Merge pull request #1546 from LDOUBLEV/dyg_db

Dyg db

deploy/cpp_infer/readme.md

@@ -122,10 +122,10 @@ build/paddle_inference_install_dir/
 * 下载之后使用下面的方法解压。
 
 ```
-tar -xf fluid_inference.tgz
+tar -xf paddle_inference.tgz
 ```
 
-最终会在当前的文件夹中生成`fluid_inference/`的子文件夹。
+最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
 
 
 ## 2 开始运行
@@ -137,11 +137,11 @@ tar -xf fluid_inference.tgz
 ```
 inference/
 |-- det_db
-|   |--model
-|   |--params
+|   |--inference.pdparams
+|   |--inference.pdimodel
 |-- rec_rcnn
-|   |--model
-|   |--params
+|   |--inference.pdparams
+|   |--inference.pdparams
 ```
 
 
@@ -180,7 +180,7 @@ cmake .. \
 make -j
 ```
 
-`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`fluid_inference`文件夹)或者编译生成的Paddle预测库地址(`build/fluid_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中；为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
+`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中；为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
 
 
 * 编译完成之后，会在`build`文件夹下生成一个名为`ocr_system`的可执行文件。
@@ -202,7 +202,6 @@ gpu_id  0 # GPU id，使用GPU时有效
 gpu_mem  4000  # 申请的GPU内存
 cpu_math_library_num_threads  10 # CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快
 use_mkldnn 1 # 是否使用mkldnn库
-use_zero_copy_run 1 # 是否使用use_zero_copy_run进行预测
 
 # det config
 max_side_len  960 # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960

deploy/cpp_infer/readme_en.md

@@ -130,10 +130,10 @@ Among them, `paddle` is the Paddle library required for C++ prediction later, an
 * After downloading, use the following method to uncompress.
 
 ```
-tar -xf fluid_inference.tgz
+tar -xf paddle_inference.tgz
 ```
 
-Finally you can see the following files in the folder of `fluid_inference/`.
+Finally you can see the following files in the folder of `paddle_inference/`.
 
 
 ## 2. Compile and run the demo
@@ -145,11 +145,11 @@ Finally you can see the following files in the folder of `fluid_inference/`.
 ```
 inference/
 |-- det_db
-|   |--model
-|   |--params
+|   |--inference.pdparams
+|   |--inference.pdimodel
 |-- rec_rcnn
-|   |--model
-|   |--params
+|   |--inference.pdparams
+|   |--inference.pdparams
 ```
 
 
@@ -188,7 +188,9 @@ cmake .. \
 make -j
 ```
 
-`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`fluid_inference` folder) or the generated Paddle inference library path (`build/fluid_inference_install_dir` folder); `CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
+`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
+or the generated Paddle inference library path (`build/paddle_inference_install_dir` folder);
+`CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
 
 
 * After the compilation is completed, an executable file named `ocr_system` will be generated in the `build` folder.
@@ -211,7 +213,6 @@ gpu_id  0 # GPU id when use_gpu is 1
 gpu_mem  4000  # GPU memory requested
 cpu_math_library_num_threads  10 # Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed
 use_mkldnn 1 # Whether to use mkdlnn library
-use_zero_copy_run 1 # Whether to use use_zero_copy_run for inference
 
 max_side_len  960 #  Limit the maximum image height and width to 960
 det_db_thresh  0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
@@ -244,4 +245,4 @@ The detection results will be shown on the screen, which is as follows.
 
 ### 2.3 Notes
 
-* Paddle2.0.0-beta0 inference model library is recommanded for this tuturial.
+* Paddle2.0.0-beta0 inference model library is recommended for this toturial.