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Merge branch 'PaddlePaddle:dygraph' into dygraph

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configs/rec/multi_language/rec_korean_lite_train.yml
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......@@ -16,7 +16,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/korean_dict.txt
character_type: korean
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/multi_language/rec_latin_lite_train.yml
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......@@ -15,7 +15,6 @@ Global:
use_visualdl: false
infer_img: null
character_dict_path: ppocr/utils/dict/latin_dict.txt
character_type: latin
max_text_length: 25
infer_mode: false
use_space_char: true
......
configs/rec/rec_icdar15_train.yml
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......@@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/ic15_dict.txt
character_type: ch
character_dict_path: ppocr/utils/en_dict.txt
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_mtb_nrtr.yml 0 → 100644
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Global:
use_gpu: True
epoch_num: 21
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/nrtr/
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/EN_symbol_dict.txt
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_nrtr.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.99
clip_norm: 5.0
lr:
name: Cosine
learning_rate: 0.0005
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0.
Architecture:
model_type: rec
algorithm: NRTR
in_channels: 1
Transform:
Backbone:
name: MTB
cnn_num: 2
Head:
name: Transformer
d_model: 512
num_encoder_layers: 6
beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation.
Loss:
name: NRTRLoss
smoothing: True
PostProcess:
name: NRTRLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg:
image_shape: [100, 32]
resize_type: PIL # PIL or OpenCV
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 512
drop_last: True
num_workers: 8
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg:
image_shape: [100, 32]
resize_type: PIL # PIL or OpenCV
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 1
use_shared_memory: False
configs/rec/rec_mv3_none_bilstm_ctc.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_mv3_none_none_ctc.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_mv3_tps_bilstm_att.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_mv3_tps_bilstm_ctc.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_r31_sar.yml 0 → 100644
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Global:
use_gpu: true
epoch_num: 5
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./sar_rec
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict90.txt
max_text_length: 30
infer_mode: False
use_space_char: False
rm_symbol: True
save_res_path: ./output/rec/predicts_sar.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Piecewise
decay_epochs: [3, 4]
values: [0.001, 0.0001, 0.00001]
regularizer:
name: 'L2'
factor: 0
Architecture:
model_type: rec
algorithm: SAR
Transform:
Backbone:
name: ResNet31
Head:
name: SARHead
Loss:
name: SARLoss
PostProcess:
name: SARLabelDecode
Metric:
name: RecMetric
Train:
dataset:
name: SimpleDataSet
label_file_list: ['./train_data/train_list.txt']
data_dir: ./train_data/
ratio_list: 1.0
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SARLabelEncode: # Class handling label
- SARRecResizeImg:
image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
width_downsample_ratio: 0.25
- KeepKeys:
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 64
drop_last: True
num_workers: 8
use_shared_memory: False
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SARLabelEncode: # Class handling label
- SARRecResizeImg:
image_shape: [3, 48, 48, 160]
width_downsample_ratio: 0.25
- KeepKeys:
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 64
num_workers: 4
use_shared_memory: False
configs/rec/rec_r34_vd_none_bilstm_ctc.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_r34_vd_none_none_ctc.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_r34_vd_tps_bilstm_att.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_r50_fpn_srn.yml
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......@@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
num_heads: 8
infer_mode: False
......
configs/rec/rec_resnet_stn_bilstm_att.yml 0 → 100644
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Global:
use_gpu: True
epoch_num: 400
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/seed
save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/EN_symbol_dict.txt
max_text_length: 100
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_seed.txt
Optimizer:
name: Adadelta
weight_deacy: 0.0
momentum: 0.9
lr:
name: Piecewise
decay_epochs: [4,5,8]
values: [1.0, 0.1, 0.01]
regularizer:
name: 'L2'
factor: 2.0e-05
Architecture:
model_type: rec
algorithm: SEED
Transform:
name: STN_ON
tps_inputsize: [32, 64]
tps_outputsize: [32, 100]
num_control_points: 20
tps_margins: [0.05,0.05]
stn_activation: none
Backbone:
name: ResNet_ASTER
Head:
name: AsterHead # AttentionHead
sDim: 512
attDim: 512
max_len_labels: 100
Loss:
name: AsterLoss
PostProcess:
name: SEEDLabelDecode
Metric:
name: RecMetric
main_indicator: acc
is_filter: True
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- Fasttext:
path: "./cc.en.300.bin"
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SEEDLabelEncode: # Class handling label
- RecResizeImg:
character_type: en
image_shape: [3, 64, 256]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length', 'fast_label'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 6
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SEEDLabelEncode: # Class handling label
- RecResizeImg:
character_type: en
image_shape: [3, 64, 256]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: True
batch_size_per_card: 256
num_workers: 4
deploy/cpp_infer/CMakeLists.txt
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project(ppocr CXX C)
cmake_minimum_required(VERSION 3.14)
option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON)
option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." OFF)
......@@ -206,9 +207,12 @@ endif()
set(DEPS ${DEPS} ${OpenCV_LIBS})
include(FetchContent)
include(external-cmake/auto-log.cmake)
include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
AUX_SOURCE_DIRECTORY(./src SRCS)
add_executable(${DEMO_NAME} ${SRCS})
target_link_libraries(${DEMO_NAME} ${DEPS})
if (WIN32 AND WITH_MKL)
......
deploy/cpp_infer/docs/vs2019_build_withgpu_config.png 0 → 100644
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deploy/cpp_infer/docs/windows_vs2019_build.md
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......@@ -5,20 +5,20 @@ PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测
## 前置条件
* Visual Studio 2019
* CUDA 9.0 / CUDA 10.0，cudnn 7+ （仅在使用GPU版本的预测库时需要）
* CUDA 10.2，cudnn 7+ （仅在使用GPU版本的预测库时需要）
* CMake 3.0+
请确保系统已经安装好上述基本软件，我们使用的是`VS2019`的社区版。
**下面所有示例以工作目录为 `D:\projects`演示**。
### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
### Step1: 下载PaddlePaddle C++ 预测库 paddle_inference
PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#windows)
解压后`D:\projects\fluid_inference`目录包含内容为：
解压后`D:\projects\paddle_inference`目录包含内容为：
```
fluid_inference
paddle_inference
├── paddle # paddle核心库和头文件
|
├── third_party # 第三方依赖库和头文件
......@@ -46,13 +46,13 @@ fluid_inference
![step2.2](https://paddleseg.bj.bcebos.com/inference/vs2019_step3.png)
3. 点击：`项目`->`cpp_inference_demo的CMake设置`
3. 点击：`项目`->`CMake设置`
![step3](https://paddleseg.bj.bcebos.com/inference/vs2019_step4.png)
4. 点击`浏览`，分别设置编译选项指定`CUDA`、`CUDNN_LIB`、`OpenCV`、`Paddle预测库`的路径
4. 分别设置编译选项指定`CUDA`、`CUDNN_LIB`、`OpenCV`、`Paddle预测库`的路径
三个编译参数的含义说明如下（带`*`表示仅在使用**GPU版本**预测库时指定, 其中CUDA库版本尽量对齐，**使用9.0、10.0版本，不使用9.2、10.1等版本CUDA库**）：
三个编译参数的含义说明如下（带`*`表示仅在使用**GPU版本**预测库时指定, 其中CUDA库版本尽量对齐）：
| 参数名 | 含义 |
| ---- | ---- |
......@@ -67,6 +67,11 @@ fluid_inference
![step4](https://paddleseg.bj.bcebos.com/inference/vs2019_step5.png)
下面给出with GPU的配置示例：
![step5](./vs2019_build_withgpu_config.png)
**注意：**
CMAKE_BACKWARDS的版本要根据平台安装cmake的版本进行设置。
**设置完成后**, 点击上图中`保存并生成CMake缓存以加载变量`。
5. 点击`生成`->`全部生成`
......@@ -74,24 +79,34 @@ fluid_inference
![step6](https://paddleseg.bj.bcebos.com/inference/vs2019_step6.png)
### Step4: 预测及可视化
### Step4: 预测
上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release`目录下，打开`cmd`，并切换到该目录：
上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release\Release`目录下，打开`cmd`，并切换到`D:\projects\PaddleOCR\deploy\cpp_infer\`：
```
cd D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release
cd D:\projects\PaddleOCR\deploy\cpp_infer
```
可执行文件`ocr_system.exe`即为样例的预测程序，其主要使用方法如下
可执行文件`ppocr.exe`即为样例的预测程序，其主要使用方法如下，更多使用方法可以参考[说明文档](../readme.md)`运行demo`部分。
```shell
#预测图片 `D:\projects\PaddleOCR\doc\imgs\10.jpg`
.\ocr_system.exe D:\projects\PaddleOCR\deploy\cpp_infer\tools\config.txt D:\projects\PaddleOCR\doc\imgs\10.jpg
#识别中文图片 `D:\projects\PaddleOCR\doc\imgs_words\ch\`
.\out\build\x64-Release\Release\ppocr.exe rec --rec_model_dir=D:\projects\PaddleOCR\ch_ppocr_mobile_v2.0_rec_infer --image_dir=D:\projects\PaddleOCR\doc\imgs_words\ch\
#识别英文图片 'D:\projects\PaddleOCR\doc\imgs_words\en\'
.\out\build\x64-Release\Release\ppocr.exe rec --rec_model_dir=D:\projects\PaddleOCR\inference\rec_mv3crnn --image_dir=D:\projects\PaddleOCR\doc\imgs_words\en\ --char_list_file=D:\projects\PaddleOCR\ppocr\utils\dict\en_dict.txt
```
第一个参数为配置文件路径，第二个参数为需要预测的图片路径。
第一个参数为配置文件路径，第二个参数为需要预测的图片路径，第三个参数为配置文本识别的字典。
### 注意
### FQA
* 在Windows下的终端中执行文件exe时，可能会发生乱码的现象，此时需要在终端中输入`CHCP 65001`，将终端的编码方式由GBK编码(默认)改为UTF-8编码，更加具体的解释可以参考这篇博客：[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)。
* 编译时，如果报错`错误：C1083 无法打开包括文件:"dirent.h":No such file or directory`，可以参考该[文档](https://blog.csdn.net/Dora_blank/article/details/117740837#41_C1083_direnthNo_such_file_or_directory_54)，新建`dirent.h`文件，并添加到`VC++`的包含目录中。
* 编译时，如果报错`错误：C1083 无法打开包括文件:"dirent.h":No such file or directory`，可以参考该[文档](https://blog.csdn.net/Dora_blank/article/details/117740837#41_C1083_direnthNo_such_file_or_directory_54)，新建`dirent.h`文件，并添加到`utility.cpp`的头文件引用中。同时修改`utility.cpp`70行：`lstat`改成`stat`。
* 编译时，如果报错`Autolog未定义`，新建`autolog.h`文件，内容为：[autolog.h](https://github.com/LDOUBLEV/AutoLog/blob/main/auto_log/autolog.h)，并添加到`main.cpp`的头文件引用中，再次编译。
* 运行时，如果弹窗报错找不到`paddle_inference.dll`或者`openblas.dll`，在`D:\projects\paddle_inference`预测库内找到这两个文件，复制到`D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release\Release`目录下。不用重新编译，再次运行即可。
* 运行时，弹窗报错提示`应用程序无法正常启动(0xc0000142)`，并且`cmd`窗口内提示`You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found.`，把tensort目录下的lib里面的所有dll文件复制到release目录下，再次运行即可。
deploy/cpp_infer/external-cmake/auto-log.cmake 0 → 100644
View file @ 006d84bf
find_package(Git REQUIRED)
include(FetchContent)
set(FETCHCONTENT_BASE_DIR "${CMAKE_CURRENT_BINARY_DIR}/third-party")
FetchContent_Declare(
extern_Autolog
PREFIX autolog
GIT_REPOSITORY https://github.com/LDOUBLEV/AutoLog.git
GIT_TAG main
)
FetchContent_MakeAvailable(extern_Autolog)
deploy/cpp_infer/readme.md
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......@@ -4,15 +4,32 @@
C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
PaddleOCR模型部署。
* [1. 准备环境](#1)
+ [1.0 运行准备](#10)
+ [1.1 编译opencv库](#11)
+ [1.2 下载或者编译Paddle预测库](#12)
- [1.2.1 直接下载安装](#121)
- [1.2.2 预测库源码编译](#122)
* [2 开始运行](#2)
+ [2.1 将模型导出为inference model](#21)
+ [2.2 编译PaddleOCR C++预测demo](#22)
+ [2.3运行demo](#23)
<a name="1"></a>
## 1. 准备环境
### 运行准备
<a name="10"></a>
### 1.0 运行准备
- Linux环境，推荐使用docker。
- Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译。
* 该文档主要介绍基于Linux环境的PaddleOCR C++预测流程，如果需要在Windows下基于预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)
<a name="11"></a>
### 1.1 编译opencv库
* 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。
......@@ -71,6 +88,8 @@ opencv3/
|-- share
```
<a name="12"></a>
### 1.2 下载或者编译Paddle预测库
* 有2种方式获取Paddle预测库，下面进行详细介绍。
......@@ -132,9 +151,12 @@ build/paddle_inference_install_dir/
其中`paddle`就是C++预测所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
<a name="2"></a>
## 2 开始运行
<a name="21"></a>
### 2.1 将模型导出为inference model
* 可以参考[模型预测章节](../../doc/doc_ch/inference.md)，导出inference model，用于模型预测。模型导出之后，假设放在`inference`目录下，则目录结构如下。
......@@ -149,6 +171,7 @@ inference/
| |--inference.pdmodel
```
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### 2.2 编译PaddleOCR C++预测demo
......@@ -172,8 +195,9 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
* 编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
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### 运行demo
### 2.3 运行demo
运行方式：
```shell
......@@ -258,6 +282,4 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
</div>
### 2.3 注意
* 在使用Paddle预测库时，推荐使用2.0.0版本的预测库。
**注意：在使用Paddle预测库时，推荐使用2.0.0版本的预测库。**
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