Merge branch 'develop_reg' into develop_reg
cuicheng01
GitHub
commit
d7f29e9f1f
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@ -1,5 +1,5 @@
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Global:
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rec_inference_model_dir: "./cartoon/model"
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rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
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batch_size: 1
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use_gpu: True
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enable_mkldnn: True
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@ -26,10 +26,11 @@ RecPreProcess:
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RecPostProcess: null
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# indexing engine config
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IndexProcess:
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index_path: "./cartoon/index/"
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image_root: "./cartoon/dataset/"
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data_file: "./cartoon/data_file.txt"
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index_path: "./dataset/cartoon_demo_data_v1.0/index/"
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image_root: "./dataset/cartoon_demo_data_v1.0/"
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data_file: "./dataset/cartoon_demo_data_v1.0/data_file.txt"
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delimiter: "\t"
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dist_type: "IP"
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pq_size: 100
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@ -1,5 +1,5 @@
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Global:
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rec_inference_model_dir: "./logo/model/"
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rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
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batch_size: 1
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use_gpu: True
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enable_mkldnn: True
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@ -26,9 +26,9 @@ RecPostProcess: null
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# indexing engine config
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IndexProcess:
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index_path: "./logo/logo_index/"
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image_root: "./logo/dataset/"
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data_file: "./logo/logo_gallery_demo.txt"
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index_path: "./dataset/logo_demo_data_v1.0/index/"
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image_root: "./dataset/logo_demo_data_v1.0/"
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data_file: "./dataset/logo_demo_data_v1.0/data_file.txt"
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delimiter: "\t"
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dist_type: "IP"
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pq_size: 100
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@ -1,5 +1,5 @@
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Global:
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rec_inference_model_dir: "./vehicle/model/"
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rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
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batch_size: 1
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use_gpu: True
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enable_mkldnn: True
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@ -26,9 +26,9 @@ RecPostProcess: null
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# indexing engine config
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IndexProcess:
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index_path: "./vehilce/vehicle_index/"
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image_root: "./vehicle/dataset/"
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data_file: "./vehilce/demo_gallery.txt"
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index_path: "./dataset/vehicle_demo_data_v1.0/index/"
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image_root: "./dataset/vehicle_demo_data_v1.0/"
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data_file: "./dataset/vehicle_demo_data_v1.0/data_file.txt"
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delimiter: " "
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dist_type: "IP"
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pq_size: 100
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@ -1,7 +1,7 @@
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Global:
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infer_imgs: "./dataset/iCartoonFace/val2/0000000.jpg"
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det_inference_model_dir: "./cartoon/det"
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rec_inference_model_dir: "./cartoon/rec"
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infer_imgs: "./dataset/cartoon_demo_data_v1.0/query/"
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det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
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rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
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batch_size: 1
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image_shape: [3, 640, 640]
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threshold: 0.5
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@ -49,6 +49,7 @@ RecPreProcess:
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RecPostProcess: null
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IndexProcess:
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index_path: "./cartoon/index/"
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index_path: "./dataset/cartoon_demo_data_v1.0/index/"
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search_budget: 100
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return_k: 10
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return_k: 5
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dist_type: "IP"
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@ -50,5 +50,5 @@ RecPostProcess: null
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IndexProcess:
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index_path: "./dataset/product_demo_data_v1.0/index"
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search_budget: 100
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return_k: 10
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return_k: 5
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dist_type: "IP"
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@ -1,7 +1,7 @@
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Global:
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infer_imgs: "./logo/demo/logo_APK.jpg"
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det_inference_model_dir: "./logo/det/"
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rec_inference_model_dir: "./logo/rec/"
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infer_imgs: "./dataset/logo_demo_data_v1.0/query/logo_AKG.jpg"
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det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
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rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
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batch_size: 1
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image_shape: [3, 640, 640]
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threshold: 0.5
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@ -48,7 +48,7 @@ RecPostProcess: null
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# indexing engine config
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IndexProcess:
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index_path: "./logo_index/"
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index_path: "./dataset/logo_demo_data_v1.0/index/"
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search_budget: 100
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return_k: 10
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return_k: 5
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dist_type: "IP"
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@ -1,7 +1,7 @@
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Global:
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infer_imgs: "./vehicle/demo/2e3521935c280c.jpg"
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det_inference_model_dir: "./det/"
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rec_inference_model_dir: "./vehicle/rec/"
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infer_imgs: "./dataset/vehicle_demo_data_v1.0/query/"
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det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
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rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
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batch_size: 1
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image_shape: [3, 640, 640]
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threshold: 0.5
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@ -50,7 +50,7 @@ RecPostProcess: null
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# indexing engine config
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IndexProcess:
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index_path: "./vehicle_index/"
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index_path: "./dataset/vehicle_demo_data_v1.0/index/"
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search_budget: 100
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return_k: 10
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return_k: 5
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dist_type: "IP"
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@ -0,0 +1,181 @@
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# 图像识别快速开始
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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。
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## 1. 环境配置
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* 请先参考[快速安装](./installation.md)配置PaddleClas运行环境。
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注意：
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**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**
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```shell
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cd deploy
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```
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## 2. inference 模型和数据下载
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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。。
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| 模型简介 | 推荐场景 | 测试数据地址 | inference模型 |
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| ------------ | ------------- | ------- | -------- |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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**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
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* 下载并解压数据与模型
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```shell
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mkdir dataset
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cd dataset
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# 下载demo数据并解压
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wget {url/of/data} && tar -xf {name/of/data/package}
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cd ..
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mkdir models
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cd models
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# 下载识别inference模型并解压
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wget {url/of/inference model} && tar -xf {name/of/inference model/package}
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cd ..
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```
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### 2.1 下载通用检测模型
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```shell
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mkdir models
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cd models
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# 下载通用检测inference模型并解压
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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
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cd ..
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```
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### 2.1 Logo识别
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以Logo识别demo为例，按照下面的命令下载demo数据与模型。
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```shell
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mkdir dataset
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cd dataset
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# 下载demo数据并解压
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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
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cd ..
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mkdir models
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cd models
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# 下载识别inference模型并解压
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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
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cd ..
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```
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解压完毕后，`dataset`文件夹下应有如下文件结构：
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```
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├── logo_demo_data_v1.0
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│ ├── data_file.txt
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│ ├── gallery
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│ ├── index
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│ └── query
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├── ...
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```
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`models`文件夹下应有如下文件结构：
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```
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├── logo_rec_ResNet50_Logo3K_v1.0_infer
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│ ├── inference.pdiparams
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│ ├── inference.pdiparams.info
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│ └── inference.pdmodel
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├── ppyolov2_r50vd_dcn_mainbody_v1.0_infer
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│ ├── inference.pdiparams
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│ ├── inference.pdiparams.info
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│ └── inference.pdmodel
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```
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按照下面的方式可以完成对于图片的检索
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```shell
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python3.7 python/predict_system.py -c configs/inference_logo.yaml
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```
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配置文件中，部分关键字段解释如下
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```yaml
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Global:
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infer_imgs: "./dataset/logo_demo_data_v1.0/query/" # 预测图像
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det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/" # 检测inference模型文件夹
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rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/" # 识别inference模型文件夹
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batch_size: 1 # 预测的批大小
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image_shape: [3, 640, 640] # 检测的图像尺寸
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threshold: 0.5 # 检测的阈值，得分超过该阈值的检测框才会被检出
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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
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# indexing engine config
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IndexProcess:
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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
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search_budget: 100
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return_k: 5 # 从底库中反馈return_k个数量的最相似内容
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dist_type: "IP"
|
||||
```
|
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|
||||
|
||||
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||||
最终输出结果如下
|
||||
|
||||
```
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||||
[{'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])}]
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||||
```
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|
||||
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。
|
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|
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```shell
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||||
python3.7 python/predict_system.py -c configs/inference_logo.yaml -o Global.infer_imgs="./dataset/logo_demo_data_v1.0/query"
|
||||
```
|
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|
||||
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。
|
||||
|
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```shell
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||||
python3.7 python/build_gallery.py -c configs/build_logo.yaml
|
||||
```
|
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|
||||
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"
|
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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) |
|
||||
|
|
@ -138,4 +138,4 @@ Metric:
|
|||
topk: [1, 5]
|
||||
Eval:
|
||||
- Recallk:
|
||||
topk: 1
|
||||
topk: [1]
|
||||
|
|
|
|||
|
|
@ -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():
|
||||
|
|
|
|||
Loading…
Reference in New Issue