Merge pull request #930 from PaddlePaddle/develop

Develop

README_en.md

@@ -50,14 +50,14 @@ Four sample solutions are provided, including product recognition, vehicle recog
 </div>
 
 ## Quick Start 
-Quick experience of image recognition：[Link](./docs/zh_CN/tutorials/quick_start_recognition.md)
+Quick experience of image recognition：[Link](./docs/en/tutorials/quick_start_recognition_en.md)
 
 ## Tutorials
 
 - [Quick Installatiopn](./docs/en/tutorials/install_en.md)
 - [Quick Start of Recognition](./docs/en/tutorials/quick_start_recognition_en.md)
-- Algorithms Introduction（Updating）
-    - Backbone Network and Pre-trained Model Library
+- Algorithms Introduction
+    - [Backbone Network and Pre-trained Model Library](./docs/en/ImageNet_models.md)
     - [Mainbody Detection](./docs/en/application/mainbody_detection_en.md)
     - [Image Classification](./docs/en/tutorials/image_classification_en.md)
     - [Feature Learning](./docs/en/application/feature_learning_en.md)
@@ -65,12 +65,12 @@ Quick experience of image recognition：[Link](./docs/zh_CN/tutorials/quick_star
         - [Vehicle Recognition](./docs/en/application/vehicle_recognition_en.md)
         - [Logo Recognition](./docs/en/application/logo_recognition_en.md)
         - [Animation Character Recognition](./docs/en/application/cartoon_character_recognition_en.md)
-        - [Vector Retrieval](./deploy/vector_search/README.md)
+    - [Vector Search](./deploy/vector_search/README.md)
 - Models Training/Evaluation
     - [Image Classification](./docs/en/tutorials/getting_started_en.md)
-    - Feature Learning
-- Inference Model Prediction（Updating）
-    - Python Inference
+    - Feature Learning (coming soon)
+- Inference Model Prediction
+    - [Python Inference](./docs/en/inference.md)
     - [C++ Inference](./deploy/cpp/readme_en.md)(only support classification for now, recognition coming soon)
 - Model Deploy (only support classification for now, recognition coming soon)
     - [Hub Serving Deployment](./deploy/hubserving/readme_en.md)

docs/en/inference.md

+# INFERING BASED ON PYTHON PREDICTION ENGINE
+
+The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
+
+The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
+
+Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
+
+Next, we first introduce how to convert a trained model into an inference model, and then we will introduce mainbody detection, feature extraction based on inference model, 
+then we introduce a recognition pipeline consist of mainbody detection, feature extraction and vector search. At last, we introduce classification base on inference model. 
+
+- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
+    - [Convert feature extraction model to inference model](#Convert_feature_extraction)
+    - [Convert classification model to inference model](#Convert_class)
+
+- [MAINBODY DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
+
+- [FEATURE EXTRACTION MODEL INFERENCE](#FEATURE_EXTRACTION_MODEL_INFERENCE)
+
+- [CONCATENATION OF MAINBODY DETECTION, FEATURE EXTRACTION AND VECTOR SEARCH](#CONCATENATION)
+
+- [CLASSIFICATION MODEL INFERENCE](#CLASSIFICATION)
+
+<a name="CONVERT"></a>
+## CONVERT TRAINING MODEL TO INFERENCE MODEL
+<a name="Convert_feature_extraction"></a>
+### Convert feature extraction model to inference model
+First please enter the root folder of PaddleClas. Download the product feature extraction model:
+```shell script
+wget -P ./product_pretrain/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams
+```
+
+The above model is trained on AliProduct with ResNet50_vd as backbone. To convert the trained model into an inference model, just run the following command:
+```
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# Global.save_inference_dir Set the address where the converted model will be saved.
+
+python3.7 tools/export_model.py -c ppcls/configs/Products/ResNet50_vd_Aliproduct.yaml -o Global.pretrained_model=./product_pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained -o Global.save_inference_dir=./deploy/models/product_ResNet50_vd_aliproduct_v1.0_infer
+```
+
+When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.pretrained_model` parameter in the configuration file.
+After the conversion is successful, there are three files in the model save directory:
+``` 
+├── product_ResNet50_vd_aliproduct_v1.0_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+
+<a name="Convert_class"></a>
+### Convert classification model to inference model
+
+Download the pretrained model:
+``` shell script
+wget -P ./cls_pretrain/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
+```
+
+the model is trained on ImageNet with ResNet50_vd as backbone, using config file `ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml`.
+The model can be converted to the inference model in the same way as the feature extraction model, as follows:
+```
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# Global.save_inference_dir Set the address where the converted model will be saved.
+
+python3.7 tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.pretrained_model=./cls_pretrain/ResNet50_vd_pretrained -o Global.save_inference_dir=./deploy/models/class_ResNet50_vd_ImageNet_infer
+```
+
+After the conversion is successful, there are three files in the model save directory:
+```
+├── class_ResNet50_vd_ImageNet_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+
+<a name="DETECTION_MODEL_INFERENCE"></a>
+## MAINBODY DETECTION MODEL INFERENCE
+
+The following will introduce the mainbody detection model inference. All the command should be run under `deploy` folder of PaddleClas:
+```shell script
+cd deploy
+```
+
+For mainbody detection model inference, you can execute the following commands:
+
+```shell script
+mkdir -p models
+cd models
+# download mainbody detection inference model
+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 ..
+# predict
+python3.7 python/predict_det.py -c configs/inference_det.yaml
+```
+
+The input example image is as follows:
+[](../images/recognition/product_demo/wangzai.jpg)
+The output will be:
+```text
+[{'class_id': 0, 'score': 0.4762245, 'bbox': array([305.55115, 226.05322, 776.61084, 930.42395], dtype=float32), 'label_name': 'foreground'}]
+```
+And the visualise result is as follows:
+[](../images/recognition/product_demo/wangzai_det_result.jpg)
+
+If you want to detect another image, you can change the value of `infer_imgs` in `configs/inference_det.yaml`, 
+or you can use `-o Global.infer_imgs` argument. For example, if you want to detect `images/anmuxi.jpg`:
+```shell script
+python3.7 python/predict_det.py -c configs/inference_det.yaml -o Global.infer_imgs=images/anmuxi.jpg
+```
+
+If you want to use the CPU for prediction, you can switch value of `use_gpu` in config file to `False`. Or you can execute the command as follows
+```
+python3.7 python/predict_det.py -c configs/inference_det.yaml  -o Global.use_gpu=False
+```
+
+<a name="FEATURE_EXTRACTION_MODEL_INFERENCE"></a>
+### FEATURE EXTRACTION MODEL INFERENCE
+
+The following will introduce the feature extraction model inference. All the command should be run under `deploy` folder of PaddleClas:
+```shell script
+cd deploy
+```
+For feature extraction model inference, you can execute the following commands:
+```shell script
+mkdir -p models
+cd models
+# download feature extraction inference model
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar && tar -xf product_ResNet50_vd_aliproduct_v1.0_infer.tar
+cd ..
+# predict
+python3.7 python/predict_rec.py -c configs/inference_rec.yaml
+```
+You can get a 512-dim feature printed in the command line.
+
+If you want to extract feature of another image, you can change the value of `infer_imgs` in `configs/inference_rec.yaml`, 
+or you can use `-o Global.infer_imgs` argument. For example, if you want to try `images/anmuxi.jpg`:
+```shell script
+python3.7 python/predict_rec.py -c configs/inference_rec.yaml -o Global.infer_imgs=images/anmuxi.jpg
+```
+
+If you want to use the CPU for prediction, you can switch value of `use_gpu` in config file to `False`. Or you can execute the command as follows
+```
+python3.7 python/predict_rec.py -c configs/inference_rec.yaml  -o Global.use_gpu=False
+```
+
+<a name="CONCATENATION"></a>
+## CONCATENATION OF MAINBODY DETECTION, FEATURE EXTRACTION AND VECTOR SEARCH
+ Please refer to [Quick Start of Recognition](./tutorials/quick_start_recognition_en.md)
+
+<a name="CLASSIFICATION"></a>
+### CLASSIFICATION MODEL INFERENCE
+The following will introduce the classification model inference. All the command should be run under `deploy` folder of PaddleClas:
+```shell script
+cd deploy
+```
+
+For classification model inference, you can execute the following commands:
+
+```shell script
+python3.7 python/predict_cls.py -c configs/inference_cls.yaml
+```
+If you want to try another image, you can change the value of `infer_imgs` in `configs/inference_cls.yaml`, 
+or you can use `-o Global.infer_imgs` argument. For example, if you want to try `images/ILSVRC2012_val_00010010.jpeg`:
+```shell script
+python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.infer_imgs=images/ILSVRC2012_val_00010010.jpeg
+
+```
+
+If you want to use the CPU for prediction, you can switch value of `use_gpu` in config file to `False`. Or you can execute the command as follows
+```
+python3.7 python/predict_cls.py -c configs/inference_cls.yaml  -o Global.use_gpu=False
+```
\ No newline at end of file

docs/zh_CN/inference.md

@@ -109,15 +109,15 @@ python3.7 python/predict_det.py -c configs/inference_det.yaml
 检测的可视化结果如下：
 [](../images/recognition/product_demo/wangzai_det_result.jpg)
 
-如果想要修改图像，可以在configs/inference_det.yaml中，修改infer_imgs的值，或使用-o Global.infer_imgs修改，
+如果想要修改图像，可以在`configs/inference_det.yaml`中，修改infer_imgs的值，或使用-o Global.infer_imgs修改，
 例如，要使用`images/anmuxi.jpg`可以运行：
 
 ```shell script
 python3.7 python/predict_det.py -c configs/inference_det.yaml -o Global.infer_imgs=images/anmuxi.jpg
 ```
 
-如果想使用CPU进行预测，可以将配置文件中use_gpu选项设置为False，或者执行命令：
-```
+如果想使用CPU进行预测，可以将配置文件中`use_gpu`选项设置为`False`，或者执行命令：
+```shell script
 python3.7 python/predict_det.py -c configs/inference_det.yaml  -o Global.use_gpu=False
 ```
 
@@ -170,7 +170,6 @@ cd deploy
 
 导出inference模型后，可以使用下面的命令预测：
 ```shell script
-
 python3.7 python/predict_cls.py -c configs/inference_cls.yaml
 ```