310 infer dvpp_aipp_mindir

model_zoo/official/cv/yolov4/README.MD

@@ -389,10 +389,46 @@ overall performance
 
 ### Convert
 
-If you want to infer the network on Ascend 310, you should convert the model to AIR:
+If you want to infer the network on Ascend 310, you should convert the model to MINDIR:
 
 ```python
-python src/export.py --pretrained=[PRETRAINED_BACKBONE] --batch_size=[BATCH_SIZE] 
+python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] 
+```
+
+The ckpt_file parameter is required,
+`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
+
+## [Inference Process](#contents)
+
+### Usage
+
+Before performing inference, the mindir file must be exported by export script on the 910 environment.
+Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space.
+
+```shell
+# Ascend310 inference
+sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] [ANN_FILE]
+```
+`DEVICE_ID` is optional, default value is 0.
+
+### result
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```text
+=============coco eval reulst=========
+Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.438
+Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.630
+Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.475
+Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.272
+Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.481
+Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.330
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.542
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.588
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.410
+Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.636
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
 ```
 
 # [Model Description](#contents)
@@ -437,7 +473,7 @@ YOLOv4 on 20K images(The annotation and data format must be the same as coco tes
 # [Description of Random Situation](#contents)
 
 In dataset.py, we set the seed inside ```create_dataset``` function.
-In var_init.py, we set seed for weight initilization
+In var_init.py, we set seed for weight initialization
 
 # [ModelZoo Homepage](#contents)
 

model_zoo/official/cv/yolov4/ascend310_infer/inc/utils.h

@@ -0,0 +1,32 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_INFERENCE_UTILS_H_
+#define MINDSPORE_INFERENCE_UTILS_H_
+
+#include <sys/stat.h>
+#include <dirent.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include "include/api/types.h"
+
+std::vector<std::string> GetAllFiles(std::string_view dirName);
+DIR *OpenDir(std::string_view dirName);
+std::string RealPath(std::string_view path);
+std::shared_ptr<mindspore::api::Tensor> ReadFileToTensor(const std::string &file);
+int WriteResult(const std::string& imageFile, const std::vector<mindspore::api::Buffer> &outputs);
+#endif

model_zoo/official/cv/yolov4/ascend310_infer/src/CMakeLists.txt

@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(MindSporeCxxTestcase[CXX])
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT}/../inc)
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main main.cc utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

model_zoo/official/cv/yolov4/ascend310_infer/src/build.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/official/cv/yolov4/ascend310_infer/src/main.cc

@@ -0,0 +1,144 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/context.h"
+#include "minddata/dataset/include/minddata_eager.h"
+#include "../inc/utils.h"
+#include "include/api/types.h"
+#include "minddata/dataset/include/vision.h"
+
+using mindspore::api::Context;
+using mindspore::api::Serialization;
+using mindspore::api::Model;
+using mindspore::api::kModelOptionInsertOpCfgPath;
+using mindspore::api::kModelOptionPrecisionMode;
+using mindspore::api::kModelOptionOpSelectImplMode;
+using mindspore::api::Status;
+using mindspore::api::MindDataEager;
+using mindspore::api::Buffer;
+using mindspore::api::ModelType;
+using mindspore::api::GraphCell;
+using mindspore::api::SUCCESS;
+using mindspore::dataset::vision::DvppDecodeResizeJpeg;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_int32(device_id, 0, "device id");
+DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
+DEFINE_string(op_select_impl_mode, "", "op select impl mode");
+DEFINE_string(input_shape, "img_data:1, 3, 768, 1280; img_info:1, 4", "input shape");
+DEFINE_string(input_format, "nchw", "input format");
+DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_mindir_path).empty()) {
+        std::cout << "Invalid mindir" << std::endl;
+        return 1;
+    }
+    if (RealPath(FLAGS_aipp_path).empty()) {
+        std::cout << "Invalid aipp path" << std::endl;
+        return 1;
+    }
+
+    Context::Instance().SetDeviceTarget("Ascend310").SetDeviceID(FLAGS_device_id);
+    auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
+    Model model((GraphCell(graph)));
+
+    std::map<std::string, std::string> build_options;
+    if (!FLAGS_precision_mode.empty()) {
+        build_options.emplace(kModelOptionPrecisionMode, FLAGS_precision_mode);
+    }
+    if (!FLAGS_op_select_impl_mode.empty()) {
+        build_options.emplace(kModelOptionOpSelectImplMode, FLAGS_op_select_impl_mode);
+    }
+
+    if (!FLAGS_aipp_path.empty()) {
+        build_options.emplace(kModelOptionInsertOpCfgPath, FLAGS_aipp_path);
+    }
+
+    Status ret = model.Build(build_options);
+    if (ret != SUCCESS) {
+        std::cout << "EEEEEEEERROR Build failed." << std::endl;
+        return 1;
+    }
+
+    auto all_files = GetAllFiles(FLAGS_dataset_path);
+    if (all_files.empty()) {
+        std::cout << "ERROR: no input data." << std::endl;
+        return 1;
+    }
+
+    std::map<double, double> costTime_map;
+    size_t size = all_files.size();
+    MindDataEager SingleOp({DvppDecodeResizeJpeg({608, 608})});
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start = {0};
+        struct timeval end = {0};
+        double startTime_ms;
+        double endTime_ms;
+        std::vector<Buffer> inputs;
+        std::vector<Buffer> outputs;
+        std::cout << "Start predict input files:" << all_files[i] << std::endl;
+        auto imgDvpp = SingleOp(ReadFileToTensor(all_files[i]));
+        std::vector<float> input_shape = {608, 608};
+
+        inputs.clear();
+        inputs.emplace_back(imgDvpp->Data(), imgDvpp->DataSize());
+        inputs.emplace_back(input_shape.data(), input_shape.size() * sizeof(float));
+        gettimeofday(&start, NULL);
+        ret = model.Predict(inputs, &outputs);
+        gettimeofday(&end, NULL);
+        if (ret != SUCCESS) {
+            std::cout << "Predict " << all_files[i] << " failed." << std::endl;
+            return 1;
+        }
+        startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+        endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+        costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
+        WriteResult(all_files[i], outputs);
+    }
+    double average = 0.0;
+    int infer_cnt = 0;
+    char tmpCh[256] = {0};
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        double diff = 0.0;
+        diff = iter->second - iter->first;
+        average += diff;
+        infer_cnt++;
+    }
+    average = average/infer_cnt;
+    snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms of infer_count %d \n", average, infer_cnt);
+    std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
+    std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
+    std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
+    file_stream << tmpCh;
+    file_stream.close();
+    costTime_map.clear();
+    return 0;
+}

model_zoo/official/cv/yolov4/ascend310_infer/src/utils.cc

@@ -0,0 +1,145 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include "../inc/utils.h"
+#include <fstream>
+#include <algorithm>
+#include <iostream>
+
+using mindspore::api::Tensor;
+using mindspore::api::Buffer;
+using mindspore::api::DataType;
+
+std::vector<std::string> GetAllFiles(std::string_view dirName) {
+    struct dirent *filename;
+    DIR *dir = OpenDir(dirName);
+    if (dir == nullptr) {
+        return {};
+    }
+
+    std::vector<std::string> res;
+    while ((filename = readdir(dir)) != nullptr) {
+        std::string dName = std::string(filename->d_name);
+        if (dName == "." ||
+            dName == ".." ||
+            filename->d_type != DT_REG)
+            continue;
+        res.emplace_back(std::string(dirName) + "/" + filename->d_name);
+    }
+
+    std::sort(res.begin(), res.end());
+    for (auto &f : res) {
+        std::cout << "image file: " << f << std::endl;
+    }
+    return res;
+}
+
+int WriteResult(const std::string& imageFile, const std::vector<Buffer> &outputs) {
+    std::string homePath = "./result_Files";
+    for (size_t i = 0; i < outputs.size(); ++i) {
+        size_t outputSize;
+        const void * netOutput;
+        netOutput = outputs[i].Data();
+        outputSize = outputs[i].DataSize();
+
+        int pos = imageFile.rfind('/');
+        std::string fileName(imageFile, pos + 1);
+        fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
+        std::string outFileName = homePath + "/" + fileName;
+        FILE * outputFile = fopen(outFileName.c_str(), "wb");
+        fwrite(netOutput, outputSize, sizeof(char), outputFile);
+
+        fclose(outputFile);
+        outputFile = nullptr;
+    }
+    return 0;
+}
+
+std::shared_ptr<Tensor>  ReadFileToTensor(const std::string &file) {
+    auto buffer = std::make_shared<Tensor>();
+    if (file.empty()) {
+      std::cout << "Pointer file is nullptr" << std::endl;
+      return buffer;
+    }
+
+    std::ifstream ifs(file);
+    if (!ifs.good()) {
+      std::cout << "File: " << file << " is not exist" << std::endl;
+      return buffer;
+    }
+
+    if (!ifs.is_open()) {
+      std::cout << "File: " << file << "open failed" << std::endl;
+      return buffer;
+    }
+
+    ifs.seekg(0, std::ios::end);
+    size_t size = ifs.tellg();
+    buffer->ResizeData(size);
+    if (buffer->DataSize() != size) {
+        std::cout << "Malloc buf failed, file: " << file << std::endl;
+        ifs.close();
+        return buffer;
+    }
+
+    ifs.seekg(0, std::ios::beg);
+    ifs.read(reinterpret_cast<char *>(buffer->MutableData()), size);
+    ifs.close();
+
+    buffer->SetDataType(DataType::kMsUint8);
+    buffer->SetShape({static_cast<int64_t>(size)});
+    return buffer;
+}
+
+DIR *OpenDir(std::string_view dirName) {
+    if (dirName.empty()) {
+        std::cout << " dirName is null ! " << std::endl;
+        return nullptr;
+    }
+
+    std::string realPath = RealPath(dirName);
+
+    struct stat s;
+    lstat(realPath.c_str(), &s);
+    if (!S_ISDIR(s.st_mode)) {
+        std::cout << "dirName is not a valid directory !" << std::endl;
+        return nullptr;
+    }
+
+    DIR *dir;
+    dir = opendir(realPath.c_str());
+    if (dir == nullptr) {
+        std::cout << "Can not open dir " << dirName << std::endl;
+        return nullptr;
+    }
+    std::cout << "Successfully opened the dir " << dirName << std::endl;
+    return dir;
+}
+
+std::string RealPath(std::string_view path) {
+    char real_path_mem[PATH_MAX] = {0};
+    char *real_path_ret = nullptr;
+    real_path_ret = realpath(path.data(), real_path_mem);
+
+    if (real_path_ret == nullptr) {
+        std::cout << "File: " << path << " is not exist.";
+        return "";
+    }
+
+    std::string real_path(real_path_mem);
+    std::cout << path << " realpath is: " << real_path << std::endl;
+    return real_path;
+}

model_zoo/official/cv/yolov4/postprocess.py

@@ -0,0 +1,98 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""YoloV4 310 infer."""
+import os
+import argparse
+import datetime
+import time
+
+import numpy as np
+from pycocotools.coco import COCO
+from src.logger import get_logger
+from eval import DetectionEngine
+
+
+parser = argparse.ArgumentParser('mindspore coco testing')
+
+# dataset related
+parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
+
+# logging related
+parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
+
+# detect_related
+parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
+parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
+parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
+
+parser.add_argument('--img_id_file_path', type=str, default='', help='path of image dataset')
+parser.add_argument('--result_files', type=str, default='./result_Files', help='path to 310 infer result floder')
+
+args, _ = parser.parse_known_args()
+
+
+class Redirct:
+    def __init__(self):
+        self.content = ""
+
+    def write(self, content):
+        self.content += content
+
+    def flush(self):
+        self.content = ""
+
+
+if __name__ == "__main__":
+    start_time = time.time()
+
+    args.outputs_dir = os.path.join(args.log_path,
+                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+    args.logger = get_logger(args.outputs_dir, 0)
+
+    # init detection engine
+    detection = DetectionEngine(args)
+
+    coco = COCO(args.ann_file)
+    result_path = args.result_files
+
+    files = os.listdir(args.img_id_file_path)
+
+    for file in files:
+        img_ids_name = file.split('.')[0]
+        img_id = int(np.squeeze(img_ids_name))
+        imgIds = coco.getImgIds(imgIds=[img_id])
+        img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
+        image_shape = ((img['width'], img['height']),)
+        img_id = (np.squeeze(img_ids_name),)
+
+        result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
+        result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
+        result_path_2 = os.path.join(result_path, img_ids_name + "_2.bin")
+
+        output_small = np.fromfile(result_path_0, dtype=np.float32).reshape(1, 19, 19, 3, 85)
+        output_me = np.fromfile(result_path_1, dtype=np.float32).reshape(1, 38, 38, 3, 85)
+        output_big = np.fromfile(result_path_2, dtype=np.float32).reshape(1, 76, 76, 3, 85)
+
+        detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, img_id)
+
+    args.logger.info('Calculating mAP...')
+    detection.do_nms_for_results()
+    result_file_path = detection.write_result()
+    args.logger.info('result file path: {}'.format(result_file_path))
+    eval_result = detection.get_eval_result()
+
+    cost_time = time.time() - start_time
+    args.logger.info('\n=============coco eval reulst=========\n' + eval_result)
+    args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))

model_zoo/official/cv/yolov4/scripts/run_infer_310.sh

@@ -0,0 +1,103 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [[ $# -lt 3 || $# -gt 4 ]]; then
+    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] [ANN_FILE]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+if [ $# == 4 ]; then
+    device_id=$3
+    if [ -z $device_id ]; then
+        device_id=0
+    else
+        device_id=$device_id
+    fi
+fi
+annotation_file=$(get_real_path $4)
+
+echo "mindir name: "$model
+echo "dataset path: "$data_path
+echo "device id: "$device_id
+echo "annotation file: "$annotation_file
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+  export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+  export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+  export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+  export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+  export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+  export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+  export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+  export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages/te.egg:$ASCEND_HOME/atc/python/site-packages/topi.egg:$ASCEND_HOME/atc/python/site-packages/auto_tune.egg::$ASCEND_HOME/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
+  export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function compile_app()
+{
+    cd ../ascend310_infer/src
+    if [ -f "Makefile" ]; then
+        make clean
+    fi
+    sh build.sh &> build.log
+}
+
+function infer()
+{
+    cd -
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+     if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --aipp_path ../src/aipp.cfg &> infer.log
+}
+
+function cal_acc()
+{
+    python3.7 ../postprocess.py --ann_file=$annotation_file --img_id_file_path=$data_path --result_files=./result_Files &> acc.log &
+}
+
+compile_app
+if [ $? -ne 0 ]; then
+    echo "compile app code failed"
+    exit 1
+fi
+infer
+if [ $? -ne 0 ]; then
+    echo "execute inference failed"
+    exit 1
+fi
+cal_acc
+if [ $? -ne 0 ]; then
+    echo "calculate accuracy failed"
+    exit 1
+fi

model_zoo/official/cv/yolov4/src/aipp.cfg

@@ -0,0 +1,26 @@
+aipp_op {
+    aipp_mode : static
+    input_format : YUV420SP_U8
+    related_input_rank : 0
+    csc_switch : true
+    rbuv_swap_switch : false
+    matrix_r0c0 : 256
+    matrix_r0c1 : 0
+    matrix_r0c2 : 359
+    matrix_r1c0 : 256
+    matrix_r1c1 : -88
+    matrix_r1c2 : -183
+    matrix_r2c0 : 256
+    matrix_r2c1 : 454
+    matrix_r2c2 : 0
+    input_bias_0 : 0
+    input_bias_1 : 128
+    input_bias_2 : 128
+    
+    mean_chn_0 : 124
+    mean_chn_1 : 117
+    mean_chn_2 : 104
+    var_reci_chn_0 : 0.0171247538316637
+    var_reci_chn_1 : 0.0175070028011204
+    var_reci_chn_2 : 0.0174291938997821
+}