faceattribute add 310 infer
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@ -229,6 +229,42 @@ cd ./scripts
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sh run_export.sh [BATCH_SIZE] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]
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```
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### Inference Process
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#### Export MindIR
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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The ckpt_file parameter is required,
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`file_format` should be in ["AIR", "MINDIR"]
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`ckpt_path` ckpt file path
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#### Infer on Ascend310
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Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
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Current batch_Size for imagenet2012 dataset can only be set to 1.
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
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```
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- `MINDIR_PATH` specifies path of used "MINDIR" OR "AIR" model.
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- `DATASET_PATH` specifies path of cifar10 datasets
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- `DEVICE_ID` is optional, default value is 0.
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#### Result
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Inference result is saved in current path, you can find result like this in acc.log file.
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```bash
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'age accuracy': 0.4937
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'gen accuracy': 0.9093
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'mask accuracy': 0.9903
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```
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# [Model Description](#contents)
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## [Performance](#contents)
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@ -0,0 +1,14 @@
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cmake_minimum_required(VERSION 3.14.1)
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project(Ascend310Infer)
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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})
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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 src/main.cc src/utils.cc)
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target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
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@ -0,0 +1,29 @@
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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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if [ -d out ]; then
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rm -rf out
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fi
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mkdir out
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cd out || exit
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if [ -f "Makefile" ]; then
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make clean
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fi
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cmake .. \
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-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 +1,35 @@
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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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mindspore::MSTensor ReadFileToTensor(const std::string &file);
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int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
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std::vector<std::string> GetAllFiles(std::string dir_name);
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std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name);
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#endif
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@ -0,0 +1,125 @@
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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 <sstream>
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#include "include/api/model.h"
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#include "include/api/context.h"
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#include "include/api/types.h"
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#include "include/api/serialization.h"
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#include "inc/utils.h"
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::Status;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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using mindspore::kSuccess;
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using mindspore::MSTensor;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(input0_path, ".", "input0 path");
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DEFINE_int32(device_id, 0, "device id");
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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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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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Model model;
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Status rst = model.Build(GraphCell(graph), context);
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if (rst != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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}
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std::vector<MSTensor> model_inputs = model.GetInputs();
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if (model_inputs.empty()) {
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std::cout << "Invalid model, inputs is empty." << std::endl;
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return 1;
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}
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auto input0_files = GetAllFiles(FLAGS_input0_path);
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if (input0_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 = input0_files.size();
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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 startTimeMs;
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double endTimeMs;
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std::vector<MSTensor> inputs;
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std::vector<MSTensor> outputs;
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std::cout << "Start predict input files:" << input0_files[i] <<std::endl;
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auto input0 = ReadFileToTensor(input0_files[i]);
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std::cout << "input0:" << std::endl;
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inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
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input0.Data().get(), input0.DataSize());
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gettimeofday(&start, nullptr);
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Status ret = model.Predict(inputs, &outputs);
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std::cout << "ret:" << std::endl;
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gettimeofday(&end, nullptr);
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if (ret != kSuccess) {
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std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
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return 1;
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}
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startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
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endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
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costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
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WriteResult(input0_files[i], outputs);
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}
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double average = 0.0;
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int inferCount = 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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inferCount++;
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}
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average = average / inferCount;
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std::stringstream timeCost;
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timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
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std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
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fileStream << timeCost.str();
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fileStream.close();
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costTime_map.clear();
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return 0;
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}
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@ -0,0 +1,185 @@
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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 <fstream>
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#include <algorithm>
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#include <iostream>
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#include "inc/utils.h"
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using mindspore::MSTensor;
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using mindspore::DataType;
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std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name) {
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std::vector<std::vector<std::string>> ret;
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DIR *dir = OpenDir(dir_name);
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if (dir == nullptr) {
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return {};
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}
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struct dirent *filename;
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/* read all the files in the dir ~ */
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std::vector<std::string> sub_dirs;
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while ((filename = readdir(dir)) != nullptr) {
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std::string d_name = std::string(filename->d_name);
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// get rid of "." and ".."
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if (d_name == "." || d_name == ".." || d_name.empty()) {
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continue;
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}
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std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name);
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struct stat s;
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lstat(dir_path.c_str(), &s);
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if (!S_ISDIR(s.st_mode)) {
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continue;
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}
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sub_dirs.emplace_back(dir_path);
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}
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std::sort(sub_dirs.begin(), sub_dirs.end());
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(void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret),
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[](const std::string &d) { return GetAllFiles(d); });
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return ret;
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}
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std::vector<std::string> GetAllFiles(std::string dir_name) {
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struct dirent *filename;
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DIR *dir = OpenDir(dir_name);
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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 d_name = std::string(filename->d_name);
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if (d_name == "." || d_name == ".." || d_name.size() <= 3) {
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continue;
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}
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res.emplace_back(std::string(dir_name) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
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return res;
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}
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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 == "." || dName == ".." || filename->d_type != DT_REG) {
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continue;
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}
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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<MSTensor> &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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std::shared_ptr<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.get(), 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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mindspore::MSTensor ReadFileToTensor(const std::string &file) {
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if (file.empty()) {
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std::cout << "Pointer file is nullptr" << std::endl;
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return mindspore::MSTensor();
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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 mindspore::MSTensor();
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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 mindspore::MSTensor();
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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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mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
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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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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 realPathMem[PATH_MAX] = {0};
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char *realPathRet = nullptr;
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realPathRet = realpath(path.data(), realPathMem);
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if (realPathRet == 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 realPath(realPathMem);
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std::cout << path << " realpath is: " << realPath << std::endl;
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return realPath;
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}
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@ -24,13 +24,13 @@ from mindspore.train.serialization import export, load_checkpoint, load_param_in
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from src.FaceAttribute.resnet18_softmax import get_resnet18
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from src.config import config
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devid = int(os.getenv('DEVICE_ID'))
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devid = 0
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=devid)
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def main(args):
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network = get_resnet18(args)
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ckpt_path = args.model_path
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ckpt_path = args.ckpt_file
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if os.path.isfile(ckpt_path):
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param_dict = load_checkpoint(ckpt_path)
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param_dict_new = {}
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|
|
@ -46,19 +46,19 @@ def main(args):
|
|||
else:
|
||||
print('-----------------------load model failed -----------------------')
|
||||
|
||||
input_data = np.random.uniform(low=0, high=1.0, size=(args.batch_size, 3, 112, 112)).astype(np.float32)
|
||||
input_data = np.random.uniform(low=0, high=1.0, size=(1, 3, 112, 112)).astype(np.float32)
|
||||
tensor_input_data = Tensor(input_data)
|
||||
|
||||
export(network, tensor_input_data, file_name=ckpt_path.replace('.ckpt', '_' + str(args.batch_size) + 'b.air'),
|
||||
file_format='AIR')
|
||||
export(network, tensor_input_data, file_name=args.file_name,
|
||||
file_format=args.file_format)
|
||||
print('-----------------------export model success-----------------------')
|
||||
|
||||
def parse_args():
|
||||
"""parse_args"""
|
||||
parser = argparse.ArgumentParser(description='Convert ckpt to air')
|
||||
parser.add_argument('--model_path', type=str, default='', help='pretrained model to load')
|
||||
parser.add_argument('--batch_size', type=int, default=8, help='batch size')
|
||||
|
||||
parser = argparse.ArgumentParser(description='Convert ckpt to designated format')
|
||||
parser.add_argument('--ckpt_file', type=str, default='', help='pretrained model to load')
|
||||
parser.add_argument('--file_name', type=str, default='faceattri', help='file name')
|
||||
parser.add_argument('--file_format', type=str, default='MINDIR', choices=['MINDIR', 'AIR'], help='file format')
|
||||
args_opt = parser.parse_args()
|
||||
return args_opt
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""postprocess for 310 inference"""
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
|
||||
batch_size = 1
|
||||
parser = argparse.ArgumentParser(description="face attribute acc postprocess")
|
||||
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
|
||||
parser.add_argument("--label_path", type=str, default="./data/label", required=True, help="image label file path.")
|
||||
args = parser.parse_args()
|
||||
|
||||
def calcul_acc(lab, preds):
|
||||
return sum(1 for x, y in zip(lab, preds) if x == y) / len(lab)
|
||||
|
||||
def get_result(result_path, img_label_path):
|
||||
"""get accuracy result"""
|
||||
files = os.listdir(img_label_path)
|
||||
preds_age = []
|
||||
preds_gen = []
|
||||
preds_mask = []
|
||||
labels_age = []
|
||||
labels_gen = []
|
||||
labels_mask = []
|
||||
for file in files:
|
||||
label = np.fromfile(os.path.join(img_label_path, file), dtype=np.int32)
|
||||
labels_age.append(int(label[0]))
|
||||
labels_gen.append(int(label[1]))
|
||||
labels_mask.append(int(label[2]))
|
||||
file_name = file.split('.')[0]
|
||||
age_result_path = os.path.join(result_path, file_name+'_0.bin')
|
||||
gen_result_path = os.path.join(result_path, file_name+'_1.bin')
|
||||
mask_result_path = os.path.join(result_path, file_name+'_2.bin')
|
||||
output_age = np.fromfile(age_result_path, dtype=np.float32)
|
||||
output_gen = np.fromfile(gen_result_path, dtype=np.float32)
|
||||
output_mask = np.fromfile(mask_result_path, dtype=np.float32)
|
||||
preds_age.append(np.argmax(output_age, axis=0))
|
||||
preds_gen.append(np.argmax(output_gen, axis=0))
|
||||
preds_mask.append(np.argmax(output_mask, axis=0))
|
||||
age_acc = calcul_acc(labels_age, preds_age)
|
||||
gen_acc = calcul_acc(labels_gen, preds_gen)
|
||||
mask_acc = calcul_acc(labels_mask, preds_mask)
|
||||
print("age accuracy: {}".format(age_acc))
|
||||
print("gen accuracy: {}".format(gen_acc))
|
||||
print("mask accuracy: {}".format(mask_acc))
|
||||
if __name__ == '__main__':
|
||||
get_result(args.result_path, args.label_path)
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""preprocess"""
|
||||
import os
|
||||
import argparse
|
||||
from src.config import config
|
||||
import mindspore.dataset as de
|
||||
import mindspore.dataset.vision.py_transforms as F
|
||||
import mindspore.dataset.transforms.py_transforms as F2
|
||||
|
||||
def parse_args():
|
||||
"""parse_args"""
|
||||
parser = argparse.ArgumentParser(description='face attribute dataset to bin')
|
||||
parser.add_argument('--model_path', type=str, default='', help='mindir path referenced')
|
||||
parser.add_argument('--mindrecord_path', type=str, default='', help='mindir file path')
|
||||
args_opt = parser.parse_args()
|
||||
return args_opt
|
||||
|
||||
def eval_data_generator(args):
|
||||
'''Build eval dataloader.'''
|
||||
mindrecord_path = args.mindrecord_path
|
||||
dst_w = args.dst_w
|
||||
dst_h = args.dst_h
|
||||
batch_size = 1
|
||||
#attri_num = args.attri_num
|
||||
transform_img = F2.Compose([F.Decode(),
|
||||
F.Resize((dst_w, dst_h)),
|
||||
F.ToTensor(),
|
||||
F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
|
||||
|
||||
de_dataset = de.MindDataset(mindrecord_path + "0", columns_list=["image", "label"])
|
||||
de_dataset = de_dataset.map(input_columns="image", operations=transform_img, num_parallel_workers=args.workers,
|
||||
python_multiprocessing=True)
|
||||
de_dataset = de_dataset.batch(batch_size)
|
||||
|
||||
#de_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
|
||||
steps_per_epoch = de_dataset.get_dataset_size()
|
||||
print("image number:{0}".format(steps_per_epoch))
|
||||
#num_classes = attri_num
|
||||
return de_dataset
|
||||
|
||||
if __name__ == "__main__":
|
||||
args_1 = parse_args()
|
||||
args_1.dst_h = config.dst_h
|
||||
args_1.dst_w = config.dst_w
|
||||
args_1.attri_num = config.attri_num
|
||||
args_1.classes = config.classes
|
||||
args_1.flat_dim = config.flat_dim
|
||||
args_1.fc_dim = config.fc_dim
|
||||
args_1.workers = config.workers
|
||||
ds = eval_data_generator(args_1)
|
||||
cur_dir = os.getcwd()
|
||||
image_path = os.path.join(cur_dir, './data/image')
|
||||
if not os.path.isdir(image_path):
|
||||
os.makedirs(image_path)
|
||||
image_label_path = os.path.join(cur_dir, './data/label')
|
||||
if not os.path.isdir(image_label_path):
|
||||
os.makedirs(image_label_path)
|
||||
total = ds.get_dataset_size()
|
||||
iter_num = 0
|
||||
for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
|
||||
file_name = "face_" + str(iter_num) + '.bin'
|
||||
img_np = data['image']
|
||||
image_label = data['label']
|
||||
img_np.tofile(os.path.join(image_path, file_name))
|
||||
image_label.tofile(os.path.join(image_label_path, file_name))
|
||||
iter_num += 1
|
||||
print("total num of images:", total)
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
#!/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 2 || $# -gt 3 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
input_path=$(get_real_path $2)
|
||||
device_id=0
|
||||
if [ $# == 3 ]; then
|
||||
device_id=$3
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "input path: "$input_path
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
export ASCEND_HOME=/usr/local/Ascend
|
||||
export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/toolkit/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib/:/usr/local/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:/usr/local/Ascend/toolkit/lib64:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages
|
||||
export PATH=/usr/local/python375/bin:$PATH
|
||||
export NPU_HOST_LIB=/usr/local/Ascend/acllib/lib64/stub
|
||||
export ASCEND_OPP_PATH=/usr/local/Ascend/opp
|
||||
export ASCEND_AICPU_PATH=/usr/local/Ascend
|
||||
export LD_LIBRARY_PATH=/usr/local/lib64/:$LD_LIBRARY_PATH
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d data ]; then
|
||||
rm -rf ./data
|
||||
fi
|
||||
mkdir data
|
||||
python3.7 ../preprocess.py --mindrecord_path=$input_path
|
||||
}
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer/ || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
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/out/main --mindir_path=$model --input0_path=./data/image --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files --label_path=./data/label &> acc.log &
|
||||
}
|
||||
|
||||
|
||||
preprocess_data
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "preprocess dataset failed"
|
||||
exit 1
|
||||
fi
|
||||
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
|
||||
Loading…
Reference in New Issue