From 456bcc3ffcf79c0a49b7827afcab6b2782c55013 Mon Sep 17 00:00:00 2001 From: HinGwenWoong Date: Tue, 10 May 2022 19:30:52 +0800 Subject: [PATCH 01/24] Fix re-reco will clear all key message --- PPOCRLabel/PPOCRLabel.py | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/PPOCRLabel/PPOCRLabel.py b/PPOCRLabel/PPOCRLabel.py index 5c89e6f89..109d4c915 100644 --- a/PPOCRLabel/PPOCRLabel.py +++ b/PPOCRLabel/PPOCRLabel.py @@ -1149,7 +1149,10 @@ class MainWindow(QMainWindow): for box in self.result_dic: trans_dic = {"label": box[1][0], "points": box[0], "difficult": False} if self.kie_mode: - trans_dic.update({"key_cls": "None"}) + if len(box) == 3: + trans_dic.update({"key_cls": box[2]}) + else: + trans_dic.update({"key_cls": "None"}) if trans_dic["label"] == "" and mode == 'Auto': continue shapes.append(trans_dic) @@ -2047,6 +2050,7 @@ class MainWindow(QMainWindow): rec_flag = 0 for shape in self.canvas.shapes: box = [[int(p.x()), int(p.y())] for p in shape.points] + kie_cls = shape.key_cls if len(box) > 4: box = self.gen_quad_from_poly(np.array(box)) @@ -2062,17 +2066,27 @@ class MainWindow(QMainWindow): if shape.line_color == DEFAULT_LOCK_COLOR: shape.label = result[0][0] result.insert(0, box) + if self.kie_mode: + result.append(kie_cls) self.result_dic_locked.append(result) else: result.insert(0, box) + if self.kie_mode: + result.append(kie_cls) self.result_dic.append(result) else: print('Can not recognise the box') if shape.line_color == DEFAULT_LOCK_COLOR: shape.label = result[0][0] - self.result_dic_locked.append([box, (self.noLabelText, 0)]) + if self.kie_mode: + self.result_dic_locked.append([box, (self.noLabelText, 0), kie_cls]) + else: + self.result_dic_locked.append([box, (self.noLabelText, 0)]) else: - self.result_dic.append([box, (self.noLabelText, 0)]) + if self.kie_mode: + self.result_dic.append([box, (self.noLabelText, 0), kie_cls]) + else: + self.result_dic.append([box, (self.noLabelText, 0)]) try: if self.noLabelText == shape.label or result[1][0] == shape.label: print('label no change') From 09187228d6e5f046182310f3adb8122ffac72900 Mon Sep 17 00:00:00 2001 From: andyjpaddle <87074272+andyjpaddle@users.noreply.github.com> Date: Wed, 11 May 2022 16:14:49 +0800 Subject: [PATCH 02/24] [CP] update doc for v3 det quant (#6250) * update cpp serving for v3 * update doc for v3 det quant * update doc for v3 infer --- deploy/slim/quantization/README.md | 4 ++-- deploy/slim/quantization/README_en.md | 4 ++-- doc/doc_ch/inference_ppocr.md | 12 ++++++------ doc/doc_en/inference_ppocr_en.md | 11 +++++------ 4 files changed, 15 insertions(+), 16 deletions(-) diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md index 894a29be0..4c1d784b9 100644 --- a/deploy/slim/quantization/README.md +++ b/deploy/slim/quantization/README.md @@ -47,9 +47,9 @@ python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3 ``` # 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 -tar xf https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar +tar xf ch_PP-OCRv3_det_distill_train.tar -python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3_det/ch_PP-OCRv3_det_cml.yml -o Global.pretrained_model='./ch_PP-OCRv3_det_distill_train/best_accuracy' Global.save_model_dir=./output/quant_model_distill/ +python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.pretrained_model='./ch_PP-OCRv3_det_distill_train/best_accuracy' Global.save_model_dir=./output/quant_model_distill/ ``` 如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。 diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md index ea77cb381..33b2c4784 100644 --- a/deploy/slim/quantization/README_en.md +++ b/deploy/slim/quantization/README_en.md @@ -54,9 +54,9 @@ Model distillation and model quantization can be used at the same time, taking t ``` # download provided model wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar -tar xf https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar +tar xf ch_PP-OCRv3_det_distill_train.tar -python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3_det/ch_PP-OCRv3_det_cml.yml -o Global.pretrained_model='./ch_PP-OCRv3_det_distill_train/best_accuracy' Global.save_model_dir=./output/quant_model_distill/ +python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.pretrained_model='./ch_PP-OCRv3_det_distill_train/best_accuracy' Global.save_model_dir=./output/quant_model_distill/ ``` If you want to quantify the text recognition model, you can modify the configuration file and loaded model parameters. diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 946307556..472c0003f 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -59,7 +59,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di ### 2.1 超轻量中文识别模型推理 -**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 需要添加参数`--rec_image_shape=3,48,320`，如果不使用`PP-OCRv3`的识别模型，则无需设置该参数。 +**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 如果使用其他识别模型，则需根据模型设置参数`--rec_image_shape`。此外，`PP-OCRv3`的识别模型默认使用的`rec_algorithm`为`SVTR_LCNet`，注意和原始`SVTR`的区别。 超轻量中文识别模型推理，可以执行如下命令： @@ -67,7 +67,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di # 下载超轻量中文识别模型： wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar tar xf ch_PP-OCRv3_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --rec_image_shape=3,48,320 +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" ``` ![](../imgs_words/ch/word_4.jpg) @@ -121,17 +121,17 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982] ## 4. 文本检测、方向分类和文字识别串联推理 -**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 需要添加参数`--rec_image_shape=3,48,320`，如果不使用`PP-OCRv3`的识别模型，则无需设置该参数。 +**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 如果使用其他识别模型，则需根据模型设置参数`--rec_image_shape`。此外，`PP-OCRv3`的识别模型默认使用的`rec_algorithm`为`SVTR_LCNet`，注意和原始`SVTR`的区别。 以超轻量中文OCR模型推理为例，在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。`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。可视化识别结果默认保存到 ./inference_results 文件夹里面。 ```shell # 使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true --rec_image_shape=3,48,320 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true # 不使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --rec_image_shape=3,48,320 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false # 使用多进程 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 --rec_image_shape=3,48,320 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` 执行命令后，识别结果图像如下： diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index dcfb738e7..935f92f51 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -56,7 +56,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di ### 1. Lightweight Chinese Recognition Model Inference -**Note**: The input shape used by the recognition model of `PP-OCRv3` is `3,48,320`, and the parameter `--rec_image_shape=3,48,320` needs to be added. If the recognition model of `PP-OCRv3` is not used, this parameter does not need to be set. +**Note**: The input shape used by the recognition model of `PP-OCRv3` is `3, 48, 320`. If you use other recognition models, you need to set the parameter `--rec_image_shape` according to the model. In addition, the `rec_algorithm` used by the recognition model of `PP-OCRv3` is `SVTR_LCNet` by default. Note the difference from the original `SVTR`. For lightweight Chinese recognition model inference, you can execute the following commands: @@ -120,19 +120,18 @@ After executing the command, the prediction results (classification angle and sc ## Text Detection Angle Classification and Recognition Inference Concatenation -**Note**: The input shape used by the recognition model of `PP-OCRv3` is `3,48,320`, and the parameter `--rec_image_shape=3,48,320` needs to be added. If the recognition model of `PP-OCRv3` is not used, this parameter does not need to be set. +**Note**: The input shape used by the recognition model of `PP-OCRv3` is `3, 48, 320`. If you use other recognition models, you need to set the parameter `--rec_image_shape` according to the model. In addition, the `rec_algorithm` used by the recognition model of `PP-OCRv3` is `SVTR_LCNet` by default. Note the difference from the original `SVTR`. When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default. ```shell # use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" --use_angle_cls=true --rec_image_shape=3,48,320 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true # not use use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer/" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --rec_image_shape=3,48,320 - +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false # use multi-process -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer/" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 --rec_image_shape=3,48,320 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` From 8de06d2370e81e0ee1473d17925fb2e05295a0fe Mon Sep 17 00:00:00 2001 From: DanielYang Date: Wed, 11 May 2022 16:55:13 +0800 Subject: [PATCH 03/24] Update quickstart_en.md --- doc/doc_en/quickstart_en.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md index d7aeb7773..53f431357 100644 --- a/doc/doc_en/quickstart_en.md +++ b/doc/doc_en/quickstart_en.md @@ -28,13 +28,13 @@ - If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install ```bash - python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple + python3 -m pip install paddlepaddle-gpu ``` - If you have no available GPU on your machine, please run the following command to install the CPU version ```bash - python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple + python3 -m pip install paddlepaddle ``` For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation. From 400989320b2495db733d45ff2703387b4721de89 Mon Sep 17 00:00:00 2001 From: zhoujun Date: Thu, 12 May 2022 10:38:13 +0800 Subject: [PATCH 04/24] Update README.md --- ppstructure/table/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ppstructure/table/README.md b/ppstructure/table/README.md index d21ef4aa3..b6804c6f0 100644 --- a/ppstructure/table/README.md +++ b/ppstructure/table/README.md @@ -18,7 +18,7 @@ The table recognition mainly contains three models The table recognition flow chart is as follows -![tableocr_pipeline](../../doc/table/tableocr_pipeline_en.jpg) +![tableocr_pipeline](../docs/table/tableocr_pipeline_en.jpg) 1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result. 2. The table structure and cell coordinates is predicted by RARE model. From d5f329d04566af84ff3734d8ef66f78f2b88fe89 Mon Sep 17 00:00:00 2001 From: yfzhou Date: Thu, 12 May 2022 12:01:39 +0800 Subject: [PATCH 05/24] the file has no function [self.vector_slope] MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit self.vector_slope 方法不存在，应该是漏掉了 --- ppocr/data/imaug/fce_targets.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/ppocr/data/imaug/fce_targets.py b/ppocr/data/imaug/fce_targets.py index 181848086..4d1903c0a 100644 --- a/ppocr/data/imaug/fce_targets.py +++ b/ppocr/data/imaug/fce_targets.py @@ -22,6 +22,9 @@ from numpy.fft import fft from numpy.linalg import norm import sys +def vector_slope(vec): + assert len(vec) == 2 + return abs(vec[1] / (vec[0] + 1e-8)) class FCENetTargets: """Generate the ground truth targets of FCENet: Fourier Contour Embedding @@ -233,9 +236,9 @@ class FCENetTargets: head_inds = [head_start, head_end] tail_inds = [tail_start, tail_end] else: - if self.vector_slope(points[1] - points[0]) + self.vector_slope( - points[3] - points[2]) < self.vector_slope(points[ - 2] - points[1]) + self.vector_slope(points[0] - points[ + if vector_slope(points[1] - points[0]) + vector_slope( + points[3] - points[2]) < vector_slope(points[ + 2] - points[1]) + vector_slope(points[0] - points[ 3]): horizontal_edge_inds = [[0, 1], [2, 3]] vertical_edge_inds = [[3, 0], [1, 2]] From 6024fc9f39a9766eb08e92c52ca17801cebf4014 Mon Sep 17 00:00:00 2001 From: Double_V Date: Thu, 12 May 2022 16:14:08 +0800 Subject: [PATCH 06/24] Update label_ops.py --- ppocr/data/imaug/label_ops.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index c9bc2e772..8b017b321 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -438,7 +438,12 @@ class KieLabelEncode(object): texts.append(ann['transcription']) text_ind = [self.dict[c] for c in text if c in self.dict] text_inds.append(text_ind) - labels.append(ann['label']) + if 'label' in ann.keys(): + labels.append(ann['label']) + elif 'key_cls' in ann.keys(): + labels.append(ann['key_cls']) + else: + raise ValueError("Cannot found 'key_cls' in ann.keys(), please check your training annotation.") edges.append(ann.get('edge', 0)) ann_infos = dict( image=data['image'], From 791712e274fa6410b91fcb19efaf8e9db6d89862 Mon Sep 17 00:00:00 2001 From: xiaoting <31891223+tink2123@users.noreply.github.com> Date: Thu, 12 May 2022 16:40:11 +0800 Subject: [PATCH 07/24] update c++ img and qps result (#6267) * update c++ img and qps result * update c++ img and qps result --- deploy/pdserving/README.md | 68 +++++++++++++++--------------- deploy/pdserving/README_CN.md | 67 +++++++++++++++-------------- deploy/pdserving/imgs/c++_qps.png | Bin 0 -> 325486 bytes 3 files changed, 66 insertions(+), 69 deletions(-) create mode 100644 deploy/pdserving/imgs/c++_qps.png diff --git a/deploy/pdserving/README.md b/deploy/pdserving/README.md index 55e03c4c2..6989502c8 100644 --- a/deploy/pdserving/README.md +++ b/deploy/pdserving/README.md @@ -163,43 +163,41 @@ The recognition model is the same. The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file. - Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 23: + Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 62.0: ``` - - 2021-05-13 03:42:36,895 ==================== TRACER ====================== - 2021-05-13 03:42:36,975 Op(rec): - 2021-05-13 03:42:36,976 in[14.472382882882883 ms] - 2021-05-13 03:42:36,976 prep[9.556855855855856 ms] - 2021-05-13 03:42:36,976 midp[59.921905405405404 ms] - 2021-05-13 03:42:36,976 postp[15.345945945945946 ms] - 2021-05-13 03:42:36,976 out[1.9921216216216215 ms] - 2021-05-13 03:42:36,976 idle[0.16254943864471572] - 2021-05-13 03:42:36,976 Op(det): - 2021-05-13 03:42:36,976 in[315.4468035714286 ms] - 2021-05-13 03:42:36,976 prep[69.5980625 ms] - 2021-05-13 03:42:36,976 midp[18.989535714285715 ms] - 2021-05-13 03:42:36,976 postp[18.857803571428573 ms] - 2021-05-13 03:42:36,977 out[3.1337544642857145 ms] - 2021-05-13 03:42:36,977 idle[0.7477961159203756] - 2021-05-13 03:42:36,977 DAGExecutor: - 2021-05-13 03:42:36,977 Query count[224] - 2021-05-13 03:42:36,977 QPS[22.4 q/s] - 2021-05-13 03:42:36,977 Succ[0.9910714285714286] - 2021-05-13 03:42:36,977 Error req[169, 170] - 2021-05-13 03:42:36,977 Latency: - 2021-05-13 03:42:36,977 ave[535.1678348214285 ms] - 2021-05-13 03:42:36,977 .50[172.651 ms] - 2021-05-13 03:42:36,977 .60[187.904 ms] - 2021-05-13 03:42:36,977 .70[245.675 ms] - 2021-05-13 03:42:36,977 .80[526.684 ms] - 2021-05-13 03:42:36,977 .90[854.596 ms] - 2021-05-13 03:42:36,977 .95[1722.728 ms] - 2021-05-13 03:42:36,977 .99[3990.292 ms] - 2021-05-13 03:42:36,978 Channel (server worker num[10]): - 2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0] - 2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0] - 2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0] + 2022-05-12 03:56:46,461 ==================== TRACER ====================== + 2022-05-12 03:56:46,860 Op(det): + 2022-05-12 03:56:46,860 in[80.32286641221374 ms] + 2022-05-12 03:56:46,860 prep[74.27364885496183 ms] + 2022-05-12 03:56:46,860 midp[33.41587786259542 ms] + 2022-05-12 03:56:46,860 postp[20.935980916030534 ms] + 2022-05-12 03:56:46,860 out[1.551145038167939 ms] + 2022-05-12 03:56:46,860 idle[0.3889510617728378] + 2022-05-12 03:56:46,860 Op(rec): + 2022-05-12 03:56:46,860 in[15.46498846153846 ms] + 2022-05-12 03:56:46,861 prep[22.565715384615384 ms] + 2022-05-12 03:56:46,861 midp[91.42518076923076 ms] + 2022-05-12 03:56:46,861 postp[11.678453846153847 ms] + 2022-05-12 03:56:46,861 out[1.1200576923076924 ms] + 2022-05-12 03:56:46,861 idle[0.11658723106110291] + 2022-05-12 03:56:46,862 DAGExecutor: + 2022-05-12 03:56:46,862 Query count[620] + 2022-05-12 03:56:46,862 QPS[62.0 q/s] + 2022-05-12 03:56:46,862 Succ[0.4193548387096774] + 2022-05-12 03:56:46,862 Latency: + 2022-05-12 03:56:46,863 ave[165.54603709677417 ms] + 2022-05-12 03:56:46,863 .50[77.863 ms] + 2022-05-12 03:56:46,863 .60[158.414 ms] + 2022-05-12 03:56:46,863 .70[237.28 ms] + 2022-05-12 03:56:46,863 .80[316.022 ms] + 2022-05-12 03:56:46,863 .90[424.416 ms] + 2022-05-12 03:56:46,863 .95[515.566 ms] + 2022-05-12 03:56:46,863 .99[762.256 ms] + 2022-05-12 03:56:46,863 Channel (server worker num[10]): + 2022-05-12 03:56:46,864 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0] + 2022-05-12 03:56:46,864 chl1(In: ['det'], Out: ['rec']) size[2/0] + 2022-05-12 03:56:46,865 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0] ``` diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md index 0891611db..761d63fa2 100644 --- a/deploy/pdserving/README_CN.md +++ b/deploy/pdserving/README_CN.md @@ -162,42 +162,41 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \ 预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。 - 在200张真实图片上测试，把检测长边限制为960。T4 GPU 上 QPS 均值可达到23左右： + 在200张真实图片上测试，把检测长边限制为960。T4 GPU 上 QPS 均值可达到62左右： ``` - 2021-05-13 03:42:36,895 ==================== TRACER ====================== - 2021-05-13 03:42:36,975 Op(rec): - 2021-05-13 03:42:36,976 in[14.472382882882883 ms] - 2021-05-13 03:42:36,976 prep[9.556855855855856 ms] - 2021-05-13 03:42:36,976 midp[59.921905405405404 ms] - 2021-05-13 03:42:36,976 postp[15.345945945945946 ms] - 2021-05-13 03:42:36,976 out[1.9921216216216215 ms] - 2021-05-13 03:42:36,976 idle[0.16254943864471572] - 2021-05-13 03:42:36,976 Op(det): - 2021-05-13 03:42:36,976 in[315.4468035714286 ms] - 2021-05-13 03:42:36,976 prep[69.5980625 ms] - 2021-05-13 03:42:36,976 midp[18.989535714285715 ms] - 2021-05-13 03:42:36,976 postp[18.857803571428573 ms] - 2021-05-13 03:42:36,977 out[3.1337544642857145 ms] - 2021-05-13 03:42:36,977 idle[0.7477961159203756] - 2021-05-13 03:42:36,977 DAGExecutor: - 2021-05-13 03:42:36,977 Query count[224] - 2021-05-13 03:42:36,977 QPS[22.4 q/s] - 2021-05-13 03:42:36,977 Succ[0.9910714285714286] - 2021-05-13 03:42:36,977 Error req[169, 170] - 2021-05-13 03:42:36,977 Latency: - 2021-05-13 03:42:36,977 ave[535.1678348214285 ms] - 2021-05-13 03:42:36,977 .50[172.651 ms] - 2021-05-13 03:42:36,977 .60[187.904 ms] - 2021-05-13 03:42:36,977 .70[245.675 ms] - 2021-05-13 03:42:36,977 .80[526.684 ms] - 2021-05-13 03:42:36,977 .90[854.596 ms] - 2021-05-13 03:42:36,977 .95[1722.728 ms] - 2021-05-13 03:42:36,977 .99[3990.292 ms] - 2021-05-13 03:42:36,978 Channel (server worker num[10]): - 2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0] - 2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0] - 2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0] + 2022-05-12 03:56:46,461 ==================== TRACER ====================== + 2022-05-12 03:56:46,860 Op(det): + 2022-05-12 03:56:46,860 in[80.32286641221374 ms] + 2022-05-12 03:56:46,860 prep[74.27364885496183 ms] + 2022-05-12 03:56:46,860 midp[33.41587786259542 ms] + 2022-05-12 03:56:46,860 postp[20.935980916030534 ms] + 2022-05-12 03:56:46,860 out[1.551145038167939 ms] + 2022-05-12 03:56:46,860 idle[0.3889510617728378] + 2022-05-12 03:56:46,860 Op(rec): + 2022-05-12 03:56:46,860 in[15.46498846153846 ms] + 2022-05-12 03:56:46,861 prep[22.565715384615384 ms] + 2022-05-12 03:56:46,861 midp[91.42518076923076 ms] + 2022-05-12 03:56:46,861 postp[11.678453846153847 ms] + 2022-05-12 03:56:46,861 out[1.1200576923076924 ms] + 2022-05-12 03:56:46,861 idle[0.11658723106110291] + 2022-05-12 03:56:46,862 DAGExecutor: + 2022-05-12 03:56:46,862 Query count[620] + 2022-05-12 03:56:46,862 QPS[62.0 q/s] + 2022-05-12 03:56:46,862 Succ[0.4193548387096774] + 2022-05-12 03:56:46,862 Latency: + 2022-05-12 03:56:46,863 ave[165.54603709677417 ms] + 2022-05-12 03:56:46,863 .50[77.863 ms] + 2022-05-12 03:56:46,863 .60[158.414 ms] + 2022-05-12 03:56:46,863 .70[237.28 ms] + 2022-05-12 03:56:46,863 .80[316.022 ms] + 2022-05-12 03:56:46,863 .90[424.416 ms] + 2022-05-12 03:56:46,863 .95[515.566 ms] + 2022-05-12 03:56:46,863 .99[762.256 ms] + 2022-05-12 03:56:46,863 Channel (server worker num[10]): + 2022-05-12 03:56:46,864 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0] + 2022-05-12 03:56:46,864 chl1(In: ['det'], Out: ['rec']) size[2/0] + 2022-05-12 03:56:46,865 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0] ``` diff --git a/deploy/pdserving/imgs/c++_qps.png b/deploy/pdserving/imgs/c++_qps.png new file mode 100644 index 0000000000000000000000000000000000000000..dc406acd624ea3f5fd51a56ae7c6d299c8211b48 GIT binary patch literal 325486 zcmd42Wmr^S^fnAqij>kJ-6<^+gCZrJ(nxnp$EcKam&90vG(+bg9RdnSOAlQ`&M@_k zzyI?*AD(aTr}v$8o$KuDoPGA$bI#gp-D}quz$E^=oT;4)bxx9fP)Es3|+1aksg8PmU2aO+VJRmhj-<;hVEBcoErvrwjNgAtK3 z^2x}MD(^$EMyoc_bNZs46%ptKtgWgozu{dxOewx~=&5Tjr{Xzp%Pxk)DNbKI!;Ve5G<$y@EM3#d7stE`G*m2x@}+7s>15sF_n#GxdPK|``ijhGF9LoWaXlI}g|x{YDsG#K$m z7-6TTQ9h&NCf8GTB+NR(X&;QBlpY~mF81FN-O}Yt8GnmcTBt;cJ*YgSzi+9S2kqcpf3jYjOISN8Z*l(>u@4n>o_y8l!)b4u%1vNgM5%#cS!ZI)hg z(*CZtT;5kk%l^EZi7K#<=?=HQ6vpR+YF8B2hjj4XX!>`&*Ryvt4W%5tMu-!O;2O>3 z&L;djYB$RxIewLJFZM>&>9g0(Kw%LhR=VXk29e|?ni!Lep##1*7u%Y z_&>}i52|nX4}QN+Qy)Edpq};H++RdxG#rGS-Jv3CV<)VC`>=)V@dHN!O)VE|K2+(+ zJ%R{!5_4rjE2)o|2cwaSh9M~=vFl5S$*I0KyF=p 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zH=C0Dcq6#>ula)iIJ}W@&+)Lqoiil)vb(v!mv@n#X%Daqd=0biWhYWo zA6Bouy>uzC?CO!|_yonnhWc&Mt(?2G?hkP`?LD3_6ug)IIw`1>0BT?wrj<(%!d%-<#~LL5IyvT01}j%1w;|#$6}ph*WCHPI=^2)@X-Fy1;3`8 z!+LovhScbRAFbOc9!Zl Date: Fri, 13 May 2022 14:58:52 +0800 Subject: [PATCH 08/24] Update PPOCRLabel Update PPOCRLabel --- PPOCRLabel/PPOCRLabel.py | 16 +- PPOCRLabel/libs/resources.py | 9564 +++++++++-------- PPOCRLabel/libs/utils.py | 4 +- .../resources/strings/strings-en.properties | 2 +- .../strings/strings-zh-CN.properties | 2 +- 5 files changed, 4807 insertions(+), 4781 deletions(-) diff --git a/PPOCRLabel/PPOCRLabel.py b/PPOCRLabel/PPOCRLabel.py index 109d4c915..ce3d66f07 100644 --- a/PPOCRLabel/PPOCRLabel.py +++ b/PPOCRLabel/PPOCRLabel.py @@ -2336,13 +2336,6 @@ class MainWindow(QMainWindow): else: labeldict[file] = [] - # if len(labeldict) != len(csv_paths): - # msg = 'ERROR, box label and excel label are not in the same number\n' + \ - # 'box label: ' + str(len(labeldict)) + '\n' + \ - # 'excel label: ' + str(len(csv_paths)) + '\n' + \ - # 'Please check the label.txt and tableRec_excel_output\n' - # QMessageBox.information(self, "Information", msg) - # return train_split, val_split, test_split = partitionDialog.getDataPartition() # check validate if train_split + val_split + test_split > 100: @@ -2365,14 +2358,8 @@ class MainWindow(QMainWindow): filename, _ = os.path.splitext(os.path.basename(image_path)) csv_path = os.path.join(TableRec_excel_dir, filename + '.xlsx') if not os.path.exists(csv_path): - msg = 'ERROR, Can not find ' + csv_path - QMessageBox.information(self, "Information", msg) - return + continue - # read xlsx file, convert to HTML - # xd = pd.ExcelFile(csv_path) - # df = xd.parse() - # structure = df.to_html(index = False) excel = xlrd.open_workbook(csv_path) sheet0 = excel.sheet_by_index(0) # only sheet 0 merged_cells = sheet0.merged_cells # (0,1,1,3) start row, end row, start col, end col @@ -2384,7 +2371,6 @@ class MainWindow(QMainWindow): token_list = convert_token(html_list) - # load box annotations cells = [] for anno in labeldict[image_path]: diff --git a/PPOCRLabel/libs/resources.py b/PPOCRLabel/libs/resources.py index 476eea2dd..e2dc0383a 100644 --- a/PPOCRLabel/libs/resources.py +++ b/PPOCRLabel/libs/resources.py @@ -9,857 +9,443 @@ from PyQt5 import QtCore qt_resource_data = b"\ -\x00\x00\x10\x78\ -\x89\ -\x50\x4e\x47\x0d\x0a\x1a\x0a\x00\x00\x00\x0d\x49\x48\x44\x52\x00\ -\x00\x00\xc8\x00\x00\x00\xc8\x08\x06\x00\x00\x00\xad\x58\xae\x9e\ -\x00\x00\x10\x3f\x49\x44\x41\x54\x78\x5e\xed\x9d\x0b\xac\x1e\x45\ -\x15\xc7\xcf\xd9\x7b\xdb\x42\x20\x42\x89\x06\x15\x09\x04\x08\x12\ -\xde\xa2\x3c\x0c\x82\x10\x04\xc4\x08\x04\x63\x45\xe5\x91\x02\x16\ -\xa9\xb6\x77\x67\xbf\xde\x46\x5e\x89\x25\x18\x21\xb5\x37\x3b\x7b\ -\xfb\x00\x0a\x3e\x80\x04\x4c\x4d\x90\x48\x22\x0a\x28\x68\x10\x91\ -\x00\x51\x8b\x12\x83\x84\x87\x8f\x08\x11\x83\x3c\xe5\xb6\x77\xc7\ -\x0c\x7e\x90\x4b\xbd\x8f\xdd\xb3\x33\xfb\xed\xec\xfe\x27\x21\x97\ 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+\x00\x6f\x00\x6c\x00\x6f\x00\x72\x00\x5f\x00\x6c\x00\x69\x00\x6e\x00\x65\ +\x00\x07\ +\x0a\xb9\x04\x03\ +\x00\x73\ +\x00\x74\x00\x72\x00\x69\x00\x6e\x00\x67\x00\x73\ +\x00\x04\ +\x00\x06\xd0\x25\ +\x00\x66\ +\x00\x69\x00\x6c\x00\x65\ +\x00\x04\ +\x00\x08\x16\x5d\ +\x00\x7a\ +\x00\x6f\x00\x6f\x00\x6d\ +\x00\x06\ +\x07\xcc\x8f\xd9\ +\x00\x76\ +\x00\x65\x00\x72\x00\x69\x00\x66\x00\x79\ +\x00\x04\ +\x00\x06\xbb\x04\ +\x00\x65\ +\x00\x64\x00\x69\x00\x74\ \x00\x04\ \x00\x07\x8c\x04\ \x00\x71\ @@ -11407,254 +11567,134 @@ qt_resource_name = b"\ \x06\x60\x56\xe4\ \x00\x7a\ \x00\x6f\x00\x6f\x00\x6d\x00\x2d\x00\x6f\x00\x75\x00\x74\ -\x00\x06\ -\x06\xac\x2c\xa5\ -\x00\x64\ -\x00\x65\x00\x6c\x00\x65\x00\x74\x00\x65\ -\x00\x04\ -\x00\x08\x16\x5d\ -\x00\x7a\ -\x00\x6f\x00\x6f\x00\x6d\ -\x00\x04\ -\x00\x06\xa6\x79\ -\x00\x63\ -\x00\x6f\x00\x70\x00\x79\ -\x00\x04\ -\x00\x06\xb6\x45\ -\x00\x64\ -\x00\x6f\x00\x6e\x00\x65\ -\x00\x0b\ -\x0a\x5a\x28\xf4\ -\x00\x72\ 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-\x00\x6f\x00\x72\x00\x6d\x00\x61\x00\x74\x00\x5f\x00\x76\x00\x6f\x00\x63\ -\x00\x04\ -\x00\x04\x8c\xaf\ -\x00\x41\ -\x00\x75\x00\x74\x00\x6f\ -\x00\x04\ -\x00\x07\x4c\xf4\ -\x00\x6e\ -\x00\x65\x00\x78\x00\x74\ \x00\x04\ \x00\x07\x78\xc6\ \x00\x70\ \x00\x72\x00\x65\x00\x76\ -\x00\x08\ -\x0c\x9c\xab\x2c\ +\x00\x0a\ +\x08\xa9\x06\x94\ \x00\x72\ -\x00\x65\x00\x73\x00\x65\x00\x74\x00\x61\x00\x6c\x00\x6c\ -\x00\x03\ -\x00\x00\x74\xc7\ -\x00\x6e\ -\x00\x65\x00\x77\ +\x00\x6f\x00\x74\x00\x61\x00\x74\x00\x65\x00\x4c\x00\x65\x00\x66\x00\x74\ \x00\x04\ -\x00\x06\xef\xa5\ -\x00\x68\ -\x00\x69\x00\x64\x00\x65\ +\x00\x07\x98\xc5\ +\x00\x73\ +\x00\x61\x00\x76\x00\x65\ " qt_resource_struct_v1 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x24\x00\x00\x00\x01\ -\x00\x00\x01\xb0\x00\x00\x00\x00\x00\x01\x00\x00\xa3\x21\ -\x00\x00\x02\x7a\x00\x00\x00\x00\x00\x01\x00\x02\xb9\x0f\ -\x00\x00\x02\x3a\x00\x00\x00\x00\x00\x01\x00\x01\xba\xda\ -\x00\x00\x00\x5e\x00\x00\x00\x00\x00\x01\x00\x00\x29\x04\ 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+\x00\x00\x02\x6c\x00\x00\x00\x00\x00\x01\x00\x02\xaf\xb8\ \x00\x00\x01\x7b\xbd\x6c\xe2\x2f\ -\x00\x00\x01\xfe\x00\x00\x00\x00\x00\x01\x00\x01\xa5\xa4\ -\x00\x00\x01\x7d\xbc\xe6\x5d\x3b\ -\x00\x00\x02\x64\x00\x00\x00\x00\x00\x01\x00\x02\xaa\xd6\ +\x00\x00\x00\xa6\x00\x00\x00\x00\x00\x01\x00\x00\x38\x1c\ +\x00\x00\x01\x7a\x50\x6d\xd4\x88\ +\x00\x00\x00\x3e\x00\x00\x00\x00\x00\x01\x00\x00\x1a\xe2\ +\x00\x00\x01\x7b\xbd\x6c\xe2\x2f\ +\x00\x00\x01\xea\x00\x00\x00\x00\x00\x01\x00\x02\x07\x91\ +\x00\x00\x01\x80\xbc\x2a\x72\x46\ +\x00\x00\x00\xc8\x00\x00\x00\x00\x00\x01\x00\x00\x4b\xb5\ \x00\x00\x01\x7a\x50\x6d\xd4\x88\ " diff --git a/PPOCRLabel/libs/utils.py b/PPOCRLabel/libs/utils.py index bf5470048..e397f139e 100644 --- a/PPOCRLabel/libs/utils.py +++ b/PPOCRLabel/libs/utils.py @@ -221,10 +221,10 @@ def convert_token(html_list): token_list.append("", ""]) token_list.append("") token_list.append("") diff --git a/PPOCRLabel/resources/strings/strings-en.properties b/PPOCRLabel/resources/strings/strings-en.properties index 7ba9af4c3..0b112c464 100644 --- a/PPOCRLabel/resources/strings/strings-en.properties +++ b/PPOCRLabel/resources/strings/strings-en.properties @@ -112,4 +112,4 @@ keyDialogTip=Enter object label keyChange=Change Box Key TableRecognition=Table Recognition cellreRecognition=Cell Re-Recognition -exportJSON=export JSON(PubTabNet) +exportJSON=Export Excel Label(PubTabNet) diff --git a/PPOCRLabel/resources/strings/strings-zh-CN.properties b/PPOCRLabel/resources/strings/strings-zh-CN.properties index 308974ef2..184247e85 100644 --- a/PPOCRLabel/resources/strings/strings-zh-CN.properties +++ b/PPOCRLabel/resources/strings/strings-zh-CN.properties @@ -84,7 +84,7 @@ mhelp=帮助 iconList=缩略图 detectionBoxposition=检测框位置 recognitionResult=识别结果 -creatPolygon=多边形标注 +creatPolygon=多点标注 drawSquares=正方形标注 rotateLeft=图片左旋转90度 rotateRight=图片右旋转90度 From 7db347787eb858115a39a1423ea9d209b32e261b Mon Sep 17 00:00:00 2001 From: Evezerest <50011306+Evezerest@users.noreply.github.com> Date: Fri, 13 May 2022 19:57:49 +0800 Subject: [PATCH 09/24] Update README.md --- PPOCRLabel/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PPOCRLabel/README.md b/PPOCRLabel/README.md index 2ae98481d..250d1fc0c 100644 --- a/PPOCRLabel/README.md +++ b/PPOCRLabel/README.md @@ -206,7 +206,7 @@ the table and pop up Excel at the same time. - Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languages​include French, German, Korean, and Japanese. For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating) -- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/PPOCRLabel/PPOCRLabel.py#L86) : +- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/PPOCRLabel/PPOCRLabel.py#L97) : add parameter `det_model_dir` in `self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang) ` From 05d7663511579290406f7edff9dec598e1a0e965 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Mon, 16 May 2022 19:27:38 +0800 Subject: [PATCH 10/24] Update knowledge_distillation.md --- doc/doc_ch/knowledge_distillation.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md index 76ab5e62e..688e05f41 100644 --- a/doc/doc_ch/knowledge_distillation.md +++ b/doc/doc_ch/knowledge_distillation.md @@ -293,8 +293,8 @@ Loss: 以上述配置为例，最终蒸馏训练的损失函数包含下面5个部分。 -- `Student`和`Teacher`最终输出(`head_out`)的CTC分支与gt的CTC loss，权重为1。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。 -- `Student`和`Teacher`最终输出(`head_out`)的SAR分支与gt的SAR loss，权重为1.0。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。 +- `Student`和`Teacher`最终输出(`head_out`)的CTC分支与gt的CTC loss，权重为1。在这里因为2个子网络都需要更新参数，因此2者都需要计算与gt的loss。 +- `Student`和`Teacher`最终输出(`head_out`)的SAR分支与gt的SAR loss，权重为1.0。在这里因为2个子网络都需要更新参数，因此2者都需要计算与gt的loss。 - `Student`和`Teacher`最终输出(`head_out`)的CTC分支之间的DML loss，权重为1。 - `Student`和`Teacher`最终输出(`head_out`)的SAR分支之间的DML loss，权重为0.5。 - `Student`和`Teacher`的骨干网络输出(`backbone_out`)之间的l2 loss，权重为1。 @@ -374,7 +374,7 @@ paddle.save(s_params, "ch_PP-OCRv3_rec_train/student.pdparams") ### 2.2 检测配置文件解析 -检测模型蒸馏的配置文件在PaddleOCR/configs/det/ch_PP-OCRv3/目录下，包含两个个蒸馏配置文件： +检测模型蒸馏的配置文件在PaddleOCR/configs/det/ch_PP-OCRv3/目录下，包含两个蒸馏配置文件： - ch_PP-OCRv3_det_cml.yml，采用cml蒸馏，采用一个大模型蒸馏两个小模型，且两个小模型互相学习的方法 - ch_PP-OCRv3_det_dml.yml，采用DML的蒸馏，两个Student模型互蒸馏的方法 @@ -383,7 +383,7 @@ paddle.save(s_params, "ch_PP-OCRv3_rec_train/student.pdparams") 知识蒸馏任务中，模型结构配置如下所示： -``` +```yaml Architecture: name: DistillationModel # 结构名称，蒸馏任务中，为DistillationModel，用于构建对应的结构 algorithm: Distillation # 算法名称 @@ -428,7 +428,7 @@ Architecture:  下面介绍[ch_PP-OCRv3_det_cml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)的配置文件参数： -``` +```yaml Architecture: name: DistillationModel algorithm: Distillation From 4e5824688d66657257c860275044947999297d0e Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Mon, 16 May 2022 19:28:29 +0800 Subject: [PATCH 11/24] Update knowledge_distillation.md --- doc/doc_ch/knowledge_distillation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md index 688e05f41..bdb66b73f 100644 --- a/doc/doc_ch/knowledge_distillation.md +++ b/doc/doc_ch/knowledge_distillation.md @@ -425,7 +425,7 @@ Architecture: ``` 如果是采用DML，即两个小模型互相学习的方法，上述配置文件里的Teacher网络结构需要设置为Student模型一样的配置，具体参考配置文件[ch_PP-OCRv3_det_dml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml)。 - + 下面介绍[ch_PP-OCRv3_det_cml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)的配置文件参数： ```yaml From 40db742e0299cbfa886d6c18fcfc8c12d8ca60cf Mon Sep 17 00:00:00 2001 From: zhoujun Date: Tue, 17 May 2022 12:43:00 +0800 Subject: [PATCH 12/24] add fce doc, test=document (#6304) --- doc/doc_en/algorithm_overview_en.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index 18c9cd7d5..383cbe39b 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -41,6 +41,12 @@ On Total-Text dataset, the text detection result is as follows: | --- | --- | --- | --- | --- | --- | |SAST|ResNet50_vd|89.63%|78.44%|83.66%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)| +On CTW1500 dataset, the text detection result is as follows: + +|Model|Backbone|Precision|Recall|Hmean| Download link| +| --- | --- | --- | --- | --- |---| +|FCE|ResNet50_dcn|88.39%|82.18%|85.27%| [trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) | + **Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from: * [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi). * [Google Drive](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing) From 5cab4c0c33c3b8a9102c5f29be120872e809e203 Mon Sep 17 00:00:00 2001 From: lgcy <865562216@qq.com> Date: Tue, 17 May 2022 20:47:45 +0800 Subject: [PATCH 13/24] update lmdb_dateset for ppocrv3 rec MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 对lmdb_dataset适配ppocrv3的RecConAug数据增强 --- ppocr/data/lmdb_dataset.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/ppocr/data/lmdb_dataset.py b/ppocr/data/lmdb_dataset.py index e1b49809d..0c1ea9465 100644 --- a/ppocr/data/lmdb_dataset.py +++ b/ppocr/data/lmdb_dataset.py @@ -88,6 +88,29 @@ class LMDBDataSet(Dataset): if imgori is None: return None return imgori + + def get_ext_data(self): + ext_data_num = 0 + for op in self.ops: + if hasattr(op, 'ext_data_num'): + ext_data_num = getattr(op, 'ext_data_num') + break + load_data_ops = self.ops[:self.ext_op_transform_idx] + ext_data = [] + + while len(ext_data) < ext_data_num: + lmdb_idx, file_idx = self.data_idx_order_list[np.random.randint(self.__len__())] + lmdb_idx = int(lmdb_idx) + file_idx = int(file_idx) + sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'], + file_idx) + if sample_info is None: + continue + img, label = sample_info + data = {'image': img, 'label': label} + outs = transform(data, load_data_ops) + ext_data.append(data) + return ext_data def get_lmdb_sample_info(self, txn, index): label_key = 'label-%09d'.encode() % index @@ -109,6 +132,7 @@ class LMDBDataSet(Dataset): return self.__getitem__(np.random.randint(self.__len__())) img, label = sample_info data = {'image': img, 'label': label} + data['ext_data'] = self.get_ext_data() outs = transform(data, self.ops) if outs is None: return self.__getitem__(np.random.randint(self.__len__())) From 0dfb536fa6eec201682d01c6e6dc256e33029db3 Mon Sep 17 00:00:00 2001 From: lgcy <865562216@qq.com> Date: Tue, 17 May 2022 21:00:39 +0800 Subject: [PATCH 14/24] Update lmdb_dataset.py --- ppocr/data/lmdb_dataset.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ppocr/data/lmdb_dataset.py b/ppocr/data/lmdb_dataset.py index 0c1ea9465..2b1ccaddc 100644 --- a/ppocr/data/lmdb_dataset.py +++ b/ppocr/data/lmdb_dataset.py @@ -37,6 +37,8 @@ class LMDBDataSet(Dataset): if self.do_shuffle: np.random.shuffle(self.data_idx_order_list) self.ops = create_operators(dataset_config['transforms'], global_config) + self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", + 2) ratio_list = dataset_config.get("ratio_list", [1.0]) self.need_reset = True in [x < 1 for x in ratio_list] From 6d08ff0a70760679dff16e8348ce3a21560e8709 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Wed, 18 May 2022 15:32:54 +0800 Subject: [PATCH 15/24] Update ppocr_introduction_en.md --- doc/doc_en/ppocr_introduction_en.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/doc_en/ppocr_introduction_en.md b/doc/doc_en/ppocr_introduction_en.md index 8fe6bc683..b13d7f9bf 100644 --- a/doc/doc_en/ppocr_introduction_en.md +++ b/doc/doc_en/ppocr_introduction_en.md @@ -38,7 +38,7 @@ On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detec PP-OCRv3 upgraded the detection model and recognition model in 9 aspects based on PP-OCRv2: - PP-OCRv3 detector upgrades the CML(Collaborative Mutual Learning) text detection strategy proposed in PP-OCRv2, and further optimizes the effect of teacher model and student model respectively. In the optimization of teacher model, a pan module with large receptive field named LK-PAN is proposed and the DML distillation strategy is adopted; In the optimization of student model, a FPN module with residual attention mechanism named RSE-FPN is proposed. -- PP-OCRv3 recognizer is optimized based on text recognition algorithm [SVTR](https://arxiv.org/abs/2205.00159). SVTR no longer adopts RNN by introducing transformers structure, which can mine the context information of text line image more effectively, so as to improve the ability of text recognition. PP-OCRv3 adopts lightweight text recognition network SVTR_LCNet, guided training of CTC loss by attention loss, data augmentation strategy TextConAug, better pre-trained model by self-supervised TextRotNet, UDML(Unified Deep Mutual Learning), and UIM (Unlabeled Images Mining) to accelerate the model and improve the effect. +- PP-OCRv3 recognizer is optimized based on text recognition algorithm [SVTR](https://arxiv.org/abs/2205.00159). SVTR no longer adopts RNN by introducing transformers structure, which can mine the context information of text line image more effectively, so as to improve the ability of text recognition. PP-OCRv3 adopts lightweight text recognition network SVTR_LCNet, guided training of CTC by attention, data augmentation strategy TextConAug, better pre-trained model by self-supervised TextRotNet, UDML(Unified Deep Mutual Learning), and UIM (Unlabeled Images Mining) to accelerate the model and improve the effect. PP-OCRv3 pipeline is as follows: From 1bb03b4dfb730e7f1eae8917dcd84485dfe7d5f8 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Mon, 16 May 2022 16:57:13 +0800 Subject: [PATCH 16/24] add support for static training (#6297) * add support for static training * fix assert info --- ppocr/modeling/architectures/__init__.py | 20 +++++++++++++++++++- tools/train.py | 3 +++ 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/ppocr/modeling/architectures/__init__.py b/ppocr/modeling/architectures/__init__.py index e9a01cf02..3f47f64a0 100755 --- a/ppocr/modeling/architectures/__init__.py +++ b/ppocr/modeling/architectures/__init__.py @@ -15,10 +15,13 @@ import copy import importlib +from paddle.jit import to_static +from paddle.static import InputSpec + from .base_model import BaseModel from .distillation_model import DistillationModel -__all__ = ['build_model'] +__all__ = ["build_model", "apply_to_static"] def build_model(config): @@ -30,3 +33,18 @@ def build_model(config): mod = importlib.import_module(__name__) arch = getattr(mod, name)(config) return arch + + +def apply_to_static(model, config, logger): + if config["Global"].get("to_static", False) is not True: + return model + assert "image_shape" in config[ + "Global"], "image_shape must be assigned for static training mode..." + supported_list = ["DB"] + assert config["Architecture"][ + "algorithm"] in supported_list, f"algorithms that supports static training must in in {supported_list} but got {config['Architecture']['algorithm']}" + + specs = [InputSpec([None] + config["Global"]["image_shape"])] + model = to_static(model, input_spec=specs) + logger.info("Successfully to apply @to_static with specs: {}".format(specs)) + return model diff --git a/tools/train.py b/tools/train.py index 42aba548d..b7c25e342 100755 --- a/tools/train.py +++ b/tools/train.py @@ -35,6 +35,7 @@ from ppocr.postprocess import build_post_process from ppocr.metrics import build_metric from ppocr.utils.save_load import load_model from ppocr.utils.utility import set_seed +from ppocr.modeling.architectures import apply_to_static import tools.program as program dist.get_world_size() @@ -121,6 +122,8 @@ def main(config, device, logger, vdl_writer): if config['Global']['distributed']: model = paddle.DataParallel(model) + model = apply_to_static(model, config, logger) + # build loss loss_class = build_loss(config['Loss']) From f4b62551db6540bedfa8d0a19584baa5ce6ed98d Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Wed, 18 May 2022 19:03:18 +0800 Subject: [PATCH 17/24] add support for svtr static training (#6328) --- ppocr/modeling/architectures/__init__.py | 26 ++++++++++++++++++++---- ppocr/modeling/heads/rec_sar_head.py | 20 +++++++++--------- 2 files changed, 32 insertions(+), 14 deletions(-) diff --git a/ppocr/modeling/architectures/__init__.py b/ppocr/modeling/architectures/__init__.py index 3f47f64a0..1c955ef3a 100755 --- a/ppocr/modeling/architectures/__init__.py +++ b/ppocr/modeling/architectures/__init__.py @@ -40,11 +40,29 @@ def apply_to_static(model, config, logger): return model assert "image_shape" in config[ "Global"], "image_shape must be assigned for static training mode..." - supported_list = ["DB"] - assert config["Architecture"][ - "algorithm"] in supported_list, f"algorithms that supports static training must in in {supported_list} but got {config['Architecture']['algorithm']}" + supported_list = ["DB", "SVTR"] + if config["Architecture"]["algorithm"] in ["Distillation"]: + algo = list(config["Architecture"]["Models"].values())[0]["algorithm"] + else: + algo = config["Architecture"]["algorithm"] + assert algo in supported_list, f"algorithms that supports static training must in in {supported_list} but got {algo}" + + specs = [ + InputSpec( + [None] + config["Global"]["image_shape"], dtype='float32') + ] + + if algo == "SVTR": + specs.append([ + InputSpec( + [None, config["Global"]["max_text_length"]], + dtype='int64'), InputSpec( + [None, config["Global"]["max_text_length"]], dtype='int64'), + InputSpec( + [None], dtype='int64'), InputSpec( + [None], dtype='float64') + ]) - specs = [InputSpec([None] + config["Global"]["image_shape"])] model = to_static(model, input_spec=specs) logger.info("Successfully to apply @to_static with specs: {}".format(specs)) return model diff --git a/ppocr/modeling/heads/rec_sar_head.py b/ppocr/modeling/heads/rec_sar_head.py index 0e6b34404..5e64cae85 100644 --- a/ppocr/modeling/heads/rec_sar_head.py +++ b/ppocr/modeling/heads/rec_sar_head.py @@ -83,7 +83,7 @@ class SAREncoder(nn.Layer): def forward(self, feat, img_metas=None): if img_metas is not None: - assert len(img_metas[0]) == feat.shape[0] + assert len(img_metas[0]) == paddle.shape(feat)[0] valid_ratios = None if img_metas is not None and self.mask: @@ -98,9 +98,10 @@ class SAREncoder(nn.Layer): if valid_ratios is not None: valid_hf = [] - T = holistic_feat.shape[1] - for i in range(len(valid_ratios)): - valid_step = min(T, math.ceil(T * valid_ratios[i])) - 1 + T = paddle.shape(holistic_feat)[1] + for i in range(paddle.shape(valid_ratios)[0]): + valid_step = paddle.minimum( + T, paddle.ceil(valid_ratios[i] * T).astype('int32')) - 1 valid_hf.append(holistic_feat[i, valid_step, :]) valid_hf = paddle.stack(valid_hf, axis=0) else: @@ -247,13 +248,14 @@ class ParallelSARDecoder(BaseDecoder): # bsz * (seq_len + 1) * h * w * attn_size attn_weight = self.conv1x1_2(attn_weight) # bsz * (seq_len + 1) * h * w * 1 - bsz, T, h, w, c = attn_weight.shape + bsz, T, h, w, c = paddle.shape(attn_weight) assert c == 1 if valid_ratios is not None: # cal mask of attention weight - for i in range(len(valid_ratios)): - valid_width = min(w, math.ceil(w * valid_ratios[i])) + for i in range(paddle.shape(valid_ratios)[0]): + valid_width = paddle.minimum( + w, paddle.ceil(valid_ratios[i] * w).astype("int32")) if valid_width < w: attn_weight[i, :, :, valid_width:, :] = float('-inf') @@ -288,7 +290,7 @@ class ParallelSARDecoder(BaseDecoder): img_metas: [label, valid_ratio] ''' if img_metas is not None: - assert len(img_metas[0]) == feat.shape[0] + assert paddle.shape(img_metas[0])[0] == paddle.shape(feat)[0] valid_ratios = None if img_metas is not None and self.mask: @@ -302,7 +304,6 @@ class ParallelSARDecoder(BaseDecoder): # bsz * (seq_len + 1) * C out_dec = self._2d_attention( in_dec, feat, out_enc, valid_ratios=valid_ratios) - # bsz * (seq_len + 1) * num_classes return out_dec[:, 1:, :] # bsz * seq_len * num_classes @@ -395,7 +396,6 @@ class SARHead(nn.Layer): if self.training: label = targets[0] # label - label = paddle.to_tensor(label, dtype='int64') final_out = self.decoder( feat, holistic_feat, label, img_metas=targets) else: From ae17dd44f13a1ebe864167b99aee92a004523adf Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Wed, 18 May 2022 11:33:15 +0000 Subject: [PATCH 18/24] fix doc link --- doc/doc_ch/models_list.md | 4 ++-- doc/doc_en/models_list_en.md | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/doc_ch/models_list.md b/doc/doc_ch/models_list.md index 318d5874f..c36cac037 100644 --- a/doc/doc_ch/models_list.md +++ b/doc/doc_ch/models_list.md @@ -22,7 +22,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训 |模型类型|模型格式|简介| |--- | --- | --- | -|推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理，[详情](./inference.md)| +|推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理，[详情](./inference_ppocr.md)| |训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息，多用于模型指标评估和恢复训练| |nb模型|\*.nb|经过飞桨Paddle-Lite工具优化后的模型，适用于移动端/IoT端等端侧部署场景（需使用飞桨Paddle Lite部署）。| @@ -114,7 +114,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训 | ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_train.tar) | | ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_train.tar) | | latin_PP-OCRv3_rec | ppocr/utils/dict/latin_dict.txt | 拉丁文识别 | [latin_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/latin_PP-OCRv3_rec.yml) |9.7M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_train.tar) | -| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/rec_arabic_lite_train.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) | +| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/arabic_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) | | cyrillic_PP-OCRv3_rec | ppocr/utils/dict/cyrillic_dict.txt | 斯拉夫字母 | [cyrillic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/cyrillic_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_train.tar) | | devanagari_PP-OCRv3_rec | ppocr/utils/dict/devanagari_dict.txt |梵文字母 | [devanagari_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/devanagari_PP-OCRv3_rec.yml) |9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_train.tar) | diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md index 8e8c1f2fe..c52f71dfe 100644 --- a/doc/doc_en/models_list_en.md +++ b/doc/doc_en/models_list_en.md @@ -20,7 +20,7 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine |model type|model format|description| |--- | --- | --- | -|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_en.md)| +|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_ppocr_en.md)| |trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.| |nb model|\*.nb| Model optimized by Paddle-Lite, which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for nb model deployment). | @@ -37,7 +37,7 @@ Relationship of the above models is as follows. |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|ch_PP-OCRv3_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/ch/ch_PP-OCRv3_det_slim_distill_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)| +|ch_PP-OCRv3_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_distill_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)| |ch_PP-OCRv3_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)| |ch_PP-OCRv2_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)| |ch_PP-OCRv2_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| @@ -75,7 +75,7 @@ Relationship of the above models is as follows. |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|ch_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/ch/ch_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) | +|ch_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) | |ch_PP-OCRv3_rec| [New] Original lightweight model, supporting Chinese, English, multilingual text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 12.4M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) | |ch_PP-OCRv2_rec_slim| Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) | |ch_PP-OCRv2_rec| Original lightweight model, supporting Chinese, English, multilingual text recognition |[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) | @@ -91,7 +91,7 @@ Relationship of the above models is as follows. |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|en_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting english, English text recognition |[en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| 3.2M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.nb) | +|en_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting english, English text recognition |[en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| 3.2M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.nb) | |en_PP-OCRv3_rec| [New] Original lightweight model, supporting english, English, multilingual text recognition |[en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| 9.6M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) | |en_number_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)| 2.7M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar) | |en_number_mobile_v2.0_rec|Original lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) | @@ -108,7 +108,7 @@ Relationship of the above models is as follows. | ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition |[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_train.tar) | | ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |Lightweight model for Tamil recognition|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_train.tar) | | latin_PP-OCRv3_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | [latin_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/latin_PP-OCRv3_rec.yml) |9.7M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_train.tar) | -| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/rec_arabic_lite_train.yml) |9.6M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) | +| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/arabic_PP-OCRv3_rec.yml) |9.6M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) | | cyrillic_PP-OCRv3_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | [cyrillic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/cyrillic_PP-OCRv3_rec.yml) |9.6M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_train.tar) | | devanagari_PP-OCRv3_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | [devanagari_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/devanagari_PP-OCRv3_rec.yml) |9.9M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_train.tar) | From a07fea8579a2690ae2481c5c36bbfdb1058fe71f Mon Sep 17 00:00:00 2001 From: tink2123 Date: Wed, 18 May 2022 20:22:14 +0800 Subject: [PATCH 19/24] add starnet doc & test=document_fix --- doc/doc_en/algorithm_rec_starnet.md | 137 ++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 doc/doc_en/algorithm_rec_starnet.md diff --git a/doc/doc_en/algorithm_rec_starnet.md b/doc/doc_en/algorithm_rec_starnet.md new file mode 100644 index 000000000..fae63f445 --- /dev/null +++ b/doc/doc_en/algorithm_rec_starnet.md @@ -0,0 +1,137 @@ +# STAR-Net + +- [1. Introduction](#1) +- [2. Environment](#2) +- [3. Model Training / Evaluation / Prediction](#3) + - [3.1 Training](#3-1) + - [3.2 Evaluation](#3-2) + - [3.3 Prediction](#3-3) +- [4. Inference and Deployment](#4) + - [4.1 Python Inference](#4-1) + - [4.2 C++ Inference](#4-2) + - [4.3 Serving](#4-3) + - [4.4 More](#4-4) +- [5. FAQ](#5) + + +## 1. Introduction + +Paper information: +> [STAR-Net: a spatial attention residue network for scene text recognition.]( http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf) +> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han. +> BMVC, pages 43.1-43.13, 2016 + +Refer to[DTRB] text Recognition Training and Evaluation Process (https://arxiv.org/abs/1904.01906). Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: + +|Models|Backbone Networks|Avg Accuracy|Configuration Files|Download Links| +| --- | --- | --- | --- | --- | +|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)| +|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[ trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)| + + + +## 2. Environment +Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code. + + +## 3. Model Training / Evaluation / Prediction + +Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example: + + +### 3.1 Training +After the data preparation is complete, the training can be started. The training command is as follows: + +```` +#Single card training (long training period, not recommended) +python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml #Multi-card training, specify the card number through the --gpus parameter +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml + ```` + + +### 3.2 Evaluation + +```` +# GPU evaluation, Global.pretrained_model is the model to be evaluated +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy + ```` + + +### 3.3 Prediction + +```` +# The configuration file used for prediction must match the training +python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png + ```` + + +## 4. Inference + + +### 4.1 Python Inference +First, convert the model saved during the STAR-Net text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example ([Model download address]( https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar ), which can be converted using the following command: + +```shell +python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet + ```` + +STAR-Net text recognition model inference, you can execute the following commands: + +```shell +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt" + ```` +![](../imgs_words_en/word_336.png) +The inference results are as follows: + + +```bash +Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073) +``` + +**Attention** Since the above model refers to the [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the ultra-lightweight Chinese recognition model training in two aspects: + +- The image resolutions used during training are different. The image resolutions used for training the above models are [3, 32, 100], while for Chinese model training, in order to ensure the recognition effect of long texts, the image resolutions used during training are [ 3, 32, 320]. The default shape parameter of the predictive inference program is the image resolution used for training Chinese, i.e. [3, 32, 320]. Therefore, when inferring the above English model here, it is necessary to set the shape of the recognized image through the parameter rec_image_shape. + +- Character list, the experiment in the DTRB paper is only for 26 lowercase English letters and 10 numbers, a total of 36 characters. All uppercase and lowercase characters are converted to lowercase characters, and characters not listed above are ignored and considered spaces. Therefore, there is no input character dictionary here, but a dictionary is generated by the following command. Therefore, the parameter rec_char_dict_path needs to be set during inference, which is specified as an English dictionary "./ppocr/utils/ic15_dict.txt". + +``` +self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" +dict_character = list(self.character_str) + + + ``` + + +### 4.2 C++ Inference + +After preparing the inference model, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial to operate. + + +### 4.3 Serving + +After preparing the inference model, refer to the [pdserving](../../deploy/pdserving/) tutorial for Serving deployment, including two modes: Python Serving and C++ Serving. + + +### 4.4 More + +The STAR-Net model also supports the following inference deployment methods: + +- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial. + + +## 5. FAQ + +## Quote + +```bibtex +@inproceedings{liu2016star, + title={STAR-Net: a spatial attention residue network for scene text recognition.}, + author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu}, + booktitle={BMVC}, + volume={2}, + pages={7}, + year={2016} +} +``` + + From f3c2bc343b49ae8e97ae695feb1ec5185b6ed24f Mon Sep 17 00:00:00 2001 From: tink2123 Date: Wed, 18 May 2022 20:23:25 +0800 Subject: [PATCH 20/24] add starnet doc & test=document_fix --- doc/doc_en/algorithm_rec_starnet.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/doc_en/algorithm_rec_starnet.md b/doc/doc_en/algorithm_rec_starnet.md index fae63f445..3147deaae 100644 --- a/doc/doc_en/algorithm_rec_starnet.md +++ b/doc/doc_en/algorithm_rec_starnet.md @@ -80,7 +80,9 @@ STAR-Net text recognition model inference, you can execute the following command ```shell python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt" ```` + ![](../imgs_words_en/word_336.png) + The inference results are as follows: From c5f56aa20c5580912ef75bcfba8b94a11414ebcc Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Wed, 18 May 2022 10:31:09 +0000 Subject: [PATCH 21/24] fix dead links --- doc/doc_ch/knowledge_distillation.md | 4 +- doc/doc_ch/multi_languages.md | 6 +- doc/doc_ch/serving_inference.md | 238 ------------------ doc/doc_ch/update.md | 6 +- doc/doc_en/algorithm_det_east_en.md | 2 +- doc/doc_en/algorithm_det_fcenet_en.md | 2 +- doc/doc_en/algorithm_det_psenet_en.md | 2 +- doc/doc_en/algorithm_e2e_pgnet_en.md | 2 +- doc/doc_en/algorithm_rec_aster_en.md | 4 +- doc/doc_en/algorithm_rec_crnn_en.md | 4 +- doc/doc_en/algorithm_rec_nrtr_en.md | 4 +- doc/doc_en/algorithm_rec_sar_en.md | 4 +- doc/doc_en/algorithm_rec_seed_en.md | 4 +- doc/doc_en/algorithm_rec_srn_en.md | 4 +- doc/doc_en/algorithm_rec_svtr_en.md | 4 +- doc/doc_en/knowledge_distillation_en.md | 4 +- doc/doc_en/multi_languages_en.md | 6 +- doc/doc_en/update_en.md | 6 +- ppstructure/vqa/README_ch.md | 4 +- .../jeston_test_train_inference_python.md | 2 +- .../docs/mac_test_train_inference_python.md | 2 +- test_tipc/docs/test_train_inference_python.md | 2 +- .../docs/win_test_train_inference_python.md | 2 +- 23 files changed, 40 insertions(+), 278 deletions(-) delete mode 100644 doc/doc_ch/serving_inference.md diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md index bdb66b73f..2adba3659 100644 --- a/doc/doc_ch/knowledge_distillation.md +++ b/doc/doc_ch/knowledge_distillation.md @@ -424,9 +424,9 @@ Architecture: ``` -如果是采用DML，即两个小模型互相学习的方法，上述配置文件里的Teacher网络结构需要设置为Student模型一样的配置，具体参考配置文件[ch_PP-OCRv3_det_dml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml)。 +如果是采用DML，即两个小模型互相学习的方法，上述配置文件里的Teacher网络结构需要设置为Student模型一样的配置，具体参考配置文件[ch_PP-OCRv3_det_dml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml)。 -下面介绍[ch_PP-OCRv3_det_cml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)的配置文件参数： +下面介绍[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)的配置文件参数： ```yaml Architecture: diff --git a/doc/doc_ch/multi_languages.md b/doc/doc_ch/multi_languages.md index 499fdd988..1f337bdf4 100644 --- a/doc/doc_ch/multi_languages.md +++ b/doc/doc_ch/multi_languages.md @@ -238,10 +238,10 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs ## 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](./inference.md) -- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md) +- [基于Python脚本预测引擎推理](./inference_ppocr.md) +- [基于C++预测引擎推理](../../deploy/cpp_infer/readme_ch.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) +- [端侧部署](../../deploy/lite/readme_ch.md) - [Benchmark](./benchmark.md) diff --git a/doc/doc_ch/serving_inference.md b/doc/doc_ch/serving_inference.md deleted file mode 100644 index 30ea7ee7c..000000000 --- a/doc/doc_ch/serving_inference.md +++ /dev/null @@ -1,238 +0,0 @@ -# 使用Paddle Serving预测推理 - -阅读本文档之前，请先阅读文档 [基于Python预测引擎推理](./inference.md) - -同本地执行预测一样，我们需要保存一份可以用于Paddle Serving的模型。 - -接下来首先介绍如何将训练的模型转换成Paddle Serving模型，然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。 - -### 一、 准备环境 -我们先安装Paddle Serving相关组件 -我们推荐用户使用GPU来做Paddle Serving的OCR服务部署 - -**CUDA版本：9.X/10.X** - -**CUDNN版本：7.X** - -**操作系统版本：Linux/Windows** - -**Python版本： 2.7/3.5/3.6/3.7** - -**Python操作指南：** - -目前Serving用于OCR的部分功能还在测试当中，因此在这里我们给出[Servnig latest package](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Latest_Packages_CN.md) -大家根据自己的环境选择需要安装的whl包即可，例如以Python 3.5为例，执行下列命令 -``` -#CPU/GPU版本选择一个 -#GPU版本服务端 -#CUDA 9 -python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py3-none-any.whl -#CUDA 10 -python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post10-py3-none-any.whl -#CPU版本服务端 -python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server-0.0.0-py3-none-any.whl -#客户端和App包使用以下链接（CPU，GPU通用） -python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp36-none-any.whl https://paddle-serving.bj.bcebos.com/whl/paddle_serving_app-0.0.0-py3-none-any.whl -``` - -## 二、训练模型转Serving模型 - -在前序文档 [基于Python预测引擎推理](./inference.md) 中，我们提供了如何把训练的checkpoint转换成Paddle模型。Paddle模型通常由一个文件夹构成，内含模型结构描述文件`model`和模型参数文件`params`。Serving模型由两个文件夹构成，用于存放客户端和服务端的配置。 - -我们以`ch_rec_r34_vd_crnn`模型作为例子，下载链接在： - -``` -wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar -tar xf ch_rec_r34_vd_crnn_infer.tar -``` -因此我们按照Serving模型转换教程，运行下列python文件。 -``` -python tools/inference_to_serving.py --model_dir ch_rec_r34_vd_crnn -``` -最终会在`serving_client_dir`和`serving_server_dir`生成客户端和服务端的模型配置。其中`serving_server_dir`和`serving_client_dir`的名字可以自定义。最终文件结构如下 - -``` -/ch_rec_r34_vd_crnn/ -├── serving_client_dir # 客户端配置文件夹 -└── serving_server_dir # 服务端配置文件夹 -``` - -## 三、文本检测模型Serving推理 - -启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表： - -|版本|特点|适用场景| -|-|-|-| -|标准版|稳定性高，分布式部署|适用于吞吐量大，需要跨机房部署的情况| -|快速版|部署方便，预测速度快|适用于对预测速度要求高，迭代速度快的场景，Windows用户只能选择快速版| - -接下来的命令中，我们会指定快速版和标准版的命令。需要说明的是，标准版只能用Linux平台，快速版可以支持Linux/Windows。 -文本检测模型推理，默认使用DB模型的配置参数，识别默认为CRNN。 - -配置文件在`params.py`中，我们贴出配置部分，如果需要做改动，也在这个文件内部进行修改。 - -``` -def read_params(): - cfg = Config() - #use gpu - cfg.use_gpu = False # 是否使用GPU - cfg.use_pdserving = True # 是否使用paddleserving，必须为True - - #params for text detector - cfg.det_algorithm = "DB" # 检测算法， DB/EAST等 - cfg.det_model_dir = "./det_mv_server/" # 检测算法模型路径 - cfg.det_max_side_len = 960 - - #DB params - cfg.det_db_thresh =0.3 - cfg.det_db_box_thresh =0.5 - cfg.det_db_unclip_ratio =2.0 - - #EAST params - cfg.det_east_score_thresh = 0.8 - cfg.det_east_cover_thresh = 0.1 - cfg.det_east_nms_thresh = 0.2 - - #params for text recognizer - cfg.rec_algorithm = "CRNN" # 识别算法， CRNN/RARE等 - cfg.rec_model_dir = "./ocr_rec_server/" # 识别算法模型路径 - - cfg.rec_image_shape = "3, 32, 320" - cfg.rec_batch_num = 30 - cfg.max_text_length = 25 - - cfg.rec_char_dict_path = "./ppocr_keys_v1.txt" # 识别算法字典文件 - cfg.use_space_char = True - - #params for text classifier - cfg.use_angle_cls = True # 是否启用分类算法 - cfg.cls_model_dir = "./ocr_clas_server/" # 分类算法模型路径 - cfg.cls_image_shape = "3, 48, 192" - cfg.label_list = ['0', '180'] - cfg.cls_batch_num = 30 - cfg.cls_thresh = 0.9 - - return cfg -``` -与本地预测不同的是，Serving预测需要一个客户端和一个服务端，因此接下来的教程都是两行代码。 - -在正式执行服务端启动命令之前，先export PYTHONPATH到工程主目录下。 -``` -export PYTHONPATH=$PWD:$PYTHONPATH -cd deploy/pdserving -``` -为了方便用户复现Demo程序，我们提供了Chinese and English ultra-lightweight OCR model (8.1M)版本的Serving模型 -``` -wget --no-check-certificate https://paddleocr.bj.bcebos.com/deploy/pdserving/ocr_pdserving_suite.tar.gz -tar xf ocr_pdserving_suite.tar.gz -``` - -### 1. 超轻量中文检测模型推理 - -超轻量中文检测模型推理，可以执行如下命令启动服务端： - -``` -#根据环境只需要启动其中一个就可以 -python det_rpc_server.py #标准版，Linux用户 -python det_local_server.py #快速版，Windows/Linux用户 -``` - -客户端 - -``` -python det_web_client.py -``` - - -Serving的推测和本地预测不同点在于，客户端发送请求到服务端，服务端需要检测到文字框之后返回框的坐标，此处没有后处理的图片，只能看到坐标值。 - -## 四、文本识别模型Serving推理 - -下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外，如果训练时修改了文本的字典，请参考下面的自定义文本识别字典的推理。 - -### 1. 超轻量中文识别模型推理 - -超轻量中文识别模型推理，可以执行如下命令启动服务端： -需要注意params.py中的`--use_gpu`的值 -``` -#根据环境只需要启动其中一个就可以 -python rec_rpc_server.py #标准版，Linux用户 -python rec_local_server.py #快速版，Windows/Linux用户 -``` -如果需要使用CPU版本，还需增加 `--use_gpu False`。 - -客户端 - -``` -python rec_web_client.py -``` - -![](../imgs_words/ch/word_4.jpg) - -执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下： - -``` -{u'result': {u'score': [u'0.89547354'], u'pred_text': ['实力活力']}} -``` - - - -## 五、方向分类模型推理 - -下面将介绍方向分类模型推理。 - - - -### 1. 方向分类模型推理 - -方向分类模型推理， 可以执行如下命令启动服务端： -需要注意params.py中的`--use_gpu`的值 -``` -#根据环境只需要启动其中一个就可以 -python clas_rpc_server.py #标准版，Linux用户 -python clas_local_server.py #快速版，Windows/Linux用户 -``` - -客户端 - -``` -python rec_web_client.py -``` - -![](../imgs_words/ch/word_4.jpg) - -执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下： - -``` -{u'result': {u'direction': [u'0'], u'score': [u'0.9999963']}} -``` - - -## 六、文本检测、方向分类和文字识别串联Serving推理 - -### 1. 超轻量中文OCR模型推理 - -在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。与本地预测不同的是，为了减少网络传输耗时，可视化识别结果目前不做处理，用户收到的是推理得到的文字字段。 - -执行如下命令启动服务端： -需要注意params.py中的`--use_gpu`的值 -``` -#标准版，Linux用户 -#GPU用户 -python -m paddle_serving_server_gpu.serve --model det_infer_server --port 9293 --gpu_id 0 -python -m paddle_serving_server_gpu.serve --model cls_infer_server --port 9294 --gpu_id 0 -python ocr_rpc_server.py -#CPU用户 -python -m paddle_serving_server.serve --model det_infer_server --port 9293 -python -m paddle_serving_server.serve --model cls_infer_server --port 9294 -python ocr_rpc_server.py - -#快速版，Windows/Linux用户 -python ocr_local_server.py -``` - -客户端 - -``` -python rec_web_client.py -``` diff --git a/doc/doc_ch/update.md b/doc/doc_ch/update.md index 9927a228d..07591ea12 100644 --- a/doc/doc_ch/update.md +++ b/doc/doc_ch/update.md @@ -1,9 +1,9 @@ # 更新 - 2022.5.9 发布PaddleOCR v2.5。发布内容包括： - - [PP-OCRv3](./doc/doc_ch/ppocr_introduction.md#pp-ocrv3)，速度可比情况下，中文场景效果相比于PP-OCRv2再提升5%，英文场景提升11%，80语种多语言模型平均识别准确率提升5%以上； - - 半自动标注工具[PPOCRLabelv2](./PPOCRLabel)：新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能； + - [PP-OCRv3](./ppocr_introduction.md#pp-ocrv3)，速度可比情况下，中文场景效果相比于PP-OCRv2再提升5%，英文场景提升11%，80语种多语言模型平均识别准确率提升5%以上； + - 半自动标注工具[PPOCRLabelv2](../../PPOCRLabel)：新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能； - OCR产业落地工具集：打通22种训练部署软硬件环境与方式，覆盖企业90%的训练部署环境需求 - - 交互式OCR开源电子书[《动手学OCR》](./doc/doc_ch/ocr_book.md)，覆盖OCR全栈技术的前沿理论与代码实践，并配套教学视频。 + - 交互式OCR开源电子书[《动手学OCR》](./ocr_book.md)，覆盖OCR全栈技术的前沿理论与代码实践，并配套教学视频。 - 2022.5.7 添加对[Weights & Biases](https://docs.wandb.ai/)训练日志记录工具的支持。 - 2021.12.21 《OCR十讲》课程开讲，12月21日起每晚八点半线上授课！ 【免费】报名地址：https://aistudio.baidu.com/aistudio/course/introduce/25207 - 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法（PSENet），3种文本识别算法（NRTR、SEED、SAR）；文档结构化算法新增1种关键信息提取算法（SDMGR），3种DocVQA算法（LayoutLM、LayoutLMv2，LayoutXLM）。 diff --git a/doc/doc_en/algorithm_det_east_en.md b/doc/doc_en/algorithm_det_east_en.md index 3955809a4..07c434a9b 100644 --- a/doc/doc_en/algorithm_det_east_en.md +++ b/doc/doc_en/algorithm_det_east_en.md @@ -40,7 +40,7 @@ Please prepare your environment referring to [prepare the environment](./environ The above EAST model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md). -After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. +After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. diff --git a/doc/doc_en/algorithm_det_fcenet_en.md b/doc/doc_en/algorithm_det_fcenet_en.md index e15fb9a07..f3c51a91a 100644 --- a/doc/doc_en/algorithm_det_fcenet_en.md +++ b/doc/doc_en/algorithm_det_fcenet_en.md @@ -37,7 +37,7 @@ Please prepare your environment referring to [prepare the environment](./environ The above FCE model is trained using the CTW1500 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md). -After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. +After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. ## 4. Inference and Deployment diff --git a/doc/doc_en/algorithm_det_psenet_en.md b/doc/doc_en/algorithm_det_psenet_en.md index d4cb3ea7d..3977a156a 100644 --- a/doc/doc_en/algorithm_det_psenet_en.md +++ b/doc/doc_en/algorithm_det_psenet_en.md @@ -39,7 +39,7 @@ Please prepare your environment referring to [prepare the environment](./environ The above PSE model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md). -After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. +After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. ## 4. Inference and Deployment diff --git a/doc/doc_en/algorithm_e2e_pgnet_en.md b/doc/doc_en/algorithm_e2e_pgnet_en.md index c7cb3221c..ab74c57bc 100644 --- a/doc/doc_en/algorithm_e2e_pgnet_en.md +++ b/doc/doc_en/algorithm_e2e_pgnet_en.md @@ -36,7 +36,7 @@ The results of detection and recognition are as follows: ## 2. Environment Configuration -Please refer to [Operation Environment Preparation](./environment_en.md) to configure PaddleOCR operating environment first, refer to [PaddleOCR Overview and Project Clone](./paddleOCR_overview_en.md) to clone the project +Please refer to [Operation Environment Preparation](./environment_en.md) to configure PaddleOCR operating environment first, refer to [Project Clone](./clone_en.md) to clone the project ## 3. Quick Use diff --git a/doc/doc_en/algorithm_rec_aster_en.md b/doc/doc_en/algorithm_rec_aster_en.md index 1540681a1..b949cb5b3 100644 --- a/doc/doc_en/algorithm_rec_aster_en.md +++ b/doc/doc_en/algorithm_rec_aster_en.md @@ -33,13 +33,13 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/algorithm_rec_crnn_en.md b/doc/doc_en/algorithm_rec_crnn_en.md index 571569ee4..8548c2fa6 100644 --- a/doc/doc_en/algorithm_rec_crnn_en.md +++ b/doc/doc_en/algorithm_rec_crnn_en.md @@ -33,13 +33,13 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/algorithm_rec_nrtr_en.md b/doc/doc_en/algorithm_rec_nrtr_en.md index 3f8fd0ade..40c9b9162 100644 --- a/doc/doc_en/algorithm_rec_nrtr_en.md +++ b/doc/doc_en/algorithm_rec_nrtr_en.md @@ -29,13 +29,13 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/algorithm_rec_sar_en.md b/doc/doc_en/algorithm_rec_sar_en.md index 8c1e6dbbf..24b87c10c 100644 --- a/doc/doc_en/algorithm_rec_sar_en.md +++ b/doc/doc_en/algorithm_rec_sar_en.md @@ -31,13 +31,13 @@ Note:In addition to using the two text recognition datasets MJSynth and SynthTex ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/algorithm_rec_seed_en.md b/doc/doc_en/algorithm_rec_seed_en.md index 21679f42f..f8d7ae6d3 100644 --- a/doc/doc_en/algorithm_rec_seed_en.md +++ b/doc/doc_en/algorithm_rec_seed_en.md @@ -31,13 +31,13 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/algorithm_rec_srn_en.md b/doc/doc_en/algorithm_rec_srn_en.md index c022a81f9..1d7fc07dc 100644 --- a/doc/doc_en/algorithm_rec_srn_en.md +++ b/doc/doc_en/algorithm_rec_srn_en.md @@ -30,13 +30,13 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/algorithm_rec_svtr_en.md b/doc/doc_en/algorithm_rec_svtr_en.md index 2e7deb4c0..d402a6b49 100644 --- a/doc/doc_en/algorithm_rec_svtr_en.md +++ b/doc/doc_en/algorithm_rec_svtr_en.md @@ -34,7 +34,7 @@ The accuracy (%) and model files of SVTR on the public dataset of scene text rec ## 2. Environment -Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. #### Dataset Preparation @@ -44,7 +44,7 @@ Please refer to ["Environment Preparation"](./environment.md) to configure the P ## 3. Model Training / Evaluation / Prediction -Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: diff --git a/doc/doc_en/knowledge_distillation_en.md b/doc/doc_en/knowledge_distillation_en.md index bd36907c9..52725e5c0 100755 --- a/doc/doc_en/knowledge_distillation_en.md +++ b/doc/doc_en/knowledge_distillation_en.md @@ -438,10 +438,10 @@ Architecture: ``` If DML is used, that is, the method of two small models learning from each other, the Teacher network structure in the above configuration file needs to be set to the same configuration as the Student model. -Refer to the configuration file for details. [ch_PP-OCRv3_det_dml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml) +Refer to the configuration file for details. [ch_PP-OCRv3_det_dml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml) -The following describes the configuration file parameters [ch_PP-OCRv3_det_cml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml): +The following describes the configuration file parameters [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml): ``` Architecture: diff --git a/doc/doc_en/multi_languages_en.md b/doc/doc_en/multi_languages_en.md index 4696a3e84..d9cb180f7 100644 --- a/doc/doc_en/multi_languages_en.md +++ b/doc/doc_en/multi_languages_en.md @@ -187,10 +187,10 @@ 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](./inference_en.md) -- [C++ Inference](../../deploy/cpp_infer/readme_en.md) +- [Python Inference](./inference_ppocr_en.md) +- [C++ Inference](../../deploy/cpp_infer/readme.md) - [Serving](../../deploy/hubserving/readme_en.md) -- [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md) +- [Mobile](../../deploy/lite/readme.md) - [Benchmark](./benchmark_en.md) diff --git a/doc/doc_en/update_en.md b/doc/doc_en/update_en.md index a900219b2..a44dd0d70 100644 --- a/doc/doc_en/update_en.md +++ b/doc/doc_en/update_en.md @@ -1,8 +1,8 @@ # RECENT UPDATES - 2022.5.9 release PaddleOCR v2.5, including: - - [PP-OCRv3](./doc/doc_en/ppocr_introduction_en.md#pp-ocrv3): With comparable speed, the effect of Chinese scene is further improved by 5% compared with PP-OCRv2, the effect of English scene is improved by 11%, and the average recognition accuracy of 80 language multilingual models is improved by more than 5%. - - [PPOCRLabelv2](./PPOCRLabel): Add the annotation function for table recognition task, key information extraction task and irregular text image. - - Interactive e-book [*"Dive into OCR"*](./doc/doc_en/ocr_book_en.md), covers the cutting-edge theory and code practice of OCR full stack technology. + - [PP-OCRv3](./ppocr_introduction_en.md#pp-ocrv3): With comparable speed, the effect of Chinese scene is further improved by 5% compared with PP-OCRv2, the effect of English scene is improved by 11%, and the average recognition accuracy of 80 language multilingual models is improved by more than 5%. + - [PPOCRLabelv2](../../PPOCRLabel): Add the annotation function for table recognition task, key information extraction task and irregular text image. + - Interactive e-book [*"Dive into OCR"*](./ocr_book_en.md), covers the cutting-edge theory and code practice of OCR full stack technology. - 2022.5.7 Add support for metric and model logging during training to [Weights & Biases](https://docs.wandb.ai/). - 2021.12.21 OCR open source online course starts. The lesson starts at 8:30 every night and lasts for ten days. Free registration: https://aistudio.baidu.com/aistudio/course/introduce/25207 - 2021.12.21 release PaddleOCR v2.4, release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR), 1 key information extraction algorithm (SDMGR) and 3 DocVQA algorithms (LayoutLM、LayoutLMv2，LayoutXLM). diff --git a/ppstructure/vqa/README_ch.md b/ppstructure/vqa/README_ch.md index ff513f8f7..b677dc07b 100644 --- a/ppstructure/vqa/README_ch.md +++ b/ppstructure/vqa/README_ch.md @@ -52,7 +52,7 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进 ### 3.1 SER -![](../../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../../doc/vqa/result_ser/zh_val_42_ser.jpg) +![](../docs/vqa/result_ser/zh_val_0_ser.jpg) | ![](../docs/vqa/result_ser/zh_val_42_ser.jpg) ---|--- 图中不同颜色的框表示不同的类别，对于XFUND数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别 @@ -65,7 +65,7 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进 ### 3.2 RE -![](../../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../../doc/vqa/result_re/zh_val_40_re.jpg) +![](../docs/vqa/result_re/zh_val_21_re.jpg) | ![](../docs/vqa/result_re/zh_val_40_re.jpg) ---|--- diff --git a/test_tipc/docs/jeston_test_train_inference_python.md b/test_tipc/docs/jeston_test_train_inference_python.md index 9e9d15fb6..b25175ed0 100644 --- a/test_tipc/docs/jeston_test_train_inference_python.md +++ b/test_tipc/docs/jeston_test_train_inference_python.md @@ -115,4 +115,4 @@ ValueError: The results of python_infer_gpu_usetrt_True_precision_fp32_batchsize ## 3. 更多教程 本文档为功能测试用，更丰富的训练预测使用教程请参考： [模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md) -[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md) +[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference_ppocr.md) diff --git a/test_tipc/docs/mac_test_train_inference_python.md b/test_tipc/docs/mac_test_train_inference_python.md index ea6e0218b..c37291a8f 100644 --- a/test_tipc/docs/mac_test_train_inference_python.md +++ b/test_tipc/docs/mac_test_train_inference_python.md @@ -152,4 +152,4 @@ ValueError: The results of python_infer_cpu_usemkldnn_False_threads_1_batchsize_ ## 3. 更多教程 本文档为功能测试用，更丰富的训练预测使用教程请参考： [模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md) -[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md) +[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference_ppocr.md) diff --git a/test_tipc/docs/test_train_inference_python.md b/test_tipc/docs/test_train_inference_python.md index fa969cbe1..99de94007 100644 --- a/test_tipc/docs/test_train_inference_python.md +++ b/test_tipc/docs/test_train_inference_python.md @@ -153,4 +153,4 @@ python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.tx ## 3. 更多教程 本文档为功能测试用，更丰富的训练预测使用教程请参考： [模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md) -[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md) +[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference_ppocr.md) diff --git a/test_tipc/docs/win_test_train_inference_python.md b/test_tipc/docs/win_test_train_inference_python.md index 95585af03..6e3ce93bb 100644 --- a/test_tipc/docs/win_test_train_inference_python.md +++ b/test_tipc/docs/win_test_train_inference_python.md @@ -156,4 +156,4 @@ ValueError: The results of python_infer_cpu_usemkldnn_False_threads_1_batchsize_ ## 3. 更多教程 本文档为功能测试用，更丰富的训练预测使用教程请参考： [模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md) -[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md) +[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference_ppocr.md) From a5de9ccd4703e4adda9b7ff68b7b7e8c53d441d0 Mon Sep 17 00:00:00 2001 From: xiaoting <31891223+tink2123@users.noreply.github.com> Date: Thu, 19 May 2022 11:36:29 +0800 Subject: [PATCH 22/24] Update algorithm_rec_starnet.md --- doc/doc_en/algorithm_rec_starnet.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/doc_en/algorithm_rec_starnet.md b/doc/doc_en/algorithm_rec_starnet.md index 3147deaae..dbb53a9c7 100644 --- a/doc/doc_en/algorithm_rec_starnet.md +++ b/doc/doc_en/algorithm_rec_starnet.md @@ -17,11 +17,11 @@ ## 1. Introduction Paper information: -> [STAR-Net: a spatial attention residue network for scene text recognition.]( http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf) +> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf) > Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han. > BMVC, pages 43.1-43.13, 2016 -Refer to[DTRB] text Recognition Training and Evaluation Process (https://arxiv.org/abs/1904.01906). Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: +Refer to [DTRB](https://arxiv.org/abs/1904.01906) text Recognition Training and Evaluation Process . Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: |Models|Backbone Networks|Avg Accuracy|Configuration Files|Download Links| | --- | --- | --- | --- | --- | @@ -69,7 +69,7 @@ python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Globa ### 4.1 Python Inference -First, convert the model saved during the STAR-Net text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example ([Model download address]( https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar ), which can be converted using the following command: +First, convert the model saved during the STAR-Net text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example [Model download address]( https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) , which can be converted using the following command: ```shell python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet From 44b4234311fe0e1781a1bf9348e9ec8327ae918c Mon Sep 17 00:00:00 2001 From: lubin10 Date: Tue, 17 May 2022 08:29:44 +0000 Subject: [PATCH 23/24] update tools/export_model.py tools/infer_rec.py --- tools/export_model.py | 2 +- tools/infer_rec.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/export_model.py b/tools/export_model.py index e971f6cb2..c0cbcd361 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -17,7 +17,7 @@ import sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, ".."))) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, ".."))) import argparse diff --git a/tools/infer_rec.py b/tools/infer_rec.py index 193e24a4d..a08fa25b4 100755 --- a/tools/infer_rec.py +++ b/tools/infer_rec.py @@ -157,7 +157,7 @@ def main(): if info is not None: logger.info("\t result: {}".format(info)) - fout.write(file + "\t" + info) + fout.write(file + "\t" + info + "\n") logger.info("success!") From d8a8ca81e1b17a9bf04618baa42c56c7c86931d3 Mon Sep 17 00:00:00 2001 From: xiaoting <31891223+tink2123@users.noreply.github.com> Date: Mon, 23 May 2022 22:14:56 +0800 Subject: [PATCH 24/24] support xpu (#6382) --- tools/infer/utility.py | 3 +++ tools/program.py | 30 +++++++++++++++++++----------- 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 74ec42ec8..d27aec63e 100644 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -34,6 +34,7 @@ def init_args(): parser = argparse.ArgumentParser() # params for prediction engine parser.add_argument("--use_gpu", type=str2bool, default=True) + parser.add_argument("--use_xpu", type=str2bool, default=False) parser.add_argument("--ir_optim", type=str2bool, default=True) parser.add_argument("--use_tensorrt", type=str2bool, default=False) parser.add_argument("--min_subgraph_size", type=int, default=15) @@ -285,6 +286,8 @@ def create_predictor(args, mode, logger): config.set_trt_dynamic_shape_info( min_input_shape, max_input_shape, opt_input_shape) + elif args.use_xpu: + config.enable_xpu(10 * 1024 * 1024) else: config.disable_gpu() if hasattr(args, "cpu_threads"): diff --git a/tools/program.py b/tools/program.py index 7c02dc014..5d726b820 100755 --- a/tools/program.py +++ b/tools/program.py @@ -112,20 +112,25 @@ def merge_config(config, opts): return config -def check_gpu(use_gpu): +def check_device(use_gpu, use_xpu=False): """ Log error and exit when set use_gpu=true in paddlepaddle cpu version. """ - err = "Config use_gpu cannot be set as true while you are " \ - "using paddlepaddle cpu version ! \nPlease try: \n" \ - "\t1. Install paddlepaddle-gpu to run model on GPU \n" \ - "\t2. Set use_gpu as false in config file to run " \ + err = "Config {} cannot be set as true while your paddle " \ + "is not compiled with {} ! \nPlease try: \n" \ + "\t1. Install paddlepaddle to run model on {} \n" \ + "\t2. Set {} as false in config file to run " \ "model on CPU" try: + if use_gpu and use_xpu: + print("use_xpu and use_gpu can not both be ture.") if use_gpu and not paddle.is_compiled_with_cuda(): - print(err) + print(err.format("use_gpu", "cuda", "gpu", "use_gpu")) + sys.exit(1) + if use_xpu and not paddle.device.is_compiled_with_xpu(): + print(err.format("use_xpu", "xpu", "xpu", "use_xpu")) sys.exit(1) except Exception as e: pass @@ -301,6 +306,7 @@ def train(config, stats['lr'] = lr train_stats.update(stats) + if log_writer is not None and dist.get_rank() == 0: log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step) @@ -547,7 +553,7 @@ def preprocess(is_train=False): # check if set use_gpu=True in paddlepaddle cpu version use_gpu = config['Global']['use_gpu'] - check_gpu(use_gpu) + use_xpu = config['Global'].get('use_xpu', False) # check if set use_xpu=True in paddlepaddle cpu/gpu version use_xpu = False @@ -562,11 +568,13 @@ def preprocess(is_train=False): 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR' ] - device = 'cpu' - if use_gpu: - device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_xpu: - device = 'xpu' + device = 'xpu:{0}'.format(os.getenv('FLAGS_selected_xpus', 0)) + else: + device = 'gpu:{}'.format(dist.ParallelEnv() + .dev_id) if use_gpu else 'cpu' + check_device(use_gpu, use_xpu) + device = paddle.set_device(device) config['Global']['distributed'] = dist.get_world_size() != 1