Complicated models help to improve the performance of the model, but it also leads to some redundancy in the model. Model tailoring reduces this redundancy by removing the sub-models in the network model, so as to reduce model calculation complexity and improve model inference performance. .
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@@ -36,7 +35,20 @@ PaddleOCR also provides a series of models [../../../doc/doc_en/models_list_en.m
### 3. Pruning sensitivity analysis
After the pre-training model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, thereby determining the pruning ratio of each network layer. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
After the pre-training model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sensitivities_0.data. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of correspoding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
@@ -10,7 +10,7 @@ The following list can be viewed via `--help`
## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE
Take `rec_chinese_lite_train.yml` as an example
Take `rec_chinese_lite_train_v1.1.yml` as an example
| Parameter | Use | Default | Note |
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@@ -32,6 +32,7 @@ Take `rec_chinese_lite_train.yml` as an example
| loss_type | Set loss type | ctc | Supports two types of loss: ctc / attention |
| distort | Set use distort | false | Support distort type ,read [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) |
| use_space_char | Wether to recognize space | false | Only support in character_type=ch mode |
label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in the direction classifier |
| reader_yml | Set the reader configuration file | ./configs/rec/rec_icdar15_reader.yml | \ |
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
