Merge remote-tracking branch 'origin/dygraph' into dy1

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

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+test_add_4.png	[]
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tests/results/det_results_gpu_trt_fp16.txt

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+test_add_18.jpg	[[[466, 788], [715, 790], [715, 813], [466, 811]], [[553, 752], [665, 757], [663, 791], [552, 786]], [[119, 539], [189, 539], [189, 570], [119, 570]], [[116, 473], [674, 486], [673, 528], [115, 516]], [[121, 429], [669, 441], [668, 470], [121, 457]], [[121, 375], [674, 381], [674, 410], [121, 404]], [[556, 262], [675, 264], [675, 278], [556, 277]], [[164, 259], [334, 259], [334, 273], [164, 273]], [[344, 195], [456, 197], [455, 220], [343, 217]], [[309, 175], [490, 175], [490, 190], [309, 190]], [[255, 128], [537, 131], [537, 169], [254, 165]], [[347, 92], [486, 94], [486, 109], [346, 107]], [[285, 41], [567, 49], [566, 82], [284, 74]], [[236, 32], [266, 32], [266, 60], [236, 60]]]
+test_add_19.jpg	[[[24, 294], [42, 294], [42, 302], [24, 302]], [[64, 293], [105, 293], [105, 303], [64, 303]], [[145, 287], [163, 287], [163, 304], [145, 304]], [[63, 280], [106, 280], [106, 290], [63, 290]], [[9, 281], [26, 281], [26, 288], [9, 288]], [[220, 279], [245, 279], [245, 291], [220, 291]], [[177, 279], [208, 279], [208, 290], [177, 290]], [[23, 279], [51, 279], [51, 290], [23, 290]], [[145, 278], [162, 278], [162, 292], [145, 292]], [[8, 267], [18, 267], [18, 276], [8, 276]], [[221, 265], [243, 265], [243, 277], [221, 277]], [[24, 265], [47, 265], [47, 277], [24, 277]], [[142, 263], [163, 263], [163, 279], [142, 279]], [[218, 252], [249, 252], [249, 265], [218, 265]], [[65, 253], [131, 253], [131, 263], [65, 263]], [[24, 252], [43, 252], [43, 264], [24, 264]], [[8, 253], [18, 253], [18, 262], [8, 262]], [[8, 240], [17, 240], [17, 249], [8, 249]], [[63, 237], [114, 237], [114, 251], [63, 251]], [[25, 236], [47, 239], [45, 251], [23, 249]], [[144, 234], [166, 237], [163, 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/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_pretrained_params
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -44,12 +44,23 @@ def main():
     # build model
     # for rec algorithm
     if hasattr(post_process_class, 'character'):
-        config['Architecture']["Head"]['out_channels'] = len(
-            getattr(post_process_class, 'character'))
+        char_num = len(getattr(post_process_class, 'character'))
+        if config['Architecture']["algorithm"] in ["Distillation",
+                                                   ]:  # distillation model
+            for key in config['Architecture']["Models"]:
+                config['Architecture']["Models"][key]["Head"][
+                    'out_channels'] = char_num
+        else:  # base rec model
+            config['Architecture']["Head"]['out_channels'] = char_num
+
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
+    if "model_type" in config['Architecture'].keys():
+        model_type = config['Architecture']['model_type']
+    else:
+        model_type = None
 
-    best_model_dict = init_model(config, model, logger)
+    best_model_dict = init_model(config, model)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():
@@ -59,10 +70,10 @@ def main():
     eval_class = build_metric(config['Metric'])
 
     # start eval
-    metirc = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class, use_srn)
+    metric = program.eval(model, valid_dataloader, post_process_class,
+                        eval_class, model_type, use_srn)
     logger.info('metric eval ***************')
-    for k, v in metirc.items():
+    for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))
 
 

tools/export_model.py

@@ -17,7 +17,7 @@ import sys
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+sys.path.append(os.path.abspath(os.path.join(__dir__, "..")))
 
 import argparse
 
@@ -31,70 +31,90 @@ from ppocr.utils.logging import get_logger
 from tools.program import load_config, merge_config, ArgsParser
 
 
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-c", "--config", help="configuration file to use")
-    parser.add_argument(
-        "-o", "--output_path", type=str, default='./output/infer/')
-    return parser.parse_args()
-
-
-def main():
-    FLAGS = ArgsParser().parse_args()
-    config = load_config(FLAGS.config)
-    merge_config(FLAGS.opt)
-    logger = get_logger()
-    # build post process
-
-    post_process_class = build_post_process(config['PostProcess'],
-                                            config['Global'])
-
-    # build model
-    # for rec algorithm
-    if hasattr(post_process_class, 'character'):
-        char_num = len(getattr(post_process_class, 'character'))
-        config['Architecture']["Head"]['out_channels'] = char_num
-    model = build_model(config['Architecture'])
-    init_model(config, model, logger)
-    model.eval()
-
-    save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
-
-    if config['Architecture']['algorithm'] == "SRN":
+def export_single_model(model, arch_config, save_path, logger):
+    if arch_config["algorithm"] == "SRN":
+        max_text_length = arch_config["Head"]["max_text_length"]
         other_shape = [
             paddle.static.InputSpec(
-                shape=[None, 1, 64, 256], dtype='float32'), [
+                shape=[None, 1, 64, 256], dtype="float32"), [
                     paddle.static.InputSpec(
                         shape=[None, 256, 1],
                         dtype="int64"), paddle.static.InputSpec(
-                            shape=[None, 25, 1],
-                            dtype="int64"), paddle.static.InputSpec(
-                                shape=[None, 8, 25, 25], dtype="int64"),
+                            shape=[None, max_text_length, 1], dtype="int64"),
                     paddle.static.InputSpec(
-                        shape=[None, 8, 25, 25], dtype="int64")
+                        shape=[None, 8, max_text_length, max_text_length],
+                        dtype="int64"), paddle.static.InputSpec(
+                            shape=[None, 8, max_text_length, max_text_length],
+                            dtype="int64")
                 ]
         ]
         model = to_static(model, input_spec=other_shape)
     else:
         infer_shape = [3, -1, -1]
-        if config['Architecture']['model_type'] == "rec":
+        if arch_config["model_type"] == "rec":
             infer_shape = [3, 32, -1]  # for rec model, H must be 32
-            if 'Transform' in config['Architecture'] and config['Architecture'][
-                    'Transform'] is not None and config['Architecture'][
-                        'Transform']['name'] == 'TPS':
+            if "Transform" in arch_config and arch_config[
+                    "Transform"] is not None and arch_config["Transform"][
+                        "name"] == "TPS":
                 logger.info(
-                    'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
+                    "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training"
                 )
                 infer_shape[-1] = 100
+        elif arch_config["model_type"] == "table":
+            infer_shape = [3, 488, 488]
         model = to_static(
             model,
             input_spec=[
                 paddle.static.InputSpec(
-                    shape=[None] + infer_shape, dtype='float32')
+                    shape=[None] + infer_shape, dtype="float32")
             ])
 
     paddle.jit.save(model, save_path)
-    logger.info('inference model is saved to {}'.format(save_path))
+    logger.info("inference model is saved to {}".format(save_path))
+    return
+
+
+def main():
+    FLAGS = ArgsParser().parse_args()
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    logger = get_logger()
+    # build post process
+
+    post_process_class = build_post_process(config["PostProcess"],
+                                            config["Global"])
+
+    # build model
+    # for rec algorithm
+    if hasattr(post_process_class, "character"):
+        char_num = len(getattr(post_process_class, "character"))
+        if config["Architecture"]["algorithm"] in ["Distillation",
+                                                   ]:  # distillation model
+            for key in config["Architecture"]["Models"]:
+                config["Architecture"]["Models"][key]["Head"][
+                    "out_channels"] = char_num
+                # just one final tensor needs to to exported for inference
+                config["Architecture"]["Models"][key][
+                    "return_all_feats"] = False
+        else:  # base rec model
+            config["Architecture"]["Head"]["out_channels"] = char_num
+    model = build_model(config["Architecture"])
+    init_model(config, model)
+    model.eval()
+
+    save_path = config["Global"]["save_inference_dir"]
+
+    arch_config = config["Architecture"]
+
+    if arch_config["algorithm"] in ["Distillation", ]:  # distillation model
+        archs = list(arch_config["Models"].values())
+        for idx, name in enumerate(model.model_name_list):
+            sub_model_save_path = os.path.join(save_path, name, "inference")
+            export_single_model(model.model_list[idx], archs[idx],
+                                sub_model_save_path, logger)
+    else:
+        save_path = os.path.join(save_path, "inference")
+        export_single_model(model, arch_config, save_path, logger)
 
 
 if __name__ == "__main__":

tools/infer/predict_cls.py

@@ -45,7 +45,7 @@ class TextClassifier(object):
             "label_list": args.label_list,
         }
         self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors = \
+        self.predictor, self.input_tensor, self.output_tensors, _ = \
             utility.create_predictor(args, 'cls', logger)
 
     def resize_norm_img(self, img):
@@ -84,9 +84,11 @@ class TextClassifier(object):
         batch_num = self.cls_batch_num
         elapse = 0
         for beg_img_no in range(0, img_num, batch_num):
+
             end_img_no = min(img_num, beg_img_no + batch_num)
             norm_img_batch = []
             max_wh_ratio = 0
+            starttime = time.time()
             for ino in range(beg_img_no, end_img_no):
                 h, w = img_list[indices[ino]].shape[0:2]
                 wh_ratio = w * 1.0 / h
@@ -97,7 +99,7 @@ class TextClassifier(object):
                 norm_img_batch.append(norm_img)
             norm_img_batch = np.concatenate(norm_img_batch)
             norm_img_batch = norm_img_batch.copy()
-            starttime = time.time()
+
             self.input_tensor.copy_from_cpu(norm_img_batch)
             self.predictor.run()
             prob_out = self.output_tensors[0].copy_to_cpu()
@@ -141,8 +143,8 @@ def main(args):
     for ino in range(len(img_list)):
         logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
                                                cls_res[ino]))
-    logger.info("Total predict time for {} images, cost: {:.3f}".format(
-        len(img_list), predict_time))
+    logger.info(
+        "The predict time about text angle classify module is as follows: ")
 
 
 if __name__ == "__main__":

tools/infer/predict_det.py

@@ -30,7 +30,7 @@ from ppocr.utils.logging import get_logger
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.data import create_operators, transform
 from ppocr.postprocess import build_post_process
-
+import json
 logger = get_logger()
 
 
@@ -39,7 +39,10 @@ class TextDetector(object):
         self.args = args
         self.det_algorithm = args.det_algorithm
         pre_process_list = [{
-            'DetResizeForTest': None
+            'DetResizeForTest': {
+                'limit_side_len': args.det_limit_side_len,
+                'limit_type': args.det_limit_type,
+            }
         }, {
             'NormalizeImage': {
                 'std': [0.229, 0.224, 0.225],
@@ -62,6 +65,7 @@ class TextDetector(object):
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             postprocess_params["use_dilation"] = args.use_dilation
+            postprocess_params["score_mode"] = args.det_db_score_mode
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh
@@ -91,9 +95,28 @@ class TextDetector(object):
 
         self.preprocess_op = create_operators(pre_process_list)
         self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
-            args, 'det', logger)  # paddle.jit.load(args.det_model_dir)
-        # self.predictor.eval()
+        self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
+            args, 'det', logger)
+
+        if args.benchmark:
+            import auto_log
+            pid = os.getpid()
+            gpu_id = utility.get_infer_gpuid()
+            self.autolog = auto_log.AutoLogger(
+                model_name="det",
+                model_precision=args.precision,
+                batch_size=1,
+                data_shape="dynamic",
+                save_path=None,
+                inference_config=self.config,
+                pids=pid,
+                process_name=None,
+                gpu_ids=gpu_id if args.use_gpu else None,
+                time_keys=[
+                    'preprocess_time', 'inference_time', 'postprocess_time'
+                ],
+                warmup=2,
+                logger=logger)
 
     def order_points_clockwise(self, pts):
         """
@@ -151,6 +174,12 @@ class TextDetector(object):
     def __call__(self, img):
         ori_im = img.copy()
         data = {'image': img}
+
+        st = time.time()
+
+        if self.args.benchmark:
+            self.autolog.times.start()
+
         data = transform(data, self.preprocess_op)
         img, shape_list = data
         if img is None:
@@ -158,7 +187,9 @@ class TextDetector(object):
         img = np.expand_dims(img, axis=0)
         shape_list = np.expand_dims(shape_list, axis=0)
         img = img.copy()
-        starttime = time.time()
+
+        if self.args.benchmark:
+            self.autolog.times.stamp()
 
         self.input_tensor.copy_from_cpu(img)
         self.predictor.run()
@@ -166,6 +197,8 @@ class TextDetector(object):
         for output_tensor in self.output_tensors:
             output = output_tensor.copy_to_cpu()
             outputs.append(output)
+        if self.args.benchmark:
+            self.autolog.times.stamp()
 
         preds = {}
         if self.det_algorithm == "EAST":
@@ -180,15 +213,19 @@ class TextDetector(object):
             preds['maps'] = outputs[0]
         else:
             raise NotImplementedError
-        self.predictor.try_shrink_memory()
+
+        #self.predictor.try_shrink_memory()
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
         if self.det_algorithm == "SAST" and self.det_sast_polygon:
             dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
         else:
             dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
-        elapse = time.time() - starttime
-        return dt_boxes, elapse
+
+        if self.args.benchmark:
+            self.autolog.times.end(stamp=True)
+        et = time.time()
+        return dt_boxes, et - st
 
 
 if __name__ == "__main__":
@@ -198,8 +235,15 @@ if __name__ == "__main__":
     count = 0
     total_time = 0
     draw_img_save = "./inference_results"
+
+    if args.warmup:
+        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
+        for i in range(2):
+            res = text_detector(img)
+
     if not os.path.exists(draw_img_save):
         os.makedirs(draw_img_save)
+    save_results = []
     for image_file in image_file_list:
         img, flag = check_and_read_gif(image_file)
         if not flag:
@@ -207,16 +251,26 @@ if __name__ == "__main__":
         if img is None:
             logger.info("error in loading image:{}".format(image_file))
             continue
-        dt_boxes, elapse = text_detector(img)
+        st = time.time()
+        dt_boxes, _ = text_detector(img)
+        elapse = time.time() - st
         if count > 0:
             total_time += elapse
         count += 1
-        logger.info("Predict time of {}: {}".format(image_file, elapse))
+        save_pred = os.path.basename(image_file) + "\t" + str(
+            json.dumps(np.array(dt_boxes).astype(np.int32).tolist())) + "\n"
+        save_results.append(save_pred)
+        logger.info(save_pred)
+        logger.info("The predict time of {}: {}".format(image_file, elapse))
         src_im = utility.draw_text_det_res(dt_boxes, image_file)
         img_name_pure = os.path.split(image_file)[-1]
         img_path = os.path.join(draw_img_save,
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
         logger.info("The visualized image saved in {}".format(img_path))
-    if count > 1:
-        logger.info("Avg Time: {}".format(total_time / (count - 1)))
+
+    with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
+        f.writelines(save_results)
+        f.close()
+    if args.benchmark:
+        text_detector.autolog.report()

tools/infer/predict_e2e.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.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import numpy as np
+import time
+import sys
+
+import tools.infer.utility as utility
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+
+logger = get_logger()
+
+
+class TextE2E(object):
+    def __init__(self, args):
+        self.args = args
+        self.e2e_algorithm = args.e2e_algorithm
+        pre_process_list = [{
+            'E2EResizeForTest': {}
+        }, {
+            'NormalizeImage': {
+                'std': [0.229, 0.224, 0.225],
+                'mean': [0.485, 0.456, 0.406],
+                'scale': '1./255.',
+                'order': 'hwc'
+            }
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': ['image', 'shape']
+            }
+        }]
+        postprocess_params = {}
+        if self.e2e_algorithm == "PGNet":
+            pre_process_list[0] = {
+                'E2EResizeForTest': {
+                    'max_side_len': args.e2e_limit_side_len,
+                    'valid_set': 'totaltext'
+                }
+            }
+            postprocess_params['name'] = 'PGPostProcess'
+            postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
+            postprocess_params["character_dict_path"] = args.e2e_char_dict_path
+            postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
+            postprocess_params["mode"] = args.e2e_pgnet_mode
+            self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
+        else:
+            logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
+            sys.exit(0)
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors, _ = utility.create_predictor(
+            args, 'e2e', logger)  # paddle.jit.load(args.det_model_dir)
+        # self.predictor.eval()
+
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points
+
+    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.clip_det_res(box, img_height, img_width)
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def __call__(self, img):
+
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img, shape_list = data
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        shape_list = np.expand_dims(shape_list, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        self.input_tensor.copy_from_cpu(img)
+        self.predictor.run()
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+
+        preds = {}
+        if self.e2e_algorithm == 'PGNet':
+            preds['f_border'] = outputs[0]
+            preds['f_char'] = outputs[1]
+            preds['f_direction'] = outputs[2]
+            preds['f_score'] = outputs[3]
+        else:
+            raise NotImplementedError
+        post_result = self.postprocess_op(preds, shape_list)
+        points, strs = post_result['points'], post_result['texts']
+        dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
+        elapse = time.time() - starttime
+        return dt_boxes, strs, elapse
+
+
+if __name__ == "__main__":
+    args = utility.parse_args()
+    image_file_list = get_image_file_list(args.image_dir)
+    text_detector = TextE2E(args)
+    count = 0
+    total_time = 0
+    draw_img_save = "./inference_results"
+    if not os.path.exists(draw_img_save):
+        os.makedirs(draw_img_save)
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        points, strs, elapse = text_detector(img)
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+        src_im = utility.draw_e2e_res(points, strs, image_file)
+        img_name_pure = os.path.split(image_file)[-1]
+        img_path = os.path.join(draw_img_save,
+                                "e2e_res_{}".format(img_name_pure))
+        cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
+    if count > 1:
+        logger.info("Avg Time: {}".format(total_time / (count - 1)))

tools/infer/predict_rec.py

@@ -62,14 +62,34 @@ class TextRecognizer(object):
                 "use_space_char": args.use_space_char
             }
         self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors = \
+        self.predictor, self.input_tensor, self.output_tensors, self.config = \
             utility.create_predictor(args, 'rec', logger)
+        self.benchmark = args.benchmark
+        if args.benchmark:
+            import auto_log
+            pid = os.getpid()
+            gpu_id = utility.get_infer_gpuid()
+            self.autolog = auto_log.AutoLogger(
+                model_name="rec",
+                model_precision=args.precision,
+                batch_size=args.rec_batch_num,
+                data_shape="dynamic",
+                save_path=None,  #args.save_log_path,
+                inference_config=self.config,
+                pids=pid,
+                process_name=None,
+                gpu_ids=gpu_id if args.use_gpu else None,
+                time_keys=[
+                    'preprocess_time', 'inference_time', 'postprocess_time'
+                ],
+                warmup=2,
+                logger=logger)
 
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         assert imgC == img.shape[2]
-        if self.character_type == "ch":
-            imgW = int((32 * max_wh_ratio))
+        max_wh_ratio = max(max_wh_ratio, imgW / imgH)
+        imgW = int((32 * max_wh_ratio))
         h, w = img.shape[:2]
         ratio = w / float(h)
         if math.ceil(imgH * ratio) > imgW:
@@ -165,17 +185,16 @@ class TextRecognizer(object):
             width_list.append(img.shape[1] / float(img.shape[0]))
         # Sorting can speed up the recognition process
         indices = np.argsort(np.array(width_list))
-
-        # rec_res = []
         rec_res = [['', 0.0]] * img_num
         batch_num = self.rec_batch_num
-        elapse = 0
+        st = time.time()
+        if self.benchmark:
+            self.autolog.times.start()
         for beg_img_no in range(0, img_num, batch_num):
             end_img_no = min(img_num, beg_img_no + batch_num)
             norm_img_batch = []
             max_wh_ratio = 0
             for ino in range(beg_img_no, end_img_no):
-                # h, w = img_list[ino].shape[0:2]
                 h, w = img_list[indices[ino]].shape[0:2]
                 wh_ratio = w * 1.0 / h
                 max_wh_ratio = max(max_wh_ratio, wh_ratio)
@@ -199,9 +218,10 @@ class TextRecognizer(object):
                     norm_img_batch.append(norm_img[0])
             norm_img_batch = np.concatenate(norm_img_batch)
             norm_img_batch = norm_img_batch.copy()
+            if self.benchmark:
+                self.autolog.times.stamp()
 
             if self.rec_algorithm == "SRN":
-                starttime = time.time()
                 encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
                 gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
                 gsrm_slf_attn_bias1_list = np.concatenate(
@@ -226,9 +246,10 @@ class TextRecognizer(object):
                 for output_tensor in self.output_tensors:
                     output = output_tensor.copy_to_cpu()
                     outputs.append(output)
+                if self.benchmark:
+                    self.autolog.times.stamp()
                 preds = {"predict": outputs[2]}
             else:
-                starttime = time.time()
                 self.input_tensor.copy_from_cpu(norm_img_batch)
                 self.predictor.run()
 
@@ -236,23 +257,30 @@ class TextRecognizer(object):
                 for output_tensor in self.output_tensors:
                     output = output_tensor.copy_to_cpu()
                     outputs.append(output)
+                if self.benchmark:
+                    self.autolog.times.stamp()
                 preds = outputs[0]
-            self.predictor.try_shrink_memory()
             rec_result = self.postprocess_op(preds)
             for rno in range(len(rec_result)):
                 rec_res[indices[beg_img_no + rno]] = rec_result[rno]
-            elapse += time.time() - starttime
-        return rec_res, elapse
+            if self.benchmark:
+                self.autolog.times.end(stamp=True)
+        return rec_res, time.time() - st
 
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_recognizer = TextRecognizer(args)
-    total_run_time = 0.0
-    total_images_num = 0
     valid_image_file_list = []
     img_list = []
-    for idx, image_file in enumerate(image_file_list):
+
+    # warmup 2 times
+    if args.warmup:
+        img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
+        for i in range(2):
+            res = text_recognizer([img] * int(args.rec_batch_num))
+
+    for image_file in image_file_list:
         img, flag = check_and_read_gif(image_file)
         if not flag:
             img = cv2.imread(image_file)
@@ -261,29 +289,18 @@ def main(args):
             continue
         valid_image_file_list.append(image_file)
         img_list.append(img)
-        if len(img_list) >= args.rec_batch_num or idx == len(
-                image_file_list) - 1:
-            try:
-                rec_res, predict_time = text_recognizer(img_list)
-                total_run_time += predict_time
-            except:
-                logger.info(traceback.format_exc())
-                logger.info(
-                    "ERROR!!!! \n"
-                    "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
-                    "If your model has tps module:  "
-                    "TPS does not support variable shape.\n"
-                    "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' "
-                )
-                exit()
-            for ino in range(len(img_list)):
-                logger.info("Predicts of {}:{}".format(valid_image_file_list[
-                    ino], rec_res[ino]))
-            total_images_num += len(valid_image_file_list)
-            valid_image_file_list = []
-            img_list = []
-    logger.info("Total predict time for {} images, cost: {:.3f}".format(
-        total_images_num, total_run_time))
+    try:
+        rec_res, _ = text_recognizer(img_list)
+
+    except Exception as E:
+        logger.info(traceback.format_exc())
+        logger.info(E)
+        exit()
+    for ino in range(len(img_list)):
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
+                                               rec_res[ino]))
+    if args.benchmark:
+        text_recognizer.autolog.report()
 
 
 if __name__ == "__main__":

tools/infer/predict_system.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 import sys
+import subprocess
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
@@ -24,6 +25,7 @@ import cv2
 import copy
 import numpy as np
 import time
+import logging
 from PIL import Image
 import tools.infer.utility as utility
 import tools.infer.predict_rec as predict_rec
@@ -31,13 +33,15 @@ import tools.infer.predict_det as predict_det
 import tools.infer.predict_cls as predict_cls
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
-from tools.infer.utility import draw_ocr_box_txt
-
+from tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image
 logger = get_logger()
 
 
 class TextSystem(object):
     def __init__(self, args):
+        if not args.show_log:
+            logger.setLevel(logging.INFO)
+
         self.text_detector = predict_det.TextDetector(args)
         self.text_recognizer = predict_rec.TextRecognizer(args)
         self.use_angle_cls = args.use_angle_cls
@@ -45,49 +49,17 @@ class TextSystem(object):
         if self.use_angle_cls:
             self.text_classifier = predict_cls.TextClassifier(args)
 
-    def get_rotate_crop_image(self, img, points):
-        '''
-        img_height, img_width = img.shape[0:2]
-        left = int(np.min(points[:, 0]))
-        right = int(np.max(points[:, 0]))
-        top = int(np.min(points[:, 1]))
-        bottom = int(np.max(points[:, 1]))
-        img_crop = img[top:bottom, left:right, :].copy()
-        points[:, 0] = points[:, 0] - left
-        points[:, 1] = points[:, 1] - top
-        '''
-        img_crop_width = int(
-            max(
-                np.linalg.norm(points[0] - points[1]),
-                np.linalg.norm(points[2] - points[3])))
-        img_crop_height = int(
-            max(
-                np.linalg.norm(points[0] - points[3]),
-                np.linalg.norm(points[1] - points[2])))
-        pts_std = np.float32([[0, 0], [img_crop_width, 0],
-                              [img_crop_width, img_crop_height],
-                              [0, img_crop_height]])
-        M = cv2.getPerspectiveTransform(points, pts_std)
-        dst_img = cv2.warpPerspective(
-            img,
-            M, (img_crop_width, img_crop_height),
-            borderMode=cv2.BORDER_REPLICATE,
-            flags=cv2.INTER_CUBIC)
-        dst_img_height, dst_img_width = dst_img.shape[0:2]
-        if dst_img_height * 1.0 / dst_img_width >= 1.5:
-            dst_img = np.rot90(dst_img)
-        return dst_img
-
     def print_draw_crop_rec_res(self, img_crop_list, rec_res):
         bbox_num = len(img_crop_list)
         for bno in range(bbox_num):
             cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
             logger.info(bno, rec_res[bno])
 
-    def __call__(self, img):
+    def __call__(self, img, cls=True):
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
-        logger.info("dt_boxes num : {}, elapse : {}".format(
+
+        logger.debug("dt_boxes num : {}, elapse : {}".format(
             len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
@@ -97,16 +69,16 @@ class TextSystem(object):
 
         for bno in range(len(dt_boxes)):
             tmp_box = copy.deepcopy(dt_boxes[bno])
-            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
+            img_crop = get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
-        if self.use_angle_cls:
+        if self.use_angle_cls and cls:
             img_crop_list, angle_list, elapse = self.text_classifier(
                 img_crop_list)
-            logger.info("cls num  : {}, elapse : {}".format(
+            logger.debug("cls num  : {}, elapse : {}".format(
                 len(img_crop_list), elapse))
 
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        logger.info("rec_res num  : {}, elapse : {}".format(
+        logger.debug("rec_res num  : {}, elapse : {}".format(
             len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         filter_boxes, filter_rec_res = [], []
@@ -141,11 +113,24 @@ def sorted_boxes(dt_boxes):
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
+    image_file_list = image_file_list[args.process_id::args.total_process_num]
     text_sys = TextSystem(args)
     is_visualize = True
     font_path = args.vis_font_path
     drop_score = args.drop_score
-    for image_file in image_file_list:
+
+    # warm up 10 times
+    if args.warmup:
+        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
+        for i in range(10):
+            res = text_sys(img)
+
+    total_time = 0
+    cpu_mem, gpu_mem, gpu_util = 0, 0, 0
+    _st = time.time()
+    count = 0
+    for idx, image_file in enumerate(image_file_list):
+
         img, flag = check_and_read_gif(image_file)
         if not flag:
             img = cv2.imread(image_file)
@@ -155,8 +140,10 @@ def main(args):
         starttime = time.time()
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
-        logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
+        total_time += elapse
 
+        logger.info(
+            str(idx) + "  Predict time of %s: %.3fs" % (image_file, elapse))
         for text, score in rec_res:
             logger.info("{}, {:.3f}".format(text, score))
 
@@ -176,12 +163,31 @@ def main(args):
             draw_img_save = "./inference_results/"
             if not os.path.exists(draw_img_save):
                 os.makedirs(draw_img_save)
+            if flag:
+                image_file = image_file[:-3] + "png"
             cv2.imwrite(
                 os.path.join(draw_img_save, os.path.basename(image_file)),
                 draw_img[:, :, ::-1])
             logger.info("The visualized image saved in {}".format(
                 os.path.join(draw_img_save, os.path.basename(image_file))))
 
+    logger.info("The predict total time is {}".format(time.time() - _st))
+    logger.info("\nThe predict total time is {}".format(total_time))
+
 
 if __name__ == "__main__":
-    main(utility.parse_args())
+    args = utility.parse_args()
+    if args.use_mp:
+        p_list = []
+        total_process_num = args.total_process_num
+        for process_id in range(total_process_num):
+            cmd = [sys.executable, "-u"] + sys.argv + [
+                "--process_id={}".format(process_id),
+                "--use_mp={}".format(False)
+            ]
+            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+            p_list.append(p)
+        for p in p_list:
+            p.wait()
+    else:
+        main(args)

tools/infer/utility.py

@@ -21,18 +21,22 @@ import json
 from PIL import Image, ImageDraw, ImageFont
 import math
 from paddle import inference
+import time
+from ppocr.utils.logging import get_logger
 
 
-def parse_args():
-    def str2bool(v):
-        return v.lower() in ("true", "t", "1")
+def str2bool(v):
+    return v.lower() in ("true", "t", "1")
+
 
+def init_args():
     parser = argparse.ArgumentParser()
     # params for prediction engine
     parser.add_argument("--use_gpu", type=str2bool, default=True)
     parser.add_argument("--ir_optim", type=str2bool, default=True)
     parser.add_argument("--use_tensorrt", type=str2bool, default=False)
-    parser.add_argument("--use_fp16", type=str2bool, default=False)
+    parser.add_argument("--min_subgraph_size", type=int, default=15)
+    parser.add_argument("--precision", type=str, default="fp32")
     parser.add_argument("--gpu_mem", type=int, default=500)
 
     # params for text detector
@@ -44,10 +48,11 @@ def parse_args():
 
     # DB parmas
     parser.add_argument("--det_db_thresh", type=float, default=0.3)
-    parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
-    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
+    parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
+    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
     parser.add_argument("--max_batch_size", type=int, default=10)
-    parser.add_argument("--use_dilation", type=bool, default=False)
+    parser.add_argument("--use_dilation", type=str2bool, default=False)
+    parser.add_argument("--det_db_score_mode", type=str, default="fast")
     # EAST parmas
     parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
     parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
@@ -56,7 +61,7 @@ def parse_args():
     # SAST parmas
     parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
     parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
-    parser.add_argument("--det_sast_polygon", type=bool, default=False)
+    parser.add_argument("--det_sast_polygon", type=str2bool, default=False)
 
     # params for text recognizer
     parser.add_argument("--rec_algorithm", type=str, default='CRNN')
@@ -74,6 +79,20 @@ def parse_args():
         "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
     parser.add_argument("--drop_score", type=float, default=0.5)
 
+    # params for e2e
+    parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
+    parser.add_argument("--e2e_model_dir", type=str)
+    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
+    parser.add_argument("--e2e_limit_type", type=str, default='max')
+
+    # PGNet parmas
+    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
+    parser.add_argument(
+        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
+    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
+    parser.add_argument("--e2e_pgnet_polygon", type=str2bool, default=True)
+    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
+
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)
     parser.add_argument("--cls_model_dir", type=str)
@@ -83,8 +102,24 @@ def parse_args():
     parser.add_argument("--cls_thresh", type=float, default=0.9)
 
     parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
+    parser.add_argument("--cpu_threads", type=int, default=10)
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
+    parser.add_argument("--warmup", type=str2bool, default=True)
+
+    # multi-process
+    parser.add_argument("--use_mp", type=str2bool, default=False)
+    parser.add_argument("--total_process_num", type=int, default=1)
+    parser.add_argument("--process_id", type=int, default=0)
+
+    parser.add_argument("--benchmark", type=str2bool, default=False)
+    parser.add_argument("--save_log_path", type=str, default="./log_output/")
 
+    parser.add_argument("--show_log", type=str2bool, default=True)
+    return parser
+
+
+def parse_args():
+    parser = init_args()
     return parser.parse_args()
 
 
@@ -93,8 +128,12 @@ def create_predictor(args, mode, logger):
         model_dir = args.det_model_dir
     elif mode == 'cls':
         model_dir = args.cls_model_dir
-    else:
+    elif mode == 'rec':
         model_dir = args.rec_model_dir
+    elif mode == 'table':
+        model_dir = args.table_model_dir
+    else:
+        model_dir = args.e2e_model_dir
 
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
@@ -102,38 +141,136 @@ def create_predictor(args, mode, logger):
     model_file_path = model_dir + "/inference.pdmodel"
     params_file_path = model_dir + "/inference.pdiparams"
     if not os.path.exists(model_file_path):
-        logger.info("not find model file path {}".format(model_file_path))
-        sys.exit(0)
+        raise ValueError("not find model file path {}".format(model_file_path))
     if not os.path.exists(params_file_path):
-        logger.info("not find params file path {}".format(params_file_path))
-        sys.exit(0)
+        raise ValueError("not find params file path {}".format(
+            params_file_path))
 
     config = inference.Config(model_file_path, params_file_path)
 
+    if hasattr(args, 'precision'):
+        if args.precision == "fp16" and args.use_tensorrt:
+            precision = inference.PrecisionType.Half
+        elif args.precision == "int8":
+            precision = inference.PrecisionType.Int8
+        else:
+            precision = inference.PrecisionType.Float32
+    else:
+        precision = inference.PrecisionType.Float32
+
     if args.use_gpu:
+        gpu_id = get_infer_gpuid()
+        if gpu_id is None:
+            raise ValueError(
+                "Not found GPU in current device. Please check your device or set args.use_gpu as False"
+            )
         config.enable_use_gpu(args.gpu_mem, 0)
         if args.use_tensorrt:
             config.enable_tensorrt_engine(
-                precision_mode=inference.PrecisionType.Half
-                if args.use_fp16 else inference.PrecisionType.Float32,
-                max_batch_size=args.max_batch_size)
+                precision_mode=precision,
+                max_batch_size=args.max_batch_size,
+                min_subgraph_size=args.min_subgraph_size)
+            # skip the minmum trt subgraph
+        if mode == "det":
+            min_input_shape = {
+                "x": [1, 3, 50, 50],
+                "conv2d_92.tmp_0": [1, 120, 20, 20],
+                "conv2d_91.tmp_0": [1, 24, 10, 10],
+                "conv2d_59.tmp_0": [1, 96, 20, 20],
+                "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
+                "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
+                "conv2d_124.tmp_0": [1, 256, 20, 20],
+                "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
+                "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
+                "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
+                "elementwise_add_7": [1, 56, 2, 2],
+                "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
+            }
+            max_input_shape = {
+                "x": [1, 3, 2000, 2000],
+                "conv2d_92.tmp_0": [1, 120, 400, 400],
+                "conv2d_91.tmp_0": [1, 24, 200, 200],
+                "conv2d_59.tmp_0": [1, 96, 400, 400],
+                "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
+                "conv2d_124.tmp_0": [1, 256, 400, 400],
+                "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
+                "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
+                "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
+                "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
+                "elementwise_add_7": [1, 56, 400, 400],
+                "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
+            }
+            opt_input_shape = {
+                "x": [1, 3, 640, 640],
+                "conv2d_92.tmp_0": [1, 120, 160, 160],
+                "conv2d_91.tmp_0": [1, 24, 80, 80],
+                "conv2d_59.tmp_0": [1, 96, 160, 160],
+                "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
+                "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
+                "conv2d_124.tmp_0": [1, 256, 160, 160],
+                "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
+                "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
+                "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
+                "elementwise_add_7": [1, 56, 40, 40],
+                "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
+            }
+            min_pact_shape = {
+                "nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
+                "nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
+                "nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
+                "nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
+            }
+            max_pact_shape = {
+                "nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
+                "nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
+                "nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
+                "nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
+            }
+            opt_pact_shape = {
+                "nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
+                "nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
+                "nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
+                "nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
+            }
+            min_input_shape.update(min_pact_shape)
+            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]}
+            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]}
+            max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
+            opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
+        else:
+            min_input_shape = {"x": [1, 3, 10, 10]}
+            max_input_shape = {"x": [1, 3, 1000, 1000]}
+            opt_input_shape = {"x": [1, 3, 500, 500]}
+        config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
+                                          opt_input_shape)
+
     else:
         config.disable_gpu()
-        config.set_cpu_math_library_num_threads(6)
+        if hasattr(args, "cpu_threads"):
+            config.set_cpu_math_library_num_threads(args.cpu_threads)
+        else:
+            # default cpu threads as 10
+            config.set_cpu_math_library_num_threads(10)
         if args.enable_mkldnn:
             # cache 10 different shapes for mkldnn to avoid memory leak
             config.set_mkldnn_cache_capacity(10)
             config.enable_mkldnn()
-            #  TODO LDOUBLEV: fix mkldnn bug when bach_size  > 1
-            #config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'})
-            args.rec_batch_num = 1
 
     # enable memory optim
     config.enable_memory_optim()
-    config.disable_glog_info()
+    #config.disable_glog_info()
 
     config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
+    if mode == 'table':
+        config.delete_pass("fc_fuse_pass")  # not supported for table
     config.switch_use_feed_fetch_ops(False)
+    config.switch_ir_optim(True)
 
     # create predictor
     predictor = inference.create_predictor(config)
@@ -145,7 +282,37 @@ def create_predictor(args, mode, logger):
     for output_name in output_names:
         output_tensor = predictor.get_output_handle(output_name)
         output_tensors.append(output_tensor)
-    return predictor, input_tensor, output_tensors
+    return predictor, input_tensor, output_tensors, config
+
+
+def get_infer_gpuid():
+    cmd = "nvidia-smi"
+    res = os.popen(cmd).readlines()
+    if len(res) == 0:
+        return None
+    cmd = "env | grep CUDA_VISIBLE_DEVICES"
+    env_cuda = os.popen(cmd).readlines()
+    if len(env_cuda) == 0:
+        return 0
+    else:
+        gpu_id = env_cuda[0].strip().split("=")[1]
+        return int(gpu_id[0])
+
+
+def draw_e2e_res(dt_boxes, strs, img_path):
+    src_im = cv2.imread(img_path)
+    for box, str in zip(dt_boxes, strs):
+        box = box.astype(np.int32).reshape((-1, 1, 2))
+        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+        cv2.putText(
+            src_im,
+            str,
+            org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+            fontFace=cv2.FONT_HERSHEY_COMPLEX,
+            fontScale=0.7,
+            color=(0, 255, 0),
+            thickness=1)
+    return src_im
 
 
 def draw_text_det_res(dt_boxes, img_path):
@@ -173,7 +340,7 @@ def draw_ocr(image,
              txts=None,
              scores=None,
              drop_score=0.5,
-             font_path="./doc/simfang.ttf"):
+             font_path="./doc/fonts/simfang.ttf"):
     """
     Visualize the results of OCR detection and recognition
     args:
@@ -380,23 +547,40 @@ def draw_boxes(image, boxes, scores=None, drop_score=0.5):
     return image
 
 
+def get_rotate_crop_image(img, points):
+    '''
+    img_height, img_width = img.shape[0:2]
+    left = int(np.min(points[:, 0]))
+    right = int(np.max(points[:, 0]))
+    top = int(np.min(points[:, 1]))
+    bottom = int(np.max(points[:, 1]))
+    img_crop = img[top:bottom, left:right, :].copy()
+    points[:, 0] = points[:, 0] - left
+    points[:, 1] = points[:, 1] - top
+    '''
+    assert len(points) == 4, "shape of points must be 4*2"
+    img_crop_width = int(
+        max(
+            np.linalg.norm(points[0] - points[1]),
+            np.linalg.norm(points[2] - points[3])))
+    img_crop_height = int(
+        max(
+            np.linalg.norm(points[0] - points[3]),
+            np.linalg.norm(points[1] - points[2])))
+    pts_std = np.float32([[0, 0], [img_crop_width, 0],
+                          [img_crop_width, img_crop_height],
+                          [0, img_crop_height]])
+    M = cv2.getPerspectiveTransform(points, pts_std)
+    dst_img = cv2.warpPerspective(
+        img,
+        M, (img_crop_width, img_crop_height),
+        borderMode=cv2.BORDER_REPLICATE,
+        flags=cv2.INTER_CUBIC)
+    dst_img_height, dst_img_width = dst_img.shape[0:2]
+    if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        dst_img = np.rot90(dst_img)
+    return dst_img
+
+
 if __name__ == '__main__':
-    test_img = "./doc/test_v2"
-    predict_txt = "./doc/predict.txt"
-    f = open(predict_txt, 'r')
-    data = f.readlines()
-    img_path, anno = data[0].strip().split('\t')
-    img_name = os.path.basename(img_path)
-    img_path = os.path.join(test_img, img_name)
-    image = Image.open(img_path)
-
-    data = json.loads(anno)
-    boxes, txts, scores = [], [], []
-    for dic in data:
-        boxes.append(dic['points'])
-        txts.append(dic['transcription'])
-        scores.append(round(dic['scores'], 3))
-
-    new_img = draw_ocr(image, boxes, txts, scores)
-
-    cv2.imwrite(img_name, new_img)
+    pass

tools/infer_cls.py

@@ -47,7 +47,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    init_model(config, model)
 
     # create data ops
     transforms = []

tools/infer_det.py

@@ -61,7 +61,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    init_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'])
@@ -112,4 +112,4 @@ def main():
 
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess()
-    main()
\ No newline at end of file
+    main()

tools/infer_e2e.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+
+
+def draw_e2e_res(dt_boxes, strs, config, img, img_name):
+    if len(dt_boxes) > 0:
+        src_im = img
+        for box, str in zip(dt_boxes, strs):
+            box = box.astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+            cv2.putText(
+                src_im,
+                str,
+                org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+                fontFace=cv2.FONT_HERSHEY_COMPLEX,
+                fontScale=0.7,
+                color=(0, 255, 0),
+                thickness=1)
+        save_det_path = os.path.dirname(config['Global'][
+            'save_res_path']) + "/e2e_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, os.path.basename(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The e2e Image saved in {}".format(save_path))
+
+
+def main():
+    global_config = config['Global']
+
+    # build model
+    model = build_model(config['Architecture'])
+
+    init_model(config, model)
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        elif op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['image', 'shape']
+        transforms.append(op)
+
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global']['save_res_path']
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    with open(save_res_path, "wb") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            images = np.expand_dims(batch[0], axis=0)
+            shape_list = np.expand_dims(batch[1], axis=0)
+            images = paddle.to_tensor(images)
+            preds = model(images)
+            post_result = post_process_class(preds, shape_list)
+            points, strs = post_result['points'], post_result['texts']
+            # write resule
+            dt_boxes_json = []
+            for poly, str in zip(points, strs):
+                tmp_json = {"transcription": str}
+                tmp_json['points'] = poly.tolist()
+                dt_boxes_json.append(tmp_json)
+            otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
+            fout.write(otstr.encode())
+            src_img = cv2.imread(file)
+            draw_e2e_res(points, strs, config, src_img, file)
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()