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squeezenet add 310 infer

This commit is contained in:
chenweitao_295 2021-05-27 18:23:40 +08:00
parent ba4c72d1e3
commit 32ff37bef2
10 changed files with 733 additions and 1 deletions
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118
model_zoo/official/cv/squeezenet/README.md
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@ -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

14
model_zoo/official/cv/squeezenet/ascend310_infer/CMakeLists.txt Normal file
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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)

23
model_zoo/official/cv/squeezenet/ascend310_infer/build.sh Normal file
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@ -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

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model_zoo/official/cv/squeezenet/ascend310_infer/inc/utils.h Normal file
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@ -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 <sys/stat.h>
#include <dirent.h>
#include <vector>
#include <string>
#include <memory>
#include "include/api/types.h"
std::vector<std::string> 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<mindspore::MSTensor> &outputs);
#endif

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model_zoo/official/cv/squeezenet/ascend310_infer/src/main.cc Normal file
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@ -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 <sys/time.h>
#include <gflags/gflags.h>
#include <dirent.h>
#include <iostream>
#include <string>
#include <algorithm>
#include <iosfwd>
#include <vector>
#include <fstream>
#include <sstream>
#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<Context>();
auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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<TensorTransform> decode(new Decode());
std::shared_ptr<TensorTransform> resize(new Resize({256, 256}));
std::shared_ptr<TensorTransform> center_crop(new CenterCrop({227, 227}));
std::shared_ptr<TensorTransform> normalize(new Normalize({123.675, 116.28, 103.53},
{58.395, 57.120, 57.375}));
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
Execute transform({decode, resize, center_crop, normalize, hwc2chw});
std::map<double, double> 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<MSTensor> inputs;
std::vector<MSTensor> 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<MSTensor> 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<double, double>(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;
}

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model_zoo/official/cv/squeezenet/ascend310_infer/src/utils.cc Normal file
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@ -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 <fstream>
#include <algorithm>
#include <iostream>
using mindspore::MSTensor;
using mindspore::DataType;
std::vector<std::string> GetAllFiles(std::string_view dirName) {
struct dirent *filename;
DIR *dir = OpenDir(dirName);
if (dir == nullptr) {
return {};
}
std::vector<std::string> 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<MSTensor> &outputs) {
std::string homePath = "./result_Files";
for (size_t i = 0; i < outputs.size(); ++i) {
size_t outputSize;
std::shared_ptr<const void> netOutput;
netOutput = outputs[i].Data();
outputSize = outputs[i].DataSize();
int pos = imageFile.rfind('/');
std::string fileName(imageFile, pos + 1);
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), ".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<int64_t>(size)}, nullptr, size);
ifs.seekg(0, std::ios::beg);
ifs.read(reinterpret_cast<char *>(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;
}

2
model_zoo/official/cv/squeezenet/export.py
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@ -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()

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model_zoo/official/cv/squeezenet/postprocess.py Normal file
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# 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)

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model_zoo/official/cv/squeezenet/preprocess.py Normal file
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# 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)

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#!/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