Merge branch 'develop_reg' into develop_reg

deploy/configs/build_cartoon.yaml

 Global:
-  rec_inference_model_dir: "./cartoon/model"
+  rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
   batch_size: 1
   use_gpu: True
   enable_mkldnn: True
@@ -26,11 +26,12 @@ RecPreProcess:
 
 RecPostProcess: null
 
+# indexing engine config
 IndexProcess:
-  index_path: "./cartoon/index/"
-  image_root: "./cartoon/dataset/"
-  data_file:  "./cartoon/data_file.txt"
-  delimiter:  "\t"
+  index_path: "./dataset/cartoon_demo_data_v1.0/index/"
+  image_root: "./dataset/cartoon_demo_data_v1.0/"
+  data_file:  "./dataset/cartoon_demo_data_v1.0/data_file.txt"
+  delimiter: "\t"
   dist_type: "IP"
   pq_size: 100
   embedding_size: 2048

deploy/configs/build_logo.yaml

 Global:
-  rec_inference_model_dir: "./logo/model/"
+  rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
   batch_size: 1
   use_gpu: True
   enable_mkldnn: True
@@ -26,9 +26,9 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./logo/logo_index/"
-  image_root: "./logo/dataset/"
-  data_file:  "./logo/logo_gallery_demo.txt"
+  index_path: "./dataset/logo_demo_data_v1.0/index/"
+  image_root: "./dataset/logo_demo_data_v1.0/"
+  data_file:  "./dataset/logo_demo_data_v1.0/data_file.txt"
   delimiter: "\t"
   dist_type: "IP"
   pq_size: 100

deploy/configs/build_vehicle.yaml

 Global:
-  rec_inference_model_dir: "./vehicle/model/"
+  rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
   batch_size: 1
   use_gpu: True
   enable_mkldnn: True
@@ -26,9 +26,9 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./vehilce/vehicle_index/"
-  image_root: "./vehicle/dataset/"
-  data_file:  "./vehilce/demo_gallery.txt"
+  index_path: "./dataset/vehicle_demo_data_v1.0/index/"
+  image_root: "./dataset/vehicle_demo_data_v1.0/"
+  data_file:  "./dataset/vehicle_demo_data_v1.0/data_file.txt"
   delimiter: " "
   dist_type: "IP"
   pq_size: 100

deploy/configs/inference_cartoon.yaml

 Global:
-  infer_imgs: "./dataset/iCartoonFace/val2/0000000.jpg"
-  det_inference_model_dir: "./cartoon/det"
-  rec_inference_model_dir: "./cartoon/rec"
+  infer_imgs: "./dataset/cartoon_demo_data_v1.0/query/"
+  det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
+  rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
   batch_size: 1
   image_shape: [3, 640, 640]
   threshold: 0.5
@@ -49,6 +49,7 @@ RecPreProcess:
 RecPostProcess: null
 
 IndexProcess:
-    index_path: "./cartoon/index/"
-    search_budget: 100
-    return_k: 10
+  index_path: "./dataset/cartoon_demo_data_v1.0/index/"
+  search_budget: 100
+  return_k: 5
+  dist_type: "IP"

deploy/configs/inference_inshop.yaml

@@ -50,5 +50,5 @@ RecPostProcess: null
 IndexProcess:
   index_path: "./dataset/product_demo_data_v1.0/index"
   search_budget: 100
-  return_k: 10
+  return_k: 5
   dist_type: "IP"

deploy/configs/inference_logo.yaml

 Global:
-  infer_imgs: "./logo/demo/logo_APK.jpg"
-  det_inference_model_dir: "./logo/det/"
-  rec_inference_model_dir: "./logo/rec/"
+  infer_imgs: "./dataset/logo_demo_data_v1.0/query/logo_AKG.jpg"
+  det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
+  rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
   batch_size: 1
   image_shape: [3, 640, 640]
   threshold: 0.5
@@ -48,7 +48,7 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./logo_index/"
+  index_path: "./dataset/logo_demo_data_v1.0/index/"
   search_budget: 100
-  return_k: 10
+  return_k: 5
   dist_type: "IP"

deploy/configs/inference_vehicle.yaml

 Global:
-  infer_imgs: "./vehicle/demo/2e3521935c280c.jpg"
-  det_inference_model_dir: "./det/"
-  rec_inference_model_dir: "./vehicle/rec/"
+  infer_imgs: "./dataset/vehicle_demo_data_v1.0/query/"
+  det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
+  rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
   batch_size: 1
   image_shape: [3, 640, 640]
   threshold: 0.5
@@ -50,7 +50,7 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-    index_path: "./vehicle_index/"
+    index_path: "./dataset/vehicle_demo_data_v1.0/index/"
     search_budget: 100
-    return_k: 10
+    return_k: 5
     dist_type: "IP"

docs/zh_CN/tutorials/quick_start_recognition.md

+# 图像识别快速开始
+
+图像识别主要包含3个部分：主体检测得到检测框、识别提取特征、根据特征进行检索。
+
+## 1. 环境配置
+
+* 请先参考[快速安装](./installation.md)配置PaddleClas运行环境。
+
+
+注意：
+
+**本部分内容需要在`deploy`文件夹下运行，在PaddleClas代码的根目录下，可以通过以下方法进入该文件夹**
+
+```shell
+cd deploy
+```
+
+
+## 2. inference 模型和数据下载
+
+检测模型与4个方向(Logo、动漫人物、车辆、商品)的识别inference模型以及测试数据下载方法如下。。
+
+| 模型简介       | 推荐场景   | 测试数据地址  | inference模型 |
+| ------------  | ------------- | ------- | -------- |
+| 通用主体检测模型 | 通用场景  | -  |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/ppyolov2_r50vd_dcn_mainbody_v1.0_infer.tar) |
+| Logo识别模型 | Logo场景  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/logo_demo_data_v1.0.tar) |  [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/logo_rec_ResNet50_Logo3K_v1.0_infer.tar) |
+| 动漫人物识别模型 | 动漫人物场景  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/cartoon_demo_data_v1.0.tar) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) |
+| 车辆细分类模型 | 车辆场景  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/vehicle_demo_data_v1.0.tar) |  [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) |
+| 商品识别模型 | 商品场景  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/product_demo_data_v1.0.tar) |  [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) |
+
+
+**注意**：windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
+
+
+* 下载并解压数据与模型
+
+```shell
+mkdir dataset
+cd dataset
+# 下载demo数据并解压
+wget {url/of/data} && tar -xf {name/of/data/package}
+cd ..
+
+mkdir models
+cd models
+# 下载识别inference模型并解压
+wget {url/of/inference model} && tar -xf {name/of/inference model/package}
+cd ..
+```
+
+
+### 2.1 下载通用检测模型
+
+```shell
+mkdir models
+cd models
+# 下载通用检测inference模型并解压
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/ppyolov2_r50vd_dcn_mainbody_v1.0_infer.tar && tar -xf ppyolov2_r50vd_dcn_mainbody_v1.0_infer.tar
+cd ..
+```
+
+
+### 2.1 Logo识别
+
+以Logo识别demo为例，按照下面的命令下载demo数据与模型。
+
+```shell
+mkdir dataset
+cd dataset
+# 下载demo数据并解压
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/logo_demo_data_v1.0.tar && tar -xf logo_demo_data_v1.0.tar
+cd ..
+
+mkdir models
+cd models
+# 下载识别inference模型并解压
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/logo_rec_ResNet50_Logo3K_v1.0_infer.tar && tar -xf logo_rec_ResNet50_Logo3K_v1.0_infer.tar
+cd ..
+```
+
+解压完毕后，`dataset`文件夹下应有如下文件结构：
+
+```
+├── logo_demo_data_v1.0
+│   ├── data_file.txt
+│   ├── gallery
+│   ├── index
+│   └── query
+├── ...
+```
+
+`models`文件夹下应有如下文件结构：
+
+```
+├── logo_rec_ResNet50_Logo3K_v1.0_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+├── ppyolov2_r50vd_dcn_mainbody_v1.0_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+
+按照下面的方式可以完成对于图片的检索
+
+```shell
+python3.7 python/predict_system.py -c configs/inference_logo.yaml
+```
+
+配置文件中，部分关键字段解释如下
+
+```yaml
+Global:
+  infer_imgs: "./dataset/logo_demo_data_v1.0/query/" # 预测图像
+  det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/" # 检测inference模型文件夹
+  rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/" # 识别inference模型文件夹
+  batch_size: 1 # 预测的批大小
+  image_shape: [3, 640, 640] # 检测的图像尺寸
+  threshold: 0.5 # 检测的阈值，得分超过该阈值的检测框才会被检出
+  max_det_results: 1 # 用于图像识别的检测框数量，符合阈值条件的检测框中，根据得分，最多对其中的max_det_results个检测框做后续的识别
+
+# indexing engine config
+IndexProcess:
+  index_path: "./dataset/logo_demo_data_v1.0/index/" # 索引文件夹，用于识别特征提取之后的索引
+  search_budget: 100
+  return_k: 5 # 从底库中反馈return_k个数量的最相似内容
+  dist_type: "IP"
+```
+
+
+
+最终输出结果如下
+
+```
+[{'bbox': [25, 21, 483, 382], 'rec_docs': ['AKG', 'AKG', 'AKG', 'AKG', 'AKG'], 'rec_scores': array([2.32288337, 2.31903863, 2.28398442, 2.16804123, 2.10190272])}]
+```
+
+其中bbox表示检测出的主体所在位置，rec_docs表示底库中与检出主体最相近的若干张图像对应的标签，rec_scores表示对应的相似度。
+
+如果希望预测文件夹内的图像，可以直接修改配置文件，也可以通过下面的`-o`参数修改对应的配置。
+
+```shell
+python3.7 python/predict_system.py -c configs/inference_logo.yaml -o Global.infer_imgs="./dataset/logo_demo_data_v1.0/query"
+```
+
+如果希望在底库中新增图像，重新构建idnex，可以使用下面的命令重新构建index。
+
+```shell
+python3.7 python/build_gallery.py -c configs/build_logo.yaml
+```
+
+其中index相关配置如下。
+
+```yaml
+# indexing engine config
+IndexProcess:
+  index_path: "./dataset/logo_demo_data_v1.0/index/" # 保存的索引地址
+  image_root: "./dataset/logo_demo_data_v1.0/" # 图像的根目录
+  data_file:  "./dataset/logo_demo_data_v1.0/data_file.txt" # 图像的数据list文本，每一行包含图像的文件名与标签信息
+  delimiter: "\t"
+  dist_type: "IP"
+  pq_size: 100
+  embedding_size: 512 # 特征维度
+```
+
+需要改动的内容为：
+1. 在图像根目录下面添加对应的图像内容（也可以在其子文件夹下面，保证最终根目录与数据list文本中添加的文件名合并之后，图像存在即可）
+2. 图像的数据list文本中添加图像新的内容，每行包含图像文件名以及对应的标签信息。
+
+
+### 2.2 其他任务的识别
+
+如果希望尝试其他方向的识别与检索效果，在下载解压好对应的demo数据与模型之后，替换对应的配置文件即可完成预测。
+
+
+| 场景   | 预测配置文件  | 构建底库的配置文件 |
+| ---- | ----- | ----- |
+| 动漫人物 | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) |
+| 车辆 | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) |
+| 商品 | [inference_inshop.yaml](../../../deploy/configs/) | [build_inshop.yaml](../../../deploy/configs/build_inshop.yaml) |

ppcls/configs/Cartoonface/ResNet50_icartoon.yaml

@@ -138,4 +138,4 @@ Metric:
         topk: [1, 5]
     Eval:
     - Recallk:
-        topk: 1
+        topk: [1]

ppcls/metric/metrics.py

@@ -69,7 +69,7 @@ class mINP(nn.Layer):
 class Recallk(nn.Layer):
     def __init__(self, topk=(1, 5)):
         super().__init__()
-        assert isinstance(topk, (int, list))
+        assert isinstance(topk, (int, list, tuple))
         if isinstance(topk, int):
             topk = [topk]
         self.topk = topk
@@ -97,6 +97,9 @@ class RetriMetric(nn.Layer):
                                    gallery_img_id, self.max_rank)
         if "Recallk" in self.config.keys():
             topk = self.config['Recallk']['topk']
+            assert isinstance(topk, (int, list, tuple))
+            if isinstance(topk, int):
+                topk = [topk]
             for k in topk:
                 metric_dict["recall{}".format(k)] = all_cmc[k - 1]
         if "mAP" in self.config.keys():