diff --git a/.gitignore b/.gitignore
index caf886a2b581b5976ea391aca5d7a56041bdbaa8..34f0e0cc9c8394d298f0bf730c5f3ac24470412d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,7 +10,7 @@ __pycache__/
inference/
inference_results/
output/
-
+train_data/
*.DS_Store
*.vs
*.user
diff --git a/configs/rec/rec_r32_gaspin_bilstm_att.yml b/configs/rec/rec_r32_gaspin_bilstm_att.yml
new file mode 100644
index 0000000000000000000000000000000000000000..236a17c43d34cf873b5ef6ffc12da6cf09194f03
--- /dev/null
+++ b/configs/rec/rec_r32_gaspin_bilstm_att.yml
@@ -0,0 +1,117 @@
+Global:
+ use_gpu: True
+ epoch_num: 6
+ log_smooth_window: 50
+ print_batch_step: 50
+ save_model_dir: ./output/rec/rec_r32_gaspin_bilstm_att/
+ save_epoch_step: 3
+ # evaluation is run every 2000 iterations after the 4000th iteration
+ eval_batch_step: [0, 2000]
+ 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: ./ppocr/utils/dict/spin_dict.txt
+ max_text_length: 25
+ infer_mode: False
+ use_space_char: False
+ save_res_path: ./output/rec/predicts_r32_gaspin_bilstm_att.txt
+
+
+Optimizer:
+ name: AdamW
+ beta1: 0.9
+ beta2: 0.999
+ lr:
+ name: Piecewise
+ decay_epochs: [3, 4, 5]
+ values: [0.001, 0.0003, 0.00009, 0.000027]
+ clip_norm: 5
+
+Architecture:
+ model_type: rec
+ algorithm: SPIN
+ in_channels: 1
+ Transform:
+ name: GA_SPIN
+ offsets: True
+ default_type: 6
+ loc_lr: 0.1
+ stn: True
+ Backbone:
+ name: ResNet32
+ out_channels: 512
+ Neck:
+ name: SequenceEncoder
+ encoder_type: cascadernn
+ hidden_size: 256
+ out_channels: [256, 512]
+ with_linear: True
+ Head:
+ name: SPINAttentionHead
+ hidden_size: 256
+
+
+Loss:
+ name: SPINAttentionLoss
+ ignore_index: 0
+
+PostProcess:
+ name: SPINAttnLabelDecode
+ character_dict_path: ./ppocr/utils/dict/spin_dict.txt
+ use_space_char: False
+
+
+Metric:
+ name: RecMetric
+ main_indicator: acc
+ is_filter: True
+
+Train:
+ dataset:
+ name: SimpleDataSet
+ data_dir: ./train_data/ic15_data/
+ label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+ transforms:
+ - NRTRDecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - SPINAttnLabelEncode: # Class handling label
+ - SPINRecResizeImg:
+ image_shape: [100, 32]
+ interpolation : 2
+ mean: [127.5]
+ std: [127.5]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: True
+ batch_size_per_card: 8
+ drop_last: True
+ num_workers: 4
+
+Eval:
+ dataset:
+ name: SimpleDataSet
+ data_dir: ./train_data/ic15_data
+ label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+ transforms:
+ - NRTRDecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - SPINAttnLabelEncode: # Class handling label
+ - SPINRecResizeImg:
+ image_shape: [100, 32]
+ interpolation : 2
+ mean: [127.5]
+ std: [127.5]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: False
+ drop_last: False
+ batch_size_per_card: 8
+ num_workers: 2
diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index 6227a21498eda7d8527e21e7f2567995251d9e47..ef95317acde07bc450204bf6144b1a90fc40dfa1 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -66,6 +66,7 @@
- [x] [SAR](./algorithm_rec_sar.md)
- [x] [SEED](./algorithm_rec_seed.md)
- [x] [SVTR](./algorithm_rec_svtr.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：
@@ -84,6 +85,7 @@
|SAR|Resnet31| 87.20% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
|SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|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) |
+|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
diff --git a/doc/doc_ch/algorithm_rec_spin.md b/doc/doc_ch/algorithm_rec_spin.md
new file mode 100644
index 0000000000000000000000000000000000000000..c996992d2fa6297e6086ffae4bc36ad3e880873d
--- /dev/null
+++ b/doc/doc_ch/algorithm_rec_spin.md
@@ -0,0 +1,112 @@
+# SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
+
+- [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：
+> [SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition](https://arxiv.org/abs/2005.13117)
+> Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu, Futai Zou
+> AAAI, 2020
+
+SPIN收录于AAAI2020。主要用于OCR识别任务。在任意形状文本识别中，矫正网络是一种较为常见的前置处理模块，但诸如RARE\ASTER\ESIR等只考虑了空间变换，并没有考虑色度变换。本文提出了一种结构Structure-Preserving Inner Offset Network (SPIN)，可以在色彩空间上进行变换。该模块是可微分的，可以加入到任意识别器中。
+使用MJSynth和SynthText两个合成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，算法复现效果如下：
+
+|模型|骨干网络|配置文件|Acc|下载链接|
+| --- | --- | --- | --- | --- |
+|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|coming soon|
+
+
+
+## 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
+
+请参考[文本识别教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。
+
+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：
+
+```
+#单卡训练（训练周期长，不建议）
+python3 tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.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_r32_gaspin_bilstm_att.yml
+```
+
+评估
+
+```
+# GPU 评估， Global.pretrained_model 为待测权重
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+预测：
+
+```
+# 预测使用的配置文件必须与训练一致
+python3 tools/infer_rec.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+```
+
+
+## 4. 推理部署
+
+
+### 4.1 Python推理
+首先将SPIN文本识别训练过程中保存的模型，转换成inference model。可以使用如下命令进行转换：
+
+```
+python3 tools/export_model.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r32_gaspin_bilstm_att
+```
+SPIN文本识别模型推理，可以执行如下命令：
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r32_gaspin_bilstm_att/" --rec_image_shape="3, 32, 100" --rec_algorithm="SPIN" --rec_char_dict_path="/ppocr/utils/dict/spin_dict.txt" --use_space_char=Falsee
+```
+
+
+### 4.2 C++推理
+
+由于C++预处理后处理还未支持SPIN，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
+
+
+## 引用
+
+```bibtex
+@article{2020SPIN,
+ title={SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition},
+ author={Chengwei Zhang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Fei Wu and Futai Zou},
+ journal={AAAI2020},
+ year={2020},
+}
+```
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index 383cbe39bbd2eb8ca85f497888920ce87cb1837e..608584e01f3c9fa2397fd39e0c10ac712f044ee2 100755
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -65,6 +65,7 @@ Supported text recognition algorithms (Click the link to get the tutorial):
- [x] [SAR](./algorithm_rec_sar_en.md)
- [x] [SEED](./algorithm_rec_seed_en.md)
- [x] [SVTR](./algorithm_rec_svtr_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:
@@ -83,6 +84,7 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|SAR|Resnet31| 87.20% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
|SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|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) |
+|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
diff --git a/doc/doc_en/algorithm_rec_spin_en.md b/doc/doc_en/algorithm_rec_spin_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..43ab30ce7d96cbb64ddf87156fee3012d666b2bf
--- /dev/null
+++ b/doc/doc_en/algorithm_rec_spin_en.md
@@ -0,0 +1,112 @@
+# SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
+
+- [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:
+> [SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition](https://arxiv.org/abs/2005.13117)
+> Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu, Futai Zou
+> AAAI, 2020
+
+Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets. The algorithm reproduction effect is as follows:
+
+|Model|Backbone|config|Acc|Download link|
+| --- | --- | --- | --- | --- |
+|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|coming soon|
+
+
+
+## 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_r32_gaspin_bilstm_att.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_r32_gaspin_bilstm_att.yml
+```
+
+Evaluation:
+
+```
+# GPU evaluation
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r32_gaspin_bilstm_att.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_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+```
+
+
+## 4. Inference and Deployment
+
+
+### 4.1 Python Inference
+First, the model saved during the SPIN text recognition training process is converted into an inference model. you can use the following command to convert:
+
+```
+python3 tools/export_model.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r32_gaspin_bilstm_att
+```
+
+For SPIN text recognition model inference, the following commands can be executed:
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r32_gaspin_bilstm_att/" --rec_image_shape="3, 32, 100" --rec_algorithm="SPIN" --rec_char_dict_path="/ppocr/utils/dict/spin_dict.txt" --use_space_char=False
+```
+
+
+### 4.2 C++ Inference
+
+Not supported
+
+
+### 4.3 Serving
+
+Not supported
+
+
+### 4.4 More
+
+Not supported
+
+
+## 5. FAQ
+
+
+## Citation
+
+```bibtex
+@article{2020SPIN,
+ title={SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition},
+ author={Chengwei Zhang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Fei Wu and Futai Zou},
+ journal={AAAI2020},
+ year={2020},
+}
+```
diff --git a/log/workerlog.0 b/log/workerlog.0
new file mode 100644
index 0000000000000000000000000000000000000000..7983c87dfd8e41231fff05786e8057bf407e2ef2
--- /dev/null
+++ b/log/workerlog.0
@@ -0,0 +1,131 @@
+D:\Projects\3rdparty\anaconda\envs\pd2\lib\site-packages\socks.py:58: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
+ from collections import Callable
+D:\Projects\3rdparty\anaconda\envs\pd2\lib\site-packages\win32\lib\pywintypes.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
+ import imp, sys, os
+D:\Projects\3rdparty\anaconda\envs\pd2\lib\site-packages\pkg_resources\_vendor\pyparsing.py:943: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
+ collections.MutableMapping.register(ParseResults)
+D:\Projects\3rdparty\anaconda\envs\pd2\lib\site-packages\pkg_resources\_vendor\pyparsing.py:3245: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
+ elif isinstance( exprs, collections.Iterable ):
+[2022/06/12 13:42:08] ppocr INFO: Architecture :
+[2022/06/12 13:42:08] ppocr INFO: Backbone :
+[2022/06/12 13:42:08] ppocr INFO: name : ResNet32
+[2022/06/12 13:42:08] ppocr INFO: out_channels : 512
+[2022/06/12 13:42:08] ppocr INFO: Head :
+[2022/06/12 13:42:08] ppocr INFO: hidden_size : 256
+[2022/06/12 13:42:08] ppocr INFO: name : SPINAttentionHead
+[2022/06/12 13:42:08] ppocr INFO: Neck :
+[2022/06/12 13:42:08] ppocr INFO: encoder_type : cascadernn
+[2022/06/12 13:42:08] ppocr INFO: hidden_size : 256
+[2022/06/12 13:42:08] ppocr INFO: name : SequenceEncoder
+[2022/06/12 13:42:08] ppocr INFO: out_channels : [256, 512]
+[2022/06/12 13:42:08] ppocr INFO: with_linear : True
+[2022/06/12 13:42:08] ppocr INFO: Transform :
+[2022/06/12 13:42:08] ppocr INFO: default_type : 6
+[2022/06/12 13:42:08] ppocr INFO: loc_lr : 0.1
+[2022/06/12 13:42:08] ppocr INFO: name : GA_SPIN
+[2022/06/12 13:42:08] ppocr INFO: offsets : True
+[2022/06/12 13:42:08] ppocr INFO: stn : True
+[2022/06/12 13:42:08] ppocr INFO: algorithm : SPIN
+[2022/06/12 13:42:08] ppocr INFO: in_channels : 1
+[2022/06/12 13:42:08] ppocr INFO: model_type : rec
+[2022/06/12 13:42:08] ppocr INFO: Eval :
+[2022/06/12 13:42:08] ppocr INFO: dataset :
+[2022/06/12 13:42:08] ppocr INFO: data_dir : ./train_data/ic15_data
+[2022/06/12 13:42:08] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_test.txt']
+[2022/06/12 13:42:08] ppocr INFO: name : SimpleDataSet
+[2022/06/12 13:42:08] ppocr INFO: transforms :
+[2022/06/12 13:42:08] ppocr INFO: NRTRDecodeImage :
+[2022/06/12 13:42:08] ppocr INFO: channel_first : False
+[2022/06/12 13:42:08] ppocr INFO: img_mode : BGR
+[2022/06/12 13:42:08] ppocr INFO: SPINAttnLabelEncode : None
+[2022/06/12 13:42:08] ppocr INFO: SPINRecResizeImg :
+[2022/06/12 13:42:08] ppocr INFO: image_shape : [100, 32]
+[2022/06/12 13:42:08] ppocr INFO: interpolation : 2
+[2022/06/12 13:42:08] ppocr INFO: mean : [127.5]
+[2022/06/12 13:42:08] ppocr INFO: std : [127.5]
+[2022/06/12 13:42:08] ppocr INFO: KeepKeys :
+[2022/06/12 13:42:08] ppocr INFO: keep_keys : ['image', 'label', 'length']
+[2022/06/12 13:42:08] ppocr INFO: loader :
+[2022/06/12 13:42:08] ppocr INFO: batch_size_per_card : 8
+[2022/06/12 13:42:08] ppocr INFO: drop_last : False
+[2022/06/12 13:42:08] ppocr INFO: num_workers : 2
+[2022/06/12 13:42:08] ppocr INFO: shuffle : False
+[2022/06/12 13:42:08] ppocr INFO: Global :
+[2022/06/12 13:42:08] ppocr INFO: cal_metric_during_train : True
+[2022/06/12 13:42:08] ppocr INFO: character_dict_path : ./ppocr/utils/dict/spin_dict.txt
+[2022/06/12 13:42:08] ppocr INFO: checkpoints : ./inference/rec_r32_gaspin_bilstm_att/best_accuracy
+[2022/06/12 13:42:08] ppocr INFO: distributed : False
+[2022/06/12 13:42:08] ppocr INFO: epoch_num : 6
+[2022/06/12 13:42:08] ppocr INFO: eval_batch_step : [0, 2000]
+[2022/06/12 13:42:08] ppocr INFO: infer_img : doc/imgs_words/ch/word_1.jpg
+[2022/06/12 13:42:08] ppocr INFO: infer_mode : False
+[2022/06/12 13:42:08] ppocr INFO: log_smooth_window : 50
+[2022/06/12 13:42:08] ppocr INFO: max_text_length : 25
+[2022/06/12 13:42:08] ppocr INFO: pretrained_model : None
+[2022/06/12 13:42:08] ppocr INFO: print_batch_step : 50
+[2022/06/12 13:42:08] ppocr INFO: save_epoch_step : 3
+[2022/06/12 13:42:08] ppocr INFO: save_inference_dir : None
+[2022/06/12 13:42:08] ppocr INFO: save_model_dir : ./output/rec/rec_r32_gaspin_bilstm_att/
+[2022/06/12 13:42:08] ppocr INFO: save_res_path : ./output/rec/predicts_r32_gaspin_bilstm_att.txt
+[2022/06/12 13:42:08] ppocr INFO: use_gpu : True
+[2022/06/12 13:42:08] ppocr INFO: use_space_char : False
+[2022/06/12 13:42:08] ppocr INFO: use_visualdl : False
+[2022/06/12 13:42:08] ppocr INFO: Loss :
+[2022/06/12 13:42:08] ppocr INFO: ignore_index : 0
+[2022/06/12 13:42:08] ppocr INFO: name : SPINAttentionLoss
+[2022/06/12 13:42:08] ppocr INFO: Metric :
+[2022/06/12 13:42:08] ppocr INFO: is_filter : True
+[2022/06/12 13:42:08] ppocr INFO: main_indicator : acc
+[2022/06/12 13:42:08] ppocr INFO: name : RecMetric
+[2022/06/12 13:42:08] ppocr INFO: Optimizer :
+[2022/06/12 13:42:08] ppocr INFO: beta1 : 0.9
+[2022/06/12 13:42:08] ppocr INFO: beta2 : 0.999
+[2022/06/12 13:42:08] ppocr INFO: clip_norm : 5
+[2022/06/12 13:42:08] ppocr INFO: lr :
+[2022/06/12 13:42:08] ppocr INFO: decay_epochs : [3, 4, 5]
+[2022/06/12 13:42:08] ppocr INFO: name : Piecewise
+[2022/06/12 13:42:08] ppocr INFO: values : [0.001, 0.0003, 9e-05, 2.7e-05]
+[2022/06/12 13:42:08] ppocr INFO: name : AdamW
+[2022/06/12 13:42:08] ppocr INFO: PostProcess :
+[2022/06/12 13:42:08] ppocr INFO: character_dict_path : ./ppocr/utils/dict/spin_dict.txt
+[2022/06/12 13:42:08] ppocr INFO: name : SPINAttnLabelDecode
+[2022/06/12 13:42:08] ppocr INFO: use_space_char : False
+[2022/06/12 13:42:08] ppocr INFO: Train :
+[2022/06/12 13:42:08] ppocr INFO: dataset :
+[2022/06/12 13:42:08] ppocr INFO: data_dir : ./train_data/ic15_data/
+[2022/06/12 13:42:08] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_train.txt']
+[2022/06/12 13:42:08] ppocr INFO: name : SimpleDataSet
+[2022/06/12 13:42:08] ppocr INFO: transforms :
+[2022/06/12 13:42:08] ppocr INFO: NRTRDecodeImage :
+[2022/06/12 13:42:08] ppocr INFO: channel_first : False
+[2022/06/12 13:42:08] ppocr INFO: img_mode : BGR
+[2022/06/12 13:42:08] ppocr INFO: SPINAttnLabelEncode : None
+[2022/06/12 13:42:08] ppocr INFO: SPINRecResizeImg :
+[2022/06/12 13:42:08] ppocr INFO: image_shape : [100, 32]
+[2022/06/12 13:42:08] ppocr INFO: interpolation : 2
+[2022/06/12 13:42:08] ppocr INFO: mean : [127.5]
+[2022/06/12 13:42:08] ppocr INFO: std : [127.5]
+[2022/06/12 13:42:08] ppocr INFO: KeepKeys :
+[2022/06/12 13:42:08] ppocr INFO: keep_keys : ['image', 'label', 'length']
+[2022/06/12 13:42:08] ppocr INFO: loader :
+[2022/06/12 13:42:08] ppocr INFO: batch_size_per_card : 8
+[2022/06/12 13:42:08] ppocr INFO: drop_last : True
+[2022/06/12 13:42:08] ppocr INFO: num_workers : 4
+[2022/06/12 13:42:08] ppocr INFO: shuffle : True
+[2022/06/12 13:42:08] ppocr INFO: profiler_options : None
+[2022/06/12 13:42:08] ppocr INFO: train with paddle 2.2.2 and device CUDAPlace(0)
+[2022/06/12 13:42:08] ppocr INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_test.txt']
+W0612 13:42:08.814790 17600 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.1, Runtime API Version: 10.2
+W0612 13:42:08.832805 17600 device_context.cc:465] device: 0, cuDNN Version: 7.6.
+[2022/06/12 13:42:12] ppocr INFO: resume from ./inference/rec_r32_gaspin_bilstm_att/best_accuracy
+[2022/06/12 13:42:12] ppocr INFO: metric in ckpt ***************
+[2022/06/12 13:42:12] ppocr INFO: acc:0.90589541082154
+[2022/06/12 13:42:12] ppocr INFO: norm_edit_dis:0.9627389225663741
+[2022/06/12 13:42:12] ppocr INFO: fps:1802.1068940938283
+[2022/06/12 13:42:12] ppocr INFO: best_epoch:6
+[2022/06/12 13:42:12] ppocr INFO: start_epoch:7
+
eval model:: 0%| | 0/2 [00:00 self.max_text_len:
+ return None
+ data['length'] = np.array(len(text))
+ target = [0] + text + [1]
+ padded_text = [0 for _ in range(self.max_text_len + 2)]
+
+ padded_text[:len(target)] = target
+ data['label'] = np.array(padded_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
\ No newline at end of file
diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py
index 32de2b3fc36757fee954de41686d09999ce4c640..8caa29e292c50035be82bd4a418ea381bf5ec0e1 100644
--- a/ppocr/data/imaug/rec_img_aug.py
+++ b/ppocr/data/imaug/rec_img_aug.py
@@ -267,6 +267,51 @@ class PRENResizeImg(object):
data['image'] = resized_img.astype(np.float32)
return data
+class SPINRecResizeImg(object):
+ def __init__(self,
+ image_shape,
+ interpolation=2,
+ mean=(127.5, 127.5, 127.5),
+ 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
+
+ def __call__(self, data):
+ img = data['image']
+ # different interpolation type corresponding the OpenCV
+ if self.interpolation == 0:
+ interpolation = cv2.INTER_NEAREST
+ elif self.interpolation == 1:
+ interpolation = cv2.INTER_LINEAR
+ elif self.interpolation == 2:
+ interpolation = cv2.INTER_CUBIC
+ elif self.interpolation == 3:
+ interpolation = cv2.INTER_AREA
+ else:
+ raise Exception("Unsupported interpolation type !!!")
+ # Deal with the image error during image loading
+ if img is None:
+ return None
+
+ img = cv2.resize(img, tuple(self.image_shape), interpolation)
+ img = np.array(img, np.float32)
+ img = np.expand_dims(img, -1)
+ img = img.transpose((2, 0, 1))
+ # normalize the image
+ to_rgb = False
+ img = img.copy().astype(np.float32)
+ mean = np.float64(self.mean.reshape(1, -1))
+ stdinv = 1 / np.float64(self.std.reshape(1, -1))
+ if to_rgb:
+ cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+ img -= mean
+ img *= stdinv
+ data['image'] = img
+ return data
def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
imgC, imgH, imgW_min, imgW_max = image_shape
diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py
index de8419b7c1cf6a30ab7195a1cbcbb10a5e52642d..f748b94cf386d1b149d5d28d75386dc0afe9a584 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_spin_att_loss import SPINAttentionLoss
# cls loss
from .cls_loss import ClsLoss
@@ -61,7 +62,8 @@ def build_loss(config):
'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss',
'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
'NRTRLoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
- 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss'
+ 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss',
+ 'SPINAttentionLoss'
]
config = copy.deepcopy(config)
module_name = config.pop('name')
diff --git a/ppocr/losses/rec_spin_att_loss.py b/ppocr/losses/rec_spin_att_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..37fd93da5750a353e30c4b9f0574e0e634b50d79
--- /dev/null
+++ b/ppocr/losses/rec_spin_att_loss.py
@@ -0,0 +1,41 @@
+# copyright (c) 2021 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 SPINAttentionLoss(nn.Layer):
+ def __init__(self, reduction='mean', ignore_index=-100, **kwargs):
+ super(SPINAttentionLoss, self).__init__()
+ self.loss_func = nn.CrossEntropyLoss(weight=None, reduction=reduction, ignore_index=ignore_index)
+
+ def forward(self, predicts, batch):
+ targets = batch[1].astype("int64")
+ targets = targets[:, 1:] # remove [eos] in label
+
+ label_lengths = batch[2].astype('int64')
+ batch_size, num_steps, num_classes = predicts.shape[0], predicts.shape[
+ 1], predicts.shape[2]
+ assert len(targets.shape) == len(list(predicts.shape)) - 1, \
+ "The target's shape and inputs's shape is [N, d] and [N, num_steps]"
+
+ inputs = paddle.reshape(predicts, [-1, predicts.shape[-1]])
+ targets = paddle.reshape(targets, [-1])
+
+ return {'loss': self.loss_func(inputs, targets)}
diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py
index 072d6e0f84d4126d256c26aa5baf17c9dc4e63df..6b525326a6bd6825c11ab697511aa477c374d32e 100755
--- a/ppocr/modeling/backbones/__init__.py
+++ b/ppocr/modeling/backbones/__init__.py
@@ -32,10 +32,11 @@ def build_backbone(config, model_type):
from .rec_micronet import MicroNet
from .rec_efficientb3_pren import EfficientNetb3_PREN
from .rec_svtrnet import SVTRNet
+ from .rec_resnet_32 import ResNet32
support_dict = [
'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB',
"ResNet31", "ResNet_ASTER", 'MicroNet', 'EfficientNetb3_PREN',
- 'SVTRNet'
+ 'SVTRNet', 'ResNet32'
]
elif model_type == "e2e":
from .e2e_resnet_vd_pg import ResNet
diff --git a/ppocr/modeling/backbones/rec_resnet_32.py b/ppocr/modeling/backbones/rec_resnet_32.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b072dc5f6fefa8792338e8cce4716bdcbaeb135
--- /dev/null
+++ b/ppocr/modeling/backbones/rec_resnet_32.py
@@ -0,0 +1,289 @@
+# copyright (c) 2021 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/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/backbones/ResNet32.py
+"""
+
+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
+import numpy as np
+
+__all__ = ["ResNet32"]
+
+conv_weight_attr = nn.initializer.KaimingNormal()
+
+class ResNet32(nn.Layer):
+ """
+ Feature Extractor is proposed in FAN Ref [1]
+
+ Ref [1]: Focusing Attention: Towards Accurate Text Recognition in Neural Images ICCV-2017
+ """
+
+ def __init__(self, in_channels, out_channels=512):
+ """
+
+ Args:
+ in_channels (int): input channel
+ output_channel (int): output channel
+ """
+ super(ResNet32, self).__init__()
+ self.out_channels = out_channels
+ self.ConvNet = ResNet(in_channels, out_channels, BasicBlock, [1, 2, 5, 3])
+
+ def forward(self, inputs):
+ """
+ Args:
+ inputs (torch.Tensor): input feature
+
+ Returns:
+ torch.Tensor: output feature
+
+ """
+ return self.ConvNet(inputs)
+
+class BasicBlock(nn.Layer):
+ """Res-net Basic Block"""
+ expansion = 1
+
+ def __init__(self, inplanes, planes,
+ stride=1, downsample=None,
+ norm_type='BN', **kwargs):
+ """
+ Args:
+ inplanes (int): input channel
+ planes (int): channels of the middle feature
+ stride (int): stride of the convolution
+ downsample (int): type of the down_sample
+ norm_type (str): type of the normalization
+ **kwargs (None): backup parameter
+ """
+ super(BasicBlock, self).__init__()
+ self.conv1 = self._conv3x3(inplanes, planes)
+ self.bn1 = nn.BatchNorm2D(planes)
+ self.conv2 = self._conv3x3(planes, planes)
+ self.bn2 = nn.BatchNorm2D(planes)
+ self.relu = nn.ReLU()
+ self.downsample = downsample
+ self.stride = stride
+
+ def _conv3x3(self, in_planes, out_planes, stride=1):
+ """
+
+ Args:
+ in_planes (int): input channel
+ out_planes (int): channels of the middle feature
+ stride (int): stride of the convolution
+ Returns:
+ nn.Module: Conv2D with kernel = 3
+
+ """
+
+ return nn.Conv2D(in_planes, out_planes,
+ kernel_size=3, stride=stride,
+ padding=1, weight_attr=conv_weight_attr,
+ bias_attr=False)
+
+ def forward(self, x):
+ """
+ Args:
+ x (torch.Tensor): input feature
+
+ Returns:
+ torch.Tensor: output feature of the BasicBlock
+
+ """
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+class ResNet(nn.Layer):
+ """Res-Net network structure"""
+ def __init__(self, input_channel,
+ output_channel, block, layers):
+ """
+
+ Args:
+ input_channel (int): input channel
+ output_channel (int): output channel
+ block (BasicBlock): convolution block
+ layers (list): layers of the block
+ """
+ super(ResNet, self).__init__()
+
+ self.output_channel_block = [int(output_channel / 4),
+ int(output_channel / 2),
+ output_channel,
+ output_channel]
+
+ self.inplanes = int(output_channel / 8)
+ self.conv0_1 = nn.Conv2D(input_channel, int(output_channel / 16),
+ kernel_size=3, stride=1,
+ padding=1,
+ weight_attr=conv_weight_attr,
+ bias_attr=False)
+ self.bn0_1 = nn.BatchNorm2D(int(output_channel / 16))
+ self.conv0_2 = nn.Conv2D(int(output_channel / 16), self.inplanes,
+ kernel_size=3, stride=1,
+ padding=1,
+ weight_attr=conv_weight_attr,
+ bias_attr=False)
+ self.bn0_2 = nn.BatchNorm2D(self.inplanes)
+ self.relu = nn.ReLU()
+
+ self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
+ self.layer1 = self._make_layer(block,
+ self.output_channel_block[0],
+ layers[0])
+ self.conv1 = nn.Conv2D(self.output_channel_block[0],
+ self.output_channel_block[0],
+ kernel_size=3, stride=1,
+ padding=1,
+ weight_attr=conv_weight_attr,
+ bias_attr=False)
+ self.bn1 = nn.BatchNorm2D(self.output_channel_block[0])
+
+ self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
+ self.layer2 = self._make_layer(block,
+ self.output_channel_block[1],
+ layers[1], stride=1)
+ self.conv2 = nn.Conv2D(self.output_channel_block[1],
+ self.output_channel_block[1],
+ kernel_size=3, stride=1,
+ padding=1,
+ weight_attr=conv_weight_attr,
+ bias_attr=False,)
+ self.bn2 = nn.BatchNorm2D(self.output_channel_block[1])
+
+ self.maxpool3 = nn.MaxPool2D(kernel_size=2,
+ stride=(2, 1),
+ padding=(0, 1))
+ self.layer3 = self._make_layer(block, self.output_channel_block[2],
+ layers[2], stride=1)
+ self.conv3 = nn.Conv2D(self.output_channel_block[2],
+ self.output_channel_block[2],
+ kernel_size=3, stride=1,
+ padding=1,
+ weight_attr=conv_weight_attr,
+ bias_attr=False)
+ self.bn3 = nn.BatchNorm2D(self.output_channel_block[2])
+
+ self.layer4 = self._make_layer(block, self.output_channel_block[3],
+ layers[3], stride=1)
+ self.conv4_1 = nn.Conv2D(self.output_channel_block[3],
+ self.output_channel_block[3],
+ kernel_size=2, stride=(2, 1),
+ padding=(0, 1),
+ weight_attr=conv_weight_attr,
+ bias_attr=False)
+ self.bn4_1 = nn.BatchNorm2D(self.output_channel_block[3])
+ self.conv4_2 = nn.Conv2D(self.output_channel_block[3],
+ self.output_channel_block[3],
+ kernel_size=2, stride=1,
+ padding=0,
+ weight_attr=conv_weight_attr,
+ bias_attr=False)
+ self.bn4_2 = nn.BatchNorm2D(self.output_channel_block[3])
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ """
+
+ Args:
+ block (block): convolution block
+ planes (int): input channels
+ blocks (list): layers of the block
+ stride (int): stride of the convolution
+
+ Returns:
+ nn.Sequential: the combination of the convolution block
+
+ """
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ nn.Conv2D(self.inplanes, planes * block.expansion,
+ kernel_size=1, stride=stride,
+ weight_attr=conv_weight_attr,
+ bias_attr=False),
+ nn.BatchNorm2D(planes * block.expansion),
+ )
+
+ layers = list()
+ layers.append(block(self.inplanes, planes, stride, downsample))
+ self.inplanes = planes * block.expansion
+ for _ in range(1, blocks):
+ layers.append(block(self.inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ """
+ Args:
+ x (torch.Tensor): input feature
+
+ Returns:
+ torch.Tensor: output feature of the Resnet
+
+ """
+ x = self.conv0_1(x)
+ x = self.bn0_1(x)
+ x = self.relu(x)
+ x = self.conv0_2(x)
+ x = self.bn0_2(x)
+ x = self.relu(x)
+
+ x = self.maxpool1(x)
+ x = self.layer1(x)
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+
+ x = self.maxpool2(x)
+ x = self.layer2(x)
+ x = self.conv2(x)
+ x = self.bn2(x)
+ x = self.relu(x)
+
+ x = self.maxpool3(x)
+ x = self.layer3(x)
+ x = self.conv3(x)
+ x = self.bn3(x)
+ x = self.relu(x)
+
+ x = self.layer4(x)
+ x = self.conv4_1(x)
+ x = self.bn4_1(x)
+ x = self.relu(x)
+ x = self.conv4_2(x)
+ x = self.bn4_2(x)
+ x = self.relu(x)
+ return x
diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py
index 1670ea38e66baa683e6faab0ec4b12bc517f3c41..9b53462b81d05af9dfba2912fcd2ec4d6ae09784 100755
--- a/ppocr/modeling/heads/__init__.py
+++ b/ppocr/modeling/heads/__init__.py
@@ -33,6 +33,7 @@ def build_head(config):
from .rec_aster_head import AsterHead
from .rec_pren_head import PRENHead
from .rec_multi_head import MultiHead
+ from .rec_spin_att_head import SPINAttentionHead
# cls head
from .cls_head import ClsHead
@@ -46,7 +47,7 @@ def build_head(config):
'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead',
'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer',
'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead',
- 'MultiHead'
+ 'MultiHead', 'SPINAttentionHead'
]
#table head
diff --git a/ppocr/modeling/heads/rec_spin_att_head.py b/ppocr/modeling/heads/rec_spin_att_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..94e69a7ede926e76fb4df4c0cb5077644cd9737f
--- /dev/null
+++ b/ppocr/modeling/heads/rec_spin_att_head.py
@@ -0,0 +1,203 @@
+# copyright (c) 2021 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
+import paddle.nn as nn
+import paddle.nn.functional as F
+import numpy as np
+
+
+class SPINAttentionHead(nn.Layer):
+ def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
+ super(SPINAttentionHead, self).__init__()
+ self.input_size = in_channels
+ self.hidden_size = hidden_size
+ self.num_classes = out_channels
+
+ self.attention_cell = AttentionLSTMCell(
+ in_channels, hidden_size, out_channels, use_gru=False)
+ self.generator = nn.Linear(hidden_size, out_channels)
+
+ def _char_to_onehot(self, input_char, onehot_dim):
+ input_ont_hot = F.one_hot(input_char, onehot_dim)
+ return input_ont_hot
+
+ def forward(self, inputs, targets=None, batch_max_length=25):
+ batch_size = paddle.shape(inputs)[0]
+ num_steps = batch_max_length + 1 # +1 for [sos] at end of sentence
+
+ hidden = (paddle.zeros((batch_size, self.hidden_size)),
+ paddle.zeros((batch_size, self.hidden_size)))
+ output_hiddens = []
+ if self.training: # for train
+ targets = targets[0]
+ for i in range(num_steps):
+ char_onehots = self._char_to_onehot(
+ targets[:, i], onehot_dim=self.num_classes)
+ (outputs, hidden), alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+ output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
+ output = paddle.concat(output_hiddens, axis=1)
+ probs = self.generator(output)
+ else:
+ targets = paddle.zeros(shape=[batch_size], dtype="int32")
+ probs = None
+ char_onehots = None
+ outputs = None
+ alpha = None
+
+ for i in range(num_steps):
+ char_onehots = self._char_to_onehot(
+ targets, onehot_dim=self.num_classes)
+ (outputs, hidden), alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+ probs_step = self.generator(outputs)
+ if probs is None:
+ probs = paddle.unsqueeze(probs_step, axis=1)
+ else:
+ probs = paddle.concat(
+ [probs, paddle.unsqueeze(
+ probs_step, axis=1)], axis=1)
+ next_input = probs_step.argmax(axis=1)
+ targets = next_input
+ if not self.training:
+ probs = paddle.nn.functional.softmax(probs, axis=2)
+ return probs
+
+
+class AttentionGRUCell(nn.Layer):
+ def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
+ super(AttentionGRUCell, self).__init__()
+ self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
+ self.h2h = nn.Linear(hidden_size, hidden_size)
+ self.score = nn.Linear(hidden_size, 1, bias_attr=False)
+
+ self.rnn = nn.GRUCell(
+ input_size=input_size + num_embeddings, hidden_size=hidden_size)
+
+ self.hidden_size = hidden_size
+
+ def forward(self, prev_hidden, batch_H, char_onehots):
+
+ batch_H_proj = self.i2h(batch_H)
+ prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1)
+
+ res = paddle.add(batch_H_proj, prev_hidden_proj)
+ res = paddle.tanh(res)
+ e = self.score(res)
+
+ alpha = F.softmax(e, axis=1)
+ alpha = paddle.transpose(alpha, [0, 2, 1])
+ context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
+ concat_context = paddle.concat([context, char_onehots], 1)
+
+ cur_hidden = self.rnn(concat_context, prev_hidden)
+
+ return cur_hidden, alpha
+
+
+class AttentionLSTM(nn.Layer):
+ def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
+ super(AttentionLSTM, self).__init__()
+ self.input_size = in_channels
+ self.hidden_size = hidden_size
+ self.num_classes = out_channels
+
+ self.attention_cell = AttentionLSTMCell(
+ in_channels, hidden_size, out_channels, use_gru=False)
+ self.generator = nn.Linear(hidden_size, out_channels)
+
+ def _char_to_onehot(self, input_char, onehot_dim):
+ input_ont_hot = F.one_hot(input_char, onehot_dim)
+ return input_ont_hot
+
+ def forward(self, inputs, targets=None, batch_max_length=25):
+ batch_size = inputs.shape[0]
+ num_steps = batch_max_length
+
+ hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros(
+ (batch_size, self.hidden_size)))
+ output_hiddens = []
+
+ if targets is not None:
+ for i in range(num_steps):
+ # one-hot vectors for a i-th char
+ char_onehots = self._char_to_onehot(
+ targets[:, i], onehot_dim=self.num_classes)
+ hidden, alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+
+ hidden = (hidden[1][0], hidden[1][1])
+ output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1))
+ output = paddle.concat(output_hiddens, axis=1)
+ probs = self.generator(output)
+
+ else:
+ targets = paddle.zeros(shape=[batch_size], dtype="int32")
+ probs = None
+
+ for i in range(num_steps):
+ char_onehots = self._char_to_onehot(
+ targets, onehot_dim=self.num_classes)
+ hidden, alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+ probs_step = self.generator(hidden[0])
+ hidden = (hidden[1][0], hidden[1][1])
+ if probs is None:
+ probs = paddle.unsqueeze(probs_step, axis=1)
+ else:
+ probs = paddle.concat(
+ [probs, paddle.unsqueeze(
+ probs_step, axis=1)], axis=1)
+
+ next_input = probs_step.argmax(axis=1)
+
+ targets = next_input
+
+ return probs
+
+
+class AttentionLSTMCell(nn.Layer):
+ def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
+ super(AttentionLSTMCell, self).__init__()
+ self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
+ self.h2h = nn.Linear(hidden_size, hidden_size)
+ self.score = nn.Linear(hidden_size, 1, bias_attr=False)
+ if not use_gru:
+ self.rnn = nn.LSTMCell(
+ input_size=input_size + num_embeddings, hidden_size=hidden_size)
+ else:
+ self.rnn = nn.GRUCell(
+ input_size=input_size + num_embeddings, hidden_size=hidden_size)
+
+ self.hidden_size = hidden_size
+
+ def forward(self, prev_hidden, batch_H, char_onehots):
+ batch_H_proj = self.i2h(batch_H)
+ prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1)
+ res = paddle.add(batch_H_proj, prev_hidden_proj)
+ res = paddle.tanh(res)
+ e = self.score(res)
+
+ alpha = F.softmax(e, axis=1)
+ alpha = paddle.transpose(alpha, [0, 2, 1])
+ context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
+ concat_context = paddle.concat([context, char_onehots], 1)
+ cur_hidden = self.rnn(concat_context, prev_hidden)
+
+ return cur_hidden, alpha
diff --git a/ppocr/modeling/necks/rnn.py b/ppocr/modeling/necks/rnn.py
index c8a774b8c543b9ccc14223c52f1b79ce690592f6..32e626c3fcfcf9c578772e99941f791083aabf31 100644
--- a/ppocr/modeling/necks/rnn.py
+++ b/ppocr/modeling/necks/rnn.py
@@ -47,6 +47,67 @@ class EncoderWithRNN(nn.Layer):
x, _ = self.lstm(x)
return x
+class BidirectionalLSTM(nn.Layer):
+ def __init__(self, input_size,
+ hidden_size,
+ output_size=None,
+ num_layers=1,
+ dropout=0,
+ direction=False,
+ time_major=False,
+ with_linear=False):
+ super(BidirectionalLSTM, self).__init__()
+ self.with_linear = with_linear
+ self.rnn = nn.LSTM(input_size,
+ hidden_size,
+ num_layers=num_layers,
+ dropout=dropout,
+ direction=direction,
+ time_major=time_major)
+
+ # text recognition the specified structure LSTM with linear
+ if self.with_linear:
+ self.linear = nn.Linear(hidden_size * 2, output_size)
+
+ def forward(self, input_feature):
+ """
+
+ Args:
+ input_feature (Torch.Tensor): visual feature [batch_size x T x input_size]
+
+ Returns:
+ Torch.Tensor: LSTM output contextual feature [batch_size x T x output_size]
+
+ """
+
+ # self.rnn.flatten_parameters() # error in export_model
+ recurrent, _ = self.rnn(input_feature) # batch_size x T x input_size -> batch_size x T x (2*hidden_size)
+ if self.with_linear:
+ output = self.linear(recurrent) # batch_size x T x output_size
+ return output
+ return recurrent
+
+class EncoderWithCascadeRNN(nn.Layer):
+ def __init__(self, in_channels, hidden_size, out_channels, num_layers=2, with_linear=False):
+ super(EncoderWithCascadeRNN, self).__init__()
+ self.out_channels = out_channels[-1]
+ self.encoder = nn.LayerList(
+ [BidirectionalLSTM(
+ in_channels if i == 0 else out_channels[i - 1],
+ hidden_size,
+ output_size=out_channels[i],
+ num_layers=1,
+ direction='bidirectional',
+ with_linear=with_linear)
+ for i in range(num_layers)]
+ )
+
+
+ def forward(self, x):
+ for i, l in enumerate(self.encoder):
+ x = l(x)
+ return x
+
class EncoderWithFC(nn.Layer):
def __init__(self, in_channels, hidden_size):
@@ -166,13 +227,17 @@ class SequenceEncoder(nn.Layer):
'reshape': Im2Seq,
'fc': EncoderWithFC,
'rnn': EncoderWithRNN,
- 'svtr': EncoderWithSVTR
+ 'svtr': EncoderWithSVTR,
+ 'cascadernn': EncoderWithCascadeRNN
}
assert encoder_type in support_encoder_dict, '{} must in {}'.format(
encoder_type, support_encoder_dict.keys())
if encoder_type == "svtr":
self.encoder = support_encoder_dict[encoder_type](
self.encoder_reshape.out_channels, **kwargs)
+ elif encoder_type == 'cascadernn':
+ self.encoder = support_encoder_dict[encoder_type](
+ self.encoder_reshape.out_channels, hidden_size, **kwargs)
else:
self.encoder = support_encoder_dict[encoder_type](
self.encoder_reshape.out_channels, hidden_size)
diff --git a/ppocr/modeling/transforms/__init__.py b/ppocr/modeling/transforms/__init__.py
index 405ab3cc6c0380654f61e42e523ddc85839139b3..7e4ffdf46854416f71e1c8f4e131d1f0283bb725 100755
--- a/ppocr/modeling/transforms/__init__.py
+++ b/ppocr/modeling/transforms/__init__.py
@@ -18,8 +18,10 @@ __all__ = ['build_transform']
def build_transform(config):
from .tps import TPS
from .stn import STN_ON
+ from .gaspin_transformer import GA_SPIN_Transformer as GA_SPIN
- support_dict = ['TPS', 'STN_ON']
+
+ support_dict = ['TPS', 'STN_ON', 'GA_SPIN']
module_name = config.pop('name')
assert module_name in support_dict, Exception(
diff --git a/ppocr/modeling/transforms/gaspin_transformer.py b/ppocr/modeling/transforms/gaspin_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..331c82aaeb0b176950621faf5e095cfc806a6257
--- /dev/null
+++ b/ppocr/modeling/transforms/gaspin_transformer.py
@@ -0,0 +1,286 @@
+# 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 numpy as np
+import itertools
+import functools
+from .tps import GridGenerator
+
+'''This code is refer from:
+https://github.com/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/transformations/gaspin_transformation.py
+'''
+
+class SP_TransformerNetwork(nn.Layer):
+ """
+ Sturture-Preserving Transformation (SPT) as Equa. (2) in Ref. [1]
+ Ref: [1] SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. AAAI-2021.
+ """
+
+ def __init__(self, nc=1, default_type=5):
+ """ Based on SPIN
+ Args:
+ nc (int): number of input channels (usually in 1 or 3)
+ default_type (int): the complexity of transformation intensities (by default set to 6 as the paper)
+ """
+ super(SP_TransformerNetwork, self).__init__()
+ self.power_list = self.cal_K(default_type)
+ self.sigmoid = nn.Sigmoid()
+ self.bn = nn.InstanceNorm2D(nc)
+
+ def cal_K(self, k=5):
+ """
+
+ Args:
+ k (int): the complexity of transformation intensities (by default set to 6 as the paper)
+
+ Returns:
+ List: the normalized intensity of each pixel in [0,1], denoted as \beta [1x(2K+1)]
+
+ """
+ from math import log
+ x = []
+ if k != 0:
+ for i in range(1, k+1):
+ lower = round(log(1-(0.5/(k+1))*i)/log((0.5/(k+1))*i), 2)
+ upper = round(1/lower, 2)
+ x.append(lower)
+ x.append(upper)
+ x.append(1.00)
+ return x
+
+ def forward(self, batch_I, weights, offsets, lambda_color=None):
+ """
+
+ Args:
+ batch_I (torch.Tensor): batch of input images [batch_size x nc x I_height x I_width]
+ weights:
+ offsets: the predicted offset by AIN, a scalar
+ lambda_color: the learnable update gate \alpha in Equa. (5) as
+ g(x) = (1 - \alpha) \odot x + \alpha \odot x_{offsets}
+
+ Returns:
+ torch.Tensor: transformed images by SPN as Equa. (4) in Ref. [1]
+ [batch_size x I_channel_num x I_r_height x I_r_width]
+
+ """
+ batch_I = (batch_I + 1) * 0.5
+ if offsets is not None:
+ batch_I = batch_I*(1-lambda_color) + offsets*lambda_color
+ batch_weight_params = paddle.unsqueeze(paddle.unsqueeze(weights, -1), -1)
+ batch_I_power = paddle.stack([batch_I.pow(p) for p in self.power_list], axis=1)
+
+ batch_weight_sum = paddle.sum(batch_I_power * batch_weight_params, axis=1)
+ batch_weight_sum = self.bn(batch_weight_sum)
+ batch_weight_sum = self.sigmoid(batch_weight_sum)
+ batch_weight_sum = batch_weight_sum * 2 - 1
+ return batch_weight_sum
+
+class GA_SPIN_Transformer(nn.Layer):
+ """
+ Geometric-Absorbed SPIN Transformation (GA-SPIN) proposed in Ref. [1]
+
+
+ Ref: [1] SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. AAAI-2021.
+ """
+
+ def __init__(self, in_channels=1,
+ I_r_size=(32, 100),
+ offsets=False,
+ norm_type='BN',
+ default_type=6,
+ loc_lr=1,
+ stn=True):
+ """
+ Args:
+ in_channels (int): channel of input features,
+ set it to 1 if the grayscale images and 3 if RGB input
+ I_r_size (tuple): size of rectified images (used in STN transformations)
+ inputDataType (str): the type of input data,
+ only support 'torch.cuda.FloatTensor' this version
+ offsets (bool): set it to False if use SPN w.o. AIN,
+ and set it to True if use SPIN (both with SPN and AIN)
+ norm_type (str): the normalization type of the module,
+ set it to 'BN' by default, 'IN' optionally
+ default_type (int): the K chromatic space,
+ set it to 3/5/6 depend on the complexity of transformation intensities
+ loc_lr (float): learning rate of location network
+
+ """
+ super(GA_SPIN_Transformer, self).__init__()
+ self.nc = in_channels
+ self.spt = True
+ self.offsets = offsets
+ self.stn = stn # set to True in GA-SPIN, while set it to False in SPIN
+ self.I_r_size = I_r_size
+ self.out_channels = in_channels
+ if norm_type == 'BN':
+ norm_layer = functools.partial(nn.BatchNorm2D, use_global_stats=True)
+ elif norm_type == 'IN':
+ norm_layer = functools.partial(nn.InstanceNorm2D, weight_attr=False,
+ use_global_stats=False)
+ else:
+ raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+
+ if self.spt:
+ self.sp_net = SP_TransformerNetwork(in_channels,
+ default_type)
+ self.spt_convnet = nn.Sequential(
+ # 32*100
+ nn.Conv2D(in_channels, 32, 3, 1, 1, bias_attr=False),
+ norm_layer(32), nn.ReLU(),
+ nn.MaxPool2D(kernel_size=2, stride=2),
+ # 16*50
+ nn.Conv2D(32, 64, 3, 1, 1, bias_attr=False),
+ norm_layer(64), nn.ReLU(),
+ nn.MaxPool2D(kernel_size=2, stride=2),
+ # 8*25
+ nn.Conv2D(64, 128, 3, 1, 1, bias_attr=False),
+ norm_layer(128), nn.ReLU(),
+ nn.MaxPool2D(kernel_size=2, stride=2),
+ # 4*12
+ )
+ self.stucture_fc1 = nn.Sequential(
+ nn.Conv2D(128, 256, 3, 1, 1, bias_attr=False),
+ norm_layer(256), nn.ReLU(),
+ nn.MaxPool2D(kernel_size=2, stride=2),
+ nn.Conv2D(256, 256, 3, 1, 1, bias_attr=False),
+ norm_layer(256), nn.ReLU(), # 2*6
+ nn.MaxPool2D(kernel_size=2, stride=2),
+ nn.Conv2D(256, 512, 3, 1, 1, bias_attr=False),
+ norm_layer(512), nn.ReLU(), # 1*3
+ nn.AdaptiveAvgPool2D(1),
+ nn.Flatten(1, -1), # batch_size x 512
+ nn.Linear(512, 256, weight_attr=nn.initializer.Normal(0.001)),
+ nn.BatchNorm1D(256), nn.ReLU()
+ )
+ self.out_weight = 2*default_type+1
+ self.spt_length = 2*default_type+1
+ if offsets:
+ self.out_weight += 1
+ if self.stn:
+ self.F = 20
+ self.out_weight += self.F * 2
+ self.GridGenerator = GridGenerator(self.F*2, self.F)
+
+ # self.out_weight*=nc
+ # Init structure_fc2 in LocalizationNetwork
+ initial_bias = self.init_spin(default_type*2)
+ initial_bias = initial_bias.reshape(-1)
+ param_attr = ParamAttr(
+ learning_rate=loc_lr,
+ initializer=nn.initializer.Assign(np.zeros([256, self.out_weight])))
+ bias_attr = ParamAttr(
+ learning_rate=loc_lr,
+ initializer=nn.initializer.Assign(initial_bias))
+ self.stucture_fc2 = nn.Linear(256, self.out_weight,
+ weight_attr=param_attr,
+ bias_attr=bias_attr)
+ self.sigmoid = nn.Sigmoid()
+
+ if offsets:
+ self.offset_fc1 = nn.Sequential(nn.Conv2D(128, 16,
+ 3, 1, 1,
+ bias_attr=False),
+ norm_layer(16),
+ nn.ReLU(),)
+ self.offset_fc2 = nn.Conv2D(16, in_channels,
+ 3, 1, 1)
+ self.pool = nn.MaxPool2D(2, 2)
+
+ def init_spin(self, nz):
+ """
+ Args:
+ nz (int): number of paired \betas exponents, which means the value of K x 2
+
+ """
+ init_id = [0.00]*nz+[5.00]
+ if self.offsets:
+ init_id += [-5.00]
+ # init_id *=3
+ init = np.array(init_id)
+
+ if self.stn:
+ F = self.F
+ ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
+ ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))
+ ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))
+ ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
+ ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
+ initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
+ initial_bias = initial_bias.reshape(-1)
+ init = np.concatenate([init, initial_bias], axis=0)
+ return init
+
+ def forward(self, x, return_weight=False):
+ """
+ Args:
+ x (torch.cuda.FloatTensor): input image batch
+ return_weight (bool): set to False by default,
+ if set to True return the predicted offsets of AIN, denoted as x_{offsets}
+
+ Returns:
+ torch.Tensor: rectified image [batch_size x I_channel_num x I_height x I_width], the same as the input size
+ """
+
+ if self.spt:
+ feat = self.spt_convnet(x)
+ fc1 = self.stucture_fc1(feat)
+ sp_weight_fusion = self.stucture_fc2(fc1)
+ sp_weight_fusion = sp_weight_fusion.reshape([x.shape[0], self.out_weight, 1])
+ if self.offsets: # SPIN w. AIN
+ lambda_color = sp_weight_fusion[:, self.spt_length, 0]
+ lambda_color = self.sigmoid(lambda_color).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+ sp_weight = sp_weight_fusion[:, :self.spt_length, :]
+ offsets = self.pool(self.offset_fc2(self.offset_fc1(feat)))
+
+ assert offsets.shape[2] == 2 # 2
+ assert offsets.shape[3] == 6 # 16
+ offsets = self.sigmoid(offsets) # v12
+
+ if return_weight:
+ return offsets
+ offsets = nn.functional.upsample(offsets, size=(x.shape[2], x.shape[3]), mode='bilinear')
+
+ if self.stn:
+ batch_C_prime = sp_weight_fusion[:, (self.spt_length + 1):, :].reshape([x.shape[0], self.F, 2])
+ build_P_prime = self.GridGenerator(batch_C_prime, self.I_r_size)
+ build_P_prime_reshape = build_P_prime.reshape([build_P_prime.shape[0],
+ self.I_r_size[0],
+ self.I_r_size[1],
+ 2])
+
+ else: # SPIN w.o. AIN
+ sp_weight = sp_weight_fusion[:, :self.spt_length, :]
+ lambda_color, offsets = None, None
+
+ if self.stn:
+ batch_C_prime = sp_weight_fusion[:, self.spt_length:, :].reshape([x.shape[0], self.F, 2])
+ build_P_prime = self.GridGenerator(batch_C_prime, self.I_r_size)
+ build_P_prime_reshape = build_P_prime.reshape([build_P_prime.shape[0],
+ self.I_r_size[0],
+ self.I_r_size[1],
+ 2])
+
+ x = self.sp_net(x, sp_weight, offsets, lambda_color)
+ if self.stn:
+ x = F.grid_sample(x=x, grid=build_P_prime_reshape, padding_mode='border')
+ return x
diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py
index f50b5f1c5f8e617066bb47636c8f4d2b171b6ecb..cf2575ee0569d89c775796e2c211baa3dc506ddb 100644
--- a/ppocr/postprocess/__init__.py
+++ b/ppocr/postprocess/__init__.py
@@ -27,7 +27,7 @@ from .sast_postprocess import SASTPostProcess
from .fce_postprocess import FCEPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \
DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \
- SEEDLabelDecode, PRENLabelDecode
+ SEEDLabelDecode, PRENLabelDecode, SPINAttnLabelDecode
from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess
from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess
@@ -42,7 +42,7 @@ def build_post_process(config, global_config=None):
'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess',
'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode',
- 'DistillationSARLabelDecode'
+ 'DistillationSARLabelDecode', 'SPINAttnLabelDecode'
]
if config['name'] == 'PSEPostProcess':
diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py
index bf0fd890bf25949361665d212bf8e1a657054e5b..0df8f3ccd159f2ddb506294da21921acbc00bc3c 100644
--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -752,3 +752,82 @@ class PRENLabelDecode(BaseRecLabelDecode):
return text
label = self.decode(label)
return text, label
+
+class SPINAttnLabelDecode(BaseRecLabelDecode):
+ """ Convert between text-label and text-index """
+
+ def __init__(self, character_dict_path=None, use_space_char=False,
+ **kwargs):
+ super(SPINAttnLabelDecode, self).__init__(character_dict_path,
+ use_space_char)
+
+ def add_special_char(self, dict_character):
+ self.beg_str = "sos"
+ self.end_str = "eos"
+ dict_character = dict_character
+ dict_character = [self.beg_str] + [self.end_str] + dict_character
+ return dict_character
+
+ 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()
+ [beg_idx, end_idx] = 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] == int(beg_idx):
+ continue
+ if int(text_index[batch_idx][idx]) == int(end_idx):
+ break
+ 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.lower(), np.mean(conf_list).tolist()))
+ return result_list
+
+ def __call__(self, preds, label=None, *args, **kwargs):
+ """
+ text = self.decode(text)
+ if label is None:
+ return text
+ else:
+ label = self.decode(label, is_remove_duplicate=False)
+ return text, label
+ """
+ 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=False)
+ if label is None:
+ return text
+ label = self.decode(label, is_remove_duplicate=False)
+ return text, label
+
+ 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
\ No newline at end of file
diff --git a/ppocr/utils/dict/spin_dict.txt b/ppocr/utils/dict/spin_dict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ee8347fd9c85228a3cf46c810d4fc28ab05c492
--- /dev/null
+++ b/ppocr/utils/dict/spin_dict.txt
@@ -0,0 +1,68 @@
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+:
+(
+'
+-
+,
+%
+>
+.
+[
+?
+)
+"
+=
+_
+*
+]
+;
+&
++
+$
+@
+/
+|
+!
+<
+#
+`
+{
+~
+\
+}
+^
\ No newline at end of file
diff --git a/test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml b/test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml
new file mode 100644
index 0000000000000000000000000000000000000000..e53396a03e7064176f911bffc9401727c2c69a6b
--- /dev/null
+++ b/test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml
@@ -0,0 +1,118 @@
+Global:
+ use_gpu: True
+ epoch_num: 6
+ log_smooth_window: 50
+ print_batch_step: 50
+ save_model_dir: ./output/rec/rec_r32_gaspin_bilstm_att/
+ save_epoch_step: 3
+ # evaluation is run every 5000 iterations after the 4000th iteration
+ eval_batch_step: [0, 2000]
+ 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: ./ppocr/utils/dict/spin_dict.txt
+ max_text_length: 25
+ infer_mode: False
+ use_space_char: False
+ save_res_path: ./output/rec/predicts_r32_gaspin_bilstm_att.txt
+
+
+Optimizer:
+ name: AdamW
+ beta1: 0.9
+ beta2: 0.999
+ lr:
+ name: Piecewise
+ decay_epochs: [3, 4, 5]
+ values: [0.001, 0.0003, 0.00009, 0.000027]
+
+ clip_norm: 5
+
+Architecture:
+ model_type: rec
+ algorithm: SPIN
+ in_channels: 1
+ Transform:
+ name: GA_SPIN
+ offsets: True
+ default_type: 6
+ loc_lr: 0.1
+ stn: True
+ Backbone:
+ name: ResNet32
+ out_channels: 512
+ Neck:
+ name: SequenceEncoder
+ encoder_type: cascadernn
+ hidden_size: 256
+ out_channels: [256, 512]
+ with_linear: True
+ Head:
+ name: SPINAttentionHead
+ hidden_size: 256
+
+
+Loss:
+ name: SPINAttentionLoss
+ ignore_index: 0
+
+PostProcess:
+ name: SPINAttnLabelDecode
+ character_dict_path: ./ppocr/utils/dict/spin_dict.txt
+ use_space_char: False
+
+
+Metric:
+ name: RecMetric
+ main_indicator: acc
+ is_filter: True
+
+Train:
+ dataset:
+ name: SimpleDataSet
+ data_dir: ./train_data/ic15_data/
+ label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+ transforms:
+ - NRTRDecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - SPINAttnLabelEncode: # Class handling label
+ - SPINRecResizeImg:
+ image_shape: [100, 32]
+ interpolation : 2
+ mean: [127.5]
+ std: [127.5]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: True
+ batch_size_per_card: 128
+ drop_last: True
+ num_workers: 4
+
+Eval:
+ dataset:
+ name: SimpleDataSet
+ data_dir: ./train_data/ic15_data
+ label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+ transforms:
+ - NRTRDecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - SPINAttnLabelEncode: # Class handling label
+ - SPINRecResizeImg:
+ image_shape: [100, 32]
+ interpolation : 2
+ mean: [127.5]
+ std: [127.5]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: False
+ drop_last: False
+ batch_size_per_card: 1
+ num_workers: 1
diff --git a/test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt b/test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4915055a576f0a5c1f7b0935a31d1d3c266903a5
--- /dev/null
+++ b/test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt
@@ -0,0 +1,53 @@
+===========================train_params===========================
+model_name:rec_r32_gaspin_bilstm_att
+python:python
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=64
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/rec_r32_gaspin_bilstm_att/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/spin_dict.txt --use_space_char=False --rec_image_shape="3,32,100" --rec_algorithm="SPIN"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:False|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,32,100]}]
diff --git a/tools/export_model.py b/tools/export_model.py
index 3ea0228f857a2fadb36678ecd3b91bc865e56e46..b8bc5e1ed835dbb8cd581c7d1034abd5b71edb19 100755
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -73,6 +73,12 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None):
shape=[None, 3, 64, 512], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
+ elif arch_config["algorithm"] == "SPIN":
+ other_shape = [
+ paddle.static.InputSpec(
+ shape=[None, 1, 32, 100], dtype="float32"),
+ ]
+ model = to_static(model, input_spec=other_shape)
else:
infer_shape = [3, -1, -1]
if arch_config["model_type"] == "rec":
diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index 3664ef2caf4b888d6a3918202256c99cc54c5eb1..09e13d8dc536a15005fbf0f4e220122e9ad8897e 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 == "SPIN":
+ postprocess_params = {
+ 'name': 'SPINAttnLabelDecode',
+ "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, self.config = \
utility.create_predictor(args, 'rec', logger)
@@ -250,6 +256,22 @@ class TextRecognizer(object):
return padding_im, resize_shape, pad_shape, valid_ratio
+ def resize_norm_img_spin(self, img):
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ # return padding_im
+ img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
+ img = np.array(img, np.float32)
+ img = np.expand_dims(img, -1)
+ img = img.transpose((2, 0, 1))
+ mean = [127.5]
+ std = [127.5]
+ mean = np.array(mean, dtype=np.float32)
+ std = np.array(std, dtype=np.float32)
+ mean = np.float32(mean.reshape(1, -1))
+ stdinv = 1 / np.float32(std.reshape(1, -1))
+ img -= mean
+ img *= stdinv
+ return img
def __call__(self, img_list):
img_num = len(img_list)
# Calculate the aspect ratio of all text bars
@@ -300,6 +322,10 @@ class TextRecognizer(object):
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, :]
+ norm_img_batch.append(norm_img)
else:
norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio)
diff --git a/tools/program.py b/tools/program.py
index aa0d2698cf66c928f87217996c31c042e1c8aa02..51c73d3e52d0de36cfda2976a0bf230650a93c06 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -207,7 +207,7 @@ def train(config,
model.train()
use_srn = config['Architecture']['algorithm'] == "SRN"
- extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"]
+ extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN"]
extra_input = False
if config['Architecture']['algorithm'] == 'Distillation':
for key in config['Architecture']["Models"]:
@@ -564,7 +564,8 @@ def preprocess(is_train=False):
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
- 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR'
+ 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR',
+ 'SPIN'
]
if use_xpu: