inference_en.md 14.0 KB
Newer Older
K
Khanh Tran 已提交
1 2 3 4 5 6 7

# Prediction from inference model

The inference model (the model saved by fluid.io.save_inference_model) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.

The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.

X
xxxpsyduck 已提交
8
Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting in deployment and accelerating inferencing, is flexible and convenient, and is suitable for integration with actual systems. For more details, please refer to the document [Classification Framework](https://paddleclas.readthedocs.io/zh_CN/latest/extension/paddle_inference.html).
K
Khanh Tran 已提交
9 10 11

Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, and the concatenation of them based on inference model.

X
xxxpsyduck 已提交
12 13
## Convert training model to inference model
### Convert detection model to inference model
K
Khanh Tran 已提交
14

X
xxxpsyduck 已提交
15
Download the lightweight Chinese detection model:
K
Khanh Tran 已提交
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
```
The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
```
python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy Global.save_inference_dir=./inference/det_db/
```
When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` and `Global.save_inference_dir` parameters in the configuration file.
`Global.checkpoints` points to the model parameter file saved during training, and `Global.save_inference_dir` is the directory where the generated inference model is saved.
After the conversion is successful, there are two files in the `save_inference_dir` directory:
```
inference/det_db/
  └─  model     Check the program file of inference model
  └─  params    Check the parameter file of the inference model
```

X
xxxpsyduck 已提交
32
### Convert recognition model to inference model
K
Khanh Tran 已提交
33

X
xxxpsyduck 已提交
34
Download the lightweight Chinese recognition model:
K
Khanh Tran 已提交
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
```

The recognition model is converted to the inference model in the same way as the detection, as follows:
```
python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
        Global.save_inference_dir=./inference/rec_crnn/
```

If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.

After the conversion is successful, there are two files in the directory:
```
/inference/rec_crnn/
  └─  model     Identify the saved model files
  └─  params    Identify the parameter files of the inference model
```

## Text detection model inference

X
xxxpsyduck 已提交
56
The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model. Because EAST and DB algorithms are very different, when inference, it is necessary to adapt the EAST text detection algorithm by passing in corresponding parameters.
K
Khanh Tran 已提交
57

X
xxxpsyduck 已提交
58
### 1. lightweight Chinese detection model inference
K
Khanh Tran 已提交
59

X
xxxpsyduck 已提交
60
For lightweight Chinese detection model inference, you can execute the following commands:
K
Khanh Tran 已提交
61 62 63 64 65 66 67

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"
```

The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:

68
![](../imgs_results/det_res_2.jpg)
K
Khanh Tran 已提交
69 70 71 72 73 74 75 76 77 78 79 80

By setting the size of the parameter `det_max_side_len`, the maximum value of picture normalization in the detection algorithm is changed. When the length and width of the picture are less than det_max_side_len, the original picture is used for prediction, otherwise the picture is scaled to the maximum value for prediction. This parameter is set to det_max_side_len=960 by default. If the resolution of the input picture is relatively large and you want to use a larger resolution for prediction, you can execute the following command:

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200
```

If you want to use the CPU for prediction, execute the command as follows
```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
```

X
xxxpsyduck 已提交
81
### 2. DB text detection model inference
K
Khanh Tran 已提交
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)), you can use the following command to convert:

```
# Set the yml configuration file of the training algorithm after -c
# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# The Global.save_inference_dir parameter sets the address where the converted model will be saved.

python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db"
```

DB text detection model inference, you can execute the following command:

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
```

The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

101
![](../imgs_results/det_res_img_10_db.jpg)
K
Khanh Tran 已提交
102 103 104

**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.

X
xxxpsyduck 已提交
105
### 3. EAST text detection model inference
K
Khanh Tran 已提交
106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123

First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)), you can use the following command to convert:

```
# Set the yml configuration file of the training algorithm after -c
# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# The Global.save_inference_dir parameter sets the address where the converted model will be saved.

python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east"
```

For EAST text detection model inference, you need to set the parameter det_algorithm, specify the detection algorithm type to EAST, run the following command:

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST"
```
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

124
![](../imgs_results/det_res_img_10_east.jpg)
K
Khanh Tran 已提交
125 126 127 128 129 130

**Note**: The Python version of NMS in EAST post-processing used in this codebase so the prediction speed is quite slow. If you use the C++ version, there will be a significant speedup.


## Text recognition model inference

X
xxxpsyduck 已提交
131
The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
K
Khanh Tran 已提交
132 133


X
xxxpsyduck 已提交
134
### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
K
Khanh Tran 已提交
135

X
xxxpsyduck 已提交
136
For lightweight Chinese recognition model inference, you can execute the following commands:
K
Khanh Tran 已提交
137 138 139 140 141

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
```

142
![](../imgs_words/ch/word_4.jpg)
K
Khanh Tran 已提交
143 144 145 146 147 148

After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.

Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]


X
xxxpsyduck 已提交
149
### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE
K
Khanh Tran 已提交
150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167

Taking STAR-Net as an example, we introduce the recognition model inference based on CTC loss. CRNN and Rosetta are used in a similar way, by setting the recognition algorithm parameter `rec_algorithm`.

First, convert the model saved in the STAR-Net text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)). It can be converted as follow:

```
# Set the yml configuration file of the training algorithm after -c
# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# The Global.save_inference_dir parameter sets the address where the converted model will be saved.

python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.checkpoints="./models/rec_r34_vd_tps_bilstm_ctc/best_accuracy" Global.save_inference_dir="./inference/starnet"
```

For STAR-Net text recognition model inference, execute the following commands:

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
```
X
xxxpsyduck 已提交
168 169

### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
170
![](../imgs_words_en/word_336.png)
K
Khanh Tran 已提交
171 172 173 174 175

After executing the command, the recognition result of the above image is as follows:

Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]

X
xxxpsyduck 已提交
176
**Note**:Since the above model refers to [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the training of lightweight Chinese recognition model in two aspects:
K
Khanh Tran 已提交
177 178 179 180 181 182 183 184 185 186

- The image resolution used in training is different: the image resolution used in training the above model is [3,32,100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`.

- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command. Therefore, the parameter `rec_char_type` needs to be set during inference, which is specified as "en" in English.

```
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```

X
xxxpsyduck 已提交
187 188
### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict.
L
LDOUBLEV 已提交
189 190 191 192 193

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
```

X
xxxpsyduck 已提交
194
## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
K
Khanh Tran 已提交
195

X
xxxpsyduck 已提交
196
### 1. LIGHTWEIGHT CHINESE MODEL
K
Khanh Tran 已提交
197

X
xxxpsyduck 已提交
198
When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visualized recognition results are saved to the `./inference_results` folder by default.
K
Khanh Tran 已提交
199 200 201 202 203 204 205

```
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
```

After executing the command, the recognition result image is as follows:

206
![](../imgs_results/2.jpg)
K
Khanh Tran 已提交
207

X
xxxpsyduck 已提交
208
### 2. OTHER MODELS
K
Khanh Tran 已提交
209 210 211 212 213 214 215 216 217

If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model, the following command uses the combination of the EAST text detection and STAR-Net text recognition:

```
python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
```

After executing the command, the recognition result image is as follows:

218
![](../imgs_results/img_10.jpg)