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PaddleOCR
“9ede3ef8eec5d81f8d43eb1db3170f1ec8db28d7”上不存在“src/git@gitcode.net:taosdata/tdengine.git”
未验证 提交 81ae8f40 编写于 作者: D Double_V 提交者： GitHub
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Merge pull request #7620 from LDOUBLEV/dygraph 

add ppocrv3 for lite deploy readme
上级 2df2c88b 1b07e429
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Showing with 62 addition and 120 deletion
deploy/cpp_infer/src/ocr_cls.cpp
浏览文件 @ 81ae8f40
...@@ -112,6 +112,11 @@ void Classifier::LoadModel(const std::string &model_dir) { ...@@ -112,6 +112,11 @@ void Classifier::LoadModel(const std::string &model_dir) {
precision = paddle_infer::Config::Precision::kInt8; precision = paddle_infer::Config::Precision::kInt8;
} }
config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false); config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
if (!Utility::PathExists("./trt_cls_shape.txt")){
config.CollectShapeRangeInfo("./trt_cls_shape.txt");
} else {
config.EnableTunedTensorRtDynamicShape("./trt_cls_shape.txt", true);
}
} }
} else { } else {
config.DisableGpu(); config.DisableGpu();
......
deploy/cpp_infer/src/ocr_det.cpp
浏览文件 @ 81ae8f40
...@@ -32,49 +32,13 @@ void DBDetector::LoadModel(const std::string &model_dir) { ...@@ -32,49 +32,13 @@ void DBDetector::LoadModel(const std::string &model_dir) {
if (this->precision_ == "int8") { if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8; precision = paddle_infer::Config::Precision::kInt8;
} }
config.EnableTensorRtEngine(1 << 20, 1, 20, precision, false, false); config.EnableTensorRtEngine(1 << 30, 1, 20, precision, false, false);
std::map<std::string, std::vector<int>> min_input_shape = { if (!Utility::PathExists("./trt_det_shape.txt")){
{"x", {1, 3, 50, 50}}, config.CollectShapeRangeInfo("./trt_det_shape.txt");
{"conv2d_92.tmp_0", {1, 120, 20, 20}}, } else {
{"conv2d_91.tmp_0", {1, 24, 10, 10}}, config.EnableTunedTensorRtDynamicShape("./trt_det_shape.txt", true);
{"conv2d_59.tmp_0", {1, 96, 20, 20}}, }
{"nearest_interp_v2_1.tmp_0", {1, 256, 10, 10}},
{"nearest_interp_v2_2.tmp_0", {1, 256, 20, 20}},
{"conv2d_124.tmp_0", {1, 256, 20, 20}},
{"nearest_interp_v2_3.tmp_0", {1, 64, 20, 20}},
{"nearest_interp_v2_4.tmp_0", {1, 64, 20, 20}},
{"nearest_interp_v2_5.tmp_0", {1, 64, 20, 20}},
{"elementwise_add_7", {1, 56, 2, 2}},
{"nearest_interp_v2_0.tmp_0", {1, 256, 2, 2}}};
std::map<std::string, std::vector<int>> max_input_shape = {
{"x", {1, 3, 1536, 1536}},
{"conv2d_92.tmp_0", {1, 120, 400, 400}},
{"conv2d_91.tmp_0", {1, 24, 200, 200}},
{"conv2d_59.tmp_0", {1, 96, 400, 400}},
{"nearest_interp_v2_1.tmp_0", {1, 256, 200, 200}},
{"nearest_interp_v2_2.tmp_0", {1, 256, 400, 400}},
{"conv2d_124.tmp_0", {1, 256, 400, 400}},
{"nearest_interp_v2_3.tmp_0", {1, 64, 400, 400}},
{"nearest_interp_v2_4.tmp_0", {1, 64, 400, 400}},
{"nearest_interp_v2_5.tmp_0", {1, 64, 400, 400}},
{"elementwise_add_7", {1, 56, 400, 400}},
{"nearest_interp_v2_0.tmp_0", {1, 256, 400, 400}}};
std::map<std::string, std::vector<int>> opt_input_shape = {
{"x", {1, 3, 640, 640}},
{"conv2d_92.tmp_0", {1, 120, 160, 160}},
{"conv2d_91.tmp_0", {1, 24, 80, 80}},
{"conv2d_59.tmp_0", {1, 96, 160, 160}},
{"nearest_interp_v2_1.tmp_0", {1, 256, 80, 80}},
{"nearest_interp_v2_2.tmp_0", {1, 256, 160, 160}},
{"conv2d_124.tmp_0", {1, 256, 160, 160}},
{"nearest_interp_v2_3.tmp_0", {1, 64, 160, 160}},
{"nearest_interp_v2_4.tmp_0", {1, 64, 160, 160}},
{"nearest_interp_v2_5.tmp_0", {1, 64, 160, 160}},
{"elementwise_add_7", {1, 56, 40, 40}},
{"nearest_interp_v2_0.tmp_0", {1, 256, 40, 40}}};
config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
opt_input_shape);
} }
} else { } else {
config.DisableGpu(); config.DisableGpu();
......
deploy/cpp_infer/src/ocr_rec.cpp
浏览文件 @ 81ae8f40
...@@ -147,20 +147,12 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) { ...@@ -147,20 +147,12 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
if (this->precision_ == "int8") { if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8; precision = paddle_infer::Config::Precision::kInt8;
} }
config.EnableTensorRtEngine(1 << 20, 10, 15, precision, false, false); if (!Utility::PathExists("./trt_rec_shape.txt")){
int imgH = this->rec_image_shape_[1]; config.CollectShapeRangeInfo("./trt_rec_shape.txt");
int imgW = this->rec_image_shape_[2]; } else {
std::map<std::string, std::vector<int>> min_input_shape = { config.EnableTunedTensorRtDynamicShape("./trt_rec_shape.txt", true);
{"x", {1, 3, imgH, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}}; }
std::map<std::string, std::vector<int>> max_input_shape = {
{"x", {this->rec_batch_num_, 3, imgH, 2500}},
{"lstm_0.tmp_0", {1000, 1, 96}}};
std::map<std::string, std::vector<int>> opt_input_shape = {
{"x", {this->rec_batch_num_, 3, imgH, imgW}},
{"lstm_0.tmp_0", {25, 1, 96}}};
config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
opt_input_shape);
} }
} else { } else {
config.DisableGpu(); config.DisableGpu();
......
deploy/lite/config.txt
浏览文件 @ 81ae8f40
...@@ -5,4 +5,4 @@ det_db_unclip_ratio 1.6 ...@@ -5,4 +5,4 @@ det_db_unclip_ratio 1.6
det_db_use_dilate 0 det_db_use_dilate 0
det_use_polygon_score 1 det_use_polygon_score 1
use_direction_classify 1 use_direction_classify 1
rec_image_height 32 rec_image_height 48
\ No newline at end of file \ No newline at end of file
deploy/lite/readme.md
浏览文件 @ 81ae8f40
...@@ -99,6 +99,8 @@ The following table also provides a series of models that can be deployed on mob ...@@ -99,6 +99,8 @@ The following table also provides a series of models that can be deployed on mob
|Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch| |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
|---|---|---|---|---|---|---| |---|---|---|---|---|---|---|
|PP-OCRv3|extra-lightweight chinese OCR optimized model|16.2M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.nb)|v2.10|
|PP-OCRv3(slim)|extra-lightweight chinese OCR optimized model|5.9M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb)|v2.10|
|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10| |PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
|PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.6M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10| |PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.6M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
...@@ -134,17 +136,16 @@ Introduction to paddle_lite_opt parameters: ...@@ -134,17 +136,16 @@ Introduction to paddle_lite_opt parameters:
The following takes the ultra-lightweight Chinese model of PaddleOCR as an example to introduce the use of the compiled opt file to complete the conversion of the inference model to the Paddle-Lite optimized model The following takes the ultra-lightweight Chinese model of PaddleOCR as an example to introduce the use of the compiled opt file to complete the conversion of the inference model to the Paddle-Lite optimized model
``` ```
# 【[Recommendation] Download the Chinese and English inference model of PP-OCRv2 # 【[Recommendation] Download the Chinese and English inference model of PP-OCRv3
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf ch_PP-OCRv2_det_slim_quant_infer.tar wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar && tar xf ch_PP-OCRv3_det_slim_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_slim_infer.tar wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_slim_infer.tar
# Convert detection model # Convert detection model
./opt --model_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdmodel --param_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv2_det_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer paddle_lite_opt --model_file=./ch_PP-OCRv3_det_slim_infer/inference.pdmodel --param_file=./ch_PP-OCRv3_det_slim_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv3_det_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
# Convert recognition model # Convert recognition model
./opt --model_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdmodel --param_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv2_rec_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer paddle_lite_opt --model_file=./ch_PP-OCRv3_rec_slim_infer/inference.pdmodel --param_file=./ch_PP-OCRv3_rec_slim_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv3_rec_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
# Convert angle classifier model # Convert angle classifier model
./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer paddle_lite_opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
``` ```
After the conversion is successful, there will be more files ending with `.nb` in the inference model directory, which is the successfully converted model file. After the conversion is successful, there will be more files ending with `.nb` in the inference model directory, which is the successfully converted model file.
...@@ -197,15 +198,15 @@ Some preparatory work is required first. ...@@ -197,15 +198,15 @@ Some preparatory work is required first.
cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
``` ```
Prepare the test image, taking PaddleOCR/doc/imgs/11.jpg as an example, copy the image file to the demo/cxx/ocr/debug/ folder. Prepare the model files optimized by the lite opt tool, ch_det_mv3_db_opt.nb, ch_rec_mv3_crnn_opt.nb, and place them under the demo/cxx/ocr/debug/ folder. Prepare the test image, taking PaddleOCR/doc/imgs/11.jpg as an example, copy the image file to the demo/cxx/ocr/debug/ folder. Prepare the model files optimized by the lite opt tool, ch_PP-OCRv3_det_slim_opt.nb , ch_PP-OCRv3_rec_slim_opt.nb , and place them under the demo/cxx/ocr/debug/ folder.
The structure of the OCR demo is as follows after the above command is executed: The structure of the OCR demo is as follows after the above command is executed:
``` ```
demo/cxx/ocr/ demo/cxx/ocr/
|-- debug/ |-- debug/
| |--ch_PP-OCRv2_det_slim_opt.nb Detection model | |--ch_PP-OCRv3_det_slim_opt.nb Detection model
| |--ch_PP-OCRv2_rec_slim_opt.nb Recognition model | |--ch_PP-OCRv3_rec_slim_opt.nb Recognition model
| |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb Text direction classification model | |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb Text direction classification model
| |--11.jpg Image for OCR | |--11.jpg Image for OCR
| |--ppocr_keys_v1.txt Dictionary file | |--ppocr_keys_v1.txt Dictionary file
...@@ -240,7 +241,7 @@ det_db_thresh 0.3 # Used to filter the binarized image of DB prediction, ...@@ -240,7 +241,7 @@ det_db_thresh 0.3 # Used to filter the binarized image of DB prediction,
det_db_box_thresh 0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate det_db_box_thresh 0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
det_db_unclip_ratio 1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text det_db_unclip_ratio 1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
use_direction_classify 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use use_direction_classify 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use
rec_image_height 32 # The height of the input image of the recognition model, the PP-OCRv3 model needs to be set to 48, and the PP-OCRv2 model needs to be set to 32 rec_image_height 48 # The height of the input image of the recognition model, the PP-OCRv3 model needs to be set to 48, and the PP-OCRv2 model needs to be set to 32
``` ```
5. Run Model on phone 5. Run Model on phone
...@@ -260,14 +261,14 @@ After the above steps are completed, you can use adb to push the file to the pho ...@@ -260,14 +261,14 @@ After the above steps are completed, you can use adb to push the file to the pho
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
# The use of ocr_db_crnn is: # The use of ocr_db_crnn is:
# ./ocr_db_crnn Mode Detection model file Orientation classifier model file Recognition model file Hardware Precision Threads Batchsize Test image path Dictionary file path # ./ocr_db_crnn Mode Detection model file Orientation classifier model file Recognition model file Hardware Precision Threads Batchsize Test image path Dictionary file path
./ocr_db_crnn system ch_PP-OCRv2_det_slim_opt.nb ch_PP-OCRv2_rec_slim_opt.nb ch_ppocr_mobile_v2.0_cls_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt ppocr_keys_v1.txt True ./ocr_db_crnn system ch_PP-OCRv3_det_slim_opt.nb ch_PP-OCRv3_rec_slim_opt.nb ch_ppocr_mobile_v2.0_cls_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt ppocr_keys_v1.txt True
# precision can be INT8 for quantitative model or FP32 for normal model. # precision can be INT8 for quantitative model or FP32 for normal model.
# Only using detection model # Only using detection model
./ocr_db_crnn det ch_PP-OCRv2_det_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt ./ocr_db_crnn det ch_PP-OCRv3_det_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt
# Only using recognition model # Only using recognition model
./ocr_db_crnn rec ch_PP-OCRv2_rec_slim_opt.nb arm8 INT8 10 1 word_1.jpg ppocr_keys_v1.txt config.txt ./ocr_db_crnn rec ch_PP-OCRv3_rec_slim_opt.nb arm8 INT8 10 1 word_1.jpg ppocr_keys_v1.txt config.txt
``` ```
If you modify the code, you need to recompile and push to the phone. If you modify the code, you need to recompile and push to the phone.
......
deploy/lite/readme_ch.md
浏览文件 @ 81ae8f40
...@@ -97,6 +97,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括 ...@@ -97,6 +97,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
|模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本| |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
|---|---|---|---|---|---|---| |---|---|---|---|---|---|---|
|PP-OCRv3|蒸馏版超轻量中文OCR移动端模型|16.2M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.nb)|v2.10|
|PP-OCRv3(slim)|蒸馏版超轻量中文OCR移动端模型|5.9M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb)|v2.10|
|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10| |PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
|PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10| |PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
...@@ -131,16 +133,16 @@ paddle_lite_opt 参数介绍： ...@@ -131,16 +133,16 @@ paddle_lite_opt 参数介绍：
下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。 下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。
``` ```
# 【推荐】 下载 PP-OCRv2版本的中英文 inference模型 # 【推荐】 下载 PP-OCRv3版本的中英文 inference模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf ch_PP-OCRv2_det_slim_quant_infer.tar wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar && tar xf ch_PP-OCRv3_det_slim_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_slim_infer.tar wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_slim_infer.tar
# 转换检测模型 # 转换检测模型
./opt --model_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdmodel --param_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv2_det_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer paddle_lite_opt --model_file=./ch_PP-OCRv3_det_slim_infer/inference.pdmodel --param_file=./ch_PP-OCRv3_det_slim_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv3_det_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
# 转换识别模型 # 转换识别模型
./opt --model_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdmodel --param_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv2_rec_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer paddle_lite_opt --model_file=./ch_PP-OCRv3_rec_slim_infer/inference.pdmodel --param_file=./ch_PP-OCRv3_rec_slim_infer/inference.pdiparams --optimize_out=./ch_PP-OCRv3_rec_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
# 转换方向分类器模型 # 转换方向分类器模型
./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer paddle_lite_opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
``` ```
...@@ -194,15 +196,15 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls ...@@ -194,15 +196,15 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls
``` ```
准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。 准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。
准备lite opt工具优化后的模型文件，比如使用`ch_PP-OCRv2_det_slim_opt.ch_PP-OCRv2_rec_slim_rec.nb, ch_ppocr_mobile_v2.0_cls_slim_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。 准备lite opt工具优化后的模型文件，比如使用`ch_PP-OCRv3_det_slim_opt.ch_PP-OCRv3_rec_slim_rec.nb, ch_ppocr_mobile_v2.0_cls_slim_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。
执行完成后，ocr文件夹下将有如下文件格式： 执行完成后，ocr文件夹下将有如下文件格式：
``` ```
demo/cxx/ocr/ demo/cxx/ocr/
|-- debug/ |-- debug/
| |--ch_PP-OCRv2_det_slim_opt.nb 优化后的检测模型文件 | |--ch_PP-OCRv3_det_slim_opt.nb 优化后的检测模型文件
| |--ch_PP-OCRv2_rec_slim_opt.nb 优化后的识别模型文件 | |--ch_PP-OCRv3_rec_slim_opt.nb 优化后的识别模型文件
| |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb 优化后的文字方向分类器模型文件 | |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb 优化后的文字方向分类器模型文件
| |--11.jpg 待测试图像 | |--11.jpg 待测试图像
| |--ppocr_keys_v1.txt 中文字典文件 | |--ppocr_keys_v1.txt 中文字典文件
...@@ -239,7 +241,7 @@ det_db_thresh 0.3 # 用于过滤DB预测的二值化图像，设置为0. ...@@ -239,7 +241,7 @@ det_db_thresh 0.3 # 用于过滤DB预测的二值化图像，设置为0.
det_db_box_thresh 0.5 # 检测器后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小 det_db_box_thresh 0.5 # 检测器后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
det_db_unclip_ratio 1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本 det_db_unclip_ratio 1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本
use_direction_classify 0 # 是否使用方向分类器，0表示不使用，1表示使用 use_direction_classify 0 # 是否使用方向分类器，0表示不使用，1表示使用
rec_image_height 32 # 识别模型输入图像的高度，PP-OCRv3模型设置为48，PP-OCRv2模型需要设置为32 rec_image_height 48 # 识别模型输入图像的高度，PP-OCRv3模型设置为48，PP-OCRv2模型需要设置为32
``` ```
5. 启动调试 5. 启动调试
...@@ -259,13 +261,13 @@ rec_image_height 32 # 识别模型输入图像的高度，PP-OCRv3模型 ...@@ -259,13 +261,13 @@ rec_image_height 32 # 识别模型输入图像的高度，PP-OCRv3模型
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
# 开始使用，ocr_db_crnn可执行文件的使用方式为: # 开始使用，ocr_db_crnn可执行文件的使用方式为:
# ./ocr_db_crnn 预测模式 检测模型文件 方向分类器模型文件 识别模型文件 运行硬件 运行精度 线程数 batchsize 测试图像路径 参数配置路径 字典文件路径 是否使用benchmark参数 # ./ocr_db_crnn 预测模式 检测模型文件 方向分类器模型文件 识别模型文件 运行硬件 运行精度 线程数 batchsize 测试图像路径 参数配置路径 字典文件路径 是否使用benchmark参数
./ocr_db_crnn system ch_PP-OCRv2_det_slim_opt.nb ch_PP-OCRv2_rec_slim_opt.nb ch_ppocr_mobile_v2.0_cls_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt ppocr_keys_v1.txt True ./ocr_db_crnn system ch_PP-OCRv3_det_slim_opt.nb ch_PP-OCRv3_rec_slim_opt.nb ch_ppocr_mobile_v2.0_cls_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt ppocr_keys_v1.txt True
# 仅使用文本检测模型，使用方式如下： # 仅使用文本检测模型，使用方式如下：
./ocr_db_crnn det ch_PP-OCRv2_det_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt ./ocr_db_crnn det ch_PP-OCRv3_det_slim_opt.nb arm8 INT8 10 1 ./11.jpg config.txt
# 仅使用文本识别模型，使用方式如下： # 仅使用文本识别模型，使用方式如下：
./ocr_db_crnn rec ch_PP-OCRv2_rec_slim_opt.nb arm8 INT8 10 1 word_1.jpg ppocr_keys_v1.txt config.txt ./ocr_db_crnn rec ch_PP-OCRv3_rec_slim_opt.nb arm8 INT8 10 1 word_1.jpg ppocr_keys_v1.txt config.txt
``` ```
如果对代码做了修改，则需要重新编译并push到手机上。 如果对代码做了修改，则需要重新编译并push到手机上。
......
deploy/slim/quantization/README.md
浏览文件 @ 81ae8f40
...@@ -22,7 +22,7 @@ ...@@ -22,7 +22,7 @@
### 1. 安装PaddleSlim ### 1. 安装PaddleSlim
```bash ```bash
pip3 install paddleslim==2.2.2 pip3 install paddleslim==2.3.2
``` ```
### 2. 准备训练好的模型 ### 2. 准备训练好的模型
...@@ -33,17 +33,7 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list. ...@@ -33,17 +33,7 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.
量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。 量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。
量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下： 量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，以PPOCRv3检测模型为例，训练指令如下：
```bash
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
# 比如下载提供的训练模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
```
模型蒸馏和模型量化可以同时使用，以PPOCRv3检测模型为例：
``` ```
# 下载检测预训练模型： # 下载检测预训练模型：
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar
...@@ -58,7 +48,7 @@ python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_ ...@@ -58,7 +48,7 @@ python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_
在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署： 在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
```bash ```bash
python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model python deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
``` ```
### 5. 量化模型部署 ### 5. 量化模型部署
......
deploy/slim/quantization/README_en.md
浏览文件 @ 81ae8f40
...@@ -25,7 +25,7 @@ After training, if you want to further compress the model size and accelerate th ...@@ -25,7 +25,7 @@ After training, if you want to further compress the model size and accelerate th
### 1. Install PaddleSlim ### 1. Install PaddleSlim
```bash ```bash
pip3 install paddleslim==2.2.2 pip3 install paddleslim==2.3.2
``` ```
...@@ -39,18 +39,7 @@ Quantization training includes offline quantization training and online quantiza ...@@ -39,18 +39,7 @@ Quantization training includes offline quantization training and online quantiza
Online quantization training is more effective. It is necessary to load the pre-trained model. Online quantization training is more effective. It is necessary to load the pre-trained model.
After the quantization strategy is defined, the model can be quantified. After the quantization strategy is defined, the model can be quantified.
The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows: The code for quantization training is located in `slim/quantization/quant.py`. For example, the training instructions of slim PPOCRv3 detection model are as follows:
```bash
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
# download provided model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
```
Model distillation and model quantization can be used at the same time, taking the PPOCRv3 detection model as an example:
``` ```
# download provided model # download provided model
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar
...@@ -66,7 +55,7 @@ If you want to quantify the text recognition model, you can modify the configura ...@@ -66,7 +55,7 @@ If you want to quantify the text recognition model, you can modify the configura
Once we got the model after pruning and fine-tuning, we can export it as an inference model for the deployment of predictive tasks: Once we got the model after pruning and fine-tuning, we can export it as an inference model for the deployment of predictive tasks:
```bash ```bash
python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model python deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
``` ```
### 5. Deploy ### 5. Deploy
......
tools/infer/utility.py
浏览文件 @ 81ae8f40
...@@ -228,19 +228,18 @@ def create_predictor(args, mode, logger): ...@@ -228,19 +228,18 @@ def create_predictor(args, mode, logger):
use_calib_mode=False) use_calib_mode=False)
# collect shape # collect shape
if args.shape_info_filename is not None: trt_shape_f = f"{os.path.dirname(args.shape_info_filename)}/{mode}_{os.path.basename(args.shape_info_filename)}"
if not os.path.exists(args.shape_info_filename): if trt_shape_f is not None:
config.collect_shape_range_info( if not os.path.exists(trt_shape_f):
args.shape_info_filename) config.collect_shape_range_info(trt_shape_f)
logger.info( logger.info(
f"collect dynamic shape info into : {args.shape_info_filename}" f"collect dynamic shape info into : {trt_shape_f}"
) )
else: else:
logger.info( logger.info(
f"dynamic shape info file( {args.shape_info_filename} ) already exists, not need to generate again." f"dynamic shape info file( {trt_shape_f} ) already exists, not need to generate again."
) )
config.enable_tuned_tensorrt_dynamic_shape( config.enable_tuned_tensorrt_dynamic_shape(trt_shape_f, True)
args.shape_info_filename, True)
else: else:
logger.info( logger.info(
f"when using tensorrt, dynamic shape is a suggested option, you can use '--shape_info_filename=shape.txt' for offline dygnamic shape tuning" f"when using tensorrt, dynamic shape is a suggested option, you can use '--shape_info_filename=shape.txt' for offline dygnamic shape tuning"
......
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