From 4a3f3bdddc0f6083123ec5c1d744753c0daa5ff7 Mon Sep 17 00:00:00 2001 From: yuzhenhua Date: Mon, 31 May 2021 15:34:54 +0800 Subject: [PATCH] ascend310 inferenct for resnet50,resnet101 --- model_zoo/official/cv/resnet/README.md | 39 +++- model_zoo/official/cv/resnet/README_CN.md | 39 +++- .../cv/resnet/ascend310_infer/inc/utils.h | 2 - .../cv/resnet/ascend310_infer/src/main.cc | 192 +++++++++--------- .../cv/resnet/ascend310_infer/src/utils.cc | 106 +++------- model_zoo/official/cv/resnet/postprocess.py | 40 +++- model_zoo/official/cv/resnet/preprocess.py | 47 +++++ .../cv/resnet/scripts/run_infer_310.sh | 45 +++- 8 files changed, 324 insertions(+), 186 deletions(-) create mode 100755 model_zoo/official/cv/resnet/preprocess.py diff --git a/model_zoo/official/cv/resnet/README.md b/model_zoo/official/cv/resnet/README.md index c2978ac5684..55d02a73057 100644 --- a/model_zoo/official/cv/resnet/README.md +++ b/model_zoo/official/cv/resnet/README.md @@ -521,18 +521,51 @@ Current batch_Size can only be set to 1. The precision calculation process needs ```shell # Ascend310 inference -bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATA_PATH] [DEVICE_ID] +bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID] ``` -- `NET_TYPE` can choose from [resnet18, se-resnet50]. +- `NET_TYPE` can choose from [resnet18, se-resnet50, resnet50, resnet101]. +- `DATASET` can choose from [cifar10, imagenet]. - `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. +- Evaluating ResNet18 with CIFAR-10 dataset + ```bash -top1_accuracy:70.42, top5_accuracy:89.7 +Total data: 10000, top1 accuracy: 0.94.26, top5 accuracy: 0.9987. +``` + +- Evaluating ResNet18 with ImageNet2012 dataset + +```bash +Total data: 50000, top1 accuracy: 0.70668, top5 accuracy: 0.89698. +``` + +- Evaluating ResNet50 with CIFAR-10 dataset + +```bash +Total data: 10000, top1 accuracy: 0.9310, top5 accuracy: 0.9980. +``` + +- Evaluating ResNet50 with ImageNet2012 dataset + +```bash +Total data: 50000, top1 accuracy: 0.0.7696, top5 accuracy: 0.93432. +``` + +- Evaluating ResNet101 with ImageNet2012 dataset + +```bash +Total data: 50000, top1 accuracy: 0.7871, top5 accuracy: 0.94354. +``` + +- Evaluating SE-ResNet50 with ImageNet2012 dataset + +```bash +Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. ``` # [Model Description](#contents) diff --git a/model_zoo/official/cv/resnet/README_CN.md b/model_zoo/official/cv/resnet/README_CN.md index 61c0f42036a..517d16d9556 100755 --- a/model_zoo/official/cv/resnet/README_CN.md +++ b/model_zoo/official/cv/resnet/README_CN.md @@ -487,18 +487,51 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [ ```shell # Ascend310 inference -bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATA_PATH] [DEVICE_ID] +bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID] ``` -- `NET_TYPE` 选择范围：[resnet18, se-resnet50]。 +- `NET_TYPE` 选择范围：[resnet18, se-resnet50, resnet50, resnet101]。 +- `DATASET` 选择范围：[cifar10, imagenet]。 - `DEVICE_ID` 可选，默认值为0。 ### 结果 推理结果保存在脚本执行的当前路径，你可以在acc.log中看到以下精度计算结果。 +- 使用CIFAR-10数据集评估ResNet18 + ```bash -top1_accuracy:70.42, top5_accuracy:89.7 +Total data: 10000, top1 accuracy: 0.94.26, top5 accuracy: 0.9987. +``` + +- 使用ImageNet2012数据集评估ResNet18 + +```bash +Total data: 50000, top1 accuracy: 0.70668, top5 accuracy: 0.89698. +``` + +- 使用CIFAR-10数据集评估ResNet50 + +```text +Total data: 10000, top1 accuracy: 0.9310, top5 accuracy: 0.9980. +``` + +- 使用ImageNet2012数据集评估ResNet50 + +```text +Total data: 50000, top1 accuracy: 0.0.7696, top5 accuracy: 0.93432. +``` + +- 使用ImageNet2012数据集评估ResNet101 + +```text +Total data: 50000, top1 accuracy: 0.7871, top5 accuracy: 0.94354. +``` + +- 使用ImageNet2012数据集评估SE-ResNet50 + +```text +Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. ``` # 模型描述 diff --git a/model_zoo/official/cv/resnet/ascend310_infer/inc/utils.h b/model_zoo/official/cv/resnet/ascend310_infer/inc/utils.h index f8ae1e5b473..0b400632f51 100644 --- a/model_zoo/official/cv/resnet/ascend310_infer/inc/utils.h +++ b/model_zoo/official/cv/resnet/ascend310_infer/inc/utils.h @@ -24,12 +24,10 @@ #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); std::vector GetAllFiles(std::string dir_name); -std::vector> GetAllInputData(std::string dir_name); #endif diff --git a/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc b/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc index 5b219766e66..6346d93974e 100644 --- a/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc +++ b/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc @@ -50,106 +50,112 @@ using mindspore::kSuccess; using mindspore::MSTensor; using mindspore::dataset::Execute; - DEFINE_string(mindir_path, "", "mindir path"); DEFINE_string(dataset_path, ".", "dataset path"); DEFINE_string(network, "resnet18", "networktype"); +DEFINE_string(dataset, "imagenet", "dataset"); 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); - 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 = GetAllInputData(FLAGS_dataset_path); - if (all_files.empty()) { - std::cout << "ERROR: no input data." << std::endl; - return 1; - } - - std::map costTime_map; - size_t size = all_files.size(); - - std::shared_ptr decode(new Decode()); - std::shared_ptr resize(new Resize({256})); - std::shared_ptr centercrop(new CenterCrop({224})); - std::shared_ptr normalize(new Normalize({123.675, 116.28, 103.53}, - {58.395, 57.12, 57.375})); - std::shared_ptr hwc2chw(new HWC2CHW()); - - std::shared_ptr sr_resize(new Resize({292})); - std::shared_ptr sr_centercrop(new CenterCrop({256})); - std::shared_ptr sr_normalize(new Normalize({123.68, 116.78, 103.94}, - {1.0, 1.0, 1.0})); - - std::vector> trans_list; - - if (FLAGS_network == "se-resnet50") { - trans_list = {decode, sr_resize, sr_centercrop, sr_normalize, hwc2chw}; - } else { - trans_list = {decode, resize, centercrop, normalize, hwc2chw}; - } - mindspore::dataset::Execute SingleOp(trans_list); - - for (size_t i = 0; i < size; ++i) { - for (size_t j = 0; j < all_files[i].size(); ++j) { - 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][j] <(); - SingleOp(ReadFileToTensor(all_files[i][j]), imgDvpp.get()); - - inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(), - imgDvpp->Data().get(), imgDvpp->DataSize()); - gettimeofday(&start, nullptr); - ret = model.Predict(inputs, &outputs); - gettimeofday(&end, nullptr); - if (ret != kSuccess) { - std::cout << "Predict " << all_files[i][j] << " failed." << std::endl; + gflags::ParseCommandLineFlags(&argc, &argv, true); + if (RealPath(FLAGS_mindir_path).empty()) { + std::cout << "Invalid mindir" << 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][j], 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; + auto context = std::make_shared(); + auto ascend310 = std::make_shared(); + ascend310->SetDeviceID(FLAGS_device_id); + 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::vector modelInputs = model.GetInputs(); + std::map costTime_map; + size_t size = all_files.size(); + + std::shared_ptr decode = std::make_shared(); + std::shared_ptr hwc2chw = std::make_shared(); + + std::shared_ptr resize = std::make_shared(std::vector{256}); + std::shared_ptr centercrop = std::make_shared(std::vector{224}); + std::shared_ptr normalize = std::make_shared( + std::vector{123.675, 116.28, 103.53}, std::vector{58.395, 57.12, 57.375}); + + std::shared_ptr normalizeResnet101 = std::make_shared( + std::vector{121.125, 115.005, 99.96}, std::vector{70.125, 68.085, 70.89}); + + std::shared_ptr sr_resize = std::make_shared(std::vector{292}); + std::shared_ptr sr_centercrop = std::make_shared(std::vector{256}); + std::shared_ptr sr_normalize = std::make_shared( + std::vector{123.68, 116.78, 103.94}, std::vector{1.0, 1.0, 1.0}); + + std::vector> trans_list; + + if (FLAGS_network == "se-resnet50") { + trans_list = {decode, sr_resize, sr_centercrop, sr_normalize, hwc2chw}; + } else if (FLAGS_network == "resnet101") { + trans_list = {decode, resize, centercrop, normalizeResnet101, hwc2chw}; + } else { + trans_list = {decode, resize, centercrop, normalize, hwc2chw}; + } + mindspore::dataset::Execute SingleOp(trans_list); + + 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] <(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++) { + average += iter->second - iter->first; + 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/resnet/ascend310_infer/src/utils.cc b/model_zoo/official/cv/resnet/ascend310_infer/src/utils.cc index d71f388b83d..728d57d9362 100644 --- a/model_zoo/official/cv/resnet/ascend310_infer/src/utils.cc +++ b/model_zoo/official/cv/resnet/ascend310_infer/src/utils.cc @@ -22,84 +22,44 @@ using mindspore::MSTensor; using mindspore::DataType; - -std::vector> GetAllInputData(std::string dir_name) { - std::vector> ret; - - DIR *dir = OpenDir(dir_name); - if (dir == nullptr) { - return {}; - } - struct dirent *filename; - /* read all the files in the dir ~ */ - std::vector sub_dirs; - while ((filename = readdir(dir)) != nullptr) { - std::string d_name = std::string(filename->d_name); - // get rid of "." and ".." - if (d_name == "." || d_name == ".." || d_name.empty()) { - continue; +std::vector GetAllFiles(std::string dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; } - std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name); - struct stat s; - lstat(dir_path.c_str(), &s); - if (!S_ISDIR(s.st_mode)) { - continue; + std::vector dirs; + std::vector files; + while ((filename = readdir(dir)) != nullptr) { + std::string dName = std::string(filename->d_name); + if (dName == "." || dName == "..") { + continue; + } else if (filename->d_type == DT_DIR) { + dirs.emplace_back(std::string(dirName) + "/" + filename->d_name); + } else if (filename->d_type == DT_REG) { + files.emplace_back(std::string(dirName) + "/" + filename->d_name); + } else { + continue; + } } - sub_dirs.emplace_back(dir_path); - } - std::sort(sub_dirs.begin(), sub_dirs.end()); - - (void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret), - [](const std::string &d) { return GetAllFiles(d); }); - - return ret; + for (auto d : dirs) { + dir = OpenDir(d); + while ((filename = readdir(dir)) != nullptr) { + std::string dName = std::string(filename->d_name); + if (dName == "." || dName == ".." || filename->d_type != DT_REG) { + continue; + } + files.emplace_back(std::string(d) + "/" + filename->d_name); + } + } + std::sort(files.begin(), files.end()); + for (auto &f : files) { + std::cout << "image file: " << f << std::endl; + } + return files; } - -std::vector GetAllFiles(std::string dir_name) { - struct dirent *filename; - DIR *dir = OpenDir(dir_name); - if (dir == nullptr) { - return {}; - } - - std::vector res; - while ((filename = readdir(dir)) != nullptr) { - std::string d_name = std::string(filename->d_name); - if (d_name == "." || d_name == ".." || d_name.size() <= 3) { - continue; - } - res.emplace_back(std::string(dir_name) + "/" + filename->d_name); - } - std::sort(res.begin(), res.end()); - - return res; -} - - -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) { diff --git a/model_zoo/official/cv/resnet/postprocess.py b/model_zoo/official/cv/resnet/postprocess.py index f7fa6d040b4..e438b627884 100644 --- a/model_zoo/official/cv/resnet/postprocess.py +++ b/model_zoo/official/cv/resnet/postprocess.py @@ -21,12 +21,45 @@ from src.config import config2 as config batch_size = 1 parser = argparse.ArgumentParser(description="resnet inference") +parser.add_argument("--dataset", type=str, required=True, help="dataset type.") parser.add_argument("--result_path", type=str, required=True, help="result files path.") parser.add_argument("--label_path", type=str, required=True, help="image file path.") args = parser.parse_args() +def get_top5_acc(top5_arg, gt_class): + sub_count = 0 + for top5, gt in zip(top5_arg, gt_class): + if gt in top5: + sub_count += 1 + return sub_count -def get_result(result_path, label_path): +def cal_acc_cifar10(result_path, label_path): + img_tot = 0 + top1_correct = 0 + top5_correct = 0 + img_tot = 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.split(".bin")[0][:-2] + ".bin") + 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 + + print(f"Total data: {img_tot}, top1 accuracy: {top1_correct / img_tot}, top5 accuracy: {top5_correct / img_tot}.") + +def cal_acc_imagenet(result_path, label_path): files = os.listdir(result_path) with open(label_path, "r") as label: labels = json.load(label) @@ -48,4 +81,7 @@ def get_result(result_path, label_path): if __name__ == '__main__': - get_result(args.result_path, args.label_path) + if args.dataset.lower() == "cifar10": + cal_acc_cifar10(args.result_path, args.label_path) + else: + cal_acc_imagenet(args.result_path, args.label_path) diff --git a/model_zoo/official/cv/resnet/preprocess.py b/model_zoo/official/cv/resnet/preprocess.py new file mode 100755 index 00000000000..81a47d3a35f --- /dev/null +++ b/model_zoo/official/cv/resnet/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. +# ============================================================================ +"""train resnet.""" +import os +import argparse +from src.dataset import create_dataset1 as create_dataset + +parser = argparse.ArgumentParser(description='preprocess data') +parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') +parser.add_argument('--output_path', type=str, default=None, help='output path') +args_opt = parser.parse_args() + +if __name__ == '__main__': + # create dataset + dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=1, + target="Ascend") + step_size = dataset.get_dataset_size() + + img_path = os.path.join(args_opt.output_path, "img_data") + label_path = os.path.join(args_opt.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 = "google_cifar10_1_" + 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) diff --git a/model_zoo/official/cv/resnet/scripts/run_infer_310.sh b/model_zoo/official/cv/resnet/scripts/run_infer_310.sh index 31cd75f788f..7559b1e7716 100644 --- a/model_zoo/official/cv/resnet/scripts/run_infer_310.sh +++ b/model_zoo/official/cv/resnet/scripts/run_infer_310.sh @@ -14,9 +14,9 @@ # limitations under the License. # ============================================================================ -if [[ $# -lt 3 || $# -gt 4 ]]; then - echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATA_PATH] [DEVICE_ID] - NET_TYPE can choose from [resnet18, se-resnet50] +if [[ $# -lt 4 || $# -gt 5 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID] + NET_TYPE can choose from [resnet18, se-resnet50, resnet50, resnet101] DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" exit 1 fi @@ -29,23 +29,25 @@ get_real_path(){ fi } model=$(get_real_path $1) -if [ $2 == 'resnet18' ] || [ $2 == 'se-resnet50' ]; then +if [ $2 == 'resnet18' ] || [ $2 == 'se-resnet50' ] || [ $2 == 'resnet50' ] || [ $2 == 'resnet101' ]; then network=$2 else echo "NET_TYPE can choose from [resnet18, se-resnet50]" exit 1 fi -data_path=$(get_real_path $3) +dataset=$3 +data_path=$(get_real_path $4) device_id=0 -if [ $# == 4 ]; then - device_id=$4 +if [ $# == 5 ]; then + device_id=$5 fi echo "mindir name: "$model echo "dataset path: "$data_path echo "network: "$network +echo "dataset: "$dataset echo "device id: "$device_id export ASCEND_HOME=/usr/local/Ascend/ @@ -71,6 +73,16 @@ function compile_app() bash build.sh &> build.log } +function preprocess_data() +{ + if [ -d preprocess_Result ]; then + rm -rf ./preprocess_Result + fi + mkdir preprocess_Result + + python3.7 ../preprocess.py --dataset_path=$data_path --output_path=./preprocess_Result +} + function infer() { cd - || exit @@ -82,15 +94,28 @@ function infer() fi mkdir result_Files mkdir time_Result - ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --network=$network --device_id=$device_id &> infer.log + ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --network=$network --dataset=$dataset --device_id=$device_id &> infer.log } function cal_acc() { - python3.7 ../create_imagenet2012_label.py --img_path=$data_path - python3.7 ../postprocess.py --result_path=./result_Files --label_path=./imagenet_label.json &> acc.log & + if [ "x${dataset}" == "xcifar10" ] || [ "x${dataset}" == "xCifar10" ]; then + python ../postprocess.py --dataset=$dataset --label_path=./preprocess_Result/label --result_path=result_Files &> acc.log + else + python3.7 ../create_imagenet2012_label.py --img_path=$data_path + python3.7 ../postprocess.py --dataset=$dataset --result_path=./result_Files --label_path=./imagenet_label.json &> acc.log + fi + if [ $? -ne 0 ]; then + echo "calculate accuracy failed" + exit 1 + fi } +if [ "x${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"