Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into test_v10

PPOCRLabel/README.md

@@ -207,6 +207,24 @@ For some data that are difficult to recognize, the recognition results will not
     pip install opencv-contrib-python-headless==4.2.0.32
     ```
 
+### Dataset division
+
+- Enter the following command in the terminal to execute the dataset division script:
+    ```
+    cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
+    python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
+    ```
+
+- Parameter Description:
+
+    trainValTestRatio is the division ratio of the number of images in the training set, validation set, and test set, set according to your actual situation, the default is 6:2:2
+    
+    labelRootPath is the storage path of the dataset labeled by PPOCRLabel, the default is ../train_data/label
+    
+    detRootPath is the path where the text detection dataset is divided according to the dataset marked by PPOCRLabel. The default is ../train_data/det
+    
+    recRootPath is the path where the character recognition dataset is divided according to the dataset marked by PPOCRLabel. The default is ../train_data/rec
+
 ### Related
 
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
\ No newline at end of file

PPOCRLabel/README_ch.md

@@ -193,7 +193,23 @@ PPOCRLabel支持三种导出方式：
     ```
     pip install opencv-contrib-python-headless==4.2.0.32
     ```
+### 数据集划分
+- 在终端中输入以下命令执行数据集划分脚本：
+    ```
+    cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+    python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
+    ```
+- 参数说明：
+
+    trainValTestRatio是训练集、验证集、测试集的图像数量划分比例，根据你的实际情况设定，默认是6:2:2
+
+    labelRootPath是PPOCRLabel标注的数据集存放路径，默认是../train_data/label
+
+    detRootPath是根据PPOCRLabel标注的数据集划分后的文本检测数据集存放的路径，默认是../train_data/det 
 
+    recRootPath是根据PPOCRLabel标注的数据集划分后的字符识别数据集存放的路径，默认是../train_data/rec
+    
+    
 ### 4. 参考资料
 
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)

PPOCRLabel/gen_ocr_train_val_test.py

+# coding:utf8
+import os
+import shutil
+import random
+import argparse
+
+
+# 删除划分的训练集、验证集、测试集文件夹，重新创建一个空的文件夹
+def isCreateOrDeleteFolder(path, flag):
+    flagPath = os.path.join(path, flag)
+
+    if os.path.exists(flagPath):
+        shutil.rmtree(flagPath)
+
+    os.makedirs(flagPath)
+    flagAbsPath = os.path.abspath(flagPath)
+    return flagAbsPath
+
+
+def splitTrainVal(root, dir, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
+    # 按照指定的比例划分训练集、验证集、测试集
+    labelPath = os.path.join(root, dir)
+    labelAbsPath = os.path.abspath(labelPath)
+
+    if flag == "det":
+        labelFilePath = os.path.join(labelAbsPath, args.detLabelFileName)
+    elif flag == "rec":
+        labelFilePath = os.path.join(labelAbsPath, args.recLabelFileName)
+
+    labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
+    labelFileContent = labelFileRead.readlines()
+    random.shuffle(labelFileContent)
+    labelRecordLen = len(labelFileContent)
+
+    for index, labelRecordInfo in enumerate(labelFileContent):
+        imageRelativePath = labelRecordInfo.split('\t')[0]
+        imageLabel = labelRecordInfo.split('\t')[1]
+        imageName = os.path.basename(imageRelativePath)
+
+        if flag == "det":
+            imagePath = os.path.join(labelAbsPath, imageName)
+        elif flag == "rec":
+            imagePath = os.path.join(labelAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
+
+        # 按预设的比例划分训练集、验证集、测试集
+        trainValTestRatio = args.trainValTestRatio.split(":")
+        trainRatio = eval(trainValTestRatio[0]) / 10
+        valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
+        curRatio = index / labelRecordLen
+
+        if curRatio < trainRatio:
+            imageCopyPath = os.path.join(absTrainRootPath, imageName)
+            shutil.copy(imagePath, imageCopyPath)
+            trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+        elif curRatio >= trainRatio and curRatio < valRatio:
+            imageCopyPath = os.path.join(absValRootPath, imageName)
+            shutil.copy(imagePath, imageCopyPath)
+            valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+        else:
+            imageCopyPath = os.path.join(absTestRootPath, imageName)
+            shutil.copy(imagePath, imageCopyPath)
+            testTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+
+
+# 删掉存在的文件
+def removeFile(path):
+    if os.path.exists(path):
+        os.remove(path)
+
+
+def genDetRecTrainVal(args):
+    detAbsTrainRootPath = isCreateOrDeleteFolder(args.detRootPath, "train")
+    detAbsValRootPath = isCreateOrDeleteFolder(args.detRootPath, "val")
+    detAbsTestRootPath = isCreateOrDeleteFolder(args.detRootPath, "test")
+    recAbsTrainRootPath = isCreateOrDeleteFolder(args.recRootPath, "train")
+    recAbsValRootPath = isCreateOrDeleteFolder(args.recRootPath, "val")
+    recAbsTestRootPath = isCreateOrDeleteFolder(args.recRootPath, "test")
+
+    removeFile(os.path.join(args.detRootPath, "train.txt"))
+    removeFile(os.path.join(args.detRootPath, "val.txt"))
+    removeFile(os.path.join(args.detRootPath, "test.txt"))
+    removeFile(os.path.join(args.recRootPath, "train.txt"))
+    removeFile(os.path.join(args.recRootPath, "val.txt"))
+    removeFile(os.path.join(args.recRootPath, "test.txt"))
+
+    detTrainTxt = open(os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8")
+    detValTxt = open(os.path.join(args.detRootPath, "val.txt"), "a", encoding="UTF-8")
+    detTestTxt = open(os.path.join(args.detRootPath, "test.txt"), "a", encoding="UTF-8")
+    recTrainTxt = open(os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8")
+    recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
+    recTestTxt = open(os.path.join(args.recRootPath, "test.txt"), "a", encoding="UTF-8")
+
+    for root, dirs, files in os.walk(args.labelRootPath):
+        for dir in dirs:
+            splitTrainVal(root, dir, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
+                          detTestTxt, "det")
+            splitTrainVal(root, dir, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
+                          recTestTxt, "rec")
+        break
+
+
+if __name__ == "__main__":
+    # 功能描述：分别划分检测和识别的训练集、验证集、测试集
+    # 说明：可以根据自己的路径和需求调整参数，图像数据往往多人合作分批标注，每一批图像数据放在一个文件夹内用PPOCRLabel进行标注，
+    # 如此会有多个标注好的图像文件夹汇总并划分训练集、验证集、测试集的需求
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--trainValTestRatio",
+        type=str,
+        default="6:2:2",
+        help="ratio of trainset:valset:testset")
+    parser.add_argument(
+        "--labelRootPath",
+        type=str,
+        default="../train_data/label",
+        help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
+    )
+    parser.add_argument(
+        "--detRootPath",
+        type=str,
+        default="../train_data/det",
+        help="the path where the divided detection dataset is placed")
+    parser.add_argument(
+        "--recRootPath",
+        type=str,
+        default="../train_data/rec",
+        help="the path where the divided recognition dataset is placed"
+    )
+    parser.add_argument(
+        "--detLabelFileName",
+        type=str,
+        default="Label.txt",
+        help="the name of the detection annotation file")
+    parser.add_argument(
+        "--recLabelFileName",
+        type=str,
+        default="rec_gt.txt",
+        help="the name of the recognition annotation file"
+    )
+    parser.add_argument(
+        "--recImageDirName",
+        type=str,
+        default="crop_img",
+        help="the name of the folder where the cropped recognition dataset is located"
+    )
+    args = parser.parse_args()
+    genDetRecTrainVal(args)

deploy/pdserving/ocr_cpp_client.py

+# 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.
+# pylint: disable=doc-string-missing
+
+from paddle_serving_client import Client
+import sys
+import numpy as np
+import base64
+import os
+import cv2
+from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from ocr_reader import OCRReader
+
+client = Client()
+# TODO:load_client need to load more than one client model.
+# this need to figure out some details.
+client.load_client_config(sys.argv[1:])
+client.connect(["127.0.0.1:9293"])
+
+import paddle
+test_img_dir = "test_img/"
+
+ocr_reader = OCRReader(char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode(
+        'utf8')  #data.tostring()).decode('utf8')
+
+
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data = file.read()
+    image = cv2_to_base64(image_data)
+    res_list = []
+    #print(image)
+    fetch_map = client.predict(
+        feed={"x": image}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True)
+    print("fetrch map:", fetch_map)
+    one_batch_res = ocr_reader.postprocess(fetch_map, with_score=True)
+    for res in one_batch_res:
+        res_list.append(res[0])
+    res = {"res": str(res_list)}
+    print(res)

deploy/pdserving/pipeline_http_client.py

@@ -18,13 +18,19 @@ import json
 import base64
 import os
 
+import argparse
+parser = argparse.ArgumentParser(description="args for paddleserving")
+parser.add_argument("--image_dir", type=str, default="../../doc/imgs/")
+args = parser.parse_args()
+
 
 def cv2_to_base64(image):
     return base64.b64encode(image).decode('utf8')
 
 
 url = "http://127.0.0.1:9998/ocr/prediction"
-test_img_dir = "../../doc/imgs/"
+test_img_dir = args.image_dir
+
 for idx, img_file in enumerate(os.listdir(test_img_dir)):
     with open(os.path.join(test_img_dir, img_file), 'rb') as file:
         image_data1 = file.read()
@@ -36,5 +42,4 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)):
         r = requests.post(url=url, data=json.dumps(data))
         print(r.json())
 
-test_img_dir = "../../doc/imgs/"
 print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))

deploy/pdserving/pipeline_rpc_client.py

@@ -30,7 +30,12 @@ def cv2_to_base64(image):
     return base64.b64encode(image).decode('utf8')
 
 
-test_img_dir = "imgs/"
+import argparse
+parser = argparse.ArgumentParser(description="args for paddleserving")
+parser.add_argument("--image_dir", type=str, default="../../doc/imgs/")
+args = parser.parse_args()
+test_img_dir = args.image_dir
+
 for img_file in os.listdir(test_img_dir):
     with open(os.path.join(test_img_dir, img_file), 'rb') as file:
         image_data = file.read()

test_tipc/configs/mac_ppocr_det_mobile_params.txt

@@ -80,7 +80,8 @@ op.det.local_service_conf.use_mkldnn:True|False
 op.det.local_service_conf.thread_num:1|6
 op.det.local_service_conf.use_trt:False|True
 op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs
+pipline:pipeline_http_client.py|pipeline_rpc_client.py
+--image_dir=../../doc/imgs
 ===========================kl_quant_params===========================
 infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
 infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o

test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/det_mobile_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
\ No newline at end of file

test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_det_mobile
+python:python3.7|cpp
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
+op.det.local_service_conf.devices:null|0
+op.det.local_service_conf.use_mkldnn:True|False
+op.det.local_service_conf.thread_num:1|6
+op.det.local_service_conf.use_trt:False|True
+op.det.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs
\ No newline at end of file

test_tipc/configs/ppocr_det_mobile_params.txt

@@ -80,7 +80,8 @@ op.det.local_service_conf.use_mkldnn:True|False
 op.det.local_service_conf.thread_num:1|6
 op.det.local_service_conf.use_trt:False|True
 op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs
 ===========================kl_quant_params===========================
 infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
 infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o

test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_server_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/det_server_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--det_model_dir:
+--image_dir:./inference/det_inference
\ No newline at end of file

test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_det_server
+python:python3.7|cpp
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_server_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_det_server_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_det_server_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
+op.det.local_service_conf.devices:null|0
+op.det.local_service_conf.use_mkldnn:True|False
+op.det.local_service_conf.thread_num:1|6
+op.det.local_service_conf.use_trt:False|True
+op.det.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ppocr_det_server_params.txt

@@ -80,4 +80,5 @@ op.det.local_service_conf.use_mkldnn:True|False
 op.det.local_service_conf.thread_num:1|6
 op.det.local_service_conf.use_trt:False|True
 op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs
+pipline:pipeline_http_client.py|pipeline_rpc_client.py
+--image_dir=../../doc/imgs

test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/rec_mobile_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--rec_model_dir:
+--image_dir:./inference/rec_inference
\ No newline at end of file

test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_rec_mobile
+python:python3.7|cpp
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
+op.rec.local_service_conf.devices:null|0
+op.rec.local_service_conf.use_mkldnn:True|False
+op.rec.local_service_conf.thread_num:1|6
+op.rec.local_service_conf.use_trt:False|True
+op.rec.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ppocr_rec_mobile_params.txt

@@ -80,4 +80,5 @@ op.rec.local_service_conf.use_mkldnn:True|False
 op.rec.local_service_conf.thread_num:1|6
 op.rec.local_service_conf.use_trt:False|True
 op.rec.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en
+pipline:pipeline_http_client.py|pipeline_rpc_client.py
+--image_dir=../../doc/imgs_words_en

test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/rec_server_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--rec_model_dir:
+--image_dir:./inference/rec_inference
\ No newline at end of file

test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_rec_server
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
+op.rec.local_service_conf.devices:null|0
+op.rec.local_service_conf.use_mkldnn:True|False
+op.rec.local_service_conf.thread_num:1|6
+op.rec.local_service_conf.use_trt:False|True
+op.rec.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ppocr_rec_server_params.txt

@@ -80,4 +80,5 @@ op.rec.local_service_conf.use_mkldnn:True|False
 op.rec.local_service_conf.thread_num:1|6
 op.rec.local_service_conf.use_trt:False|True
 op.rec.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en
+pipline:pipeline_http_client.py|pipeline_rpc_client.py
+--image_dir=../../doc/imgs_words_en

test_tipc/configs/win_ppocr_det_mobile_params.txt

@@ -80,7 +80,8 @@ op.det.local_service_conf.use_mkldnn:True|False
 op.det.local_service_conf.thread_num:1|6
 op.det.local_service_conf.use_trt:False|True
 op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs
+pipline:pipeline_http_client.py|pipeline_rpc_client.py
+--image_dir=../../doc/imgs
 ===========================kl_quant_params===========================
 infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
 infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o

test_tipc/docs/test_paddle2onnx.md

@@ -18,10 +18,10 @@ PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测
 先运行`prepare.sh`准备数据和模型，然后运行`test_paddle2onnx.sh`进行测试，最终在```test_tipc/output```目录下生成`paddle2onnx_infer_*.log`后缀的日志文件。
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "paddle2onnx_infer"
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt "paddle2onnx_infer"
 
 # 用法:
-bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
+bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
 ```  
 
 #### 运行结果

test_tipc/docs/test_serving.md

@@ -20,10 +20,10 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试
 先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "serving_infer"
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt "serving_infer"
 
 # 用法:
-bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
+bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
 ```  
 
 #### 运行结果

test_tipc/test_lite.sh

@@ -3,7 +3,7 @@ source ./common_func.sh
 export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
 
 FILENAME=$1
-dataline=$(awk 'NR==101, NR==110{print}'  $FILENAME)
+dataline=$(awk 'NR==102, NR==111{print}'  $FILENAME)
 echo $dataline
 # parser params
 IFS=$'\n'

test_tipc/test_paddle2onnx.sh

@@ -11,7 +11,7 @@ python=$(func_parser_value "${lines[2]}")
 
 
 # parser params
-dataline=$(awk 'NR==111, NR==123{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==12{print}'  $FILENAME)
 IFS=$'\n'
 lines=(${dataline})
 

test_tipc/test_serving.sh

@@ -2,7 +2,7 @@
 source test_tipc/common_func.sh
 
 FILENAME=$1
-dataline=$(awk 'NR==67, NR==83{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==18{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'
@@ -35,6 +35,8 @@ web_use_trt_list=$(func_parser_value "${lines[14]}")
 web_precision_key=$(func_parser_key "${lines[15]}")
 web_precision_list=$(func_parser_value "${lines[15]}")
 pipeline_py=$(func_parser_value "${lines[16]}")
+image_dir_key=$(func_parser_key "${lines[17]}")
+image_dir_value=$(func_parser_value "${lines[17]}")
 
 LOG_PATH="../../test_tipc/output"
 mkdir -p ./test_tipc/output
@@ -51,67 +53,98 @@ function func_serving(){
     set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
     set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
     set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
+    set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
     trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
     eval $trans_model_cmd
     cd ${serving_dir_value}
     echo $PWD
     unset https_proxy
     unset http_proxy
-    for use_gpu in ${web_use_gpu_list[*]}; do
-        echo ${ues_gpu}
-        if [ ${use_gpu} = "null" ]; then
-            for use_mkldnn in ${web_use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ]; then
-                    continue
-                fi
-                for threads in ${web_cpu_threads_list[*]}; do
-                      _save_log_path="${LOG_PATH}/server_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
-                      set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
-                      web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
-                      eval $web_service_cmd
-                      sleep 2s
-                      pipeline_cmd="${python} ${pipeline_py} > ${_save_log_path} 2>&1 "
-                      eval $pipeline_cmd
-                      last_status=${PIPESTATUS[0]}
-                      eval "cat ${_save_log_path}"
-                      status_check $last_status "${pipeline_cmd}" "${status_log}"
-                      PID=$!
-                      kill $PID
-                      sleep 2s
-                      ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
-                done
-            done
-        elif [ ${use_gpu} = "0" ]; then
-            for use_trt in ${web_use_trt_list[*]}; do
-                for precision in ${web_precision_list[*]}; do
-                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
-                        continue
-                    fi
-                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
-                        continue
-                    fi
-                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
-                        continue
-                    fi
-                    _save_log_path="${LOG_PATH}/server_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
-                    set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
-                    set_precision=$(func_set_params "${web_precision_key}" "${precision}")
-                    web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
+    for python in ${python[*]}; do
+        if [ ${python} = "cpp"]; then
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                if [ ${use_gpu} = "null" ]; then
+                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
                     eval $web_service_cmd
                     sleep 2s
-                    pipeline_cmd="${python} ${pipeline_py} > ${_save_log_path} 2>&1"
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
                     eval $pipeline_cmd
-                    last_status=${PIPESTATUS[0]}
-                    eval "cat ${_save_log_path}"
                     status_check $last_status "${pipeline_cmd}" "${status_log}"
-                    PID=$!
-                    kill $PID
                     sleep 2s
                     ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
-                done
+                else
+                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
+                    eval $web_service_cmd
+                    sleep 2s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 2s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9                
+                fi
             done
         else
-            echo "Does not support hardware other than CPU and GPU Currently!"
+            # python serving
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                echo ${ues_gpu}
+                if [ ${use_gpu} = "null" ]; then
+                    for use_mkldnn in ${web_use_mkldnn_list[*]}; do
+                        if [ ${use_mkldnn} = "False" ]; then
+                            continue
+                        fi
+                        for threads in ${web_cpu_threads_list[*]}; do
+                            set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
+                            web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
+                            eval $web_service_cmd
+                            sleep 2s
+                            for pipeline in ${pipeline_py[*]}; do
+                                _save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
+                                pipeline_cmd="${python} ${pipeline} ${set_image_dir} > ${_save_log_path} 2>&1 "
+                                eval $pipeline_cmd
+                                last_status=${PIPESTATUS[0]}
+                                eval "cat ${_save_log_path}"
+                                status_check $last_status "${pipeline_cmd}" "${status_log}"
+                                sleep 2s
+                            done
+                            ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                        done
+                    done
+                elif [ ${use_gpu} = "0" ]; then
+                    for use_trt in ${web_use_trt_list[*]}; do
+                        for precision in ${web_precision_list[*]}; do
+                            if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
+                                continue
+                            fi
+                            if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
+                                continue
+                            fi
+                            if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
+                                continue
+                            fi
+                            set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
+                            set_precision=$(func_set_params "${web_precision_key}" "${precision}")
+                            web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
+                            eval $web_service_cmd
+                        
+                            sleep 2s
+                            for pipeline in ${pipeline_py[*]}; do
+                                _save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
+                                pipeline_cmd="${python} ${pipeline} ${set_image_dir}> ${_save_log_path} 2>&1"
+                                eval $pipeline_cmd
+                                last_status=${PIPESTATUS[0]}
+                                eval "cat ${_save_log_path}"
+                                status_check $last_status "${pipeline_cmd}" "${status_log}"
+                                sleep 2s
+                            done
+                            ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                        done
+                    done
+                else
+                    echo "Does not support hardware other than CPU and GPU Currently!"
+                fi
+            done
         fi
     done
 }

test_tipc/test_train_inference_python.sh

@@ -90,7 +90,7 @@ infer_value1=$(func_parser_value "${lines[50]}")
 
 # parser klquant_infer
 if [ ${MODE} = "klquant_whole_infer" ]; then
-    dataline=$(awk 'NR==82, NR==98{print}'  $FILENAME)
+    dataline=$(awk 'NR==85 NR==101{print}'  $FILENAME)
     lines=(${dataline})
     # parser inference model 
     infer_model_dir_list=$(func_parser_value "${lines[1]}")
@@ -244,7 +244,7 @@ else
     export Count=0
     USE_GPU_KEY=(${train_use_gpu_value})
     for gpu in ${gpu_list[*]}; do
-        use_gpu=${USE_GPU_KEY[Count]}
+        train_use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
         ips=""
         if [ ${gpu} = "-1" ];then
@@ -302,11 +302,20 @@ else
                 set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
                 set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
                 set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
-                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
-                save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-                
+                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
+                if [ ${#ips} -le 26 ];then
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+                    nodes=1
+                else
+                    IFS=","
+                    ips_array=(${ips})
+                    IFS="|"
+                    nodes=${#ips_array[@]}
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
+                fi
+
                 # load pretrain from norm training if current trainer is pact or fpgm trainer
-                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
+                if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
                     set_pretrain="${load_norm_train_model}"
                 fi
 
@@ -325,7 +334,7 @@ else
 
                 set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
                 # save norm trained models to set pretrain for pact training and fpgm training 
-                if [ ${trainer} = ${trainer_norm} ]; then
+                if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
                     load_norm_train_model=${set_eval_pretrain}
                 fi
                 # run eval