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!18356 ssd_mobilenetv2 add 310infer & resnet152 bug fix 

Merge pull request !18356 from ZeyangGAO/ssd_mobilev2
This commit is contained in:
i-robot 2021-06-17 20:22:54 +08:00 committed by Gitee
parent ff0a9228f8 3a5a469f2f
commit 9089a6f3bc
17 changed files with 1236 additions and 18 deletions
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42
model_zoo/official/cv/resnet152/export.py
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@ -15,27 +15,33 @@
"""Export DPN
suggest run as python export.py --file_name [filename] --file_format [file format] --checkpoint_path [ckpt path]
"""
import argparse
import numpy as np
from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
from src.dpn import dpns
from src.model_utils.config import config
from mindspore import Tensor, context
from mindspore.train.serialization import export, load_checkpoint
from src.resnet import resnet152 as resnet
from src.config import config5 as config
parser = argparse.ArgumentParser(description="resnet152 export ")
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--ckpt_file", type=str, required=True, help="checkpoint file path")
parser.add_argument("--dataset", type=str, default="imagenet2012", help="Dataset, either cifar10 or imagenet2012")
parser.add_argument("--width", type=int, default=224, help="input width")
parser.add_argument("--height", type=int, default=224, help="input height")
parser.add_argument("--file_name", type=str, default='resnet152', help="output file name")
parser.add_argument("--file_format", type=str, choices=['AIR', 'ONNX', 'MINDIR'], default='AIR', help="Device id")
parser.add_argument("--device_target", type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend', help="target")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
if config.device_target == "Ascend":
context.set_context(device_id=config.device_id)
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
if __name__ == "__main__":
target = args.device_target
if target != "GPU":
context.set_context(device_id=args.device_id)
# define net
backbone = config.backbone
num_classes = config.num_classes
net = dpns[backbone](num_classes=num_classes)
# load checkpoint
param_dict = load_checkpoint(config.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
image = Tensor(np.zeros([1, 3, config.height, config.width], np.float32))
export(net, image, file_name=config.file_name, file_format=config.file_format)
network = resnet(class_num=config.class_num)
param_dict = load_checkpoint(args.ckpt_file, net=network)
network.set_train(False)
input_data = Tensor(np.zeros([1, 3, args.height, args.width]).astype(np.float32))
export(network, input_data, file_name=args.file_name, file_format=args.file_format)

49
model_zoo/research/cv/ssd_mobilenetV2/README.md
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@ -15,6 +15,10 @@
- [Training on Ascend](#training-on-ascend)
- [Evaluation Process](#evaluation-process)
- [Evaluation on Ascend](#evaluation-on-ascend)
- [Inference Process](#inference-process)
- [Export MindIR](#export-mindir)
- [Infer on Ascend](#infer-on-ascend)
- [Result](#result)
- [Model Description](#model-description)
- [Performance](#performance)
- [Evaluation Performance](#evaluation-performance)
@ -282,6 +286,51 @@ Inference result will be stored in the example path, whose folder name begins wi
mAP: 0.2527925497483538
```
### Inference Process
#### [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
#### [Infer on Ascend310](#contents)
Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
```
- `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. Note that the image shape of ssd_vgg16 inference is [300, 300], The DVPP hardware restricts width 16-alignment and height even-alignment. Therefore, the network needs to use the CPU operator to process images.
- `ANNO_PATH` is mandatory, and must specify annotation file path including file name.
- `DEVICE_ID` is optional, default value is 0.
#### [Result](#contents)
Inference result is saved in current path, you can find result like this in acc.log file.
```bash
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.256
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.422
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.262
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.230
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.263
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.410
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.466
Average Recall (AR) @[ IoU=0.50:0.95 | area=large | maxDets=100 ] = 0.714
mAP: 0.2561487588412723
```
## [Model Description](#contents)
### [Performance](#contents)

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model_zoo/research/cv/ssd_mobilenetV2/ascend310_infer/CMakeLists.txt Normal file
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@ -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)

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model_zoo/research/cv/ssd_mobilenetV2/ascend310_infer/build.sh Normal file
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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 [ -d out ]; then
rm -rf out
fi
mkdir out
cd out || exit
if [ -f "Makefile" ]; then
make clean
fi
cmake .. \
-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

32
model_zoo/research/cv/ssd_mobilenetV2/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/research/cv/ssd_mobilenetV2/ascend310_infer/src/main.cc 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.
*/
#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::DvppDecodeResizeJpeg;
using mindspore::dataset::vision::Resize;
using mindspore::dataset::vision::HWC2CHW;
using mindspore::dataset::vision::Normalize;
using mindspore::dataset::vision::Decode;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
DEFINE_string(cpu_dvpp, "DVPP", "cpu or dvpp process");
DEFINE_int32(image_height, 640, "image height");
DEFINE_int32(image_width, 640, "image width");
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->SetBufferOptimizeMode("off_optimize");
context->MutableDeviceInfo().push_back(ascend310);
mindspore::Graph graph;
Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
if (FLAGS_cpu_dvpp == "DVPP") {
if (RealPath(FLAGS_aipp_path).empty()) {
std::cout << "Invalid aipp path" << std::endl;
return 1;
} else {
ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
}
}
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::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;
if (FLAGS_cpu_dvpp == "DVPP") {
auto resizeShape = {static_cast <uint32_t>(FLAGS_image_height), static_cast <uint32_t>(FLAGS_image_width)};
Execute resize_op(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg(resizeShape)));
auto imgDvpp = std::make_shared<MSTensor>();
resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get());
inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
imgDvpp->Data().get(), imgDvpp->DataSize());
} else {
std::shared_ptr<TensorTransform> decode(new Decode());
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
std::shared_ptr<TensorTransform> normalize(
new Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375}));
auto resizeShape = {FLAGS_image_height, FLAGS_image_width};
std::shared_ptr<TensorTransform> resize(new Resize(resizeShape));
Execute composeDecode({decode, resize, normalize, hwc2chw});
auto img = MSTensor();
auto image = ReadFileToTensor(all_files[i]);
composeDecode(image, &img);
std::vector<MSTensor> model_inputs = model.GetInputs();
if (model_inputs.empty()) {
std::cout << "Invalid model, inputs is empty." << std::endl;
return 1;
}
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
img.Data().get(), img.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/research/cv/ssd_mobilenetV2/ascend310_infer/src/utils.cc 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.
*/
#include <fstream>
#include <algorithm>
#include <iostream>
#include "inc/utils.h"
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('.'), '_' + std::to_string(i) + ".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;
}

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model_zoo/research/cv/ssd_mobilenetV2/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
from PIL import Image
from src.config import config
from src.eval_utils import metrics
parser = argparse.ArgumentParser(description="ssd_mobilenetv2 acc calculation")
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
parser.add_argument("--anno_path", type=str, required=True, help="annotation json file path.")
parser.add_argument("--drop", action="store_true", help="drop iscrowd images or not.")
args = parser.parse_args()
def get_imgSize(file_name):
img = Image.open(file_name)
return img.size
def get_result(result_path, img_id_file_path):
'''calculate map result for this net'''
anno_json = args.anno_path
if args.drop:
from pycocotools.coco import COCO
train_cls = config.classes
train_cls_dict = {}
for i, cls in enumerate(train_cls):
train_cls_dict[cls] = i
coco = COCO(anno_json)
classs_dict = {}
cat_ids = coco.loadCats(coco.getCatIds())
for cat in cat_ids:
classs_dict[cat["id"]] = cat["name"]
files = os.listdir(img_id_file_path)
pred_data = []
for file in files:
img_ids_name = file.split('.')[0]
img_id = int(np.squeeze(img_ids_name))
if args.drop:
anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
anno = coco.loadAnns(anno_ids)
annos = []
iscrowd = False
for label in anno:
bbox = label["bbox"]
class_name = classs_dict[label["category_id"]]
iscrowd = iscrowd or label["iscrowd"]
if class_name in train_cls:
x_min, x_max = bbox[0], bbox[0] + bbox[2]
y_min, y_max = bbox[1], bbox[1] + bbox[3]
annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])
if iscrowd or (not annos):
continue
img_size = get_imgSize(os.path.join(img_id_file_path, file))
image_shape = np.array([img_size[1], img_size[0]])
result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
boxes = np.fromfile(result_path_0, dtype=np.float32).reshape(config.num_ssd_boxes, 4)
box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(config.num_ssd_boxes, config.num_classes)
pred_data.append({
"boxes": boxes,
"box_scores": box_scores,
"img_id": img_id,
"image_shape": image_shape
})
mAP = metrics(pred_data, anno_json)
print(f" mAP:{mAP}")
if __name__ == '__main__':
get_result(args.result_path, args.img_path)

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model_zoo/research/cv/ssd_mobilenetV2/scripts/run_infer_310.sh Normal file
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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 3 || $# -gt 4 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
ANNO_PATH is mandatory, and should specify annotation file path of your data including file name.
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)
anno=$(get_real_path $3)
device_id=0
if [ $# == 4 ]; then
device_id=$4
fi
echo "mindir name: "$model
echo "dataset path: "$data_path
echo "image process mode: "$DVPP
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=$ASCEND_HOME/opp
fi
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --cpu_dvpp='CPU' --device_id=$device_id --image_height=320 --image_width=320 &> infer.log
}
function cal_acc()
{
python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --anno_path=$anno --drop &> acc.log &
}
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"
exit 1
fi
infer
if [ $? -ne 0 ]; then
echo " execute inference failed"
exit 1
fi
cal_acc
if [ $? -ne 0 ]; then
echo "calculate accuracy failed"
exit 1
fi

49
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@ -15,6 +15,10 @@
- [Training on Ascend](#training-on-ascend)
- [Evaluation Process](#evaluation-process)
- [Evaluation on Ascend](#evaluation-on-ascend)
- [Inference Process](#inference-process)
- [Export MindIR](#export-mindir)
- [Infer on Ascend](#infer-on-ascend)
- [Result](#result)
- [Model Description](#model-description)
- [Performance](#performance)
- [Evaluation Performance](#evaluation-performance)
@ -286,6 +290,51 @@ Inference result will be stored in the example path, whose folder name begins wi
mAP: 0.23368420287379554
```
### Inference Process
#### [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
#### [Infer on Ascend310](#contents)
Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
```
- `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. Note that the image shape of ssd_vgg16 inference is [300, 300], The DVPP hardware restricts width 16-alignment and height even-alignment. Therefore, the network needs to use the CPU operator to process images.
- `ANNO_PATH` is mandatory, and must specify annotation file path including file name.
- `DEVICE_ID` is optional, default value is 0.
#### [Result](#contents)
Inference result is saved in current path, you can find result like this in acc.log file.
```bash
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.264
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.430
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.279
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.263
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.466
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.528
Average Recall (AR) @[ IoU=0.50:0.95 | area=large | maxDets=100 ] = 0.675
mAP: 0.2645785822173796
```
## [Model Description](#contents)
### [Performance](#contents)

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model_zoo/research/cv/ssd_mobilenetV2_FPNlite/ascend310_infer/CMakeLists.txt Normal file
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@ -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)

29
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@ -0,0 +1,29 @@
#!/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
rm -rf out
fi
mkdir out
cd out || exit
if [ -f "Makefile" ]; then
make clean
fi
cmake .. \
-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

32
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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

165
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@ -0,0 +1,165 @@
/**
* 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::DvppDecodeResizeJpeg;
using mindspore::dataset::vision::Resize;
using mindspore::dataset::vision::HWC2CHW;
using mindspore::dataset::vision::Normalize;
using mindspore::dataset::vision::Decode;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
DEFINE_string(cpu_dvpp, "DVPP", "cpu or dvpp process");
DEFINE_int32(image_height, 640, "image height");
DEFINE_int32(image_width, 640, "image width");
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->SetBufferOptimizeMode("off_optimize");
context->MutableDeviceInfo().push_back(ascend310);
mindspore::Graph graph;
Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
if (FLAGS_cpu_dvpp == "DVPP") {
if (RealPath(FLAGS_aipp_path).empty()) {
std::cout << "Invalid aipp path" << std::endl;
return 1;
} else {
ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
}
}
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::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;
if (FLAGS_cpu_dvpp == "DVPP") {
auto resizeShape = {static_cast <uint32_t>(FLAGS_image_height), static_cast <uint32_t>(FLAGS_image_width)};
Execute resize_op(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg(resizeShape)));
auto imgDvpp = std::make_shared<MSTensor>();
resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get());
inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
imgDvpp->Data().get(), imgDvpp->DataSize());
} else {
std::shared_ptr<TensorTransform> decode(new Decode());
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
std::shared_ptr<TensorTransform> normalize(
new Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375}));
auto resizeShape = {FLAGS_image_height, FLAGS_image_width};
std::shared_ptr<TensorTransform> resize(new Resize(resizeShape));
Execute composeDecode({decode, resize, normalize, hwc2chw});
auto img = MSTensor();
auto image = ReadFileToTensor(all_files[i]);
composeDecode(image, &img);
std::vector<MSTensor> model_inputs = model.GetInputs();
if (model_inputs.empty()) {
std::cout << "Invalid model, inputs is empty." << std::endl;
return 1;
}
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
img.Data().get(), img.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;
}

129
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@ -0,0 +1,129 @@
/**
* 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 <fstream>
#include <algorithm>
#include <iostream>
#include "inc/utils.h"
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('.'), '_' + std::to_string(i) + ".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;
}

89
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@ -0,0 +1,89 @@
# 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
from PIL import Image
from src.config import config
from src.eval_utils import metrics
parser = argparse.ArgumentParser(description="ssd_mobilenetv2 acc calculation")
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
parser.add_argument("--anno_path", type=str, required=True, help="annotation json file path.")
parser.add_argument("--drop", action="store_true", help="drop iscrowd images or not.")
args = parser.parse_args()
def get_imgSize(file_name):
img = Image.open(file_name)
return img.size
def get_result(result_path, img_id_file_path):
'''calculate map result for this net'''
anno_json = args.anno_path
if args.drop:
from pycocotools.coco import COCO
train_cls = config.classes
train_cls_dict = {}
for i, cls in enumerate(train_cls):
train_cls_dict[cls] = i
coco = COCO(anno_json)
classs_dict = {}
cat_ids = coco.loadCats(coco.getCatIds())
for cat in cat_ids:
classs_dict[cat["id"]] = cat["name"]
files = os.listdir(img_id_file_path)
pred_data = []
for file in files:
img_ids_name = file.split('.')[0]
img_id = int(np.squeeze(img_ids_name))
if args.drop:
anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
anno = coco.loadAnns(anno_ids)
annos = []
iscrowd = False
for label in anno:
bbox = label["bbox"]
class_name = classs_dict[label["category_id"]]
iscrowd = iscrowd or label["iscrowd"]
if class_name in train_cls:
x_min, x_max = bbox[0], bbox[0] + bbox[2]
y_min, y_max = bbox[1], bbox[1] + bbox[3]
annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])
if iscrowd or (not annos):
continue
img_size = get_imgSize(os.path.join(img_id_file_path, file))
image_shape = np.array([img_size[1], img_size[0]])
result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
boxes = np.fromfile(result_path_0, dtype=np.float32).reshape(config.num_ssd_boxes, 4)
box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(config.num_ssd_boxes, config.num_classes)
pred_data.append({
"boxes": boxes,
"box_scores": box_scores,
"img_id": img_id,
"image_shape": image_shape
})
mAP = metrics(pred_data, anno_json)
print(f" mAP:{mAP}")
if __name__ == '__main__':
get_result(args.result_path, args.img_path)

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@ -0,0 +1,99 @@
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [[ $# -lt 3 || $# -gt 4 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
ANNO_PATH is mandatory, and should specify annotation file path of your data including file name.
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)
anno=$(get_real_path $3)
device_id=0
if [ $# == 4 ]; then
device_id=$4
fi
echo "mindir name: "$model
echo "dataset path: "$data_path
echo "image process mode: "$DVPP
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=$ASCEND_HOME/opp
fi
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --cpu_dvpp='CPU' --device_id=$device_id --image_height=320 --image_width=320 &> infer.log
}
function cal_acc()
{
python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --anno_path=$anno --drop &> acc.log &
}
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"
exit 1
fi
infer
if [ $? -ne 0 ]; then
echo " execute inference failed"
exit 1
fi
cal_acc
if [ $? -ne 0 ]; then
echo "calculate accuracy failed"
exit 1
fi