From 3f65b360efe8d2f910caf34a300f6661c8e741fa Mon Sep 17 00:00:00 2001 From: andyjpaddle <87074272+andyjpaddle@users.noreply.github.com> Date: Tue, 9 Aug 2022 11:29:43 +0800 Subject: [PATCH] Add rec algo VisionLAN (#6943) * add vl * add vl * add vl * add ref * fix head out * add visionlan doc * fix vl infer * update dict --- configs/rec/rec_r45_visionlan.yml | 106 +++++ doc/doc_ch/algorithm_overview.md | 2 + doc/doc_ch/algorithm_rec_visionlan.md | 154 ++++++ doc/doc_en/algorithm_overview_en.md | 2 + doc/doc_en/algorithm_rec_visionlan_en.md | 135 ++++++ ppocr/data/imaug/__init__.py | 5 +- ppocr/data/imaug/label_ops.py | 71 ++- ppocr/data/imaug/rec_img_aug.py | 36 +- ppocr/losses/__init__.py | 3 +- ppocr/losses/rec_vl_loss.py | 70 +++ ppocr/modeling/backbones/rec_resnet_45.py | 25 +- ppocr/modeling/backbones/rec_resnet_aster.py | 2 +- ppocr/modeling/heads/__init__.py | 4 +- ppocr/modeling/heads/rec_visionlan_head.py | 468 +++++++++++++++++++ ppocr/optimizer/optimizer.py | 57 ++- ppocr/postprocess/__init__.py | 37 +- ppocr/postprocess/rec_postprocess.py | 105 ++++- tools/eval.py | 2 +- tools/export_model.py | 8 +- tools/infer/predict_rec.py | 22 + tools/infer_rec.py | 1 - tools/program.py | 12 +- 22 files changed, 1268 insertions(+), 59 deletions(-) create mode 100644 configs/rec/rec_r45_visionlan.yml create mode 100644 doc/doc_ch/algorithm_rec_visionlan.md create mode 100644 doc/doc_en/algorithm_rec_visionlan_en.md create mode 100644 ppocr/losses/rec_vl_loss.py create mode 100644 ppocr/modeling/heads/rec_visionlan_head.py diff --git a/configs/rec/rec_r45_visionlan.yml b/configs/rec/rec_r45_visionlan.yml new file mode 100644 index 00000000..25017653 --- /dev/null +++ b/configs/rec/rec_r45_visionlan.yml @@ -0,0 +1,106 @@ +Global: + use_gpu: true + epoch_num: 8 + log_smooth_window: 200 + print_batch_step: 200 + save_model_dir: ./output/rec/r45_visionlan + save_epoch_step: 1 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: True + infer_img: doc/imgs_words/en/word_2.png + # for data or label process + character_dict_path: + max_text_length: &max_text_length 25 + training_step: &training_step LA + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_visionlan.txt + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + clip_norm: 20.0 + group_lr: true + training_step: *training_step + lr: + name: Piecewise + decay_epochs: [6] + values: [0.0001, 0.00001] + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: VisionLAN + Transform: + Backbone: + name: ResNet45 + strides: [2, 2, 2, 1, 1] + Head: + name: VLHead + n_layers: 3 + n_position: 256 + n_dim: 512 + max_text_length: *max_text_length + training_step: *training_step + +Loss: + name: VLLoss + mode: *training_step + weight_res: 0.5 + weight_mas: 0.5 + +PostProcess: + name: VLLabelDecode + +Metric: + name: RecMetric + is_filter: true + + +Train: + dataset: + name: LMDBDataSet + data_dir: ./train_data/data_lmdb_release/training/ + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - ABINetRecAug: + - VLLabelEncode: # Class handling label + - VLRecResizeImg: + image_shape: [3, 64, 256] + - KeepKeys: + keep_keys: ['image', 'label', 'label_res', 'label_sub', 'label_id', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 220 + drop_last: True + num_workers: 4 + +Eval: + dataset: + name: LMDBDataSet + data_dir: ./train_data/data_lmdb_release/validation/ + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VLLabelEncode: # Class handling label + - VLRecResizeImg: + image_shape: [3, 64, 256] + - KeepKeys: + keep_keys: ['image', 'label', 'label_res', 'label_sub', 'label_id', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 64 + num_workers: 4 + diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index fbd3ce9e..9d725a86 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -69,6 +69,7 @@ - [x] [SVTR](./algorithm_rec_svtr.md) - [x] [ViTSTR](./algorithm_rec_vitstr.md) - [x] [ABINet](./algorithm_rec_abinet.md) +- [x] [VisionLAN](./algorithm_rec_visionlan.md) - [x] [SPIN](./algorithm_rec_spin.md) 参考[DTRB](https://arxiv.org/abs/1904.01906)[3]文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： @@ -90,6 +91,7 @@ |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | |ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) | |ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) | |SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon | diff --git a/doc/doc_ch/algorithm_rec_visionlan.md b/doc/doc_ch/algorithm_rec_visionlan.md new file mode 100644 index 00000000..0c4fe86e --- /dev/null +++ b/doc/doc_ch/algorithm_rec_visionlan.md @@ -0,0 +1,154 @@ +# 场景文本识别算法-VisionLAN + +- [1. 算法简介](#1) +- [2. 环境配置](#2) +- [3. 模型训练、评估、预测](#3) + - [3.1 训练](#3-1) + - [3.2 评估](#3-2) + - [3.3 预测](#3-3) +- [4. 推理部署](#4) + - [4.1 Python推理](#4-1) + - [4.2 C++推理](#4-2) + - [4.3 Serving服务化部署](#4-3) + - [4.4 更多推理部署](#4-4) +- [5. FAQ](#5) + + +## 1. 算法简介 + +论文信息： +> [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://arxiv.org/abs/2108.09661) +> Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang +> ICCV, 2021 + + + +`VisionLAN`使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下： + +|模型|骨干网络|配置文件|Acc|下载链接| +| --- | --- | --- | --- | --- | +|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)| + + +## 2. 环境配置 +请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。 + + + +## 3. 模型训练、评估、预测 + + +### 3.1 模型训练 + +请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练`VisionLAN`识别模型时需要**更换配置文件**为`VisionLAN`的[配置文件](../../configs/rec/rec_r45_visionlan.yml)。 + +#### 启动训练 + + +具体地，在完成数据准备后，便可以启动训练，训练命令如下： +```shell +#单卡训练（训练周期长，不建议） +python3 tools/train.py -c configs/rec/rec_r45_visionlan.yml + +#多卡训练，通过--gpus参数指定卡号 +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r45_visionlan.yml +``` + + +### 3.2 评估 + +可下载已训练完成的[模型文件](#model)，使用如下命令进行评估： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/eval.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy +``` + + +### 3.3 预测 + +使用如下命令进行单张图片预测： +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_img='./doc/imgs_words/en/word_2.png' Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy +# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/imgs_words_en/'。 +``` + + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar))，可以使用如下命令进行转换： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/ +``` +**注意：** +- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。 +- 如果您修改了训练时的输入大小，请修改`tools/export_model.py`文件中的对应VisionLAN的`infer_shape`。 + +转换成功后，在目录下有三个文件： +``` +./inference/rec_r45_visionlan/ + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 +``` + +执行如下命令进行模型推理： + +```shell +python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt' +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。 +``` + +![](../imgs_words/en/word_2.png) + +执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下： +结果如下： +```shell +Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982) +``` + +**注意**： + +- 训练上述模型采用的图像分辨率是[3，64，256]，需要通过参数`rec_image_shape`设置为您训练时的识别图像形状。 +- 在推理时需要设置参数`rec_char_dict_path`指定字典，如果您修改了字典，请修改该参数为您的字典文件。 +- 如果您修改了预处理方法，需修改`tools/infer/predict_rec.py`中VisionLAN的预处理为您的预处理方法。 + + + +### 4.2 C++推理部署 + +由于C++预处理后处理还未支持VisionLAN，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + +1. MJSynth和SynthText两种数据集来自于[VisionLAN源repo](https://github.com/wangyuxin87/VisionLAN) 。 +2. 我们使用VisionLAN作者提供的预训练模型进行finetune训练。 + +## 引用 + +```bibtex +@inproceedings{wang2021two, + title={From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network}, + author={Wang, Yuxin and Xie, Hongtao and Fang, Shancheng and Wang, Jing and Zhu, Shenggao and Zhang, Yongdong}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={14194--14203}, + year={2021} +} +``` diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index a579d244..dfd8ecda 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -68,6 +68,7 @@ Supported text recognition algorithms (Click the link to get the tutorial): - [x] [SVTR](./algorithm_rec_svtr_en.md) - [x] [ViTSTR](./algorithm_rec_vitstr_en.md) - [x] [ABINet](./algorithm_rec_abinet_en.md) +- [x] [VisionLAN](./algorithm_rec_visionlan_en.md) - [x] [SPIN](./algorithm_rec_spin_en.md) Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow: @@ -89,6 +90,7 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | |ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) | |ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) | |SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon | diff --git a/doc/doc_en/algorithm_rec_visionlan_en.md b/doc/doc_en/algorithm_rec_visionlan_en.md new file mode 100644 index 00000000..ebd02d52 --- /dev/null +++ b/doc/doc_en/algorithm_rec_visionlan_en.md @@ -0,0 +1,135 @@ +# VisionLAN + +- [1. Introduction](#1) +- [2. Environment](#2) +- [3. Model Training / Evaluation / Prediction](#3) + - [3.1 Training](#3-1) + - [3.2 Evaluation](#3-2) + - [3.3 Prediction](#3-3) +- [4. Inference and Deployment](#4) + - [4.1 Python Inference](#4-1) + - [4.2 C++ Inference](#4-2) + - [4.3 Serving](#4-3) + - [4.4 More](#4-4) +- [5. FAQ](#5) + + +## 1. Introduction + +Paper: +> [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://arxiv.org/abs/2108.09661) +> Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang +> ICCV, 2021 + +Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: + +|Model|Backbone|config|Acc|Download link| +| --- | --- | --- | --- | --- | +|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)| + + +## 2. Environment +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. + + + +## 3. Model Training / Evaluation / Prediction + +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. + +Training: + +Specifically, after the data preparation is completed, the training can be started. The training command is as follows: + +``` +#Single GPU training (long training period, not recommended) +python3 tools/train.py -c configs/rec/rec_r45_visionlan.yml + +#Multi GPU training, specify the gpu number through the --gpus parameter +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r45_visionlan.yml +``` + +Evaluation: + +``` +# GPU evaluation +python3 tools/eval.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model={path/to/weights}/best_accuracy +``` + +Prediction: + +``` +# The configuration file used for prediction must match the training +python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_img='./doc/imgs_words/en/word_2.png' Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy +``` + + +## 4. Inference and Deployment + + +### 4.1 Python Inference +First, the model saved during the VisionLAN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)) ), you can use the following command to convert: + +``` +python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/ +``` + +**Note:** +- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file. +- If you modified the input size during training, please modify the `infer_shape` corresponding to VisionLAN in the `tools/export_model.py` file. + +After the conversion is successful, there are three files in the directory: +``` +./inference/rec_r45_visionlan/ + ├── inference.pdiparams + ├── inference.pdiparams.info + └── inference.pdmodel +``` + + +For VisionLAN text recognition model inference, the following commands can be executed: + +``` +python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt' +``` + +![](../imgs_words/en/word_2.png) + +After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: +The result is as follows: +```shell +Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982) +``` + + +### 4.2 C++ Inference + +Not supported + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + +1. Note that the MJSynth and SynthText datasets come from [VisionLAN repo](https://github.com/wangyuxin87/VisionLAN). +2. We use the pre-trained model provided by the VisionLAN authors for finetune training. + +## Citation + +```bibtex +@inproceedings{wang2021two, + title={From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network}, + author={Wang, Yuxin and Xie, Hongtao and Fang, Shancheng and Wang, Jing and Zhu, Shenggao and Zhang, Yongdong}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={14194--14203}, + year={2021} +} +``` diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index d41eed9d..a2332b6c 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -25,8 +25,9 @@ from .make_pse_gt import MakePseGt from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \ - SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \ - ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug, SPINRecResizeImg + SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \ + ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug, VLRecResizeImg, SPINRecResizeImg + from .ssl_img_aug import SSLRotateResize from .randaugment import RandAugment from .copy_paste import CopyPaste diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 180fe97b..1656c695 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -23,6 +23,8 @@ import string from shapely.geometry import LineString, Point, Polygon import json import copy +from random import sample + from ppocr.utils.logging import get_logger from ppocr.data.imaug.vqa.augment import order_by_tbyx @@ -98,12 +100,13 @@ class BaseRecLabelEncode(object): def __init__(self, max_text_length, character_dict_path=None, - use_space_char=False): + use_space_char=False, + lower=False): self.max_text_len = max_text_length self.beg_str = "sos" self.end_str = "eos" - self.lower = False + self.lower = lower if character_dict_path is None: logger = get_logger() @@ -1266,3 +1269,67 @@ class SPINLabelEncode(AttnLabelEncode): padded_text[:len(target)] = target data['label'] = np.array(padded_text) return data + + +class VLLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + lower=True, + **kwargs): + super(VLLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char, lower) + self.character = self.character[10:] + self.character[ + 1:10] + [self.character[0]] + self.dict = {} + for i, char in enumerate(self.character): + self.dict[char] = i + + def __call__(self, data): + text = data['label'] # original string + # generate occluded text + len_str = len(text) + if len_str <= 0: + return None + change_num = 1 + order = list(range(len_str)) + change_id = sample(order, change_num)[0] + label_sub = text[change_id] + if change_id == (len_str - 1): + label_res = text[:change_id] + elif change_id == 0: + label_res = text[1:] + else: + label_res = text[:change_id] + text[change_id + 1:] + + data['label_res'] = label_res # remaining string + data['label_sub'] = label_sub # occluded character + data['label_id'] = change_id # character index + # encode label + text = self.encode(text) + if text is None: + return None + text = [i + 1 for i in text] + data['length'] = np.array(len(text)) + text = text + [0] * (self.max_text_len - len(text)) + data['label'] = np.array(text) + label_res = self.encode(label_res) + label_sub = self.encode(label_sub) + if label_res is None: + label_res = [] + else: + label_res = [i + 1 for i in label_res] + if label_sub is None: + label_sub = [] + else: + label_sub = [i + 1 for i in label_sub] + data['length_res'] = np.array(len(label_res)) + data['length_sub'] = np.array(len(label_sub)) + label_res = label_res + [0] * (self.max_text_len - len(label_res)) + label_sub = label_sub + [0] * (self.max_text_len - len(label_sub)) + data['label_res'] = np.array(label_res) + data['label_sub'] = np.array(label_sub) + return data diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py index c5d8a3b2..725b4b06 100644 --- a/ppocr/data/imaug/rec_img_aug.py +++ b/ppocr/data/imaug/rec_img_aug.py @@ -205,6 +205,38 @@ class RecResizeImg(object): return data +class VLRecResizeImg(object): + def __init__(self, + image_shape, + infer_mode=False, + character_dict_path='./ppocr/utils/ppocr_keys_v1.txt', + padding=True, + **kwargs): + self.image_shape = image_shape + self.infer_mode = infer_mode + self.character_dict_path = character_dict_path + self.padding = padding + + def __call__(self, data): + img = data['image'] + + imgC, imgH, imgW = self.image_shape + resized_image = cv2.resize( + img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_w = imgW + resized_image = resized_image.astype('float32') + if self.image_shape[0] == 1: + resized_image = resized_image / 255 + norm_img = resized_image[np.newaxis, :] + else: + norm_img = resized_image.transpose((2, 0, 1)) / 255 + valid_ratio = min(1.0, float(resized_w / imgW)) + + data['image'] = norm_img + data['valid_ratio'] = valid_ratio + return data + + class SRNRecResizeImg(object): def __init__(self, image_shape, num_heads, max_text_length, **kwargs): self.image_shape = image_shape @@ -259,6 +291,7 @@ class PRENResizeImg(object): data['image'] = resized_img.astype(np.float32) return data + class SPINRecResizeImg(object): def __init__(self, image_shape, @@ -267,7 +300,7 @@ class SPINRecResizeImg(object): std=(127.5, 127.5, 127.5), **kwargs): self.image_shape = image_shape - + self.mean = np.array(mean, dtype=np.float32) self.std = np.array(std, dtype=np.float32) self.interpolation = interpolation @@ -303,6 +336,7 @@ class SPINRecResizeImg(object): data['image'] = img return data + class GrayRecResizeImg(object): def __init__(self, image_shape, diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 30120ac5..bb82c7e0 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -35,6 +35,7 @@ from .rec_sar_loss import SARLoss from .rec_aster_loss import AsterLoss from .rec_pren_loss import PRENLoss from .rec_multi_loss import MultiLoss +from .rec_vl_loss import VLLoss from .rec_spin_att_loss import SPINAttentionLoss # cls loss @@ -63,7 +64,7 @@ def build_loss(config): 'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss', 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss', - 'TableMasterLoss', 'SPINAttentionLoss' + 'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss' ] config = copy.deepcopy(config) module_name = config.pop('name') diff --git a/ppocr/losses/rec_vl_loss.py b/ppocr/losses/rec_vl_loss.py new file mode 100644 index 00000000..5cd87c70 --- /dev/null +++ b/ppocr/losses/rec_vl_loss.py @@ -0,0 +1,70 @@ +# copyright (c) 2022 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. +""" +This code is refer from: +https://github.com/wangyuxin87/VisionLAN +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn + + +class VLLoss(nn.Layer): + def __init__(self, mode='LF_1', weight_res=0.5, weight_mas=0.5, **kwargs): + super(VLLoss, self).__init__() + self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="mean") + assert mode in ['LF_1', 'LF_2', 'LA'] + self.mode = mode + self.weight_res = weight_res + self.weight_mas = weight_mas + + def flatten_label(self, target): + label_flatten = [] + label_length = [] + for i in range(0, target.shape[0]): + cur_label = target[i].tolist() + label_flatten += cur_label[:cur_label.index(0) + 1] + label_length.append(cur_label.index(0) + 1) + label_flatten = paddle.to_tensor(label_flatten, dtype='int64') + label_length = paddle.to_tensor(label_length, dtype='int32') + return (label_flatten, label_length) + + def _flatten(self, sources, lengths): + return paddle.concat([t[:l] for t, l in zip(sources, lengths)]) + + def forward(self, predicts, batch): + text_pre = predicts[0] + target = batch[1].astype('int64') + label_flatten, length = self.flatten_label(target) + text_pre = self._flatten(text_pre, length) + if self.mode == 'LF_1': + loss = self.loss_func(text_pre, label_flatten) + else: + text_rem = predicts[1] + text_mas = predicts[2] + target_res = batch[2].astype('int64') + target_sub = batch[3].astype('int64') + label_flatten_res, length_res = self.flatten_label(target_res) + label_flatten_sub, length_sub = self.flatten_label(target_sub) + text_rem = self._flatten(text_rem, length_res) + text_mas = self._flatten(text_mas, length_sub) + loss_ori = self.loss_func(text_pre, label_flatten) + loss_res = self.loss_func(text_rem, label_flatten_res) + loss_mas = self.loss_func(text_mas, label_flatten_sub) + loss = loss_ori + loss_res * self.weight_res + loss_mas * self.weight_mas + return {'loss': loss} diff --git a/ppocr/modeling/backbones/rec_resnet_45.py b/ppocr/modeling/backbones/rec_resnet_45.py index 9093d0bc..083eb7f4 100644 --- a/ppocr/modeling/backbones/rec_resnet_45.py +++ b/ppocr/modeling/backbones/rec_resnet_45.py @@ -84,11 +84,15 @@ class BasicBlock(nn.Layer): class ResNet45(nn.Layer): - def __init__(self, block=BasicBlock, layers=[3, 4, 6, 6, 3], in_channels=3): + def __init__(self, + in_channels=3, + block=BasicBlock, + layers=[3, 4, 6, 6, 3], + strides=[2, 1, 2, 1, 1]): self.inplanes = 32 super(ResNet45, self).__init__() self.conv1 = nn.Conv2D( - 3, + in_channels, 32, kernel_size=3, stride=1, @@ -98,18 +102,13 @@ class ResNet45(nn.Layer): self.bn1 = nn.BatchNorm2D(32) self.relu = nn.ReLU() - self.layer1 = self._make_layer(block, 32, layers[0], stride=2) - self.layer2 = self._make_layer(block, 64, layers[1], stride=1) - self.layer3 = self._make_layer(block, 128, layers[2], stride=2) - self.layer4 = self._make_layer(block, 256, layers[3], stride=1) - self.layer5 = self._make_layer(block, 512, layers[4], stride=1) + self.layer1 = self._make_layer(block, 32, layers[0], stride=strides[0]) + self.layer2 = self._make_layer(block, 64, layers[1], stride=strides[1]) + self.layer3 = self._make_layer(block, 128, layers[2], stride=strides[2]) + self.layer4 = self._make_layer(block, 256, layers[3], stride=strides[3]) + self.layer5 = self._make_layer(block, 512, layers[4], stride=strides[4]) self.out_channels = 512 - # for m in self.modules(): - # if isinstance(m, nn.Conv2D): - # n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels - # m.weight.data.normal_(0, math.sqrt(2. / n)) - def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: @@ -137,11 +136,9 @@ class ResNet45(nn.Layer): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) - # print(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) - # print(x) x = self.layer4(x) x = self.layer5(x) return x diff --git a/ppocr/modeling/backbones/rec_resnet_aster.py b/ppocr/modeling/backbones/rec_resnet_aster.py index 6a2710df..782dc393 100644 --- a/ppocr/modeling/backbones/rec_resnet_aster.py +++ b/ppocr/modeling/backbones/rec_resnet_aster.py @@ -140,4 +140,4 @@ class ResNet_ASTER(nn.Layer): rnn_feat, _ = self.rnn(cnn_feat) return rnn_feat else: - return cnn_feat + return cnn_feat \ No newline at end of file diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index b4f18b37..b465cedb 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -35,6 +35,7 @@ def build_head(config): from .rec_multi_head import MultiHead from .rec_spin_att_head import SPINAttentionHead from .rec_abinet_head import ABINetHead + from .rec_visionlan_head import VLHead # cls head from .cls_head import ClsHead @@ -49,7 +50,8 @@ def build_head(config): 'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead', - 'MultiHead', 'ABINetHead', 'TableMasterHead', 'SPINAttentionHead' + 'MultiHead', 'ABINetHead', 'TableMasterHead', 'SPINAttentionHead', + 'VLHead' ] #table head diff --git a/ppocr/modeling/heads/rec_visionlan_head.py b/ppocr/modeling/heads/rec_visionlan_head.py new file mode 100644 index 00000000..86054d9b --- /dev/null +++ b/ppocr/modeling/heads/rec_visionlan_head.py @@ -0,0 +1,468 @@ +# copyright (c) 2022 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. +""" +This code is refer from: +https://github.com/wangyuxin87/VisionLAN +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F +from paddle.nn.initializer import Normal, XavierNormal +import numpy as np + + +class PositionalEncoding(nn.Layer): + def __init__(self, d_hid, n_position=200): + super(PositionalEncoding, self).__init__() + self.register_buffer( + 'pos_table', self._get_sinusoid_encoding_table(n_position, d_hid)) + + def _get_sinusoid_encoding_table(self, n_position, d_hid): + ''' Sinusoid position encoding table ''' + + def get_position_angle_vec(position): + return [ + position / np.power(10000, 2 * (hid_j // 2) / d_hid) + for hid_j in range(d_hid) + ] + + sinusoid_table = np.array( + [get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + sinusoid_table = paddle.to_tensor(sinusoid_table, dtype='float32') + sinusoid_table = paddle.unsqueeze(sinusoid_table, axis=0) + return sinusoid_table + + def forward(self, x): + return x + self.pos_table[:, :x.shape[1]].clone().detach() + + +class ScaledDotProductAttention(nn.Layer): + "Scaled Dot-Product Attention" + + def __init__(self, temperature, attn_dropout=0.1): + super(ScaledDotProductAttention, self).__init__() + self.temperature = temperature + self.dropout = nn.Dropout(attn_dropout) + self.softmax = nn.Softmax(axis=2) + + def forward(self, q, k, v, mask=None): + k = paddle.transpose(k, perm=[0, 2, 1]) + attn = paddle.bmm(q, k) + attn = attn / self.temperature + if mask is not None: + attn = attn.masked_fill(mask, -1e9) + if mask.dim() == 3: + mask = paddle.unsqueeze(mask, axis=1) + elif mask.dim() == 2: + mask = paddle.unsqueeze(mask, axis=1) + mask = paddle.unsqueeze(mask, axis=1) + repeat_times = [ + attn.shape[1] // mask.shape[1], attn.shape[2] // mask.shape[2] + ] + mask = paddle.tile(mask, [1, repeat_times[0], repeat_times[1], 1]) + attn[mask == 0] = -1e9 + attn = self.softmax(attn) + attn = self.dropout(attn) + output = paddle.bmm(attn, v) + return output + + +class MultiHeadAttention(nn.Layer): + " Multi-Head Attention module" + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + super(MultiHeadAttention, self).__init__() + self.n_head = n_head + self.d_k = d_k + self.d_v = d_v + self.w_qs = nn.Linear( + d_model, + n_head * d_k, + weight_attr=ParamAttr(initializer=Normal( + mean=0, std=np.sqrt(2.0 / (d_model + d_k))))) + self.w_ks = nn.Linear( + d_model, + n_head * d_k, + weight_attr=ParamAttr(initializer=Normal( + mean=0, std=np.sqrt(2.0 / (d_model + d_k))))) + self.w_vs = nn.Linear( + d_model, + n_head * d_v, + weight_attr=ParamAttr(initializer=Normal( + mean=0, std=np.sqrt(2.0 / (d_model + d_v))))) + + self.attention = ScaledDotProductAttention(temperature=np.power(d_k, + 0.5)) + self.layer_norm = nn.LayerNorm(d_model) + self.fc = nn.Linear( + n_head * d_v, + d_model, + weight_attr=ParamAttr(initializer=XavierNormal())) + self.dropout = nn.Dropout(dropout) + + def forward(self, q, k, v, mask=None): + d_k, d_v, n_head = self.d_k, self.d_v, self.n_head + sz_b, len_q, _ = q.shape + sz_b, len_k, _ = k.shape + sz_b, len_v, _ = v.shape + residual = q + + q = self.w_qs(q) + q = paddle.reshape( + q, shape=[-1, len_q, n_head, d_k]) # 4*21*512 ---- 4*21*8*64 + k = self.w_ks(k) + k = paddle.reshape(k, shape=[-1, len_k, n_head, d_k]) + v = self.w_vs(v) + v = paddle.reshape(v, shape=[-1, len_v, n_head, d_v]) + + q = paddle.transpose(q, perm=[2, 0, 1, 3]) + q = paddle.reshape(q, shape=[-1, len_q, d_k]) # (n*b) x lq x dk + k = paddle.transpose(k, perm=[2, 0, 1, 3]) + k = paddle.reshape(k, shape=[-1, len_k, d_k]) # (n*b) x lk x dk + v = paddle.transpose(v, perm=[2, 0, 1, 3]) + v = paddle.reshape(v, shape=[-1, len_v, d_v]) # (n*b) x lv x dv + + mask = paddle.tile( + mask, + [n_head, 1, 1]) if mask is not None else None # (n*b) x .. x .. + output = self.attention(q, k, v, mask=mask) + output = paddle.reshape(output, shape=[n_head, -1, len_q, d_v]) + output = paddle.transpose(output, perm=[1, 2, 0, 3]) + output = paddle.reshape( + output, shape=[-1, len_q, n_head * d_v]) # b x lq x (n*dv) + output = self.dropout(self.fc(output)) + output = self.layer_norm(output + residual) + return output + + +class PositionwiseFeedForward(nn.Layer): + def __init__(self, d_in, d_hid, dropout=0.1): + super(PositionwiseFeedForward, self).__init__() + self.w_1 = nn.Conv1D(d_in, d_hid, 1) # position-wise + self.w_2 = nn.Conv1D(d_hid, d_in, 1) # position-wise + self.layer_norm = nn.LayerNorm(d_in) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + residual = x + x = paddle.transpose(x, perm=[0, 2, 1]) + x = self.w_2(F.relu(self.w_1(x))) + x = paddle.transpose(x, perm=[0, 2, 1]) + x = self.dropout(x) + x = self.layer_norm(x + residual) + return x + + +class EncoderLayer(nn.Layer): + ''' Compose with two layers ''' + + def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1): + super(EncoderLayer, self).__init__() + self.slf_attn = MultiHeadAttention( + n_head, d_model, d_k, d_v, dropout=dropout) + self.pos_ffn = PositionwiseFeedForward( + d_model, d_inner, dropout=dropout) + + def forward(self, enc_input, slf_attn_mask=None): + enc_output = self.slf_attn( + enc_input, enc_input, enc_input, mask=slf_attn_mask) + enc_output = self.pos_ffn(enc_output) + return enc_output + + +class Transformer_Encoder(nn.Layer): + def __init__(self, + n_layers=2, + n_head=8, + d_word_vec=512, + d_k=64, + d_v=64, + d_model=512, + d_inner=2048, + dropout=0.1, + n_position=256): + super(Transformer_Encoder, self).__init__() + self.position_enc = PositionalEncoding( + d_word_vec, n_position=n_position) + self.dropout = nn.Dropout(p=dropout) + self.layer_stack = nn.LayerList([ + EncoderLayer( + d_model, d_inner, n_head, d_k, d_v, dropout=dropout) + for _ in range(n_layers) + ]) + self.layer_norm = nn.LayerNorm(d_model, epsilon=1e-6) + + def forward(self, enc_output, src_mask, return_attns=False): + enc_output = self.dropout( + self.position_enc(enc_output)) # position embeding + for enc_layer in self.layer_stack: + enc_output = enc_layer(enc_output, slf_attn_mask=src_mask) + enc_output = self.layer_norm(enc_output) + return enc_output + + +class PP_layer(nn.Layer): + def __init__(self, n_dim=512, N_max_character=25, n_position=256): + + super(PP_layer, self).__init__() + self.character_len = N_max_character + self.f0_embedding = nn.Embedding(N_max_character, n_dim) + self.w0 = nn.Linear(N_max_character, n_position) + self.wv = nn.Linear(n_dim, n_dim) + self.we = nn.Linear(n_dim, N_max_character) + self.active = nn.Tanh() + self.softmax = nn.Softmax(axis=2) + + def forward(self, enc_output): + # enc_output: b,256,512 + reading_order = paddle.arange(self.character_len, dtype='int64') + reading_order = reading_order.unsqueeze(0).expand( + [enc_output.shape[0], self.character_len]) # (S,) -> (B, S) + reading_order = self.f0_embedding(reading_order) # b,25,512 + + # calculate attention + reading_order = paddle.transpose(reading_order, perm=[0, 2, 1]) + t = self.w0(reading_order) # b,512,256 + t = self.active( + paddle.transpose( + t, perm=[0, 2, 1]) + self.wv(enc_output)) # b,256,512 + t = self.we(t) # b,256,25 + t = self.softmax(paddle.transpose(t, perm=[0, 2, 1])) # b,25,256 + g_output = paddle.bmm(t, enc_output) # b,25,512 + return g_output + + +class Prediction(nn.Layer): + def __init__(self, + n_dim=512, + n_position=256, + N_max_character=25, + n_class=37): + super(Prediction, self).__init__() + self.pp = PP_layer( + n_dim=n_dim, N_max_character=N_max_character, n_position=n_position) + self.pp_share = PP_layer( + n_dim=n_dim, N_max_character=N_max_character, n_position=n_position) + self.w_vrm = nn.Linear(n_dim, n_class) # output layer + self.w_share = nn.Linear(n_dim, n_class) # output layer + self.nclass = n_class + + def forward(self, cnn_feature, f_res, f_sub, train_mode=False, + use_mlm=True): + if train_mode: + if not use_mlm: + g_output = self.pp(cnn_feature) # b,25,512 + g_output = self.w_vrm(g_output) + f_res = 0 + f_sub = 0 + return g_output, f_res, f_sub + g_output = self.pp(cnn_feature) # b,25,512 + f_res = self.pp_share(f_res) + f_sub = self.pp_share(f_sub) + g_output = self.w_vrm(g_output) + f_res = self.w_share(f_res) + f_sub = self.w_share(f_sub) + return g_output, f_res, f_sub + else: + g_output = self.pp(cnn_feature) # b,25,512 + g_output = self.w_vrm(g_output) + return g_output + + +class MLM(nn.Layer): + "Architecture of MLM" + + def __init__(self, n_dim=512, n_position=256, max_text_length=25): + super(MLM, self).__init__() + self.MLM_SequenceModeling_mask = Transformer_Encoder( + n_layers=2, n_position=n_position) + self.MLM_SequenceModeling_WCL = Transformer_Encoder( + n_layers=1, n_position=n_position) + self.pos_embedding = nn.Embedding(max_text_length, n_dim) + self.w0_linear = nn.Linear(1, n_position) + self.wv = nn.Linear(n_dim, n_dim) + self.active = nn.Tanh() + self.we = nn.Linear(n_dim, 1) + self.sigmoid = nn.Sigmoid() + + def forward(self, x, label_pos): + # transformer unit for generating mask_c + feature_v_seq = self.MLM_SequenceModeling_mask(x, src_mask=None) + # position embedding layer + label_pos = paddle.to_tensor(label_pos, dtype='int64') + pos_emb = self.pos_embedding(label_pos) + pos_emb = self.w0_linear(paddle.unsqueeze(pos_emb, axis=2)) + pos_emb = paddle.transpose(pos_emb, perm=[0, 2, 1]) + # fusion position embedding with features V & generate mask_c + att_map_sub = self.active(pos_emb + self.wv(feature_v_seq)) + att_map_sub = self.we(att_map_sub) # b,256,1 + att_map_sub = paddle.transpose(att_map_sub, perm=[0, 2, 1]) + att_map_sub = self.sigmoid(att_map_sub) # b,1,256 + # WCL + ## generate inputs for WCL + att_map_sub = paddle.transpose(att_map_sub, perm=[0, 2, 1]) + f_res = x * (1 - att_map_sub) # second path with remaining string + f_sub = x * att_map_sub # first path with occluded character + ## transformer units in WCL + f_res = self.MLM_SequenceModeling_WCL(f_res, src_mask=None) + f_sub = self.MLM_SequenceModeling_WCL(f_sub, src_mask=None) + return f_res, f_sub, att_map_sub + + +def trans_1d_2d(x): + b, w_h, c = x.shape # b, 256, 512 + x = paddle.transpose(x, perm=[0, 2, 1]) + x = paddle.reshape(x, [-1, c, 32, 8]) + x = paddle.transpose(x, perm=[0, 1, 3, 2]) # [b, c, 8, 32] + return x + + +class MLM_VRM(nn.Layer): + """ + MLM+VRM, MLM is only used in training. + ratio controls the occluded number in a batch. + The pipeline of VisionLAN in testing is very concise with only a backbone + sequence modeling(transformer unit) + prediction layer(pp layer). + x: input image + label_pos: character index + training_step: LF or LA process + output + text_pre: prediction of VRM + test_rem: prediction of remaining string in MLM + text_mas: prediction of occluded character in MLM + mask_c_show: visualization of Mask_c + """ + + def __init__(self, + n_layers=3, + n_position=256, + n_dim=512, + max_text_length=25, + nclass=37): + super(MLM_VRM, self).__init__() + self.MLM = MLM(n_dim=n_dim, + n_position=n_position, + max_text_length=max_text_length) + self.SequenceModeling = Transformer_Encoder( + n_layers=n_layers, n_position=n_position) + self.Prediction = Prediction( + n_dim=n_dim, + n_position=n_position, + N_max_character=max_text_length + + 1, # N_max_character = 1 eos + 25 characters + n_class=nclass) + self.nclass = nclass + self.max_text_length = max_text_length + + def forward(self, x, label_pos, training_step, train_mode=False): + b, c, h, w = x.shape + nT = self.max_text_length + x = paddle.transpose(x, perm=[0, 1, 3, 2]) + x = paddle.reshape(x, [-1, c, h * w]) + x = paddle.transpose(x, perm=[0, 2, 1]) + if train_mode: + if training_step == 'LF_1': + f_res = 0 + f_sub = 0 + x = self.SequenceModeling(x, src_mask=None) + text_pre, test_rem, text_mas = self.Prediction( + x, f_res, f_sub, train_mode=True, use_mlm=False) + return text_pre, text_pre, text_pre, text_pre + elif training_step == 'LF_2': + # MLM + f_res, f_sub, mask_c = self.MLM(x, label_pos) + x = self.SequenceModeling(x, src_mask=None) + text_pre, test_rem, text_mas = self.Prediction( + x, f_res, f_sub, train_mode=True) + mask_c_show = trans_1d_2d(mask_c) + return text_pre, test_rem, text_mas, mask_c_show + elif training_step == 'LA': + # MLM + f_res, f_sub, mask_c = self.MLM(x, label_pos) + ## use the mask_c (1 for occluded character and 0 for remaining characters) to occlude input + ## ratio controls the occluded number in a batch + character_mask = paddle.zeros_like(mask_c) + + ratio = b // 2 + if ratio >= 1: + with paddle.no_grad(): + character_mask[0:ratio, :, :] = mask_c[0:ratio, :, :] + else: + character_mask = mask_c + x = x * (1 - character_mask) + # VRM + ## transformer unit for VRM + x = self.SequenceModeling(x, src_mask=None) + ## prediction layer for MLM and VSR + text_pre, test_rem, text_mas = self.Prediction( + x, f_res, f_sub, train_mode=True) + mask_c_show = trans_1d_2d(mask_c) + return text_pre, test_rem, text_mas, mask_c_show + else: + raise NotImplementedError + else: # VRM is only used in the testing stage + f_res = 0 + f_sub = 0 + contextual_feature = self.SequenceModeling(x, src_mask=None) + text_pre = self.Prediction( + contextual_feature, + f_res, + f_sub, + train_mode=False, + use_mlm=False) + text_pre = paddle.transpose( + text_pre, perm=[1, 0, 2]) # (26, b, 37)) + return text_pre, x + + +class VLHead(nn.Layer): + """ + Architecture of VisionLAN + """ + + def __init__(self, + in_channels, + out_channels=36, + n_layers=3, + n_position=256, + n_dim=512, + max_text_length=25, + training_step='LA'): + super(VLHead, self).__init__() + self.MLM_VRM = MLM_VRM( + n_layers=n_layers, + n_position=n_position, + n_dim=n_dim, + max_text_length=max_text_length, + nclass=out_channels + 1) + self.training_step = training_step + + def forward(self, feat, targets=None): + + if self.training: + label_pos = targets[-2] + text_pre, test_rem, text_mas, mask_map = self.MLM_VRM( + feat, label_pos, self.training_step, train_mode=True) + return text_pre, test_rem, text_mas, mask_map + else: + text_pre, x = self.MLM_VRM( + feat, targets, self.training_step, train_mode=False) + return text_pre, x diff --git a/ppocr/optimizer/optimizer.py b/ppocr/optimizer/optimizer.py index dd8544e2..144f011c 100644 --- a/ppocr/optimizer/optimizer.py +++ b/ppocr/optimizer/optimizer.py @@ -77,11 +77,62 @@ class Adam(object): self.grad_clip = grad_clip self.name = name self.lazy_mode = lazy_mode + self.group_lr = kwargs.get('group_lr', False) + self.training_step = kwargs.get('training_step', None) def __call__(self, model): - train_params = [ - param for param in model.parameters() if param.trainable is True - ] + if self.group_lr: + if self.training_step == 'LF_2': + import paddle + if isinstance(model, paddle.fluid.dygraph.parallel. + DataParallel): # multi gpu + mlm = model._layers.head.MLM_VRM.MLM.parameters() + pre_mlm_pp = model._layers.head.MLM_VRM.Prediction.pp_share.parameters( + ) + pre_mlm_w = model._layers.head.MLM_VRM.Prediction.w_share.parameters( + ) + else: # single gpu + mlm = model.head.MLM_VRM.MLM.parameters() + pre_mlm_pp = model.head.MLM_VRM.Prediction.pp_share.parameters( + ) + pre_mlm_w = model.head.MLM_VRM.Prediction.w_share.parameters( + ) + + total = [] + for param in mlm: + total.append(id(param)) + for param in pre_mlm_pp: + total.append(id(param)) + for param in pre_mlm_w: + total.append(id(param)) + + group_base_params = [ + param for param in model.parameters() if id(param) in total + ] + group_small_params = [ + param for param in model.parameters() + if id(param) not in total + ] + train_params = [{ + 'params': group_base_params + }, { + 'params': group_small_params, + 'learning_rate': self.learning_rate.values[0] * 0.1 + }] + + else: + print( + 'group lr currently only support VisionLAN in LF_2 training step' + ) + train_params = [ + param for param in model.parameters() + if param.trainable is True + ] + else: + train_params = [ + param for param in model.parameters() if param.trainable is True + ] + opt = optim.Adam( learning_rate=self.learning_rate, beta1=self.beta1, diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 6fa871a4..7c0c7fd0 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -28,7 +28,7 @@ from .fce_postprocess import FCEPostProcess from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \ DistillationCTCLabelDecode, NRTRLabelDecode, SARLabelDecode, \ SEEDLabelDecode, PRENLabelDecode, ViTSTRLabelDecode, ABINetLabelDecode, \ - SPINLabelDecode + SPINLabelDecode, VLLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess, DistillationSerPostProcess @@ -38,31 +38,16 @@ from .table_postprocess import TableMasterLabelDecode, TableLabelDecode def build_post_process(config, global_config=None): support_dict = [ - 'DBPostProcess', - 'EASTPostProcess', - 'SASTPostProcess', - 'FCEPostProcess', - 'CTCLabelDecode', - 'AttnLabelDecode', - 'ClsPostProcess', - 'SRNLabelDecode', - 'PGPostProcess', - 'DistillationCTCLabelDecode', - 'TableLabelDecode', - 'DistillationDBPostProcess', - 'NRTRLabelDecode', - 'SARLabelDecode', - 'SEEDLabelDecode', - 'VQASerTokenLayoutLMPostProcess', - 'VQAReTokenLayoutLMPostProcess', - 'PRENLabelDecode', - 'DistillationSARLabelDecode', - 'ViTSTRLabelDecode', - 'ABINetLabelDecode', - 'TableMasterLabelDecode', - 'SPINLabelDecode', - 'DistillationSerPostProcess', - 'DistillationRePostProcess', + 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'FCEPostProcess', + 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', + 'PGPostProcess', 'DistillationCTCLabelDecode', 'TableLabelDecode', + 'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode', + 'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess', + 'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode', + 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode', + 'TableMasterLabelDecode', 'SPINLabelDecode', + 'DistillationSerPostProcess', 'DistillationRePostProcess', + 'VLLabelDecode' ] if config['name'] == 'PSEPostProcess': diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 3fe29aab..7b994f81 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -668,6 +668,7 @@ class ABINetLabelDecode(NRTRLabelDecode): dict_character = [''] + dict_character return dict_character + class SPINLabelDecode(AttnLabelDecode): """ Convert between text-label and text-index """ @@ -681,4 +682,106 @@ class SPINLabelDecode(AttnLabelDecode): self.end_str = "eos" dict_character = dict_character dict_character = [self.beg_str] + [self.end_str] + dict_character - return dict_character \ No newline at end of file + return dict_character + + +class VLLabelDecode(BaseRecLabelDecode): + """ Convert between text-label and text-index """ + + def __init__(self, character_dict_path=None, use_space_char=False, + **kwargs): + super(VLLabelDecode, self).__init__(character_dict_path, use_space_char) + self.max_text_length = kwargs.get('max_text_length', 25) + self.nclass = len(self.character) + 1 + self.character = self.character[10:] + self.character[ + 1:10] + [self.character[0]] + + 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): + selection = np.ones(len(text_index[batch_idx]), dtype=bool) + if is_remove_duplicate: + selection[1:] = text_index[batch_idx][1:] != text_index[ + batch_idx][:-1] + for ignored_token in ignored_tokens: + selection &= text_index[batch_idx] != ignored_token + + char_list = [ + self.character[text_id - 1] + for text_id in text_index[batch_idx][selection] + ] + if text_prob is not None: + conf_list = text_prob[batch_idx][selection] + else: + conf_list = [1] * len(selection) + if len(conf_list) == 0: + conf_list = [0] + + text = ''.join(char_list) + result_list.append((text, np.mean(conf_list).tolist())) + return result_list + + def __call__(self, preds, label=None, length=None, *args, **kwargs): + if len(preds) == 2: # eval mode + text_pre, x = preds + b = text_pre.shape[1] + lenText = self.max_text_length + nsteps = self.max_text_length + + if not isinstance(text_pre, paddle.Tensor): + text_pre = paddle.to_tensor(text_pre, dtype='float32') + + out_res = paddle.zeros( + shape=[lenText, b, self.nclass], dtype=x.dtype) + out_length = paddle.zeros(shape=[b], dtype=x.dtype) + now_step = 0 + for _ in range(nsteps): + if 0 in out_length and now_step < nsteps: + tmp_result = text_pre[now_step, :, :] + out_res[now_step] = tmp_result + tmp_result = tmp_result.topk(1)[1].squeeze(axis=1) + for j in range(b): + if out_length[j] == 0 and tmp_result[j] == 0: + out_length[j] = now_step + 1 + now_step += 1 + for j in range(0, b): + if int(out_length[j]) == 0: + out_length[j] = nsteps + start = 0 + output = paddle.zeros( + shape=[int(out_length.sum()), self.nclass], dtype=x.dtype) + for i in range(0, b): + cur_length = int(out_length[i]) + output[start:start + cur_length] = out_res[0:cur_length, i, :] + start += cur_length + net_out = output + length = out_length + + else: # train mode + net_out = preds[0] + length = length + net_out = paddle.concat([t[:l] for t, l in zip(net_out, length)]) + text = [] + if not isinstance(net_out, paddle.Tensor): + net_out = paddle.to_tensor(net_out, dtype='float32') + net_out = F.softmax(net_out, axis=1) + for i in range(0, length.shape[0]): + preds_idx = net_out[int(length[:i].sum()):int(length[:i].sum( + ) + length[i])].topk(1)[1][:, 0].tolist() + preds_text = ''.join([ + self.character[idx - 1] + if idx > 0 and idx <= len(self.character) else '' + for idx in preds_idx + ]) + preds_prob = net_out[int(length[:i].sum()):int(length[:i].sum( + ) + length[i])].topk(1)[0][:, 0] + preds_prob = paddle.exp( + paddle.log(preds_prob).sum() / (preds_prob.shape[0] + 1e-6)) + text.append((preds_text, preds_prob)) + if label is None: + return text + label = self.decode(label) + return text, label diff --git a/tools/eval.py b/tools/eval.py index cab28334..2fc53488 100755 --- a/tools/eval.py +++ b/tools/eval.py @@ -73,7 +73,7 @@ def main(): config['Architecture']["Head"]['out_channels'] = char_num model = build_model(config['Architecture']) - extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"] + extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR", "VisionLAN"] extra_input = False if config['Architecture']['algorithm'] == 'Distillation': for key in config['Architecture']["Models"]: diff --git a/tools/export_model.py b/tools/export_model.py index 69ac904c..78932c98 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -97,6 +97,12 @@ def export_single_model(model, shape=[None, 1, 32, 100], dtype="float32"), ] model = to_static(model, input_spec=other_shape) + elif arch_config["algorithm"] == "VisionLAN": + other_shape = [ + paddle.static.InputSpec( + shape=[None, 3, 64, 256], dtype="float32"), + ] + model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]: input_spec = [ paddle.static.InputSpec( @@ -217,4 +223,4 @@ def main(): if __name__ == "__main__": - main() + main() \ No newline at end of file diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index fdbf429b..4e4150c5 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -69,6 +69,12 @@ class TextRecognizer(object): "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } + elif self.rec_algorithm == "VisionLAN": + postprocess_params = { + 'name': 'VLLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } elif self.rec_algorithm == 'ViTSTR': postprocess_params = { 'name': 'ViTSTRLabelDecode', @@ -157,6 +163,16 @@ class TextRecognizer(object): padding_im[:, :, 0:resized_w] = resized_image return padding_im + def resize_norm_img_vl(self, img, image_shape): + + imgC, imgH, imgW = image_shape + img = img[:, :, ::-1] # bgr2rgb + resized_image = cv2.resize( + img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_image = resized_image.astype('float32') + resized_image = resized_image.transpose((2, 0, 1)) / 255 + return resized_image + def resize_norm_img_srn(self, img, image_shape): imgC, imgH, imgW = image_shape @@ -280,6 +296,7 @@ class TextRecognizer(object): img -= mean img *= stdinv return img + def resize_norm_img_svtr(self, img, image_shape): imgC, imgH, imgW = image_shape @@ -359,6 +376,11 @@ class TextRecognizer(object): self.rec_image_shape) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) + elif self.rec_algorithm == "VisionLAN": + norm_img = self.resize_norm_img_vl(img_list[indices[ino]], + self.rec_image_shape) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) elif self.rec_algorithm == 'SPIN': norm_img = self.resize_norm_img_spin(img_list[indices[ino]]) norm_img = norm_img[np.newaxis, :] diff --git a/tools/infer_rec.py b/tools/infer_rec.py index a08fa25b..182694e6 100755 --- a/tools/infer_rec.py +++ b/tools/infer_rec.py @@ -131,7 +131,6 @@ def main(): if config['Architecture']['algorithm'] == "SAR": valid_ratio = np.expand_dims(batch[-1], axis=0) img_metas = [paddle.to_tensor(valid_ratio)] - images = np.expand_dims(batch[0], axis=0) images = paddle.to_tensor(images) if config['Architecture']['algorithm'] == "SRN": diff --git a/tools/program.py b/tools/program.py index 051fdf58..fd4e662b 100755 --- a/tools/program.py +++ b/tools/program.py @@ -227,7 +227,9 @@ def train(config, model.train() use_srn = config['Architecture']['algorithm'] == "SRN" - extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN"] + extra_input_models = [ + "SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN", "VisionLAN" + ] extra_input = False if config['Architecture']['algorithm'] == 'Distillation': for key in config['Architecture']["Models"]: @@ -269,7 +271,6 @@ def train(config, images = batch[0] if use_srn: model_average = True - # use amp if scaler: with paddle.amp.auto_cast(level='O2'): @@ -310,6 +311,9 @@ def train(config, ]: # for multi head loss post_result = post_process_class( preds['ctc'], batch[1]) # for CTC head out + elif config['Loss']['name'] in ['VLLoss']: + post_result = post_process_class(preds, batch[1], + batch[-1]) else: post_result = post_process_class(preds, batch[1]) eval_class(post_result, batch) @@ -612,7 +616,7 @@ def preprocess(is_train=False): 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE', - 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN' + 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN' ] if use_xpu: @@ -631,7 +635,7 @@ def preprocess(is_train=False): if 'use_visualdl' in config['Global'] and config['Global']['use_visualdl']: save_model_dir = config['Global']['save_model_dir'] vdl_writer_path = '{}/vdl/'.format(save_model_dir) - log_writer = VDLLogger(save_model_dir) + log_writer = VDLLogger(vdl_writer_path) loggers.append(log_writer) if ('use_wandb' in config['Global'] and config['Global']['use_wandb']) or 'wandb' in config: -- GitLab