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add ssd network 310 dvpp aipp mindir inference

tags/v1.2.0-rc1
zhangxiaoxiao16 5 years ago
parent
36f3c4740d
commit
bcbea55953
9 changed files with 574 additions and 30 deletions
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  1. +65
    -30
      model_zoo/official/cv/ssd/README.md
  2. +32
    -0
      model_zoo/official/cv/ssd/ascend310_infer/inc/utils.h
  3. +14
    -0
      model_zoo/official/cv/ssd/ascend310_infer/src/CMakeLists.txt
  4. +18
    -0
      model_zoo/official/cv/ssd/ascend310_infer/src/build.sh
  5. +127
    -0
      model_zoo/official/cv/ssd/ascend310_infer/src/main.cc
  6. +133
    -0
      model_zoo/official/cv/ssd/ascend310_infer/src/utils.cc
  7. +61
    -0
      model_zoo/official/cv/ssd/postprocess.py
  8. +98
    -0
      model_zoo/official/cv/ssd/scripts/run_infer_310.sh
  9. +26
    -0
      model_zoo/official/cv/ssd/src/aipp.cfg

+ 65
- 30
model_zoo/official/cv/ssd/README.md View File

@@ -37,7 +37,7 @@ The SSD approach is based on a feed-forward convolutional network that produces

We present two different base architecture.

- **ssd300**, reference from the paper. Using mobilenetv2 as backbone and the same bbox predictor as the paper pressent.
- **ssd300**, reference from the paper. Using mobilenetv2 as backbone and the same bbox predictor as the paper present.
- ***ssd-mobilenet-v1-fpn**, using mobilenet-v1 and FPN as feature extractor with weight-shared box predcitors.

## [Dataset](#contents)
@@ -85,7 +85,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
```

2. If VOC dataset is used. **Select dataset to voc when run script.**
Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config.py`. `voc_json` is the path of json file with coco format for evalution, `voc_root` is the path of VOC dataset, the directory structure is as follows:
Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config.py`. `voc_json` is the path of json file with coco format for evaluation, `voc_root` is the path of VOC dataset, the directory structure is as follows:

```shell
.
@@ -105,7 +105,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
```

3. If your own dataset is used. **Select dataset to other when run script.**
Organize the dataset infomation into a TXT file, each row in the file is as follows:
Organize the dataset information into a TXT file, each row in the file is as follows:

```shell
train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
@@ -117,15 +117,15 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)

### Prepare the model

1. Chose the model by chaning the `using_model` in `src/confgi.py`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`.
2. Change the datset config in the corresponding config. `src/config_ssd300.py` or `src/config_ssd_mobilenet_v1_fpn.py`.
1. Chose the model by changing the `using_model` in `src/confgi.py`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`.
2. Change the dataset config in the corresponding config. `src/config_ssd300.py` or `src/config_ssd_mobilenet_v1_fpn.py`.
3. If you are running with `ssd_mobilenet_v1_fpn`, you need a pretrained model for `mobilenet_v1`. Set the checkpoint path to `feature_extractor_base_param` in `src/config_ssd_mobilenet_v1_fpn.py`. For more detail about training mobilnet_v1, please refer to the mobilenetv1 model.

### Run the scripts

After installing MindSpore via the official website, you can start training and evaluation as follows:

- runing on Ascend
- running on Ascend

```shell
# distributed training on Ascend
@@ -135,7 +135,7 @@ sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
```

- runing on GPU
- running on GPU

```shell
# distributed training on GPU
@@ -145,7 +145,7 @@ sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET]
sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
```

- runing on CPU(support Windows and Ubuntu)
- running on CPU(support Windows and Ubuntu)

**CPU is usually used for fine-tuning, which needs pre_trained checkpoint.**

@@ -200,15 +200,15 @@ python eval.py --run_platform=CPU --dataset=[DATASET] --checkpoint_path=[PRETRAI
"pre_trained_epoch_size": 0 # Pretrained epoch size
"save_checkpoint_epochs": 10 # The epoch interval between two checkpoints. By default, the checkpoint will be saved per 10 epochs
"loss_scale": 1024 # Loss scale
"filter_weight": False # Load paramters in head layer or not. If the class numbers of train dataset is different from the class numbers in pre_trained checkpoint, please set True.
"freeze_layer": "none" # Freeze the backbone paramters or not, support none and backbone.
"filter_weight": False # Load parameters in head layer or not. If the class numbers of train dataset is different from the class numbers in pre_trained checkpoint, please set True.
"freeze_layer": "none" # Freeze the backbone parameters or not, support none and backbone.

"class_num": 81 # Dataset class number
"image_shape": [300, 300] # Image height and width used as input to the model
"mindrecord_dir": "/data/MindRecord_COCO" # MindRecord path
"coco_root": "/data/coco2017" # COCO2017 dataset path
"voc_root": "/data/voc_dataset" # VOC original dataset path
"voc_json": "annotations/voc_instances_val.json" # is the path of json file with coco format for evalution
"voc_json": "annotations/voc_instances_val.json" # is the path of json file with coco format for evaluation
"image_dir": "" # Other dataset image path, if coco or voc used, it will be useless
"anno_path": "" # Other dataset annotation path, if coco or voc used, it will be useless

@@ -304,18 +304,18 @@ We need two parameters for this scripts.
Inference result will be stored in the example path, whose folder name begins with "eval". Under this, you can find result like the followings in log.

```shell
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.240
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.240
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.039
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.198
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.438
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.389
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.434
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.389
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.434
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697

========================================

@@ -339,18 +339,18 @@ We need two parameters for this scripts.
Inference result will be stored in the example path, whose folder name begins with "eval". Under this, you can find result like the followings in log.

```shell
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.375
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.228
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.375
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.228
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.034
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686

========================================

@@ -363,7 +363,42 @@ mAP: 0.2244936111705981
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```

The ckpt_file parameter is required.
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]

## Inference Process

### Usage

Before performing inference, the air file must bu exported by export script on the 910 environment.
Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space.

```shell
# Ascend310 inference
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
```

`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.

```bash
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.459
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.432
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.228
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.255
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.409
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.670
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733
mAP: 0.35406563212712244
```

## [Model Description](#contents)



+ 32
- 0
model_zoo/official/cv/ssd/ascend310_infer/inc/utils.h View File

@@ -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);
std::shared_ptr<mindspore::api::Tensor> ReadFileToTensor(const std::string &file);
int WriteResult(const std::string& imageFile, const std::vector<mindspore::api::Buffer> &outputs);
#endif

+ 14
- 0
model_zoo/official/cv/ssd/ascend310_infer/src/CMakeLists.txt View File

@@ -0,0 +1,14 @@
cmake_minimum_required(VERSION 3.14.1)
project(MindSporeCxxTestcase[CXX])
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}/../inc)
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)

add_executable(main main.cc utils.cc)
target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

+ 18
- 0
model_zoo/official/cv/ssd/ascend310_infer/src/build.sh View File

@@ -0,0 +1,18 @@
#!/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.
# ============================================================================

cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

+ 127
- 0
model_zoo/official/cv/ssd/ascend310_infer/src/main.cc View File

@@ -0,0 +1,127 @@
/**
* 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 "include/api/model.h"
#include "include/api/context.h"
#include "minddata/dataset/include/minddata_eager.h"
#include "../inc/utils.h"
#include "include/api/types.h"
#include "include/api/serialization.h"
#include "minddata/dataset/include/vision.h"

using mindspore::api::Context;
using mindspore::api::Serialization;
using mindspore::api::Model;
using mindspore::api::kModelOptionInsertOpCfgPath;
using mindspore::api::Status;
using mindspore::api::MindDataEager;
using mindspore::api::Buffer;
using mindspore::api::ModelType;
using mindspore::api::GraphCell;
using mindspore::api::SUCCESS;
using mindspore::dataset::vision::DvppDecodeResizeJpeg;

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");

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;
}
if (RealPath(FLAGS_aipp_path).empty()) {
std::cout << "Invalid aipp path" << std::endl;
return 1;
}

Context::Instance().SetDeviceTarget("Ascend310").SetDeviceID(FLAGS_device_id);
auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
Model model((GraphCell(graph)));
std::map<std::string, std::string> build_options;
if (!FLAGS_aipp_path.empty()) {
build_options.emplace(kModelOptionInsertOpCfgPath, FLAGS_aipp_path);
}

Status ret = model.Build(build_options);
if (ret != SUCCESS) {
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();
MindDataEager SingleOp({DvppDecodeResizeJpeg({640, 640})});
for (size_t i = 0; i < size; ++i) {
struct timeval start = {0};
struct timeval end = {0};
double startTimeMs;
double endTimeMs;
std::vector<Buffer> inputs;
std::vector<Buffer> outputs;
std::cout << "Start predict input files:" << all_files[i] << std::endl;
auto imgDvpp = SingleOp(ReadFileToTensor(all_files[i]));

inputs.emplace_back(imgDvpp->Data(), imgDvpp->DataSize());
gettimeofday(&start, nullptr);
ret = model.Predict(inputs, &outputs);
gettimeofday(&end, nullptr);
if (ret != SUCCESS) {
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;
char tmpCh[256] = {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;
snprintf(tmpCh, sizeof(tmpCh), \
"NN inference cost average time: %4.3f ms of infer_count %d \n", average, inferCount);
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 << tmpCh;
fileStream.close();
costTime_map.clear();
return 0;
}

+ 133
- 0
model_zoo/official/cv/ssd/ascend310_infer/src/utils.cc View File

@@ -0,0 +1,133 @@
/**
* 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::api::Tensor;
using mindspore::api::Buffer;
using mindspore::api::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<Buffer> &outputs) {
std::string homePath = "./result_Files";
for (size_t i = 0; i < outputs.size(); ++i) {
size_t outputSize;
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, outputSize, sizeof(char), outputFile);
fclose(outputFile);
outputFile = nullptr;
}
return 0;
}

std::shared_ptr<Tensor> ReadFileToTensor(const std::string &file) {
auto buffer = std::make_shared<Tensor>();
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return buffer;
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return buffer;
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return buffer;
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
buffer->ResizeData(size);
if (buffer->DataSize() != size) {
std::cout << "Malloc buf failed, file: " << file << std::endl;
ifs.close();
return buffer;
}
ifs.seekg(0, std::ios::beg);
ifs.read(reinterpret_cast<char *>(buffer->MutableData()), size);
ifs.close();
buffer->SetDataType(DataType::kMsUint8);
buffer->SetShape({static_cast<int64_t>(size)});
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;
}

+ 61
- 0
model_zoo/official/cv/ssd/postprocess.py View File

@@ -0,0 +1,61 @@
# 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
#
# less 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

batch_size = 1
parser = argparse.ArgumentParser(description="ssd_mobilenet_v1_fpn inference")
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.")
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):
anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
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))
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(51150, 4)
box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(51150, 81)

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)

+ 98
- 0
model_zoo/official/cv/ssd/scripts/run_infer_310.sh View File

@@ -0,0 +1,98 @@
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

if [[ $# -lt 2 || $# -gt 3 ]]; then
echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi

get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)

device_id=0
if [ $# == 3 ]; then
device_id=$3
fi

echo "mindir name: "$model
echo "dataset path: "$data_path
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 compile_app()
{
cd ../ascend310_infer/src
if [ -f "Makefile" ]; then
make clean
fi
sh build.sh &> build.log
}

function infer()
{
cd -
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/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --aipp_path ../src/aipp.cfg &> infer.log
}

function cal_acc()
{
python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path &> 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

+ 26
- 0
model_zoo/official/cv/ssd/src/aipp.cfg View File

@@ -0,0 +1,26 @@
aipp_op {
aipp_mode : static
input_format : YUV420SP_U8
related_input_rank : 0
csc_switch : true
rbuv_swap_switch : false
matrix_r0c0 : 256
matrix_r0c1 : 0
matrix_r0c2 : 359
matrix_r1c0 : 256
matrix_r1c1 : -88
matrix_r1c2 : -183
matrix_r2c0 : 256
matrix_r2c1 : 454
matrix_r2c2 : 0
input_bias_0 : 0
input_bias_1 : 128
input_bias_2 : 128
mean_chn_0 : 124
mean_chn_1 : 117
mean_chn_2 : 104
var_reci_chn_0 : 0.0171247538316637
var_reci_chn_1 : 0.0175070028011204
var_reci_chn_2 : 0.0174291938997821
}

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