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.gitignore vendored
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@ -10,7 +10,7 @@ __pycache__/
inference/
inference_results/
output/
train_data/
*.DS_Store
*.vs
*.user

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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

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doc/doc_ch/algorithm_overview.md
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@ -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 |
<a name="2"></a>

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# 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)
<a name="1"></a>
## 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|
<a name="2"></a>
## 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。
<a name="3"></a>
## 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
```
<a name="4"></a>
## 4. 推理部署
<a name="4-1"></a>
### 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
```
<a name="4-2"></a>
### 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
<a name="4-3"></a>
### 4.3 Serving服务化部署
暂不支持
<a name="4-4"></a>
### 4.4 更多推理部署
暂不支持
<a name="5"></a>
## 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},
}
```

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doc/doc_en/algorithm_overview_en.md
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@ -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 |
<a name="2"></a>

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# 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)
<a name="1"></a>
## 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|
<a name="2"></a>
## 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.
<a name="3"></a>
## 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
```
<a name="4"></a>
## 4. Inference and Deployment
<a name="4-1"></a>
### 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
```
<a name="4-2"></a>
### 4.2 C++ Inference
Not supported
<a name="4-3"></a>
### 4.3 Serving
Not supported
<a name="4-4"></a>
### 4.4 More
Not supported
<a name="5"></a>
## 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},
}
```

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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<?, ?it/s] eval model:: 50%|¨€¨€¨€¨€¨€ | 1/2 [00:00<00:00, 4.67it/s]
[2022/06/12 13:42:12] ppocr INFO: metric eval ***************
[2022/06/12 13:42:12] ppocr INFO: acc:0.9999987500015626
[2022/06/12 13:42:12] ppocr INFO: norm_edit_dis:1.0
[2022/06/12 13:42:12] ppocr INFO: fps:57.50210270524082

3
ppocr/data/imaug/__init__.py
View File

@ -23,7 +23,8 @@ from .random_crop_data import EastRandomCropData, RandomCropImgMask
from .make_pse_gt import MakePseGt
from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg, \
SPINRecResizeImg
from .ssl_img_aug import SSLRotateResize
from .randaugment import RandAugment
from .copy_paste import CopyPaste

49
ppocr/data/imaug/label_ops.py
View File

@ -1044,3 +1044,52 @@ class MultiLabelEncode(BaseRecLabelEncode):
data_out['label_sar'] = sar['label']
data_out['length'] = ctc['length']
return data_out
class SPINAttnLabelEncode(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(SPINAttnLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
self.lower = lower
def add_special_char(self, dict_character):
self.beg_str = "sos"
self.end_str = "eos"
dict_character = [self.beg_str] + [self.end_str] + dict_character
return dict_character
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
if len(text) > 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

45
ppocr/data/imaug/rec_img_aug.py
View File

@ -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

4
ppocr/losses/__init__.py
View File

@ -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')

41
ppocr/losses/rec_spin_att_loss.py 100644
View File

@ -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)}

3
ppocr/modeling/backbones/__init__.py
View File

@ -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

289
ppocr/modeling/backbones/rec_resnet_32.py 100644
View File

@ -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

3
ppocr/modeling/heads/__init__.py
View File

@ -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

203
ppocr/modeling/heads/rec_spin_att_head.py 100644
View File

@ -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

67
ppocr/modeling/necks/rnn.py
View File

@ -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)

4
ppocr/modeling/transforms/__init__.py
View File

@ -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(

286
ppocr/modeling/transforms/gaspin_transformer.py 100644
View File

@ -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

4
ppocr/postprocess/__init__.py
View File

@ -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':

79
ppocr/postprocess/rec_postprocess.py
View File

@ -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

68
ppocr/utils/dict/spin_dict.txt 100644
View File

@ -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
:
(
'
-
,
%
>
.
[
?
)
"
=
_
*
]
;
&
+
$
@
/
|
!
<
#
`
{
~
\
}
^

118
test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml 100644
View File

@ -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

53
test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt 100644
View File

@ -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]}]

6
tools/export_model.py
View File

@ -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":

26
tools/infer/predict_rec.py
View File

@ -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)

5
tools/program.py
View File

@ -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: