fix: export.py -> export_model.py

deploy/slim/README.md

@@ -125,7 +125,7 @@ python3.7 -m paddle.distributed.launch \
 在得到在线量化训练、模型剪枝保存的模型后，可以将其导出为inference model，用于预测部署，以模型剪枝为例：
 
 ```bash
-python3.7 tools/export.py \
+python3.7 tools/export_model.py \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
     -o Global.pretrained_model=./output/ResNet50_vd/best_model \
     -o Global.save_inference_dir=./inference

deploy/slim/README_en.md

@@ -127,7 +127,7 @@ python3.7 -m paddle.distributed.launch \
 After getting the compressed model, we can export it as inference model for predictive deployment. Using pruned model as example:
 
 ```bash
-python3.7 tools/export.py \
+python3.7 tools/export_model.py \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
     -o Global.pretrained_model=./output/ResNet50_vd/best_model
     -o Global.save_inference_dir=./inference

docs/en/advanced_tutorials/model_prune_quantization_en.md

@@ -157,7 +157,7 @@ python3.7 -m paddle.distributed.launch \
 Having obtained the saved model after online quantization training and pruning, it can be exported as an inference model for inference deployment. Here we take model pruning as an example:
 
 ```
-python3.7 tools/export.py \
+python3.7 tools/export_model.py \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
     -o Global.pretrained_model=./output/ResNet50_vd/best_model \
     -o Global.save_inference_dir=./inference

docs/zh_CN/training/advanced/prune_quantization.md

@@ -151,7 +151,7 @@ python3.7 -m paddle.distributed.launch \
 在得到在线量化训练、模型剪枝保存的模型后，可以将其导出为 inference model，用于预测部署，以模型剪枝为例：
 
 ```bash
-python3.7 tools/export.py \
+python3.7 tools/export_model.py \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
     -o Global.pretrained_model=./output/ResNet50_vd/best_model \
     -o Global.save_inference_dir=./inference