add inference to serving model tool
This commit is contained in:
wangjiawei04
parent
e53c427330
commit
0005f4d171
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@ -22,9 +22,9 @@ import time
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import re
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import base64
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from tools.infer.predict_cls import TextClassifier
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import tools.infer.utility as utility
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from params import read_params
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global_args = utility.parse_args()
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -22,9 +22,9 @@ import time
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import re
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import base64
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from tools.infer.predict_cls import TextClassifier
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import tools.infer.utility as utility
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from params import read_params
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global_args = utility.parse_args()
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -21,9 +21,9 @@ import time
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import re
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import base64
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from tools.infer.predict_det import TextDetector
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import tools.infer.utility as utility
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from params import read_params
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global_args = utility.parse_args()
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -21,9 +21,9 @@ import time
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import re
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import base64
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from tools.infer.predict_det import TextDetector
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import tools.infer.utility as utility
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from params import read_params
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global_args = utility.parse_args()
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -24,12 +24,13 @@ import base64
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from clas_local_server import TextClassifierHelper
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from det_local_server import TextDetectorHelper
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from rec_local_server import TextRecognizerHelper
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import tools.infer.utility as utility
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from tools.infer.predict_system import TextSystem, sorted_boxes
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from paddle_serving_app.local_predict import Debugger
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import copy
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from params import read_params
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global_args = read_params()
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global_args = utility.parse_args()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -84,8 +85,7 @@ class TextSystemHelper(TextSystem):
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class OCRService(WebService):
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def init_rec(self):
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args = utility.parse_args()
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self.text_system = TextSystemHelper(args)
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self.text_system = TextSystemHelper(global_args)
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def preprocess(self, feed=[], fetch=[]):
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# TODO: to handle batch rec images
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@ -24,11 +24,11 @@ import base64
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from clas_rpc_server import TextClassifierHelper
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from det_rpc_server import TextDetectorHelper
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from rec_rpc_server import TextRecognizerHelper
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import tools.infer.utility as utility
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from tools.infer.predict_system import TextSystem, sorted_boxes
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import copy
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from params import read_params
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global_args = utility.parse_args()
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -87,8 +87,7 @@ class TextSystemHelper(TextSystem):
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class OCRService(WebService):
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def init_rec(self):
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args = utility.parse_args()
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self.text_system = TextSystemHelper(args)
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self.text_system = TextSystemHelper(global_args)
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def preprocess(self, feed=[], fetch=[]):
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# TODO: to handle batch rec images
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@ -0,0 +1,50 @@
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# -*- coding:utf-8 -*-
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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class Config(object):
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pass
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def read_params():
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cfg = Config()
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#use gpu
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cfg.use_gpu = False
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cfg.use_pdserving = True
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#params for text detector
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cfg.det_algorithm = "DB"
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cfg.det_model_dir = "./det_mv_server/"
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cfg.det_max_side_len = 960
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#DB parmas
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cfg.det_db_thresh =0.3
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cfg.det_db_box_thresh =0.5
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cfg.det_db_unclip_ratio =2.0
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#EAST parmas
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cfg.det_east_score_thresh = 0.8
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cfg.det_east_cover_thresh = 0.1
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cfg.det_east_nms_thresh = 0.2
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#params for text recognizer
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cfg.rec_algorithm = "CRNN"
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cfg.rec_model_dir = "./ocr_rec_server/"
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cfg.rec_image_shape = "3, 32, 320"
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cfg.rec_char_type = 'ch'
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cfg.rec_batch_num = 30
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cfg.max_text_length = 25
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cfg.rec_char_dict_path = "./ppocr_keys_v1.txt"
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cfg.use_space_char = True
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#params for text classifier
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cfg.use_angle_cls = True
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cfg.cls_model_dir = "./ocr_clas_server/"
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cfg.cls_image_shape = "3, 48, 192"
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cfg.label_list = ['0', '180']
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cfg.cls_batch_num = 30
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cfg.cls_thresh = 0.9
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return cfg
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@ -22,9 +22,10 @@ import time
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import re
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import base64
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from tools.infer.predict_rec import TextRecognizer
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import tools.infer.utility as utility
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from params import read_params
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global_args = read_params()
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global_args = utility.parse_args()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -22,9 +22,9 @@ import time
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import re
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import base64
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from tools.infer.predict_rec import TextRecognizer
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import tools.infer.utility as utility
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from params import read_params
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global_args = utility.parse_args()
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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@ -10,117 +10,100 @@
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## 一、训练模型转Serving模型
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### 检测模型转Serving模型
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在前序文档 [基于Python预测引擎推理](./inference.md) 中，我们提供了如何把训练的checkpoint转换成Paddle模型。Paddle模型通常由一个文件夹构成，内含模型结构描述文件`model`和模型参数文件`params`。Serving模型由两个文件夹构成，用于存放客户端和服务端的配置。
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下载超轻量级中文检测模型：
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我们以`ch_rec_r34_vd_crnn`模型作为例子，下载链接在：
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```
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
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wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
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tar xf ch_rec_r34_vd_crnn_infer.tar
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```
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上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成Serving模型只需要运行如下命令：
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因此我们按照Serving模型转换教程，运行下列python文件。
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```
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python tools/inference_to_serving.py --model_dir ch_rec_r34_vd_crnn
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```
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最终会在`serving_client_dir`和`serving_server_dir`生成客户端和服务端的模型配置。其中`serving_server_dir`和`serving_client_dir`的名字可以自定义。最终文件结构如下
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```
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# -c后面设置训练算法的yml配置文件
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# -o配置可选参数
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# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
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python tools/export_serving_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy Global.save_inference_dir=./inference/det_db/
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```
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转Serving模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`、`Global.save_inference_dir`参数。 其中`Global.checkpoints`指向训练中保存的模型参数文件，`Global.save_inference_dir`是生成的inference模型要保存的目录。 转换成功后，在`save_inference_dir`目录下有两个文件：
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```
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inference/det_db/
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├── serving_client_dir # 客户端配置文件夹
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└── serving_server_dir # 服务端配置文件夹
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```
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### 识别模型转Serving模型
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下载超轻量中文识别模型：
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```
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
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```
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识别模型转inference模型与检测的方式相同，如下：
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```
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# -c后面设置训练算法的yml配置文件
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# -o配置可选参数
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# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
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python3 tools/export_serving_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
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Global.save_inference_dir=./inference/rec_crnn/
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```
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**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
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转换成功后，在目录下有两个文件：
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```
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/inference/rec_crnn/
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/ch_rec_r34_vd_crnn/
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├── serving_client_dir # 客户端配置文件夹
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└── serving_server_dir # 服务端配置文件夹
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```
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### 方向分类模型转Serving模型
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下载方向分类模型：
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```
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/
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```
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方向分类模型转inference模型与检测的方式相同，如下：
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```
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# -c后面设置训练算法的yml配置文件
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# -o配置可选参数
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# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
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python3 tools/export_serving_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
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Global.save_inference_dir=./inference/cls/
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```
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转换成功后，在目录下有两个文件：
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```
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/inference/cls/
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├── serving_client_dir # 客户端配置文件夹
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└── serving_server_dir # 服务端配置文件夹
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```
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在接下来的教程中，我们将给出推理的demo模型下载链接。
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```
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wget --no-check-certificate https://paddleocr.bj.bcebos.com/deploy/pdserving/ocr_pdserving_suite.tar.gz
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tar zxf ocr_pdserving_suite.tar.gz
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```
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## 二、文本检测模型Serving推理
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文本检测模型推理，默认使用DB模型的配置参数。当不使用DB模型时，在推理时，需要通过传入相应的参数进行算法适配，细节参考下文。
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启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表：
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与本地预测不同的是，Serving预测需要一个客户端和一个服务端，因此接下来的教程都是两行代码。所有的
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|版本|特点|适用场景|
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|标准版|稳定性高，分布式部署|适用于吞吐量大，需要跨机房部署的情况|
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|快速版|部署方便，预测速度快|适用于对预测速度要求高，迭代速度快的场景，Windows用户只能选择快速版|
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接下来的命令中，我们会指定快速版和标准版的命令。需要说明的是，标准版只能用Linux平台，快速版可以支持Linux/Windows。
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文本检测模型推理，默认使用DB模型的配置参数，识别默认为CRNN。
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配置文件在`params.py`中，我们贴出配置部分，如果需要做改动，也在这个文件内部进行修改。
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```
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def read_params():
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cfg = Config()
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#use gpu
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cfg.use_gpu = False # 是否使用GPU
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cfg.use_pdserving = True # 是否使用paddleserving，必须为True
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#params for text detector
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cfg.det_algorithm = "DB" # 检测算法， DB/EAST等
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cfg.det_model_dir = "./det_mv_server/" # 检测算法模型路径
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cfg.det_max_side_len = 960
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#DB params
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cfg.det_db_thresh =0.3
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cfg.det_db_box_thresh =0.5
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cfg.det_db_unclip_ratio =2.0
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#EAST params
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cfg.det_east_score_thresh = 0.8
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cfg.det_east_cover_thresh = 0.1
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cfg.det_east_nms_thresh = 0.2
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#params for text recognizer
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cfg.rec_algorithm = "CRNN" # 识别算法， CRNN/RARE等
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cfg.rec_model_dir = "./ocr_rec_server/" # 识别算法模型路径
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cfg.rec_image_shape = "3, 32, 320"
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cfg.rec_char_type = 'ch'
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cfg.rec_batch_num = 30
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cfg.max_text_length = 25
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cfg.rec_char_dict_path = "./ppocr_keys_v1.txt" # 识别算法字典文件
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cfg.use_space_char = True
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#params for text classifier
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cfg.use_angle_cls = True # 是否启用分类算法
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cfg.cls_model_dir = "./ocr_clas_server/" # 分类算法模型路径
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cfg.cls_image_shape = "3, 48, 192"
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cfg.label_list = ['0', '180']
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cfg.cls_batch_num = 30
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cfg.cls_thresh = 0.9
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return cfg
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```
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与本地预测不同的是，Serving预测需要一个客户端和一个服务端，因此接下来的教程都是两行代码。
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在正式执行服务端启动命令之前，先export PYTHONPATH到工程主目录下。
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```
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export PYTHONPATH=$PWD:$PYTHONPATH
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cd deploy/pdserving
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```
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### 1. 超轻量中文检测模型推理
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超轻量中文检测模型推理，可以执行如下命令启动服务端：
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```
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#根据环境只需要启动其中一个就可以
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python det_rpc_server.py --use_pdserving True --det_model_dir det_mv_server #标准版，Linux用户
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python det_local_server.py --use_pdserving True --det_model_dir det_mv_server #快速版，Windows/Linux用户
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python det_rpc_server.py #标准版，Linux用户
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python det_local_server.py #快速版，Windows/Linux用户
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```
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如果需要使用CPU版本，还需增加 `--use_gpu False`。
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客户端
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@ -129,23 +112,8 @@ python det_web_client.py
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```
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Serving的推测和本地预测不同点在于，客户端发送请求到服务端，服务端需要检测到文字框之后返回框的坐标，此处没有后处理的图片，只能看到坐标值。
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### 2. DB文本检测模型推理
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首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar))，可以使用如下命令进行转换：
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```
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# -c后面设置训练算法的yml配置文件
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# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
|
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
|
||||
|
||||
python3 tools/export_serving_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db"
|
||||
```
|
||||
|
||||
经过转换之后，会在`./inference/det_db` 目录下出现`serving_server_dir`和`serving_client_dir`，然后指定`det_model_dir` 。
|
||||
|
||||
## 三、文本识别模型Serving推理
|
||||
|
||||
下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外，如果训练时修改了文本的字典，请参考下面的自定义文本识别字典的推理。
|
||||
|
|
@ -153,11 +121,11 @@ python3 tools/export_serving_model.py -c configs/det/det_r50_vd_db.yml -o Global
|
|||
### 1. 超轻量中文识别模型推理
|
||||
|
||||
超轻量中文识别模型推理，可以执行如下命令启动服务端：
|
||||
|
||||
需要注意params.py中的`--use_gpu`的值
|
||||
```
|
||||
#根据环境只需要启动其中一个就可以
|
||||
python rec_rpc_server.py --use_pdserving True --rec_model_dir ocr_rec_server #标准版，Linux用户
|
||||
python rec_local_server.py --use_pdserving True --rec_model_dir ocr_rec_server #快速版，Windows/Linux用户
|
||||
python rec_rpc_server.py #标准版，Linux用户
|
||||
python rec_local_server.py #快速版，Windows/Linux用户
|
||||
```
|
||||
如果需要使用CPU版本，还需增加 `--use_gpu False`。
|
||||
|
||||
|
|
@ -186,13 +154,12 @@ python rec_web_client.py
|
|||
### 1. 方向分类模型推理
|
||||
|
||||
方向分类模型推理， 可以执行如下命令启动服务端：
|
||||
|
||||
需要注意params.py中的`--use_gpu`的值
|
||||
```
|
||||
#根据环境只需要启动其中一个就可以
|
||||
python clas_rpc_server.py --use_pdserving True --cls_model_dir ocr_clas_server #标准版，Linux用户
|
||||
python clas_local_server.py --use_pdserving True --cls_model_dir ocr_clas_server #快速版，Windows/Linux用户
|
||||
python clas_rpc_server.py #标准版，Linux用户
|
||||
python clas_local_server.py #快速版，Windows/Linux用户
|
||||
```
|
||||
如果需要使用CPU版本，还需增加 `--use_gpu False`。
|
||||
|
||||
客户端
|
||||
|
||||
|
|
@ -216,20 +183,20 @@ python rec_web_client.py
|
|||
在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。与本地预测不同的是，为了减少网络传输耗时，可视化识别结果目前不做处理，用户收到的是推理得到的文字字段。
|
||||
|
||||
执行如下命令启动服务端：
|
||||
|
||||
需要注意params.py中的`--use_gpu`的值
|
||||
```
|
||||
#标准版，Linux用户
|
||||
#GPU用户
|
||||
python -m paddle_serving_server_gpu.serve --model det_mv_server --port 9293 --gpu_id 0
|
||||
python -m paddle_serving_server_gpu.serve --model ocr_cls_server --port 9294 --gpu_id 0
|
||||
python ocr_rpc_server.py --use_pdserving True --use_gpu True --rec_model_dir ocr_rec_server
|
||||
python ocr_rpc_server.py
|
||||
#CPU用户
|
||||
python -m paddle_serving_server.serve --model det_mv_server --port 9293
|
||||
python -m paddle_serving_server.serve --model ocr_cls_server --port 9294
|
||||
python ocr_rpc_server.py --use_pdserving True --use_gpu False --rec_model_dir ocr_rec_server
|
||||
python ocr_rpc_server.py
|
||||
|
||||
#快速版，Windows/Linux用户
|
||||
python ocr_local_server.py --use_gpu False --use_pdserving True --rec_model_dir ocr_rec_server/ --det_model_dir det_mv_server/ --cls_model_dir ocr_clas_server/ --rec_char_dict_path ppocr_keys_v1.txt --use_angle_cls True
|
||||
python ocr_local_server.py
|
||||
```
|
||||
|
||||
客户端
|
||||
|
|
|
|||
|
|
@ -21,12 +21,10 @@ from ppocr.utils.utility import initial_logger, check_and_read_gif
|
|||
logger = initial_logger()
|
||||
|
||||
import tools.infer.utility as utility
|
||||
args = utility.parse_args()
|
||||
if args.use_pdserving is False:
|
||||
from .data_augment import AugmentData
|
||||
from .random_crop_data import RandomCropData
|
||||
from .make_shrink_map import MakeShrinkMap
|
||||
from .make_border_map import MakeBorderMap
|
||||
from .data_augment import AugmentData
|
||||
from .random_crop_data import RandomCropData
|
||||
from .make_shrink_map import MakeShrinkMap
|
||||
from .make_border_map import MakeBorderMap
|
||||
|
||||
|
||||
class DBProcessTrain(object):
|
||||
|
|
|
|||
|
|
@ -1,78 +0,0 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import sys
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
|
||||
|
||||
|
||||
def set_paddle_flags(**kwargs):
|
||||
for key, value in kwargs.items():
|
||||
if os.environ.get(key, None) is None:
|
||||
os.environ[key] = str(value)
|
||||
|
||||
|
||||
# NOTE(paddle-dev): All of these flags should be
|
||||
# set before `import paddle`. Otherwise, it would
|
||||
# not take any effect.
|
||||
set_paddle_flags(
|
||||
FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
|
||||
)
|
||||
|
||||
import program
|
||||
from paddle import fluid
|
||||
from ppocr.utils.utility import initial_logger
|
||||
logger = initial_logger()
|
||||
from ppocr.utils.save_load import init_model
|
||||
from paddle_serving_client.io import save_model
|
||||
|
||||
|
||||
def main():
|
||||
startup_prog, eval_program, place, config, _ = program.preprocess()
|
||||
|
||||
feeded_var_names, target_vars, fetches_var_name = program.build_export(
|
||||
config, eval_program, startup_prog)
|
||||
eval_program = eval_program.clone(for_test=True)
|
||||
exe = fluid.Executor(place)
|
||||
exe.run(startup_prog)
|
||||
|
||||
init_model(config, eval_program, exe)
|
||||
|
||||
save_inference_dir = config['Global']['save_inference_dir']
|
||||
if not os.path.exists(save_inference_dir):
|
||||
os.makedirs(save_inference_dir)
|
||||
serving_client_dir = "{}/serving_client_dir".format(save_inference_dir)
|
||||
serving_server_dir = "{}/serving_server_dir".format(save_inference_dir)
|
||||
|
||||
feed_dict = {
|
||||
x: eval_program.global_block().var(x)
|
||||
for x in feeded_var_names
|
||||
}
|
||||
fetch_dict = {x.name: x for x in target_vars}
|
||||
save_model(serving_server_dir, serving_client_dir, feed_dict, fetch_dict,
|
||||
eval_program)
|
||||
print(
|
||||
"paddle serving model saved in {}/serving_server_dir and {}/serving_client_dir".
|
||||
format(save_inference_dir, save_inference_dir))
|
||||
print("save success, output_name_list:", fetches_var_name)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -36,10 +36,10 @@ class TextClassifier(object):
|
|||
if args.use_pdserving is False:
|
||||
self.predictor, self.input_tensor, self.output_tensors = \
|
||||
utility.create_predictor(args, mode="cls")
|
||||
self.use_zero_copy_run = args.use_zero_copy_run
|
||||
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
|
||||
self.cls_batch_num = args.rec_batch_num
|
||||
self.label_list = args.label_list
|
||||
self.use_zero_copy_run = args.use_zero_copy_run
|
||||
self.cls_thresh = args.cls_thresh
|
||||
|
||||
def resize_norm_img(self, img):
|
||||
|
|
|
|||
|
|
@ -42,7 +42,6 @@ class TextDetector(object):
|
|||
def __init__(self, args):
|
||||
max_side_len = args.det_max_side_len
|
||||
self.det_algorithm = args.det_algorithm
|
||||
self.use_zero_copy_run = args.use_zero_copy_run
|
||||
preprocess_params = {'max_side_len': max_side_len}
|
||||
postprocess_params = {}
|
||||
if self.det_algorithm == "DB":
|
||||
|
|
@ -76,6 +75,7 @@ class TextDetector(object):
|
|||
logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
|
||||
sys.exit(0)
|
||||
if args.use_pdserving is False:
|
||||
self.use_zero_copy_run = args.use_zero_copy_run
|
||||
self.predictor, self.input_tensor, self.output_tensors =\
|
||||
utility.create_predictor(args, mode="det")
|
||||
|
||||
|
|
|
|||
|
|
@ -37,12 +37,12 @@ class TextRecognizer(object):
|
|||
if args.use_pdserving is False:
|
||||
self.predictor, self.input_tensor, self.output_tensors =\
|
||||
utility.create_predictor(args, mode="rec")
|
||||
self.use_zero_copy_run = args.use_zero_copy_run
|
||||
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
|
||||
self.character_type = args.rec_char_type
|
||||
self.rec_batch_num = args.rec_batch_num
|
||||
self.rec_algorithm = args.rec_algorithm
|
||||
self.text_len = args.max_text_length
|
||||
self.use_zero_copy_run = args.use_zero_copy_run
|
||||
char_ops_params = {
|
||||
"character_type": args.rec_char_type,
|
||||
"character_dict_path": args.rec_char_dict_path,
|
||||
|
|
|
|||
|
|
@ -37,7 +37,6 @@ def parse_args():
|
|||
parser.add_argument("--ir_optim", type=str2bool, default=True)
|
||||
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
|
||||
parser.add_argument("--gpu_mem", type=int, default=8000)
|
||||
parser.add_argument("--use_pdserving", type=str2bool, default=False)
|
||||
|
||||
# params for text detector
|
||||
parser.add_argument("--image_dir", type=str)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,29 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import argparse
|
||||
from paddle_serving_client.io import inference_model_to_serving
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--model_dir", type=str)
|
||||
parser.add_argument("--server_dir", type=str, default="serving_server_dir")
|
||||
parser.add_argument("--client_dir", type=str, default="serving_client_dir")
|
||||
return parser.parse_args()
|
||||
|
||||
args = parse_args()
|
||||
inference_model_dir = args.model_dir
|
||||
serving_client_dir = args.server_dir
|
||||
serving_server_dir = args.client_dir
|
||||
feed_var_names, fetch_var_names = inference_model_to_serving(
|
||||
inference_model_dir, serving_client_dir, serving_server_dir, model_filename="model", params_filename="params")
|
||||
Loading…
Reference in New Issue