diff --git a/PPOCRLabel/PPOCRLabel.py b/PPOCRLabel/PPOCRLabel.py index 92d80c8a..8babac65 100644 --- a/PPOCRLabel/PPOCRLabel.py +++ b/PPOCRLabel/PPOCRLabel.py @@ -1031,7 +1031,7 @@ class MainWindow(QMainWindow, WindowMixin): for box in self.result_dic: trans_dic = {"label": box[1][0], "points": box[0], 'difficult': False} - if trans_dic["label"] is "" and mode == 'Auto': + if trans_dic["label"] == "" and mode == 'Auto': continue shapes.append(trans_dic) @@ -1763,7 +1763,7 @@ class MainWindow(QMainWindow, WindowMixin): QMessageBox.information(self, "Information", msg) return result = self.ocr.ocr(img_crop, cls=True, det=False) - if result[0][0] is not '': + if result[0][0] != '': result.insert(0, box) print('result in reRec is ', result) self.result_dic.append(result) @@ -1794,7 +1794,7 @@ class MainWindow(QMainWindow, WindowMixin): QMessageBox.information(self, "Information", msg) return result = self.ocr.ocr(img_crop, cls=True, det=False) - if result[0][0] is not '': + if result[0][0] != '': result.insert(0, box) print('result in reRec is ', result) if result[1][0] == shape.label: @@ -1999,7 +1999,7 @@ if __name__ == '__main__': resource_file = './libs/resources.py' if not os.path.exists(resource_file): output = os.system('pyrcc5 -o libs/resources.py resources.qrc') - assert output is 0, "operate the cmd have some problems ,please check whether there is a in the lib " \ + assert output == 0, "operate the cmd have some problems ,please check whether there is a in the lib " \ "directory resources.py " import libs.resources sys.exit(main()) diff --git a/README.md b/README.md index 5b6e4bd0..67d65e98 100644 --- a/README.md +++ b/README.md @@ -5,10 +5,11 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools ## Notice PaddleOCR supports both dynamic graph and static graph programming paradigm -- Dynamic graph: dygraph branch (default), **supported by paddle 2.0rc1+ ([installation](./doc/doc_en/installation_en.md))** +- Dynamic graph: dygraph branch (default), **supported by paddle 2.0.0 ([installation](./doc/doc_en/installation_en.md))** - Static graph: develop branch **Recent updates** +- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048). - 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image. - 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly. - 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941 diff --git a/README_ch.md b/README_ch.md index 2de6fdf5..d627ec45 100755 --- a/README_ch.md +++ b/README_ch.md @@ -4,11 +4,13 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。 ## 注意 PaddleOCR同时支持动态图与静态图两种编程范式 -- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0rc1+（[快速安装](./doc/doc_ch/installation.md)） +- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0.0（[快速安装](./doc/doc_ch/installation.md)） - 静态图版本：develop分支 **近期更新** -- 2021.1.18 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数152个，每周一都会更新，欢迎大家持续关注。 +- 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802) +- 2021.1.25 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数157个，每周一都会更新，欢迎大家持续关注。 +- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，[多语言模型下载](./doc/doc_ch/models_list.md)，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048) - 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。 - 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。 - 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941 diff --git a/StyleText/README.md b/StyleText/README.md index df4fbf3c..65a72ac8 100644 --- a/StyleText/README.md +++ b/StyleText/README.md @@ -72,7 +72,7 @@ fusion_generator: python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en ``` -* Note 1: The language options is correspond to the corpus. Currently, the tool only supports English, Simplified Chinese and Korean. +* Note 1: The language options is correspond to the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko). * Note 2: Synth-Text is mainly used to generate images for OCR recognition models. So the height of style images should be around 32 pixels. Images in other sizes may behave poorly. * Note 3: You can modify `use_gpu` in `configs/config.yml` to determine whether to use GPU for prediction. @@ -120,7 +120,7 @@ In actual application scenarios, it is often necessary to synthesize pictures in * `with_label`：Whether the `label_file` is label file list. * `CorpusGenerator`： * `method`：Method of CorpusGenerator，supports `FileCorpus` and `EnNumCorpus`. If `EnNumCorpus` is used，No other configuration is needed，otherwise you need to set `corpus_file` and `language`. - * `language`：Language of the corpus. + * `language`：Language of the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko). * `corpus_file`: Filepath of the corpus. Corpus file should be a text file which will be split by line-endings（'\n'）. Corpus generator samples one line each time. diff --git a/StyleText/README_ch.md b/StyleText/README_ch.md index fd259ca0..ccd1efaf 100644 --- a/StyleText/README_ch.md +++ b/StyleText/README_ch.md @@ -63,10 +63,10 @@ fusion_generator: ```python python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en ``` -* 注1：语言选项和语料相对应，目前该工具只支持英文、简体中文和韩语。 +* 注1：语言选项和语料相对应，目前支持英文(en)、简体中文(ch)和韩语(ko)。 * 注2：Style-Text生成的数据主要应用于OCR识别场景。基于当前PaddleOCR识别模型的设计，我们主要支持高度在32左右的风格图像。 如果输入图像尺寸相差过多，效果可能不佳。 -* 注3：可以通过修改配置文件中的`use_gpu`(true或者false)参数来决定是否使用GPU进行预测。 +* 注3：可以通过修改配置文件`configs/config.yml`中的`use_gpu`(true或者false)参数来决定是否使用GPU进行预测。 例如，输入如下图片和语料"PaddleOCR": @@ -105,7 +105,7 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_ * `with_label`：标志`label_file`是否为label文件。 * `CorpusGenerator`： * `method`：语料生成方法，目前有`FileCorpus`和`EnNumCorpus`可选。如果使用`EnNumCorpus`，则不需要填写其他配置，否则需要修改`corpus_file`和`language`； - * `language`：语料的语种； + * `language`：语料的语种，目前支持英文(en)、简体中文(ch)和韩语(ko)； * `corpus_file`: 语料文件路径。语料文件应使用文本文件。语料生成器首先会将语料按行切分，之后每次随机选取一行。 语料文件格式示例： diff --git a/configs/rec/multi_language/rec_en_number_lite_train.yml b/configs/rec/multi_language/rec_en_number_lite_train.yml index cee05121..13eda848 100644 --- a/configs/rec/multi_language/rec_en_number_lite_train.yml +++ b/configs/rec/multi_language/rec_en_number_lite_train.yml @@ -16,7 +16,7 @@ Global: infer_img: # for data or label process character_dict_path: ppocr/utils/dict/en_dict.txt - character_type: ch + character_type: EN max_text_length: 25 infer_mode: False use_space_char: False diff --git a/configs/rec/rec_mv3_none_bilstm_ctc.yml b/configs/rec/rec_mv3_none_bilstm_ctc.yml index 38f1e869..00c1db88 100644 --- a/configs/rec/rec_mv3_none_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml @@ -1,5 +1,5 @@ Global: - use_gpu: true + use_gpu: True epoch_num: 72 log_smooth_window: 20 print_batch_step: 10 @@ -59,7 +59,7 @@ Metric: Train: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image @@ -78,7 +78,7 @@ Train: Eval: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/validation/ transforms: - DecodeImage: # load image diff --git a/configs/rec/rec_mv3_none_none_ctc.yml b/configs/rec/rec_mv3_none_none_ctc.yml index 33079ad4..6711b1d2 100644 --- a/configs/rec/rec_mv3_none_none_ctc.yml +++ b/configs/rec/rec_mv3_none_none_ctc.yml @@ -58,7 +58,7 @@ Metric: Train: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image @@ -77,7 +77,7 @@ Train: Eval: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/validation/ transforms: - DecodeImage: # load image diff --git a/configs/rec/rec_mv3_tps_bilstm_ctc.yml b/configs/rec/rec_mv3_tps_bilstm_ctc.yml index 08f68939..1b9fb0a0 100644 --- a/configs/rec/rec_mv3_tps_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml @@ -63,7 +63,7 @@ Metric: Train: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image @@ -82,7 +82,7 @@ Train: Eval: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/validation/ transforms: - DecodeImage: # load image diff --git a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml index 4ad2ff89..e4d301a6 100644 --- a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml @@ -58,7 +58,7 @@ Metric: Train: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image @@ -77,7 +77,7 @@ Train: Eval: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/validation/ transforms: - DecodeImage: # load image diff --git a/configs/rec/rec_r34_vd_none_none_ctc.yml b/configs/rec/rec_r34_vd_none_none_ctc.yml index 9c1eeb30..4a17a004 100644 --- a/configs/rec/rec_r34_vd_none_none_ctc.yml +++ b/configs/rec/rec_r34_vd_none_none_ctc.yml @@ -56,7 +56,7 @@ Metric: Train: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image @@ -75,7 +75,7 @@ Train: Eval: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/validation/ transforms: - DecodeImage: # load image diff --git a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml index aeded492..62edf843 100644 --- a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml @@ -62,7 +62,7 @@ Metric: Train: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image @@ -81,7 +81,7 @@ Train: Eval: dataset: - name: LMDBDateSet + name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/validation/ transforms: - DecodeImage: # load image diff --git a/configs/rec/rec_r50_fpn_srn.yml b/configs/rec/rec_r50_fpn_srn.yml new file mode 100644 index 00000000..ec7f1705 --- /dev/null +++ b/configs/rec/rec_r50_fpn_srn.yml @@ -0,0 +1,107 @@ +Global: + use_gpu: True + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 5 + save_model_dir: ./output/rec/srn_new + save_epoch_step: 3 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [0, 5000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_words/ch/word_1.jpg + # for data or label process + character_dict_path: + character_type: en + max_text_length: 25 + num_heads: 8 + infer_mode: False + use_space_char: False + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + clip_norm: 10.0 + lr: + learning_rate: 0.0001 + +Architecture: + model_type: rec + algorithm: SRN + in_channels: 1 + Transform: + Backbone: + name: ResNetFPN + Head: + name: SRNHead + max_text_length: 25 + num_heads: 8 + num_encoder_TUs: 2 + num_decoder_TUs: 4 + hidden_dims: 512 + +Loss: + name: SRNLoss + +PostProcess: + name: SRNLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: LMDBDataSet + data_dir: ./train_data/srn_train_data_duiqi + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - SRNLabelEncode: # Class handling label + - SRNRecResizeImg: + image_shape: [1, 64, 256] + - KeepKeys: + keep_keys: ['image', + 'label', + 'length', + 'encoder_word_pos', + 'gsrm_word_pos', + 'gsrm_slf_attn_bias1', + 'gsrm_slf_attn_bias2'] # dataloader will return list in this order + loader: + shuffle: False + batch_size_per_card: 64 + drop_last: False + num_workers: 4 + +Eval: + dataset: + name: LMDBDataSet + data_dir: ./train_data/data_lmdb_release/evaluation + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - SRNLabelEncode: # Class handling label + - SRNRecResizeImg: + image_shape: [1, 64, 256] + - KeepKeys: + keep_keys: ['image', + 'label', + 'length', + 'encoder_word_pos', + 'gsrm_word_pos', + 'gsrm_slf_attn_bias1', + 'gsrm_slf_attn_bias2'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 32 + num_workers: 4 diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md index ccd4d06b..4ac3f7c3 100644 --- a/deploy/slim/quantization/README.md +++ b/deploy/slim/quantization/README.md @@ -42,7 +42,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global # 比如下载提供的训练模型 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar tar -xf ch_ppocr_mobile_v2.0_det_train.tar -python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model +python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./output/quant_inference_model ``` 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 7da0b3e7..36407a2b 100644 --- a/deploy/slim/quantization/README_en.md +++ b/deploy/slim/quantization/README_en.md @@ -58,7 +58,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment: ```bash -python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model +python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model ``` ### 5. Deploy diff --git a/doc/doc_ch/FAQ.md b/doc/doc_ch/FAQ.md index 37b9834d..11a9f35d 100755 --- a/doc/doc_ch/FAQ.md +++ b/doc/doc_ch/FAQ.md @@ -9,42 +9,43 @@ ## PaddleOCR常见问题汇总(持续更新) -* [近期更新（2021.1.18）](#近期更新) +* [近期更新（2021.1.25）](#近期更新) * [【精选】OCR精选10个问题](#OCR精选10个问题) * [【理论篇】OCR通用32个问题](#OCR通用问题) * [基础知识7题](#基础知识) * [数据集7题](#数据集2) * [模型训练调优18题](#模型训练调优2) -* [【实战篇】PaddleOCR实战110个问题](#PaddleOCR实战问题) - * [使用咨询36题](#使用咨询) +* [【实战篇】PaddleOCR实战115个问题](#PaddleOCR实战问题) + * [使用咨询38题](#使用咨询) * [数据集17题](#数据集3) * [模型训练调优28题](#模型训练调优3) - * [预测部署29题](#预测部署3) + * [预测部署32题](#预测部署3) -## 近期更新（2021.1.18） +## 近期更新（2021.1.25） +#### Q3.1.37: 小语种模型只有识别模型，没有检测模型吗？ -#### Q2.3.18: 在PP-OCR系统中，文本检测的骨干网络为什么没有使用SE模块？ +**A**：小语种（包括纯英文数字）的检测模型和中文的检测模型是共用的，在训练中文检测模型时加入了多语言数据。https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/models_list_en.md#1-text-detection-model。 -**A**：SE模块是MobileNetV3网络一个重要模块，目的是估计特征图每个特征通道重要性，给特征图每个特征分配权重，提高网络的表达能力。但是，对于文本检测，输入网络的分辨率比较大，一般是640\*640，利用SE模块估计特征图每个特征通道重要性比较困难，网络提升能力有限，但是该模块又比较耗时，因此在PP-OCR系统中，文本检测的骨干网络没有使用SE模块。实验也表明，当去掉SE模块，超轻量模型大小可以减小40%，文本检测效果基本不受影响。详细可以参考PP-OCR技术文章，https://arxiv.org/abs/2009.09941. +#### Q3.1.38: module 'paddle.distributed' has no attribute ‘get_rank’。 -#### Q3.3.27: PaddleOCR关于文本识别模型的训练，支持的数据增强方式有哪些？ +**A**：Paddle版本问题，请安装2.0版本Paddle：pip install paddlepaddle==2.0.0。 -**A**：文本识别支持的数据增强方式有随机小幅度裁剪、图像平衡、添加白噪声、颜色漂移、图像反色和Text Image Augmentation（TIA）变换等。可以参考[代码](../../ppocr/data/imaug/rec_img_aug.py)中的warp函数。 +#### Q3.4.30: PaddleOCR是否支持在华为鲲鹏920CPU上部署？ -#### Q3.3.28: 关于dygraph分支中，文本识别模型训练，要使用数据增强应该如何设置？ +**A**：目前Paddle的预测库是支持华为鲲鹏920CPU的，但是OCR还没在这些芯片上测试过，可以自己调试，有问题反馈给我们。 -**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4.由于tia数据增强特殊性，默认不采用，可以通过添加use_tia设置，使tia数据增强生效。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。 +#### Q3.4.31: 采用Paddle-Lite进行端侧部署，出现问题，环境没问题。 -#### Q3.4.28: PP-OCR系统中，文本检测的结果有置信度吗？ +**A**：如果你的预测库是自己编译的，那么你的nb文件也要自己编译，用同一个lite版本。不能直接用下载的nb文件，因为版本不同。 -**A**：文本检测的结果有置信度，由于推理过程中没有使用，所以没有显示的返回到最终结果中。如果需要文本检测结果的置信度，可以在[文本检测DB的后处理代码](../../ppocr/postprocess/db_postprocess.py)的155行，添加scores信息。这样，在[检测预测代码](../../tools/infer/predict_det.py)的197行，就可以拿到文本检测的scores信息。 +#### Q3.4.32: PaddleOCR的模型支持onnx转换吗？ -#### Q3.4.29: DB文本检测，特征提取网络金字塔构建的部分代码在哪儿？ - -**A**：特征提取网络金字塔构建的部分:[代码位置](../../ppocr/modeling/necks/db_fpn.py)。ppocr/modeling文件夹里面是组网相关的代码，其中architectures是文本检测或者文本识别整体流程代码；backbones是骨干网络相关代码；necks是类似与FPN的颈函数代码；heads是提取文本检测或者文本识别预测结果相关的头函数；transforms是类似于TPS特征预处理模块。更多的信息可以参考[代码组织结构](./tree.md)。 +**A**：我们目前已经通过Paddle2ONNX来支持各模型套件的转换，PaddleOCR基于PaddlePaddle 2.0的版本（dygraph分支）已经支持导出为ONNX，欢迎关注Paddle2ONNX，了解更多项目的进展： +Paddle2ONNX项目：https://github.com/PaddlePaddle/Paddle2ONNX +Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/model_zoo.md#%E5%9B%BE%E5%83%8Focr) ## 【精选】OCR精选10个问题 @@ -396,13 +397,13 @@ **A**：动态图版本正在紧锣密鼓开发中，将于2020年12月16日发布，敬请关注。 #### Q3.1.22：ModuleNotFoundError: No module named 'paddle.nn'， -**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0rc1的版本，安装方式为 +**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0的版本，安装方式为 ``` -python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple ``` #### Q3.1.23： ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so) -**A**：这个问题是glibc版本不足导致的，Paddle2.0rc1版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。 +**A**：这个问题是glibc版本不足导致的，Paddle2.0.0版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。 如果您的环境不满足这个要求，或者使用的docker镜像为: `hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev` `hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`，安装Paddle2.0rc版本可能会出现上述错误，2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`。 @@ -414,7 +415,7 @@ python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/py - develop：基于Paddle静态图开发的分支，推荐使用paddle1.8 或者2.0版本，该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能，领先于release/1.1分支。 - release/1.1：PaddleOCR 发布的第一个稳定版本，基于静态图开发，具备完善的训练、预测、推理部署、量化裁剪等功能。 -- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0rc1版本，目前仍在开发中。 +- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0.0版本。 - release/2.0-rc1-0：PaddleOCR发布的第二个稳定版本，基于动态图和paddle2.0版本开发，动态图开发的工程更易于调试，目前支，支持模型训练、预测，暂不支持移动端部署。 如果您已经上手过PaddleOCR，并且希望在各种环境上部署PaddleOCR，目前建议使用静态图分支，develop或者release/1.1分支。如果您是初学者，想快速训练，调试PaddleOCR中的算法，建议尝鲜PaddleOCR dygraph分支。 @@ -431,7 +432,7 @@ python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/py #### Q3.1.27: 如何可视化acc,loss曲线图,模型网络结构图等？ -**A**：在配置文件里有`use_visualdl`的参数，设置为True即可，更多的使用命令可以参考：[VisualDL使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc1/guides/03_VisualDL/visualdl.html)。 +**A**：在配置文件里有`use_visualdl`的参数，设置为True即可，更多的使用命令可以参考：[VisualDL使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/03_VisualDL/visualdl.html)。 #### Q3.1.28: 在使用StyleText数据合成工具的时候，报错`ModuleNotFoundError: No module named 'utils.config'`，这是为什么呢？ @@ -450,7 +451,7 @@ https://github.com/PaddlePaddle/PaddleOCR/blob/de3e2e7cd3b8b65ee02d7a41e570fa5b5 #### Q3.1.31: 怎么输出网络结构以及每层的参数信息？ -**A**：可以使用 `paddle.summary`， 具体参考:https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc1/api/paddle/hapi/model_summary/summary_cn.html#summary。 +**A**：可以使用 `paddle.summary`， 具体参考:https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/hapi/model_summary/summary_cn.html。 #### Q3.1.32 能否修改StyleText配置文件中的分辨率？ @@ -474,9 +475,18 @@ StyleText的用途主要是：提取style_image中的字体、背景等style信 例如识别身份证照片，可以先匹配"姓名"，"性别"等关键字，根据这些关键字的坐标去推测其他信息的位置，再与识别的结果匹配。 #### Q3.1.36 如何识别竹简上的古文？ + **A**：对于字符都是普通的汉字字符的情况，只要标注足够的数据，finetune模型就可以了。如果数据量不足，您可以尝试StyleText工具。 而如果使用的字符是特殊的古文字、甲骨文、象形文字等，那么首先需要构建一个古文字的字典，之后再进行训练。 +#### Q3.1.37: 小语种模型只有识别模型，没有检测模型吗？ + +**A**：小语种（包括纯英文数字）的检测模型和中文的检测模型是共用的，在训练中文检测模型时加入了多语言数据。https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/models_list_en.md#1-text-detection-model。 + +#### Q3.1.38: module 'paddle.distributed' has no attribute ‘get_rank’。 + +**A**：Paddle版本问题，请安装2.0版本Paddle：pip install paddlepaddle==2.0.0。 + ### 数据集 @@ -854,3 +864,17 @@ img = cv.imdecode(img_array, -1) #### Q3.4.29: DB文本检测，特征提取网络金字塔构建的部分代码在哪儿？ **A**：特征提取网络金字塔构建的部分:[代码位置](../../ppocr/modeling/necks/db_fpn.py)。ppocr/modeling文件夹里面是组网相关的代码，其中architectures是文本检测或者文本识别整体流程代码；backbones是骨干网络相关代码；necks是类似与FPN的颈函数代码；heads是提取文本检测或者文本识别预测结果相关的头函数；transforms是类似于TPS特征预处理模块。更多的信息可以参考[代码组织结构](./tree.md)。 + +#### Q3.4.30: PaddleOCR是否支持在华为鲲鹏920CPU上部署？ + +**A**：目前Paddle的预测库是支持华为鲲鹏920CPU的，但是OCR还没在这些芯片上测试过，可以自己调试，有问题反馈给我们。 + +#### Q3.4.31: 采用Paddle-Lite进行端侧部署，出现问题，环境没问题。 + +**A**：如果你的预测库是自己编译的，那么你的nb文件也要自己编译，用同一个lite版本。不能直接用下载的nb文件，因为版本不同。 + +#### Q3.4.32: PaddleOCR的模型支持onnx转换吗？ + +**A**：我们目前已经通过Paddle2ONNX来支持各模型套件的转换，PaddleOCR基于PaddlePaddle 2.0的版本（dygraph分支）已经支持导出为ONNX，欢迎关注Paddle2ONNX，了解更多项目的进展： +Paddle2ONNX项目：https://github.com/PaddlePaddle/Paddle2ONNX +Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/model_zoo.md#%E5%9B%BE%E5%83%8Focr) diff --git a/doc/doc_ch/angle_class.md b/doc/doc_ch/angle_class.md index 4d7ff0d7..6e68134a 100644 --- a/doc/doc_ch/angle_class.md +++ b/doc/doc_ch/angle_class.md @@ -63,7 +63,7 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本。 *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* ``` -# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号，如果使用的paddle版本小于2.0rc1，请使用'--select_gpus'参数选择要使用的GPU +# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号。 # 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可 python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml ``` diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index 8f0f6979..a8dee65a 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -76,7 +76,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model # 单机单卡训练 mv3_db 模型 python3 tools/train.py -c configs/det/det_mv3_db.yml \ -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ -# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID；如果使用的paddle版本小于2.0rc1，请使用'--select_gpus'参数选择要使用的GPU +# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \ -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ ``` diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index ab548703..c4601e15 100755 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -306,10 +306,10 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ### 4. 多语言模型的推理 如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果， -需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/` 路径下有默认提供的小语种字体，例如韩文识别： +需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： ``` -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf" +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` ![](../imgs_words/korean/1.jpg) diff --git a/doc/doc_ch/installation.md b/doc/doc_ch/installation.md index 36565cd4..fce151eb 100644 --- a/doc/doc_ch/installation.md +++ b/doc/doc_ch/installation.md @@ -2,7 +2,7 @@ 经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23 PaddleOCR 工作环境 -- PaddlePaddle 1.8+ ，推荐使用 PaddlePaddle 2.0rc1 +- PaddlePaddle 2.0.0 - python3.7 - glibc 2.23 - cuDNN 7.6+ (GPU) @@ -35,11 +35,11 @@ sudo docker container exec -it ppocr /bin/bash pip3 install --upgrade pip 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装 -python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple 如果您的机器是CPU，请运行以下命令安装 -python3 -m pip install paddlepaddle==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 ``` diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md index b473f3ac..c5f459bd 100644 --- a/doc/doc_ch/recognition.md +++ b/doc/doc_ch/recognition.md @@ -195,8 +195,6 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t | :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | | [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc | -| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | -| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc | | rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc | | rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc | | rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc | @@ -272,16 +270,109 @@ Eval: - 小语种 -PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的提供了多语言的配置文件，目前PaddleOCR支持的多语言算法有： +PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 -| 配置文件 | 算法名称 | backbone | trans | seq | pred | language | -| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | -| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语 | -| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 | -| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 | -| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 | -| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 | +您有两种方式创建所需的配置文件： +1. 通过脚本自动生成 + +[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) 可以帮助您生成多语言模型的配置文件 + +- 以意大利语为例，如果您的数据是按如下格式准备的： + ``` + |-train_data + |- it_train.txt # 训练集标签 + |- it_val.txt # 验证集标签 + |- data + |- word_001.jpg + |- word_002.jpg + |- word_003.jpg + | ... + ``` + + 可以使用默认参数，生成配置文件： + + ```bash + # 该代码需要在指定目录运行 + cd PaddleOCR/configs/rec/multi_language/ + # 通过-l或者--language参数设置需要生成的语种的配置文件，该命令会将默认参数写入配置文件 + python3 generate_multi_language_configs.py -l it + ``` + +- 如果您的数据放置在其他位置，或希望使用自己的字典，可以通过指定相关参数来生成配置文件: + + ```bash + # -l或者--language字段是必须的 + # --train修改训练集，--val修改验证集，--data_dir修改数据集目录，--dict修改字典路径， -o修改对应默认参数 + cd PaddleOCR/configs/rec/multi_language/ + python3 generate_multi_language_configs.py -l it \ # 语种 + --train {path/of/train_label.txt} \ # 训练标签文件的路径 + --val {path/of/val_label.txt} \ # 验证集标签文件的路径 + --data_dir {train_data/path} \ # 训练数据的根目录 + --dict {path/of/dict} \ # 字典文件路径 + -o Global.use_gpu=False # 是否使用gpu + ... + + ``` + +2. 手动修改配置文件 + + 您也可以手动修改模版中的以下几个字段: + + ``` + Global: + use_gpu: True + epoch_num: 500 + ... + character_type: it # 需要识别的语种 + character_dict_path: {path/of/dict} # 字典文件所在路径 + + Train: + dataset: + name: SimpleDataSet + data_dir: train_data/ # 数据存放根目录 + label_file_list: ["./train_data/train_list.txt"] # 训练集label路径 + ... + + Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/ # 数据存放根目录 + label_file_list: ["./train_data/val_list.txt"] # 验证集label路径 + ... + + ``` + +目前PaddleOCR支持的多语言算法有： + +| 配置文件 | 算法名称 | backbone | trans | seq | pred | language | character_type | +| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: | +| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 中文繁体 | chinese_cht| +| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语（区分大小写） | EN | +| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 | french | +| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 | german | +| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 | japan | +| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 | korean | +| rec_it_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 意大利语 | it | +| rec_xi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 西班牙语 | xi | +| rec_pu_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 葡萄牙语 | pu | +| rec_ru_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 俄罗斯语 | ru | +| rec_ar_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 阿拉伯语 | ar | +| rec_hi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 印地语 | hi | +| rec_ug_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 维吾尔语 | ug | +| rec_fa_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 波斯语 | fa | +| rec_ur_ite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 乌尔都语 | ur | +| rec_rs_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 塞尔维亚(latin)语 | rs | +| rec_oc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 欧西坦语 | oc | +| rec_mr_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 马拉地语 | mr | +| rec_ne_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 尼泊尔语 | ne | +| rec_rsc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 塞尔维亚(cyrillic)语 | rsc | +| rec_bg_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 保加利亚语 | bg | +| rec_uk_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 乌克兰语 | uk | +| rec_be_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 白俄罗斯语 | be | +| rec_te_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 泰卢固语 | te | +| rec_ka_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 卡纳达语 | ka | +| rec_ta_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 泰米尔语 | ta | 多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。 diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md index 8d932870..d1cc712f 100644 --- a/doc/doc_en/angle_class_en.md +++ b/doc/doc_en/angle_class_en.md @@ -66,7 +66,7 @@ Start training: ``` # Set PYTHONPATH path export PYTHONPATH=$PYTHONPATH:. -# GPU training Support single card and multi-card training, specify the card number through --gpus. If your paddle version is less than 2.0rc1, please use '--selected_gpus' +# GPU training Support single card and multi-card training, specify the card number through --gpus. # Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml ``` diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index 5c4a63e2..3ee9092c 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -76,7 +76,7 @@ You can also use `-o` to change the training parameters without modifying the ym python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 # multi-GPU training -# Set the GPU ID used by the '--gpus' parameter; If your paddle version is less than 2.0rc1, please use '--selected_gpus' +# Set the GPU ID used by the '--gpus' parameter. python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index 98e3ef63..ccbb7184 100755 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -315,10 +315,10 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ### 4. MULTILINGAUL MODEL INFERENCE If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, -You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition: +You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: ``` -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf" +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` ![](../imgs_words/korean/1.jpg) diff --git a/doc/doc_en/installation_en.md b/doc/doc_en/installation_en.md index 7f1f0e83..35c1881d 100644 --- a/doc/doc_en/installation_en.md +++ b/doc/doc_en/installation_en.md @@ -3,7 +3,7 @@ After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility. PaddleOCR working environment: -- PaddlePaddle 1.8+, Recommend PaddlePaddle 2.0rc1 +- PaddlePaddle 2.0.0 - python3.7 - glibc 2.23 @@ -38,10 +38,10 @@ sudo docker container exec -it ppocr /bin/bash pip3 install --upgrade pip # If you have cuda9 or cuda10 installed on your machine, please run the following command to install -python3 -m pip install paddlepaddle-gpu==2.0rc1 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple # If you only have cpu on your machine, please run the following command to install -python3 -m pip install paddlepaddle==2.0rc1 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple ``` For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation. diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md index 3eb0cd23..33033f83 100644 --- a/doc/doc_en/models_list_en.md +++ b/doc/doc_en/models_list_en.md @@ -93,7 +93,7 @@ python3 generate_multi_language_configs.py -l it \ |model name|description|config|model size|download| | --- | --- | --- | --- | --- | | french_mobile_v2.0_rec |Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) | -| german_mobile_v2.0_rec |Lightweight model for French recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) | +| german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) | | korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) | | japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) | | it_mobile_v2.0_rec |Lightweight model for Italian recognition|rec_it_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) | diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md index 7723d20b..22f89cde 100644 --- a/doc/doc_en/recognition_en.md +++ b/doc/doc_en/recognition_en.md @@ -266,15 +266,116 @@ Eval: - Multi-language -PaddleOCR also provides multi-language. The configuration file in `configs/rec/multi_languages` provides multi-language configuration files. Currently, the multi-language algorithms supported by PaddleOCR are: +PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is +provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 + +There are two ways to create the required configuration file:： + +1. Automatically generated by script + +[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models + +- Take Italian as an example, if your data is prepared in the following format: + ``` + |-train_data + |- it_train.txt # train_set label + |- it_val.txt # val_set label + |- data + |- word_001.jpg + |- word_002.jpg + |- word_003.jpg + | ... + ``` + + You can use the default parameters to generate a configuration file: + + ```bash + # The code needs to be run in the specified directory + cd PaddleOCR/configs/rec/multi_language/ + # Set the configuration file of the language to be generated through the -l or --language parameter. + # This command will write the default parameters into the configuration file + python3 generate_multi_language_configs.py -l it + ``` + +- If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters: + + ```bash + # -l or --language field is required + # --train to modify the training set + # --val to modify the validation set + # --data_dir to modify the data set directory + # --dict to modify the dict path + # -o to modify the corresponding default parameters + cd PaddleOCR/configs/rec/multi_language/ + python3 generate_multi_language_configs.py -l it \ # language + --train {path/of/train_label.txt} \ # path of train_label + --val {path/of/val_label.txt} \ # path of val_label + --data_dir {train_data/path} \ # root directory of training data + --dict {path/of/dict} \ # path of dict + -o Global.use_gpu=False # whether to use gpu + ... + + ``` + +2. Manually modify the configuration file + + You can also manually modify the following fields in the template: + + ``` + Global: + use_gpu: True + epoch_num: 500 + ... + character_type: it # language + character_dict_path: {path/of/dict} # path of dict + + Train: + dataset: + name: SimpleDataSet + data_dir: train_data/ # root directory of training data + label_file_list: ["./train_data/train_list.txt"] # train label path + ... + + Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/ # root directory of val data + label_file_list: ["./train_data/val_list.txt"] # val label path + ... + + ``` + +Currently, the multi-language algorithms supported by PaddleOCR are: + +| Configuration file | Algorithm name | backbone | trans | seq | pred | language | character_type | +| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: | +| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | chinese traditional | chinese_cht| +| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English(Case sensitive) | EN | +| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French | french | +| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German | german | +| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese | japan | +| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean | korean | +| rec_it_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Italian | it | +| rec_xi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Spanish | xi | +| rec_pu_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Portuguese | pu | +| rec_ru_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Russia | ru | +| rec_ar_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Arabic | ar | +| rec_hi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Hindi | hi | +| rec_ug_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Uyghur | ug | +| rec_fa_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Persian(Farsi) | fa | +| rec_ur_ite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Urdu | ur | +| rec_rs_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Serbian(latin) | rs | +| rec_oc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Occitan | oc | +| rec_mr_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Marathi | mr | +| rec_ne_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Nepali | ne | +| rec_rsc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Serbian(cyrillic) | rsc | +| rec_bg_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Bulgarian | bg | +| rec_uk_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Ukranian | uk | +| rec_be_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Belarusian | be | +| rec_te_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Telugu | te | +| rec_ka_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Kannada | ka | +| rec_ta_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Tamil | ta | -| Configuration file | Algorithm name | backbone | trans | seq | pred | language ｜ -| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | -| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English ｜ -| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French ｜ -| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German ｜ -| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese ｜ -| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean ｜ The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi. diff --git a/doc/fonts/arabic.ttf b/doc/fonts/arabic.ttf new file mode 100644 index 00000000..064b6041 Binary files /dev/null and b/doc/fonts/arabic.ttf differ diff --git a/doc/fonts/chinese_cht.ttf b/doc/fonts/chinese_cht.ttf new file mode 100644 index 00000000..3416754f Binary files /dev/null and b/doc/fonts/chinese_cht.ttf differ diff --git a/doc/fonts/cyrillic.ttf b/doc/fonts/cyrillic.ttf new file mode 100644 index 00000000..be4bf660 Binary files /dev/null and b/doc/fonts/cyrillic.ttf differ diff --git a/doc/french.ttf b/doc/fonts/french.ttf similarity index 100% rename from doc/french.ttf rename to doc/fonts/french.ttf diff --git a/doc/german.ttf b/doc/fonts/german.ttf similarity index 100% rename from doc/german.ttf rename to doc/fonts/german.ttf diff --git a/doc/fonts/hindi.ttf b/doc/fonts/hindi.ttf new file mode 100644 index 00000000..8b0c36f5 Binary files /dev/null and b/doc/fonts/hindi.ttf differ diff --git a/doc/japan.ttc b/doc/fonts/japan.ttc similarity index 100% rename from doc/japan.ttc rename to doc/fonts/japan.ttc diff --git a/doc/fonts/kannada.ttf b/doc/fonts/kannada.ttf new file mode 100644 index 00000000..43b60d42 Binary files /dev/null and b/doc/fonts/kannada.ttf differ diff --git a/doc/korean.ttf b/doc/fonts/korean.ttf similarity index 100% rename from doc/korean.ttf rename to doc/fonts/korean.ttf diff --git a/doc/fonts/latin.ttf b/doc/fonts/latin.ttf new file mode 100644 index 00000000..e392413a Binary files /dev/null and b/doc/fonts/latin.ttf differ diff --git a/doc/fonts/marathi.ttf b/doc/fonts/marathi.ttf new file mode 100644 index 00000000..a796d3ed Binary files /dev/null and b/doc/fonts/marathi.ttf differ diff --git a/doc/fonts/nepali.ttf b/doc/fonts/nepali.ttf new file mode 100644 index 00000000..8b0c36f5 Binary files /dev/null and b/doc/fonts/nepali.ttf differ diff --git a/doc/fonts/persian.ttf b/doc/fonts/persian.ttf new file mode 100644 index 00000000..bdb1c8d7 Binary files /dev/null and b/doc/fonts/persian.ttf differ diff --git a/doc/simfang.ttf b/doc/fonts/simfang.ttf similarity index 100% rename from doc/simfang.ttf rename to doc/fonts/simfang.ttf diff --git a/doc/fonts/spanish.ttf b/doc/fonts/spanish.ttf new file mode 100644 index 00000000..532353d2 Binary files /dev/null and b/doc/fonts/spanish.ttf differ diff --git a/doc/fonts/tamil.ttf b/doc/fonts/tamil.ttf new file mode 100644 index 00000000..2e9998e8 Binary files /dev/null and b/doc/fonts/tamil.ttf differ diff --git a/doc/fonts/telugu.ttf b/doc/fonts/telugu.ttf new file mode 100644 index 00000000..12c91e41 Binary files /dev/null and b/doc/fonts/telugu.ttf differ diff --git a/doc/fonts/urdu.ttf b/doc/fonts/urdu.ttf new file mode 100644 index 00000000..625feee2 Binary files /dev/null and b/doc/fonts/urdu.ttf differ diff --git a/doc/fonts/uyghur.ttf b/doc/fonts/uyghur.ttf new file mode 100644 index 00000000..625feee2 Binary files /dev/null and b/doc/fonts/uyghur.ttf differ diff --git a/ppocr/data/__init__.py b/ppocr/data/__init__.py index 4e1ff0ae..7cb50d7a 100644 --- a/ppocr/data/__init__.py +++ b/ppocr/data/__init__.py @@ -33,7 +33,7 @@ import paddle.distributed as dist from ppocr.data.imaug import transform, create_operators from ppocr.data.simple_dataset import SimpleDataSet -from ppocr.data.lmdb_dataset import LMDBDateSet +from ppocr.data.lmdb_dataset import LMDBDataSet __all__ = ['build_dataloader', 'transform', 'create_operators'] @@ -51,20 +51,21 @@ signal.signal(signal.SIGINT, term_mp) signal.signal(signal.SIGTERM, term_mp) -def build_dataloader(config, mode, device, logger): +def build_dataloader(config, mode, device, logger, seed=None): config = copy.deepcopy(config) - support_dict = ['SimpleDataSet', 'LMDBDateSet'] + support_dict = ['SimpleDataSet', 'LMDBDataSet'] module_name = config[mode]['dataset']['name'] assert module_name in support_dict, Exception( 'DataSet only support {}'.format(support_dict)) assert mode in ['Train', 'Eval', 'Test' ], "Mode should be Train, Eval or Test." - dataset = eval(module_name)(config, mode, logger) + dataset = eval(module_name)(config, mode, logger, seed) loader_config = config[mode]['loader'] batch_size = loader_config['batch_size_per_card'] drop_last = loader_config['drop_last'] + shuffle = loader_config['shuffle'] num_workers = loader_config['num_workers'] if 'use_shared_memory' in loader_config.keys(): use_shared_memory = loader_config['use_shared_memory'] @@ -75,14 +76,14 @@ def build_dataloader(config, mode, device, logger): batch_sampler = DistributedBatchSampler( dataset=dataset, batch_size=batch_size, - shuffle=False, + shuffle=shuffle, drop_last=drop_last) else: #Distribute data to single card batch_sampler = BatchSampler( dataset=dataset, batch_size=batch_size, - shuffle=False, + shuffle=shuffle, drop_last=drop_last) data_loader = DataLoader( diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 6ea4dd8e..250ac75e 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -21,7 +21,7 @@ from .make_border_map import MakeBorderMap from .make_shrink_map import MakeShrinkMap from .random_crop_data import EastRandomCropData, PSERandomCrop -from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg +from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg from .randaugment import RandAugment from .operators import * from .label_ops import * diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index af3308a5..61c0c196 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -18,6 +18,7 @@ from __future__ import print_function from __future__ import unicode_literals import numpy as np +import string class ClsLabelEncode(object): @@ -92,18 +93,28 @@ class BaseRecLabelEncode(object): character_type='ch', use_space_char=False): support_character_type = [ - 'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean' + 'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean', + 'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', + 'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', + 'mr', 'ne' ] assert character_type in support_character_type, "Only {} are supported now but get {}".format( support_character_type, character_type) self.max_text_len = max_text_length + self.beg_str = "sos" + self.end_str = "eos" if character_type == "en": self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" dict_character = list(self.character_str) - elif character_type in ["ch", "french", "german", "japan", "korean"]: + elif character_type == "EN_symbol": + # same with ASTER setting (use 94 char). + self.character_str = string.printable[:-6] + dict_character = list(self.character_str) + elif character_type in support_character_type: self.character_str = "" - assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch" + assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format( + character_type) with open(character_dict_path, "rb") as fin: lines = fin.readlines() for line in lines: @@ -112,11 +123,6 @@ class BaseRecLabelEncode(object): if use_space_char: self.character_str += " " dict_character = list(self.character_str) - elif character_type == "en_sensitive": - # same with ASTER setting (use 94 char). - import string - self.character_str = string.printable[:-6] - dict_character = list(self.character_str) self.character_type = character_type dict_character = self.add_special_char(dict_character) self.dict = {} @@ -213,3 +219,49 @@ class AttnLabelEncode(BaseRecLabelEncode): assert False, "Unsupport type %s in get_beg_end_flag_idx" \ % beg_or_end return idx + + +class SRNLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length=25, + character_dict_path=None, + character_type='en', + use_space_char=False, + **kwargs): + super(SRNLabelEncode, + self).__init__(max_text_length, character_dict_path, + character_type, use_space_char) + + def add_special_char(self, dict_character): + dict_character = dict_character + [self.beg_str, self.end_str] + return dict_character + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + char_num = len(self.character_str) + if text is None: + return None + if len(text) > self.max_text_len: + return None + data['length'] = np.array(len(text)) + text = text + [char_num] * (self.max_text_len - len(text)) + data['label'] = np.array(text) + return data + + def get_ignored_tokens(self): + beg_idx = self.get_beg_end_flag_idx("beg") + end_idx = self.get_beg_end_flag_idx("end") + return [beg_idx, end_idx] + + def get_beg_end_flag_idx(self, beg_or_end): + if beg_or_end == "beg": + idx = np.array(self.dict[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict[self.end_str]) + else: + assert False, "Unsupport type %s in get_beg_end_flag_idx" \ + % beg_or_end + return idx diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py index 2ccb2d1d..28e6bd0b 100644 --- a/ppocr/data/imaug/rec_img_aug.py +++ b/ppocr/data/imaug/rec_img_aug.py @@ -12,20 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - import math import cv2 import numpy as np @@ -77,6 +63,26 @@ class RecResizeImg(object): return data +class SRNRecResizeImg(object): + def __init__(self, image_shape, num_heads, max_text_length, **kwargs): + self.image_shape = image_shape + self.num_heads = num_heads + self.max_text_length = max_text_length + + def __call__(self, data): + img = data['image'] + norm_img = resize_norm_img_srn(img, self.image_shape) + data['image'] = norm_img + [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ + srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length) + + data['encoder_word_pos'] = encoder_word_pos + data['gsrm_word_pos'] = gsrm_word_pos + data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1 + data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2 + return data + + def resize_norm_img(img, image_shape): imgC, imgH, imgW = image_shape h = img.shape[0] @@ -103,7 +109,7 @@ def resize_norm_img(img, image_shape): def resize_norm_img_chinese(img, image_shape): imgC, imgH, imgW = image_shape # todo: change to 0 and modified image shape - max_wh_ratio = 0 + max_wh_ratio = imgW * 1.0 / imgH h, w = img.shape[0], img.shape[1] ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, ratio) @@ -126,6 +132,60 @@ def resize_norm_img_chinese(img, image_shape): return padding_im +def resize_norm_img_srn(img, image_shape): + imgC, imgH, imgW = image_shape + + img_black = np.zeros((imgH, imgW)) + im_hei = img.shape[0] + im_wid = img.shape[1] + + if im_wid <= im_hei * 1: + img_new = cv2.resize(img, (imgH * 1, imgH)) + elif im_wid <= im_hei * 2: + img_new = cv2.resize(img, (imgH * 2, imgH)) + elif im_wid <= im_hei * 3: + img_new = cv2.resize(img, (imgH * 3, imgH)) + else: + img_new = cv2.resize(img, (imgW, imgH)) + + img_np = np.asarray(img_new) + img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) + img_black[:, 0:img_np.shape[1]] = img_np + img_black = img_black[:, :, np.newaxis] + + row, col, c = img_black.shape + c = 1 + + return np.reshape(img_black, (c, row, col)).astype(np.float32) + + +def srn_other_inputs(image_shape, num_heads, max_text_length): + + imgC, imgH, imgW = image_shape + feature_dim = int((imgH / 8) * (imgW / 8)) + + encoder_word_pos = np.array(range(0, feature_dim)).reshape( + (feature_dim, 1)).astype('int64') + gsrm_word_pos = np.array(range(0, max_text_length)).reshape( + (max_text_length, 1)).astype('int64') + + gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) + gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( + [1, max_text_length, max_text_length]) + gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, + [num_heads, 1, 1]) * [-1e9] + + gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( + [1, max_text_length, max_text_length]) + gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, + [num_heads, 1, 1]) * [-1e9] + + return [ + encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2 + ] + + def flag(): """ flag diff --git a/ppocr/data/lmdb_dataset.py b/ppocr/data/lmdb_dataset.py index e7bb6dd3..bd0630f6 100644 --- a/ppocr/data/lmdb_dataset.py +++ b/ppocr/data/lmdb_dataset.py @@ -21,7 +21,7 @@ from .imaug import transform, create_operators class LMDBDateSet(Dataset): - def __init__(self, config, mode, logger): + def __init__(self, config, mode, logger, seed=None): super(LMDBDateSet, self).__init__() global_config = config['Global'] diff --git a/ppocr/data/simple_dataset.py b/ppocr/data/simple_dataset.py index ab17dd1a..d2a86b0f 100644 --- a/ppocr/data/simple_dataset.py +++ b/ppocr/data/simple_dataset.py @@ -20,7 +20,7 @@ from .imaug import transform, create_operators class SimpleDataSet(Dataset): - def __init__(self, config, mode, logger): + def __init__(self, config, mode, logger, seed=None): super(SimpleDataSet, self).__init__() self.logger = logger @@ -41,6 +41,7 @@ class SimpleDataSet(Dataset): self.data_dir = dataset_config['data_dir'] self.do_shuffle = loader_config['shuffle'] + self.seed = seed logger.info("Initialize indexs of datasets:%s" % label_file_list) self.data_lines = self.get_image_info_list(label_file_list, ratio_list) self.data_idx_order_list = list(range(len(self.data_lines))) @@ -55,6 +56,7 @@ class SimpleDataSet(Dataset): for idx, file in enumerate(file_list): with open(file, "rb") as f: lines = f.readlines() + random.seed(self.seed) lines = random.sample(lines, round(len(lines) * ratio_list[idx])) data_lines.extend(lines) @@ -62,6 +64,7 @@ class SimpleDataSet(Dataset): def shuffle_data_random(self): if self.do_shuffle: + random.seed(self.seed) random.shuffle(self.data_lines) return diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 4673d35c..b280eb33 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -23,11 +23,14 @@ def build_loss(config): # rec loss from .rec_ctc_loss import CTCLoss + from .rec_srn_loss import SRNLoss # cls loss from .cls_loss import ClsLoss - support_dict = ['DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss'] + support_dict = [ + 'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'SRNLoss' + ] config = copy.deepcopy(config) module_name = config.pop('name') diff --git a/ppocr/losses/rec_srn_loss.py b/ppocr/losses/rec_srn_loss.py new file mode 100644 index 00000000..7d5b65eb --- /dev/null +++ b/ppocr/losses/rec_srn_loss.py @@ -0,0 +1,47 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn + + +class SRNLoss(nn.Layer): + def __init__(self, **kwargs): + super(SRNLoss, self).__init__() + self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="sum") + + def forward(self, predicts, batch): + predict = predicts['predict'] + word_predict = predicts['word_out'] + gsrm_predict = predicts['gsrm_out'] + label = batch[1] + + casted_label = paddle.cast(x=label, dtype='int64') + casted_label = paddle.reshape(x=casted_label, shape=[-1, 1]) + + cost_word = self.loss_func(word_predict, label=casted_label) + cost_gsrm = self.loss_func(gsrm_predict, label=casted_label) + cost_vsfd = self.loss_func(predict, label=casted_label) + + cost_word = paddle.reshape(x=paddle.sum(cost_word), shape=[1]) + cost_gsrm = paddle.reshape(x=paddle.sum(cost_gsrm), shape=[1]) + cost_vsfd = paddle.reshape(x=paddle.sum(cost_vsfd), shape=[1]) + + sum_cost = cost_word * 3.0 + cost_vsfd + cost_gsrm * 0.15 + + return {'loss': sum_cost, 'word_loss': cost_word, 'img_loss': cost_vsfd} diff --git a/ppocr/metrics/rec_metric.py b/ppocr/metrics/rec_metric.py index a86fc838..b3aa9f38 100644 --- a/ppocr/metrics/rec_metric.py +++ b/ppocr/metrics/rec_metric.py @@ -33,8 +33,6 @@ class RecMetric(object): if pred == target: correct_num += 1 all_num += 1 - # if all_num < 10 and kwargs.get('show_str', False): - # print('{} -> {}'.format(pred, target)) self.correct_num += correct_num self.all_num += all_num self.norm_edit_dis += norm_edit_dis @@ -50,7 +48,7 @@ class RecMetric(object): 'norm_edit_dis': 0, } """ - acc = self.correct_num / self.all_num + acc = 1.0 * self.correct_num / self.all_num norm_edit_dis = 1 - self.norm_edit_dis / self.all_num self.reset() return {'acc': acc, 'norm_edit_dis': norm_edit_dis} diff --git a/ppocr/modeling/architectures/base_model.py b/ppocr/modeling/architectures/base_model.py index ab44b53a..09b6e034 100644 --- a/ppocr/modeling/architectures/base_model.py +++ b/ppocr/modeling/architectures/base_model.py @@ -68,11 +68,14 @@ class BaseModel(nn.Layer): config["Head"]['in_channels'] = in_channels self.head = build_head(config["Head"]) - def forward(self, x): + def forward(self, x, data=None): if self.use_transform: x = self.transform(x) x = self.backbone(x) if self.use_neck: x = self.neck(x) - x = self.head(x) + if data is None: + x = self.head(x) + else: + x = self.head(x, data) return x diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index 43103e53..03c15508 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/ppocr/modeling/backbones/__init__.py @@ -24,7 +24,8 @@ def build_backbone(config, model_type): elif model_type == 'rec' or model_type == 'cls': from .rec_mobilenet_v3 import MobileNetV3 from .rec_resnet_vd import ResNet - support_dict = ['MobileNetV3', 'ResNet', 'ResNet_FPN'] + from .rec_resnet_fpn import ResNetFPN + support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN'] else: raise NotImplementedError diff --git a/ppocr/modeling/backbones/det_mobilenet_v3.py b/ppocr/modeling/backbones/det_mobilenet_v3.py index f97bcfca..bb451bbe 100755 --- a/ppocr/modeling/backbones/det_mobilenet_v3.py +++ b/ppocr/modeling/backbones/det_mobilenet_v3.py @@ -58,15 +58,15 @@ class MobileNetV3(nn.Layer): [5, 72, 40, True, 'relu', 2], [5, 120, 40, True, 'relu', 1], [5, 120, 40, True, 'relu', 1], - [3, 240, 80, False, 'hard_swish', 2], - [3, 200, 80, False, 'hard_swish', 1], - [3, 184, 80, False, 'hard_swish', 1], - [3, 184, 80, False, 'hard_swish', 1], - [3, 480, 112, True, 'hard_swish', 1], - [3, 672, 112, True, 'hard_swish', 1], - [5, 672, 160, True, 'hard_swish', 2], - [5, 960, 160, True, 'hard_swish', 1], - [5, 960, 160, True, 'hard_swish', 1], + [3, 240, 80, False, 'hardswish', 2], + [3, 200, 80, False, 'hardswish', 1], + [3, 184, 80, False, 'hardswish', 1], + [3, 184, 80, False, 'hardswish', 1], + [3, 480, 112, True, 'hardswish', 1], + [3, 672, 112, True, 'hardswish', 1], + [5, 672, 160, True, 'hardswish', 2], + [5, 960, 160, True, 'hardswish', 1], + [5, 960, 160, True, 'hardswish', 1], ] cls_ch_squeeze = 960 elif model_name == "small": @@ -75,14 +75,14 @@ class MobileNetV3(nn.Layer): [3, 16, 16, True, 'relu', 2], [3, 72, 24, False, 'relu', 2], [3, 88, 24, False, 'relu', 1], - [5, 96, 40, True, 'hard_swish', 2], - [5, 240, 40, True, 'hard_swish', 1], - [5, 240, 40, True, 'hard_swish', 1], - [5, 120, 48, True, 'hard_swish', 1], - [5, 144, 48, True, 'hard_swish', 1], - [5, 288, 96, True, 'hard_swish', 2], - [5, 576, 96, True, 'hard_swish', 1], - [5, 576, 96, True, 'hard_swish', 1], + [5, 96, 40, True, 'hardswish', 2], + [5, 240, 40, True, 'hardswish', 1], + [5, 240, 40, True, 'hardswish', 1], + [5, 120, 48, True, 'hardswish', 1], + [5, 144, 48, True, 'hardswish', 1], + [5, 288, 96, True, 'hardswish', 2], + [5, 576, 96, True, 'hardswish', 1], + [5, 576, 96, True, 'hardswish', 1], ] cls_ch_squeeze = 576 else: @@ -102,7 +102,7 @@ class MobileNetV3(nn.Layer): padding=1, groups=1, if_act=True, - act='hard_swish', + act='hardswish', name='conv1') self.stages = [] @@ -112,7 +112,8 @@ class MobileNetV3(nn.Layer): inplanes = make_divisible(inplanes * scale) for (k, exp, c, se, nl, s) in cfg: se = se and not self.disable_se - if s == 2 and i > 2: + start_idx = 2 if model_name == 'large' else 0 + if s == 2 and i > start_idx: self.out_channels.append(inplanes) self.stages.append(nn.Sequential(*block_list)) block_list = [] @@ -137,7 +138,7 @@ class MobileNetV3(nn.Layer): padding=0, groups=1, if_act=True, - act='hard_swish', + act='hardswish', name='conv_last')) self.stages.append(nn.Sequential(*block_list)) self.out_channels.append(make_divisible(scale * cls_ch_squeeze)) @@ -191,10 +192,11 @@ class ConvBNLayer(nn.Layer): if self.if_act: if self.act == "relu": x = F.relu(x) - elif self.act == "hard_swish": - x = F.activation.hard_swish(x) + elif self.act == "hardswish": + x = F.hardswish(x) else: - print("The activation function is selected incorrectly.") + print("The activation function({}) is selected incorrectly.". + format(self.act)) exit() return x @@ -281,5 +283,5 @@ class SEModule(nn.Layer): outputs = self.conv1(outputs) outputs = F.relu(outputs) outputs = self.conv2(outputs) - outputs = F.activation.hard_sigmoid(outputs) + outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5) return inputs * outputs diff --git a/ppocr/modeling/backbones/rec_mobilenet_v3.py b/ppocr/modeling/backbones/rec_mobilenet_v3.py index bdf4a616..1ff17159 100644 --- a/ppocr/modeling/backbones/rec_mobilenet_v3.py +++ b/ppocr/modeling/backbones/rec_mobilenet_v3.py @@ -51,15 +51,15 @@ class MobileNetV3(nn.Layer): [5, 72, 40, True, 'relu', (large_stride[2], 1)], [5, 120, 40, True, 'relu', 1], [5, 120, 40, True, 'relu', 1], - [3, 240, 80, False, 'hard_swish', 1], - [3, 200, 80, False, 'hard_swish', 1], - [3, 184, 80, False, 'hard_swish', 1], - [3, 184, 80, False, 'hard_swish', 1], - [3, 480, 112, True, 'hard_swish', 1], - [3, 672, 112, True, 'hard_swish', 1], - [5, 672, 160, True, 'hard_swish', (large_stride[3], 1)], - [5, 960, 160, True, 'hard_swish', 1], - [5, 960, 160, True, 'hard_swish', 1], + [3, 240, 80, False, 'hardswish', 1], + [3, 200, 80, False, 'hardswish', 1], + [3, 184, 80, False, 'hardswish', 1], + [3, 184, 80, False, 'hardswish', 1], + [3, 480, 112, True, 'hardswish', 1], + [3, 672, 112, True, 'hardswish', 1], + [5, 672, 160, True, 'hardswish', (large_stride[3], 1)], + [5, 960, 160, True, 'hardswish', 1], + [5, 960, 160, True, 'hardswish', 1], ] cls_ch_squeeze = 960 elif model_name == "small": @@ -68,14 +68,14 @@ class MobileNetV3(nn.Layer): [3, 16, 16, True, 'relu', (small_stride[0], 1)], [3, 72, 24, False, 'relu', (small_stride[1], 1)], [3, 88, 24, False, 'relu', 1], - [5, 96, 40, True, 'hard_swish', (small_stride[2], 1)], - [5, 240, 40, True, 'hard_swish', 1], - [5, 240, 40, True, 'hard_swish', 1], - [5, 120, 48, True, 'hard_swish', 1], - [5, 144, 48, True, 'hard_swish', 1], - [5, 288, 96, True, 'hard_swish', (small_stride[3], 1)], - [5, 576, 96, True, 'hard_swish', 1], - [5, 576, 96, True, 'hard_swish', 1], + [5, 96, 40, True, 'hardswish', (small_stride[2], 1)], + [5, 240, 40, True, 'hardswish', 1], + [5, 240, 40, True, 'hardswish', 1], + [5, 120, 48, True, 'hardswish', 1], + [5, 144, 48, True, 'hardswish', 1], + [5, 288, 96, True, 'hardswish', (small_stride[3], 1)], + [5, 576, 96, True, 'hardswish', 1], + [5, 576, 96, True, 'hardswish', 1], ] cls_ch_squeeze = 576 else: @@ -96,7 +96,7 @@ class MobileNetV3(nn.Layer): padding=1, groups=1, if_act=True, - act='hard_swish', + act='hardswish', name='conv1') i = 0 block_list = [] @@ -124,7 +124,7 @@ class MobileNetV3(nn.Layer): padding=0, groups=1, if_act=True, - act='hard_swish', + act='hardswish', name='conv_last') self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) diff --git a/ppocr/modeling/backbones/rec_resnet_fpn.py b/ppocr/modeling/backbones/rec_resnet_fpn.py new file mode 100644 index 00000000..a7e876a2 --- /dev/null +++ b/ppocr/modeling/backbones/rec_resnet_fpn.py @@ -0,0 +1,307 @@ +#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import paddle.fluid as fluid +import paddle +import numpy as np + +__all__ = ["ResNetFPN"] + + +class ResNetFPN(nn.Layer): + def __init__(self, in_channels=1, layers=50, **kwargs): + super(ResNetFPN, self).__init__() + supported_layers = { + 18: { + 'depth': [2, 2, 2, 2], + 'block_class': BasicBlock + }, + 34: { + 'depth': [3, 4, 6, 3], + 'block_class': BasicBlock + }, + 50: { + 'depth': [3, 4, 6, 3], + 'block_class': BottleneckBlock + }, + 101: { + 'depth': [3, 4, 23, 3], + 'block_class': BottleneckBlock + }, + 152: { + 'depth': [3, 8, 36, 3], + 'block_class': BottleneckBlock + } + } + stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)] + num_filters = [64, 128, 256, 512] + self.depth = supported_layers[layers]['depth'] + self.F = [] + self.conv = ConvBNLayer( + in_channels=in_channels, + out_channels=64, + kernel_size=7, + stride=2, + act="relu", + name="conv1") + self.block_list = [] + in_ch = 64 + if layers >= 50: + for block in range(len(self.depth)): + for i in range(self.depth[block]): + if layers in [101, 152] and block == 2: + if i == 0: + conv_name = "res" + str(block + 2) + "a" + else: + conv_name = "res" + str(block + 2) + "b" + str(i) + else: + conv_name = "res" + str(block + 2) + chr(97 + i) + block_list = self.add_sublayer( + "bottleneckBlock_{}_{}".format(block, i), + BottleneckBlock( + in_channels=in_ch, + out_channels=num_filters[block], + stride=stride_list[block] if i == 0 else 1, + name=conv_name)) + in_ch = num_filters[block] * 4 + self.block_list.append(block_list) + self.F.append(block_list) + else: + for block in range(len(self.depth)): + for i in range(self.depth[block]): + conv_name = "res" + str(block + 2) + chr(97 + i) + if i == 0 and block != 0: + stride = (2, 1) + else: + stride = (1, 1) + basic_block = self.add_sublayer( + conv_name, + BasicBlock( + in_channels=in_ch, + out_channels=num_filters[block], + stride=stride_list[block] if i == 0 else 1, + is_first=block == i == 0, + name=conv_name)) + in_ch = basic_block.out_channels + self.block_list.append(basic_block) + out_ch_list = [in_ch // 4, in_ch // 2, in_ch] + self.base_block = [] + self.conv_trans = [] + self.bn_block = [] + for i in [-2, -3]: + in_channels = out_ch_list[i + 1] + out_ch_list[i] + + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_0".format(i), + nn.Conv2D( + in_channels=in_channels, + out_channels=out_ch_list[i], + kernel_size=1, + weight_attr=ParamAttr(trainable=True), + bias_attr=ParamAttr(trainable=True)))) + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_1".format(i), + nn.Conv2D( + in_channels=out_ch_list[i], + out_channels=out_ch_list[i], + kernel_size=3, + padding=1, + weight_attr=ParamAttr(trainable=True), + bias_attr=ParamAttr(trainable=True)))) + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_2".format(i), + nn.BatchNorm( + num_channels=out_ch_list[i], + act="relu", + param_attr=ParamAttr(trainable=True), + bias_attr=ParamAttr(trainable=True)))) + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_3".format(i), + nn.Conv2D( + in_channels=out_ch_list[i], + out_channels=512, + kernel_size=1, + bias_attr=ParamAttr(trainable=True), + weight_attr=ParamAttr(trainable=True)))) + self.out_channels = 512 + + def __call__(self, x): + x = self.conv(x) + fpn_list = [] + F = [] + for i in range(len(self.depth)): + fpn_list.append(np.sum(self.depth[:i + 1])) + + for i, block in enumerate(self.block_list): + x = block(x) + for number in fpn_list: + if i + 1 == number: + F.append(x) + base = F[-1] + + j = 0 + for i, block in enumerate(self.base_block): + if i % 3 == 0 and i < 6: + j = j + 1 + b, c, w, h = F[-j - 1].shape + if [w, h] == list(base.shape[2:]): + base = base + else: + base = self.conv_trans[j - 1](base) + base = self.bn_block[j - 1](base) + base = paddle.concat([base, F[-j - 1]], axis=1) + base = block(base) + return base + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + groups=1, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=2 if stride == (1, 1) else kernel_size, + dilation=2 if stride == (1, 1) else 1, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'), + bias_attr=False, ) + + if name == "conv1": + bn_name = "bn_" + name + else: + bn_name = "bn" + name[3:] + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name=name + '.output.1.w_0'), + bias_attr=ParamAttr(name=name + '.output.1.b_0'), + moving_mean_name=bn_name + "_mean", + moving_variance_name=bn_name + "_variance") + + def __call__(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class ShortCut(nn.Layer): + def __init__(self, in_channels, out_channels, stride, name, is_first=False): + super(ShortCut, self).__init__() + self.use_conv = True + + if in_channels != out_channels or stride != 1 or is_first == True: + if stride == (1, 1): + self.conv = ConvBNLayer( + in_channels, out_channels, 1, 1, name=name) + else: # stride==(2,2) + self.conv = ConvBNLayer( + in_channels, out_channels, 1, stride, name=name) + else: + self.use_conv = False + + def forward(self, x): + if self.use_conv: + x = self.conv(x) + return x + + +class BottleneckBlock(nn.Layer): + def __init__(self, in_channels, out_channels, stride, name): + super(BottleneckBlock, self).__init__() + self.conv0 = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + act='relu', + name=name + "_branch2a") + self.conv1 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + act='relu', + name=name + "_branch2b") + + self.conv2 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels * 4, + kernel_size=1, + act=None, + name=name + "_branch2c") + + self.short = ShortCut( + in_channels=in_channels, + out_channels=out_channels * 4, + stride=stride, + is_first=False, + name=name + "_branch1") + self.out_channels = out_channels * 4 + + def forward(self, x): + y = self.conv0(x) + y = self.conv1(y) + y = self.conv2(y) + y = y + self.short(x) + y = F.relu(y) + return y + + +class BasicBlock(nn.Layer): + def __init__(self, in_channels, out_channels, stride, name, is_first): + super(BasicBlock, self).__init__() + self.conv0 = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + act='relu', + stride=stride, + name=name + "_branch2a") + self.conv1 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + act=None, + name=name + "_branch2b") + self.short = ShortCut( + in_channels=in_channels, + out_channels=out_channels, + stride=stride, + is_first=is_first, + name=name + "_branch1") + self.out_channels = out_channels + + def forward(self, x): + y = self.conv0(x) + y = self.conv1(y) + y = y + self.short(x) + return F.relu(y) diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 78074709..1a39ca41 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -23,10 +23,13 @@ def build_head(config): # rec head from .rec_ctc_head import CTCHead + from .rec_srn_head import SRNHead # cls head from .cls_head import ClsHead - support_dict = ['DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead'] + support_dict = [ + 'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'SRNHead' + ] module_name = config.pop('name') assert module_name in support_dict, Exception('head only support {}'.format( diff --git a/ppocr/modeling/heads/rec_srn_head.py b/ppocr/modeling/heads/rec_srn_head.py new file mode 100644 index 00000000..8aaf65e1 --- /dev/null +++ b/ppocr/modeling/heads/rec_srn_head.py @@ -0,0 +1,279 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import paddle.fluid as fluid +import numpy as np +from .self_attention import WrapEncoderForFeature +from .self_attention import WrapEncoder +from paddle.static import Program +from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN +import paddle.fluid.framework as framework + +from collections import OrderedDict +gradient_clip = 10 + + +class PVAM(nn.Layer): + def __init__(self, in_channels, char_num, max_text_length, num_heads, + num_encoder_tus, hidden_dims): + super(PVAM, self).__init__() + self.char_num = char_num + self.max_length = max_text_length + self.num_heads = num_heads + self.num_encoder_TUs = num_encoder_tus + self.hidden_dims = hidden_dims + # Transformer encoder + t = 256 + c = 512 + self.wrap_encoder_for_feature = WrapEncoderForFeature( + src_vocab_size=1, + max_length=t, + n_layer=self.num_encoder_TUs, + n_head=self.num_heads, + d_key=int(self.hidden_dims / self.num_heads), + d_value=int(self.hidden_dims / self.num_heads), + d_model=self.hidden_dims, + d_inner_hid=self.hidden_dims, + prepostprocess_dropout=0.1, + attention_dropout=0.1, + relu_dropout=0.1, + preprocess_cmd="n", + postprocess_cmd="da", + weight_sharing=True) + + # PVAM + self.flatten0 = paddle.nn.Flatten(start_axis=0, stop_axis=1) + self.fc0 = paddle.nn.Linear( + in_features=in_channels, + out_features=in_channels, ) + self.emb = paddle.nn.Embedding( + num_embeddings=self.max_length, embedding_dim=in_channels) + self.flatten1 = paddle.nn.Flatten(start_axis=0, stop_axis=2) + self.fc1 = paddle.nn.Linear( + in_features=in_channels, out_features=1, bias_attr=False) + + def forward(self, inputs, encoder_word_pos, gsrm_word_pos): + b, c, h, w = inputs.shape + conv_features = paddle.reshape(inputs, shape=[-1, c, h * w]) + conv_features = paddle.transpose(conv_features, perm=[0, 2, 1]) + # transformer encoder + b, t, c = conv_features.shape + + enc_inputs = [conv_features, encoder_word_pos, None] + word_features = self.wrap_encoder_for_feature(enc_inputs) + + # pvam + b, t, c = word_features.shape + word_features = self.fc0(word_features) + word_features_ = paddle.reshape(word_features, [-1, 1, t, c]) + word_features_ = paddle.tile(word_features_, [1, self.max_length, 1, 1]) + word_pos_feature = self.emb(gsrm_word_pos) + word_pos_feature_ = paddle.reshape(word_pos_feature, + [-1, self.max_length, 1, c]) + word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1]) + y = word_pos_feature_ + word_features_ + y = F.tanh(y) + attention_weight = self.fc1(y) + attention_weight = paddle.reshape( + attention_weight, shape=[-1, self.max_length, t]) + attention_weight = F.softmax(attention_weight, axis=-1) + pvam_features = paddle.matmul(attention_weight, + word_features) #[b, max_length, c] + return pvam_features + + +class GSRM(nn.Layer): + def __init__(self, in_channels, char_num, max_text_length, num_heads, + num_encoder_tus, num_decoder_tus, hidden_dims): + super(GSRM, self).__init__() + self.char_num = char_num + self.max_length = max_text_length + self.num_heads = num_heads + self.num_encoder_TUs = num_encoder_tus + self.num_decoder_TUs = num_decoder_tus + self.hidden_dims = hidden_dims + + self.fc0 = paddle.nn.Linear( + in_features=in_channels, out_features=self.char_num) + self.wrap_encoder0 = WrapEncoder( + src_vocab_size=self.char_num + 1, + max_length=self.max_length, + n_layer=self.num_decoder_TUs, + n_head=self.num_heads, + d_key=int(self.hidden_dims / self.num_heads), + d_value=int(self.hidden_dims / self.num_heads), + d_model=self.hidden_dims, + d_inner_hid=self.hidden_dims, + prepostprocess_dropout=0.1, + attention_dropout=0.1, + relu_dropout=0.1, + preprocess_cmd="n", + postprocess_cmd="da", + weight_sharing=True) + + self.wrap_encoder1 = WrapEncoder( + src_vocab_size=self.char_num + 1, + max_length=self.max_length, + n_layer=self.num_decoder_TUs, + n_head=self.num_heads, + d_key=int(self.hidden_dims / self.num_heads), + d_value=int(self.hidden_dims / self.num_heads), + d_model=self.hidden_dims, + d_inner_hid=self.hidden_dims, + prepostprocess_dropout=0.1, + attention_dropout=0.1, + relu_dropout=0.1, + preprocess_cmd="n", + postprocess_cmd="da", + weight_sharing=True) + + self.mul = lambda x: paddle.matmul(x=x, + y=self.wrap_encoder0.prepare_decoder.emb0.weight, + transpose_y=True) + + def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2): + # ===== GSRM Visual-to-semantic embedding block ===== + b, t, c = inputs.shape + pvam_features = paddle.reshape(inputs, [-1, c]) + word_out = self.fc0(pvam_features) + word_ids = paddle.argmax(F.softmax(word_out), axis=1) + word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1]) + + #===== GSRM Semantic reasoning block ===== + """ + This module is achieved through bi-transformers, + ngram_feature1 is the froward one, ngram_fetaure2 is the backward one + """ + pad_idx = self.char_num + + word1 = paddle.cast(word_ids, "float32") + word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC") + word1 = paddle.cast(word1, "int64") + word1 = word1[:, :-1, :] + word2 = word_ids + + enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1] + enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2] + + gsrm_feature1 = self.wrap_encoder0(enc_inputs_1) + gsrm_feature2 = self.wrap_encoder1(enc_inputs_2) + + gsrm_feature2 = F.pad(gsrm_feature2, [0, 1], + value=0., + data_format="NLC") + gsrm_feature2 = gsrm_feature2[:, 1:, ] + gsrm_features = gsrm_feature1 + gsrm_feature2 + + gsrm_out = self.mul(gsrm_features) + + b, t, c = gsrm_out.shape + gsrm_out = paddle.reshape(gsrm_out, [-1, c]) + + return gsrm_features, word_out, gsrm_out + + +class VSFD(nn.Layer): + def __init__(self, in_channels=512, pvam_ch=512, char_num=38): + super(VSFD, self).__init__() + self.char_num = char_num + self.fc0 = paddle.nn.Linear( + in_features=in_channels * 2, out_features=pvam_ch) + self.fc1 = paddle.nn.Linear( + in_features=pvam_ch, out_features=self.char_num) + + def forward(self, pvam_feature, gsrm_feature): + b, t, c1 = pvam_feature.shape + b, t, c2 = gsrm_feature.shape + combine_feature_ = paddle.concat([pvam_feature, gsrm_feature], axis=2) + img_comb_feature_ = paddle.reshape( + combine_feature_, shape=[-1, c1 + c2]) + img_comb_feature_map = self.fc0(img_comb_feature_) + img_comb_feature_map = F.sigmoid(img_comb_feature_map) + img_comb_feature_map = paddle.reshape( + img_comb_feature_map, shape=[-1, t, c1]) + combine_feature = img_comb_feature_map * pvam_feature + ( + 1.0 - img_comb_feature_map) * gsrm_feature + img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1]) + + out = self.fc1(img_comb_feature) + return out + + +class SRNHead(nn.Layer): + def __init__(self, in_channels, out_channels, max_text_length, num_heads, + num_encoder_TUs, num_decoder_TUs, hidden_dims, **kwargs): + super(SRNHead, self).__init__() + self.char_num = out_channels + self.max_length = max_text_length + self.num_heads = num_heads + self.num_encoder_TUs = num_encoder_TUs + self.num_decoder_TUs = num_decoder_TUs + self.hidden_dims = hidden_dims + + self.pvam = PVAM( + in_channels=in_channels, + char_num=self.char_num, + max_text_length=self.max_length, + num_heads=self.num_heads, + num_encoder_tus=self.num_encoder_TUs, + hidden_dims=self.hidden_dims) + + self.gsrm = GSRM( + in_channels=in_channels, + char_num=self.char_num, + max_text_length=self.max_length, + num_heads=self.num_heads, + num_encoder_tus=self.num_encoder_TUs, + num_decoder_tus=self.num_decoder_TUs, + hidden_dims=self.hidden_dims) + self.vsfd = VSFD(in_channels=in_channels) + + self.gsrm.wrap_encoder1.prepare_decoder.emb0 = self.gsrm.wrap_encoder0.prepare_decoder.emb0 + + def forward(self, inputs, others): + encoder_word_pos = others[0] + gsrm_word_pos = others[1] + gsrm_slf_attn_bias1 = others[2] + gsrm_slf_attn_bias2 = others[3] + + pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos) + + gsrm_feature, word_out, gsrm_out = self.gsrm( + pvam_feature, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2) + + final_out = self.vsfd(pvam_feature, gsrm_feature) + if not self.training: + final_out = F.softmax(final_out, axis=1) + + _, decoded_out = paddle.topk(final_out, k=1) + + predicts = OrderedDict([ + ('predict', final_out), + ('pvam_feature', pvam_feature), + ('decoded_out', decoded_out), + ('word_out', word_out), + ('gsrm_out', gsrm_out), + ]) + + return predicts diff --git a/ppocr/modeling/heads/self_attention.py b/ppocr/modeling/heads/self_attention.py new file mode 100644 index 00000000..51d5198f --- /dev/null +++ b/ppocr/modeling/heads/self_attention.py @@ -0,0 +1,409 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import paddle +from paddle import ParamAttr, nn +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import paddle.fluid as fluid +import numpy as np +gradient_clip = 10 + + +class WrapEncoderForFeature(nn.Layer): + def __init__(self, + src_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + bos_idx=0): + super(WrapEncoderForFeature, self).__init__() + + self.prepare_encoder = PrepareEncoder( + src_vocab_size, + d_model, + max_length, + prepostprocess_dropout, + bos_idx=bos_idx, + word_emb_param_name="src_word_emb_table") + self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model, + d_inner_hid, prepostprocess_dropout, + attention_dropout, relu_dropout, preprocess_cmd, + postprocess_cmd) + + def forward(self, enc_inputs): + conv_features, src_pos, src_slf_attn_bias = enc_inputs + enc_input = self.prepare_encoder(conv_features, src_pos) + enc_output = self.encoder(enc_input, src_slf_attn_bias) + return enc_output + + +class WrapEncoder(nn.Layer): + """ + embedder + encoder + """ + + def __init__(self, + src_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + bos_idx=0): + super(WrapEncoder, self).__init__() + + self.prepare_decoder = PrepareDecoder( + src_vocab_size, + d_model, + max_length, + prepostprocess_dropout, + bos_idx=bos_idx) + self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model, + d_inner_hid, prepostprocess_dropout, + attention_dropout, relu_dropout, preprocess_cmd, + postprocess_cmd) + + def forward(self, enc_inputs): + src_word, src_pos, src_slf_attn_bias = enc_inputs + enc_input = self.prepare_decoder(src_word, src_pos) + enc_output = self.encoder(enc_input, src_slf_attn_bias) + return enc_output + + +class Encoder(nn.Layer): + """ + encoder + """ + + def __init__(self, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd="n", + postprocess_cmd="da"): + + super(Encoder, self).__init__() + + self.encoder_layers = list() + for i in range(n_layer): + self.encoder_layers.append( + self.add_sublayer( + "layer_%d" % i, + EncoderLayer(n_head, d_key, d_value, d_model, d_inner_hid, + prepostprocess_dropout, attention_dropout, + relu_dropout, preprocess_cmd, + postprocess_cmd))) + self.processer = PrePostProcessLayer(preprocess_cmd, d_model, + prepostprocess_dropout) + + def forward(self, enc_input, attn_bias): + for encoder_layer in self.encoder_layers: + enc_output = encoder_layer(enc_input, attn_bias) + enc_input = enc_output + enc_output = self.processer(enc_output) + return enc_output + + +class EncoderLayer(nn.Layer): + """ + EncoderLayer + """ + + def __init__(self, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd="n", + postprocess_cmd="da"): + + super(EncoderLayer, self).__init__() + self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model, + prepostprocess_dropout) + self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head, + attention_dropout) + self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model, + prepostprocess_dropout) + + self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model, + prepostprocess_dropout) + self.ffn = FFN(d_inner_hid, d_model, relu_dropout) + self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model, + prepostprocess_dropout) + + def forward(self, enc_input, attn_bias): + attn_output = self.self_attn( + self.preprocesser1(enc_input), None, None, attn_bias) + attn_output = self.postprocesser1(attn_output, enc_input) + ffn_output = self.ffn(self.preprocesser2(attn_output)) + ffn_output = self.postprocesser2(ffn_output, attn_output) + return ffn_output + + +class MultiHeadAttention(nn.Layer): + """ + Multi-Head Attention + """ + + def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.): + super(MultiHeadAttention, self).__init__() + self.n_head = n_head + self.d_key = d_key + self.d_value = d_value + self.d_model = d_model + self.dropout_rate = dropout_rate + self.q_fc = paddle.nn.Linear( + in_features=d_model, out_features=d_key * n_head, bias_attr=False) + self.k_fc = paddle.nn.Linear( + in_features=d_model, out_features=d_key * n_head, bias_attr=False) + self.v_fc = paddle.nn.Linear( + in_features=d_model, out_features=d_value * n_head, bias_attr=False) + self.proj_fc = paddle.nn.Linear( + in_features=d_value * n_head, out_features=d_model, bias_attr=False) + + def _prepare_qkv(self, queries, keys, values, cache=None): + if keys is None: # self-attention + keys, values = queries, queries + static_kv = False + else: # cross-attention + static_kv = True + + q = self.q_fc(queries) + q = paddle.reshape(x=q, shape=[0, 0, self.n_head, self.d_key]) + q = paddle.transpose(x=q, perm=[0, 2, 1, 3]) + + if cache is not None and static_kv and "static_k" in cache: + # for encoder-decoder attention in inference and has cached + k = cache["static_k"] + v = cache["static_v"] + else: + k = self.k_fc(keys) + v = self.v_fc(values) + k = paddle.reshape(x=k, shape=[0, 0, self.n_head, self.d_key]) + k = paddle.transpose(x=k, perm=[0, 2, 1, 3]) + v = paddle.reshape(x=v, shape=[0, 0, self.n_head, self.d_value]) + v = paddle.transpose(x=v, perm=[0, 2, 1, 3]) + + if cache is not None: + if static_kv and not "static_k" in cache: + # for encoder-decoder attention in inference and has not cached + cache["static_k"], cache["static_v"] = k, v + elif not static_kv: + # for decoder self-attention in inference + cache_k, cache_v = cache["k"], cache["v"] + k = paddle.concat([cache_k, k], axis=2) + v = paddle.concat([cache_v, v], axis=2) + cache["k"], cache["v"] = k, v + + return q, k, v + + def forward(self, queries, keys, values, attn_bias, cache=None): + # compute q ,k ,v + keys = queries if keys is None else keys + values = keys if values is None else values + q, k, v = self._prepare_qkv(queries, keys, values, cache) + + # scale dot product attention + product = paddle.matmul(x=q, y=k, transpose_y=True) + product = product * self.d_model**-0.5 + if attn_bias is not None: + product += attn_bias + weights = F.softmax(product) + if self.dropout_rate: + weights = F.dropout( + weights, p=self.dropout_rate, mode="downscale_in_infer") + out = paddle.matmul(weights, v) + + # combine heads + out = paddle.transpose(out, perm=[0, 2, 1, 3]) + out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]]) + + # project to output + out = self.proj_fc(out) + + return out + + +class PrePostProcessLayer(nn.Layer): + """ + PrePostProcessLayer + """ + + def __init__(self, process_cmd, d_model, dropout_rate): + super(PrePostProcessLayer, self).__init__() + self.process_cmd = process_cmd + self.functors = [] + for cmd in self.process_cmd: + if cmd == "a": # add residual connection + self.functors.append(lambda x, y: x + y if y is not None else x) + elif cmd == "n": # add layer normalization + self.functors.append( + self.add_sublayer( + "layer_norm_%d" % len( + self.sublayers(include_sublayers=False)), + paddle.nn.LayerNorm( + normalized_shape=d_model, + weight_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(1.)), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.))))) + elif cmd == "d": # add dropout + self.functors.append(lambda x: F.dropout( + x, p=dropout_rate, mode="downscale_in_infer") + if dropout_rate else x) + + def forward(self, x, residual=None): + for i, cmd in enumerate(self.process_cmd): + if cmd == "a": + x = self.functors[i](x, residual) + else: + x = self.functors[i](x) + return x + + +class PrepareEncoder(nn.Layer): + def __init__(self, + src_vocab_size, + src_emb_dim, + src_max_len, + dropout_rate=0, + bos_idx=0, + word_emb_param_name=None, + pos_enc_param_name=None): + super(PrepareEncoder, self).__init__() + self.src_emb_dim = src_emb_dim + self.src_max_len = src_max_len + self.emb = paddle.nn.Embedding( + num_embeddings=self.src_max_len, + embedding_dim=self.src_emb_dim, + sparse=True) + self.dropout_rate = dropout_rate + + def forward(self, src_word, src_pos): + src_word_emb = src_word + src_word_emb = fluid.layers.cast(src_word_emb, 'float32') + src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5) + src_pos = paddle.squeeze(src_pos, axis=-1) + src_pos_enc = self.emb(src_pos) + src_pos_enc.stop_gradient = True + enc_input = src_word_emb + src_pos_enc + if self.dropout_rate: + out = F.dropout( + x=enc_input, p=self.dropout_rate, mode="downscale_in_infer") + else: + out = enc_input + return out + + +class PrepareDecoder(nn.Layer): + def __init__(self, + src_vocab_size, + src_emb_dim, + src_max_len, + dropout_rate=0, + bos_idx=0, + word_emb_param_name=None, + pos_enc_param_name=None): + super(PrepareDecoder, self).__init__() + self.src_emb_dim = src_emb_dim + """ + self.emb0 = Embedding(num_embeddings=src_vocab_size, + embedding_dim=src_emb_dim) + """ + self.emb0 = paddle.nn.Embedding( + num_embeddings=src_vocab_size, + embedding_dim=self.src_emb_dim, + padding_idx=bos_idx, + weight_attr=paddle.ParamAttr( + name=word_emb_param_name, + initializer=nn.initializer.Normal(0., src_emb_dim**-0.5))) + self.emb1 = paddle.nn.Embedding( + num_embeddings=src_max_len, + embedding_dim=self.src_emb_dim, + weight_attr=paddle.ParamAttr(name=pos_enc_param_name)) + self.dropout_rate = dropout_rate + + def forward(self, src_word, src_pos): + src_word = fluid.layers.cast(src_word, 'int64') + src_word = paddle.squeeze(src_word, axis=-1) + src_word_emb = self.emb0(src_word) + src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5) + src_pos = paddle.squeeze(src_pos, axis=-1) + src_pos_enc = self.emb1(src_pos) + src_pos_enc.stop_gradient = True + enc_input = src_word_emb + src_pos_enc + if self.dropout_rate: + out = F.dropout( + x=enc_input, p=self.dropout_rate, mode="downscale_in_infer") + else: + out = enc_input + return out + + +class FFN(nn.Layer): + """ + Feed-Forward Network + """ + + def __init__(self, d_inner_hid, d_model, dropout_rate): + super(FFN, self).__init__() + self.dropout_rate = dropout_rate + self.fc1 = paddle.nn.Linear( + in_features=d_model, out_features=d_inner_hid) + self.fc2 = paddle.nn.Linear( + in_features=d_inner_hid, out_features=d_model) + + def forward(self, x): + hidden = self.fc1(x) + hidden = F.relu(hidden) + if self.dropout_rate: + hidden = F.dropout( + hidden, p=self.dropout_rate, mode="downscale_in_infer") + out = self.fc2(hidden) + return out diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index c9b42e08..0156e438 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -26,11 +26,12 @@ def build_post_process(config, global_config=None): from .db_postprocess import DBPostProcess from .east_postprocess import EASTPostProcess from .sast_postprocess import SASTPostProcess - from .rec_postprocess import CTCLabelDecode, AttnLabelDecode + from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode from .cls_postprocess import ClsPostProcess support_dict = [ - 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess' + 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', + 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode' ] config = copy.deepcopy(config) diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 4d078994..76a700e1 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import numpy as np +import string import paddle from paddle.nn import functional as F @@ -24,19 +25,28 @@ class BaseRecLabelDecode(object): character_type='ch', use_space_char=False): support_character_type = [ - 'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean', 'it', - 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc', 'rsc', 'bg', - 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr', 'ne' + 'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean', + 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc', + 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr', + 'ne', 'EN' ] assert character_type in support_character_type, "Only {} are supported now but get {}".format( support_character_type, character_type) + self.beg_str = "sos" + self.end_str = "eos" + if character_type == "en": self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" dict_character = list(self.character_str) - elif character_type in ["ch", "french", "german", "japan", "korean"]: + elif character_type == "EN_symbol": + # same with ASTER setting (use 94 char). + self.character_str = string.printable[:-6] + dict_character = list(self.character_str) + elif character_type in support_character_type: self.character_str = "" - assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch" + assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format( + character_type) with open(character_dict_path, "rb") as fin: lines = fin.readlines() for line in lines: @@ -45,11 +55,7 @@ class BaseRecLabelDecode(object): if use_space_char: self.character_str += " " dict_character = list(self.character_str) - elif character_type == "en_sensitive": - # same with ASTER setting (use 94 char). - import string - self.character_str = string.printable[:-6] - dict_character = list(self.character_str) + else: raise NotImplementedError self.character_type = character_type @@ -106,7 +112,6 @@ class CTCLabelDecode(BaseRecLabelDecode): def __call__(self, preds, label=None, *args, **kwargs): if isinstance(preds, paddle.Tensor): preds = preds.numpy() - preds_idx = preds.argmax(axis=2) preds_prob = preds.max(axis=2) text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True) @@ -155,3 +160,84 @@ class AttnLabelDecode(BaseRecLabelDecode): assert False, "unsupport type %s in get_beg_end_flag_idx" \ % beg_or_end return idx + + +class SRNLabelDecode(BaseRecLabelDecode): + """ Convert between text-label and text-index """ + + def __init__(self, + character_dict_path=None, + character_type='en', + use_space_char=False, + **kwargs): + super(SRNLabelDecode, self).__init__(character_dict_path, + character_type, use_space_char) + + def __call__(self, preds, label=None, *args, **kwargs): + pred = preds['predict'] + char_num = len(self.character_str) + 2 + if isinstance(pred, paddle.Tensor): + pred = pred.numpy() + pred = np.reshape(pred, [-1, char_num]) + + preds_idx = np.argmax(pred, axis=1) + preds_prob = np.max(pred, axis=1) + + preds_idx = np.reshape(preds_idx, [-1, 25]) + + preds_prob = np.reshape(preds_prob, [-1, 25]) + + text = self.decode(preds_idx, preds_prob) + + if label is None: + text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False) + return text + label = self.decode(label) + return text, label + + def decode(self, text_index, text_prob=None, is_remove_duplicate=False): + """ convert text-index into text-label. """ + result_list = [] + ignored_tokens = self.get_ignored_tokens() + batch_size = len(text_index) + + for batch_idx in range(batch_size): + char_list = [] + conf_list = [] + for idx in range(len(text_index[batch_idx])): + if text_index[batch_idx][idx] in ignored_tokens: + continue + if is_remove_duplicate: + # only for predict + if idx > 0 and text_index[batch_idx][idx - 1] == text_index[ + batch_idx][idx]: + continue + char_list.append(self.character[int(text_index[batch_idx][ + idx])]) + if text_prob is not None: + conf_list.append(text_prob[batch_idx][idx]) + else: + conf_list.append(1) + + text = ''.join(char_list) + result_list.append((text, np.mean(conf_list))) + return result_list + + def add_special_char(self, dict_character): + dict_character = dict_character + [self.beg_str, self.end_str] + return dict_character + + def get_ignored_tokens(self): + beg_idx = self.get_beg_end_flag_idx("beg") + end_idx = self.get_beg_end_flag_idx("end") + return [beg_idx, end_idx] + + def get_beg_end_flag_idx(self, beg_or_end): + if beg_or_end == "beg": + idx = np.array(self.dict[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict[self.end_str]) + else: + assert False, "unsupport type %s in get_beg_end_flag_idx" \ + % beg_or_end + return idx diff --git a/tools/export_model.py b/tools/export_model.py index 74357d58..1e9526e0 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -31,6 +31,14 @@ from ppocr.utils.logging import get_logger from tools.program import load_config, merge_config, ArgsParser +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("-c", "--config", help="configuration file to use") + parser.add_argument( + "-o", "--output_path", type=str, default='./output/infer/') + return parser.parse_args() + + def main(): FLAGS = ArgsParser().parse_args() config = load_config(FLAGS.config) @@ -51,14 +59,40 @@ def main(): model.eval() save_path = '{}/inference'.format(config['Global']['save_inference_dir']) - infer_shape = [3, 32, 100] if config['Architecture'][ - 'model_type'] != "det" else [3, 640, 640] - model = to_static( - model, - input_spec=[ + + if config['Architecture']['algorithm'] == "SRN": + other_shape = [ paddle.static.InputSpec( - shape=[None] + infer_shape, dtype='float32') - ]) + shape=[None, 1, 64, 256], dtype='float32'), [ + paddle.static.InputSpec( + shape=[None, 256, 1], + dtype="int64"), paddle.static.InputSpec( + shape=[None, 25, 1], + dtype="int64"), paddle.static.InputSpec( + shape=[None, 8, 25, 25], dtype="int64"), + paddle.static.InputSpec( + shape=[None, 8, 25, 25], dtype="int64") + ] + ] + model = to_static(model, input_spec=other_shape) + else: + infer_shape = [3, -1, -1] + if config['Architecture']['model_type'] == "rec": + infer_shape = [3, 32, -1] # for rec model, H must be 32 + if 'Transform' in config['Architecture'] and config['Architecture'][ + 'Transform'] is not None and config['Architecture'][ + 'Transform']['name'] == 'TPS': + logger.info( + 'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training' + ) + infer_shape[-1] = 100 + model = to_static( + model, + input_spec=[ + paddle.static.InputSpec( + shape=[None] + infer_shape, dtype='float32') + ]) + paddle.jit.save(model, save_path) logger.info('inference model is saved to {}'.format(save_path)) diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index 974fdbb6..fd895e50 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -25,6 +25,7 @@ import numpy as np import math import time import traceback +import paddle import tools.infer.utility as utility from ppocr.postprocess import build_post_process @@ -46,6 +47,13 @@ class TextRecognizer(object): "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } + if self.rec_algorithm == "SRN": + postprocess_params = { + 'name': 'SRNLabelDecode', + "character_type": args.rec_char_type, + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors = \ utility.create_predictor(args, 'rec', logger) @@ -70,6 +78,78 @@ class TextRecognizer(object): padding_im[:, :, 0:resized_w] = resized_image return padding_im + def resize_norm_img_srn(self, img, image_shape): + imgC, imgH, imgW = image_shape + + img_black = np.zeros((imgH, imgW)) + im_hei = img.shape[0] + im_wid = img.shape[1] + + if im_wid <= im_hei * 1: + img_new = cv2.resize(img, (imgH * 1, imgH)) + elif im_wid <= im_hei * 2: + img_new = cv2.resize(img, (imgH * 2, imgH)) + elif im_wid <= im_hei * 3: + img_new = cv2.resize(img, (imgH * 3, imgH)) + else: + img_new = cv2.resize(img, (imgW, imgH)) + + img_np = np.asarray(img_new) + img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) + img_black[:, 0:img_np.shape[1]] = img_np + img_black = img_black[:, :, np.newaxis] + + row, col, c = img_black.shape + c = 1 + + return np.reshape(img_black, (c, row, col)).astype(np.float32) + + def srn_other_inputs(self, image_shape, num_heads, max_text_length): + + imgC, imgH, imgW = image_shape + feature_dim = int((imgH / 8) * (imgW / 8)) + + encoder_word_pos = np.array(range(0, feature_dim)).reshape( + (feature_dim, 1)).astype('int64') + gsrm_word_pos = np.array(range(0, max_text_length)).reshape( + (max_text_length, 1)).astype('int64') + + gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) + gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( + [-1, 1, max_text_length, max_text_length]) + gsrm_slf_attn_bias1 = np.tile( + gsrm_slf_attn_bias1, + [1, num_heads, 1, 1]).astype('float32') * [-1e9] + + gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( + [-1, 1, max_text_length, max_text_length]) + gsrm_slf_attn_bias2 = np.tile( + gsrm_slf_attn_bias2, + [1, num_heads, 1, 1]).astype('float32') * [-1e9] + + encoder_word_pos = encoder_word_pos[np.newaxis, :] + gsrm_word_pos = gsrm_word_pos[np.newaxis, :] + + return [ + encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2 + ] + + def process_image_srn(self, img, image_shape, num_heads, max_text_length): + norm_img = self.resize_norm_img_srn(img, image_shape) + norm_img = norm_img[np.newaxis, :] + + [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ + self.srn_other_inputs(image_shape, num_heads, max_text_length) + + gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) + gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) + encoder_word_pos = encoder_word_pos.astype(np.int64) + gsrm_word_pos = gsrm_word_pos.astype(np.int64) + + return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2) + def __call__(self, img_list): img_num = len(img_list) # Calculate the aspect ratio of all text bars @@ -93,21 +173,64 @@ class TextRecognizer(object): wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) for ino in range(beg_img_no, end_img_no): - # norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio) - norm_img = self.resize_norm_img(img_list[indices[ino]], - max_wh_ratio) - norm_img = norm_img[np.newaxis, :] - norm_img_batch.append(norm_img) + if self.rec_algorithm != "SRN": + norm_img = self.resize_norm_img(img_list[indices[ino]], + max_wh_ratio) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + else: + norm_img = self.process_image_srn( + img_list[indices[ino]], self.rec_image_shape, 8, 25) + encoder_word_pos_list = [] + gsrm_word_pos_list = [] + gsrm_slf_attn_bias1_list = [] + gsrm_slf_attn_bias2_list = [] + encoder_word_pos_list.append(norm_img[1]) + gsrm_word_pos_list.append(norm_img[2]) + gsrm_slf_attn_bias1_list.append(norm_img[3]) + gsrm_slf_attn_bias2_list.append(norm_img[4]) + norm_img_batch.append(norm_img[0]) norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = norm_img_batch.copy() - starttime = time.time() - self.input_tensor.copy_from_cpu(norm_img_batch) - self.predictor.run() - outputs = [] - for output_tensor in self.output_tensors: - output = output_tensor.copy_to_cpu() - outputs.append(output) - preds = outputs[0] + + if self.rec_algorithm == "SRN": + starttime = time.time() + encoder_word_pos_list = np.concatenate(encoder_word_pos_list) + gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list) + gsrm_slf_attn_bias1_list = np.concatenate( + gsrm_slf_attn_bias1_list) + gsrm_slf_attn_bias2_list = np.concatenate( + gsrm_slf_attn_bias2_list) + + inputs = [ + norm_img_batch, + encoder_word_pos_list, + gsrm_word_pos_list, + gsrm_slf_attn_bias1_list, + gsrm_slf_attn_bias2_list, + ] + input_names = self.predictor.get_input_names() + for i in range(len(input_names)): + input_tensor = self.predictor.get_input_handle(input_names[ + i]) + input_tensor.copy_from_cpu(inputs[i]) + self.predictor.run() + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + preds = {"predict": outputs[2]} + else: + starttime = time.time() + self.input_tensor.copy_from_cpu(norm_img_batch) + self.predictor.run() + + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + preds = outputs[0] + rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 966fa3cc..4171a29b 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -70,7 +70,7 @@ def parse_args(): default="./ppocr/utils/ppocr_keys_v1.txt") parser.add_argument("--use_space_char", type=str2bool, default=True) parser.add_argument( - "--vis_font_path", type=str, default="./doc/simfang.ttf") + "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf") parser.add_argument("--drop_score", type=float, default=0.5) # params for text classifier diff --git a/tools/infer_rec.py b/tools/infer_rec.py index 7e4b0811..075ec261 100755 --- a/tools/infer_rec.py +++ b/tools/infer_rec.py @@ -62,7 +62,13 @@ def main(): elif op_name in ['RecResizeImg']: op[op_name]['infer_mode'] = True elif op_name == 'KeepKeys': - op[op_name]['keep_keys'] = ['image'] + if config['Architecture']['algorithm'] == "SRN": + op[op_name]['keep_keys'] = [ + 'image', 'encoder_word_pos', 'gsrm_word_pos', + 'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2' + ] + else: + op[op_name]['keep_keys'] = ['image'] transforms.append(op) global_config['infer_mode'] = True ops = create_operators(transforms, global_config) @@ -74,10 +80,25 @@ def main(): img = f.read() data = {'image': img} batch = transform(data, ops) + if config['Architecture']['algorithm'] == "SRN": + encoder_word_pos_list = np.expand_dims(batch[1], axis=0) + gsrm_word_pos_list = np.expand_dims(batch[2], axis=0) + gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0) + gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0) + + others = [ + paddle.to_tensor(encoder_word_pos_list), + paddle.to_tensor(gsrm_word_pos_list), + paddle.to_tensor(gsrm_slf_attn_bias1_list), + paddle.to_tensor(gsrm_slf_attn_bias2_list) + ] images = np.expand_dims(batch[0], axis=0) images = paddle.to_tensor(images) - preds = model(images) + if config['Architecture']['algorithm'] == "SRN": + preds = model(images, others) + else: + preds = model(images) post_result = post_process_class(preds) for rec_reuslt in post_result: logger.info('\t result: {}'.format(rec_reuslt)) diff --git a/tools/program.py b/tools/program.py index c2915426..694d6415 100755 --- a/tools/program.py +++ b/tools/program.py @@ -174,6 +174,7 @@ def train(config, best_model_dict = {main_indicator: 0} best_model_dict.update(pre_best_model_dict) train_stats = TrainingStats(log_smooth_window, ['lr']) + model_average = False model.train() if 'start_epoch' in best_model_dict: @@ -182,8 +183,8 @@ def train(config, start_epoch = 1 for epoch in range(start_epoch, epoch_num + 1): - if epoch > 0: - train_dataloader = build_dataloader(config, 'Train', device, logger) + train_dataloader = build_dataloader( + config, 'Train', device, logger, seed=epoch) train_batch_cost = 0.0 train_reader_cost = 0.0 batch_sum = 0 @@ -194,7 +195,12 @@ def train(config, break lr = optimizer.get_lr() images = batch[0] - preds = model(images) + if config['Architecture']['algorithm'] == "SRN": + others = batch[-4:] + preds = model(images, others) + model_average = True + else: + preds = model(images) loss = loss_class(preds, batch) avg_loss = loss['loss'] avg_loss.backward() @@ -212,7 +218,7 @@ def train(config, stats['lr'] = lr train_stats.update(stats) - if cal_metric_during_train: # onlt rec and cls need + if cal_metric_during_train: # only rec and cls need batch = [item.numpy() for item in batch] post_result = post_process_class(preds, batch[1]) eval_class(post_result, batch) @@ -238,21 +244,28 @@ def train(config, # eval if global_step > start_eval_step and \ (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0: + if model_average: + Model_Average = paddle.incubate.optimizer.ModelAverage( + 0.15, + parameters=model.parameters(), + min_average_window=10000, + max_average_window=15625) + Model_Average.apply() cur_metirc = eval(model, valid_dataloader, post_process_class, eval_class) - cur_metirc_str = 'cur metirc, {}'.format(', '.join( - ['{}: {}'.format(k, v) for k, v in cur_metirc.items()])) - logger.info(cur_metirc_str) + cur_metric_str = 'cur metric, {}'.format(', '.join( + ['{}: {}'.format(k, v) for k, v in cur_metric.items()])) + logger.info(cur_metric_str) # logger metric if vdl_writer is not None: - for k, v in cur_metirc.items(): + for k, v in cur_metric.items(): if isinstance(v, (float, int)): vdl_writer.add_scalar('EVAL/{}'.format(k), - cur_metirc[k], global_step) - if cur_metirc[main_indicator] >= best_model_dict[ + cur_metric[k], global_step) + if cur_metric[main_indicator] >= best_model_dict[ main_indicator]: - best_model_dict.update(cur_metirc) + best_model_dict.update(cur_metric) best_model_dict['best_epoch'] = epoch save_model( model, @@ -263,7 +276,7 @@ def train(config, prefix='best_accuracy', best_model_dict=best_model_dict, epoch=epoch) - best_str = 'best metirc, {}'.format(', '.join([ + best_str = 'best metric, {}'.format(', '.join([ '{}: {}'.format(k, v) for k, v in best_model_dict.items() ])) logger.info(best_str) @@ -273,6 +286,7 @@ def train(config, best_model_dict[main_indicator], global_step) global_step += 1 + optimizer.clear_grad() batch_start = time.time() if dist.get_rank() == 0: save_model( @@ -294,7 +308,7 @@ def train(config, prefix='iter_epoch_{}'.format(epoch), best_model_dict=best_model_dict, epoch=epoch) - best_str = 'best metirc, {}'.format(', '.join( + best_str = 'best metric, {}'.format(', '.join( ['{}: {}'.format(k, v) for k, v in best_model_dict.items()])) logger.info(best_str) if dist.get_rank() == 0 and vdl_writer is not None: @@ -312,8 +326,9 @@ def eval(model, valid_dataloader, post_process_class, eval_class): if idx >= len(valid_dataloader): break images = batch[0] + others = batch[-4:] start = time.time() - preds = model(images) + preds = model(images, others) batch = [item.numpy() for item in batch] # Obtain usable results from post-processing methods @@ -323,13 +338,13 @@ def eval(model, valid_dataloader, post_process_class, eval_class): eval_class(post_result, batch) pbar.update(1) total_frame += len(images) - # Get final metirc，eg. acc or hmean - metirc = eval_class.get_metric() + # Get final metric，eg. acc or hmean + metric = eval_class.get_metric() pbar.close() model.train() - metirc['fps'] = total_frame / total_time - return metirc + metric['fps'] = total_frame / total_time + return metric def preprocess(is_train=False): diff --git a/train.sh b/train.sh index c511c516..8fe861a3 100644 --- a/train.sh +++ b/train.sh @@ -1,5 +1,2 @@ -# for paddle.__version__ >= 2.0rc1 +# recommended paddle.__version__ == 2.0.0 python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml - -# for paddle.__version__ < 2.0rc1 -# python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml