Merge pull request #3963 from LDOUBLEV/update_doc_2.3

rm tests and update inference_ppocr doc

doc/doc_ch/inference_ppocr.md

@@ -21,9 +21,10 @@
 
 ```
 # 下载超轻量中文检测模型：
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
+
 ```
 
 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
@@ -41,13 +42,13 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_m
 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216：
 
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 
 如果想使用CPU进行预测，执行命令如下
 
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/"  --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 
 
@@ -64,9 +65,9 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di
 
 ```
 # 下载超轻量中文识别模型：
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 
 ![](../imgs_words/ch/word_4.jpg)
@@ -81,10 +82,9 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
 
 ### 2.2 多语言模型的推理
 
-如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
-需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
-
+如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
 ```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 
@@ -125,14 +125,13 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
 
 ```shell
 # 使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 # 不使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 # 使用多进程
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```
 
 执行命令后，识别结果图像如下：
 
 ![](../imgs_results/system_res_00018069.jpg)
-

doc/doc_en/inference_ppocr_en.md

@@ -9,7 +9,7 @@ This article introduces the use of the Python inference engine for the PP-OCR mo
 - [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
     - [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
     - [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
-    
+
 - [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
 
 - [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
@@ -22,10 +22,10 @@ The default configuration is based on the inference setting of the DB text detec
 
 ```
 # download DB text detection inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-# predict
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
+# run inference
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
 ```
 
 The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:
@@ -42,12 +42,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si
 
 If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 
 If you want to use the CPU for prediction, execute the command as follows
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 
 <a name="RECOGNITION_MODEL_INFERENCE"></a>
@@ -62,9 +62,10 @@ For lightweight Chinese recognition model inference, you can execute the followi
 
 ```
 # download CRNN text recognition inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
+# run inference
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 
 ![](../imgs_words_en/word_10.png)
@@ -78,10 +79,12 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
 
 ### 2. Multilingaul Model Inference
-If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
+If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
 
 ```
+wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
+
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
@@ -120,13 +123,13 @@ When performing prediction, you need to specify the path of a single image or a
 
 ```shell
 # use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 
 # not use use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 
 # use multi-process
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```
 
 

tests/compare_results.py

-import numpy as np
-import os
-import subprocess
-import json
-import argparse
-import glob
-
-
-def init_args():
-    parser = argparse.ArgumentParser()
-    # params for testing assert allclose
-    parser.add_argument("--atol", type=float, default=1e-3)
-    parser.add_argument("--rtol", type=float, default=1e-3)
-    parser.add_argument("--gt_file", type=str, default="")
-    parser.add_argument("--log_file", type=str, default="")
-    parser.add_argument("--precision", type=str, default="fp32")
-    return parser
-
-
-def parse_args():
-    parser = init_args()
-    return parser.parse_args()
-
-
-def run_shell_command(cmd):
-    p = subprocess.Popen(
-        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
-    out, err = p.communicate()
-
-    if p.returncode == 0:
-        return out.decode('utf-8')
-    else:
-        return None
-
-
-def parser_results_from_log_by_name(log_path, names_list):
-    if not os.path.exists(log_path):
-        raise ValueError("The log file {} does not exists!".format(log_path))
-
-    if names_list is None or len(names_list) < 1:
-        return []
-
-    parser_results = {}
-    for name in names_list:
-        cmd = "grep {} {}".format(name, log_path)
-        outs = run_shell_command(cmd)
-        outs = outs.split("\n")[0]
-        result = outs.split("{}".format(name))[-1]
-        result = json.loads(result)
-        parser_results[name] = result
-    return parser_results
-
-
-def load_gt_from_file(gt_file):
-    if not os.path.exists(gt_file):
-        raise ValueError("The log file {} does not exists!".format(gt_file))
-    with open(gt_file, 'r') as f:
-        data = f.readlines()
-        f.close()
-    parser_gt = {}
-    for line in data:
-        image_name, result = line.strip("\n").split("\t")
-        result = json.loads(result)
-        parser_gt[image_name] = result
-    return parser_gt
-
-
-def load_gt_from_txts(gt_file):
-    gt_list = glob.glob(gt_file)
-    gt_collection = {}
-    for gt_f in gt_list:
-        gt_dict = load_gt_from_file(gt_f)
-        basename = os.path.basename(gt_f)
-        if "fp32" in basename:
-            gt_collection["fp32"] = [gt_dict, gt_f]
-        elif "fp16" in basename:
-            gt_collection["fp16"] = [gt_dict, gt_f]
-        elif "int8" in basename:
-            gt_collection["int8"] = [gt_dict, gt_f]
-        else:
-            continue
-    return gt_collection
-
-
-def collect_predict_from_logs(log_path, key_list):
-    log_list = glob.glob(log_path)
-    pred_collection = {}
-    for log_f in log_list:
-        pred_dict = parser_results_from_log_by_name(log_f, key_list)
-        key = os.path.basename(log_f)
-        pred_collection[key] = pred_dict
-
-    return pred_collection
-
-
-def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7):
-    for k in dict_x:
-        np.testing.assert_allclose(
-            np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol)
-
-
-if __name__ == "__main__":
-    # Usage:
-    # python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt  --log_file=./tests/output/infer_*.log
-
-    args = parse_args()
-
-    gt_collection = load_gt_from_txts(args.gt_file)
-    key_list = gt_collection["fp32"][0].keys()
-
-    pred_collection = collect_predict_from_logs(args.log_file, key_list)
-    for filename in pred_collection.keys():
-        if "fp32" in filename:
-            gt_dict, gt_filename = gt_collection["fp32"]
-        elif "fp16" in filename:
-            gt_dict, gt_filename = gt_collection["fp16"]
-        elif "int8" in filename:
-            gt_dict, gt_filename = gt_collection["int8"]
-        else:
-            continue
-        pred_dict = pred_collection[filename]
-
-        try:
-            testing_assert_allclose(
-                gt_dict, pred_dict, atol=args.atol, rtol=args.rtol)
-            print(
-                "Assert allclose passed! The results of {} and {} are consistent!".
-                format(filename, gt_filename))
-        except Exception as E:
-            print(E)
-            raise ValueError(
-                "The results of {} and the results of {} are inconsistent!".
-                format(filename, gt_filename))

tests/ocr_det_params.txt

-===========================train_params===========================
-model_name:ocr_det
-python:python3.7
-gpu_list:0|0,1
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=1|whole_train_infer=300
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
-null:null
-##
-trainer:norm_train|pact_train
-norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
-pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
-fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params=========================== 
-eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o 
-fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o 
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-infer_export:null
-infer_quant:False
-inference:tools/infer/predict_det.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
-null:null
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr det
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
-

tests/ocr_det_server_params.txt

-===========================train_params===========================
-model_name:ocr_server_det
-python:python3.7
-gpu_list:0|0,1
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=2|whole_train_infer=300
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
-null:null
-##
-trainer:norm_train|pact_train
-norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=""
-pact_train:null
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params=========================== 
-eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o 
-quant_export:null 
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
-infer_export:null
-infer_quant:False
-inference:tools/infer/predict_det.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
-null:null
-

tests/ocr_rec_params.txt

-===========================train_params===========================
-model_name:ocr_rec
-python:python3.7
-gpu_list:0|2,3
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=2|whole_train_infer=300
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./train_data/ic15_data/train
-null:null
-##
-trainer:norm_train|pact_train
-norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o
-pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
-quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
-infer_export:null
-infer_quant:False
-inference:tools/infer/predict_rec.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:True|False
---precision:fp32|fp16|int8
---rec_model_dir:
---image_dir:./inference/rec_inference
---save_log_path:./test/output/
---benchmark:True
-null:null

tests/prepare.sh

-#!/bin/bash
-FILENAME=$1
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
-MODE=$2
-
-dataline=$(cat ${FILENAME})
-
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-function func_parser_key(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[0]}
-    echo ${tmp}
-}
-function func_parser_value(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[1]}
-    echo ${tmp}
-}
-IFS=$'\n'
-# The training params
-model_name=$(func_parser_value "${lines[1]}")
-
-trainer_list=$(func_parser_value "${lines[14]}")
-
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
-MODE=$2
-
-if [ ${MODE} = "lite_train_infer" ];then
-    # pretrain lite train data
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    wget -nc -P ./pretrain_models/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
-    cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
-    rm -rf ./train_data/icdar2015
-    rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
-    wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle
-    
-    cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
-    ln -s ./icdar2015_lite ./icdar2015
-    cd ../
-elif [ ${MODE} = "whole_train_infer" ];then
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    rm -rf ./train_data/icdar2015
-    rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
-    cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
-elif [ ${MODE} = "whole_infer" ];then
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    rm -rf ./train_data/icdar2015
-    rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
-    cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
-    ln -s ./icdar2015_infer ./icdar2015
-    cd ../
-elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
-    if [ ${model_name} = "ocr_det" ]; then
-        eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
-        rm -rf ./train_data/icdar2015
-        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
-    elif [ ${model_name} = "ocr_server_det" ]; then
-        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar
-        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
-        cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
-    else 
-        rm -rf ./train_data/ic15_data
-        eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
-        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ic15_data.tar && cd ../
-    fi 
-fi
-
-if [ ${MODE} = "cpp_infer" ];then
-    cd deploy/cpp_infer
-    use_opencv=$(func_parser_value "${lines[52]}")
-    if [ ${use_opencv} = "True" ]; then
-        echo "################### build opencv ###################"
-        rm -rf 3.4.7.tar.gz opencv-3.4.7/
-        wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
-        tar -xf 3.4.7.tar.gz
-
-        cd opencv-3.4.7/
-        install_path=$(pwd)/opencv-3.4.7/opencv3
-
-        rm -rf build
-        mkdir build
-        cd build
-
-        cmake .. \
-            -DCMAKE_INSTALL_PREFIX=${install_path} \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DBUILD_SHARED_LIBS=OFF \
-            -DWITH_IPP=OFF \
-            -DBUILD_IPP_IW=OFF \
-            -DWITH_LAPACK=OFF \
-            -DWITH_EIGEN=OFF \
-            -DCMAKE_INSTALL_LIBDIR=lib64 \
-            -DWITH_ZLIB=ON \
-            -DBUILD_ZLIB=ON \
-            -DWITH_JPEG=ON \
-            -DBUILD_JPEG=ON \
-            -DWITH_PNG=ON \
-            -DBUILD_PNG=ON \
-            -DWITH_TIFF=ON \
-            -DBUILD_TIFF=ON
-
-        make -j
-        make install
-        cd ../
-        echo "################### build opencv finished ###################"
-    fi
-
-
-    echo "################### build PaddleOCR demo ####################"
-    if [ ${use_opencv} = "True" ]; then
-        OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
-    else
-        OPENCV_DIR=''
-    fi
-    LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
-    CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
-    CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
-    
-    BUILD_DIR=build
-    rm -rf ${BUILD_DIR}
-    mkdir ${BUILD_DIR}
-    cd ${BUILD_DIR}
-    cmake .. \
-        -DPADDLE_LIB=${LIB_DIR} \
-        -DWITH_MKL=ON \
-        -DWITH_GPU=OFF \
-        -DWITH_STATIC_LIB=OFF \
-        -DWITH_TENSORRT=OFF \
-        -DOPENCV_DIR=${OPENCV_DIR} \
-        -DCUDNN_LIB=${CUDNN_LIB_DIR} \
-        -DCUDA_LIB=${CUDA_LIB_DIR} \
-        -DTENSORRT_DIR=${TENSORRT_DIR} \
-
-    make -j
-    echo "################### build PaddleOCR demo finished ###################"
-fi
\ No newline at end of file

tests/readme.md

-
-# 介绍
-
-test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。
-
-# 安装依赖
-- 安装PaddlePaddle >= 2.0
-- 安装PaddleOCR依赖
-    ```
-    pip3 install  -r ../requirements.txt
-    ```
-- 安装autolog
-    ```
-    git clone https://github.com/LDOUBLEV/AutoLog
-    cd AutoLog
-    pip3 install -r requirements.txt
-    python3 setup.py bdist_wheel
-    pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
-    cd ../
-    ```
-
-# 目录介绍
-
-```bash
-tests/
-├── ocr_det_params.txt   # 测试OCR检测模型的参数配置文件
-├── ocr_rec_params.txt   # 测试OCR识别模型的参数配置文件
-└── prepare.sh           # 完成test.sh运行所需要的数据和模型下载
-└── test.sh              # 根据
-```
-
-# 使用方法
-test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
-- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
-```
-bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
-```
-- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
-```
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
-```
-
-- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
-```
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
-用法1:
-bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
-用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
-```
-
-模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度
-```
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-```

tests/results/det_results_gpu_fp32.txt

-00008790.jpg	[[[209, 406], [280, 406], [280, 419], [209, 419]], [[60, 398], [105, 398], [105, 411], [60, 411]], [[198, 389], [291, 389], [291, 402], [198, 402]], [[162, 391], [173, 391], [173, 401], [162, 401]], [[35, 380], [133, 380], [133, 393], [35, 393]], [[199, 371], [292, 371], [292, 384], [199, 384]], [[218, 310], [272, 310], [272, 324], [218, 324]], [[162, 305], [172, 305], [172, 314], [162, 314]], [[371, 302], [436, 302], [436, 316], [371, 316]], [[31, 302], [134, 301], [134, 315], [31, 316]], [[223, 292], [269, 292], [269, 306], [223, 306]], [[60, 225], [104, 225], [104, 236], [60, 236]], [[218, 223], [272, 223], [272, 237], [218, 237]], [[162, 219], [173, 219], [173, 227], [162, 227]], [[33, 207], [131, 207], [131, 220], [33, 220]], [[223, 206], [269, 206], [269, 220], [223, 220]], [[74, 146], [383, 146], [383, 159], [74, 159]], [[54, 120], [117, 120], [117, 134], [54, 134]], [[74, 51], [296, 51], [296, 65], [74, 65]], [[56, 18], [116, 18], [116, 32], [56, 32]]]
-00018946.jpg	[[[441, 328], [474, 328], [474, 339], [441, 339]], [[86, 284], [141, 286], [140, 307], [85, 305]], [[302, 279], [377, 279], [377, 297], [302, 297]], [[197, 265], [281, 274], [279, 293], [195, 284]], [[198, 197], [452, 219], [450, 242], [196, 220]], [[343, 182], [376, 182], [376, 192], [343, 192]], [[199, 164], [340, 171], [339, 192], [198, 185]], [[177, 101], [415, 118], [413, 145], [175, 128]]]
-00034387.jpg	[[[265, 460], [740, 460], [740, 484], [265, 484]], [[348, 417], [420, 417], [420, 443], [348, 443]], [[545, 418], [568, 418], [568, 442], [545, 442]], [[685, 417], [710, 417], [710, 443], [685, 443]], [[175, 415], [226, 415], [226, 443], [175, 443]], [[874, 414], [908, 414], [908, 446], [874, 446]], [[56, 417], [74, 417], [74, 442], [56, 442]], [[856, 373], [925, 373], [925, 400], [856, 400]], [[348, 372], [418, 372], [418, 397], [348, 397]], [[674, 372], [723, 372], [723, 401], [674, 401]], [[539, 373], [570, 373], [570, 400], [539, 400]], [[151, 365], [228, 369], [226, 402], [149, 398]], [[56, 372], [74, 372], [74, 397], [56, 397]], [[857, 329], [925, 329], [925, 355], [857, 355]], [[351, 330], [419, 330], [419, 356], [351, 356]], [[674, 328], [723, 328], [723, 356], [674, 356]], [[541, 329], [570, 329], [570, 357], [541, 357]], [[171, 327], [227, 324], [229, 355], [173, 358]], [[57, 330], [74, 330], [74, 356], [57, 356]], [[298, 327], [316, 327], [316, 334], [298, 334]], [[855, 286], [925, 286], [925, 312], [855, 312]], [[674, 286], [723, 286], [723, 313], [674, 313]], [[346, 286], [426, 283], [427, 313], [347, 316]], [[540, 285], [569, 285], [569, 312], [540, 312]], [[172, 282], [226, 282], [226, 313], [172, 313]], [[56, 287], [73, 287], [73, 312], [56, 312]], [[857, 242], [925, 242], [925, 268], [857, 268]], [[348, 242], [460, 242], [460, 268], [348, 268]], [[156, 242], [227, 242], [227, 269], [156, 269]], [[674, 241], [724, 241], [724, 269], [674, 269]], [[531, 241], [572, 241], [572, 270], [531, 270]], [[56, 242], [74, 242], [74, 268], [56, 268]], [[855, 197], [925, 200], [924, 226], [854, 224]], [[674, 198], [726, 198], [726, 226], [674, 226]], [[344, 200], [434, 195], [436, 223], [346, 228]], [[176, 197], [227, 197], [227, 225], [176, 225]], [[56, 200], [73, 200], [73, 226], [56, 226]], [[527, 194], [576, 194], [576, 226], [527, 226]], [[349, 155], [419, 155], [419, 181], [349, 181]], [[854, 154], [925, 154], [925, 180], [854, 180]], [[176, 154], [226, 154], [226, 183], [176, 183]], [[670, 153], [723, 153], [723, 181], [670, 181]], [[538, 154], [571, 154], [571, 182], [538, 182]], [[56, 156], [74, 156], [74, 182], [56, 182]], [[349, 111], [419, 111], [419, 137], [349, 137]], [[174, 111], [227, 111], [227, 139], [174, 139]], [[546, 113], [564, 113], [564, 137], [546, 137]], [[52, 112], [75, 112], [75, 139], [52, 139]], [[639, 108], [727, 105], [728, 138], [640, 141]], [[817, 103], [927, 110], [925, 139], [815, 132]], [[814, 68], [951, 68], [951, 92], [814, 92]], [[307, 66], [446, 68], [446, 93], [306, 90]], [[673, 67], [723, 67], [723, 93], [673, 93]], [[175, 65], [228, 68], [226, 95], [174, 92]], [[39, 65], [90, 68], [88, 97], [37, 94]], [[528, 65], [580, 65], [580, 94], [528, 94]], [[334, 20], [670, 20], [670, 43], [334, 43]]]
-00037951.jpg	[[[434, 976], [551, 978], [550, 993], [434, 991]], [[433, 932], [553, 932], [553, 969], [433, 969]], [[30, 522], [98, 522], [98, 545], [30, 545]], [[31, 443], [145, 443], [145, 464], [31, 464]], [[234, 335], [326, 332], [327, 354], [235, 356]], [[124, 252], [436, 252], [436, 284], [124, 284]], [[182, 206], [378, 206], [378, 227], [182, 227]], [[258, 106], [320, 123], [304, 181], [242, 163]], [[28, 65], [33, 65], [33, 71], [28, 71]], [[37, 58], [147, 58], [147, 80], [37, 80]]]
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-test_add_4.png	[]
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tests/results/det_results_gpu_trt_fp16.txt

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253], [142, 249]], [[494, 226], [531, 226], [531, 239], [494, 239]], [[335, 226], [354, 226], [354, 237], [335, 237]], [[288, 226], [314, 226], [314, 237], [288, 237]], [[63, 226], [113, 226], [113, 236], [63, 236]], [[7, 227], [17, 227], [17, 234], [7, 234]], [[221, 225], [248, 225], [248, 235], [221, 235]], [[143, 225], [165, 222], [167, 234], [145, 237]], [[24, 224], [48, 224], [48, 238], [24, 238]], [[495, 213], [524, 213], [524, 224], [495, 224]], [[420, 212], [437, 212], [437, 225], [420, 225]], [[336, 212], [398, 212], [398, 223], [336, 223]], [[292, 212], [320, 212], [320, 223], [292, 223]], [[222, 212], [249, 212], [249, 223], [222, 223]], [[145, 212], [166, 212], [166, 223], [145, 223]], [[61, 211], [113, 209], [114, 222], [62, 224]], [[26, 211], [48, 211], [48, 223], [26, 223]], [[337, 199], [383, 199], [383, 209], [337, 209]], [[65, 200], [87, 200], [87, 207], [65, 207]], [[493, 197], [541, 197], [541, 211], [493, 211]], [[445, 202], [455, 196], [462, 206], [452, 212]], [[178, 198], [205, 198], [205, 208], [178, 208]], [[146, 199], [157, 199], [157, 208], [146, 208]], [[32, 194], [43, 204], [33, 214], [22, 203]], [[422, 193], [440, 201], [432, 215], [415, 207]], [[65, 186], [132, 186], [132, 196], [65, 196]], [[337, 185], [399, 185], [399, 196], [337, 196]], [[445, 190], [456, 182], [465, 191], [454, 200]], [[292, 188], [308, 182], [313, 193], [297, 200]], [[220, 183], [255, 183], [255, 197], [220, 197]], [[142, 184], [158, 184], [158, 197], [142, 197]], [[493, 182], [518, 182], [518, 197], [493, 197]], [[425, 180], [437, 191], [427, 202], [414, 190]], [[32, 179], [42, 189], [32, 199], [22, 189]], [[182, 179], [195, 185], [188, 198], [175, 192]], [[335, 172], [400, 169], [400, 183], [336, 185]], [[492, 170], [519, 170], [519, 185], [492, 185]], [[412, 177], [428, 164], [440, 178], [425, 190]], [[293, 171], [315, 171], [315, 185], [293, 185]], [[220, 170], [251, 170], [251, 184], [220, 184]], [[178, 172], [188, 172], [188, 183], [178, 183]], [[64, 172], [125, 170], [125, 181], [64, 182]], [[454, 168], [464, 176], [454, 185], [445, 176]], [[142, 172], [159, 168], [163, 180], [145, 185]], [[30, 165], [43, 174], [34, 186], [20, 177]], [[493, 160], [523, 160], [523, 170], [493, 170]], [[402, 161], [435, 161], [435, 168], [402, 168]], [[335, 159], [401, 159], [401, 169], [335, 169]], [[296, 159], [325, 159], [325, 170], [296, 170]], [[221, 158], [251, 158], [251, 169], [221, 169]], [[174, 161], [183, 156], [190, 167], [181, 172]], [[145, 158], [162, 158], [162, 170], [145, 170]], [[61, 158], [125, 157], [125, 168], [62, 169]], [[20, 161], [33, 154], [40, 167], [28, 174]], [[492, 143], [542, 143], [542, 157], [492, 157]], [[450, 144], [479, 144], [479, 157], [450, 157]], [[335, 143], [439, 143], [439, 156], [335, 156]], [[294, 143], [327, 143], [327, 157], [294, 157]], [[220, 143], [253, 143], [253, 157], [220, 157]], [[178, 145], [187, 145], [187, 156], [178, 156]], [[63, 144], [104, 144], [104, 155], [63, 155]], [[144, 140], [164, 145], [160, 159], [141, 154]], [[31, 137], [44, 149], [31, 162], [17, 149]], [[286, 135], [291, 135], [291, 140], [286, 140]], [[177, 133], [193, 133], [193, 144], [177, 144]], [[336, 132], [388, 132], [388, 141], [336, 141]], [[492, 131], [525, 131], [525, 141], [492, 141]], [[450, 131], [477, 131], [477, 141], [450, 141]], [[292, 131], [321, 131], [321, 141], [292, 141]], [[218, 132], [255, 130], [256, 141], [219, 144]], [[63, 131], [95, 131], [95, 141], [63, 141]], [[417, 130], [437, 130], [437, 141], [417, 141]], [[145, 130], [159, 130], [159, 143], [145, 143]], [[30, 124], [43, 133], [32, 147], [19, 138]], [[493, 118], [535, 118], [535, 129], [493, 129]], [[336, 118], [388, 118], [388, 129], [336, 129]], [[218, 118], [255, 118], [255, 128], [218, 128]], [[451, 117], [478, 117], [478, 129], [451, 129]], [[418, 117], [438, 117], [438, 130], [418, 130]], [[177, 116], [209, 116], [209, 130], [177, 130]], [[145, 117], [162, 117], [162, 130], [145, 130]], [[62, 116], [88, 116], [88, 131], [62, 131]], [[19, 121], [33, 111], [43, 124], [29, 134]], [[491, 107], [523, 107], [523, 113], [491, 113]], [[449, 107], [477, 107], [477, 113], [449, 113]], [[420, 107], [436, 107], [436, 113], [420, 113]], [[295, 107], [319, 107], [319, 114], [295, 114]], [[220, 107], [242, 107], [242, 113], [220, 113]], [[176, 107], [203, 107], [203, 113], [176, 113]], [[145, 107], [161, 107], [161, 114], [145, 114]], [[334, 105], [372, 105], [372, 114], [334, 114]], [[63, 106], [86, 106], [86, 113], [63, 113]], [[483, 89], [522, 89], [522, 99], [483, 99]], [[331, 88], [380, 88], [380, 99], [331, 99]], [[276, 88], [325, 88], [325, 99], [276, 99]], [[214, 88], [246, 88], [246, 99], [214, 99]], [[411, 86], [474, 86], [474, 100], [411, 100]], [[6, 86], [102, 86], [102, 100], [6, 100]], [[415, 66], [461, 66], [461, 77], [415, 77]], [[288, 66], [333, 66], [333, 77], [288, 77]], [[157, 64], [206, 64], [206, 78], [157, 78]], [[416, 48], [523, 49], [523, 63], [415, 62]], [[288, 49], [375, 49], [375, 63], [288, 63]], [[159, 49], [269, 49], [269, 62], [159, 62]], [[24, 53], [36, 46], [45, 59], [33, 67]], [[416, 36], [481, 36], [481, 46], [416, 46]], [[25, 38], [39, 32], [46, 46], [33, 52]], [[157, 34], [205, 34], [205, 47], [157, 47]], [[412, 4], [527, 4], [527, 17], [412, 17]], [[146, 4], [345, 2], [345, 15], [146, 17]]]
-test_add_20.jpg	[[[31, 346], [605, 346], [605, 370], [31, 370]], [[217, 294], [510, 294], [510, 322], [217, 322]], [[473, 271], [525, 271], [525, 286], [473, 286]], [[220, 267], [287, 267], [287, 286], [220, 286]], [[219, 239], [484, 239], [484, 263], [219, 263]], [[221, 217], [303, 217], [303, 234], [221, 234]], [[402, 192], [417, 192], [417, 205], [402, 205]], [[222, 187], [341, 187], [341, 207], [222, 207]], [[221, 162], [287, 162], [287, 180], [221, 180]], [[375, 122], [475, 124], [475, 146], [375, 143]], [[222, 124], [356, 122], [356, 143], [222, 146]], [[218, 81], [352, 84], [352, 116], [218, 113]], [[440, 35], [605, 35], [605, 60], [440, 60]], [[72, 16], [398, 16], [398, 44], [72, 44]]]
-test_add_3.jpg	[[[169, 327], [337, 326], [337, 341], [169, 342]], [[170, 288], [307, 290], [307, 312], [170, 310]], [[171, 221], [323, 221], [323, 234], [171, 234]], [[340, 221], [449, 217], [449, 231], [341, 234]], [[169, 201], [372, 201], [372, 214], [169, 214]], [[170, 183], [418, 183], [418, 196], [170, 196]], [[170, 149], [416, 149], [416, 163], [170, 163]], [[171, 119], [418, 119], [418, 140], [171, 140]], [[326, 64], [478, 64], [478, 91], [326, 91]], [[173, 64], [306, 60], [306, 89], [174, 93]]]
-test_add_4.png	[]
-test_add_5.png	[[[48, 164], [108, 164], [108, 174], [48, 174]], [[52, 121], [169, 121], [169, 134], [52, 134]], [[50, 102], [165, 102], [165, 118], [50, 118]], [[52, 83], [164, 83], [164, 100], [52, 100]], [[51, 68], [166, 68], [166, 84], [51, 84]], [[51, 50], [145, 47], [145, 64], [52, 67]]]
-test_add_6.jpg	[[[123, 223], [219, 227], [218, 251], [122, 247]], [[172, 172], [186, 186], [172, 200], [158, 186]]]
-test_add_7.jpg	[[[48, 938], [174, 936], [174, 962], [48, 964]], [[227, 873], [629, 876], [628, 953], [226, 949]], [[56, 745], [638, 745], [638, 790], [56, 790]], [[150, 674], [545, 678], [544, 721], [150, 718]], [[73, 504], [633, 504], [633, 601], [73, 601]], [[59, 270], [655, 279], [652, 441], [56, 432]], [[513, 193], [553, 193], [553, 223], [513, 223]], [[61, 175], [532, 175], [532, 239], [61, 239]], [[533, 178], [642, 178], [642, 236], [533, 236]]]
-test_add_8.jpg	[[[251, 586], [454, 580], [454, 606], [252, 613]], [[107, 533], [457, 527], [457, 560], [108, 566]], [[336, 494], [384, 494], [384, 507], [336, 507]], [[27, 307], [355, 297], [356, 320], [28, 330]], [[22, 259], [445, 251], [445, 274], [23, 282]], [[78, 209], [445, 205], [445, 225], [78, 229]], [[160, 23], [319, 30], [317, 79], [158, 72]]]
-test_add_9.png	[[[266, 687], [486, 687], [486, 696], [266, 696]], [[196, 668], [554, 668], [554, 681], [196, 681]], [[154, 596], [597, 596], [597, 606], [154, 606]], [[215, 578], [541, 578], [541, 588], [215, 588]], [[134, 560], [615, 560], [615, 570], [134, 570]], [[85, 543], [665, 543], [665, 553], [85, 553]], [[96, 522], [653, 522], [653, 535], [96, 535]], [[362, 449], [389, 449], [389, 460], [362, 460]], [[238, 376], [513, 376], [513, 389], [238, 389]], [[177, 356], [574, 356], [574, 368], [177, 368]], [[344, 281], [408, 283], [407, 297], [343, 294]], [[257, 205], [493, 205], [493, 219], [257, 219]]]

tests/test.sh

-#!/bin/bash
-FILENAME=$1
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
-MODE=$2
-
-dataline=$(cat ${FILENAME})
-
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-
-function func_parser_key(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[0]}
-    echo ${tmp}
-}
-function func_parser_value(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[1]}
-    echo ${tmp}
-}
-function func_set_params(){
-    key=$1
-    value=$2
-    if [ ${key} = "null" ];then
-        echo " "
-    elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
-        echo " "
-    else 
-        echo "${key}=${value}"
-    fi
-}
-function func_parser_params(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    key=${array[0]}
-    tmp=${array[1]}
-    IFS="|"
-    res=""
-    for _params in ${tmp[*]}; do
-        IFS="="
-        array=(${_params})
-        mode=${array[0]}
-        value=${array[1]}
-        if [[ ${mode} = ${MODE} ]]; then
-            IFS="|"
-            #echo $(func_set_params "${mode}" "${value}")
-            echo $value
-            break
-        fi
-        IFS="|"
-    done
-    echo ${res}
-}
-function status_check(){
-    last_status=$1   # the exit code
-    run_command=$2
-    run_log=$3
-    if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    else
-        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    fi
-}
-
-IFS=$'\n'
-# The training params
-model_name=$(func_parser_value "${lines[1]}")
-python=$(func_parser_value "${lines[2]}")
-gpu_list=$(func_parser_value "${lines[3]}")
-train_use_gpu_key=$(func_parser_key "${lines[4]}")
-train_use_gpu_value=$(func_parser_value "${lines[4]}")
-autocast_list=$(func_parser_value "${lines[5]}")
-autocast_key=$(func_parser_key "${lines[5]}")
-epoch_key=$(func_parser_key "${lines[6]}")
-epoch_num=$(func_parser_params "${lines[6]}")
-save_model_key=$(func_parser_key "${lines[7]}")
-train_batch_key=$(func_parser_key "${lines[8]}")
-train_batch_value=$(func_parser_params "${lines[8]}")
-pretrain_model_key=$(func_parser_key "${lines[9]}")
-pretrain_model_value=$(func_parser_value "${lines[9]}")
-train_model_name=$(func_parser_value "${lines[10]}")
-train_infer_img_dir=$(func_parser_value "${lines[11]}")
-train_param_key1=$(func_parser_key "${lines[12]}")
-train_param_value1=$(func_parser_value "${lines[12]}")
-
-trainer_list=$(func_parser_value "${lines[14]}")
-trainer_norm=$(func_parser_key "${lines[15]}")
-norm_trainer=$(func_parser_value "${lines[15]}")
-pact_key=$(func_parser_key "${lines[16]}")
-pact_trainer=$(func_parser_value "${lines[16]}")
-fpgm_key=$(func_parser_key "${lines[17]}")
-fpgm_trainer=$(func_parser_value "${lines[17]}")
-distill_key=$(func_parser_key "${lines[18]}")
-distill_trainer=$(func_parser_value "${lines[18]}")
-trainer_key1=$(func_parser_key "${lines[19]}")
-trainer_value1=$(func_parser_value "${lines[19]}")
-trainer_key2=$(func_parser_key "${lines[20]}")
-trainer_value2=$(func_parser_value "${lines[20]}")
-
-eval_py=$(func_parser_value "${lines[23]}")
-eval_key1=$(func_parser_key "${lines[24]}")
-eval_value1=$(func_parser_value "${lines[24]}")
-
-save_infer_key=$(func_parser_key "${lines[27]}")
-export_weight=$(func_parser_key "${lines[28]}")
-norm_export=$(func_parser_value "${lines[29]}")
-pact_export=$(func_parser_value "${lines[30]}")
-fpgm_export=$(func_parser_value "${lines[31]}")
-distill_export=$(func_parser_value "${lines[32]}")
-export_key1=$(func_parser_key "${lines[33]}")
-export_value1=$(func_parser_value "${lines[33]}")
-export_key2=$(func_parser_key "${lines[34]}")
-export_value2=$(func_parser_value "${lines[34]}")
-
-# parser inference model 
-infer_model_dir_list=$(func_parser_value "${lines[36]}")
-infer_export_list=$(func_parser_value "${lines[37]}")
-infer_is_quant=$(func_parser_value "${lines[38]}")
-# parser inference 
-inference_py=$(func_parser_value "${lines[39]}")
-use_gpu_key=$(func_parser_key "${lines[40]}")
-use_gpu_list=$(func_parser_value "${lines[40]}")
-use_mkldnn_key=$(func_parser_key "${lines[41]}")
-use_mkldnn_list=$(func_parser_value "${lines[41]}")
-cpu_threads_key=$(func_parser_key "${lines[42]}")
-cpu_threads_list=$(func_parser_value "${lines[42]}")
-batch_size_key=$(func_parser_key "${lines[43]}")
-batch_size_list=$(func_parser_value "${lines[43]}")
-use_trt_key=$(func_parser_key "${lines[44]}")
-use_trt_list=$(func_parser_value "${lines[44]}")
-precision_key=$(func_parser_key "${lines[45]}")
-precision_list=$(func_parser_value "${lines[45]}")
-infer_model_key=$(func_parser_key "${lines[46]}")
-image_dir_key=$(func_parser_key "${lines[47]}")
-infer_img_dir=$(func_parser_value "${lines[47]}")
-save_log_key=$(func_parser_key "${lines[48]}")
-benchmark_key=$(func_parser_key "${lines[49]}")
-benchmark_value=$(func_parser_value "${lines[49]}")
-infer_key1=$(func_parser_key "${lines[50]}")
-infer_value1=$(func_parser_value "${lines[50]}")
-
-if [ ${MODE} = "cpp_infer" ]; then
-    # parser cpp inference model 
-    cpp_infer_model_dir_list=$(func_parser_value "${lines[53]}")
-    cpp_infer_is_quant=$(func_parser_value "${lines[54]}")
-    # parser cpp inference 
-    inference_cmd=$(func_parser_value "${lines[55]}")
-    cpp_use_gpu_key=$(func_parser_key "${lines[56]}")
-    cpp_use_gpu_list=$(func_parser_value "${lines[56]}")
-    cpp_use_mkldnn_key=$(func_parser_key "${lines[57]}")
-    cpp_use_mkldnn_list=$(func_parser_value "${lines[57]}")
-    cpp_cpu_threads_key=$(func_parser_key "${lines[58]}")
-    cpp_cpu_threads_list=$(func_parser_value "${lines[58]}")
-    cpp_batch_size_key=$(func_parser_key "${lines[59]}")
-    cpp_batch_size_list=$(func_parser_value "${lines[59]}")
-    cpp_use_trt_key=$(func_parser_key "${lines[60]}")
-    cpp_use_trt_list=$(func_parser_value "${lines[60]}")
-    cpp_precision_key=$(func_parser_key "${lines[61]}")
-    cpp_precision_list=$(func_parser_value "${lines[61]}")
-    cpp_infer_model_key=$(func_parser_key "${lines[62]}")
-    cpp_image_dir_key=$(func_parser_key "${lines[63]}")
-    cpp_infer_img_dir=$(func_parser_value "${lines[63]}")
-    cpp_save_log_key=$(func_parser_key "${lines[64]}")
-    cpp_benchmark_key=$(func_parser_key "${lines[65]}")
-    cpp_benchmark_value=$(func_parser_value "${lines[65]}")
-fi
-
-
-LOG_PATH="./tests/output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
-
-
-function func_inference(){
-    IFS='|'
-    _python=$1
-    _script=$2
-    _model_dir=$3
-    _log_path=$4
-    _img_dir=$5
-    _flag_quant=$6
-    # inference 
-    for use_gpu in ${use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
-            for use_mkldnn in ${use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                    continue
-                fi
-                for threads in ${cpu_threads_list[*]}; do
-                    for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
-            for use_trt in ${use_trt_list[*]}; do
-                for precision in ${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
-                    for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
-                        set_precision=$(func_set_params "${precision_key}" "${precision}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                        
-                    done
-                done
-            done
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
-
-function func_cpp_inference(){
-    IFS='|'
-    _script=$1
-    _model_dir=$2
-    _log_path=$3
-    _img_dir=$4
-    _flag_quant=$5
-    # inference 
-    for use_gpu in ${cpp_use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
-            for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                    continue
-                fi
-                for threads in ${cpp_cpu_threads_list[*]}; do
-                    for batch_size in ${cpp_batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
-                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
-                        set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
-                        set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
-                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
-            for use_trt in ${cpp_use_trt_list[*]}; do
-                for precision in ${cpp_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
-                    for batch_size in ${cpp_batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
-                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
-                        set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
-                        set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
-                        set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
-                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                        
-                    done
-                done
-            done
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
-
-if [ ${MODE} = "infer" ]; then
-    GPUID=$3
-    if [ ${#GPUID} -le 0 ];then
-        env=" "
-    else
-        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-    fi
-    # set CUDA_VISIBLE_DEVICES
-    eval $env
-    export Count=0
-    IFS="|"
-    infer_run_exports=(${infer_export_list})
-    infer_quant_flag=(${infer_is_quant})
-    for infer_model in ${infer_model_dir_list[*]}; do
-        # run export
-        if [ ${infer_run_exports[Count]} != "null" ];then
-            save_infer_dir=$(dirname $infer_model)
-            set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
-            set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
-            export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
-            eval $export_cmd
-            status_export=$?
-            if [ ${status_export} = 0 ];then
-                status_check $status_export "${export_cmd}" "${status_log}"
-            fi
-        else
-            save_infer_dir=${infer_model}
-        fi
-        #run inference
-        is_quant=${infer_quant_flag[Count]}
-        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
-        Count=$(($Count + 1))
-    done
-
-elif [ ${MODE} = "cpp_infer" ]; then
-    GPUID=$3
-    if [ ${#GPUID} -le 0 ];then
-        env=" "
-    else
-        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-    fi
-    # set CUDA_VISIBLE_DEVICES
-    eval $env
-    export Count=0
-    IFS="|"
-    infer_quant_flag=(${cpp_infer_is_quant})
-    for infer_model in ${cpp_infer_model_dir_list[*]}; do
-        #run inference
-        is_quant=${infer_quant_flag[Count]}
-        func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
-        Count=$(($Count + 1))
-    done
-
-else
-    IFS="|"
-    export Count=0
-    USE_GPU_KEY=(${train_use_gpu_value})
-    for gpu in ${gpu_list[*]}; do
-        use_gpu=${USE_GPU_KEY[Count]}
-        Count=$(($Count + 1))
-        if [ ${gpu} = "-1" ];then
-            env=""
-        elif [ ${#gpu} -le 1 ];then
-            env="export CUDA_VISIBLE_DEVICES=${gpu}"
-            eval ${env}
-        elif [ ${#gpu} -le 15 ];then
-            IFS=","
-            array=(${gpu})
-            env="export CUDA_VISIBLE_DEVICES=${array[0]}"
-            IFS="|"
-        else
-            IFS=";"
-            array=(${gpu})
-            ips=${array[0]}
-            gpu=${array[1]}
-            IFS="|"
-            env=" "
-        fi
-        for autocast in ${autocast_list[*]}; do 
-            for trainer in ${trainer_list[*]}; do 
-                flag_quant=False
-                if [ ${trainer} = ${pact_key} ]; then
-                    run_train=${pact_trainer}
-                    run_export=${pact_export}
-                    flag_quant=True
-                elif [ ${trainer} = "${fpgm_key}" ]; then
-                    run_train=${fpgm_trainer}
-                    run_export=${fpgm_export}
-                elif [ ${trainer} = "${distill_key}" ]; then
-                    run_train=${distill_trainer}
-                    run_export=${distill_export}
-                elif [ ${trainer} = ${trainer_key1} ]; then
-                    run_train=${trainer_value1}
-                    run_export=${export_value1}
-                elif [[ ${trainer} = ${trainer_key2} ]]; then
-                    run_train=${trainer_value2}
-                    run_export=${export_value2}
-                else
-                    run_train=${norm_trainer}
-                    run_export=${norm_export}
-                fi
-
-                if [ ${run_train} = "null" ]; then
-                    continue
-                fi
-                
-                set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
-                set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
-                set_pretrain=$(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}"
-                
-                # load pretrain from norm training if current trainer is pact or fpgm trainer
-                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
-                    set_pretrain="${load_norm_train_model}"
-                fi
-
-                set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
-                if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
-                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
-                elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
-                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
-                else     # train with multi-machine
-                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
-                fi
-                # run train
-                eval "unset CUDA_VISIBLE_DEVICES"
-                eval $cmd
-                status_check $? "${cmd}" "${status_log}"
-
-                set_eval_pretrain=$(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} = ${trainer_norm} ]; then
-                    load_norm_train_model=${set_eval_pretrain}
-                fi
-                # run eval 
-                if [ ${eval_py} != "null" ]; then
-                    set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
-                    eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" 
-                    eval $eval_cmd
-                    status_check $? "${eval_cmd}" "${status_log}"
-                fi
-                # run export model
-                if [ ${run_export} != "null" ]; then 
-                    # run export model
-                    save_infer_path="${save_log}"
-                    set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
-                    set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
-                    export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
-                    eval $export_cmd
-                    status_check $? "${export_cmd}" "${status_log}"
-
-                    #run inference
-                    eval $env
-                    save_infer_path="${save_log}"
-                    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
-                    eval "unset CUDA_VISIBLE_DEVICES"
-                fi
-            done  # done with:    for trainer in ${trainer_list[*]}; do 
-        done      # done with:    for autocast in ${autocast_list[*]}; do 
-    done          # done with:    for gpu in ${gpu_list[*]}; do
-fi  # end if [ ${MODE} = "infer" ]; then

tools/infer/utility.py

@@ -236,11 +236,11 @@ def create_predictor(args, mode, logger):
             max_input_shape.update(max_pact_shape)
             opt_input_shape.update(opt_pact_shape)
         elif mode == "rec":
-            min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
+            min_input_shape = {"x": [1, 3, 32, 10]}
             max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
             opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
         elif mode == "cls":
-            min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
+            min_input_shape = {"x": [1, 3, 48, 10]}
             max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
             opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
         else: