提交 bbd7665c 编写于 作者: L LDOUBLEV

fix commit

上级 5fc33c12
...@@ -30,7 +30,7 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=hos ...@@ -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 sudo docker container exec -it ppocr /bin/bash
``` ```
**2. 安装PaddlePaddle v2.0** **2. 安装PaddlePaddle 2.0**
``` ```
pip3 install --upgrade pip pip3 install --upgrade pip
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...@@ -6,7 +6,7 @@ The inference model (the model saved by `paddle.jit.save`) is generally a solidi ...@@ -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. 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. 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. 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.
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...@@ -33,7 +33,7 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags ...@@ -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 sudo docker container exec -it ppocr /bin/bash
``` ```
**2. Install PaddlePaddle v2.0** **2. Install PaddlePaddle 2.0**
``` ```
pip3 install --upgrade pip pip3 install --upgrade pip
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...@@ -64,7 +64,7 @@ class TextDetector(object): ...@@ -64,7 +64,7 @@ class TextDetector(object):
postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000 postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio 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": elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess' postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh postprocess_params["score_thresh"] = args.det_east_score_thresh
......
...@@ -124,6 +124,7 @@ def create_predictor(args, mode, logger): ...@@ -124,6 +124,7 @@ def create_predictor(args, mode, logger):
# cache 10 different shapes for mkldnn to avoid memory leak # cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10) config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn() config.enable_mkldnn()
# TODO LDOUBLEV: fix mkldnn bug when bach_size > 1
#config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'}) #config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'})
args.rec_batch_num = 1 args.rec_batch_num = 1
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