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Merge remote-tracking branch 'upstream/develop' into zxdev

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zhangxin 2020-06-24 19:06:18 +08:00
parent 2814d997a4 824563c63c
commit 9136cba8cb
4 changed files with 15 additions and 13 deletions
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12
doc/doc_ch/FAQ.md
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@ -38,15 +38,13 @@ PaddleOCR已完成Windows和Mac系统适配，运行时注意两点：1、在[
中文数据集，LSVT街景数据集训练数据3w张图片
- 识别：
英文数据集，MJSynth和SynthText合成数据，数据量上千万。
中文数据集，LSVT街景数据集根据真值将图crop出来，并进行位置校准，总共30w张图像。此外基于LSVT的语料，合成数据500w。
中文数据集，LSVT街景数据集根据真值将图crop出来，并进行位置校准，总共30w张图像。此外基于LSVT的语料，合成数据500w。
其中，公开数据集都是开源的，用户可自行搜索下载，也可参考[中文数据集](./datasets.md)，合成数据暂不开源，用户可使用开源合成工具自行合成，可参考的合成工具包括[text_renderer](https://github.com/Sanster/text_renderer)、[SynthText](https://github.com/ankush-me/SynthText)、[TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator)等。
10. **使用带TPS的识别模型预测报错**
报错信息：Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](320) != Grid dimension[2](100)
10. **使用带TPS的识别模型预测报错**
报错信息：Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](320) != Grid dimension[2](100)
原因：TPS模块暂时无法支持变长的输入，请设置 --rec_image_shape='3,32,100' --rec_char_type='en' 固定输入shape
11. **自定义字典训练的模型，识别结果出现字典里没出现的字**
11. **自定义字典训练的模型，识别结果出现字典里没出现的字**
预测时没有设置采用的自定义字典路径。设置方法是在预测时，通过增加输入参数rec_char_dict_path来设置。

2
doc/doc_ch/inference.md
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@ -127,7 +127,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
## 文本识别模型推理
下面将介绍超轻量中文识别模型推理和基于CTC损失的识别模型推理。**而基于Attention损失的识别模型推理还在调试中**。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。
下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外，如果训练时修改了文本的字典，请参考下面的自定义文本识别字典的推理。
### 1.超轻量中文识别模型推理

6
doc/doc_en/FAQ_en.md
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@ -44,8 +44,6 @@ At present, the open source model, dataset and magnitude are as follows:
Among them, the public datasets are opensourced, users can search and download by themselves, or refer to [Chinese data set](./datasets_en.md), synthetic data is not opensourced, users can use open-source synthesis tools to synthesize data themselves. Current available synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator), etc.
10. **Error in using the model with TPS module for prediction**
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](108) != Grid dimension[2](100)
10. **Error in using the model with TPS module for prediction**
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](108) != Grid dimension[2](100)
Solution：TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'

8
tools/eval_utils/eval_det_utils.py
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@ -59,7 +59,13 @@ def cal_det_res(exe, config, eval_info_dict):
img_list.append(data[ino][0])
ratio_list.append(data[ino][1])
img_name_list.append(data[ino][2])
img_list = np.concatenate(img_list, axis=0)
try:
img_list = np.concatenate(img_list, axis=0)
except:
err = "concatenate error usually caused by different input image shapes in evaluation or testing.\n \
Please set \"test_batch_size_per_card\" in main yml as 1\n \
or add \"test_image_shape: [h, w]\" in reader yml for EvalReader."
raise Exception(err)
outs = exe.run(eval_info_dict['program'], \
feed={'image': img_list}, \
fetch_list=eval_info_dict['fetch_varname_list'])