update can docs

doc/doc_ch/algorithm_rec_can.md

@@ -57,24 +57,22 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs
 **注意：**
 - 我们提供的数据集，即[`CROHME数据集`](https://paddleocr.bj.bcebos.com/dataset/CROHME.tar)将手写公式存储为黑底白字的格式，若您自行准备的数据集与之相反，即以白底黑字模式存储，请在训练时做出如下修改
 ```
-python3 tools/train.py -c configs/rec/rec_d28_can.yml
--o Train.dataset.transforms.GrayImageChannelFormat.inverse=False
+python3 tools/train.py -c configs/rec/rec_d28_can.yml -o Train.dataset.transforms.GrayImageChannelFormat.inverse=False
 ```
 - 默认每训练1个epoch（1105次iteration）进行1次评估，若您更改训练的batch_size，或更换数据集，请在训练时作出如下修改
 ```
-python3 tools/train.py -c configs/rec/rec_d28_can.yml
--o Global.eval_batch_step=[0, {length_of_dataset//batch_size}]
+python3 tools/train.py -c configs/rec/rec_d28_can.yml -o Global.eval_batch_step=[0, {length_of_dataset//batch_size}]
 ```
 
 #
 <a name="3-2"></a>
 ### 3.2 评估
 
-可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)，使用如下命令进行评估：
+可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)，使用如下命令进行评估：
 
 ```shell
 # 注意将pretrained_model的路径设置为本地路径。若使用自行训练保存的模型，请注意修改路径和文件名为{path/to/weights}/{model_name}。
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
 ```
 
 <a name="3-3"></a>
@@ -83,7 +81,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
 使用如下命令进行单张图片预测：
 ```shell
 # 注意将pretrained_model的路径设置为本地路径。
-python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/datasets/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN
+python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/datasets/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
 
 # 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/datasets/crohme_demo/'。
 ```
@@ -94,11 +92,11 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a
 
 <a name="4-1"></a>
 ### 4.1 Python推理
-首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/contribution/can_train.tar) )，可以使用如下命令进行转换：
+首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar) )，可以使用如下命令进行转换：
 
 ```shell
 # 注意将pretrained_model的路径设置为本地路径。
-python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
+python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
 
 # 目前的静态图模型默认的输出长度最大为36，如果您需要预测更长的序列，请在导出模型时指定其输出序列为合适的值，例如 Architecture.Head.max_text_length=72
 ```

doc/doc_en/algorithm_rec_can_en.md

@@ -53,14 +53,14 @@ Evaluation:
 
 ```
 # GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
 ```
 
 Prediction:
 
 ```
 # The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN
+python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
 ```
 
 <a name="4"></a>
@@ -71,7 +71,7 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a
 First, the model saved during the CAN handwritten mathematical expression recognition training process is converted into an inference model. you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
+python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
 
 # The default output max length of the model is 36. If you need to predict a longer sequence, please specify its output sequence as an appropriate value when exporting the model, as: Architecture.Head.max_ text_ length=72
 ```
@@ -79,7 +79,7 @@ python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_infe
 For CAN handwritten mathematical expression recognition model inference, the following commands can be executed:
 
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
+python3 tools/infer/predict_rec.py --image_dir="./doc/datasets/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
 
 # If you need to predict on a picture with black characters on a white background, please set: -- rec_ image_ inverse=False
 ```