!11880 add yolov4 310 dvpp_aipp_mindir infer
From: @lihongkang1 Reviewed-by: Signed-off-by:pull/11880/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
0e73e82bab
@ -0,0 +1,32 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_INFERENCE_UTILS_H_
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#define MINDSPORE_INFERENCE_UTILS_H_
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#include <sys/stat.h>
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#include <dirent.h>
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#include <vector>
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#include <string>
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#include <memory>
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#include "include/api/types.h"
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std::vector<std::string> GetAllFiles(std::string_view dirName);
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DIR *OpenDir(std::string_view dirName);
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std::string RealPath(std::string_view path);
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std::shared_ptr<mindspore::api::Tensor> ReadFileToTensor(const std::string &file);
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int WriteResult(const std::string& imageFile, const std::vector<mindspore::api::Buffer> &outputs);
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#endif
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cmake_minimum_required(VERSION 3.14.1)
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project(MindSporeCxxTestcase[CXX])
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add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
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set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
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option(MINDSPORE_PATH "mindspore install path" "")
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include_directories(${MINDSPORE_PATH})
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include_directories(${MINDSPORE_PATH}/include)
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include_directories(${PROJECT_SRC_ROOT}/../inc)
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find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
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file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
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add_executable(main main.cc utils.cc)
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target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
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make
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@ -0,0 +1,144 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <sys/time.h>
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#include <gflags/gflags.h>
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#include <dirent.h>
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#include <iostream>
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#include <string>
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#include <algorithm>
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#include <iosfwd>
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#include <vector>
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#include <fstream>
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#include "include/api/model.h"
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#include "include/api/serialization.h"
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#include "include/api/context.h"
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#include "minddata/dataset/include/minddata_eager.h"
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#include "../inc/utils.h"
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#include "include/api/types.h"
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#include "minddata/dataset/include/vision.h"
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using mindspore::api::Context;
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using mindspore::api::Serialization;
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using mindspore::api::Model;
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using mindspore::api::kModelOptionInsertOpCfgPath;
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using mindspore::api::kModelOptionPrecisionMode;
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using mindspore::api::kModelOptionOpSelectImplMode;
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using mindspore::api::Status;
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using mindspore::api::MindDataEager;
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using mindspore::api::Buffer;
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using mindspore::api::ModelType;
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using mindspore::api::GraphCell;
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using mindspore::api::SUCCESS;
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using mindspore::dataset::vision::DvppDecodeResizeJpeg;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(dataset_path, ".", "dataset path");
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DEFINE_int32(device_id, 0, "device id");
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DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
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DEFINE_string(op_select_impl_mode, "", "op select impl mode");
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DEFINE_string(input_shape, "img_data:1, 3, 768, 1280; img_info:1, 4", "input shape");
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DEFINE_string(input_format, "nchw", "input format");
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DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
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int main(int argc, char **argv) {
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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if (RealPath(FLAGS_mindir_path).empty()) {
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std::cout << "Invalid mindir" << std::endl;
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return 1;
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}
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if (RealPath(FLAGS_aipp_path).empty()) {
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std::cout << "Invalid aipp path" << std::endl;
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return 1;
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}
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Context::Instance().SetDeviceTarget("Ascend310").SetDeviceID(FLAGS_device_id);
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auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
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Model model((GraphCell(graph)));
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std::map<std::string, std::string> build_options;
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if (!FLAGS_precision_mode.empty()) {
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build_options.emplace(kModelOptionPrecisionMode, FLAGS_precision_mode);
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}
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if (!FLAGS_op_select_impl_mode.empty()) {
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build_options.emplace(kModelOptionOpSelectImplMode, FLAGS_op_select_impl_mode);
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}
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if (!FLAGS_aipp_path.empty()) {
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build_options.emplace(kModelOptionInsertOpCfgPath, FLAGS_aipp_path);
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}
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Status ret = model.Build(build_options);
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if (ret != SUCCESS) {
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std::cout << "EEEEEEEERROR Build failed." << std::endl;
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return 1;
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}
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auto all_files = GetAllFiles(FLAGS_dataset_path);
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if (all_files.empty()) {
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std::cout << "ERROR: no input data." << std::endl;
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return 1;
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}
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std::map<double, double> costTime_map;
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size_t size = all_files.size();
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MindDataEager SingleOp({DvppDecodeResizeJpeg({608, 608})});
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for (size_t i = 0; i < size; ++i) {
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struct timeval start = {0};
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struct timeval end = {0};
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double startTime_ms;
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double endTime_ms;
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std::vector<Buffer> inputs;
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std::vector<Buffer> outputs;
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std::cout << "Start predict input files:" << all_files[i] << std::endl;
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auto imgDvpp = SingleOp(ReadFileToTensor(all_files[i]));
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std::vector<float> input_shape = {608, 608};
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inputs.clear();
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inputs.emplace_back(imgDvpp->Data(), imgDvpp->DataSize());
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inputs.emplace_back(input_shape.data(), input_shape.size() * sizeof(float));
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gettimeofday(&start, NULL);
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ret = model.Predict(inputs, &outputs);
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gettimeofday(&end, NULL);
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if (ret != SUCCESS) {
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std::cout << "Predict " << all_files[i] << " failed." << std::endl;
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return 1;
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}
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startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
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endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
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costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
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WriteResult(all_files[i], outputs);
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}
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double average = 0.0;
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int infer_cnt = 0;
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char tmpCh[256] = {0};
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for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
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double diff = 0.0;
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diff = iter->second - iter->first;
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average += diff;
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infer_cnt++;
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}
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average = average/infer_cnt;
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snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms of infer_count %d \n", average, infer_cnt);
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
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std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
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file_stream << tmpCh;
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file_stream.close();
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costTime_map.clear();
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return 0;
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}
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@ -0,0 +1,145 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "../inc/utils.h"
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#include <fstream>
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#include <algorithm>
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#include <iostream>
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using mindspore::api::Tensor;
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using mindspore::api::Buffer;
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using mindspore::api::DataType;
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std::vector<std::string> GetAllFiles(std::string_view dirName) {
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struct dirent *filename;
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DIR *dir = OpenDir(dirName);
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if (dir == nullptr) {
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return {};
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}
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std::vector<std::string> res;
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while ((filename = readdir(dir)) != nullptr) {
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std::string dName = std::string(filename->d_name);
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if (dName == "." ||
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dName == ".." ||
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filename->d_type != DT_REG)
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continue;
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res.emplace_back(std::string(dirName) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
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for (auto &f : res) {
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std::cout << "image file: " << f << std::endl;
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}
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return res;
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}
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int WriteResult(const std::string& imageFile, const std::vector<Buffer> &outputs) {
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std::string homePath = "./result_Files";
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for (size_t i = 0; i < outputs.size(); ++i) {
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size_t outputSize;
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const void * netOutput;
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netOutput = outputs[i].Data();
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outputSize = outputs[i].DataSize();
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int pos = imageFile.rfind('/');
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std::string fileName(imageFile, pos + 1);
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fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
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std::string outFileName = homePath + "/" + fileName;
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FILE * outputFile = fopen(outFileName.c_str(), "wb");
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fwrite(netOutput, outputSize, sizeof(char), outputFile);
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fclose(outputFile);
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outputFile = nullptr;
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}
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return 0;
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}
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std::shared_ptr<Tensor> ReadFileToTensor(const std::string &file) {
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auto buffer = std::make_shared<Tensor>();
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if (file.empty()) {
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std::cout << "Pointer file is nullptr" << std::endl;
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return buffer;
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}
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std::ifstream ifs(file);
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if (!ifs.good()) {
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std::cout << "File: " << file << " is not exist" << std::endl;
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return buffer;
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}
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if (!ifs.is_open()) {
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std::cout << "File: " << file << "open failed" << std::endl;
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return buffer;
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}
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ifs.seekg(0, std::ios::end);
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size_t size = ifs.tellg();
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buffer->ResizeData(size);
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if (buffer->DataSize() != size) {
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std::cout << "Malloc buf failed, file: " << file << std::endl;
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ifs.close();
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return buffer;
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}
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ifs.seekg(0, std::ios::beg);
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ifs.read(reinterpret_cast<char *>(buffer->MutableData()), size);
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ifs.close();
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buffer->SetDataType(DataType::kMsUint8);
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buffer->SetShape({static_cast<int64_t>(size)});
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return buffer;
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}
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DIR *OpenDir(std::string_view dirName) {
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if (dirName.empty()) {
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std::cout << " dirName is null ! " << std::endl;
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return nullptr;
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}
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std::string realPath = RealPath(dirName);
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struct stat s;
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lstat(realPath.c_str(), &s);
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if (!S_ISDIR(s.st_mode)) {
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std::cout << "dirName is not a valid directory !" << std::endl;
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return nullptr;
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}
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DIR *dir;
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dir = opendir(realPath.c_str());
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if (dir == nullptr) {
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std::cout << "Can not open dir " << dirName << std::endl;
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return nullptr;
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}
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std::cout << "Successfully opened the dir " << dirName << std::endl;
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return dir;
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}
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std::string RealPath(std::string_view path) {
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char real_path_mem[PATH_MAX] = {0};
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char *real_path_ret = nullptr;
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real_path_ret = realpath(path.data(), real_path_mem);
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if (real_path_ret == nullptr) {
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std::cout << "File: " << path << " is not exist.";
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return "";
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}
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std::string real_path(real_path_mem);
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std::cout << path << " realpath is: " << real_path << std::endl;
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return real_path;
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}
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@ -0,0 +1,98 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# 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
|
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#
|
||||
# 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.
|
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# ============================================================================
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"""YoloV4 310 infer."""
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import os
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import argparse
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import datetime
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import time
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import numpy as np
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from pycocotools.coco import COCO
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from src.logger import get_logger
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from eval import DetectionEngine
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parser = argparse.ArgumentParser('mindspore coco testing')
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# dataset related
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parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
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# logging related
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parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
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# detect_related
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parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
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parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
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parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
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parser.add_argument('--img_id_file_path', type=str, default='', help='path of image dataset')
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parser.add_argument('--result_files', type=str, default='./result_Files', help='path to 310 infer result floder')
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args, _ = parser.parse_known_args()
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class Redirct:
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def __init__(self):
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self.content = ""
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def write(self, content):
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self.content += content
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def flush(self):
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self.content = ""
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if __name__ == "__main__":
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start_time = time.time()
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, 0)
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# init detection engine
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detection = DetectionEngine(args)
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coco = COCO(args.ann_file)
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result_path = args.result_files
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files = os.listdir(args.img_id_file_path)
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for file in files:
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img_ids_name = file.split('.')[0]
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img_id = int(np.squeeze(img_ids_name))
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imgIds = coco.getImgIds(imgIds=[img_id])
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img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
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image_shape = ((img['width'], img['height']),)
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img_id = (np.squeeze(img_ids_name),)
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result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
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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.))
|
||||
@ -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
|
||||
@ -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
|
||||
}
|
||||
Loading…
Reference in new issue