepare_data.py \
     --input_dirs ${DOTA_ROOT}/test/ \
     --output_dir ${OUTPUT_DIR}/test1024/ \
     --coco_json_file DOTA_test1024.json \
     --subsize 1024 \
-    --gap 200 \
-    --rates 1.0 \
+    --gap 500 \
+    --rates 0.5 1.0 1.5 \
     --image_only
 ```
 

configs/rotate/s2anet/README.md

-# S2ANet模型
+简体中文 | [English](README_en.md)
+
+# S2ANet
 
 ## 内容
 - [简介](#简介)
-- [开始训练](#开始训练)
 - [模型库](#模型库)
+- [使用说明](#使用说明)
 - [预测部署](#预测部署)
+- [引用](#引用)
 
 ## 简介
 
-[S2ANet](https://arxiv.org/pdf/2008.09397.pdf)是用于检测旋转框的模型，在DOTA 1.0数据集上单尺度训练能达到74.0的mAP.
+[S2ANet](https://arxiv.org/pdf/2008.09397.pdf)是用于检测旋转框的模型.
+
+## 模型库
+
+| 模型 | Conv类型 | mAP | 学习率策略 | 角度表示 | 数据增广 | GPU数目 | 每GPU图片数目 | 模型下载 | 配置文件 |
+|:---:|:------:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:|
+| S2ANet | Conv | 71.45 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_conv_2x_dota.yml) |
+| S2ANet | AlignConv | 73.84 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml) |
+
+**注意：**
+
+- 如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
+- 模型库中的模型默认使用单尺度训练单尺度测试。如果数据增广一栏标明MS，意味着使用多尺度训练和多尺度测试。如果数据增广一栏标明RR，意味着使用RandomRotate数据增广进行训练。
+- 这里使用`multiclass_nms`，与原作者使用nms略有不同。
+
+
+## 使用说明
 
-## 开始训练
+参考[数据准备](../README.md#数据准备)准备数据。
 
 ### 1. 训练
 
@@ -22,13 +41,13 @@ python tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
 
 GPU多卡训练
 ```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
 ```
 
 可以通过`--eval`开启边训练边测试。
 
-### 3. 评估
+### 2. 评估
 ```bash
 python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams
 
@@ -36,7 +55,7 @@ python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=out
 python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams
 ```
 
-### 4. 预测
+### 3. 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
 python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams --infer_img=demo/39006.jpg --draw_threshold=0.3
@@ -46,37 +65,26 @@ python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=ou
 python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams --infer_img=demo/39006.jpg --draw_threshold=0.3
 ```
 
-### 5. DOTA数据评估
+### 4. 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。
 ```
-python tools/infer.py -c configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml -o weights=./weights/s2anet_alignconv_2x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output --visualize=False --save_results=True
+python tools/infer.py -c configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output --visualize=False --save_results=True
 ```
 参考[DOTA Task](https://captain-whu.github.io/DOTA/tasks.html), 评估DOTA数据集需要生成一个包含所有检测结果的zip文件，每一类的检测结果储存在一个txt文件中，txt文件中每行格式为：`image_name score x1 y1 x2 y2 x3 y3 x4 y4`。将生成的zip文件提交到[DOTA Evaluation](https://captain-whu.github.io/DOTA/evaluation.html)的Task1进行评估。你可以执行以下命令生成评估文件
 ```
 python configs/rotate/tools/generate_result.py --pred_txt_dir=output/ --output_dir=submit/ --data_type=dota10
+
 zip -r submit.zip submit
 ```
 
-## 模型库
-
-### S2ANet模型
-
-|     模型     |  Conv类型  |   mAP    |   模型下载   |   配置文件   |
-|:-----------:|:----------:|:--------:| :----------:| :---------: |
-|   S2ANet    |   Conv     |   71.42  |  [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_conv_2x_dota.yml)                   |
-|   S2ANet    |  AlignConv |   74.0   |  [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml)                   |
-
-**注意：** 这里使用`multiclass_nms`，与原作者使用nms略有不同。
-
-
 ## 预测部署
 
 Paddle中`multiclass_nms`算子的输入支持四边形输入，因此部署时可以不需要依赖旋转框IOU计算算子。
 
-部署教程请参考[预测部署](../../deploy/README.md)
+部署教程请参考[预测部署](../../../deploy/README.md)
 
 
-## Citations
+## 引用
 ```
 @article{han2021align,  
   author={J. {Han} and J. {Ding} and J. {Li} and G. -S. {Xia}},  

configs/rotate/s2anet/README_en.md

-# S2ANet Model
+English | [简体中文](README.md)
+
+# S2ANet
 
 ## Content
-- [S2ANet Model](#s2anet-model)
-  - [Content](#content)
-  - [Introduction](#introduction)
-  - [Start Training](#start-training)
-    - [1. Train](#1-train)
-    - [2. Evaluation](#2-evaluation)
-    - [3. Prediction](#3-prediction)
-    - [4. DOTA Data evaluation](#4-dota-data-evaluation)
-  - [Model Library](#model-library)
-    - [S2ANet Model](#s2anet-model-1)
-  - [Predict Deployment](#predict-deployment)
-  - [Citations](#citations)
+- [Introduction](#Introduction)
+- [Model Zoo](#Model-Zoo)
+- [Getting Start](#Getting-Start)
+- [Deployment](#Deployment)
+- [Citations](#Citations)
 
 ## Introduction
 
-[S2ANet](https://arxiv.org/pdf/2008.09397.pdf) is used to detect rotated objects and acheives 74.0 mAP on DOTA 1.0 dataset.
+[S2ANet](https://arxiv.org/pdf/2008.09397.pdf) is used to detect rotated objects.
+
+## Model Zoo
+| Model | Conv Type | mAP | Lr Scheduler | Angle | Aug | GPU Number | images/GPU | download | config |
+|:---:|:------:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:|
+| S2ANet | Conv | 71.45 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_conv_2x_dota.yml) |
+| S2ANet | AlignConv | 73.84 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml) |
+
+**Notes:**
+- if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
+- Models in model zoo is trained and tested with single scale by default. If `MS` is indicated in the data augmentation column, it means that multi-scale training and multi-scale testing are used. If `RR` is indicated in the data augmentation column, it means that RandomRotate data augmentation is used for training.
+- `multiclass_nms` is used here, which is slightly different from the original author's use of NMS.
 
-## Start Training
+## Getting Start
 
-### 2. Train
+Refer to [Data-Preparation](../README_en.md#Data-Preparation) to prepare data.
+
+### 1. Train
 
 Single GPU Training
 ```bash
@@ -30,13 +38,13 @@ python tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
 
 Multiple GPUs Training
 ```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
 ```
 
 You can use `--eval`to enable train-by-test.
 
-### 3. Evaluation
+### 2. Evaluation
 ```bash
 python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams
 
@@ -44,7 +52,7 @@ python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=out
 python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams
 ```
 
-### 4. Prediction
+### 3. Prediction
 Executing the following command will save the image prediction results to the `output` folder.
 ```bash
 python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams --infer_img=demo/39006.jpg --draw_threshold=0.3
@@ -54,35 +62,24 @@ Prediction using models that provide training:
 python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams --infer_img=demo/39006.jpg --draw_threshold=0.3
 ```
 
-### 5. DOTA Data evaluation
+### 4. DOTA Data evaluation
 Execute the following command, will save each image prediction result in `output` folder txt text with the same folder name.
 ```
-python tools/infer.py -c configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml -o weights=./weights/s2anet_alignconv_2x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output --visualize=False --save_results=True
+python tools/infer.py -c configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output --visualize=False --save_results=True
 ```
 Refering to [DOTA Task](https://captain-whu.github.io/DOTA/tasks.html), You need to submit a zip file containing results for all test images for evaluation. The detection results of each category are stored in a txt file, each line of which is in the following format
 `image_id score x1 y1 x2 y2 x3 y3 x4 y4`. To evaluate, you should submit the generated zip file to the Task1 of [DOTA Evaluation](https://captain-whu.github.io/DOTA/evaluation.html). You can execute the following command to generate the file
 ```
 python configs/rotate/tools/generate_result.py --pred_txt_dir=output/ --output_dir=submit/ --data_type=dota10
+
 zip -r submit.zip submit
 ```
 
-## Model Library
-
-### S2ANet Model
-
-|     Model     |  Conv Type  |   mAP    |   Model Download   |   Configuration File   |
-|:-----------:|:----------:|:--------:| :----------:| :---------: |
-|   S2ANet    |   Conv     |   71.42  |  [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_conv_2x_dota.yml)                   |
-|   S2ANet    |  AlignConv |   74.0   |  [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml)                   |
-
-**Attention:** `multiclass_nms` is used here, which is slightly different from the original author's use of NMS.
-
-
-## Predict Deployment
+## Deployment
 
 The inputs of the `multiclass_nms` operator in Paddle support quadrilateral inputs, so deployment can be done without relying on the rotating frame IOU operator.
 
-Please refer to the deployment tutorial[Predict deployment](../../deploy/README_en.md)
+Please refer to the deployment tutorial[Predict deployment](../../../deploy/README_en.md)
 
 
 ## Citations

configs/rotate/tools/slicebase.py

@@ -222,8 +222,10 @@ class SliceBase(object):
         windows = self.get_windows(height, width)
         self.slice_image_single(resize_img, windows, output_dir, base_name)
         if not self.image_only:
-            self.slice_anno_single(info['annotation'], windows, output_dir,
-                                   base_name)
+            annos = info['annotation']
+            for anno in annos:
+                anno['poly'] = list(map(lambda x: rate * x, anno['poly']))
+            self.slice_anno_single(annos, windows, output_dir, base_name)
 
     def check_or_mkdirs(self, path):
         if not os.path.exists(path):

test_tipc/configs/dota/s2anet_alignconv_2x_spine.yml

 _BASE_: [
   '../../../configs/datasets/spine_coco.yml',
   '../../../configs/runtime.yml',
-  '../../../configs/dota/_base_/s2anet_optimizer_2x.yml',
-  '../../../configs/dota/_base_/s2anet.yml',
-  '../../../configs/dota/_base_/s2anet_reader.yml',
+  '../../../configs/rotate/s2anet/_base_/s2anet_optimizer_2x.yml',
+  '../../../configs/rotate/s2anet/_base_/s2anet.yml',
+  '../../../configs/rotate/s2anet/_base_/s2anet_reader.yml',
 ]
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
 

test_tipc/configs/dota/s2anet_conv_2x_spine.yml

 _BASE_: [
   '../../../configs/datasets/spine_coco.yml',
   '../../../configs/runtime.yml',
-  '../../../configs/dota/_base_/s2anet_optimizer_2x.yml',
-  '../../../configs/dota/_base_/s2anet.yml',
-  '../../../configs/dota/_base_/s2anet_reader.yml',
+  '../../../configs/rotate/s2anet/_base_/s2anet_optimizer_2x.yml',
+  '../../../configs/rotate/s2anet/_base_/s2anet.yml',
+  '../../../configs/rotate/s2anet/_base_/s2anet_reader.yml',
 ]
 weights: output/s2anet_conv_1x_dota/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams

test_tipc/prepare.sh

@@ -152,8 +152,8 @@ else
     cd ./dataset/wider_face/ && tar -xvf wider_tipc.tar && mv -n wider_tipc/* .
     rm -rf wider_tipc/ && cd ../../
     # download spine_coco lite data
-    wget -nc -P ./dataset/spine_coco/ https://paddledet.bj.bcebos.com/data/tipc/spine_tipc.tar --no-check-certificate
-    cd ./dataset/spine_coco/ && tar -xvf spine_tipc.tar && mv -n spine_tipc/* .
+    wget -nc -P ./dataset/spine_coco/ https://paddledet.bj.bcebos.com/data/tipc/spine_coco_tipc.tar --no-check-certificate
+    cd ./dataset/spine_coco/ && tar -xvf spine_coco_tipc.tar && mv -n spine_coco_tipc/* .
     rm -rf spine_tipc/ && cd ../../
     if [[ ${model_name} =~ "s2anet" ]]; then
         cd ./ppdet/ext_op && eval "${python} setup.py install"