fix commit

doc/doc_ch/installation.md

@@ -30,7 +30,7 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=hos
 sudo docker container exec -it ppocr /bin/bash
 ```
 
-**2. 安装PaddlePaddle v2.0**
+**2. 安装PaddlePaddle 2.0**
 ```
 pip3 install --upgrade pip
 

doc/doc_en/inference_en.md

@@ -6,7 +6,7 @@ The inference model (the model saved by `paddle.jit.save`) is generally a solidi
 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.
 
 Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
-For more details, please refer to the document [Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md).
+For more details, please refer to the document [Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/docs/zh_CN/extension/paddle_mobile_inference.md).
 
 Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model.
 

doc/doc_en/installation_en.md

@@ -33,7 +33,7 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags
 sudo docker container exec -it ppocr /bin/bash
 ```
 
-**2. Install PaddlePaddle v2.0**
+**2. Install PaddlePaddle 2.0**
 ```
 pip3 install --upgrade pip
 

tools/infer/predict_det.py

@@ -64,7 +64,7 @@ def __init__(self, args):
  postprocess_params["box_thresh"] = args.det_db_box_thresh
  postprocess_params["max_candidates"] = 1000
  postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
- postprocess_params["use_dilation"] = args.use_dilation
+ postprocess_params["use_dilation"] = True
  elif self.det_algorithm == "EAST":
  postprocess_params['name'] = 'EASTPostProcess'
  postprocess_params["score_thresh"] = args.det_east_score_thresh

tools/infer/utility.py

@@ -124,6 +124,7 @@ def create_predictor(args, mode, logger):
  # cache 10 different shapes for mkldnn to avoid memory leak
  config.set_mkldnn_cache_capacity(10)
  config.enable_mkldnn()
+ # TODO LDOUBLEV: fix mkldnn bug when bach_size > 1
  #config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'})
  args.rec_batch_num = 1