diff --git a/model_zoo/official/cv/squeezenet/README.md b/model_zoo/official/cv/squeezenet/README.md index 8b05af1a297..487817251ab 100644 --- a/model_zoo/official/cv/squeezenet/README.md +++ b/model_zoo/official/cv/squeezenet/README.md @@ -12,10 +12,15 @@ - [Script Parameters](#script-parameters) - [Training Process](#training-process) - [Evaluation Process](#evaluation-process) + - [Inference Process](#inference-process) + - [Export MindIR](#export-mindir) + - [Infer on Ascend310](#infer-on-ascend310) + - [result](#result) - [Model Description](#model-description) - [Performance](#performance) - [Evaluation Performance](#evaluation-performance) - [Inference Performance](#inference-performance) + - [310 Inference Performance](#310-inference-performance) - [How to use](#how-to-use) - [Inference](#inference) - [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model) @@ -105,10 +110,12 @@ After installing MindSpore via the official website, you can start training and . └── squeezenet ├── README.md + ├── ascend310_infer # application for 310 inference ├── scripts ├── run_distribute_train.sh # launch ascend distributed training(8 pcs) ├── run_standalone_train.sh # launch ascend standalone training(1 pcs) ├── run_eval.sh # launch ascend evaluation + ├── run_infer_310.sh # shell script for 310 infer ├── src ├── config.py # parameter configuration ├── dataset.py # data preprocessing @@ -118,6 +125,8 @@ After installing MindSpore via the official website, you can start training and ├── train.py # train net ├── eval.py # eval net └── export.py # export checkpoint files into geir/onnx + ├── postprocess.py # postprocess script + ├── preprocess.py # preprocess script ``` ## [Script Parameters](#contents) @@ -330,6 +339,61 @@ result: {'top_1_accuracy': 0.9077524038461539, 'top_5_accuracy': 0.9969951923076 result: {'top_1_accuracy': 0.6094950384122919, 'top_5_accuracy': 0.826324423815621} ``` +## [Inference process](#contents) + +### Export MindIR + +```shell +python export.py --ckpt_file [CKPT_PATH] --batch_size [BATCH_SIZE] --net [NET] --dataset [DATASET] --file_format [EXPORT_FORMAT] +``` + +The ckpt_file parameter is required, +`BATCH_SIZE` can only be set to 1 +`NET` should be in ["squeezenet", "squeezenet_residual"] +`DATASET` should be in ["cifar10", "imagenet"] +`EXPORT_FORMAT` should be in ["AIR", "MINDIR"] + +### Infer on Ascend310 + +Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model. + +```shell +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [DATASET] [DATA_PATH] [LABEL_PATH] [DEVICE_ID] +``` + +- `DATASET` should be in ["imagenet", "cifar10"]. +- `LABEL_PATH` label.txt path, LABEL_FILE is only useful for imagenet. Write a py script to sort the category under the dataset, map the file names under the categories and category sort values,Such as[file name : sort value], and write the mapping results to the labe.txt 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. + +- Infer SqueezeNet with CIFAR-10 dataset + +```bash +'Top1_Accuracy': 83.62% 'Top5_Accuracy': 99.31% +``` + +- Infer SqueezeNet with ImageNet dataset + +```bash +'Top1_Accuracy': 59.30% 'Top5_Accuracy': 81.40% +``` + +- Infer SqueezeNet_Residual with CIFAR-10 dataset + +```bash +'Top1_Accuracy': 87.28% 'Top5_Accuracy': 99.58% +``` + +- Infer SqueezeNet_Residual with ImageNet dataset + +```bash +'Top1_Accuracy': 60.82% 'Top5_Accuracy': 82.56% +``` + # [Model Description](#contents) ## [Performance](#contents) @@ -470,6 +534,60 @@ result: {'top_1_accuracy': 0.6094950384122919, 'top_5_accuracy': 0.8263244238156 | outputs | probability | | Accuracy | 8pcs: 60.9%(TOP1), 82.6%(TOP5) | +### 310 Inference Performance + +#### SqueezeNet on CIFAR-10 + +| Parameters | Ascend | +| ------------------- | --------------------------- | +| Model Version | SqueezeNet | +| Resource | Ascend 310; OS Euler2.8 | +| Uploaded Date | 27/05/2021 (month/day/year) | +| MindSpore Version | 1.2.0 | +| Dataset | CIFAR-10 | +| batch_size | 1 | +| outputs | Accuracy | +| Accuracy | TOP1: 83.62%, TOP5: 99.31% | + +#### SqueezeNet on ImageNet + +| Parameters | Ascend | +| ------------------- | --------------------------- | +| Model Version | SqueezeNet | +| Resource | Ascend 310; OS Euler2.8 | +| Uploaded Date | 27/05/2020 (month/day/year) | +| MindSpore Version | 1.2.0 | +| Dataset | ImageNet | +| batch_size | 1 | +| outputs | Accuracy | +| Accuracy | TOP1: 59.30%, TOP5: 81.40% | + +#### SqueezeNet_Residual on CIFAR-10 + +| Parameters | Ascend | +| ------------------- | --------------------------- | +| Model Version | SqueezeNet_Residual | +| Resource | Ascend 310; OS Euler2.8 | +| Uploaded Date | 27/05/2020 (month/day/year) | +| MindSpore Version | 1.2.0 | +| Dataset | CIFAR-10 | +| batch_size | 1 | +| outputs | Accuracy | +| Accuracy | TOP1: 87.28%, TOP5: 99.58% | + +#### SqueezeNet_Residual on ImageNet + +| Parameters | Ascend | +| ------------------- | --------------------------- | +| Model Version | SqueezeNet_Residual | +| Resource | Ascend 310; OS Euler2.8 | +| Uploaded Date | 27/05/2020 (month/day/year) | +| MindSpore Version | 1.2.0 | +| Dataset | ImageNet | +| batch_size | 1 | +| outputs | Accuracy | +| Accuracy | TOP1: 60.82%, TOP5: 82.56% | + ## [How to use](#contents) ### Inference diff --git a/model_zoo/official/cv/squeezenet/ascend310_infer/CMakeLists.txt b/model_zoo/official/cv/squeezenet/ascend310_infer/CMakeLists.txt new file mode 100644 index 00000000000..ee3c8544734 --- /dev/null +++ b/model_zoo/official/cv/squeezenet/ascend310_infer/CMakeLists.txt @@ -0,0 +1,14 @@ +cmake_minimum_required(VERSION 3.14.1) +project(Ascend310Infer) +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}) +find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib) +file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*) + +add_executable(main src/main.cc src/utils.cc) +target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags) diff --git a/model_zoo/official/cv/squeezenet/ascend310_infer/build.sh b/model_zoo/official/cv/squeezenet/ascend310_infer/build.sh new file mode 100644 index 00000000000..770a8851efa --- /dev/null +++ b/model_zoo/official/cv/squeezenet/ascend310_infer/build.sh @@ -0,0 +1,23 @@ +#!/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 [ ! -d out ]; then + mkdir out +fi +cd out || exit +cmake .. \ + -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`" +make diff --git a/model_zoo/official/cv/squeezenet/ascend310_infer/inc/utils.h b/model_zoo/official/cv/squeezenet/ascend310_infer/inc/utils.h new file mode 100644 index 00000000000..efebe03a8c1 --- /dev/null +++ b/model_zoo/official/cv/squeezenet/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); +mindspore::MSTensor ReadFileToTensor(const std::string &file); +int WriteResult(const std::string& imageFile, const std::vector &outputs); +#endif diff --git a/model_zoo/official/cv/squeezenet/ascend310_infer/src/main.cc b/model_zoo/official/cv/squeezenet/ascend310_infer/src/main.cc new file mode 100644 index 00000000000..9e36970f4ac --- /dev/null +++ b/model_zoo/official/cv/squeezenet/ascend310_infer/src/main.cc @@ -0,0 +1,151 @@ +/** + * 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 "include/api/model.h" +#include "include/api/context.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "include/dataset/vision_ascend.h" +#include "include/dataset/execute.h" +#include "include/dataset/vision.h" +#include "inc/utils.h" + +using mindspore::Context; +using mindspore::Serialization; +using mindspore::Model; +using mindspore::Status; +using mindspore::ModelType; +using mindspore::GraphCell; +using mindspore::kSuccess; +using mindspore::MSTensor; +using mindspore::dataset::Execute; +using mindspore::dataset::TensorTransform; +using mindspore::dataset::vision::Resize; +using mindspore::dataset::vision::HWC2CHW; +using mindspore::dataset::vision::Normalize; +using mindspore::dataset::vision::Decode; +using mindspore::dataset::vision::CenterCrop; + + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(dataset, "Imagenet", "dataset：Imagenet or Cifar10"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); + + +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; + } + + auto context = std::make_shared(); + auto ascend310 = std::make_shared(); + ascend310->SetDeviceID(FLAGS_device_id); + ascend310->SetOpSelectImplMode("high_precision"); + ascend310->SetBufferOptimizeMode("off_optimize"); + context->MutableDeviceInfo().push_back(ascend310); + + mindspore::Graph graph; + Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph); + + Model model; + Status ret = model.Build(GraphCell(graph), context); + if (ret != kSuccess) { + std::cout << "ERROR: 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::shared_ptr decode(new Decode()); + std::shared_ptr resize(new Resize({256, 256})); + std::shared_ptr center_crop(new CenterCrop({227, 227})); + std::shared_ptr normalize(new Normalize({123.675, 116.28, 103.53}, + {58.395, 57.120, 57.375})); + std::shared_ptr hwc2chw(new HWC2CHW()); + Execute transform({decode, resize, center_crop, normalize, hwc2chw}); + + std::map costTime_map; + size_t size = all_files.size(); + + for (size_t i = 0; i < size; ++i) { + struct timeval start = {0}; + struct timeval end = {0}; + double startTimeMs; + double endTimeMs; + std::vector inputs; + std::vector outputs; + + std::cout << "Start predict input files:" << all_files[i] << std::endl; + mindspore::MSTensor image = ReadFileToTensor(all_files[i]); + if (FLAGS_dataset.compare("imagenet") == 0) { + transform(image, &image); + } + + std::vector model_inputs = model.GetInputs(); + inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(), + image.Data().get(), image.DataSize()); + + + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + if (ret != kSuccess) { + std::cout << "Predict " << all_files[i] << " failed." << std::endl; + return 1; + } + startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000; + endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000; + costTime_map.insert(std::pair(startTimeMs, endTimeMs)); + WriteResult(all_files[i], outputs); + } + double average = 0.0; + int inferCount = 0; + + for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) { + double diff = 0.0; + diff = iter->second - iter->first; + average += diff; + inferCount++; + } + average = average / inferCount; + std::stringstream timeCost; + timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl; + std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl; + + std::string fileName = "./time_Result" + std::string("/test_perform_static.txt"); + std::ofstream fileStream(fileName.c_str(), std::ios::trunc); + fileStream << timeCost.str(); + fileStream.close(); + costTime_map.clear(); + return 0; +} diff --git a/model_zoo/official/cv/squeezenet/ascend310_infer/src/utils.cc b/model_zoo/official/cv/squeezenet/ascend310_infer/src/utils.cc new file mode 100644 index 00000000000..cc5e872a937 --- /dev/null +++ b/model_zoo/official/cv/squeezenet/ascend310_infer/src/utils.cc @@ -0,0 +1,130 @@ +/** + * 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::MSTensor; +using mindspore::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; + std::shared_ptr 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('.'), ".bin"); + std::string outFileName = homePath + "/" + fileName; + FILE * outputFile = fopen(outFileName.c_str(), "wb"); + fwrite(netOutput.get(), outputSize, sizeof(char), outputFile); + fclose(outputFile); + outputFile = nullptr; + } + return 0; +} + +mindspore::MSTensor ReadFileToTensor(const std::string &file) { + if (file.empty()) { + std::cout << "Pointer file is nullptr" << std::endl; + return mindspore::MSTensor(); + } + + std::ifstream ifs(file); + if (!ifs.good()) { + std::cout << "File: " << file << " is not exist" << std::endl; + return mindspore::MSTensor(); + } + + if (!ifs.is_open()) { + std::cout << "File: " << file << "open failed" << std::endl; + return mindspore::MSTensor(); + } + + ifs.seekg(0, std::ios::end); + size_t size = ifs.tellg(); + mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast(buffer.MutableData()), size); + ifs.close(); + + 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 realPathMem[PATH_MAX] = {0}; + char *realPathRet = nullptr; + realPathRet = realpath(path.data(), realPathMem); + + if (realPathRet == nullptr) { + std::cout << "File: " << path << " is not exist."; + return ""; + } + + std::string realPath(realPathMem); + std::cout << path << " realpath is: " << realPath << std::endl; + return realPath; +} diff --git a/model_zoo/official/cv/squeezenet/export.py b/model_zoo/official/cv/squeezenet/export.py index 896aadadb92..1d962b32646 100755 --- a/model_zoo/official/cv/squeezenet/export.py +++ b/model_zoo/official/cv/squeezenet/export.py @@ -31,7 +31,7 @@ parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezene help='Model.') parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.') parser.add_argument("--file_name", type=str, default="squeezenet", help="output file name.") -parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format") +parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="AIR", help="file format") parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)") args = parser.parse_args() diff --git a/model_zoo/official/cv/squeezenet/postprocess.py b/model_zoo/official/cv/squeezenet/postprocess.py new file mode 100644 index 00000000000..80feb3c3ec0 --- /dev/null +++ b/model_zoo/official/cv/squeezenet/postprocess.py @@ -0,0 +1,100 @@ +# 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. +# ============================================================================ +"""post process for 310 inference""" +import os +import argparse +import numpy as np + +parser = argparse.ArgumentParser(description='squeeze calcul top1 and top5 acc') +parser.add_argument("--dataset", type=str, required=True, default="imagenet", help="dataset: Imagenet or Cifar10") +parser.add_argument("--result_path", type=str, required=True, default='', help="result file path") +parser.add_argument("--label_file", type=str, required=True, default='', help="label file") +args = parser.parse_args() + + +def get_top5_acc(top_arg, gt_class): + sub_count = 0 + for top5, gt in zip(top_arg, gt_class): + if gt in top5: + sub_count += 1 + return sub_count + + +def read_label(label_file): + with open(label_file, 'r') as f: + lines = f.readlines() + img_dict = {} + for line in lines: + img_id = line.split(':')[0] + label = line.split(':')[1] + img_dict[img_id] = label + return img_dict + + +def cal_acc_cifar10(result_path, label_path): + img_tot = 0 + top1_correct = 0 + top5_correct = 0 + result_shape = (1, 10) + files = os.listdir(result_path) + for file in files: + full_file_path = os.path.join(result_path, file) + if os.path.isfile(full_file_path): + result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape) + label_file = os.path.join(label_path, file) + gt_classes = np.fromfile(label_file, dtype=np.int32) + top1_output = np.argmax(result, (-1)) + top5_output = np.argsort(result)[:, -5:] + t1_correct = np.equal(top1_output, gt_classes).sum() + top1_correct += t1_correct + top5_correct += get_top5_acc(top5_output, [gt_classes]) + img_tot += 1 + acc1 = 100 * top1_correct / img_tot + acc5 = 100 * top5_correct / img_tot + print('total={}, top1_correct={}, acc={:.2f}%'.format(img_tot, top1_correct, acc1)) + print('total={}, top5_correct={}, acc={:.2f}%'.format(img_tot, top5_correct, acc5)) + + +def cal_acc_imagenet(result_path, label_file): + img_label = read_label(label_file) + img_tot = 0 + top1_correct = 0 + top5_correct = 0 + result_shape = (1, 1000) + files = os.listdir(result_path) + for file in files: + full_file_path = os.path.join(result_path, file) + if os.path.isfile(full_file_path): + result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape) + gt_classes = int(img_label[file.split('.')[0]]) + + top1_output = np.argmax(result, (-1)) + top5_output = np.argsort(result)[:, -5:] + + t1_correct = np.equal(top1_output, gt_classes).sum() + top1_correct += t1_correct + top5_correct += get_top5_acc(top5_output, [gt_classes]) + img_tot += 1 + acc1 = 100 * top1_correct / img_tot + acc5 = 100 * top5_correct / img_tot + print('total={}, top1_correct={}, acc={:.2f}%'.format(img_tot, top1_correct, acc1)) + print('total={}, top5_correct={}, acc={:.2f}%'.format(img_tot, top5_correct, acc5)) + + +if __name__ == '__main__': + if args.dataset.lower() == "cifar10": + cal_acc_cifar10(args.result_path, args.label_file) + else: + cal_acc_imagenet(args.result_path, args.label_file) diff --git a/model_zoo/official/cv/squeezenet/preprocess.py b/model_zoo/official/cv/squeezenet/preprocess.py new file mode 100644 index 00000000000..24591333728 --- /dev/null +++ b/model_zoo/official/cv/squeezenet/preprocess.py @@ -0,0 +1,47 @@ +# 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. +# ============================================================================ +"""pre process for 310 inference""" +import os +import argparse +from src.dataset import create_dataset_cifar + +parser = argparse.ArgumentParser(description="squeeze cifar10 preprocess") +parser.add_argument("--dataset_path", type=str, required=True, help="dataset path.") +parser.add_argument("--output_path", type=str, required=True, help="output path.") +args = parser.parse_args() + + +def preprocess(dataset_path, output_path): + dataset = create_dataset_cifar(dataset_path, False, batch_size=1) + img_path = os.path.join(output_path, "img_data") + label_path = os.path.join(output_path, "label") + os.makedirs(img_path) + os.makedirs(label_path) + + for idx, data in enumerate(dataset.create_dict_iterator(output_numpy=True, num_epochs=1)): + img_data = data["image"] + img_label = data["label"] + file_name = "squeeze_cifar10" + "_" + str(idx) + '.bin' + img_file_path = os.path.join(img_path, file_name) + img_data.tofile(img_file_path) + + label_file_path = os.path.join(label_path, file_name) + img_label.tofile(label_file_path) + + print("=" * 20, "export bin files finished", "=" * 20) + + +if __name__ == '__main__': + preprocess(args.dataset_path, args.output_path) diff --git a/model_zoo/official/cv/squeezenet/scripts/run_infer_310.sh b/model_zoo/official/cv/squeezenet/scripts/run_infer_310.sh new file mode 100644 index 00000000000..ad009a1af02 --- /dev/null +++ b/model_zoo/official/cv/squeezenet/scripts/run_infer_310.sh @@ -0,0 +1,117 @@ +#!/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 4 || $# -gt 5 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET] [DATA_PATH] [LABEL_FILE] [DEVICE_ID] + DEVICE_ID is optional, default value is zero, LABEL_FILE is only useful for imagenet " +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +dataset=$2 +data_path=$(get_real_path $3) +label_file=$(get_real_path $4) + +device_id=0 +if [ $# == 5 ]; then + device_id=$5 +fi + +echo "mindir name: "$model +echo "dataset: "$dataset +echo "dataset path: "$data_path +echo "label file path: "$label_file +echo "device id: "$device_id + +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=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/opp +fi + +function preprocess_data() +{ + if [ -d preprocess_Result ]; then + rm -rf ./preprocess_Result + fi + mkdir preprocess_Result + + python3.7 ../preprocess.py --dataset_path=$data_path --output_path=./preprocess_Result &> preprocess.log +} + +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=$model --dataset=$dataset --dataset_path=$data_path --device_id=$device_id &> infer.log +} + +function cal_acc() +{ + if [ "x${dataset}" == "xcifar10" ] || [ "x${dataset}" == "xCifar10" ]; then + python ../postprocess.py --dataset=$dataset --label_file=./preprocess_Result/label --result_path=result_Files &> acc.log & + else + python ../postprocess.py --dataset=$dataset --label_file=$label_file --result_path=result_Files &> acc.log & + fi +} + +if [ "x${dataset}" == "xcifar10" ] || [ "x${dataset}" == "xCifar10" ]; then + preprocess_data + data_path=./preprocess_Result/img_data +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 \ No newline at end of file