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PaddleOCR
提交 22a9f2ad 编写于 作者: W WenmuZhou
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PPOCRLabel/PPOCRLabel.py
浏览文件 @ 22a9f2ad
......@@ -1031,7 +1031,7 @@ class MainWindow(QMainWindow, WindowMixin):
for box in self.result_dic:
trans_dic = {"label": box[1][0], "points": box[0], 'difficult': False}
if trans_dic["label"] is "" and mode == 'Auto':
if trans_dic["label"] == "" and mode == 'Auto':
continue
shapes.append(trans_dic)
......@@ -1450,7 +1450,7 @@ class MainWindow(QMainWindow, WindowMixin):
item = QListWidgetItem(closeicon, filename)
self.fileListWidget.addItem(item)
print('dirPath in importDirImages is', dirpath)
print('DirPath in importDirImages is', dirpath)
self.iconlist.clear()
self.additems5(dirpath)
self.changeFileFolder = True
......@@ -1459,7 +1459,6 @@ class MainWindow(QMainWindow, WindowMixin):
self.reRecogButton.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
self.actions.reRec.setEnabled(True)
self.actions.saveLabel.setEnabled(True)
def openPrevImg(self, _value=False):
......@@ -1764,7 +1763,7 @@ class MainWindow(QMainWindow, WindowMixin):
QMessageBox.information(self, "Information", msg)
return
result = self.ocr.ocr(img_crop, cls=True, det=False)
if result[0][0] is not '':
if result[0][0] != '':
result.insert(0, box)
print('result in reRec is ', result)
self.result_dic.append(result)
......@@ -1795,7 +1794,7 @@ class MainWindow(QMainWindow, WindowMixin):
QMessageBox.information(self, "Information", msg)
return
result = self.ocr.ocr(img_crop, cls=True, det=False)
if result[0][0] is not '':
if result[0][0] != '':
result.insert(0, box)
print('result in reRec is ', result)
if result[1][0] == shape.label:
......@@ -1862,6 +1861,8 @@ class MainWindow(QMainWindow, WindowMixin):
for each in states:
file, state = each.split('\t')
self.fileStatedict[file] = 1
self.actions.saveLabel.setEnabled(True)
self.actions.saveRec.setEnabled(True)
def saveFilestate(self):
......@@ -1919,22 +1920,29 @@ class MainWindow(QMainWindow, WindowMixin):
rec_gt_dir = os.path.dirname(self.PPlabelpath) + '/rec_gt.txt'
crop_img_dir = os.path.dirname(self.PPlabelpath) + '/crop_img/'
ques_img = []
if not os.path.exists(crop_img_dir):
os.mkdir(crop_img_dir)
with open(rec_gt_dir, 'w', encoding='utf-8') as f:
for key in self.fileStatedict:
idx = self.getImglabelidx(key)
for i, label in enumerate(self.PPlabel[idx]):
if label['difficult']: continue
try:
img = cv2.imread(key)
img_crop = get_rotate_crop_image(img, np.array(label['points'], np.float32))
img_name = os.path.splitext(os.path.basename(idx))[0] + '_crop_'+str(i)+'.jpg'
cv2.imwrite(crop_img_dir+img_name, img_crop)
f.write('crop_img/'+ img_name + '\t')
f.write(label['transcription'] + '\n')
QMessageBox.information(self, "Information", "Cropped images has been saved in "+str(crop_img_dir))
for i, label in enumerate(self.PPlabel[idx]):
if label['difficult']: continue
img_crop = get_rotate_crop_image(img, np.array(label['points'], np.float32))
img_name = os.path.splitext(os.path.basename(idx))[0] + '_crop_'+str(i)+'.jpg'
cv2.imwrite(crop_img_dir+img_name, img_crop)
f.write('crop_img/'+ img_name + '\t')
f.write(label['transcription'] + '\n')
except Exception as e:
ques_img.append(key)
print("Can not read image ",e)
if ques_img:
QMessageBox.information(self, "Information", "The following images can not be saved, "
"please check the image path and labels.\n" + "".join(str(i)+'\n' for i in ques_img))
QMessageBox.information(self, "Information", "Cropped images have been saved in "+str(crop_img_dir))
def speedChoose(self):
if self.labelDialogOption.isChecked():
......@@ -1991,7 +1999,7 @@ if __name__ == '__main__':
resource_file = './libs/resources.py'
if not os.path.exists(resource_file):
output = os.system('pyrcc5 -o libs/resources.py resources.qrc')
assert output is 0, "operate the cmd have some problems ,please check whether there is a in the lib " \
assert output == 0, "operate the cmd have some problems ,please check whether there is a in the lib " \
"directory resources.py "
import libs.resources
sys.exit(main())
README.md
浏览文件 @ 22a9f2ad
......@@ -5,10 +5,11 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
## Notice
PaddleOCR supports both dynamic graph and static graph programming paradigm
- Dynamic graph: dygraph branch (default), **supported by paddle 2.0rc1+ ([installation](./doc/doc_en/installation_en.md))**
- Dynamic graph: dygraph branch (default), **supported by paddle 2.0.0 ([installation](./doc/doc_en/installation_en.md))**
- Static graph: develop branch
**Recent updates**
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
......
README_ch.md
浏览文件 @ 22a9f2ad
......@@ -4,11 +4,13 @@
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
## 注意
PaddleOCR同时支持动态图与静态图两种编程范式
- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0rc1+（[快速安装](./doc/doc_ch/installation.md)）
- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0.0（[快速安装](./doc/doc_ch/installation.md)）
- 静态图版本：develop分支
**近期更新**
- 2021.2.1 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数157个，每周一都会更新，欢迎大家持续关注。
- 2021.2.1 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数162个，每周一都会更新，欢迎大家持续关注。
- 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802)
- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，[多语言模型下载](./doc/doc_ch/models_list.md)，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)
- 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
......
StyleText/README.md
浏览文件 @ 22a9f2ad
......@@ -72,7 +72,7 @@ fusion_generator:
python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
```
* Note 1: The language options is correspond to the corpus. Currently, the tool only supports English, Simplified Chinese and Korean.
* Note 1: The language options is correspond to the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko).
* Note 2: Synth-Text is mainly used to generate images for OCR recognition models.
So the height of style images should be around 32 pixels. Images in other sizes may behave poorly.
* Note 3: You can modify `use_gpu` in `configs/config.yml` to determine whether to use GPU for prediction.
......@@ -120,7 +120,7 @@ In actual application scenarios, it is often necessary to synthesize pictures in
* `with_label`：Whether the `label_file` is label file list.
* `CorpusGenerator`：
* `method`：Method of CorpusGenerator，supports `FileCorpus` and `EnNumCorpus`. If `EnNumCorpus` is used，No other configuration is needed，otherwise you need to set `corpus_file` and `language`.
* `language`：Language of the corpus.
* `language`：Language of the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko).
* `corpus_file`: Filepath of the corpus. Corpus file should be a text file which will be split by line-endings（'\n'）. Corpus generator samples one line each time.
......
StyleText/README_ch.md
浏览文件 @ 22a9f2ad
......@@ -63,10 +63,10 @@ fusion_generator:
```python
python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
```
* 注1：语言选项和语料相对应，目前该工具只支持英文、简体中文和韩语。
* 注1：语言选项和语料相对应，目前支持英文(en)、简体中文(ch)和韩语(ko)。
* 注2：Style-Text生成的数据主要应用于OCR识别场景。基于当前PaddleOCR识别模型的设计，我们主要支持高度在32左右的风格图像。
如果输入图像尺寸相差过多，效果可能不佳。
* 注3：可以通过修改配置文件中的`use_gpu`(true或者false)参数来决定是否使用GPU进行预测。
* 注3：可以通过修改配置文件`configs/config.yml`中的`use_gpu`(true或者false)参数来决定是否使用GPU进行预测。
例如，输入如下图片和语料"PaddleOCR":
......@@ -105,7 +105,7 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_
* `with_label`：标志`label_file`是否为label文件。
* `CorpusGenerator`：
* `method`：语料生成方法，目前有`FileCorpus`和`EnNumCorpus`可选。如果使用`EnNumCorpus`，则不需要填写其他配置，否则需要修改`corpus_file`和`language`；
* `language`：语料的语种；
* `language`：语料的语种，目前支持英文(en)、简体中文(ch)和韩语(ko)；
* `corpus_file`: 语料文件路径。语料文件应使用文本文件。语料生成器首先会将语料按行切分，之后每次随机选取一行。
语料文件格式示例：
......
configs/rec/multi_language/rec_en_number_lite_train.yml
浏览文件 @ 22a9f2ad
......@@ -16,7 +16,7 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/en_dict.txt
character_type: ch
character_type: EN
max_text_length: 25
infer_mode: False
use_space_char: False
......
configs/rec/rec_mv3_none_bilstm_ctc.yml
浏览文件 @ 22a9f2ad
Global:
use_gpu: true
use_gpu: True
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
......@@ -59,7 +59,7 @@ Metric:
Train:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
......@@ -78,7 +78,7 @@ Train:
Eval:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
......
configs/rec/rec_mv3_none_none_ctc.yml
浏览文件 @ 22a9f2ad
......@@ -58,7 +58,7 @@ Metric:
Train:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
......@@ -77,7 +77,7 @@ Train:
Eval:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
......
configs/rec/rec_mv3_tps_bilstm_ctc.yml
浏览文件 @ 22a9f2ad
......@@ -63,7 +63,7 @@ Metric:
Train:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
......@@ -82,7 +82,7 @@ Train:
Eval:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
......
configs/rec/rec_r34_vd_none_bilstm_ctc.yml
浏览文件 @ 22a9f2ad
......@@ -58,7 +58,7 @@ Metric:
Train:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
......@@ -77,7 +77,7 @@ Train:
Eval:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
......
configs/rec/rec_r34_vd_none_none_ctc.yml
浏览文件 @ 22a9f2ad
......@@ -56,7 +56,7 @@ Metric:
Train:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
......@@ -75,7 +75,7 @@ Train:
Eval:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
......
configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
浏览文件 @ 22a9f2ad
......@@ -62,7 +62,7 @@ Metric:
Train:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
......@@ -81,7 +81,7 @@ Train:
Eval:
dataset:
name: LMDBDateSet
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
......
configs/rec/rec_r50_fpn_srn.yml 0 → 100644
浏览文件 @ 22a9f2ad
Global:
use_gpu: True
epoch_num: 72
log_smooth_window: 20
print_batch_step: 5
save_model_dir: ./output/rec/srn_new
save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 5000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
num_heads: 8
infer_mode: False
use_space_char: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
clip_norm: 10.0
lr:
learning_rate: 0.0001
Architecture:
model_type: rec
algorithm: SRN
in_channels: 1
Transform:
Backbone:
name: ResNetFPN
Head:
name: SRNHead
max_text_length: 25
num_heads: 8
num_encoder_TUs: 2
num_decoder_TUs: 4
hidden_dims: 512
Loss:
name: SRNLoss
PostProcess:
name: SRNLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/srn_train_data_duiqi
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SRNLabelEncode: # Class handling label
- SRNRecResizeImg:
image_shape: [1, 64, 256]
- KeepKeys:
keep_keys: ['image',
'label',
'length',
'encoder_word_pos',
'gsrm_word_pos',
'gsrm_slf_attn_bias1',
'gsrm_slf_attn_bias2'] # dataloader will return list in this order
loader:
shuffle: False
batch_size_per_card: 64
drop_last: False
num_workers: 4
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SRNLabelEncode: # Class handling label
- SRNRecResizeImg:
image_shape: [1, 64, 256]
- KeepKeys:
keep_keys: ['image',
'label',
'length',
'encoder_word_pos',
'gsrm_word_pos',
'gsrm_slf_attn_bias1',
'gsrm_slf_attn_bias2']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 32
num_workers: 4
deploy/slim/quantization/README.md
浏览文件 @ 22a9f2ad
......@@ -42,7 +42,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
# 比如下载提供的训练模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./output/quant_inference_model
```
如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
......
deploy/slim/quantization/README_en.md
浏览文件 @ 22a9f2ad
......@@ -58,7 +58,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
```
### 5. Deploy
......
doc/doc_ch/FAQ.md
浏览文件 @ 22a9f2ad
......@@ -15,34 +15,30 @@
* [基础知识7题](#基础知识)
* [数据集7题](#数据集2)
* [模型训练调优18题](#模型训练调优2)
* [【实战篇】PaddleOCR实战115个问题](#PaddleOCR实战问题)
* [使用咨询36题](#使用咨询)
* [【实战篇】PaddleOCR实战120个问题](#PaddleOCR实战问题)
* [使用咨询38题](#使用咨询)
* [数据集18题](#数据集3)
* [模型训练调优30题](#模型训练调优3)
* [预测部署31题](#预测部署3)
* [预测部署34题](#预测部署3)
<a name="近期更新"></a>
## 近期更新（2021.1.18）
## 近期更新（2021.2.1）
#### Q3.2.18: PaddleOCR动态图版本如何finetune？
**A**：finetune需要将配置文件里的 Global.load_static_weights设置为false，如果没有此字段可以手动添加，然后将模型地址放到Global.pretrained_model字段下即可。
**A**：finetune需要将配置文件里的 Global.load_static_weights设置为false，如果没有此字段可以手动添加，然后将模型地址放到Global.pretrained_model字段下即可
#### Q3.3.29: 微调v1.1预训练的模型，可以直接用文字垂直排列和上下颠倒的图片吗？还是必须要水平排列的？
**A**：1.1和2.0的模型一样，微调时，垂直排列的文字需要逆时针旋转90%后加入训练，上下颠倒的需要旋转为水平的。
#### Q3.3.30: 模型训练过程中如何得到 best_accuracy 模型？
**A**：配置文件里的eval_batch_step字段用来控制多少次iter进行一次eval，在eval完成后会自动生成 best_accuracy 模型，所以将eval_batch_step改小一点(例如，10))就能得到best_accuracy模型了。
#### Q3.4.30: 如何多进程运行paddleocr？
#### Q3.4.33: 如何多进程运行paddleocr？
**A**：实例化多个paddleocr服务，然后将服务注册到注册中心，之后通过注册中心统一调度即可。
#### Q3.4.31: 2.0训练出来的模型，能否在1.1版本上进行部署？
#### Q3.4.34: 2.0训练出来的模型，能否在1.1版本上进行部署？
**A**：这个是不建议的，2.0训练出来的模型建议使用dygraph分支里提供的部署代码。
<a name="OCR精选10个问题"></a>
......@@ -395,13 +391,13 @@
**A**：动态图版本正在紧锣密鼓开发中，将于2020年12月16日发布，敬请关注。
#### Q3.1.22：ModuleNotFoundError: No module named 'paddle.nn'，
**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0rc1的版本，安装方式为
**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0的版本，安装方式为
```
python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
```
#### Q3.1.23： ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so)
**A**：这个问题是glibc版本不足导致的，Paddle2.0rc1版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。
**A**：这个问题是glibc版本不足导致的，Paddle2.0.0版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。
如果您的环境不满足这个要求，或者使用的docker镜像为:
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`，安装Paddle2.0rc版本可能会出现上述错误，2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`。
......@@ -413,8 +409,8 @@ python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/py
- develop：基于Paddle静态图开发的分支，推荐使用paddle1.8 或者2.0版本，该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能，领先于release/1.1分支。
- release/1.1：PaddleOCR 发布的第一个稳定版本，基于静态图开发，具备完善的训练、预测、推理部署、量化裁剪等功能。
- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0rc1版本，目前仍在开发中。
- release/2.0：PaddleOCR发布的第二个稳定版本，基于动态图和paddle2.0rc1版本开发，动态图开发的工程更易于调试，目前支，支持模型训练、预测，暂不支持移动端部署。
- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0.0版本。
- release/2.0-rc1-0：PaddleOCR发布的第二个稳定版本，基于动态图和paddle2.0版本开发，动态图开发的工程更易于调试，目前支，支持模型训练、预测，暂不支持移动端部署。
如果您已经上手过PaddleOCR，并且希望在各种环境上部署PaddleOCR，目前建议使用静态图分支，develop或者release/1.1分支。如果您是初学者，想快速训练，调试PaddleOCR中的算法，建议尝鲜PaddleOCR dygraph分支。
......@@ -430,7 +426,7 @@ python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/py
#### Q3.1.27: 如何可视化acc,loss曲线图,模型网络结构图等？
**A**：在配置文件里有`use_visualdl`的参数，设置为True即可，更多的使用命令可以参考：[VisualDL使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc1/guides/03_VisualDL/visualdl.html)。
**A**：在配置文件里有`use_visualdl`的参数，设置为True即可，更多的使用命令可以参考：[VisualDL使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/03_VisualDL/visualdl.html)。
#### Q3.1.28: 在使用StyleText数据合成工具的时候，报错`ModuleNotFoundError: No module named 'utils.config'`，这是为什么呢？
......@@ -449,7 +445,7 @@ https://github.com/PaddlePaddle/PaddleOCR/blob/de3e2e7cd3b8b65ee02d7a41e570fa5b5
#### Q3.1.31: 怎么输出网络结构以及每层的参数信息？
**A**：可以使用 `paddle.summary`， 具体参考:https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc1/api/paddle/hapi/model_summary/summary_cn.html#summary。
**A**：可以使用 `paddle.summary`， 具体参考:https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/hapi/model_summary/summary_cn.html。
#### Q3.1.32 能否修改StyleText配置文件中的分辨率？
......@@ -473,9 +469,18 @@ StyleText的用途主要是：提取style_image中的字体、背景等style信
例如识别身份证照片，可以先匹配"姓名"，"性别"等关键字，根据这些关键字的坐标去推测其他信息的位置，再与识别的结果匹配。
#### Q3.1.36 如何识别竹简上的古文？
**A**：对于字符都是普通的汉字字符的情况，只要标注足够的数据，finetune模型就可以了。如果数据量不足，您可以尝试StyleText工具。
而如果使用的字符是特殊的古文字、甲骨文、象形文字等，那么首先需要构建一个古文字的字典，之后再进行训练。
#### Q3.1.37: 小语种模型只有识别模型，没有检测模型吗？
**A**：小语种（包括纯英文数字）的检测模型和中文的检测模型是共用的，在训练中文检测模型时加入了多语言数据。https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/models_list_en.md#1-text-detection-model。
#### Q3.1.38: module 'paddle.distributed' has no attribute ‘get_rank’。
**A**：Paddle版本问题，请安装2.0版本Paddle：pip install paddlepaddle==2.0.0。
<a name="数据集3"></a>
### 数据集
......@@ -567,11 +572,8 @@ StyleText的用途主要是：提取style_image中的字体、背景等style信
**A**：PPOCRLabel可运行于Linux、Windows、MacOS等多种系统。操作步骤可以参考文档，https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/README.md
#### Q3.2.18: PaddleOCR动态图版本如何finetune？
**A**：finetune需要将配置文件里的 Global.load_static_weights设置为false，如果没有此字段可以手动添加，然后将模型地址放到Global.pretrained_model字段下即可
**A**：finetune需要将配置文件里的 Global.load_static_weights设置为false，如果没有此字段可以手动添加，然后将模型地址放到Global.pretrained_model字段下即可。
<a name="模型训练调优3"></a>
......@@ -719,14 +721,11 @@ ps -axu | grep train.py | awk '{print $2}' | xargs kill -9
**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4.由于tia数据增强特殊性，默认不采用，可以通过添加use_tia设置，使tia数据增强生效。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。
#### Q3.3.29: 微调v1.1预训练的模型，可以直接用文字垂直排列和上下颠倒的图片吗？还是必须要水平排列的？
**A**：1.1和2.0的模型一样，微调时，垂直排列的文字需要逆时针旋转90%后加入训练，上下颠倒的需要旋转为水平的。
#### Q3.3.30: 模型训练过程中如何得到 best_accuracy 模型？
**A**：配置文件里的eval_batch_step字段用来控制多少次iter进行一次eval，在eval完成后会自动生成 best_accuracy 模型，所以将eval_batch_step改小一点(例如，10))就能得到best_accuracy模型了。
<a name="预测部署3"></a>
### 预测部署
......@@ -869,11 +868,23 @@ img = cv.imdecode(img_array, -1)
**A**：特征提取网络金字塔构建的部分:[代码位置](../../ppocr/modeling/necks/db_fpn.py)。ppocr/modeling文件夹里面是组网相关的代码，其中architectures是文本检测或者文本识别整体流程代码；backbones是骨干网络相关代码；necks是类似与FPN的颈函数代码；heads是提取文本检测或者文本识别预测结果相关的头函数；transforms是类似于TPS特征预处理模块。更多的信息可以参考[代码组织结构](./tree.md)。
#### Q3.4.30: 如何多进程运行paddleocr？
#### Q3.4.30: PaddleOCR是否支持在华为鲲鹏920CPU上部署？
**A**：实例化多个paddleocr服务，然后将服务注册到注册中心，之后通过注册中心统一调度即可。
**A**：目前Paddle的预测库是支持华为鲲鹏920CPU的，但是OCR还没在这些芯片上测试过，可以自己调试，有问题反馈给我们。
#### Q3.4.31: 采用Paddle-Lite进行端侧部署，出现问题，环境没问题。
#### Q3.4.31: 2.0训练出来的模型，能否在1.1版本上进行部署？
**A**：如果你的预测库是自己编译的，那么你的nb文件也要自己编译，用同一个lite版本。不能直接用下载的nb文件，因为版本不同。
#### Q3.4.32: PaddleOCR的模型支持onnx转换吗？
**A**：我们目前已经通过Paddle2ONNX来支持各模型套件的转换，PaddleOCR基于PaddlePaddle 2.0的版本（dygraph分支）已经支持导出为ONNX，欢迎关注Paddle2ONNX，了解更多项目的进展：
Paddle2ONNX项目：https://github.com/PaddlePaddle/Paddle2ONNX
Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/model_zoo.md#%E5%9B%BE%E5%83%8Focr)
#### Q3.4.33: 如何多进程运行paddleocr？
**A**：实例化多个paddleocr服务，然后将服务注册到注册中心，之后通过注册中心统一调度即可。
#### Q3.4.34: 2.0训练出来的模型，能否在1.1版本上进行部署？
**A**：这个是不建议的，2.0训练出来的模型建议使用dygraph分支里提供的部署代码。
doc/doc_ch/algorithm_overview.md
浏览文件 @ 22a9f2ad
......@@ -41,7 +41,7 @@ PaddleOCR基于动态图开源的文本识别算法列表：
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
参考[DTRB][3](https://arxiv.org/abs/1904.01906)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：
......@@ -53,5 +53,6 @@ PaddleOCR基于动态图开源的文本识别算法列表：
|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) |
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。
doc/doc_ch/angle_class.md
浏览文件 @ 22a9f2ad
......@@ -63,7 +63,7 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本。
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
```
# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号，如果使用的paddle版本小于2.0rc1，请使用'--select_gpus'参数选择要使用的GPU
# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号。
# 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
```
......
doc/doc_ch/detection.md
浏览文件 @ 22a9f2ad
......@@ -76,7 +76,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
# 单机单卡训练 mv3_db 模型
python3 tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID；如果使用的paddle版本小于2.0rc1，请使用'--select_gpus'参数选择要使用的GPU
# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
```
......
doc/doc_ch/inference.md
浏览文件 @ 22a9f2ad
......@@ -22,8 +22,9 @@ inference 模型（`paddle.jit.save`保存的模型）
- [三、文本识别模型推理](#文本识别模型推理)
- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
- [3. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- [4. 多语言模型的推理](#多语言模型的推理)
- [3. 基于SRN损失的识别模型推理](#基于SRN损失的识别模型推理)
- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- [5. 多语言模型的推理](#多语言模型的推理)
- [四、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理)
......@@ -295,8 +296,20 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
<a name="基于SRN损失的识别模型推理"></a>
### 3. 基于SRN损失的识别模型推理
基于SRN损失的识别模型，需要额外设置识别算法参数 --rec_algorithm="SRN"。
同时需要保证预测shape与训练时一致，如： --rec_image_shape="1, 64, 256"
### 3. 自定义文本识别字典的推理
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
--rec_model_dir="./inference/srn/" \
--rec_image_shape="1, 64, 256" \
--rec_char_type="en" \
--rec_algorithm="SRN"
```
### 4. 自定义文本识别字典的推理
如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，并且设置 `rec_char_type=ch`
```
......@@ -304,12 +317,12 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
```
<a name="多语言模型的推理"></a>
### 4. 多语言模型的推理
### 5. 多语言模型的推理
如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/` 路径下有默认提供的小语种字体，例如韩文识别：
需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
```
![](../imgs_words/korean/1.jpg)
......
doc/doc_ch/installation.md
浏览文件 @ 22a9f2ad
......@@ -2,7 +2,7 @@
经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23
PaddleOCR 工作环境
- PaddlePaddle 1.8+ ，推荐使用 PaddlePaddle 2.0rc1
- PaddlePaddle 2.0.0
- python3.7
- glibc 2.23
- cuDNN 7.6+ (GPU)
......@@ -35,11 +35,11 @@ sudo docker container exec -it ppocr /bin/bash
pip3 install --upgrade pip
如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
如果您的机器是CPU，请运行以下命令安装
python3 -m pip install paddlepaddle==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
```
......
doc/doc_ch/recognition.md
浏览文件 @ 22a9f2ad
......@@ -36,6 +36,7 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
* 数据下载
若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
<a name="自定义数据集"></a>
* 使用自己数据集
......@@ -195,13 +196,12 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
......@@ -272,16 +272,109 @@ Eval:
<a name="小语种"></a>
- 小语种
PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的提供了多语言的配置文件，目前PaddleOCR支持的多语言算法有：
PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
| 配置文件 | 算法名称 | backbone | trans | seq | pred | language |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语 |
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 |
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 |
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 |
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 |
您有两种方式创建所需的配置文件：
1. 通过脚本自动生成
[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) 可以帮助您生成多语言模型的配置文件
- 以意大利语为例，如果您的数据是按如下格式准备的：
```
|-train_data
|- it_train.txt # 训练集标签
|- it_val.txt # 验证集标签
|- data
|- word_001.jpg
|- word_002.jpg
|- word_003.jpg
| ...
```
可以使用默认参数，生成配置文件：
```bash
# 该代码需要在指定目录运行
cd PaddleOCR/configs/rec/multi_language/
# 通过-l或者--language参数设置需要生成的语种的配置文件，该命令会将默认参数写入配置文件
python3 generate_multi_language_configs.py -l it
```
- 如果您的数据放置在其他位置，或希望使用自己的字典，可以通过指定相关参数来生成配置文件:
```bash
# -l或者--language字段是必须的
# --train修改训练集，--val修改验证集，--data_dir修改数据集目录，--dict修改字典路径， -o修改对应默认参数
cd PaddleOCR/configs/rec/multi_language/
python3 generate_multi_language_configs.py -l it \ # 语种
--train {path/of/train_label.txt} \ # 训练标签文件的路径
--val {path/of/val_label.txt} \ # 验证集标签文件的路径
--data_dir {train_data/path} \ # 训练数据的根目录
--dict {path/of/dict} \ # 字典文件路径
-o Global.use_gpu=False # 是否使用gpu
...
```
2. 手动修改配置文件
您也可以手动修改模版中的以下几个字段:
```
Global:
use_gpu: True
epoch_num: 500
...
character_type: it # 需要识别的语种
character_dict_path: {path/of/dict} # 字典文件所在路径
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/ # 数据存放根目录
label_file_list: ["./train_data/train_list.txt"] # 训练集label路径
...
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/ # 数据存放根目录
label_file_list: ["./train_data/val_list.txt"] # 验证集label路径
...
```
目前PaddleOCR支持的多语言算法有：
| 配置文件 | 算法名称 | backbone | trans | seq | pred | language | character_type |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: |
| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 中文繁体 | chinese_cht|
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语（区分大小写） | EN |
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 | french |
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 | german |
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 | japan |
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 | korean |
| rec_it_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 意大利语 | it |
| rec_xi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 西班牙语 | xi |
| rec_pu_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 葡萄牙语 | pu |
| rec_ru_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 俄罗斯语 | ru |
| rec_ar_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 阿拉伯语 | ar |
| rec_hi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 印地语 | hi |
| rec_ug_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 维吾尔语 | ug |
| rec_fa_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 波斯语 | fa |
| rec_ur_ite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 乌尔都语 | ur |
| rec_rs_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 塞尔维亚(latin)语 | rs |
| rec_oc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 欧西坦语 | oc |
| rec_mr_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 马拉地语 | mr |
| rec_ne_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 尼泊尔语 | ne |
| rec_rsc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 塞尔维亚(cyrillic)语 | rsc |
| rec_bg_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 保加利亚语 | bg |
| rec_uk_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 乌克兰语 | uk |
| rec_be_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 白俄罗斯语 | be |
| rec_te_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 泰卢固语 | te |
| rec_ka_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 卡纳达语 | ka |
| rec_ta_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 泰米尔语 | ta |
多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。
......
doc/doc_en/algorithm_overview_en.md
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......@@ -43,7 +43,7 @@ PaddleOCR open-source text recognition algorithms list:
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
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:
......@@ -55,5 +55,6 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)
doc/doc_en/angle_class_en.md
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......@@ -66,7 +66,7 @@ Start training:
```
# Set PYTHONPATH path
export PYTHONPATH=$PYTHONPATH:.
# GPU training Support single card and multi-card training, specify the card number through --gpus. If your paddle version is less than 2.0rc1, please use '--selected_gpus'
# GPU training Support single card and multi-card training, specify the card number through --gpus.
# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
```
......
doc/doc_en/detection_en.md
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......@@ -76,7 +76,7 @@ You can also use `-o` to change the training parameters without modifying the ym
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
# multi-GPU training
# Set the GPU ID used by the '--gpus' parameter; If your paddle version is less than 2.0rc1, please use '--selected_gpus'
# Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
......
doc/doc_en/inference_en.md
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......@@ -25,6 +25,7 @@ Next, we first introduce how to convert a trained model into an inference model,
- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
- [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
- [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
- [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
......@@ -304,8 +305,23 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
<a name="SRN-BASED_RECOGNITION"></a>
### 3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
The recognition model based on SRN requires additional setting of the recognition algorithm parameter
--rec_algorithm="SRN". At the same time, it is necessary to ensure that the predicted shape is consistent
with the training, such as: --rec_image_shape="1, 64, 256"
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
--rec_model_dir="./inference/srn/" \
--rec_image_shape="1, 64, 256" \
--rec_char_type="en" \
--rec_algorithm="SRN"
```
<a name="USING_CUSTOM_CHARACTERS"></a>
### 3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
```
......@@ -313,12 +329,12 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
```
<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
### 4. MULTILINGAUL MODEL INFERENCE
### 5. MULTILINGAUL MODEL INFERENCE
If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition:
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
```
![](../imgs_words/korean/1.jpg)
......
doc/doc_en/installation_en.md
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......@@ -3,7 +3,7 @@
After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility.
PaddleOCR working environment:
- PaddlePaddle 1.8+, Recommend PaddlePaddle 2.0rc1
- PaddlePaddle 2.0.0
- python3.7
- glibc 2.23
......@@ -38,10 +38,10 @@ sudo docker container exec -it ppocr /bin/bash
pip3 install --upgrade pip
# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
python3 -m pip install paddlepaddle-gpu==2.0rc1 -i https://mirror.baidu.com/pypi/simple
python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
# If you only have cpu on your machine, please run the following command to install
python3 -m pip install paddlepaddle==2.0rc1 -i https://mirror.baidu.com/pypi/simple
python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
```
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
......
doc/doc_en/models_list_en.md
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......@@ -93,7 +93,7 @@ python3 generate_multi_language_configs.py -l it \
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
| french_mobile_v2.0_rec |Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec |Lightweight model for French recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| it_mobile_v2.0_rec |Lightweight model for Italian recognition|rec_it_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |
......
doc/doc_en/recognition_en.md
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......@@ -195,6 +195,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
For training Chinese data, it is recommended to use
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
......@@ -266,15 +267,116 @@ Eval:
<a name="Multi_language"></a>
- Multi-language
PaddleOCR also provides multi-language. The configuration file in `configs/rec/multi_languages` provides multi-language configuration files. Currently, the multi-language algorithms supported by PaddleOCR are:
PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
There are two ways to create the required configuration file:：
1. Automatically generated by script
[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models
- Take Italian as an example, if your data is prepared in the following format:
```
|-train_data
|- it_train.txt # train_set label
|- it_val.txt # val_set label
|- data
|- word_001.jpg
|- word_002.jpg
|- word_003.jpg
| ...
```
You can use the default parameters to generate a configuration file:
```bash
# The code needs to be run in the specified directory
cd PaddleOCR/configs/rec/multi_language/
# Set the configuration file of the language to be generated through the -l or --language parameter.
# This command will write the default parameters into the configuration file
python3 generate_multi_language_configs.py -l it
```
- If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters:
```bash
# -l or --language field is required
# --train to modify the training set
# --val to modify the validation set
# --data_dir to modify the data set directory
# --dict to modify the dict path
# -o to modify the corresponding default parameters
cd PaddleOCR/configs/rec/multi_language/
python3 generate_multi_language_configs.py -l it \ # language
--train {path/of/train_label.txt} \ # path of train_label
--val {path/of/val_label.txt} \ # path of val_label
--data_dir {train_data/path} \ # root directory of training data
--dict {path/of/dict} \ # path of dict
-o Global.use_gpu=False # whether to use gpu
...
```
2. Manually modify the configuration file
You can also manually modify the following fields in the template:
```
Global:
use_gpu: True
epoch_num: 500
...
character_type: it # language
character_dict_path: {path/of/dict} # path of dict
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/ # root directory of training data
label_file_list: ["./train_data/train_list.txt"] # train label path
...
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/ # root directory of val data
label_file_list: ["./train_data/val_list.txt"] # val label path
...
```
Currently, the multi-language algorithms supported by PaddleOCR are:
| Configuration file | Algorithm name | backbone | trans | seq | pred | language | character_type |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: |
| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | chinese traditional | chinese_cht|
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English(Case sensitive) | EN |
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French | french |
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German | german |
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese | japan |
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean | korean |
| rec_it_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Italian | it |
| rec_xi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Spanish | xi |
| rec_pu_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Portuguese | pu |
| rec_ru_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Russia | ru |
| rec_ar_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Arabic | ar |
| rec_hi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Hindi | hi |
| rec_ug_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Uyghur | ug |
| rec_fa_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Persian(Farsi) | fa |
| rec_ur_ite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Urdu | ur |
| rec_rs_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Serbian(latin) | rs |
| rec_oc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Occitan | oc |
| rec_mr_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Marathi | mr |
| rec_ne_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Nepali | ne |
| rec_rsc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Serbian(cyrillic) | rsc |
| rec_bg_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Bulgarian | bg |
| rec_uk_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Ukranian | uk |
| rec_be_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Belarusian | be |
| rec_te_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Telugu | te |
| rec_ka_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Kannada | ka |
| rec_ta_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Tamil | ta |
| Configuration file | Algorithm name | backbone | trans | seq | pred | language ｜
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English ｜
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French ｜
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German ｜
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese ｜
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean ｜
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
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  • 2-up
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  • Onion skin
ppocr/data/__init__.py
浏览文件 @ 22a9f2ad
......@@ -33,7 +33,7 @@ import paddle.distributed as dist
from ppocr.data.imaug import transform, create_operators
from ppocr.data.simple_dataset import SimpleDataSet
from ppocr.data.lmdb_dataset import LMDBDateSet
from ppocr.data.lmdb_dataset import LMDBDataSet
__all__ = ['build_dataloader', 'transform', 'create_operators']
......@@ -51,20 +51,21 @@ signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
def build_dataloader(config, mode, device, logger):
def build_dataloader(config, mode, device, logger, seed=None):
config = copy.deepcopy(config)
support_dict = ['SimpleDataSet', 'LMDBDateSet']
support_dict = ['SimpleDataSet', 'LMDBDataSet']
module_name = config[mode]['dataset']['name']
assert module_name in support_dict, Exception(
'DataSet only support {}'.format(support_dict))
assert mode in ['Train', 'Eval', 'Test'
], "Mode should be Train, Eval or Test."
dataset = eval(module_name)(config, mode, logger)
dataset = eval(module_name)(config, mode, logger, seed)
loader_config = config[mode]['loader']
batch_size = loader_config['batch_size_per_card']
drop_last = loader_config['drop_last']
shuffle = loader_config['shuffle']
num_workers = loader_config['num_workers']
if 'use_shared_memory' in loader_config.keys():
use_shared_memory = loader_config['use_shared_memory']
......@@ -75,14 +76,14 @@ def build_dataloader(config, mode, device, logger):
batch_sampler = DistributedBatchSampler(
dataset=dataset,
batch_size=batch_size,
shuffle=False,
shuffle=shuffle,
drop_last=drop_last)
else:
#Distribute data to single card
batch_sampler = BatchSampler(
dataset=dataset,
batch_size=batch_size,
shuffle=False,
shuffle=shuffle,
drop_last=drop_last)
data_loader = DataLoader(
......
ppocr/data/imaug/__init__.py
浏览文件 @ 22a9f2ad
......@@ -21,7 +21,7 @@ from .make_border_map import MakeBorderMap
from .make_shrink_map import MakeShrinkMap
from .random_crop_data import EastRandomCropData, PSERandomCrop
from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg
from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg
from .randaugment import RandAugment
from .operators import *
from .label_ops import *
......
ppocr/data/imaug/label_ops.py
浏览文件 @ 22a9f2ad
......@@ -18,6 +18,7 @@ from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import string
class ClsLabelEncode(object):
......@@ -92,18 +93,28 @@ class BaseRecLabelEncode(object):
character_type='ch',
use_space_char=False):
support_character_type = [
'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean'
'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs',
'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi',
'mr', 'ne'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
self.max_text_len = max_text_length
self.beg_str = "sos"
self.end_str = "eos"
if character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif character_type in ["ch", "french", "german", "japan", "korean"]:
elif character_type == "EN_symbol":
# same with ASTER setting (use 94 char).
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
elif character_type in support_character_type:
self.character_str = ""
assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch"
assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
character_type)
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
......@@ -112,11 +123,6 @@ class BaseRecLabelEncode(object):
if use_space_char:
self.character_str += " "
dict_character = list(self.character_str)
elif character_type == "en_sensitive":
# same with ASTER setting (use 94 char).
import string
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
self.character_type = character_type
dict_character = self.add_special_char(dict_character)
self.dict = {}
......@@ -213,3 +219,49 @@ class AttnLabelEncode(BaseRecLabelEncode):
assert False, "Unsupport type %s in get_beg_end_flag_idx" \
% beg_or_end
return idx
class SRNLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length=25,
character_dict_path=None,
character_type='en',
use_space_char=False,
**kwargs):
super(SRNLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
def add_special_char(self, dict_character):
dict_character = dict_character + [self.beg_str, self.end_str]
return dict_character
def __call__(self, data):
text = data['label']
text = self.encode(text)
char_num = len(self.character_str)
if text is None:
return None
if len(text) > self.max_text_len:
return None
data['length'] = np.array(len(text))
text = text + [char_num] * (self.max_text_len - len(text))
data['label'] = np.array(text)
return data
def get_ignored_tokens(self):
beg_idx = self.get_beg_end_flag_idx("beg")
end_idx = self.get_beg_end_flag_idx("end")
return [beg_idx, end_idx]
def get_beg_end_flag_idx(self, beg_or_end):
if beg_or_end == "beg":
idx = np.array(self.dict[self.beg_str])
elif beg_or_end == "end":
idx = np.array(self.dict[self.end_str])
else:
assert False, "Unsupport type %s in get_beg_end_flag_idx" \
% beg_or_end
return idx
ppocr/data/imaug/rec_img_aug.py
浏览文件 @ 22a9f2ad
......@@ -12,20 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import cv2
import numpy as np
......@@ -77,6 +63,26 @@ class RecResizeImg(object):
return data
class SRNRecResizeImg(object):
def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
self.image_shape = image_shape
self.num_heads = num_heads
self.max_text_length = max_text_length
def __call__(self, data):
img = data['image']
norm_img = resize_norm_img_srn(img, self.image_shape)
data['image'] = norm_img
[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)
data['encoder_word_pos'] = encoder_word_pos
data['gsrm_word_pos'] = gsrm_word_pos
data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1
data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2
return data
def resize_norm_img(img, image_shape):
imgC, imgH, imgW = image_shape
h = img.shape[0]
......@@ -103,7 +109,7 @@ def resize_norm_img(img, image_shape):
def resize_norm_img_chinese(img, image_shape):
imgC, imgH, imgW = image_shape
# todo: change to 0 and modified image shape
max_wh_ratio = 0
max_wh_ratio = imgW * 1.0 / imgH
h, w = img.shape[0], img.shape[1]
ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, ratio)
......@@ -126,6 +132,60 @@ def resize_norm_img_chinese(img, image_shape):
return padding_im
def resize_norm_img_srn(img, image_shape):
imgC, imgH, imgW = image_shape
img_black = np.zeros((imgH, imgW))
im_hei = img.shape[0]
im_wid = img.shape[1]
if im_wid <= im_hei * 1:
img_new = cv2.resize(img, (imgH * 1, imgH))
elif im_wid <= im_hei * 2:
img_new = cv2.resize(img, (imgH * 2, imgH))
elif im_wid <= im_hei * 3:
img_new = cv2.resize(img, (imgH * 3, imgH))
else:
img_new = cv2.resize(img, (imgW, imgH))
img_np = np.asarray(img_new)
img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
img_black[:, 0:img_np.shape[1]] = img_np
img_black = img_black[:, :, np.newaxis]
row, col, c = img_black.shape
c = 1
return np.reshape(img_black, (c, row, col)).astype(np.float32)
def srn_other_inputs(image_shape, num_heads, max_text_length):
imgC, imgH, imgW = image_shape
feature_dim = int((imgH / 8) * (imgW / 8))
encoder_word_pos = np.array(range(0, feature_dim)).reshape(
(feature_dim, 1)).astype('int64')
gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
(max_text_length, 1)).astype('int64')
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
[1, max_text_length, max_text_length])
gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
[num_heads, 1, 1]) * [-1e9]
gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
[1, max_text_length, max_text_length])
gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
[num_heads, 1, 1]) * [-1e9]
return [
encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2
]
def flag():
"""
flag
......
ppocr/data/lmdb_dataset.py
浏览文件 @ 22a9f2ad
......@@ -20,9 +20,9 @@ import cv2
from .imaug import transform, create_operators
class LMDBDateSet(Dataset):
def __init__(self, config, mode, logger):
super(LMDBDateSet, self).__init__()
class LMDBDataSet(Dataset):
def __init__(self, config, mode, logger, seed=None):
super(LMDBDataSet, self).__init__()
global_config = config['Global']
dataset_config = config[mode]['dataset']
......
ppocr/data/simple_dataset.py
浏览文件 @ 22a9f2ad
......@@ -20,7 +20,7 @@ from .imaug import transform, create_operators
class SimpleDataSet(Dataset):
def __init__(self, config, mode, logger):
def __init__(self, config, mode, logger, seed=None):
super(SimpleDataSet, self).__init__()
self.logger = logger
......@@ -41,6 +41,7 @@ class SimpleDataSet(Dataset):
self.data_dir = dataset_config['data_dir']
self.do_shuffle = loader_config['shuffle']
self.seed = seed
logger.info("Initialize indexs of datasets:%s" % label_file_list)
self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
self.data_idx_order_list = list(range(len(self.data_lines)))
......@@ -55,6 +56,7 @@ class SimpleDataSet(Dataset):
for idx, file in enumerate(file_list):
with open(file, "rb") as f:
lines = f.readlines()
random.seed(self.seed)
lines = random.sample(lines,
round(len(lines) * ratio_list[idx]))
data_lines.extend(lines)
......@@ -62,6 +64,7 @@ class SimpleDataSet(Dataset):
def shuffle_data_random(self):
if self.do_shuffle:
random.seed(self.seed)
random.shuffle(self.data_lines)
return
......
ppocr/losses/__init__.py
浏览文件 @ 22a9f2ad
......@@ -23,11 +23,14 @@ def build_loss(config):
# rec loss
from .rec_ctc_loss import CTCLoss
from .rec_srn_loss import SRNLoss
# cls loss
from .cls_loss import ClsLoss
support_dict = ['DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss']
support_dict = [
'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'SRNLoss'
]
config = copy.deepcopy(config)
module_name = config.pop('name')
......
ppocr/losses/rec_srn_loss.py 0 → 100644
浏览文件 @ 22a9f2ad
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
class SRNLoss(nn.Layer):
def __init__(self, **kwargs):
super(SRNLoss, self).__init__()
self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="sum")
def forward(self, predicts, batch):
predict = predicts['predict']
word_predict = predicts['word_out']
gsrm_predict = predicts['gsrm_out']
label = batch[1]
casted_label = paddle.cast(x=label, dtype='int64')
casted_label = paddle.reshape(x=casted_label, shape=[-1, 1])
cost_word = self.loss_func(word_predict, label=casted_label)
cost_gsrm = self.loss_func(gsrm_predict, label=casted_label)
cost_vsfd = self.loss_func(predict, label=casted_label)
cost_word = paddle.reshape(x=paddle.sum(cost_word), shape=[1])
cost_gsrm = paddle.reshape(x=paddle.sum(cost_gsrm), shape=[1])
cost_vsfd = paddle.reshape(x=paddle.sum(cost_vsfd), shape=[1])
sum_cost = cost_word * 3.0 + cost_vsfd + cost_gsrm * 0.15
return {'loss': sum_cost, 'word_loss': cost_word, 'img_loss': cost_vsfd}
ppocr/metrics/rec_metric.py
浏览文件 @ 22a9f2ad
......@@ -33,8 +33,6 @@ class RecMetric(object):
if pred == target:
correct_num += 1
all_num += 1
# if all_num < 10 and kwargs.get('show_str', False):
# print('{} -> {}'.format(pred, target))
self.correct_num += correct_num
self.all_num += all_num
self.norm_edit_dis += norm_edit_dis
......@@ -50,7 +48,7 @@ class RecMetric(object):
'norm_edit_dis': 0,
}
"""
acc = self.correct_num / self.all_num
acc = 1.0 * self.correct_num / self.all_num
norm_edit_dis = 1 - self.norm_edit_dis / self.all_num
self.reset()
return {'acc': acc, 'norm_edit_dis': norm_edit_dis}
......
ppocr/modeling/architectures/base_model.py
浏览文件 @ 22a9f2ad
......@@ -68,11 +68,14 @@ class BaseModel(nn.Layer):
config["Head"]['in_channels'] = in_channels
self.head = build_head(config["Head"])
def forward(self, x):
def forward(self, x, data=None):
if self.use_transform:
x = self.transform(x)
x = self.backbone(x)
if self.use_neck:
x = self.neck(x)
x = self.head(x)
if data is None:
x = self.head(x)
else:
x = self.head(x, data)
return x
ppocr/modeling/backbones/__init__.py
浏览文件 @ 22a9f2ad
......@@ -24,7 +24,8 @@ def build_backbone(config, model_type):
elif model_type == 'rec' or model_type == 'cls':
from .rec_mobilenet_v3 import MobileNetV3
from .rec_resnet_vd import ResNet
support_dict = ['MobileNetV3', 'ResNet', 'ResNet_FPN']
from .rec_resnet_fpn import ResNetFPN
support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
else:
raise NotImplementedError
......
ppocr/modeling/backbones/det_mobilenet_v3.py
浏览文件 @ 22a9f2ad
......@@ -58,15 +58,15 @@ class MobileNetV3(nn.Layer):
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hard_swish', 2],
[3, 200, 80, False, 'hard_swish', 1],
[3, 184, 80, False, 'hard_swish', 1],
[3, 184, 80, False, 'hard_swish', 1],
[3, 480, 112, True, 'hard_swish', 1],
[3, 672, 112, True, 'hard_swish', 1],
[5, 672, 160, True, 'hard_swish', 2],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
[3, 240, 80, False, 'hardswish', 2],
[3, 200, 80, False, 'hardswish', 1],
[3, 184, 80, False, 'hardswish', 1],
[3, 184, 80, False, 'hardswish', 1],
[3, 480, 112, True, 'hardswish', 1],
[3, 672, 112, True, 'hardswish', 1],
[5, 672, 160, True, 'hardswish', 2],
[5, 960, 160, True, 'hardswish', 1],
[5, 960, 160, True, 'hardswish', 1],
]
cls_ch_squeeze = 960
elif model_name == "small":
......@@ -75,14 +75,14 @@ class MobileNetV3(nn.Layer):
[3, 16, 16, True, 'relu', 2],
[3, 72, 24, False, 'relu', 2],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', 2],
[5, 240, 40, True, 'hard_swish', 1],
[5, 240, 40, True, 'hard_swish', 1],
[5, 120, 48, True, 'hard_swish', 1],
[5, 144, 48, True, 'hard_swish', 1],
[5, 288, 96, True, 'hard_swish', 2],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
[5, 96, 40, True, 'hardswish', 2],
[5, 240, 40, True, 'hardswish', 1],
[5, 240, 40, True, 'hardswish', 1],
[5, 120, 48, True, 'hardswish', 1],
[5, 144, 48, True, 'hardswish', 1],
[5, 288, 96, True, 'hardswish', 2],
[5, 576, 96, True, 'hardswish', 1],
[5, 576, 96, True, 'hardswish', 1],
]
cls_ch_squeeze = 576
else:
......@@ -102,7 +102,7 @@ class MobileNetV3(nn.Layer):
padding=1,
groups=1,
if_act=True,
act='hard_swish',
act='hardswish',
name='conv1')
self.stages = []
......@@ -138,7 +138,7 @@ class MobileNetV3(nn.Layer):
padding=0,
groups=1,
if_act=True,
act='hard_swish',
act='hardswish',
name='conv_last'))
self.stages.append(nn.Sequential(*block_list))
self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
......@@ -192,10 +192,11 @@ class ConvBNLayer(nn.Layer):
if self.if_act:
if self.act == "relu":
x = F.relu(x)
elif self.act == "hard_swish":
x = F.activation.hard_swish(x)
elif self.act == "hardswish":
x = F.hardswish(x)
else:
print("The activation function is selected incorrectly.")
print("The activation function({}) is selected incorrectly.".
format(self.act))
exit()
return x
......@@ -282,5 +283,5 @@ class SEModule(nn.Layer):
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = F.activation.hard_sigmoid(outputs)
outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
return inputs * outputs
ppocr/modeling/backbones/rec_mobilenet_v3.py
浏览文件 @ 22a9f2ad
......@@ -51,15 +51,15 @@ class MobileNetV3(nn.Layer):
[5, 72, 40, True, 'relu', (large_stride[2], 1)],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hard_swish', 1],
[3, 200, 80, False, 'hard_swish', 1],
[3, 184, 80, False, 'hard_swish', 1],
[3, 184, 80, False, 'hard_swish', 1],
[3, 480, 112, True, 'hard_swish', 1],
[3, 672, 112, True, 'hard_swish', 1],
[5, 672, 160, True, 'hard_swish', (large_stride[3], 1)],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
[3, 240, 80, False, 'hardswish', 1],
[3, 200, 80, False, 'hardswish', 1],
[3, 184, 80, False, 'hardswish', 1],
[3, 184, 80, False, 'hardswish', 1],
[3, 480, 112, True, 'hardswish', 1],
[3, 672, 112, True, 'hardswish', 1],
[5, 672, 160, True, 'hardswish', (large_stride[3], 1)],
[5, 960, 160, True, 'hardswish', 1],
[5, 960, 160, True, 'hardswish', 1],
]
cls_ch_squeeze = 960
elif model_name == "small":
......@@ -68,14 +68,14 @@ class MobileNetV3(nn.Layer):
[3, 16, 16, True, 'relu', (small_stride[0], 1)],
[3, 72, 24, False, 'relu', (small_stride[1], 1)],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', (small_stride[2], 1)],
[5, 240, 40, True, 'hard_swish', 1],
[5, 240, 40, True, 'hard_swish', 1],
[5, 120, 48, True, 'hard_swish', 1],
[5, 144, 48, True, 'hard_swish', 1],
[5, 288, 96, True, 'hard_swish', (small_stride[3], 1)],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
[5, 96, 40, True, 'hardswish', (small_stride[2], 1)],
[5, 240, 40, True, 'hardswish', 1],
[5, 240, 40, True, 'hardswish', 1],
[5, 120, 48, True, 'hardswish', 1],
[5, 144, 48, True, 'hardswish', 1],
[5, 288, 96, True, 'hardswish', (small_stride[3], 1)],
[5, 576, 96, True, 'hardswish', 1],
[5, 576, 96, True, 'hardswish', 1],
]
cls_ch_squeeze = 576
else:
......@@ -96,7 +96,7 @@ class MobileNetV3(nn.Layer):
padding=1,
groups=1,
if_act=True,
act='hard_swish',
act='hardswish',
name='conv1')
i = 0
block_list = []
......@@ -124,7 +124,7 @@ class MobileNetV3(nn.Layer):
padding=0,
groups=1,
if_act=True,
act='hard_swish',
act='hardswish',
name='conv_last')
self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
......
ppocr/modeling/backbones/rec_resnet_fpn.py 0 → 100644
浏览文件 @ 22a9f2ad
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import paddle.fluid as fluid
import paddle
import numpy as np
__all__ = ["ResNetFPN"]
class ResNetFPN(nn.Layer):
def __init__(self, in_channels=1, layers=50, **kwargs):
super(ResNetFPN, self).__init__()
supported_layers = {
18: {
'depth': [2, 2, 2, 2],
'block_class': BasicBlock
},
34: {
'depth': [3, 4, 6, 3],
'block_class': BasicBlock
},
50: {
'depth': [3, 4, 6, 3],
'block_class': BottleneckBlock
},
101: {
'depth': [3, 4, 23, 3],
'block_class': BottleneckBlock
},
152: {
'depth': [3, 8, 36, 3],
'block_class': BottleneckBlock
}
}
stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)]
num_filters = [64, 128, 256, 512]
self.depth = supported_layers[layers]['depth']
self.F = []
self.conv = ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=7,
stride=2,
act="relu",
name="conv1")
self.block_list = []
in_ch = 64
if layers >= 50:
for block in range(len(self.depth)):
for i in range(self.depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
block_list = self.add_sublayer(
"bottleneckBlock_{}_{}".format(block, i),
BottleneckBlock(
in_channels=in_ch,
out_channels=num_filters[block],
stride=stride_list[block] if i == 0 else 1,
name=conv_name))
in_ch = num_filters[block] * 4
self.block_list.append(block_list)
self.F.append(block_list)
else:
for block in range(len(self.depth)):
for i in range(self.depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
basic_block = self.add_sublayer(
conv_name,
BasicBlock(
in_channels=in_ch,
out_channels=num_filters[block],
stride=stride_list[block] if i == 0 else 1,
is_first=block == i == 0,
name=conv_name))
in_ch = basic_block.out_channels
self.block_list.append(basic_block)
out_ch_list = [in_ch // 4, in_ch // 2, in_ch]
self.base_block = []
self.conv_trans = []
self.bn_block = []
for i in [-2, -3]:
in_channels = out_ch_list[i + 1] + out_ch_list[i]
self.base_block.append(
self.add_sublayer(
"F_{}_base_block_0".format(i),
nn.Conv2D(
in_channels=in_channels,
out_channels=out_ch_list[i],
kernel_size=1,
weight_attr=ParamAttr(trainable=True),
bias_attr=ParamAttr(trainable=True))))
self.base_block.append(
self.add_sublayer(
"F_{}_base_block_1".format(i),
nn.Conv2D(
in_channels=out_ch_list[i],
out_channels=out_ch_list[i],
kernel_size=3,
padding=1,
weight_attr=ParamAttr(trainable=True),
bias_attr=ParamAttr(trainable=True))))
self.base_block.append(
self.add_sublayer(
"F_{}_base_block_2".format(i),
nn.BatchNorm(
num_channels=out_ch_list[i],
act="relu",
param_attr=ParamAttr(trainable=True),
bias_attr=ParamAttr(trainable=True))))
self.base_block.append(
self.add_sublayer(
"F_{}_base_block_3".format(i),
nn.Conv2D(
in_channels=out_ch_list[i],
out_channels=512,
kernel_size=1,
bias_attr=ParamAttr(trainable=True),
weight_attr=ParamAttr(trainable=True))))
self.out_channels = 512
def __call__(self, x):
x = self.conv(x)
fpn_list = []
F = []
for i in range(len(self.depth)):
fpn_list.append(np.sum(self.depth[:i + 1]))
for i, block in enumerate(self.block_list):
x = block(x)
for number in fpn_list:
if i + 1 == number:
F.append(x)
base = F[-1]
j = 0
for i, block in enumerate(self.base_block):
if i % 3 == 0 and i < 6:
j = j + 1
b, c, w, h = F[-j - 1].shape
if [w, h] == list(base.shape[2:]):
base = base
else:
base = self.conv_trans[j - 1](base)
base = self.bn_block[j - 1](base)
base = paddle.concat([base, F[-j - 1]], axis=1)
base = block(base)
return base
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=2 if stride == (1, 1) else kernel_size,
dilation=2 if stride == (1, 1) else 1,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'),
bias_attr=False, )
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self.bn = nn.BatchNorm(
num_channels=out_channels,
act=act,
param_attr=ParamAttr(name=name + '.output.1.w_0'),
bias_attr=ParamAttr(name=name + '.output.1.b_0'),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
def __call__(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class ShortCut(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, is_first=False):
super(ShortCut, self).__init__()
self.use_conv = True
if in_channels != out_channels or stride != 1 or is_first == True:
if stride == (1, 1):
self.conv = ConvBNLayer(
in_channels, out_channels, 1, 1, name=name)
else: # stride==(2,2)
self.conv = ConvBNLayer(
in_channels, out_channels, 1, stride, name=name)
else:
self.use_conv = False
def forward(self, x):
if self.use_conv:
x = self.conv(x)
return x
class BottleneckBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
act='relu',
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
name=name + "_branch2b")
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * 4,
kernel_size=1,
act=None,
name=name + "_branch2c")
self.short = ShortCut(
in_channels=in_channels,
out_channels=out_channels * 4,
stride=stride,
is_first=False,
name=name + "_branch1")
self.out_channels = out_channels * 4
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = self.conv2(y)
y = y + self.short(x)
y = F.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, is_first):
super(BasicBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
act='relu',
stride=stride,
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
act=None,
name=name + "_branch2b")
self.short = ShortCut(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
is_first=is_first,
name=name + "_branch1")
self.out_channels = out_channels
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = y + self.short(x)
return F.relu(y)
ppocr/modeling/heads/__init__.py
浏览文件 @ 22a9f2ad
......@@ -23,10 +23,13 @@ def build_head(config):
# rec head
from .rec_ctc_head import CTCHead
from .rec_srn_head import SRNHead
# cls head
from .cls_head import ClsHead
support_dict = ['DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead']
support_dict = [
'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'SRNHead'
]
module_name = config.pop('name')
assert module_name in support_dict, Exception('head only support {}'.format(
......
ppocr/modeling/heads/rec_srn_head.py 0 → 100644
浏览文件 @ 22a9f2ad
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import paddle.fluid as fluid
import numpy as np
from .self_attention import WrapEncoderForFeature
from .self_attention import WrapEncoder
from paddle.static import Program
from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
import paddle.fluid.framework as framework
from collections import OrderedDict
gradient_clip = 10
class PVAM(nn.Layer):
def __init__(self, in_channels, char_num, max_text_length, num_heads,
num_encoder_tus, hidden_dims):
super(PVAM, self).__init__()
self.char_num = char_num
self.max_length = max_text_length
self.num_heads = num_heads
self.num_encoder_TUs = num_encoder_tus
self.hidden_dims = hidden_dims
# Transformer encoder
t = 256
c = 512
self.wrap_encoder_for_feature = WrapEncoderForFeature(
src_vocab_size=1,
max_length=t,
n_layer=self.num_encoder_TUs,
n_head=self.num_heads,
d_key=int(self.hidden_dims / self.num_heads),
d_value=int(self.hidden_dims / self.num_heads),
d_model=self.hidden_dims,
d_inner_hid=self.hidden_dims,
prepostprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
preprocess_cmd="n",
postprocess_cmd="da",
weight_sharing=True)
# PVAM
self.flatten0 = paddle.nn.Flatten(start_axis=0, stop_axis=1)
self.fc0 = paddle.nn.Linear(
in_features=in_channels,
out_features=in_channels, )
self.emb = paddle.nn.Embedding(
num_embeddings=self.max_length, embedding_dim=in_channels)
self.flatten1 = paddle.nn.Flatten(start_axis=0, stop_axis=2)
self.fc1 = paddle.nn.Linear(
in_features=in_channels, out_features=1, bias_attr=False)
def forward(self, inputs, encoder_word_pos, gsrm_word_pos):
b, c, h, w = inputs.shape
conv_features = paddle.reshape(inputs, shape=[-1, c, h * w])
conv_features = paddle.transpose(conv_features, perm=[0, 2, 1])
# transformer encoder
b, t, c = conv_features.shape
enc_inputs = [conv_features, encoder_word_pos, None]
word_features = self.wrap_encoder_for_feature(enc_inputs)
# pvam
b, t, c = word_features.shape
word_features = self.fc0(word_features)
word_features_ = paddle.reshape(word_features, [-1, 1, t, c])
word_features_ = paddle.tile(word_features_, [1, self.max_length, 1, 1])
word_pos_feature = self.emb(gsrm_word_pos)
word_pos_feature_ = paddle.reshape(word_pos_feature,
[-1, self.max_length, 1, c])
word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1])
y = word_pos_feature_ + word_features_
y = F.tanh(y)
attention_weight = self.fc1(y)
attention_weight = paddle.reshape(
attention_weight, shape=[-1, self.max_length, t])
attention_weight = F.softmax(attention_weight, axis=-1)
pvam_features = paddle.matmul(attention_weight,
word_features) #[b, max_length, c]
return pvam_features
class GSRM(nn.Layer):
def __init__(self, in_channels, char_num, max_text_length, num_heads,
num_encoder_tus, num_decoder_tus, hidden_dims):
super(GSRM, self).__init__()
self.char_num = char_num
self.max_length = max_text_length
self.num_heads = num_heads
self.num_encoder_TUs = num_encoder_tus
self.num_decoder_TUs = num_decoder_tus
self.hidden_dims = hidden_dims
self.fc0 = paddle.nn.Linear(
in_features=in_channels, out_features=self.char_num)
self.wrap_encoder0 = WrapEncoder(
src_vocab_size=self.char_num + 1,
max_length=self.max_length,
n_layer=self.num_decoder_TUs,
n_head=self.num_heads,
d_key=int(self.hidden_dims / self.num_heads),
d_value=int(self.hidden_dims / self.num_heads),
d_model=self.hidden_dims,
d_inner_hid=self.hidden_dims,
prepostprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
preprocess_cmd="n",
postprocess_cmd="da",
weight_sharing=True)
self.wrap_encoder1 = WrapEncoder(
src_vocab_size=self.char_num + 1,
max_length=self.max_length,
n_layer=self.num_decoder_TUs,
n_head=self.num_heads,
d_key=int(self.hidden_dims / self.num_heads),
d_value=int(self.hidden_dims / self.num_heads),
d_model=self.hidden_dims,
d_inner_hid=self.hidden_dims,
prepostprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
preprocess_cmd="n",
postprocess_cmd="da",
weight_sharing=True)
self.mul = lambda x: paddle.matmul(x=x,
y=self.wrap_encoder0.prepare_decoder.emb0.weight,
transpose_y=True)
def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2):
# ===== GSRM Visual-to-semantic embedding block =====
b, t, c = inputs.shape
pvam_features = paddle.reshape(inputs, [-1, c])
word_out = self.fc0(pvam_features)
word_ids = paddle.argmax(F.softmax(word_out), axis=1)
word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1])
#===== GSRM Semantic reasoning block =====
"""
This module is achieved through bi-transformers,
ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
"""
pad_idx = self.char_num
word1 = paddle.cast(word_ids, "float32")
word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC")
word1 = paddle.cast(word1, "int64")
word1 = word1[:, :-1, :]
word2 = word_ids
enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1]
enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2]
gsrm_feature1 = self.wrap_encoder0(enc_inputs_1)
gsrm_feature2 = self.wrap_encoder1(enc_inputs_2)
gsrm_feature2 = F.pad(gsrm_feature2, [0, 1],
value=0.,
data_format="NLC")
gsrm_feature2 = gsrm_feature2[:, 1:, ]
gsrm_features = gsrm_feature1 + gsrm_feature2
gsrm_out = self.mul(gsrm_features)
b, t, c = gsrm_out.shape
gsrm_out = paddle.reshape(gsrm_out, [-1, c])
return gsrm_features, word_out, gsrm_out
class VSFD(nn.Layer):
def __init__(self, in_channels=512, pvam_ch=512, char_num=38):
super(VSFD, self).__init__()
self.char_num = char_num
self.fc0 = paddle.nn.Linear(
in_features=in_channels * 2, out_features=pvam_ch)
self.fc1 = paddle.nn.Linear(
in_features=pvam_ch, out_features=self.char_num)
def forward(self, pvam_feature, gsrm_feature):
b, t, c1 = pvam_feature.shape
b, t, c2 = gsrm_feature.shape
combine_feature_ = paddle.concat([pvam_feature, gsrm_feature], axis=2)
img_comb_feature_ = paddle.reshape(
combine_feature_, shape=[-1, c1 + c2])
img_comb_feature_map = self.fc0(img_comb_feature_)
img_comb_feature_map = F.sigmoid(img_comb_feature_map)
img_comb_feature_map = paddle.reshape(
img_comb_feature_map, shape=[-1, t, c1])
combine_feature = img_comb_feature_map * pvam_feature + (
1.0 - img_comb_feature_map) * gsrm_feature
img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1])
out = self.fc1(img_comb_feature)
return out
class SRNHead(nn.Layer):
def __init__(self, in_channels, out_channels, max_text_length, num_heads,
num_encoder_TUs, num_decoder_TUs, hidden_dims, **kwargs):
super(SRNHead, self).__init__()
self.char_num = out_channels
self.max_length = max_text_length
self.num_heads = num_heads
self.num_encoder_TUs = num_encoder_TUs
self.num_decoder_TUs = num_decoder_TUs
self.hidden_dims = hidden_dims
self.pvam = PVAM(
in_channels=in_channels,
char_num=self.char_num,
max_text_length=self.max_length,
num_heads=self.num_heads,
num_encoder_tus=self.num_encoder_TUs,
hidden_dims=self.hidden_dims)
self.gsrm = GSRM(
in_channels=in_channels,
char_num=self.char_num,
max_text_length=self.max_length,
num_heads=self.num_heads,
num_encoder_tus=self.num_encoder_TUs,
num_decoder_tus=self.num_decoder_TUs,
hidden_dims=self.hidden_dims)
self.vsfd = VSFD(in_channels=in_channels, char_num=self.char_num)
self.gsrm.wrap_encoder1.prepare_decoder.emb0 = self.gsrm.wrap_encoder0.prepare_decoder.emb0
def forward(self, inputs, others):
encoder_word_pos = others[0]
gsrm_word_pos = others[1]
gsrm_slf_attn_bias1 = others[2]
gsrm_slf_attn_bias2 = others[3]
pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos)
gsrm_feature, word_out, gsrm_out = self.gsrm(
pvam_feature, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2)
final_out = self.vsfd(pvam_feature, gsrm_feature)
if not self.training:
final_out = F.softmax(final_out, axis=1)
_, decoded_out = paddle.topk(final_out, k=1)
predicts = OrderedDict([
('predict', final_out),
('pvam_feature', pvam_feature),
('decoded_out', decoded_out),
('word_out', word_out),
('gsrm_out', gsrm_out),
])
return predicts
ppocr/modeling/heads/self_attention.py 0 → 100644
浏览文件 @ 22a9f2ad
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import ParamAttr, nn
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import paddle.fluid as fluid
import numpy as np
gradient_clip = 10
class WrapEncoderForFeature(nn.Layer):
def __init__(self,
src_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
bos_idx=0):
super(WrapEncoderForFeature, self).__init__()
self.prepare_encoder = PrepareEncoder(
src_vocab_size,
d_model,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx,
word_emb_param_name="src_word_emb_table")
self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd)
def forward(self, enc_inputs):
conv_features, src_pos, src_slf_attn_bias = enc_inputs
enc_input = self.prepare_encoder(conv_features, src_pos)
enc_output = self.encoder(enc_input, src_slf_attn_bias)
return enc_output
class WrapEncoder(nn.Layer):
"""
embedder + encoder
"""
def __init__(self,
src_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
bos_idx=0):
super(WrapEncoder, self).__init__()
self.prepare_decoder = PrepareDecoder(
src_vocab_size,
d_model,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx)
self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd)
def forward(self, enc_inputs):
src_word, src_pos, src_slf_attn_bias = enc_inputs
enc_input = self.prepare_decoder(src_word, src_pos)
enc_output = self.encoder(enc_input, src_slf_attn_bias)
return enc_output
class Encoder(nn.Layer):
"""
encoder
"""
def __init__(self,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd="n",
postprocess_cmd="da"):
super(Encoder, self).__init__()
self.encoder_layers = list()
for i in range(n_layer):
self.encoder_layers.append(
self.add_sublayer(
"layer_%d" % i,
EncoderLayer(n_head, d_key, d_value, d_model, d_inner_hid,
prepostprocess_dropout, attention_dropout,
relu_dropout, preprocess_cmd,
postprocess_cmd)))
self.processer = PrePostProcessLayer(preprocess_cmd, d_model,
prepostprocess_dropout)
def forward(self, enc_input, attn_bias):
for encoder_layer in self.encoder_layers:
enc_output = encoder_layer(enc_input, attn_bias)
enc_input = enc_output
enc_output = self.processer(enc_output)
return enc_output
class EncoderLayer(nn.Layer):
"""
EncoderLayer
"""
def __init__(self,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd="n",
postprocess_cmd="da"):
super(EncoderLayer, self).__init__()
self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
prepostprocess_dropout)
self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
attention_dropout)
self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
prepostprocess_dropout)
self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
prepostprocess_dropout)
self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
prepostprocess_dropout)
def forward(self, enc_input, attn_bias):
attn_output = self.self_attn(
self.preprocesser1(enc_input), None, None, attn_bias)
attn_output = self.postprocesser1(attn_output, enc_input)
ffn_output = self.ffn(self.preprocesser2(attn_output))
ffn_output = self.postprocesser2(ffn_output, attn_output)
return ffn_output
class MultiHeadAttention(nn.Layer):
"""
Multi-Head Attention
"""
def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.):
super(MultiHeadAttention, self).__init__()
self.n_head = n_head
self.d_key = d_key
self.d_value = d_value
self.d_model = d_model
self.dropout_rate = dropout_rate
self.q_fc = paddle.nn.Linear(
in_features=d_model, out_features=d_key * n_head, bias_attr=False)
self.k_fc = paddle.nn.Linear(
in_features=d_model, out_features=d_key * n_head, bias_attr=False)
self.v_fc = paddle.nn.Linear(
in_features=d_model, out_features=d_value * n_head, bias_attr=False)
self.proj_fc = paddle.nn.Linear(
in_features=d_value * n_head, out_features=d_model, bias_attr=False)
def _prepare_qkv(self, queries, keys, values, cache=None):
if keys is None: # self-attention
keys, values = queries, queries
static_kv = False
else: # cross-attention
static_kv = True
q = self.q_fc(queries)
q = paddle.reshape(x=q, shape=[0, 0, self.n_head, self.d_key])
q = paddle.transpose(x=q, perm=[0, 2, 1, 3])
if cache is not None and static_kv and "static_k" in cache:
# for encoder-decoder attention in inference and has cached
k = cache["static_k"]
v = cache["static_v"]
else:
k = self.k_fc(keys)
v = self.v_fc(values)
k = paddle.reshape(x=k, shape=[0, 0, self.n_head, self.d_key])
k = paddle.transpose(x=k, perm=[0, 2, 1, 3])
v = paddle.reshape(x=v, shape=[0, 0, self.n_head, self.d_value])
v = paddle.transpose(x=v, perm=[0, 2, 1, 3])
if cache is not None:
if static_kv and not "static_k" in cache:
# for encoder-decoder attention in inference and has not cached
cache["static_k"], cache["static_v"] = k, v
elif not static_kv:
# for decoder self-attention in inference
cache_k, cache_v = cache["k"], cache["v"]
k = paddle.concat([cache_k, k], axis=2)
v = paddle.concat([cache_v, v], axis=2)
cache["k"], cache["v"] = k, v
return q, k, v
def forward(self, queries, keys, values, attn_bias, cache=None):
# compute q ,k ,v
keys = queries if keys is None else keys
values = keys if values is None else values
q, k, v = self._prepare_qkv(queries, keys, values, cache)
# scale dot product attention
product = paddle.matmul(x=q, y=k, transpose_y=True)
product = product * self.d_model**-0.5
if attn_bias is not None:
product += attn_bias
weights = F.softmax(product)
if self.dropout_rate:
weights = F.dropout(
weights, p=self.dropout_rate, mode="downscale_in_infer")
out = paddle.matmul(weights, v)
# combine heads
out = paddle.transpose(out, perm=[0, 2, 1, 3])
out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
# project to output
out = self.proj_fc(out)
return out
class PrePostProcessLayer(nn.Layer):
"""
PrePostProcessLayer
"""
def __init__(self, process_cmd, d_model, dropout_rate):
super(PrePostProcessLayer, self).__init__()
self.process_cmd = process_cmd
self.functors = []
for cmd in self.process_cmd:
if cmd == "a": # add residual connection
self.functors.append(lambda x, y: x + y if y is not None else x)
elif cmd == "n": # add layer normalization
self.functors.append(
self.add_sublayer(
"layer_norm_%d" % len(
self.sublayers(include_sublayers=False)),
paddle.nn.LayerNorm(
normalized_shape=d_model,
weight_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(1.)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(0.)))))
elif cmd == "d": # add dropout
self.functors.append(lambda x: F.dropout(
x, p=dropout_rate, mode="downscale_in_infer")
if dropout_rate else x)
def forward(self, x, residual=None):
for i, cmd in enumerate(self.process_cmd):
if cmd == "a":
x = self.functors[i](x, residual)
else:
x = self.functors[i](x)
return x
class PrepareEncoder(nn.Layer):
def __init__(self,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
super(PrepareEncoder, self).__init__()
self.src_emb_dim = src_emb_dim
self.src_max_len = src_max_len
self.emb = paddle.nn.Embedding(
num_embeddings=self.src_max_len,
embedding_dim=self.src_emb_dim,
sparse=True)
self.dropout_rate = dropout_rate
def forward(self, src_word, src_pos):
src_word_emb = src_word
src_word_emb = fluid.layers.cast(src_word_emb, 'float32')
src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
src_pos = paddle.squeeze(src_pos, axis=-1)
src_pos_enc = self.emb(src_pos)
src_pos_enc.stop_gradient = True
enc_input = src_word_emb + src_pos_enc
if self.dropout_rate:
out = F.dropout(
x=enc_input, p=self.dropout_rate, mode="downscale_in_infer")
else:
out = enc_input
return out
class PrepareDecoder(nn.Layer):
def __init__(self,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
super(PrepareDecoder, self).__init__()
self.src_emb_dim = src_emb_dim
"""
self.emb0 = Embedding(num_embeddings=src_vocab_size,
embedding_dim=src_emb_dim)
"""
self.emb0 = paddle.nn.Embedding(
num_embeddings=src_vocab_size,
embedding_dim=self.src_emb_dim,
padding_idx=bos_idx,
weight_attr=paddle.ParamAttr(
name=word_emb_param_name,
initializer=nn.initializer.Normal(0., src_emb_dim**-0.5)))
self.emb1 = paddle.nn.Embedding(
num_embeddings=src_max_len,
embedding_dim=self.src_emb_dim,
weight_attr=paddle.ParamAttr(name=pos_enc_param_name))
self.dropout_rate = dropout_rate
def forward(self, src_word, src_pos):
src_word = fluid.layers.cast(src_word, 'int64')
src_word = paddle.squeeze(src_word, axis=-1)
src_word_emb = self.emb0(src_word)
src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
src_pos = paddle.squeeze(src_pos, axis=-1)
src_pos_enc = self.emb1(src_pos)
src_pos_enc.stop_gradient = True
enc_input = src_word_emb + src_pos_enc
if self.dropout_rate:
out = F.dropout(
x=enc_input, p=self.dropout_rate, mode="downscale_in_infer")
else:
out = enc_input
return out
class FFN(nn.Layer):
"""
Feed-Forward Network
"""
def __init__(self, d_inner_hid, d_model, dropout_rate):
super(FFN, self).__init__()
self.dropout_rate = dropout_rate
self.fc1 = paddle.nn.Linear(
in_features=d_model, out_features=d_inner_hid)
self.fc2 = paddle.nn.Linear(
in_features=d_inner_hid, out_features=d_model)
def forward(self, x):
hidden = self.fc1(x)
hidden = F.relu(hidden)
if self.dropout_rate:
hidden = F.dropout(
hidden, p=self.dropout_rate, mode="downscale_in_infer")
out = self.fc2(hidden)
return out
ppocr/postprocess/__init__.py
浏览文件 @ 22a9f2ad
......@@ -26,11 +26,12 @@ def build_post_process(config, global_config=None):
from .db_postprocess import DBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
from .cls_postprocess import ClsPostProcess
support_dict = [
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess'
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
]
config = copy.deepcopy(config)
......
ppocr/postprocess/rec_postprocess.py
浏览文件 @ 22a9f2ad
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import string
import paddle
from paddle.nn import functional as F
......@@ -24,19 +25,28 @@ class BaseRecLabelDecode(object):
character_type='ch',
use_space_char=False):
support_character_type = [
'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean', 'it',
'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc', 'rsc', 'bg',
'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr', 'ne'
'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
'ne', 'EN'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
self.beg_str = "sos"
self.end_str = "eos"
if character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif character_type in ["ch", "french", "german", "japan", "korean"]:
elif character_type == "EN_symbol":
# same with ASTER setting (use 94 char).
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
elif character_type in support_character_type:
self.character_str = ""
assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch"
assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
character_type)
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
......@@ -45,11 +55,7 @@ class BaseRecLabelDecode(object):
if use_space_char:
self.character_str += " "
dict_character = list(self.character_str)
elif character_type == "en_sensitive":
# same with ASTER setting (use 94 char).
import string
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
else:
raise NotImplementedError
self.character_type = character_type
......@@ -106,7 +112,6 @@ class CTCLabelDecode(BaseRecLabelDecode):
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
......@@ -155,3 +160,84 @@ class AttnLabelDecode(BaseRecLabelDecode):
assert False, "unsupport type %s in get_beg_end_flag_idx" \
% beg_or_end
return idx
class SRNLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='en',
use_space_char=False,
**kwargs):
super(SRNLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
pred = preds['predict']
char_num = len(self.character_str) + 2
if isinstance(pred, paddle.Tensor):
pred = pred.numpy()
pred = np.reshape(pred, [-1, char_num])
preds_idx = np.argmax(pred, axis=1)
preds_prob = np.max(pred, axis=1)
preds_idx = np.reshape(preds_idx, [-1, 25])
preds_prob = np.reshape(preds_prob, [-1, 25])
text = self.decode(preds_idx, preds_prob)
if label is None:
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
return text
label = self.decode(label)
return text, label
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
ignored_tokens = self.get_ignored_tokens()
batch_size = len(text_index)
for batch_idx in range(batch_size):
char_list = []
conf_list = []
for idx in range(len(text_index[batch_idx])):
if text_index[batch_idx][idx] in ignored_tokens:
continue
if is_remove_duplicate:
# only for predict
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
batch_idx][idx]:
continue
char_list.append(self.character[int(text_index[batch_idx][
idx])])
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:
conf_list.append(1)
text = ''.join(char_list)
result_list.append((text, np.mean(conf_list)))
return result_list
def add_special_char(self, dict_character):
dict_character = dict_character + [self.beg_str, self.end_str]
return dict_character
def get_ignored_tokens(self):
beg_idx = self.get_beg_end_flag_idx("beg")
end_idx = self.get_beg_end_flag_idx("end")
return [beg_idx, end_idx]
def get_beg_end_flag_idx(self, beg_or_end):
if beg_or_end == "beg":
idx = np.array(self.dict[self.beg_str])
elif beg_or_end == "end":
idx = np.array(self.dict[self.end_str])
else:
assert False, "unsupport type %s in get_beg_end_flag_idx" \
% beg_or_end
return idx
tools/export_model.py
浏览文件 @ 22a9f2ad
......@@ -31,6 +31,14 @@ from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config", help="configuration file to use")
parser.add_argument(
"-o", "--output_path", type=str, default='./output/infer/')
return parser.parse_args()
def main():
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
......@@ -51,14 +59,40 @@ def main():
model.eval()
save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
infer_shape = [3, 32, 100] if config['Architecture'][
'model_type'] != "det" else [3, 640, 640]
model = to_static(
model,
input_spec=[
if config['Architecture']['algorithm'] == "SRN":
other_shape = [
paddle.static.InputSpec(
shape=[None] + infer_shape, dtype='float32')
])
shape=[None, 1, 64, 256], dtype='float32'), [
paddle.static.InputSpec(
shape=[None, 256, 1],
dtype="int64"), paddle.static.InputSpec(
shape=[None, 25, 1],
dtype="int64"), paddle.static.InputSpec(
shape=[None, 8, 25, 25], dtype="int64"),
paddle.static.InputSpec(
shape=[None, 8, 25, 25], dtype="int64")
]
]
model = to_static(model, input_spec=other_shape)
else:
infer_shape = [3, -1, -1]
if config['Architecture']['model_type'] == "rec":
infer_shape = [3, 32, -1] # for rec model, H must be 32
if 'Transform' in config['Architecture'] and config['Architecture'][
'Transform'] is not None and config['Architecture'][
'Transform']['name'] == 'TPS':
logger.info(
'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
)
infer_shape[-1] = 100
model = to_static(
model,
input_spec=[
paddle.static.InputSpec(
shape=[None] + infer_shape, dtype='float32')
])
paddle.jit.save(model, save_path)
logger.info('inference model is saved to {}'.format(save_path))
......
tools/infer/predict_rec.py
浏览文件 @ 22a9f2ad
......@@ -25,6 +25,7 @@ import numpy as np
import math
import time
import traceback
import paddle
import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
......@@ -46,6 +47,13 @@ class TextRecognizer(object):
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
if self.rec_algorithm == "SRN":
postprocess_params = {
'name': 'SRNLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'rec', logger)
......@@ -70,6 +78,78 @@ class TextRecognizer(object):
padding_im[:, :, 0:resized_w] = resized_image
return padding_im
def resize_norm_img_srn(self, img, image_shape):
imgC, imgH, imgW = image_shape
img_black = np.zeros((imgH, imgW))
im_hei = img.shape[0]
im_wid = img.shape[1]
if im_wid <= im_hei * 1:
img_new = cv2.resize(img, (imgH * 1, imgH))
elif im_wid <= im_hei * 2:
img_new = cv2.resize(img, (imgH * 2, imgH))
elif im_wid <= im_hei * 3:
img_new = cv2.resize(img, (imgH * 3, imgH))
else:
img_new = cv2.resize(img, (imgW, imgH))
img_np = np.asarray(img_new)
img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
img_black[:, 0:img_np.shape[1]] = img_np
img_black = img_black[:, :, np.newaxis]
row, col, c = img_black.shape
c = 1
return np.reshape(img_black, (c, row, col)).astype(np.float32)
def srn_other_inputs(self, image_shape, num_heads, max_text_length):
imgC, imgH, imgW = image_shape
feature_dim = int((imgH / 8) * (imgW / 8))
encoder_word_pos = np.array(range(0, feature_dim)).reshape(
(feature_dim, 1)).astype('int64')
gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
(max_text_length, 1)).astype('int64')
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
[-1, 1, max_text_length, max_text_length])
gsrm_slf_attn_bias1 = np.tile(
gsrm_slf_attn_bias1,
[1, num_heads, 1, 1]).astype('float32') * [-1e9]
gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
[-1, 1, max_text_length, max_text_length])
gsrm_slf_attn_bias2 = np.tile(
gsrm_slf_attn_bias2,
[1, num_heads, 1, 1]).astype('float32') * [-1e9]
encoder_word_pos = encoder_word_pos[np.newaxis, :]
gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
return [
encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2
]
def process_image_srn(self, img, image_shape, num_heads, max_text_length):
norm_img = self.resize_norm_img_srn(img, image_shape)
norm_img = norm_img[np.newaxis, :]
[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
self.srn_other_inputs(image_shape, num_heads, max_text_length)
gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
encoder_word_pos = encoder_word_pos.astype(np.int64)
gsrm_word_pos = gsrm_word_pos.astype(np.int64)
return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2)
def __call__(self, img_list):
img_num = len(img_list)
# Calculate the aspect ratio of all text bars
......@@ -93,21 +173,64 @@ class TextRecognizer(object):
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
# norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
if self.rec_algorithm != "SRN":
norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
else:
norm_img = self.process_image_srn(
img_list[indices[ino]], self.rec_image_shape, 8, 25)
encoder_word_pos_list = []
gsrm_word_pos_list = []
gsrm_slf_attn_bias1_list = []
gsrm_slf_attn_bias2_list = []
encoder_word_pos_list.append(norm_img[1])
gsrm_word_pos_list.append(norm_img[2])
gsrm_slf_attn_bias1_list.append(norm_img[3])
gsrm_slf_attn_bias2_list.append(norm_img[4])
norm_img_batch.append(norm_img[0])
norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy()
starttime = time.time()
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = outputs[0]
if self.rec_algorithm == "SRN":
starttime = time.time()
encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
gsrm_slf_attn_bias1_list = np.concatenate(
gsrm_slf_attn_bias1_list)
gsrm_slf_attn_bias2_list = np.concatenate(
gsrm_slf_attn_bias2_list)
inputs = [
norm_img_batch,
encoder_word_pos_list,
gsrm_word_pos_list,
gsrm_slf_attn_bias1_list,
gsrm_slf_attn_bias2_list,
]
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[
i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = {"predict": outputs[2]}
else:
starttime = time.time()
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = outputs[0]
rec_result = self.postprocess_op(preds)
for rno in range(len(rec_result)):
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
......
tools/infer/utility.py
浏览文件 @ 22a9f2ad
......@@ -70,7 +70,7 @@ def parse_args():
default="./ppocr/utils/ppocr_keys_v1.txt")
parser.add_argument("--use_space_char", type=str2bool, default=True)
parser.add_argument(
"--vis_font_path", type=str, default="./doc/simfang.ttf")
"--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
parser.add_argument("--drop_score", type=float, default=0.5)
# params for text classifier
......
tools/infer_rec.py
浏览文件 @ 22a9f2ad
......@@ -62,7 +62,13 @@ def main():
elif op_name in ['RecResizeImg']:
op[op_name]['infer_mode'] = True
elif op_name == 'KeepKeys':
op[op_name]['keep_keys'] = ['image']
if config['Architecture']['algorithm'] == "SRN":
op[op_name]['keep_keys'] = [
'image', 'encoder_word_pos', 'gsrm_word_pos',
'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
]
else:
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
global_config['infer_mode'] = True
ops = create_operators(transforms, global_config)
......@@ -74,10 +80,25 @@ def main():
img = f.read()
data = {'image': img}
batch = transform(data, ops)
if config['Architecture']['algorithm'] == "SRN":
encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
others = [
paddle.to_tensor(encoder_word_pos_list),
paddle.to_tensor(gsrm_word_pos_list),
paddle.to_tensor(gsrm_slf_attn_bias1_list),
paddle.to_tensor(gsrm_slf_attn_bias2_list)
]
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
preds = model(images)
if config['Architecture']['algorithm'] == "SRN":
preds = model(images, others)
else:
preds = model(images)
post_result = post_process_class(preds)
for rec_reuslt in post_result:
logger.info('\t result: {}'.format(rec_reuslt))
......
tools/program.py
浏览文件 @ 22a9f2ad
......@@ -174,6 +174,7 @@ def train(config,
best_model_dict = {main_indicator: 0}
best_model_dict.update(pre_best_model_dict)
train_stats = TrainingStats(log_smooth_window, ['lr'])
model_average = False
model.train()
if 'start_epoch' in best_model_dict:
......@@ -182,8 +183,8 @@ def train(config,
start_epoch = 1
for epoch in range(start_epoch, epoch_num + 1):
if epoch > 0:
train_dataloader = build_dataloader(config, 'Train', device, logger)
train_dataloader = build_dataloader(
config, 'Train', device, logger, seed=epoch)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
......@@ -194,7 +195,12 @@ def train(config,
break
lr = optimizer.get_lr()
images = batch[0]
preds = model(images)
if config['Architecture']['algorithm'] == "SRN":
others = batch[-4:]
preds = model(images, others)
model_average = True
else:
preds = model(images)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
avg_loss.backward()
......@@ -212,7 +218,7 @@ def train(config,
stats['lr'] = lr
train_stats.update(stats)
if cal_metric_during_train: # onlt rec and cls need
if cal_metric_during_train: # only rec and cls need
batch = [item.numpy() for item in batch]
post_result = post_process_class(preds, batch[1])
eval_class(post_result, batch)
......@@ -238,21 +244,28 @@ def train(config,
# eval
if global_step > start_eval_step and \
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
if model_average:
Model_Average = paddle.incubate.optimizer.ModelAverage(
0.15,
parameters=model.parameters(),
min_average_window=10000,
max_average_window=15625)
Model_Average.apply()
cur_metirc = eval(model, valid_dataloader, post_process_class,
eval_class)
cur_metirc_str = 'cur metirc, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metirc.items()]))
logger.info(cur_metirc_str)
cur_metric_str = 'cur metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
logger.info(cur_metric_str)
# logger metric
if vdl_writer is not None:
for k, v in cur_metirc.items():
for k, v in cur_metric.items():
if isinstance(v, (float, int)):
vdl_writer.add_scalar('EVAL/{}'.format(k),
cur_metirc[k], global_step)
if cur_metirc[main_indicator] >= best_model_dict[
cur_metric[k], global_step)
if cur_metric[main_indicator] >= best_model_dict[
main_indicator]:
best_model_dict.update(cur_metirc)
best_model_dict.update(cur_metric)
best_model_dict['best_epoch'] = epoch
save_model(
model,
......@@ -263,7 +276,7 @@ def train(config,
prefix='best_accuracy',
best_model_dict=best_model_dict,
epoch=epoch)
best_str = 'best metirc, {}'.format(', '.join([
best_str = 'best metric, {}'.format(', '.join([
'{}: {}'.format(k, v) for k, v in best_model_dict.items()
]))
logger.info(best_str)
......@@ -273,6 +286,7 @@ def train(config,
best_model_dict[main_indicator],
global_step)
global_step += 1
optimizer.clear_grad()
batch_start = time.time()
if dist.get_rank() == 0:
save_model(
......@@ -294,7 +308,7 @@ def train(config,
prefix='iter_epoch_{}'.format(epoch),
best_model_dict=best_model_dict,
epoch=epoch)
best_str = 'best metirc, {}'.format(', '.join(
best_str = 'best metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
logger.info(best_str)
if dist.get_rank() == 0 and vdl_writer is not None:
......@@ -313,7 +327,11 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
break
images = batch[0]
start = time.time()
preds = model(images)
if "SRN" in str(model.head):
others = batch[-4:]
preds = model(images, others)
else:
preds = model(images)
batch = [item.numpy() for item in batch]
# Obtain usable results from post-processing methods
......@@ -323,13 +341,13 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
eval_class(post_result, batch)
pbar.update(1)
total_frame += len(images)
# Get final metirc，eg. acc or hmean
metirc = eval_class.get_metric()
# Get final metric，eg. acc or hmean
metric = eval_class.get_metric()
pbar.close()
model.train()
metirc['fps'] = total_frame / total_time
return metirc
metric['fps'] = total_frame / total_time
return metric
def preprocess(is_train=False):
......
train.sh
浏览文件 @ 22a9f2ad
# for paddle.__version__ >= 2.0rc1
# recommended paddle.__version__ == 2.0.0
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
# for paddle.__version__ < 2.0rc1
# python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
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