diff --git a/README.md b/README.md index df47cc64b0c33dc3dc2798de4e7ff8092b7bb2bb..887d486c87fcfeab6e703f22f77d8c13b2dc1dc2 100755 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ The above pictures are the visualizations of the English recognition model. For - Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
- +
diff --git a/README_ch.md b/README_ch.md index eafce6c2eecee4f65d4c8cfbfd9b0558deed3d9e..0c5d4e1c3e8237a0e3a96d6a581f8662e221e37a 100755 --- a/README_ch.md +++ b/README_ch.md @@ -8,7 +8,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 - 静态图版本：develop分支 **近期更新** -- 2021.4.20 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数208个，每周一都会更新，欢迎大家持续关注。 +- 2021.4.26 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数213个，每周一都会更新，欢迎大家持续关注。 - PaddleOCR研发团队对最新发版内容技术深入解读，4月13日晚上19:00，[直播地址](https://live.bilibili.com/21689802)。 - 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](./doc/doc_ch/pgnet.md)开源，[多语言模型](./doc/doc_ch/multi_languages.md)支持种类增加到80+。 - 2021.2.8 正式发布PaddleOCRv2.0(branch release/2.0)并设置为推荐用户使用的默认分支. 发布的详细内容，请参考: https://github.com/PaddlePaddle/PaddleOCR/releases/tag/v2.0.0 @@ -45,7 +45,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 - 微信扫描二维码加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
- +
## 快速体验 @@ -78,7 +78,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 - 算法介绍 - [文本检测](./doc/doc_ch/algorithm_overview.md) - [文本识别](./doc/doc_ch/algorithm_overview.md) - - [PP-OCR Pipline](#PP-OCR) + - [PP-OCR Pipeline](#PP-OCR) - [端到端PGNet算法](./doc/doc_ch/pgnet.md) - 模型训练/评估 - [文本检测](./doc/doc_ch/detection.md) @@ -113,7 +113,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 -## PP-OCR Pipline +## PP-OCR Pipeline
diff --git a/configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml b/configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml index 6a524e22cf4dea4c573d6b67e752c8527e973185..717c16814bac2f6fca78aa63566df12bd8cbf67b 100644 --- a/configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml +++ b/configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: True + save_res_path: ./output/rec/predicts_chinese_common_v2.0.txt Optimizer: diff --git a/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml b/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml index c96621c5684f3861a7bc2f5aa8d9684e6512e228..660465f301047110db7001db7a32e687f2917b61 100644 --- a/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml +++ b/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: True + save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt Optimizer: diff --git a/configs/rec/rec_icdar15_train.yml b/configs/rec/rec_icdar15_train.yml index 5ae47c67d8b062746d422daac44011fb5aca38e2..79e3ff88869d9e0fc7be5563830eed1244e38b76 100644 --- a/configs/rec/rec_icdar15_train.yml +++ b/configs/rec/rec_icdar15_train.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_ic15.txt Optimizer: name: Adam diff --git a/configs/rec/rec_mv3_none_bilstm_ctc.yml b/configs/rec/rec_mv3_none_bilstm_ctc.yml index 900e98b6b34de824d9afb91b1867a86fe2debc24..9e0bd23edba053b44fc7241c0a587ced5cd1ac76 100644 --- a/configs/rec/rec_mv3_none_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_mv3_none_bilstm_ctc.txt Optimizer: name: Adam diff --git a/configs/rec/rec_mv3_none_none_ctc.yml b/configs/rec/rec_mv3_none_none_ctc.yml index 6d86b90c007cab9708cab6db6d8e3045dd5187fb..904afe1134b565d6459cdcda4cbfa43ae4925b92 100644 --- a/configs/rec/rec_mv3_none_none_ctc.yml +++ b/configs/rec/rec_mv3_none_none_ctc.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_mv3_none_none_ctc.txt Optimizer: name: Adam diff --git a/configs/rec/rec_mv3_tps_bilstm_att.yml b/configs/rec/rec_mv3_tps_bilstm_att.yml index 33aed74d83f9ab18b7bdfb12d0ed315c6eebc010..feaeb0545c687774938521e4c45c026207172f11 100644 --- a/configs/rec/rec_mv3_tps_bilstm_att.yml +++ b/configs/rec/rec_mv3_tps_bilstm_att.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt Optimizer: diff --git a/configs/rec/rec_mv3_tps_bilstm_ctc.yml b/configs/rec/rec_mv3_tps_bilstm_ctc.yml index 026c6a9dfbd6b6b543c0b4260c43cbf98e192e7b..65ab23c42aff54ee548867e3482d7400603551ad 100644 --- a/configs/rec/rec_mv3_tps_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_mv3_tps_bilstm_ctc.txt Optimizer: name: Adam diff --git a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml index 4052d426e51aa8c6e82ec216cfd65226922be602..331bb36ed84b83dc62a0f9b15524457238dedc13 100644 --- a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_r34_vd_none_bilstm_ctc.txt Optimizer: name: Adam diff --git a/configs/rec/rec_r34_vd_none_none_ctc.yml b/configs/rec/rec_r34_vd_none_none_ctc.yml index c3e1d9a3a91ab6a51e28d458623aea788b952ca0..695a46958f669e4cb9508646080b45ac0767b8c9 100644 --- a/configs/rec/rec_r34_vd_none_none_ctc.yml +++ b/configs/rec/rec_r34_vd_none_none_ctc.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_r34_vd_none_none_ctc.txt Optimizer: name: Adam diff --git a/configs/rec/rec_r34_vd_tps_bilstm_att.yml b/configs/rec/rec_r34_vd_tps_bilstm_att.yml index 87a14559849abd0a47c45f52b80d06bb0790ef0e..fdd3588c844ffd7ed61de73077ae2994f0ad498d 100644 --- a/configs/rec/rec_r34_vd_tps_bilstm_att.yml +++ b/configs/rec/rec_r34_vd_tps_bilstm_att.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt Optimizer: diff --git a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml index 9c51962e58763838eb4a101d1d52a1a030e4b643..67108a6eaca2dd6f239261f5184341e5ade00dc0 100644 --- a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml @@ -19,6 +19,7 @@ Global: max_text_length: 25 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_r34_vd_tps_bilstm_ctc.txt Optimizer: name: Adam diff --git a/configs/rec/rec_r50_fpn_srn.yml b/configs/rec/rec_r50_fpn_srn.yml index 34a997f3c3f6bc3dccdc62757c6adb1e1d17cc53..fa7b1ae4e5fed41d3aa3670d6672cca01b63c359 100644 --- a/configs/rec/rec_r50_fpn_srn.yml +++ b/configs/rec/rec_r50_fpn_srn.yml @@ -20,6 +20,7 @@ Global: num_heads: 8 infer_mode: False use_space_char: False + save_res_path: ./output/rec/predicts_srn.txt Optimizer: diff --git a/deploy/cpp_infer/include/config.h b/deploy/cpp_infer/include/config.h index dbfbc2df141042f1065b380010e1ea3ff3ccedab..cd02a997e304850ebc04ce2288f4e497dbb4be4a 100644 --- a/deploy/cpp_infer/include/config.h +++ b/deploy/cpp_infer/include/config.h @@ -49,6 +49,8 @@ public: this->det_db_unclip_ratio = stod(config_map_["det_db_unclip_ratio"]); + this->use_polygon_score = bool(stoi(config_map_["use_polygon_score"])); + this->det_model_dir.assign(config_map_["det_model_dir"]); this->rec_model_dir.assign(config_map_["rec_model_dir"]); @@ -86,6 +88,8 @@ public: double det_db_unclip_ratio = 2.0; + bool use_polygon_score = false; + std::string det_model_dir; std::string rec_model_dir; diff --git a/deploy/cpp_infer/include/ocr_det.h b/deploy/cpp_infer/include/ocr_det.h index bab9c95fa4a3f1cb160ccbf9ca4587fa4c2ba16a..18318c9c4e37136db62c1338db1b58f82859f037 100644 --- a/deploy/cpp_infer/include/ocr_det.h +++ b/deploy/cpp_infer/include/ocr_det.h @@ -44,7 +44,8 @@ public: const bool &use_mkldnn, const int &max_side_len, const double &det_db_thresh, const double &det_db_box_thresh, - const double &det_db_unclip_ratio, const bool &visualize, + const double &det_db_unclip_ratio, + const bool &use_polygon_score, const bool &visualize, const bool &use_tensorrt, const bool &use_fp16) { this->use_gpu_ = use_gpu; this->gpu_id_ = gpu_id; @@ -57,6 +58,7 @@ public: this->det_db_thresh_ = det_db_thresh; this->det_db_box_thresh_ = det_db_box_thresh; this->det_db_unclip_ratio_ = det_db_unclip_ratio; + this->use_polygon_score_ = use_polygon_score; this->visualize_ = visualize; this->use_tensorrt_ = use_tensorrt; @@ -85,6 +87,7 @@ private: double det_db_thresh_ = 0.3; double det_db_box_thresh_ = 0.5; double det_db_unclip_ratio_ = 2.0; + bool use_polygon_score_ = false; bool visualize_ = true; bool use_tensorrt_ = false; diff --git a/deploy/cpp_infer/include/postprocess_op.h b/deploy/cpp_infer/include/postprocess_op.h index a600ea6d106706af2fbadb249c862fc764714f9e..b384b79b3041bfcb96f042c6450d3c6e54f00498 100644 --- a/deploy/cpp_infer/include/postprocess_op.h +++ b/deploy/cpp_infer/include/postprocess_op.h @@ -55,7 +55,8 @@ public: std::vector>> BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap, - const float &box_thresh, const float &det_db_unclip_ratio); + const float &box_thresh, const float &det_db_unclip_ratio, + const bool &use_polygon_score); std::vector>> FilterTagDetRes(std::vector>> boxes, diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md index 4b65c51f63d70c2e62315a839e1765b4d61d5c6c..da74abb93730c3d7dcd7a265532a3c2e0a3ff5c7 100644 --- a/deploy/cpp_infer/readme.md +++ b/deploy/cpp_infer/readme.md @@ -183,7 +183,7 @@ cmake .. \ make -j ``` -`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/`。 +`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。 @@ -211,6 +211,7 @@ max_side_len 960 # 输入图像长宽大于960时，等比例缩放图像，使 det_db_thresh 0.3 # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显 det_db_box_thresh 0.5 # DB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 +use_polygon_score 1 # 是否使用多边形框计算bbox score，0表示使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。 det_model_dir ./inference/det_db # 检测模型inference model地址 # cls config diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md index 1749a12b5690e5a4b4bfff8cfe380cc7efa7367c..4a02ac38a3306a4f54abeeb759e062648bfb4cca 100644 --- a/deploy/cpp_infer/readme_en.md +++ b/deploy/cpp_infer/readme_en.md @@ -219,6 +219,7 @@ 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 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 +use_polygon_score 1 # Whether to use polygon box to calculate bbox score, 0 means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area. det_model_dir ./inference/det_db # Address of detection inference model # cls config diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index 5c9042d4e80c569cfd4a57a84ad5c594e69dc350..588c8374ab341163835aea2ba6c7132640c74c64 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -59,7 +59,8 @@ int main(int argc, char **argv) { config.gpu_mem, config.cpu_math_library_num_threads, config.use_mkldnn, config.max_side_len, config.det_db_thresh, config.det_db_box_thresh, config.det_db_unclip_ratio, - config.visualize, config.use_tensorrt, config.use_fp16); + config.use_polygon_score, config.visualize, + config.use_tensorrt, config.use_fp16); Classifier *cls = nullptr; if (config.use_angle_cls == true) { diff --git a/deploy/cpp_infer/src/ocr_det.cpp b/deploy/cpp_infer/src/ocr_det.cpp index 489940f062fa9f8093282d20441704dd5cb8b382..9bfee6138577288156496d9b533b4da906ae7268 100644 --- a/deploy/cpp_infer/src/ocr_det.cpp +++ b/deploy/cpp_infer/src/ocr_det.cpp @@ -109,9 +109,9 @@ void DBDetector::Run(cv::Mat &img, cv::Mat dilation_map; cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2)); cv::dilate(bit_map, dilation_map, dila_ele); - boxes = post_processor_.BoxesFromBitmap(pred_map, dilation_map, - this->det_db_box_thresh_, - this->det_db_unclip_ratio_); + boxes = post_processor_.BoxesFromBitmap( + pred_map, dilation_map, this->det_db_box_thresh_, + this->det_db_unclip_ratio_, this->use_polygon_score_); boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg); diff --git a/deploy/cpp_infer/src/postprocess_op.cpp b/deploy/cpp_infer/src/postprocess_op.cpp index 1b71c210497778fcb70ffe8630e29245ad00136d..e7db70f3bff81390728c6b373b89cf06c74e4eca 100644 --- a/deploy/cpp_infer/src/postprocess_op.cpp +++ b/deploy/cpp_infer/src/postprocess_op.cpp @@ -160,35 +160,49 @@ std::vector> PostProcessor::GetMiniBoxes(cv::RotatedRect box, } float PostProcessor::PolygonScoreAcc(std::vector contour, - cv::Mat pred){ + cv::Mat pred) { int width = pred.cols; int height = pred.rows; std::vector box_x; std::vector box_y; - for(int i=0; i> box_array, return score; } -std::vector>> -PostProcessor::BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap, - const float &box_thresh, - const float &det_db_unclip_ratio) { +std::vector>> PostProcessor::BoxesFromBitmap( + const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh, + const float &det_db_unclip_ratio, const bool &use_polygon_score) { const int min_size = 3; const int max_candidates = 1000; @@ -267,9 +280,12 @@ PostProcessor::BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap, } float score; - score = BoxScoreFast(array, pred); - /* compute using polygon*/ - // score = PolygonScoreAcc(contours[_i], pred); + if (use_polygon_score) + /* compute using polygon*/ + score = PolygonScoreAcc(contours[_i], pred); + else + score = BoxScoreFast(array, pred); + if (score < box_thresh) continue; diff --git a/deploy/cpp_infer/src/preprocess_op.cpp b/deploy/cpp_infer/src/preprocess_op.cpp old mode 100644 new mode 100755 index 87d8dbbd774f7dd29843d9a2c2533368f1914272..37e33aee44167738261db4138793754aa657f22c --- a/deploy/cpp_infer/src/preprocess_op.cpp +++ b/deploy/cpp_infer/src/preprocess_op.cpp @@ -77,19 +77,10 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img, int resize_h = int(float(h) * ratio); int resize_w = int(float(w) * ratio); - if (resize_h % 32 == 0) - resize_h = resize_h; - else if (resize_h / 32 < 1 + 1e-5) - resize_h = 32; - else - resize_h = (resize_h / 32) * 32; - if (resize_w % 32 == 0) - resize_w = resize_w; - else if (resize_w / 32 < 1 + 1e-5) - resize_w = 32; - else - resize_w = (resize_w / 32) * 32; + resize_h = max(int(round(float(resize_h) / 32) * 32), 32); + resize_w = max(int(round(float(resize_w) / 32) * 32), 32); + if (!use_tensorrt) { cv::resize(img, resize_img, cv::Size(resize_w, resize_h)); ratio_h = float(resize_h) / float(h); diff --git a/deploy/cpp_infer/tools/config.txt b/deploy/cpp_infer/tools/config.txt index 28085ca408d279fc61a1bce1abf1df9c05115c78..5f415a6af2f3b1a695f14e8191ed7b9e55932910 100644 --- a/deploy/cpp_infer/tools/config.txt +++ b/deploy/cpp_infer/tools/config.txt @@ -10,6 +10,7 @@ max_side_len 960 det_db_thresh 0.3 det_db_box_thresh 0.5 det_db_unclip_ratio 1.6 +use_polygon_score 1 det_model_dir ./inference/ch_ppocr_mobile_v2.0_det_infer/ # cls config diff --git a/deploy/hubserving/ocr_cls/module.py b/deploy/hubserving/ocr_cls/module.py index 803d5ac27e74d04177b21b5886675ac0b5f31698..e159e0d3f23e9654c2d0342fbe6fa86b257ed24b 100644 --- a/deploy/hubserving/ocr_cls/module.py +++ b/deploy/hubserving/ocr_cls/module.py @@ -16,6 +16,7 @@ import paddlehub as hub from tools.infer.utility import base64_to_cv2 from tools.infer.predict_cls import TextClassifier from tools.infer.utility import parse_args +from deploy.hubserving.ocr_cls.params import read_params @moduleinfo( @@ -55,7 +56,6 @@ class OCRCls(hub.Module): sys.argv = sys.argv[:1] cfg = parse_args() - from ocr_det.params import read_params update_cfg_map = vars(read_params()) for key in update_cfg_map: diff --git a/doc/doc_ch/FAQ.md b/doc/doc_ch/FAQ.md index 6f008d97faacc80cdf28dc6b47fe3a403a98425e..631b2594c24e4a0d34b4967ea13fd8106052e605 100755 --- a/doc/doc_ch/FAQ.md +++ b/doc/doc_ch/FAQ.md @@ -9,41 +9,35 @@ ## PaddleOCR常见问题汇总(持续更新) -* [近期更新（2021.4.20）](#近期更新) +* [近期更新（2021.4.26）](#近期更新) * [【精选】OCR精选10个问题](#OCR精选10个问题) * [【理论篇】OCR通用43个问题](#OCR通用问题) * [基础知识13题](#基础知识) * [数据集9题](#数据集2) * [模型训练调优21题](#模型训练调优2) -* [【实战篇】PaddleOCR实战150个问题](#PaddleOCR实战问题) - * [使用咨询61题](#使用咨询) +* [【实战篇】PaddleOCR实战160个问题](#PaddleOCR实战问题) + * [使用咨询63题](#使用咨询) * [数据集18题](#数据集3) - * [模型训练调优34题](#模型训练调优3) - * [预测部署42题](#预测部署3) + * [模型训练调优35题](#模型训练调优3) + * [预测部署44题](#预测部署3) -## 近期更新（2021.4.20） +## 近期更新（2021.4.26） -#### Q3.1.58: 使用PGNet进行eval报错？ -**A**: 需要注意，我们目前在release/2.1更新了评测代码，目前支持A，B两种评测模式： -* A模式：该模式主要为了方便用户使用，与训练集一样的标注文件就可以正常进行eval操作, 代码中默认是A模式。 -* B模式：该模式主要为了保证我们的评测代码可以和Total Text官方的评测方式对齐，该模式下直接加载官方提供的mat文件进行eval。 +#### Q3.1.62: 弯曲文本（如略微形变的文档图像）漏检问题 +**A**: db后处理中计算文本框平均得分时，是求rectangle区域的平均分数，容易造成弯曲文本漏检，已新增求polygon区域的平均分数，会更准确，但速度有所降低，可按需选择，在相关pr中可查看[可视化对比效果](https://github.com/PaddlePaddle/PaddleOCR/pull/2604)。该功能通过参数 [det_db_score_mode](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/tools/infer/utility.py#L51)进行选择，参数值可选[`fast`(默认)、`slow`]，`fast`对应原始的rectangle方式，`slow`对应polygon方式。感谢用户[buptlihang](https://github.com/buptlihang)提[pr](https://github.com/PaddlePaddle/PaddleOCR/pull/2574)帮助解决该问题🌹。 -#### Q3.1.59: 使用预训练模型进行预测，对于特定字符识别识别效果较差，怎么解决？ -**A**: 由于我们所提供的识别模型是基于通用大规模数据集进行训练的，部分字符可能在训练集中包含较少，因此您可以构建特定场景的数据集，基于我们提供的预训练模型进行微调。建议用于微调的数据集中，每个字符出现的样本数量不低于300，但同时需要注意不同字符的数量均衡。具体可以参考：[微调](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/recognition.md#2-%E5%90%AF%E5%8A%A8%E8%AE%AD%E7%BB%83) +#### Q3.1.63: 请问端到端的pgnet相比于DB+CRNN在准确率上有优势吗？或者是pgnet最擅长的场景是什么场景呢？ +**A**: pgnet是端到端算法，检测识别一步到位，不用分开训练2个模型，也支持弯曲文本的识别，但是在中文上的效果还没有充分验证；db+crnn的验证更充分，应用相对成熟，常规非弯曲的文本都能解的不错。 -#### Q3.1.60: PGNet有中文预训练模型吗？ -**A**: 目前我们尚未提供针对中文的预训练模型，如有需要，可以尝试自己训练。具体需要修改的地方有： - 1. [config文件中](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/configs/e2e/e2e_r50_vd_pg.yml#L23-L24)，字典文件路径及语种设置； - 1. [网络结构中](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/ppocr/modeling/heads/e2e_pg_head.py#L181)，`out_channels`修改为字典中的字符数目+1（考虑到空格）； - 1. [loss中](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/ppocr/losses/e2e_pg_loss.py#L93)，修改`37`为字典中的字符数目+1（考虑到空格）； +#### Q3.3.35: SRN训练不收敛（loss不降）或SRN训练acc一直为0。 +**A**: 如果loss下降不正常，需要确认没有修改yml文件中的image_shape，默认[1, 64, 256]，代码中针对这个配置写死了，修改可能会造成无法收敛。如果确认参数无误，loss正常下降，可以多迭代一段时间观察下，开始acc为0是正常的。 -#### Q3.1.61: 用于PGNet的训练集，文本框的标注有要求吗？ -**A**: PGNet支持多点标注，比如4点、8点、14点等。但需要注意的是，标注点尽可能分布均匀（相邻标注点间隔距离均匀一致），且label文件中的标注点需要从标注框的左上角开始，按标注点顺时针顺序依次编写，以上问题都可能对训练精度造成影响。 -我们提供的，基于Total Text数据集的PGNet预训练模型使用了14点标注方式。 +#### Q3.4.43: 预测时显存爆炸、内存泄漏问题？ +**A**: 打开显存/内存优化开关`enable_memory_optim`可以解决该问题，相关代码已合入，[查看详情](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/tools/infer/utility.py#L153)。 -#### Q3.4.42: 在使用PaddleLite进行预测部署时，启动预测后卡死/手机死机？ -**A**: 请检查模型转换时所用PaddleLite的版本，和预测库的版本是否对齐。即PaddleLite版本为2.8，则预测库版本也要为2.8。 +#### Q3.4.44: 如何多进程预测？ +**A**: 近期PaddleOCR新增了[多进程预测控制参数](https://github.com/PaddlePaddle/PaddleOCR/blob/a312647be716776c1aac33ff939ae358a39e8188/tools/infer/utility.py#L103)，`use_mp`表示是否使用多进程，`total_process_num`表示在使用多进程时的进程数。具体使用方式请参考[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/inference.md#1-%E8%B6%85%E8%BD%BB%E9%87%8F%E4%B8%AD%E6%96%87ocr%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86)。 ## 【精选】OCR精选10个问题 @@ -638,6 +632,11 @@ repo中config.yml文件的前后处理参数和inference预测默认的超参数 **A**: PGNet支持多点标注，比如4点、8点、14点等。但需要注意的是，标注点尽可能分布均匀（相邻标注点间隔距离均匀一致），且label文件中的标注点需要从标注框的左上角开始，按标注点顺时针顺序依次编写，以上问题都可能对训练精度造成影响。 我们提供的，基于Total Text数据集的PGNet预训练模型使用了14点标注方式。 +#### Q3.1.62: 弯曲文本（如略微形变的文档图像）漏检问题 +**A**: db后处理中计算文本框平均得分时，是求rectangle区域的平均分数，容易造成弯曲文本漏检，已新增求polygon区域的平均分数，会更准确，但速度有所降低，可按需选择，在相关pr中可查看[可视化对比效果](https://github.com/PaddlePaddle/PaddleOCR/pull/2604)。该功能通过参数 [det_db_score_mode](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/tools/infer/utility.py#L51)进行选择，参数值可选[`fast`(默认)、`slow`]，`fast`对应原始的rectangle方式，`slow`对应polygon方式。感谢用户[buptlihang](https://github.com/buptlihang)提[pr](https://github.com/PaddlePaddle/PaddleOCR/pull/2574)帮助解决该问题🌹。 + +#### Q3.1.63: 请问端到端的pgnet相比于DB+CRNN在准确率上有优势吗？或者是pgnet最擅长的场景是什么场景呢？ +**A**: pgnet是端到端算法，检测识别一步到位，不用分开训练2个模型，也支持弯曲文本的识别，但是在中文上的效果还没有充分验证；db+crnn的验证更充分，应用相对成熟，常规非弯曲的文本都能解的不错。 @@ -911,8 +910,10 @@ lr: #### Q3.3.34: 表格识别中，如何提高单字的识别结果？ **A**: 首先需要确认一下检测模型有没有有效的检测出单个字符，如果没有的话，需要在训练集当中添加相应的单字数据集。 - +#### Q3.3.35: SRN训练不收敛（loss不降）或SRN训练acc一直为0。 +**A**: 如果loss下降不正常，需要确认没有修改yml文件中的image_shape，默认[1, 64, 256]，代码中针对这个配置写死了，修改可能会造成无法收敛。如果确认参数无误，loss正常下降，可以多迭代一段时间观察下，开始acc为0是正常的。 + ### 预测部署 @@ -956,10 +957,6 @@ lr: **A**：在安卓APK上无法设置，没有暴露这个接口，如果使用的是PaddledOCR/deploy/lite/的demo，可以修改config.txt中的对应参数来设置 -#### Q3.4.9：PaddleOCR模型是否可以转换成ONNX模型? - -**A**：目前暂不支持转ONNX，相关工作在研发中。 - #### Q3.4.10：使用opt工具对检测模型转换时报错 can not found op arguments for node conv2_b_attr **A**：这个问题大概率是编译opt工具的Paddle-Lite不是develop分支，建议使用Paddle-Lite 的develop分支编译opt工具。 @@ -1114,3 +1111,9 @@ nvidia-smi --lock-gpu-clocks=1590 -i 0 #### Q3.4.42: 在使用PaddleLite进行预测部署时，启动预测后卡死/手机死机？ **A**: 请检查模型转换时所用PaddleLite的版本，和预测库的版本是否对齐。即PaddleLite版本为2.8，则预测库版本也要为2.8。 + +#### Q3.4.43: 预测时显存爆炸、内存泄漏问题？ +**A**: 打开显存/内存优化开关`enable_memory_optim`可以解决该问题，相关代码已合入，[查看详情](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/tools/infer/utility.py#L153)。 + +#### Q3.4.44: 如何多进程预测？ +**A**: 近期PaddleOCR新增了[多进程预测控制参数](https://github.com/PaddlePaddle/PaddleOCR/blob/a312647be716776c1aac33ff939ae358a39e8188/tools/infer/utility.py#L103)，`use_mp`表示是否使用多进程，`total_process_num`表示在使用多进程时的进程数。具体使用方式请参考[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/inference.md#1-%E8%B6%85%E8%BD%BB%E9%87%8F%E4%B8%AD%E6%96%87ocr%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86)。 diff --git a/doc/doc_ch/multi_languages.md b/doc/doc_ch/multi_languages.md index a3bc6f1083a285fa818de7089cfaf337b36e45e5..306eba36e463cb4aef20a1d8ff895ecfcc77d0ef 100755 --- a/doc/doc_ch/multi_languages.md +++ b/doc/doc_ch/multi_languages.md @@ -47,7 +47,7 @@ PaddleOCR 旨在打造一套丰富、领先、且实用的OCR工具库，不仅 pip install paddlepaddle # gpu -pip instll paddlepaddle-gpu +pip install paddlepaddle-gpu ``` @@ -179,11 +179,11 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别 ## 4 预测部署 除了安装whl包进行快速预测，ppocr 也提供了多种预测部署方式，如有需求可阅读相关文档： -- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md) -- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) -- [服务化部署](./deploy/hubserving/readme.md) +- [基于Python脚本预测引擎推理](./inference.md) +- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md) +- [服务化部署](../../deploy/hubserving/readme.md) - [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md) -- [Benchmark](./doc/doc_ch/benchmark.md) +- [Benchmark](./benchmark.md) diff --git a/doc/doc_en/multi_languages_en.md b/doc/doc_en/multi_languages_en.md index fa00bdd3f8c9917cb501bfbb8bf95e8e0c51edde..e58b782ca18d55dbd954382fd0df6f53910e2e52 100755 --- a/doc/doc_en/multi_languages_en.md +++ b/doc/doc_en/multi_languages_en.md @@ -48,7 +48,7 @@ This document will briefly introduce how to use the multilingual model. pip install paddlepaddle # gpu -pip instll paddlepaddle-gpu +pip install paddlepaddle-gpu ``` @@ -181,11 +181,11 @@ In addition to installing the whl package for quick forecasting, ppocr also provides a variety of forecasting deployment methods. If necessary, you can read related documents: -- [Python Inference](./doc/doc_en/inference_en.md) -- [C++ Inference](./deploy/cpp_infer/readme_en.md) -- [Serving](./deploy/hubserving/readme_en.md) +- [Python Inference](./inference_en.md) +- [C++ Inference](../../deploy/cpp_infer/readme_en.md) +- [Serving](../../deploy/hubserving/readme_en.md) - [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md) -- [Benchmark](./doc/doc_en/benchmark_en.md) +- [Benchmark](./benchmark_en.md) diff --git a/ppocr/modeling/backbones/rec_resnet_vd.py b/ppocr/modeling/backbones/rec_resnet_vd.py index 6837ea0fb2da3347fd8e115f859224e2a61fd578..0187deb96f111a2c2b545c7be42dba48c7352e17 100644 --- a/ppocr/modeling/backbones/rec_resnet_vd.py +++ b/ppocr/modeling/backbones/rec_resnet_vd.py @@ -249,7 +249,7 @@ class ResNet(nn.Layer): name=conv_name)) shortcut = True self.block_list.append(bottleneck_block) - self.out_channels = num_filters[block] + self.out_channels = num_filters[block] * 4 else: for block in range(len(depth)): shortcut = False diff --git a/ppocr/modeling/heads/self_attention.py b/ppocr/modeling/heads/self_attention.py index 51d5198f558dcb7e0351f04b3a884b71707104d4..6c27fdbe434166e9277cc8d695bce2743cbd8ec6 100644 --- a/ppocr/modeling/heads/self_attention.py +++ b/ppocr/modeling/heads/self_attention.py @@ -285,8 +285,7 @@ class PrePostProcessLayer(nn.Layer): elif cmd == "n": # add layer normalization self.functors.append( self.add_sublayer( - "layer_norm_%d" % len( - self.sublayers(include_sublayers=False)), + "layer_norm_%d" % len(self.sublayers()), paddle.nn.LayerNorm( normalized_shape=d_model, weight_attr=fluid.ParamAttr( @@ -320,9 +319,7 @@ class PrepareEncoder(nn.Layer): self.src_emb_dim = src_emb_dim self.src_max_len = src_max_len self.emb = paddle.nn.Embedding( - num_embeddings=self.src_max_len, - embedding_dim=self.src_emb_dim, - sparse=True) + num_embeddings=self.src_max_len, embedding_dim=self.src_emb_dim) self.dropout_rate = dropout_rate def forward(self, src_word, src_pos): diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index 717a09ecdad9362b7ea6e556c1136f5791d2f06c..3707cb1791482bf2ac458025677db8b7ee98f2ad 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -39,7 +39,10 @@ class TextDetector(object): self.args = args self.det_algorithm = args.det_algorithm pre_process_list = [{ - 'DetResizeForTest': None + 'DetResizeForTest': { + 'limit_side_len': args.det_limit_side_len, + 'limit_type': args.det_limit_type + } }, { 'NormalizeImage': { 'std': [0.229, 0.224, 0.225], @@ -160,7 +163,6 @@ class TextDetector(object): shape_list = np.expand_dims(shape_list, axis=0) img = img.copy() starttime = time.time() - self.input_tensor.copy_from_cpu(img) self.predictor.run() outputs = [] diff --git a/tools/infer_rec.py b/tools/infer_rec.py index 075ec261e492cf21c668364ae6119fb4903f823b..2563f5a8197ed39b1b5d44c7cfee32797e760758 100755 --- a/tools/infer_rec.py +++ b/tools/infer_rec.py @@ -73,35 +73,45 @@ def main(): global_config['infer_mode'] = True ops = create_operators(transforms, global_config) + save_res_path = config['Global'].get('save_res_path', + "./output/rec/predicts_rec.txt") + if not os.path.exists(os.path.dirname(save_res_path)): + os.makedirs(os.path.dirname(save_res_path)) + model.eval() - for file in get_image_file_list(config['Global']['infer_img']): - logger.info("infer_img: {}".format(file)) - with open(file, 'rb') as f: - img = f.read() - data = {'image': img} - batch = transform(data, ops) - if config['Architecture']['algorithm'] == "SRN": - encoder_word_pos_list = np.expand_dims(batch[1], axis=0) - gsrm_word_pos_list = np.expand_dims(batch[2], axis=0) - gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0) - gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0) - - others = [ - paddle.to_tensor(encoder_word_pos_list), - paddle.to_tensor(gsrm_word_pos_list), - paddle.to_tensor(gsrm_slf_attn_bias1_list), - paddle.to_tensor(gsrm_slf_attn_bias2_list) - ] - - images = np.expand_dims(batch[0], axis=0) - images = paddle.to_tensor(images) - if config['Architecture']['algorithm'] == "SRN": - preds = model(images, others) - else: - preds = model(images) - post_result = post_process_class(preds) - for rec_reuslt in post_result: - logger.info('\t result: {}'.format(rec_reuslt)) + + with open(save_res_path, "w") as fout: + for file in get_image_file_list(config['Global']['infer_img']): + logger.info("infer_img: {}".format(file)) + with open(file, 'rb') as f: + img = f.read() + data = {'image': img} + batch = transform(data, ops) + if config['Architecture']['algorithm'] == "SRN": + encoder_word_pos_list = np.expand_dims(batch[1], axis=0) + gsrm_word_pos_list = np.expand_dims(batch[2], axis=0) + gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0) + gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0) + + others = [ + paddle.to_tensor(encoder_word_pos_list), + paddle.to_tensor(gsrm_word_pos_list), + paddle.to_tensor(gsrm_slf_attn_bias1_list), + paddle.to_tensor(gsrm_slf_attn_bias2_list) + ] + + images = np.expand_dims(batch[0], axis=0) + images = paddle.to_tensor(images) + if config['Architecture']['algorithm'] == "SRN": + preds = model(images, others) + else: + preds = model(images) + post_result = post_process_class(preds) + for rec_reuslt in post_result: + logger.info('\t result: {}'.format(rec_reuslt)) + if len(rec_reuslt) >= 2: + fout.write(file + "\t" + rec_reuslt[0] + "\t" + str( + rec_reuslt[1]) + "\n") logger.info("success!") diff --git a/tools/program.py b/tools/program.py index c22bf18b991a8aed6d47a1ea242aa3b7bb02aacc..ad6fcbd9b5a1213e7e88ef7c82fde07ff29bcb80 100755 --- a/tools/program.py +++ b/tools/program.py @@ -18,6 +18,7 @@ from __future__ import print_function import os import sys +import platform import yaml import time import shutil @@ -196,9 +197,11 @@ def train(config, train_reader_cost = 0.0 batch_sum = 0 batch_start = time.time() + max_iter = len(train_dataloader) - 1 if platform.system( + ) == "Windows" else len(train_dataloader) for idx, batch in enumerate(train_dataloader): train_reader_cost += time.time() - batch_start - if idx >= len(train_dataloader): + if idx >= max_iter: break lr = optimizer.get_lr() images = batch[0] @@ -335,8 +338,10 @@ def eval(model, valid_dataloader, post_process_class, eval_class, total_frame = 0.0 total_time = 0.0 pbar = tqdm(total=len(valid_dataloader), desc='eval model:') + max_iter = len(valid_dataloader) - 1 if platform.system( + ) == "Windows" else len(valid_dataloader) for idx, batch in enumerate(valid_dataloader): - if idx >= len(valid_dataloader): + if idx >= max_iter: break images = batch[0] start = time.time()