diff --git a/model_zoo/official/cv/yolov4/README.MD b/model_zoo/official/cv/yolov4/README.MD index 29110f6323..a0db70c87a 100644 --- a/model_zoo/official/cv/yolov4/README.MD +++ b/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) diff --git a/model_zoo/official/cv/yolov4/ascend310_infer/inc/utils.h b/model_zoo/official/cv/yolov4/ascend310_infer/inc/utils.h new file mode 100644 index 0000000000..54f53bb1ab --- /dev/null +++ b/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 +#include +#include +#include +#include +#include "include/api/types.h" + +std::vector GetAllFiles(std::string_view dirName); +DIR *OpenDir(std::string_view dirName); +std::string RealPath(std::string_view path); +std::shared_ptr ReadFileToTensor(const std::string &file); +int WriteResult(const std::string& imageFile, const std::vector &outputs); +#endif diff --git a/model_zoo/official/cv/yolov4/ascend310_infer/src/CMakeLists.txt b/model_zoo/official/cv/yolov4/ascend310_infer/src/CMakeLists.txt new file mode 100644 index 0000000000..9550b6a74a --- /dev/null +++ b/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) diff --git a/model_zoo/official/cv/yolov4/ascend310_infer/src/build.sh b/model_zoo/official/cv/yolov4/ascend310_infer/src/build.sh new file mode 100644 index 0000000000..3e205ecce7 --- /dev/null +++ b/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 diff --git a/model_zoo/official/cv/yolov4/ascend310_infer/src/main.cc b/model_zoo/official/cv/yolov4/ascend310_infer/src/main.cc new file mode 100644 index 0000000000..ffc85da0e8 --- /dev/null +++ b/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 +#include +#include +#include +#include +#include +#include +#include +#include + +#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 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 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 inputs; + std::vector outputs; + std::cout << "Start predict input files:" << all_files[i] << std::endl; + auto imgDvpp = SingleOp(ReadFileToTensor(all_files[i])); + std::vector 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(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; +} diff --git a/model_zoo/official/cv/yolov4/ascend310_infer/src/utils.cc b/model_zoo/official/cv/yolov4/ascend310_infer/src/utils.cc new file mode 100644 index 0000000000..e3d518c669 --- /dev/null +++ b/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 +#include +#include + +using mindspore::api::Tensor; +using mindspore::api::Buffer; +using mindspore::api::DataType; + +std::vector GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + + std::vector 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 &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 ReadFileToTensor(const std::string &file) { + auto buffer = std::make_shared(); + 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(buffer->MutableData()), size); + ifs.close(); + + buffer->SetDataType(DataType::kMsUint8); + buffer->SetShape({static_cast(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; +} diff --git a/model_zoo/official/cv/yolov4/postprocess.py b/model_zoo/official/cv/yolov4/postprocess.py new file mode 100644 index 0000000000..3e16f186e2 --- /dev/null +++ b/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.)) diff --git a/model_zoo/official/cv/yolov4/scripts/run_infer_310.sh b/model_zoo/official/cv/yolov4/scripts/run_infer_310.sh new file mode 100644 index 0000000000..1b786ba0ed --- /dev/null +++ b/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 diff --git a/model_zoo/official/cv/yolov4/src/aipp.cfg b/model_zoo/official/cv/yolov4/src/aipp.cfg new file mode 100644 index 0000000000..56606c880b --- /dev/null +++ b/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 +}