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!15584 faster_rcnn mask_rcnn mindir 310推理 

From: @yuzhenhua666
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34
This commit is contained in:
mindspore-ci-bot 2021-04-26 10:53:24 +08:00 committed by Gitee
parent d352244963 1b2e2d005a
commit 196d65c0bd
41 changed files with 951 additions and 3995 deletions
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7
model_zoo/official/cv/faster_rcnn/README.md
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@ -177,7 +177,7 @@ sh run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
```shell
# inference
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
# Script Description
@ -210,7 +210,6 @@ sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
├─resnet50.py // backbone network
├─roi_align.py // roi align network
└─rpn.py // region proposal network
├─aipp.cfg // aipp config file
├─config.py // total config
├─dataset.py // create dataset and process dataset
├─lr_schedule.py // learning ratio generator
@ -339,7 +338,7 @@ Eval result will be stored in the example path, whose folder name is "eval". Und
python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
```
`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
## Inference Process
@ -349,7 +348,7 @@ Before performing inference, the air file must bu exported by export script on t
```shell
# Ascend310 inference
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
### result

7
model_zoo/official/cv/faster_rcnn/README_CN.md
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@ -120,7 +120,7 @@ sh run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
sh run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
#推理
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DEVICE_ID]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
## 在GPU上运行
@ -211,7 +211,6 @@ sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DEVICE_ID]
├─resnet50.py // 骨干网络
├─roi_align.py // ROI对齐网络
└─rpn.py // 区域候选网络
├─aipp.cfg // aipp 配置文件
├─config.py // 总配置
├─dataset.py // 创建并处理数据集
├─lr_schedule.py // 学习率生成器
@ -340,7 +339,7 @@ sh run_eval_gpu.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
```
`EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"]
`EXPORT_FORMAT` 可选 ["AIR", "MINDIR"]
## 推理过程
@ -350,7 +349,7 @@ python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_
```shell
# Ascend310 inference
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DEVICE_ID]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
### 结果

14
model_zoo/official/cv/faster_rcnn/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
model_zoo/official/cv/faster_rcnn/ascend310_infer/build.sh Normal file
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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

62
model_zoo/official/cv/faster_rcnn/ascend310_infer/inc/AclProcess.h
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@ -1,62 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 ACLMANAGER_H
#define ACLMANAGER_H
#include <map>
#include <iostream>
#include <string>
#include <memory>
#include <vector>
#include "acl/acl.h"
#include "CommonDataType.h"
#include "ModelProcess.h"
#include "DvppCommon.h"
struct ModelInfo {
std::string modelPath;
uint32_t modelWidth;
uint32_t modelHeight;
uint32_t outputNum;
};
class AclProcess {
public:
AclProcess(int deviceId, const std::string &om_path, uint32_t width, uint32_t height);
~AclProcess() {}
void Release();
int InitResource();
int Process(const std::string& imageFile, std::map<double, double> *costTime_map);
private:
int InitModule();
int Preprocess(const std::string& imageFile);
int ModelInfer(std::map<double, double> *costTime_map);
int WriteResult(const std::string& imageFile);
int ReadFile(const std::string &filePath, RawData *fileData);
int32_t deviceId_;
ModelInfo modelInfo_;
aclrtContext context_;
aclrtStream stream_;
std::shared_ptr<ModelProcess> modelProcess_;
std::shared_ptr<DvppCommon> dvppCommon_;
bool keepRatio_;
std::vector<void *> outputBuffers_;
std::vector<size_t> outputSizes_;
};
#endif

95
model_zoo/official/cv/faster_rcnn/ascend310_infer/inc/CommonDataType.h
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@ -1,95 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 COMMONDATATYPE_H
#define COMMONDATATYPE_H
#include <stdio.h>
#include <iostream>
#include <memory>
#include <vector>
#include "acl/acl.h"
#include "acl/ops/acl_dvpp.h"
#define DVPP_ALIGN_UP(x, align) ((((x) + ((align)-1)) / (align)) * (align))
#define OK 0
#define ERROR -1
#define INVALID_POINTER -2
#define READ_FILE_FAIL -3
#define OPEN_FILE_FAIL -4
#define INIT_FAIL -5
#define INVALID_PARAM -6
#define DECODE_FAIL -7
const float SEC2MS = 1000.0;
const int YUV_BGR_SIZE_CONVERT_3 = 3;
const int YUV_BGR_SIZE_CONVERT_2 = 2;
const int VPC_WIDTH_ALIGN = 16;
const int VPC_HEIGHT_ALIGN = 2;
// Description of image data
struct ImageInfo {
uint32_t width; // Image width
uint32_t height; // Image height
uint32_t lenOfByte; // Size of image data, bytes
std::shared_ptr<uint8_t> data; // Smart pointer of image data
};
// Description of data in device
struct RawData {
size_t lenOfByte; // Size of memory, bytes
std::shared_ptr<void> data; // Smart pointer of data
};
// define the structure of an rectangle
struct Rectangle {
uint32_t leftTopX;
uint32_t leftTopY;
uint32_t rightBottomX;
uint32_t rightBottomY;
};
enum VpcProcessType {
VPC_PT_DEFAULT = 0,
VPC_PT_PADDING, // Resize with locked ratio and paste on upper left corner
VPC_PT_FIT, // Resize with locked ratio and paste on middle location
VPC_PT_FILL, // Resize with locked ratio and paste on whole locatin, the input image may be cropped
};
struct DvppDataInfo {
uint32_t width = 0; // Width of image
uint32_t height = 0; // Height of image
uint32_t widthStride = 0; // Width after align up
uint32_t heightStride = 0; // Height after align up
acldvppPixelFormat format = PIXEL_FORMAT_YUV_SEMIPLANAR_420; // Format of image
uint32_t frameId = 0; // Needed by video
uint32_t dataSize = 0; // Size of data in byte
uint8_t *data = nullptr; // Image data
};
struct CropRoiConfig {
uint32_t left;
uint32_t right;
uint32_t down;
uint32_t up;
};
struct DvppCropInputInfo {
DvppDataInfo dataInfo;
CropRoiConfig roi;
};
#endif

139
model_zoo/official/cv/faster_rcnn/ascend310_infer/inc/DvppCommon.h
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@ -1,139 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 DVPP_COMMON_H
#define DVPP_COMMON_H
#include <memory>
#include "CommonDataType.h"
#include "acl/ops/acl_dvpp.h"
const int MODULUS_NUM_2 = 2;
const uint32_t ODD_NUM_1 = 1;
const uint32_t JPEGD_STRIDE_WIDTH = 128; // Jpegd module output width need to align up to 128
const uint32_t JPEGD_STRIDE_HEIGHT = 16; // Jpegd module output height need to align up to 16
const uint32_t VPC_STRIDE_WIDTH = 16; // Vpc module output width need to align up to 16
const uint32_t VPC_STRIDE_HEIGHT = 2; // Vpc module output height need to align up to 2
const uint32_t YUV422_WIDTH_NU = 2; // Width of YUV422, WidthStride = Width * 2
const uint32_t YUV444_RGB_WIDTH_NU = 3; // Width of YUV444 and RGB888, WidthStride = Width * 3
const uint32_t XRGB_WIDTH_NU = 4; // Width of XRGB8888, WidthStride = Width * 4
const uint32_t JPEG_OFFSET = 8; // Offset of input file for jpegd module
const uint32_t MAX_JPEGD_WIDTH = 8192; // Max width of jpegd module
const uint32_t MAX_JPEGD_HEIGHT = 8192; // Max height of jpegd module
const uint32_t MIN_JPEGD_WIDTH = 32; // Min width of jpegd module
const uint32_t MIN_JPEGD_HEIGHT = 32; // Min height of jpegd module
const uint32_t MAX_RESIZE_WIDTH = 4096; // Max width stride of resize module
const uint32_t MAX_RESIZE_HEIGHT = 4096; // Max height stride of resize module
const uint32_t MIN_RESIZE_WIDTH = 32; // Min width stride of resize module
const uint32_t MIN_RESIZE_HEIGHT = 6; // Min height stride of resize module
const float MIN_RESIZE_SCALE = 0.03125; // Min resize scale of resize module
const float MAX_RESIZE_SCALE = 16.0; // Min resize scale of resize module
const uint32_t MAX_VPC_WIDTH = 4096; // Max width of picture to VPC(resize/crop)
const uint32_t MAX_VPC_HEIGHT = 4096; // Max height of picture to VPC(resize/crop)
const uint32_t MIN_VPC_WIDTH = 32; // Min width of picture to VPC(resize/crop)
const uint32_t MIN_VPC_HEIGHT = 6; // Min height of picture to VPC(resize/crop)
const uint32_t MIN_CROP_WIDTH = 10; // Min width of crop area
const uint32_t MIN_CROP_HEIGHT = 6; // Min height of crop area
const uint8_t YUV_GREYER_VALUE = 128; // Filling value of the resized YUV image
#define CONVERT_TO_ODD(NUM) (((NUM) % MODULUS_NUM_2 != 0) ? (NUM) : ((NUM) - 1))
#define CONVERT_TO_EVEN(NUM) (((NUM) % MODULUS_NUM_2 == 0) ? (NUM) : ((NUM) - 1))
#define CHECK_ODD(num) ((num) % MODULUS_NUM_2 != 0)
#define CHECK_EVEN(num) ((num) % MODULUS_NUM_2 == 0)
#define RELEASE_DVPP_DATA(dvppDataPtr) do { \
int retMacro; \
if (dvppDataPtr != nullptr) { \
retMacro = acldvppFree(dvppDataPtr); \
if (retMacro != OK) { \
std::cout << "Failed to free memory on dvpp, ret = " << retMacro << "." << std::endl; \
} \
dvppDataPtr = nullptr; \
} \
} while (0);
class DvppCommon {
public:
explicit DvppCommon(aclrtStream dvppStream);
~DvppCommon();
int Init(void);
int DeInit(void);
static int GetVpcDataSize(uint32_t widthVpc, uint32_t heightVpc, acldvppPixelFormat format,
uint32_t *vpcSize);
static int GetVpcInputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride);
static int GetVpcOutputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride);
static void GetJpegDecodeStrideSize(uint32_t width, uint32_t height, uint32_t *widthStride, uint32_t *heightStride);
static int GetJpegImageInfo(const void *data, uint32_t dataSize, uint32_t *width, uint32_t *height,
int32_t *components);
static int GetJpegDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
uint32_t *decSize);
int VpcResize(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output, bool withSynchronize,
VpcProcessType processType = VPC_PT_DEFAULT);
int JpegDecode(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output, bool withSynchronize);
int CombineResizeProcess(std::shared_ptr<DvppDataInfo> input, const DvppDataInfo &output, bool withSynchronize,
VpcProcessType processType = VPC_PT_DEFAULT);
int CombineJpegdProcess(const RawData& imageInfo, acldvppPixelFormat format, bool withSynchronize);
std::shared_ptr<DvppDataInfo> GetInputImage();
std::shared_ptr<DvppDataInfo> GetDecodedImage();
std::shared_ptr<DvppDataInfo> GetResizedImage();
void ReleaseDvppBuffer();
private:
int SetDvppPicDescData(std::shared_ptr<DvppDataInfo> dataInfo, std::shared_ptr<acldvppPicDesc>picDesc);
int ResizeProcess(std::shared_ptr<acldvppPicDesc> inputDesc,
std::shared_ptr<acldvppPicDesc> outputDesc, bool withSynchronize);
int ResizeWithPadding(std::shared_ptr<acldvppPicDesc> inputDesc, std::shared_ptr<acldvppPicDesc> outputDesc,
const CropRoiConfig &cropRoi, const CropRoiConfig &pasteRoi, bool withSynchronize);
void GetCropRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *cropRoi);
void GetPasteRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *pasteRoi);
int CheckResizeParams(const DvppDataInfo &input, const DvppDataInfo &output);
int TransferImageH2D(const RawData& imageInfo, const std::shared_ptr<DvppDataInfo>& jpegInput);
int CreateStreamDesc(std::shared_ptr<DvppDataInfo> data);
int DestroyResource();
std::shared_ptr<acldvppRoiConfig> cropAreaConfig_ = nullptr;
std::shared_ptr<acldvppRoiConfig> pasteAreaConfig_ = nullptr;
std::shared_ptr<acldvppPicDesc> resizeInputDesc_ = nullptr;
std::shared_ptr<acldvppPicDesc> resizeOutputDesc_ = nullptr;
std::shared_ptr<acldvppPicDesc> decodeOutputDesc_ = nullptr;
std::shared_ptr<acldvppResizeConfig> resizeConfig_ = nullptr;
acldvppChannelDesc *dvppChannelDesc_ = nullptr;
aclrtStream dvppStream_ = nullptr;
std::shared_ptr<DvppDataInfo> inputImage_ = nullptr;
std::shared_ptr<DvppDataInfo> decodedImage_ = nullptr;
std::shared_ptr<DvppDataInfo> resizedImage_ = nullptr;
};
#endif

63
model_zoo/official/cv/faster_rcnn/ascend310_infer/inc/ModelProcess.h
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@ -1,63 +0,0 @@
/*
* Copyright(C) 2020. Huawei Technologies Co.,Ltd. All rights reserved.
*
* 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 MODELPROCSS_H
#define MODELPROCSS_H
#include <cstdio>
#include <vector>
#include <unordered_map>
#include <mutex>
#include <map>
#include <memory>
#include <string>
#include "acl/acl.h"
#include "CommonDataType.h"
class ModelProcess {
public:
explicit ModelProcess(const int deviceId);
ModelProcess();
~ModelProcess();
int Init(const std::string &modelPath);
int DeInit();
int ModelInference(const std::vector<void *> &inputBufs,
const std::vector<size_t> &inputSizes,
const std::vector<void *> &ouputBufs,
const std::vector<size_t> &outputSizes,
std::map<double, double> *costTime_map);
aclmdlDesc *GetModelDesc();
int ReadBinaryFile(const std::string &fileName, uint8_t **buffShared, int *buffLength);
private:
aclmdlDataset *CreateAndFillDataset(const std::vector<void *> &bufs, const std::vector<size_t> &sizes);
void DestroyDataset(aclmdlDataset *dataset);
std::mutex mtx_ = {};
int deviceId_ = 0;
uint32_t modelId_ = 0;
void *modelDevPtr_ = nullptr;
size_t modelDevPtrSize_ = 0;
void *weightDevPtr_ = nullptr;
size_t weightDevPtrSize_ = 0;
aclrtContext contextModel_ = nullptr;
std::shared_ptr<aclmdlDesc> modelDesc_ = nullptr;
bool isDeInit_ = false;
};
#endif

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model_zoo/official/cv/faster_rcnn/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

367
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/AclProcess.cpp
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@ -1,367 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 "AclProcess.h"
#include <sys/time.h>
#include <thread>
#include <string>
/*
* @description Implementation of constructor for class AclProcess with parameter list
* @attention context is passed in as a parameter after being created in ResourceManager::InitResource
*/
AclProcess::AclProcess(int deviceId, const std::string &om_path, uint32_t width, uint32_t height)
: deviceId_(deviceId), stream_(nullptr), modelProcess_(nullptr), dvppCommon_(nullptr), keepRatio_(true) {
modelInfo_.modelPath = om_path;
modelInfo_.modelWidth = width;
modelInfo_.modelHeight = height;
}
/*
* @description Release all the resource
* @attention context will be released in ResourceManager::Release
*/
void AclProcess::Release() {
// Synchronize stream and release Dvpp channel
dvppCommon_->DeInit();
// Release stream
if (stream_ != nullptr) {
int ret = aclrtDestroyStream(stream_);
if (ret != OK) {
std::cout << "Failed to destroy the stream, ret = " << ret << ".";
}
stream_ = nullptr;
}
// Destroy resources of modelProcess_
modelProcess_->DeInit();
// Release Dvpp buffer
dvppCommon_->ReleaseDvppBuffer();
return;
}
/*
* @description Initialize the modules used by this sample
* @return int int code
*/
int AclProcess::InitModule() {
// Create Dvpp common object
if (dvppCommon_ == nullptr) {
dvppCommon_ = std::make_shared<DvppCommon>(stream_);
int retDvppCommon = dvppCommon_->Init();
if (retDvppCommon != OK) {
std::cout << "Failed to initialize dvppCommon, ret = " << retDvppCommon << std::endl;
return retDvppCommon;
}
}
// Create model inference object
if (modelProcess_ == nullptr) {
modelProcess_ = std::make_shared<ModelProcess>(deviceId_);
}
// Initialize ModelProcess module
int ret = modelProcess_->Init(modelInfo_.modelPath);
if (ret != OK) {
std::cout << "Failed to initialize the model process module, ret = " << ret << "." << std::endl;
return ret;
}
std::cout << "Initialized the model process module successfully." << std::endl;
return OK;
}
/*
* @description Create resource for this sample
* @return int int code
*/
int AclProcess::InitResource() {
int ret = aclInit(nullptr); // Initialize ACL
if (ret != OK) {
std::cout << "Failed to init acl, ret = " << ret << std::endl;
return ret;
}
ret = aclrtSetDevice(deviceId_);
if (ret != ACL_SUCCESS) {
std::cout << "acl set device " << deviceId_ << "intCode = "<< static_cast<int32_t>(ret) << std::endl;
return ret;
}
std::cout << "set device "<< deviceId_ << " success" << std::endl;
// create context (set current)
ret = aclrtCreateContext(&context_, deviceId_);
if (ret != ACL_SUCCESS) {
std::cout << "acl create context failed, deviceId = " << deviceId_ <<
"intCode = "<< static_cast<int32_t>(ret) << std::endl;
return ret;
}
std::cout << "create context success" << std::endl;
ret = aclrtCreateStream(&stream_); // Create stream for application
if (ret != OK) {
std::cout << "Failed to create the acl stream, ret = " << ret << "." << std::endl;
return ret;
}
std::cout << "Created the acl stream successfully." << std::endl;
// Initialize dvpp module
if (InitModule() != OK) {
return INIT_FAIL;
}
aclmdlDesc *modelDesc = modelProcess_->GetModelDesc();
size_t outputSize = aclmdlGetNumOutputs(modelDesc);
modelInfo_.outputNum = outputSize;
for (size_t i = 0; i < outputSize; i++) {
size_t bufferSize = aclmdlGetOutputSizeByIndex(modelDesc, i);
void *outputBuffer = nullptr;
ret = aclrtMalloc(&outputBuffer, bufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
if (ret != OK) {
std::cout << "Failed to malloc buffer, ret = " << ret << "." << std::endl;
return ret;
}
outputBuffers_.push_back(outputBuffer);
outputSizes_.push_back(bufferSize);
}
return OK;
}
int AclProcess::WriteResult(const std::string& imageFile) {
std::string homePath = "./result_Files";
void *resHostBuf = nullptr;
for (size_t i = 0; i < outputBuffers_.size(); ++i) {
size_t output_size;
void *netOutput;
netOutput = outputBuffers_[i];
output_size = outputSizes_[i];
int ret = aclrtMallocHost(&resHostBuf, output_size);
if (ret != OK) {
std::cout << "Failed to print the result, malloc host failed, ret = " << ret << "." << std::endl;
return ret;
}
ret = aclrtMemcpy(resHostBuf, output_size, netOutput,
output_size, ACL_MEMCPY_DEVICE_TO_HOST);
if (ret != OK) {
std::cout << "Failed to print result, memcpy device to host failed, ret = " << ret << "." << std::endl;
return ret;
}
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;
try {
FILE *outputFile = fopen(outFileName.c_str(), "wb");
if (outputFile == nullptr) {
std::cout << "open result file " << outFileName << " failed" << std::endl;
return INVALID_POINTER;
}
size_t size = fwrite(resHostBuf, sizeof(char), output_size, outputFile);
if (size != output_size) {
fclose(outputFile);
outputFile = nullptr;
std::cout << "write result file " << outFileName << " failed, write size[" << size <<
"] is smaller than output size[" << output_size << "], maybe the disk is full." << std::endl;
return ERROR;
}
fclose(outputFile);
outputFile = nullptr;
} catch (std::exception &e) {
std::cout << "write result file " << outFileName << " failed, error info: " << e.what() << std::endl;
std::exit(1);
}
ret = aclrtFreeHost(resHostBuf);
if (ret != OK) {
std::cout << "aclrtFree host output memory failed" << std::endl;
return ret;
}
}
return OK;
}
/**
* Read a file, store it into the RawData structure
*
* @param filePath file to read to
* @param fileData RawData structure to store in
* @return OK if create success, int code otherwise
*/
int AclProcess::ReadFile(const std::string &filePath, RawData *fileData) {
// Open file with reading mode
FILE *fp = fopen(filePath.c_str(), "rb");
if (fp == nullptr) {
std::cout << "Failed to open file, filePath = " << filePath << std::endl;
return OPEN_FILE_FAIL;
}
// Get the length of input file
fseek(fp, 0, SEEK_END);
size_t fileSize = ftell(fp);
fseek(fp, 0, SEEK_SET);
// If file not empty, read it into FileInfo and return it
if (fileSize > 0) {
fileData->lenOfByte = fileSize;
fileData->data = std::make_shared<uint8_t>();
fileData->data.reset(new uint8_t[fileSize], std::default_delete<uint8_t[]>());
uint32_t readRet = fread(fileData->data.get(), 1, fileSize, fp);
if (readRet == 0) {
fclose(fp);
return READ_FILE_FAIL;
}
fclose(fp);
return OK;
}
fclose(fp);
return INVALID_PARAM;
}
/*
* @description Preprocess the input image
* @param imageFile input image path
* @return int int code
*/
int AclProcess::Preprocess(const std::string& imageFile) {
RawData imageInfo;
int ret = ReadFile(imageFile, &imageInfo); // Read image data from input image file
if (ret != OK) {
std::cout << "Failed to read file, ret = " << ret << "." << std::endl;
return ret;
}
// Run process of jpegD
ret = dvppCommon_->CombineJpegdProcess(imageInfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420, true);
if (ret != OK) {
std::cout << "Failed to execute image decoded of preprocess module, ret = " << ret << "." << std::endl;
return ret;
}
// Get output of decode jpeg image
std::shared_ptr<DvppDataInfo> decodeOutData = dvppCommon_->GetDecodedImage();
// Run resize application function
DvppDataInfo resizeOutData;
resizeOutData.height = modelInfo_.modelHeight;
resizeOutData.width = modelInfo_.modelWidth;
resizeOutData.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
ret = dvppCommon_->CombineResizeProcess(decodeOutData, resizeOutData, true, VPC_PT_PADDING);
if (ret != OK) {
std::cout << "Failed to execute image resized of preprocess module, ret = " << ret << "." << std::endl;
return ret;
}
RELEASE_DVPP_DATA(decodeOutData->data);
return OK;
}
/*
* @description Inference of model
* @return int int code
*/
int AclProcess::ModelInfer(std::map<double, double> *costTime_map) {
// Get output of resize module
std::shared_ptr<DvppDataInfo> resizeOutData = dvppCommon_->GetResizedImage();
std::shared_ptr<DvppDataInfo> inputImg = dvppCommon_->GetInputImage();
float widthScale, heightScale;
if (keepRatio_) {
widthScale = static_cast<float>(resizeOutData->width) / inputImg->width;
if (widthScale > static_cast<float>(resizeOutData->height) / inputImg->height) {
widthScale = static_cast<float>(resizeOutData->height) / inputImg->height;
}
heightScale = widthScale;
} else {
widthScale = static_cast<float>(resizeOutData->width) / inputImg->width;
heightScale = static_cast<float>(resizeOutData->height) / inputImg->height;
}
float im_info[4];
im_info[0] = static_cast<float>(inputImg->height);
im_info[1] = static_cast<float>(inputImg->width);
im_info[2] = heightScale;
im_info[3] = widthScale;
void *imInfo_dst = nullptr;
int ret = aclrtMalloc(&imInfo_dst, 16, ACL_MEM_MALLOC_NORMAL_ONLY);
if (ret != ACL_ERROR_NONE) {
std::cout << "aclrtMalloc failed, ret = " << ret << std::endl;
aclrtFree(imInfo_dst);
return ret;
}
ret = aclrtMemcpy(reinterpret_cast<uint8_t *>(imInfo_dst), 16, im_info, 16, ACL_MEMCPY_HOST_TO_DEVICE);
if (ret != ACL_ERROR_NONE) {
std::cout << "aclrtMemcpy failed, ret = " << ret << std::endl;
aclrtFree(imInfo_dst);
return ret;
}
std::vector<void *> inputBuffers({resizeOutData->data, imInfo_dst});
std::vector<size_t> inputSizes({resizeOutData->dataSize, 4 * 4});
for (size_t i = 0; i < modelInfo_.outputNum; i++) {
aclrtMemset(outputBuffers_[i], outputSizes_[i], 0, outputSizes_[i]);
}
// Execute classification model
ret = modelProcess_->ModelInference(inputBuffers, inputSizes, outputBuffers_, outputSizes_, costTime_map);
if (ret != OK) {
aclrtFree(imInfo_dst);
std::cout << "Failed to execute the classification model, ret = " << ret << "." << std::endl;
return ret;
}
ret = aclrtFree(imInfo_dst);
if (ret != OK) {
std::cout << "aclrtFree image info failed" << std::endl;
return ret;
}
RELEASE_DVPP_DATA(resizeOutData->data);
return OK;
}
/*
* @description Process classification
*
* @par Function
* 1.Dvpp module preprocess
* 2.Execute classification model
* 3.Execute single operator
* 4.Write result
*
* @param imageFile input file path
* @return int int code
*/
int AclProcess::Process(const std::string& imageFile, std::map<double, double> *costTime_map) {
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
int ret = Preprocess(imageFile);
if (ret != OK) {
return ret;
}
ret = ModelInfer(costTime_map);
if (ret != OK) {
return ret;
}
ret = WriteResult(imageFile);
if (ret != OK) {
std::cout << "write result failed." << std::endl;
return ret;
}
gettimeofday(&end, nullptr);
const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
std::cout << "[Process Delay] cost: " << costMs << "ms." << std::endl;
return OK;
}

42
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/CMakeLists.txt
View File

@ -1,42 +0,0 @@
# Copyright (c) Huawei Technologies Co., Ltd. 2020. All rights reserved.
# CMake lowest version requirement
cmake_minimum_required(VERSION 3.5.1)
# Add definitions ENABLE_DVPP_INTERFACE to use dvpp api
add_definitions(-DENABLE_DVPP_INTERFACE)
# project information
project(InferClassification)
# Check environment variable
if(NOT DEFINED ENV{ASCEND_HOME})
message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
endif()
# Compile options
add_compile_options(-std=c++11 -fPIE -g -fstack-protector-all -Werror -Wreturn-type)
# Skip build rpath
set(CMAKE_SKIP_BUILD_RPATH True)
# Set output directory
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/out)
# Set include directory and library directory
set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
# Header path
include_directories(${ACL_LIB_DIR}/include/)
include_directories(${ATLAS_ACL_LIB_DIR}/include/)
include_directories(${PROJECT_SRC_ROOT}/../inc)
# add host lib path
link_directories(${ACL_LIB_DIR})
find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
find_library(acl_dvpp libacl_dvpp.so ${ACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
add_executable(main AclProcess.cpp
DvppCommon.cpp
ModelProcess.cpp
main.cpp)
target_link_libraries(main ${acl} gflags ${acl_dvpp} pthread)

735
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/DvppCommon.cpp
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@ -1,735 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 <iostream>
#include <memory>
#include "../inc/DvppCommon.h"
#include "../inc/CommonDataType.h"
static auto g_resizeConfigDeleter = [](acldvppResizeConfig *p) { acldvppDestroyResizeConfig(p); };
static auto g_picDescDeleter = [](acldvppPicDesc *picDesc) { acldvppDestroyPicDesc(picDesc); };
static auto g_roiConfigDeleter = [](acldvppRoiConfig *p) { acldvppDestroyRoiConfig(p); };
DvppCommon::DvppCommon(aclrtStream dvppStream):dvppStream_(dvppStream) {}
/*
* @description: Create a channel for processing image data,
* the channel description is created by acldvppCreateChannelDesc
* @return: OK if success, other values if failure
*/
int DvppCommon::Init(void) {
dvppChannelDesc_ = acldvppCreateChannelDesc();
if (dvppChannelDesc_ == nullptr) {
return -1;
}
int ret = acldvppCreateChannel(dvppChannelDesc_);
if (ret != 0) {
std::cout << "Failed to create dvpp channel, ret = " << ret << "." << std::endl;
acldvppDestroyChannelDesc(dvppChannelDesc_);
dvppChannelDesc_ = nullptr;
return ret;
}
return OK;
}
/*
* @description: destroy the channel and the channel description used by image.
* @return: OK if success, other values if failure
*/
int DvppCommon::DeInit(void) {
int ret = aclrtSynchronizeStream(dvppStream_); // int ret
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppDestroyChannel(dvppChannelDesc_);
if (ret != OK) {
std::cout << "Failed to destroy dvpp channel, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppDestroyChannelDesc(dvppChannelDesc_);
if (ret != OK) {
std::cout << "Failed to destroy dvpp channel description, ret = " << ret << "." << std::endl;
return ret;
}
return OK;
}
/*
* @description: Release the memory that is allocated in the interfaces which are started with "Combine"
*/
void DvppCommon::ReleaseDvppBuffer() {
if (resizedImage_ != nullptr) {
RELEASE_DVPP_DATA(resizedImage_->data);
}
if (decodedImage_ != nullptr) {
RELEASE_DVPP_DATA(decodedImage_->data);
}
if (inputImage_ != nullptr) {
RELEASE_DVPP_DATA(inputImage_->data);
}
}
/*
* @description: Get the size of buffer used to save image for VPC according to width, height and format
* @param width specifies the width of the output image
* @param height specifies the height of the output image
* @param format specifies the format of the output image
* @param: vpcSize is used to save the result size
* @return: OK if success, other values if failure
*/
int DvppCommon::GetVpcDataSize(uint32_t width, uint32_t height, acldvppPixelFormat format, uint32_t *vpcSize) {
if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
std::cout << "Format[" << format << "] for VPC is not supported, just support NV12 or NV21." << std::endl;
return INVALID_PARAM;
}
uint32_t widthStride = DVPP_ALIGN_UP(width, VPC_WIDTH_ALIGN);
uint32_t heightStride = DVPP_ALIGN_UP(height, VPC_HEIGHT_ALIGN);
*vpcSize = widthStride * heightStride * YUV_BGR_SIZE_CONVERT_3 / YUV_BGR_SIZE_CONVERT_2;
return OK;
}
/*
* @description: Get the aligned width and height of the input image according to the image format
* @param: width specifies the width before alignment
* @param: height specifies the height before alignment
* @param: format specifies the image format
* @param: widthStride is used to save the width after alignment
* @param: heightStride is used to save the height after alignment
* @return: OK if success, other values if failure
*/
int DvppCommon::GetVpcInputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride) {
uint32_t inputWidthStride;
if (format >= PIXEL_FORMAT_YUV_400 && format <= PIXEL_FORMAT_YVU_SEMIPLANAR_444) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH);
} else if (format >= PIXEL_FORMAT_YUYV_PACKED_422 && format <= PIXEL_FORMAT_VYUY_PACKED_422) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH) * YUV422_WIDTH_NU;
} else if (format >= PIXEL_FORMAT_YUV_PACKED_444 && format <= PIXEL_FORMAT_BGR_888) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH) * YUV444_RGB_WIDTH_NU;
} else if (format >= PIXEL_FORMAT_ARGB_8888 && format <= PIXEL_FORMAT_BGRA_8888) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH) * XRGB_WIDTH_NU;
} else {
std::cout << "Input format[" << format << "] for VPC is invalid, please check it." << std::endl;
return INVALID_PARAM;
}
uint32_t inputHeightStride = DVPP_ALIGN_UP(height, VPC_STRIDE_HEIGHT);
if (inputWidthStride > MAX_RESIZE_WIDTH || inputWidthStride < MIN_RESIZE_WIDTH) {
std::cout << "Input width stride " << inputWidthStride << " is invalid, not in [" << MIN_RESIZE_WIDTH \
<< ", " << MAX_RESIZE_WIDTH << "]." << std::endl;
return INVALID_PARAM;
}
if (inputHeightStride > MAX_RESIZE_HEIGHT || inputHeightStride < MIN_RESIZE_HEIGHT) {
std::cout << "Input height stride " << inputHeightStride << " is invalid, not in [" << MIN_RESIZE_HEIGHT \
<< ", " << MAX_RESIZE_HEIGHT << "]." << std::endl;
return INVALID_PARAM;
}
*widthStride = inputWidthStride;
*heightStride = inputHeightStride;
return OK;
}
/*
* @description: Get the aligned width and height of the output image according to the image format
* @param: width specifies the width before alignment
* @param: height specifies the height before alignment
* @param: format specifies the image format
* @param: widthStride is used to save the width after alignment
* @param: heightStride is used to save the height after alignment
* @return: OK if success, other values if failure
*/
int DvppCommon::GetVpcOutputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride) {
if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
std::cout << "Output format[" << format << "] is not supported, just support NV12 or NV21." << std::endl;
return INVALID_PARAM;
}
*widthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH);
*heightStride = DVPP_ALIGN_UP(height, VPC_STRIDE_HEIGHT);
return OK;
}
/*
* @description: Set picture description information and execute resize function
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: withSynchronize specifies whether to execute synchronously
* @param: processType specifies whether to perform proportional scaling, default is non-proportional resize
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::VpcResize(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
bool withSynchronize, VpcProcessType processType) {
acldvppPicDesc *inputDesc = acldvppCreatePicDesc();
acldvppPicDesc *outputDesc = acldvppCreatePicDesc();
resizeInputDesc_.reset(inputDesc, g_picDescDeleter);
resizeOutputDesc_.reset(outputDesc, g_picDescDeleter);
// Set dvpp picture descriptin info of input image
int ret = SetDvppPicDescData(input, resizeInputDesc_);
if (ret != OK) {
std::cout << "Failed to set dvpp input picture description, ret = " << ret << "." << std::endl;
return ret;
}
// Set dvpp picture descriptin info of output image
ret = SetDvppPicDescData(output, resizeOutputDesc_);
if (ret != OK) {
std::cout << "Failed to set dvpp output picture description, ret = " << ret << "." << std::endl;
return ret;
}
if (processType == VPC_PT_DEFAULT) {
return ResizeProcess(resizeInputDesc_, resizeOutputDesc_, withSynchronize);
}
// Get crop area according to the processType
CropRoiConfig cropRoi = {0};
GetCropRoi(input, output, processType, &cropRoi);
// The width and height of the original image will be resized by the same ratio
CropRoiConfig pasteRoi = {0};
GetPasteRoi(input, output, processType, &pasteRoi);
return ResizeWithPadding(resizeInputDesc_, resizeOutputDesc_, cropRoi, pasteRoi, withSynchronize);
}
/*
* @description: Set image description information
* @param: dataInfo specifies the image information
* @param: picsDesc specifies the picture description information to be set
* @return: OK if success, other values if failure
*/
int DvppCommon::SetDvppPicDescData(std::shared_ptr<DvppDataInfo> dataInfo, std::shared_ptr<acldvppPicDesc>picDesc) {
int ret = acldvppSetPicDescData(picDesc.get(), dataInfo->data);
if (ret != OK) {
std::cout << "Failed to set data for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescSize(picDesc.get(), dataInfo->dataSize);
if (ret != OK) {
std::cout << "Failed to set size for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescFormat(picDesc.get(), dataInfo->format);
if (ret != OK) {
std::cout << "Failed to set format for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescWidth(picDesc.get(), dataInfo->width);
if (ret != OK) {
std::cout << "Failed to set width for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescHeight(picDesc.get(), dataInfo->height);
if (ret != OK) {
std::cout << "Failed to set height for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescWidthStride(picDesc.get(), dataInfo->widthStride);
if (ret != OK) {
std::cout << "Failed to set aligned width for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescHeightStride(picDesc.get(), dataInfo->heightStride);
if (ret != OK) {
std::cout << "Failed to set aligned height for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
return OK;
}
/*
* @description: Check whether the image format and zoom ratio meet the requirements
* @param: input specifies the input image information
* @param: output specifies the output image information
* @return: OK if success, other values if failure
*/
int DvppCommon::CheckResizeParams(const DvppDataInfo &input, const DvppDataInfo &output) {
if (output.format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && output.format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
std::cout << "Output format[" << output.format << "]is not supported, just support NV12 or NV21." << std::endl;
return INVALID_PARAM;
}
float heightScale = static_cast<float>(output.height) / input.height;
if (heightScale < MIN_RESIZE_SCALE || heightScale > MAX_RESIZE_SCALE) {
std::cout << "Resize scale should be in range [1/16, 32], which is " << heightScale << "." << std::endl;
return INVALID_PARAM;
}
float widthScale = static_cast<float>(output.width) / input.width;
if (widthScale < MIN_RESIZE_SCALE || widthScale > MAX_RESIZE_SCALE) {
std::cout << "Resize scale should be in range [1/16, 32], which is " << widthScale << "." << std::endl;
return INVALID_PARAM;
}
return OK;
}
/*
* @description: Scale the input image to the size specified by the output image and
* saves the result to the output image (non-proportionate scaling)
* @param: inputDesc specifies the description information of the input image
* @param: outputDesc specifies the description information of the output image
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
*/
int DvppCommon::ResizeProcess(std::shared_ptr<acldvppPicDesc>inputDesc,
std::shared_ptr<acldvppPicDesc>outputDesc,
bool withSynchronize) {
acldvppResizeConfig *resizeConfig = acldvppCreateResizeConfig();
if (resizeConfig == nullptr) {
std::cout << "Failed to create dvpp resize config." << std::endl;
return INVALID_POINTER;
}
resizeConfig_.reset(resizeConfig, g_resizeConfigDeleter);
int ret = acldvppVpcResizeAsync(dvppChannelDesc_, inputDesc.get(), outputDesc.get(),
resizeConfig_.get(), dvppStream_);
if (ret != OK) {
std::cout << "Failed to resize asynchronously, ret = " << ret << "." << std::endl;
return ret;
}
if (withSynchronize) {
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
return ret;
}
}
return OK;
}
/*
* @description: Crop the image from the input image based on the specified area and
* paste the cropped image to the specified position of the target image
* as the output image
* @param: inputDesc specifies the description information of the input image
* @param: outputDesc specifies the description information of the output image
* @param: cropRoi specifies the cropped area
* @param: pasteRoi specifies the pasting area
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
* @attention: If the width and height of the crop area are different from those of the
* paste area, the image is scaled again
*/
int DvppCommon::ResizeWithPadding(std::shared_ptr<acldvppPicDesc> inputDesc,
std::shared_ptr<acldvppPicDesc> outputDesc,
const CropRoiConfig &cropRoi, const CropRoiConfig &pasteRoi, bool withSynchronize) {
acldvppRoiConfig *cropRoiCfg = acldvppCreateRoiConfig(cropRoi.left, cropRoi.right, cropRoi.up, cropRoi.down);
if (cropRoiCfg == nullptr) {
std::cout << "Failed to create dvpp roi config for corp area." << std::endl;
return INVALID_POINTER;
}
cropAreaConfig_.reset(cropRoiCfg, g_roiConfigDeleter);
acldvppRoiConfig *pastRoiCfg = acldvppCreateRoiConfig(pasteRoi.left, pasteRoi.right, pasteRoi.up, pasteRoi.down);
if (pastRoiCfg == nullptr) {
std::cout << "Failed to create dvpp roi config for paster area." << std::endl;
return INVALID_POINTER;
}
pasteAreaConfig_.reset(pastRoiCfg, g_roiConfigDeleter);
int ret = acldvppVpcCropAndPasteAsync(dvppChannelDesc_, inputDesc.get(), outputDesc.get(), cropAreaConfig_.get(),
pasteAreaConfig_.get(), dvppStream_);
if (ret != OK) {
// release resource.
std::cout << "Failed to crop and paste asynchronously, ret = " << ret << "." << std::endl;
return ret;
}
if (withSynchronize) {
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed tp synchronize stream, ret = " << ret << "." << std::endl;
return ret;
}
}
return OK;
}
/*
* @description: Get crop area
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: processType specifies whether to perform proportional scaling
* @param: cropRoi is used to save the info of the crop roi area
* @return: OK if success, other values if failure
*/
void DvppCommon::GetCropRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *cropRoi) {
// When processType is not VPC_PT_FILL, crop area is the whole input image
if (processType != VPC_PT_FILL) {
cropRoi->right = CONVERT_TO_ODD(input->width - ODD_NUM_1);
cropRoi->down = CONVERT_TO_ODD(input->height - ODD_NUM_1);
return;
}
bool widthRatioSmaller = true;
// The scaling ratio is based on the smaller ratio to ensure the smallest edge to fill the targe edge
float resizeRatio = static_cast<float>(input->width) / output->width;
if (resizeRatio > (static_cast<float>(input->height) / output->height)) {
resizeRatio = static_cast<float>(input->height) / output->height;
widthRatioSmaller = false;
}
const int halfValue = 2;
// The left and up must be even, right and down must be odd which is required by acl
if (widthRatioSmaller) {
cropRoi->left = 0;
cropRoi->right = CONVERT_TO_ODD(input->width - ODD_NUM_1);
cropRoi->up = CONVERT_TO_EVEN(static_cast<uint32_t>((input->height - output->height * resizeRatio) /
halfValue));
cropRoi->down = CONVERT_TO_ODD(input->height - cropRoi->up - ODD_NUM_1);
return;
}
cropRoi->up = 0;
cropRoi->down = CONVERT_TO_ODD(input->height - ODD_NUM_1);
cropRoi->left = CONVERT_TO_EVEN(static_cast<uint32_t>((input->width - output->width * resizeRatio) / halfValue));
cropRoi->right = CONVERT_TO_ODD(input->width - cropRoi->left - ODD_NUM_1);
return;
}
/*
* @description: Get paste area
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: processType specifies whether to perform proportional scaling
* @param: pasteRio is used to save the info of the paste area
* @return: OK if success, other values if failure
*/
void DvppCommon::GetPasteRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *pasteRoi) {
if (processType == VPC_PT_FILL) {
pasteRoi->right = CONVERT_TO_ODD(output->width - ODD_NUM_1);
pasteRoi->down = CONVERT_TO_ODD(output->height - ODD_NUM_1);
return;
}
bool widthRatioLarger = true;
// The scaling ratio is based on the larger ratio to ensure the largest edge to fill the targe edge
float resizeRatio = static_cast<float>(input->width) / output->width;
if (resizeRatio < (static_cast<float>(input->height) / output->height)) {
resizeRatio = static_cast<float>(input->height) / output->height;
widthRatioLarger = false;
}
// Left and up is 0 when the roi paste on the upper left corner
if (processType == VPC_PT_PADDING) {
pasteRoi->right = (input->width / resizeRatio) - ODD_NUM_1;
pasteRoi->down = (input->height / resizeRatio) - ODD_NUM_1;
pasteRoi->right = CONVERT_TO_ODD(pasteRoi->right);
pasteRoi->down = CONVERT_TO_ODD(pasteRoi->down);
return;
}
const int halfValue = 2;
// Left and up is 0 when the roi paste on the middler location
if (widthRatioLarger) {
pasteRoi->left = 0;
pasteRoi->right = output->width - ODD_NUM_1;
pasteRoi->up = (output->height - (input->height / resizeRatio)) / halfValue;
pasteRoi->down = output->height - pasteRoi->up - ODD_NUM_1;
} else {
pasteRoi->up = 0;
pasteRoi->down = output->height - ODD_NUM_1;
pasteRoi->left = (output->width - (input->width / resizeRatio)) / halfValue;
pasteRoi->right = output->width - pasteRoi->left - ODD_NUM_1;
}
// The left must be even and align to 16, up must be even, right and down must be odd which is required by acl
pasteRoi->left = DVPP_ALIGN_UP(CONVERT_TO_EVEN(pasteRoi->left), VPC_WIDTH_ALIGN);
pasteRoi->right = CONVERT_TO_ODD(pasteRoi->right);
pasteRoi->up = CONVERT_TO_EVEN(pasteRoi->up);
pasteRoi->down = CONVERT_TO_ODD(pasteRoi->down);
return;
}
/*
* @description: Resize the image specified by input and save the result to member variable resizedImage_
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: withSynchronize specifies whether to execute synchronously
* @param: processType specifies whether to perform proportional scaling, default is non-proportional resize
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::CombineResizeProcess(std::shared_ptr<DvppDataInfo> input, const DvppDataInfo &output,
bool withSynchronize, VpcProcessType processType) {
int ret = CheckResizeParams(*input, output);
if (ret != OK) {
return ret;
}
// Get widthStride and heightStride for input and output image according to the format
ret = GetVpcInputStrideSize(input->widthStride, input->heightStride, input->format,
&(input->widthStride), &(input->heightStride));
if (ret != OK) {
return ret;
}
resizedImage_ = std::make_shared<DvppDataInfo>();
resizedImage_->width = output.width;
resizedImage_->height = output.height;
resizedImage_->format = output.format;
ret = GetVpcOutputStrideSize(output.width, output.height, output.format, &(resizedImage_->widthStride),
&(resizedImage_->heightStride));
if (ret != OK) {
return ret;
}
// Get output buffer size for resize output
ret = GetVpcDataSize(output.width, output.height, output.format, &(resizedImage_->dataSize));
if (ret != OK) {
return ret;
}
// Malloc buffer for output of resize module
// Need to pay attention to release of the buffer
ret = acldvppMalloc(reinterpret_cast<void **>(&(resizedImage_->data)), resizedImage_->dataSize);
if (ret != OK) {
std::cout << "Failed to malloc " << resizedImage_->dataSize << " bytes on dvpp for resize" << std::endl;
return ret;
}
aclrtMemset(resizedImage_->data, resizedImage_->dataSize, YUV_GREYER_VALUE, resizedImage_->dataSize);
resizedImage_->frameId = input->frameId;
ret = VpcResize(input, resizedImage_, withSynchronize, processType);
if (ret != OK) {
// Release the output buffer when resize failed, otherwise release it after use
RELEASE_DVPP_DATA(resizedImage_->data);
}
return ret;
}
/*
* @description: Set the description of the output image and decode
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::JpegDecode(std::shared_ptr<DvppDataInfo> input,
std::shared_ptr<DvppDataInfo> output,
bool withSynchronize) {
acldvppPicDesc *outputDesc = acldvppCreatePicDesc();
decodeOutputDesc_.reset(outputDesc, g_picDescDeleter);
int ret = SetDvppPicDescData(output, decodeOutputDesc_);
if (ret != OK) {
return ret;
}
ret = acldvppJpegDecodeAsync(dvppChannelDesc_, input->data, input->dataSize, decodeOutputDesc_.get(), dvppStream_);
if (ret != OK) {
std::cout << "Failed to decode jpeg, ret = " << ret << "." << std::endl;
return ret;
}
if (withSynchronize) {
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
return DECODE_FAIL;
}
}
return OK;
}
/*
* @description: Get the aligned width and height of the image after decoding
* @param: width specifies the width before alignment
* @param: height specifies the height before alignment
* @param: widthStride is used to save the width after alignment
* @param: heightStride is used to save the height after alignment
* @return: OK if success, other values if failure
*/
void DvppCommon::GetJpegDecodeStrideSize(uint32_t width, uint32_t height,
uint32_t *widthStride, uint32_t *heightStride) {
*widthStride = DVPP_ALIGN_UP(width, JPEGD_STRIDE_WIDTH);
*heightStride = DVPP_ALIGN_UP(height, JPEGD_STRIDE_HEIGHT);
}
/*
* @description: Get picture width and height and number of channels from image data
* @param: data specifies the memory to store the image data
* @param: dataSize specifies the size of the image data
* @param: width is used to save the image width
* @param: height is used to save the image height
* @param: components is used to save the number of channels
* @return: OK if success, other values if failure
*/
int DvppCommon::GetJpegImageInfo(const void *data, uint32_t dataSize, uint32_t *width, uint32_t *height,
int32_t *components) {
uint32_t widthTmp;
uint32_t heightTmp;
int32_t componentsTmp;
int ret = acldvppJpegGetImageInfo(data, dataSize, &widthTmp, &heightTmp, &componentsTmp);
if (ret != OK) {
std::cout << "Failed to get image info of jpeg, ret = " << ret << "." << std::endl;
return ret;
}
if (widthTmp > MAX_JPEGD_WIDTH || widthTmp < MIN_JPEGD_WIDTH) {
std::cout << "Input width is invalid, not in [" << MIN_JPEGD_WIDTH << ", "
<< MAX_JPEGD_WIDTH << "]." << std::endl;
return INVALID_PARAM;
}
if (heightTmp > MAX_JPEGD_HEIGHT || heightTmp < MIN_JPEGD_HEIGHT) {
std::cout << "Input height is invalid, not in [" << MIN_JPEGD_HEIGHT << ", "
<< MAX_JPEGD_HEIGHT << "]." << std::endl;
return INVALID_PARAM;
}
*width = widthTmp;
*height = heightTmp;
*components = componentsTmp;
return OK;
}
/*
* @description: Get the size of the buffer for storing decoded images based on the image data, size, and format
* @param: data specifies the memory to store the image data
* @param: dataSize specifies the size of the image data
* @param: format specifies the image format
* @param: decSize is used to store the result size
* @return: OK if success, other values if failure
*/
int DvppCommon::GetJpegDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
uint32_t *decSize) {
uint32_t outputSize;
int ret = acldvppJpegPredictDecSize(data, dataSize, format, &outputSize);
if (ret != OK) {
std::cout << "Failed to predict decode size of jpeg image, ret = " << ret << "." << std::endl;
return ret;
}
*decSize = outputSize;
return OK;
}
/*
* @description: Decode the image specified by imageInfo and save the result to member variable decodedImage_
* @param: imageInfo specifies image information
* @param: format specifies the image format
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::CombineJpegdProcess(const RawData& imageInfo, acldvppPixelFormat format, bool withSynchronize) {
int32_t components;
inputImage_ = std::make_shared<DvppDataInfo>();
inputImage_->format = format;
int ret = GetJpegImageInfo(imageInfo.data.get(), imageInfo.lenOfByte, &(inputImage_->width), &(inputImage_->height),
&components);
if (ret != OK) {
std::cout << "Failed to get input image info, ret = " << ret << "." << std::endl;
return ret;
}
// Get the buffer size of decode output according to the input data and output format
uint32_t outBuffSize;
ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, &outBuffSize);
if (ret != OK) {
std::cout << "Failed to get size of decode output buffer, ret = " << ret << "." << std::endl;
return ret;
}
// In TransferImageH2D function, device buffer will be allocated to store the input image
// Need to pay attention to release of the buffer
ret = TransferImageH2D(imageInfo, inputImage_);
if (ret != OK) {
return ret;
}
decodedImage_ = std::make_shared<DvppDataInfo>();
decodedImage_->format = format;
decodedImage_->width = inputImage_->width;
decodedImage_->height = inputImage_->height;
GetJpegDecodeStrideSize(inputImage_->width, inputImage_->height, &(decodedImage_->widthStride),
&(decodedImage_->heightStride));
decodedImage_->dataSize = outBuffSize;
// Need to pay attention to release of the buffer
ret = acldvppMalloc(reinterpret_cast<void **>(&decodedImage_->data), decodedImage_->dataSize);
if (ret != OK) {
std::cout << "Failed to malloc memory on dvpp, ret = " << ret << "." << std::endl;
RELEASE_DVPP_DATA(inputImage_->data);
return ret;
}
ret = JpegDecode(inputImage_, decodedImage_, withSynchronize);
if (ret != OK) {
RELEASE_DVPP_DATA(inputImage_->data);
inputImage_->data = nullptr;
RELEASE_DVPP_DATA(decodedImage_->data);
decodedImage_->data = nullptr;
return ret;
}
return OK;
}
/*
* @description: Transfer data from host to device
* @param: imageInfo specifies the image data on the host
* @param: jpegInput is used to save the buffer and its size which is allocate on the device
* @return: OK if success, other values if failure
*/
int DvppCommon::TransferImageH2D(const RawData& imageInfo, const std::shared_ptr<DvppDataInfo>& jpegInput) {
if (imageInfo.lenOfByte == 0) {
std::cout << "The input buffer size on host should not be empty." << std::endl;
return INVALID_PARAM;
}
uint8_t* inDevBuff = nullptr;
int ret = acldvppMalloc(reinterpret_cast<void **>(&inDevBuff), imageInfo.lenOfByte);
if (ret != OK) {
std::cout << "Failed to malloc " << imageInfo.lenOfByte << " bytes on dvpp, ret = " << ret << "." << std::endl;
return ret;
}
// Copy the image data from host to device
ret = aclrtMemcpyAsync(inDevBuff, imageInfo.lenOfByte, imageInfo.data.get(), imageInfo.lenOfByte,
ACL_MEMCPY_HOST_TO_DEVICE, dvppStream_);
if (ret != OK) {
std::cout << "Failed to copy " << imageInfo.lenOfByte << " bytes from host to device" << std::endl;
RELEASE_DVPP_DATA(inDevBuff);
return ret;
}
// Attention: We must call the aclrtSynchronizeStream to ensure the task of memory replication has been completed
// after calling aclrtMemcpyAsync
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
RELEASE_DVPP_DATA(inDevBuff);
return ret;
}
jpegInput->data = inDevBuff;
jpegInput->dataSize = imageInfo.lenOfByte;
return OK;
}
std::shared_ptr<DvppDataInfo> DvppCommon::GetInputImage() {
return inputImage_;
}
std::shared_ptr<DvppDataInfo> DvppCommon::GetDecodedImage() {
return decodedImage_;
}
std::shared_ptr<DvppDataInfo> DvppCommon::GetResizedImage() {
return resizedImage_;
}
DvppCommon::~DvppCommon() {}

226
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/ModelProcess.cpp
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@ -1,226 +0,0 @@
/*
* Copyright(C) 2020. Huawei Technologies Co.,Ltd. All rights reserved.
*
* 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 <fstream>
#include "../inc/ModelProcess.h"
ModelProcess::ModelProcess(const int deviceId) {
deviceId_ = deviceId;
}
ModelProcess::ModelProcess() {}
ModelProcess::~ModelProcess() {
if (!isDeInit_) {
DeInit();
}
}
void ModelProcess::DestroyDataset(aclmdlDataset *dataset) {
// Just release the DataBuffer object and DataSet object, remain the buffer, because it is managerd by user
if (dataset != nullptr) {
for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(dataset); i++) {
aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(dataset, i);
if (dataBuffer != nullptr) {
aclDestroyDataBuffer(dataBuffer);
dataBuffer = nullptr;
}
}
aclmdlDestroyDataset(dataset);
}
}
aclmdlDesc *ModelProcess::GetModelDesc() {
return modelDesc_.get();
}
int ModelProcess::ModelInference(const std::vector<void *> &inputBufs,
const std::vector<size_t> &inputSizes,
const std::vector<void *> &ouputBufs,
const std::vector<size_t> &outputSizes,
std::map<double, double> *costTime_map) {
std::cout << "ModelProcess:Begin to inference." << std::endl;
aclmdlDataset *input = nullptr;
input = CreateAndFillDataset(inputBufs, inputSizes);
if (input == nullptr) {
return INVALID_POINTER;
}
int ret;
aclmdlDataset *output = nullptr;
output = CreateAndFillDataset(ouputBufs, outputSizes);
if (output == nullptr) {
DestroyDataset(input);
input = nullptr;
return INVALID_POINTER;
}
struct timeval start;
struct timeval end;
double startTime_ms;
double endTime_ms;
mtx_.lock();
gettimeofday(&start, NULL);
ret = aclmdlExecute(modelId_, input, output);
gettimeofday(&end, NULL);
startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
costTime_map->insert(std::pair<double, double>(startTime_ms, endTime_ms));
mtx_.unlock();
if (ret != OK) {
std::cout << "aclmdlExecute failed, ret[" << ret << "]." << std::endl;
return ret;
}
DestroyDataset(input);
DestroyDataset(output);
return OK;
}
int ModelProcess::DeInit() {
isDeInit_ = true;
int ret = aclmdlUnload(modelId_);
if (ret != OK) {
std::cout << "aclmdlUnload failed, ret["<< ret << "]." << std::endl;
return ret;
}
if (modelDevPtr_ != nullptr) {
ret = aclrtFree(modelDevPtr_);
if (ret != OK) {
std::cout << "aclrtFree failed, ret[" << ret << "]." << std::endl;
return ret;
}
modelDevPtr_ = nullptr;
}
if (weightDevPtr_ != nullptr) {
ret = aclrtFree(weightDevPtr_);
if (ret != OK) {
std::cout << "aclrtFree failed, ret[" << ret << "]." << std::endl;
return ret;
}
weightDevPtr_ = nullptr;
}
return OK;
}
/**
* Read a binary file, store the data into a uint8_t array
*
* @param fileName the file for reading
* @param buffShared a shared pointer to a uint8_t array for storing file
* @param buffLength the length of the array
* @return OK if create success, error code otherwise
*/
int ModelProcess::ReadBinaryFile(const std::string &fileName, uint8_t **buffShared, int *buffLength) {
std::ifstream inFile(fileName, std::ios::in | std::ios::binary);
if (!inFile) {
std::cout << "FaceFeatureLib: read file " << fileName << " fail." <<std::endl;
return READ_FILE_FAIL;
}
inFile.seekg(0, inFile.end);
*buffLength = inFile.tellg();
inFile.seekg(0, inFile.beg);
uint8_t *tempShared = reinterpret_cast<uint8_t *>(malloc(*buffLength));
inFile.read(reinterpret_cast<char *>(tempShared), *buffLength);
inFile.close();
*buffShared = tempShared;
std::cout << "read file: fileName=" << fileName << ", size=" << *buffLength << "." << std::endl;
return OK;
}
int ModelProcess::Init(const std::string &modelPath) {
std::cout << "ModelProcess:Begin to init instance." << std::endl;
int modelSize = 0;
uint8_t *modelData = nullptr;
int ret = ReadBinaryFile(modelPath, &modelData, &modelSize);
if (ret != OK) {
std::cout << "read model file failed, ret[" << ret << "]." << std::endl;
return ret;
}
ret = aclmdlQuerySizeFromMem(modelData, modelSize, &modelDevPtrSize_, &weightDevPtrSize_);
if (ret != OK) {
std::cout << "aclmdlQuerySizeFromMem failed, ret[" << ret << "]." << std::endl;
return ret;
}
std::cout << "modelDevPtrSize_[" << modelDevPtrSize_ << "]" << std::endl;
std::cout << " weightDevPtrSize_[" << weightDevPtrSize_ << "]." << std::endl;
ret = aclrtMalloc(&modelDevPtr_, modelDevPtrSize_, ACL_MEM_MALLOC_HUGE_FIRST);
if (ret != OK) {
std::cout << "aclrtMalloc dev_ptr failed, ret[" << ret << "]." << std::endl;
return ret;
}
ret = aclrtMalloc(&weightDevPtr_, weightDevPtrSize_, ACL_MEM_MALLOC_HUGE_FIRST);
if (ret != OK) {
std::cout << "aclrtMalloc weight_ptr failed, ret[" << ret << "] " << std::endl;
return ret;
}
ret = aclmdlLoadFromMemWithMem(modelData, modelSize, &modelId_, modelDevPtr_, modelDevPtrSize_,
weightDevPtr_, weightDevPtrSize_);
if (ret != OK) {
std::cout << "aclmdlLoadFromMemWithMem failed, ret[" << ret << "]." << std::endl;
return ret;
}
ret = aclrtGetCurrentContext(&contextModel_);
if (ret != OK) {
std::cout << "aclrtMalloc weight_ptr failed, ret[" << ret << "]." << std::endl;
return ret;
}
aclmdlDesc *modelDesc = aclmdlCreateDesc();
if (modelDesc == nullptr) {
std::cout << "aclmdlCreateDesc failed." << std::endl;
return ret;
}
ret = aclmdlGetDesc(modelDesc, modelId_);
if (ret != OK) {
std::cout << "aclmdlGetDesc ret fail, ret:" << ret << "." << std::endl;
return ret;
}
modelDesc_.reset(modelDesc, aclmdlDestroyDesc);
free(modelData);
return OK;
}
aclmdlDataset *ModelProcess::CreateAndFillDataset(const std::vector<void *> &bufs, const std::vector<size_t> &sizes) {
aclmdlDataset *dataset = aclmdlCreateDataset();
if (dataset == nullptr) {
std::cout << "ACL_ModelInputCreate failed." << std::endl;
return nullptr;
}
for (size_t i = 0; i < bufs.size(); ++i) {
aclDataBuffer *data = aclCreateDataBuffer(bufs[i], sizes[i]);
if (data == nullptr) {
DestroyDataset(dataset);
std::cout << "aclCreateDataBuffer failed." << std::endl;
return nullptr;
}
int ret = aclmdlAddDatasetBuffer(dataset, data);
if (ret != OK) {
DestroyDataset(dataset);
std::cout << "ACL_ModelInputDataAdd failed, ret[" << ret << "]." << std::endl;
return nullptr;
}
}
return dataset;
}

56
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/build.sh
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@ -1,56 +0,0 @@
#!/bin/bash
# Copyright 2020 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.
# ============================================================================
path_cur=$(cd "`dirname $0`" || exit; pwd)
build_type="Release"
function preparePath() {
rm -rf $1
mkdir -p $1
cd $1 || exit
}
function buildA300() {
if [ ! "${ARCH_PATTERN}" ]; then
# set ARCH_PATTERN to acllib when it was not specified by user
export ARCH_PATTERN=acllib
echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
else
echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
export ARCH_PATTERN=${ARCH_PATTERN}/acllib
fi
path_build=$path_cur/build
preparePath $path_build
cmake -DCMAKE_BUILD_TYPE=$build_type ..
make -j
ret=$?
cd ..
return ${ret}
}
# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
if [ ! "${ASCEND_VERSION}" ]; then
export ASCEND_VERSION=ascend-toolkit/latest
echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
else
echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
fi
buildA300
if [ $? -ne 0 ]; then
exit 1
fi

236
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,236 @@
/**
* 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/context.h"
#include "include/api/model.h"
#include "include/api/types.h"
#include "include/api/serialization.h"
#include "include/minddata/dataset/include/vision.h"
#include "include/minddata/dataset/include/execute.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::DataType;
using mindspore::dataset::Execute;
using mindspore::dataset::TensorTransform;
using mindspore::dataset::vision::Resize;
using mindspore::dataset::vision::Pad;
using mindspore::dataset::vision::HWC2CHW;
using mindspore::dataset::vision::Normalize;
using mindspore::dataset::vision::SwapRedBlue;
using mindspore::dataset::vision::Decode;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
const int IMAGEWIDTH = 1280;
const int IMAGEHEIGHT = 768;
int PadImage(const MSTensor &input, MSTensor *output) {
std::shared_ptr<TensorTransform> normalize(new Normalize({103.53, 116.28, 123.675},
{57.375, 57.120, 58.395}));
Execute composeNormalize({normalize});
std::vector<int64_t> shape = input.Shape();
auto imgResize = MSTensor();
auto imgPad = MSTensor();
float widthScale, heightScale;
widthScale = static_cast<float>(IMAGEWIDTH) / shape[1];
heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0];
Status ret;
if (widthScale < heightScale) {
int heightSize = shape[0]*widthScale;
std::shared_ptr<TensorTransform> resize(new Resize({heightSize, IMAGEWIDTH}));
Execute composeResizeWidth({resize});
ret = composeResizeWidth(input, &imgResize);
if (ret != kSuccess) {
std::cout << "ERROR: Resize Width failed." << std::endl;
return 1;
}
int paddingSize = IMAGEHEIGHT - heightSize;
std::shared_ptr<TensorTransform> pad(new Pad({0, 0, 0, paddingSize}));
Execute composePad({pad});
ret = composePad(imgResize, &imgPad);
if (ret != kSuccess) {
std::cout << "ERROR: Height Pad failed." << std::endl;
return 1;
}
ret = composeNormalize(imgPad, output);
if (ret != kSuccess) {
std::cout << "ERROR: Normalize failed." << std::endl;
return 1;
}
} else {
int widthSize = shape[1]*heightScale;
std::shared_ptr<TensorTransform> resize(new Resize({IMAGEHEIGHT, widthSize}));
Execute composeResizeHeight({resize});
ret = composeResizeHeight(input, &imgResize);
if (ret != kSuccess) {
std::cout << "ERROR: Resize Height failed." << std::endl;
return 1;
}
int paddingSize = IMAGEWIDTH - widthSize;
std::shared_ptr<TensorTransform> pad(new Pad({0, 0, paddingSize, 0}));
Execute composePad({pad});
ret = composePad(imgResize, &imgPad);
if (ret != kSuccess) {
std::cout << "ERROR: Width Pad failed." << std::endl;
return 1;
}
ret = composeNormalize(imgPad, output);
if (ret != kSuccess) {
std::cout << "ERROR: Normalize failed." << std::endl;
return 1;
}
}
return 0;
}
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->SetPrecisionMode("allow_fp32_to_fp16");
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;
}
std::vector<MSTensor> model_inputs = model.GetInputs();
if (model_inputs.empty()) {
std::cout << "Invalid model, inputs is empty." << 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();
std::shared_ptr<TensorTransform> decode(new Decode());
Execute composeDecode({decode});
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
Execute composeTranspose({hwc2chw});
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;
auto imgDecode = MSTensor();
auto image = ReadFileToTensor(all_files[i]);
ret = composeDecode(image, &imgDecode);
if (ret != kSuccess) {
std::cout << "ERROR: Decode failed." << std::endl;
return 1;
}
auto imgPad = MSTensor();
PadImage(imgDecode, &imgPad);
auto img = MSTensor();
composeTranspose(imgPad, &img);
std::vector<int64_t> shape = imgDecode.Shape();
float widthScale = static_cast<float>(IMAGEWIDTH) / shape[1];
float heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0];
float resizeScale = widthScale < heightScale ? widthScale : heightScale;
float imgInfo[4];
imgInfo[0] = shape[0];
imgInfo[1] = shape[1];
imgInfo[2] = resizeScale;
imgInfo[3] = resizeScale;
MSTensor imgMeta("imgMeta", DataType::kNumberTypeFloat32, {static_cast<int64_t>(4)}, imgInfo, 16);
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
img.Data().get(), img.DataSize());
inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
imgMeta.Data().get(), imgMeta.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;
}

136
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/main.cpp
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@ -1,136 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 <dirent.h>
#include <sys/stat.h>
#include <gflags/gflags.h>
#include <unistd.h>
#include <cstring>
#include <fstream>
#include <sstream>
#include "../inc/AclProcess.h"
#include "../inc/CommonDataType.h"
DEFINE_string(om_path, "./fasterrcnn.om", "om model path.");
DEFINE_string(data_path, "./test.jpg", "om model path.");
DEFINE_int32(width, 1280, "width");
DEFINE_int32(height, 768, "height");
DEFINE_int32(device_id, 0, "height");
static bool is_file(const std::string &filename) {
struct stat buffer;
return (stat(filename.c_str(), &buffer) == 0 && S_ISREG(buffer.st_mode));
}
static bool is_dir(const std::string &filefodler) {
struct stat buffer;
return (stat(filefodler.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode));
}
/*
* @description Initialize and run AclProcess module
* @param resourceInfo resource info of deviceIds, model info, single Operator Path, etc
* @param file the absolute path of input file
* @return int int code
*/
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
std::cout << "OM File Path :" << FLAGS_om_path << std::endl;
std::cout << "data Path :" << FLAGS_data_path << std::endl;
std::cout << "width :" << FLAGS_width << std::endl;
std::cout << "height :" << FLAGS_height << std::endl;
std::cout << "deviceId :" << FLAGS_device_id << std::endl;
char omAbsPath[PATH_MAX];
if (realpath(FLAGS_om_path.c_str(), omAbsPath) == nullptr) {
std::cout << "Failed to get the om real path." << std::endl;
return INVALID_PARAM;
}
if (access(omAbsPath, R_OK) == -1) {
std::cout << "ModelPath " << omAbsPath << " doesn't exist or read failed." << std::endl;
return INVALID_PARAM;
}
char dataAbsPath[PATH_MAX];
if (realpath(FLAGS_data_path.c_str(), dataAbsPath) == nullptr) {
std::cout << "Failed to get the data real path." << std::endl;
return INVALID_PARAM;
}
if (access(dataAbsPath, R_OK) == -1) {
std::cout << "data paeh " << dataAbsPath << " doesn't exist or read failed." << std::endl;
return INVALID_PARAM;
}
std::map<double, double> costTime_map;
AclProcess aclProcess(FLAGS_device_id, FLAGS_om_path, FLAGS_width, FLAGS_height);
int ret = aclProcess.InitResource();
if (ret != OK) {
aclProcess.Release();
return ret;
}
if (is_file(FLAGS_data_path)) {
ret = aclProcess.Process(FLAGS_data_path, &costTime_map);
if (ret != OK) {
std::cout << "model process failed, errno = " << ret << std::endl;
aclProcess.Release();
return ret;
}
} else if (is_dir(FLAGS_data_path)) {
struct dirent *filename;
DIR *dir;
dir = opendir(FLAGS_data_path.c_str());
if (dir == nullptr) {
aclProcess.Release();
return ERROR;
}
while ((filename = readdir(dir)) != nullptr) {
if (strcmp(filename->d_name, ".") == 0 || strcmp(filename->d_name, "..") == 0) {
continue;
}
std::string wholePath = FLAGS_data_path + "/" + filename->d_name;
ret = aclProcess.Process(wholePath, &costTime_map);
if (ret != OK) {
std::cout << "model process failed, errno = " << ret << std::endl;
aclProcess.Release();
return ret;
}
}
} else {
std::cout << " input image path error" << std::endl;
}
double average = 0.0;
int infer_cnt = 0;
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
double diff = 0.0;
diff = iter->second - iter->first;
average += diff;
infer_cnt++;
}
average = average / infer_cnt;
std::stringstream timeCost;
timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
file_stream << timeCost.str();
file_stream.close();
costTime_map.clear();
aclProcess.Release();
return OK;
}

129
model_zoo/official/cv/faster_rcnn/ascend310_infer/src/utils.cc Normal file
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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;
}
MSTensor ReadFileToTensor(const std::string &file) {
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return MSTensor();
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return MSTensor();
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return MSTensor();
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
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;
}

15
model_zoo/official/cv/faster_rcnn/postprocess.py
View File

@ -13,6 +13,7 @@
# limitations under the License.
# ============================================================================
"""post process for 310 inference"""
import os
import argparse
import numpy as np
from pycocotools.coco import COCO
@ -25,13 +26,13 @@ dst_height = 768
parser = argparse.ArgumentParser(description="FasterRcnn inference")
parser.add_argument("--ann_file", type=str, required=True, help="ann file.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
parser.add_argument("--result_path", type=str, required=True, help="result file path.")
args = parser.parse_args()
def get_eval_result(ann_file):
def get_eval_result(ann_file, result_path):
""" get evaluation result of faster rcnn"""
max_num = 128
result_path = "./result_Files/"
result_path = result_path
outputs = []
@ -41,9 +42,9 @@ def get_eval_result(ann_file):
for img_id in img_ids:
file_id = str(img_id).zfill(12)
bbox_result_file = result_path + file_id + "_0.bin"
label_result_file = result_path + file_id + "_1.bin"
mask_result_file = result_path + file_id + "_2.bin"
bbox_result_file = os.path.join(result_path, file_id + "_0.bin")
label_result_file = os.path.join(result_path, file_id + "_1.bin")
mask_result_file = os.path.join(result_path, file_id + "_2.bin")
all_bbox = np.fromfile(bbox_result_file, dtype=np.float16).reshape(80000, 5)
all_label = np.fromfile(label_result_file, dtype=np.int32).reshape(80000, 1)
@ -70,4 +71,4 @@ def get_eval_result(ann_file):
coco_eval(result_files, eval_types, dataset_coco, single_result=False)
if __name__ == '__main__':
get_eval_result(args.ann_file)
get_eval_result(args.ann_file, args.result_path)

55
model_zoo/official/cv/faster_rcnn/scripts/run_infer_310.sh Executable file → Normal file
View File

@ -14,61 +14,51 @@
# limitations under the License.
# ============================================================================
if [[ $# -lt 3 || $# -gt 4 ]]; then
echo "Usage: sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DEVICE_ID]
if [[ $# -lt 3 || $# -gt 4 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)
ann_file=$(get_real_path $3)
device_id=0
if [ $# == 4 ]; then
device_id=$4
elif [ $# == 3 ]; then
# shellcheck disable=SC2153 #DEVICE_ID is en
if [ -z $device_id ]; then
device_id=0
else
device_id=$device_id
fi
fi
echo $model
echo $data_path
echo $ann_file
echo $device_id
echo "mindir name: "$model
echo "dataset path: "$data_path
echo "ann_file: " $ann_file
echo "device id: "$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/acllib/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ones:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/acllib/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-ones:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/lib:$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=$ASCEND_HOME/opp
fi
function air_to_om()
{
atc --input_format=NCHW --framework=1 --model=$model --input_shape="x:1, 3, 768, 1280; im_info: 1, 4" --output=fasterrcnn --insert_op_conf=../src/aipp.cfg --precision_mode=allow_fp32_to_fp16 --soc_version=Ascend310 &> atc.log
}
function compile_app()
{
cd ../ascend310_infer/src || exit
sh build.sh &> build.log
cd ../ascend310_infer || exit
bash build.sh &> build.log
cd - || exit
}
@ -77,24 +67,19 @@ function infer()
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/src/out/main --om_path=./fasterrcnn.om --data_path=$data_path --device_id=$device_id &> infer.log
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
}
function cal_acc()
{
python ../postprocess.py --ann_file=$ann_file --img_path=$data_path &> acc.log &
python3.7 ../postprocess.py --ann_file=$ann_file --result_path=./result_Files &> acc.log &
}
air_to_om
if [ $? -ne 0 ]; then
echo "air to om failed"
exit 1
fi
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"

26
model_zoo/official/cv/faster_rcnn/src/aipp.cfg
View File

@ -1,26 +0,0 @@
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
}

7
model_zoo/official/cv/maskrcnn/README.md
View File

@ -172,7 +172,7 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
```shell
# inference
bash run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
# [Script Description](#contents)
@ -203,7 +203,6 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
├─resnet50.py # backbone network
├─roi_align.py # roi align network
└─rpn.py # reagion proposal network
├─aipp.cfg #aipp config file
├─config.py # network configuration
├─convert_checkpoint.py # convert resnet50 backbone checkpoint
├─dataset.py # dataset utils
@ -502,7 +501,7 @@ Accumulating evaluation results...
python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
```
`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
## Inference Process
@ -513,7 +512,7 @@ Current batch_ Size can only be set to 1. The inference process needs about 600G
```shell
# Ascend310 inference
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
### result

7
model_zoo/official/cv/maskrcnn/README_CN.md
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@ -129,7 +129,7 @@ pip install mmcv=0.2.14
```bash
# 评估
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
注：
@ -197,7 +197,6 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
├─resnet50.py # 骨干网
├─roi_align.py # 兴趣点对齐网络
└─rpn.py # 区域候选网络
├─aipp.cfg #aipp 配置文件
├─config.py # 网络配置
├─convert_checkpoint.py # 转换预训练checkpoint文件
├─dataset.py # 数据集工具
@ -500,7 +499,7 @@ sh run_eval.sh [VALIDATION_ANN_FILE_JSON] [CHECKPOINT_PATH]
python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
```
`EXPORT_FORMAT` 选项 ["AIR", "ONNX", "MINDIR"]
`EXPORT_FORMAT` 选项 ["AIR", "MINDIR"]
## 推理过程
@ -510,7 +509,7 @@ python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_
```shell
# Ascend310 推理
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
### 结果

14
model_zoo/official/cv/maskrcnn/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
model_zoo/official/cv/maskrcnn/ascend310_infer/build.sh Normal file
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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

1
model_zoo/official/cv/maskrcnn/ascend310_infer/fusion_switch.cfg Normal file
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@ -0,0 +1 @@
ConvBatchnormFusionPass:off

62
model_zoo/official/cv/maskrcnn/ascend310_infer/inc/AclProcess.h
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@ -1,62 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 ACLMANAGER_H
#define ACLMANAGER_H
#include <map>
#include <iostream>
#include <string>
#include <memory>
#include <vector>
#include "acl/acl.h"
#include "CommonDataType.h"
#include "ModelProcess.h"
#include "DvppCommon.h"
struct ModelInfo {
std::string modelPath;
uint32_t modelWidth;
uint32_t modelHeight;
uint32_t outputNum;
};
class AclProcess {
public:
AclProcess(int deviceId, const std::string &om_path, uint32_t width, uint32_t height);
~AclProcess() {}
void Release();
int InitResource();
int Process(const std::string& imageFile, std::map<double, double> *costTime_map);
private:
int InitModule();
int Preprocess(const std::string& imageFile);
int ModelInfer(std::map<double, double> *costTime_map);
int WriteResult(const std::string& imageFile);
int ReadFile(const std::string &filePath, RawData *fileData);
int32_t deviceId_;
ModelInfo modelInfo_;
aclrtContext context_;
aclrtStream stream_;
std::shared_ptr<ModelProcess> modelProcess_;
std::shared_ptr<DvppCommon> dvppCommon_;
bool keepRatio_;
std::vector<void *> outputBuffers_;
std::vector<size_t> outputSizes_;
};
#endif

95
model_zoo/official/cv/maskrcnn/ascend310_infer/inc/CommonDataType.h
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@ -1,95 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 COMMONDATATYPE_H
#define COMMONDATATYPE_H
#include <stdio.h>
#include <iostream>
#include <memory>
#include <vector>
#include "acl/acl.h"
#include "acl/ops/acl_dvpp.h"
#define DVPP_ALIGN_UP(x, align) ((((x) + ((align)-1)) / (align)) * (align))
#define OK 0
#define ERROR -1
#define INVALID_POINTER -2
#define READ_FILE_FAIL -3
#define OPEN_FILE_FAIL -4
#define INIT_FAIL -5
#define INVALID_PARAM -6
#define DECODE_FAIL -7
const float SEC2MS = 1000.0;
const int YUV_BGR_SIZE_CONVERT_3 = 3;
const int YUV_BGR_SIZE_CONVERT_2 = 2;
const int VPC_WIDTH_ALIGN = 16;
const int VPC_HEIGHT_ALIGN = 2;
// Description of image data
struct ImageInfo {
uint32_t width; // Image width
uint32_t height; // Image height
uint32_t lenOfByte; // Size of image data, bytes
std::shared_ptr<uint8_t> data; // Smart pointer of image data
};
// Description of data in device
struct RawData {
size_t lenOfByte; // Size of memory, bytes
std::shared_ptr<void> data; // Smart pointer of data
};
// define the structure of an rectangle
struct Rectangle {
uint32_t leftTopX;
uint32_t leftTopY;
uint32_t rightBottomX;
uint32_t rightBottomY;
};
enum VpcProcessType {
VPC_PT_DEFAULT = 0,
VPC_PT_PADDING, // Resize with locked ratio and paste on upper left corner
VPC_PT_FIT, // Resize with locked ratio and paste on middle location
VPC_PT_FILL, // Resize with locked ratio and paste on whole locatin, the input image may be cropped
};
struct DvppDataInfo {
uint32_t width = 0; // Width of image
uint32_t height = 0; // Height of image
uint32_t widthStride = 0; // Width after align up
uint32_t heightStride = 0; // Height after align up
acldvppPixelFormat format = PIXEL_FORMAT_YUV_SEMIPLANAR_420; // Format of image
uint32_t frameId = 0; // Needed by video
uint32_t dataSize = 0; // Size of data in byte
uint8_t *data = nullptr; // Image data
};
struct CropRoiConfig {
uint32_t left;
uint32_t right;
uint32_t down;
uint32_t up;
};
struct DvppCropInputInfo {
DvppDataInfo dataInfo;
CropRoiConfig roi;
};
#endif

139
model_zoo/official/cv/maskrcnn/ascend310_infer/inc/DvppCommon.h
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@ -1,139 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 DVPP_COMMON_H
#define DVPP_COMMON_H
#include <memory>
#include "CommonDataType.h"
#include "acl/ops/acl_dvpp.h"
const int MODULUS_NUM_2 = 2;
const uint32_t ODD_NUM_1 = 1;
const uint32_t JPEGD_STRIDE_WIDTH = 128; // Jpegd module output width need to align up to 128
const uint32_t JPEGD_STRIDE_HEIGHT = 16; // Jpegd module output height need to align up to 16
const uint32_t VPC_STRIDE_WIDTH = 16; // Vpc module output width need to align up to 16
const uint32_t VPC_STRIDE_HEIGHT = 2; // Vpc module output height need to align up to 2
const uint32_t YUV422_WIDTH_NU = 2; // Width of YUV422, WidthStride = Width * 2
const uint32_t YUV444_RGB_WIDTH_NU = 3; // Width of YUV444 and RGB888, WidthStride = Width * 3
const uint32_t XRGB_WIDTH_NU = 4; // Width of XRGB8888, WidthStride = Width * 4
const uint32_t JPEG_OFFSET = 8; // Offset of input file for jpegd module
const uint32_t MAX_JPEGD_WIDTH = 8192; // Max width of jpegd module
const uint32_t MAX_JPEGD_HEIGHT = 8192; // Max height of jpegd module
const uint32_t MIN_JPEGD_WIDTH = 32; // Min width of jpegd module
const uint32_t MIN_JPEGD_HEIGHT = 32; // Min height of jpegd module
const uint32_t MAX_RESIZE_WIDTH = 4096; // Max width stride of resize module
const uint32_t MAX_RESIZE_HEIGHT = 4096; // Max height stride of resize module
const uint32_t MIN_RESIZE_WIDTH = 32; // Min width stride of resize module
const uint32_t MIN_RESIZE_HEIGHT = 6; // Min height stride of resize module
const float MIN_RESIZE_SCALE = 0.03125; // Min resize scale of resize module
const float MAX_RESIZE_SCALE = 16.0; // Min resize scale of resize module
const uint32_t MAX_VPC_WIDTH = 4096; // Max width of picture to VPC(resize/crop)
const uint32_t MAX_VPC_HEIGHT = 4096; // Max height of picture to VPC(resize/crop)
const uint32_t MIN_VPC_WIDTH = 32; // Min width of picture to VPC(resize/crop)
const uint32_t MIN_VPC_HEIGHT = 6; // Min height of picture to VPC(resize/crop)
const uint32_t MIN_CROP_WIDTH = 10; // Min width of crop area
const uint32_t MIN_CROP_HEIGHT = 6; // Min height of crop area
const uint8_t YUV_GREYER_VALUE = 128; // Filling value of the resized YUV image
#define CONVERT_TO_ODD(NUM) (((NUM) % MODULUS_NUM_2 != 0) ? (NUM) : ((NUM) - 1))
#define CONVERT_TO_EVEN(NUM) (((NUM) % MODULUS_NUM_2 == 0) ? (NUM) : ((NUM) - 1))
#define CHECK_ODD(num) ((num) % MODULUS_NUM_2 != 0)
#define CHECK_EVEN(num) ((num) % MODULUS_NUM_2 == 0)
#define RELEASE_DVPP_DATA(dvppDataPtr) do { \
int retMacro; \
if (dvppDataPtr != nullptr) { \
retMacro = acldvppFree(dvppDataPtr); \
if (retMacro != OK) { \
std::cout << "Failed to free memory on dvpp, ret = " << retMacro << "." << std::endl; \
} \
dvppDataPtr = nullptr; \
} \
} while (0);
class DvppCommon {
public:
explicit DvppCommon(aclrtStream dvppStream);
~DvppCommon();
int Init(void);
int DeInit(void);
static int GetVpcDataSize(uint32_t widthVpc, uint32_t heightVpc, acldvppPixelFormat format,
uint32_t *vpcSize);
static int GetVpcInputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride);
static int GetVpcOutputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride);
static void GetJpegDecodeStrideSize(uint32_t width, uint32_t height, uint32_t *widthStride, uint32_t *heightStride);
static int GetJpegImageInfo(const void *data, uint32_t dataSize, uint32_t *width, uint32_t *height,
int32_t *components);
static int GetJpegDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
uint32_t *decSize);
int VpcResize(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output, bool withSynchronize,
VpcProcessType processType = VPC_PT_DEFAULT);
int JpegDecode(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output, bool withSynchronize);
int CombineResizeProcess(std::shared_ptr<DvppDataInfo> input, const DvppDataInfo &output, bool withSynchronize,
VpcProcessType processType = VPC_PT_DEFAULT);
int CombineJpegdProcess(const RawData& imageInfo, acldvppPixelFormat format, bool withSynchronize);
std::shared_ptr<DvppDataInfo> GetInputImage();
std::shared_ptr<DvppDataInfo> GetDecodedImage();
std::shared_ptr<DvppDataInfo> GetResizedImage();
void ReleaseDvppBuffer();
private:
int SetDvppPicDescData(std::shared_ptr<DvppDataInfo> dataInfo, std::shared_ptr<acldvppPicDesc>picDesc);
int ResizeProcess(std::shared_ptr<acldvppPicDesc> inputDesc,
std::shared_ptr<acldvppPicDesc> outputDesc, bool withSynchronize);
int ResizeWithPadding(std::shared_ptr<acldvppPicDesc> inputDesc, std::shared_ptr<acldvppPicDesc> outputDesc,
const CropRoiConfig &cropRoi, const CropRoiConfig &pasteRoi, bool withSynchronize);
void GetCropRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *cropRoi);
void GetPasteRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *pasteRoi);
int CheckResizeParams(const DvppDataInfo &input, const DvppDataInfo &output);
int TransferImageH2D(const RawData& imageInfo, const std::shared_ptr<DvppDataInfo>& jpegInput);
int CreateStreamDesc(std::shared_ptr<DvppDataInfo> data);
int DestroyResource();
std::shared_ptr<acldvppRoiConfig> cropAreaConfig_ = nullptr;
std::shared_ptr<acldvppRoiConfig> pasteAreaConfig_ = nullptr;
std::shared_ptr<acldvppPicDesc> resizeInputDesc_ = nullptr;
std::shared_ptr<acldvppPicDesc> resizeOutputDesc_ = nullptr;
std::shared_ptr<acldvppPicDesc> decodeOutputDesc_ = nullptr;
std::shared_ptr<acldvppResizeConfig> resizeConfig_ = nullptr;
acldvppChannelDesc *dvppChannelDesc_ = nullptr;
aclrtStream dvppStream_ = nullptr;
std::shared_ptr<DvppDataInfo> inputImage_ = nullptr;
std::shared_ptr<DvppDataInfo> decodedImage_ = nullptr;
std::shared_ptr<DvppDataInfo> resizedImage_ = nullptr;
};
#endif

63
model_zoo/official/cv/maskrcnn/ascend310_infer/inc/ModelProcess.h
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@ -1,63 +0,0 @@
/*
* Copyright(C) 2020. Huawei Technologies Co.,Ltd. All rights reserved.
*
* 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 MODELPROCSS_H
#define MODELPROCSS_H
#include <cstdio>
#include <vector>
#include <unordered_map>
#include <mutex>
#include <map>
#include <memory>
#include <string>
#include "acl/acl.h"
#include "CommonDataType.h"
class ModelProcess {
public:
explicit ModelProcess(const int deviceId);
ModelProcess();
~ModelProcess();
int Init(const std::string &modelPath);
int DeInit();
int ModelInference(const std::vector<void *> &inputBufs,
const std::vector<size_t> &inputSizes,
const std::vector<void *> &ouputBufs,
const std::vector<size_t> &outputSizes,
std::map<double, double> *costTime_map);
aclmdlDesc *GetModelDesc();
int ReadBinaryFile(const std::string &fileName, uint8_t **buffShared, int *buffLength);
private:
aclmdlDataset *CreateAndFillDataset(const std::vector<void *> &bufs, const std::vector<size_t> &sizes);
void DestroyDataset(aclmdlDataset *dataset);
std::mutex mtx_ = {};
int deviceId_ = 0;
uint32_t modelId_ = 0;
void *modelDevPtr_ = nullptr;
size_t modelDevPtrSize_ = 0;
void *weightDevPtr_ = nullptr;
size_t weightDevPtrSize_ = 0;
aclrtContext contextModel_ = nullptr;
std::shared_ptr<aclmdlDesc> modelDesc_ = nullptr;
bool isDeInit_ = false;
};
#endif

32
model_zoo/official/cv/maskrcnn/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

371
model_zoo/official/cv/maskrcnn/ascend310_infer/src/AclProcess.cpp
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@ -1,371 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 "AclProcess.h"
#include <sys/time.h>
#include <thread>
#include <string>
/*
* @description Implementation of constructor for class AclProcess with parameter list
* @attention context is passed in as a parameter after being created in ResourceManager::InitResource
*/
AclProcess::AclProcess(int deviceId, const std::string &om_path, uint32_t width, uint32_t height)
: deviceId_(deviceId), stream_(nullptr), modelProcess_(nullptr), dvppCommon_(nullptr), keepRatio_(true) {
modelInfo_.modelPath = om_path;
modelInfo_.modelWidth = width;
modelInfo_.modelHeight = height;
}
/*
* @description Release all the resource
* @attention context will be released in ResourceManager::Release
*/
void AclProcess::Release() {
// Synchronize stream and release Dvpp channel
dvppCommon_->DeInit();
// Release stream
if (stream_ != nullptr) {
int ret = aclrtDestroyStream(stream_);
if (ret != OK) {
std::cout << "Failed to destroy the stream, ret = " << ret << ".";
}
stream_ = nullptr;
}
// Destroy resources of modelProcess_
modelProcess_->DeInit();
// Release Dvpp buffer
dvppCommon_->ReleaseDvppBuffer();
return;
}
/*
* @description Initialize the modules used by this sample
* @return int int code
*/
int AclProcess::InitModule() {
// Create Dvpp common object
if (dvppCommon_ == nullptr) {
dvppCommon_ = std::make_shared<DvppCommon>(stream_);
int retDvppCommon = dvppCommon_->Init();
if (retDvppCommon != OK) {
std::cout << "Failed to initialize dvppCommon, ret = " << retDvppCommon << std::endl;
return retDvppCommon;
}
}
// Create model inference object
if (modelProcess_ == nullptr) {
modelProcess_ = std::make_shared<ModelProcess>(deviceId_);
}
// Initialize ModelProcess module
int ret = modelProcess_->Init(modelInfo_.modelPath);
if (ret != OK) {
std::cout << "Failed to initialize the model process module, ret = " << ret << "." << std::endl;
return ret;
}
std::cout << "Initialized the model process module successfully." << std::endl;
return OK;
}
/*
* @description Create resource for this sample
* @return int int code
*/
int AclProcess::InitResource() {
int ret = aclInit(nullptr); // Initialize ACL
if (ret != OK) {
std::cout << "Failed to init acl, ret = " << ret << std::endl;
return ret;
}
ret = aclrtSetDevice(deviceId_);
if (ret != ACL_SUCCESS) {
std::cout << "acl set device " << deviceId_ << "intCode = "<< static_cast<int32_t>(ret) << std::endl;
return ret;
}
std::cout << "set device "<< deviceId_ << " success" << std::endl;
// create context (set current)
ret = aclrtCreateContext(&context_, deviceId_);
if (ret != ACL_SUCCESS) {
std::cout << "acl create context failed, deviceId = " << deviceId_ <<
"intCode = "<< static_cast<int32_t>(ret) << std::endl;
return ret;
}
std::cout << "create context success" << std::endl;
ret = aclrtCreateStream(&stream_); // Create stream for application
if (ret != OK) {
std::cout << "Failed to create the acl stream, ret = " << ret << "." << std::endl;
return ret;
}
std::cout << "Created the acl stream successfully." << std::endl;
// Initialize dvpp module
if (InitModule() != OK) {
return INIT_FAIL;
}
aclmdlDesc *modelDesc = modelProcess_->GetModelDesc();
size_t outputSize = aclmdlGetNumOutputs(modelDesc);
modelInfo_.outputNum = outputSize;
for (size_t i = 0; i < outputSize; i++) {
size_t bufferSize = aclmdlGetOutputSizeByIndex(modelDesc, i);
void *outputBuffer = nullptr;
ret = aclrtMalloc(&outputBuffer, bufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
if (ret != OK) {
std::cout << "Failed to malloc buffer, ret = " << ret << "." << std::endl;
return ret;
}
outputBuffers_.push_back(outputBuffer);
outputSizes_.push_back(bufferSize);
}
return OK;
}
int AclProcess::WriteResult(const std::string& imageFile) {
std::string homePath = "./result_Files";
void *resHostBuf = nullptr;
for (size_t i = 0; i < outputBuffers_.size(); ++i) {
size_t output_size;
void *netOutput;
netOutput = outputBuffers_[i];
output_size = outputSizes_[i];
int ret = aclrtMallocHost(&resHostBuf, output_size);
if (ret != OK) {
std::cout << "Failed to print the result, malloc host failed, ret = " << ret << "." << std::endl;
return ret;
}
ret = aclrtMemcpy(resHostBuf, output_size, netOutput,
output_size, ACL_MEMCPY_DEVICE_TO_HOST);
if (ret != OK) {
std::cout << "Failed to print result, memcpy device to host failed, ret = " << ret << "." << std::endl;
return ret;
}
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;
try {
FILE *outputFile = fopen(outFileName.c_str(), "wb");
if (outputFile == nullptr) {
std::cout << "open result file " << outFileName << " failed" << std::endl;
return INVALID_POINTER;
}
size_t size = fwrite(resHostBuf, sizeof(char), output_size, outputFile);
if (size != output_size) {
fclose(outputFile);
outputFile = nullptr;
std::cout << "write result file " << outFileName << " failed, write size[" << size <<
"] is smaller than output size[" << output_size << "], maybe the disk is full." << std::endl;
return ERROR;
}
fclose(outputFile);
outputFile = nullptr;
} catch (std::exception &e) {
std::cout << "write result file " << outFileName << " failed, error info: " << e.what() << std::endl;
std::exit(1);
}
ret = aclrtFreeHost(resHostBuf);
if (ret != OK) {
std::cout << "aclrtFree host output memory failed" << std::endl;
return ret;
}
}
return OK;
}
/**
* Read a file, store it into the RawData structure
*
* @param filePath file to read to
* @param fileData RawData structure to store in
* @return OK if create success, int code otherwise
*/
int AclProcess::ReadFile(const std::string &filePath, RawData *fileData) {
// Open file with reading mode
FILE *fp = fopen(filePath.c_str(), "rb");
if (fp == nullptr) {
std::cout << "Failed to open file, filePath = " << filePath << std::endl;
return OPEN_FILE_FAIL;
}
// Get the length of input file
fseek(fp, 0, SEEK_END);
size_t fileSize = ftell(fp);
fseek(fp, 0, SEEK_SET);
// If file not empty, read it into FileInfo and return it
if (fileSize > 0) {
fileData->lenOfByte = fileSize;
fileData->data = std::make_shared<uint8_t>();
fileData->data.reset(new uint8_t[fileSize], std::default_delete<uint8_t[]>());
uint32_t readRet = fread(fileData->data.get(), 1, fileSize, fp);
if (readRet == 0) {
fclose(fp);
return READ_FILE_FAIL;
}
fclose(fp);
return OK;
}
fclose(fp);
return INVALID_PARAM;
}
/*
* @description Preprocess the input image
* @param imageFile input image path
* @return int int code
*/
int AclProcess::Preprocess(const std::string& imageFile) {
RawData imageInfo;
int ret = ReadFile(imageFile, &imageInfo); // Read image data from input image file
if (ret != OK) {
std::cout << "Failed to read file, ret = " << ret << "." << std::endl;
return ret;
}
// Run process of jpegD
ret = dvppCommon_->CombineJpegdProcess(imageInfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420, true);
if (ret != OK) {
std::cout << "Failed to execute image decoded of preprocess module, ret = " << ret << "." << std::endl;
return ret;
}
// Get output of decode jpeg image
std::shared_ptr<DvppDataInfo> decodeOutData = dvppCommon_->GetDecodedImage();
// Run resize application function
DvppDataInfo resizeOutData;
resizeOutData.height = modelInfo_.modelHeight;
resizeOutData.width = modelInfo_.modelWidth;
resizeOutData.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
ret = dvppCommon_->CombineResizeProcess(decodeOutData, resizeOutData, true, VPC_PT_PADDING);
if (ret != OK) {
std::cout << "Failed to execute image resized of preprocess module, ret = " << ret << "." << std::endl;
return ret;
}
RELEASE_DVPP_DATA(decodeOutData->data);
return OK;
}
/*
* @description Inference of model
* @return int int code
*/
int AclProcess::ModelInfer(std::map<double, double> *costTime_map) {
// Get output of resize module
std::shared_ptr<DvppDataInfo> resizeOutData = dvppCommon_->GetResizedImage();
std::shared_ptr<DvppDataInfo> inputImg = dvppCommon_->GetInputImage();
float widthScale, heightScale;
if (keepRatio_) {
widthScale = static_cast<float>(resizeOutData->width) / inputImg->width;
if (widthScale > static_cast<float>(resizeOutData->height) / inputImg->height) {
widthScale = static_cast<float>(resizeOutData->height) / inputImg->height;
}
heightScale = widthScale;
} else {
widthScale = static_cast<float>(resizeOutData->width) / inputImg->width;
heightScale = static_cast<float>(resizeOutData->height) / inputImg->height;
}
aclFloat16 inputWidth = aclFloatToFloat16(static_cast<float>(inputImg->width));
aclFloat16 inputHeight = aclFloatToFloat16(static_cast<float>(inputImg->height));
aclFloat16 resizeWidthRatioFp16 = aclFloatToFloat16(widthScale);
aclFloat16 resizeHeightRatioFp16 = aclFloatToFloat16(heightScale);
aclFloat16 *im_info = reinterpret_cast<aclFloat16 *>(malloc(sizeof(aclFloat16) * 4));
im_info[0] = inputHeight;
im_info[1] = inputWidth;
im_info[2] = resizeHeightRatioFp16;
im_info[3] = resizeWidthRatioFp16;
void *imInfo_dst = nullptr;
int ret = aclrtMalloc(&imInfo_dst, 8, ACL_MEM_MALLOC_NORMAL_ONLY);
if (ret != ACL_ERROR_NONE) {
std::cout << "aclrtMalloc failed, ret = " << ret << std::endl;
aclrtFree(imInfo_dst);
return ret;
}
ret = aclrtMemcpy(reinterpret_cast<uint8_t *>(imInfo_dst), 8, im_info, 8, ACL_MEMCPY_HOST_TO_DEVICE);
if (ret != ACL_ERROR_NONE) {
std::cout << "aclrtMemcpy failed, ret = " << ret << std::endl;
aclrtFree(imInfo_dst);
return ret;
}
std::vector<void *> inputBuffers({resizeOutData->data, imInfo_dst});
std::vector<size_t> inputSizes({resizeOutData->dataSize, 4 * 2});
for (size_t i = 0; i < modelInfo_.outputNum; i++) {
aclrtMemset(outputBuffers_[i], outputSizes_[i], 0, outputSizes_[i]);
}
// Execute classification model
ret = modelProcess_->ModelInference(inputBuffers, inputSizes, outputBuffers_, outputSizes_, costTime_map);
if (ret != OK) {
aclrtFree(imInfo_dst);
std::cout << "Failed to execute the classification model, ret = " << ret << "." << std::endl;
return ret;
}
ret = aclrtFree(imInfo_dst);
if (ret != OK) {
std::cout << "aclrtFree image info failed" << std::endl;
return ret;
}
RELEASE_DVPP_DATA(resizeOutData->data);
return OK;
}
/*
* @description Process classification
*
* @par Function
* 1.Dvpp module preprocess
* 2.Execute classification model
* 3.Execute single operator
* 4.Write result
*
* @param imageFile input file path
* @return int int code
*/
int AclProcess::Process(const std::string& imageFile, std::map<double, double> *costTime_map) {
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
int ret = Preprocess(imageFile);
if (ret != OK) {
return ret;
}
ret = ModelInfer(costTime_map);
if (ret != OK) {
return ret;
}
ret = WriteResult(imageFile);
if (ret != OK) {
std::cout << "write result failed." << std::endl;
return ret;
}
gettimeofday(&end, nullptr);
const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
std::cout << "[Process Delay] cost: " << costMs << "ms." << std::endl;
return OK;
}

42
model_zoo/official/cv/maskrcnn/ascend310_infer/src/CMakeLists.txt
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@ -1,42 +0,0 @@
# Copyright (c) Huawei Technologies Co., Ltd. 2020. All rights reserved.
# CMake lowest version requirement
cmake_minimum_required(VERSION 3.5.1)
# Add definitions ENABLE_DVPP_INTERFACE to use dvpp api
add_definitions(-DENABLE_DVPP_INTERFACE)
# project information
project(InferClassification)
# Check environment variable
if(NOT DEFINED ENV{ASCEND_HOME})
message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
endif()
# Compile options
add_compile_options(-std=c++11 -fPIE -g -fstack-protector-all -Werror -Wreturn-type)
# Skip build rpath
set(CMAKE_SKIP_BUILD_RPATH True)
# Set output directory
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/out)
# Set include directory and library directory
set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
# Header path
include_directories(${ACL_LIB_DIR}/include/)
include_directories(${ATLAS_ACL_LIB_DIR}/include/)
include_directories(${PROJECT_SRC_ROOT}/../inc)
# add host lib path
link_directories(${ACL_LIB_DIR})
find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
find_library(acl_dvpp libacl_dvpp.so ${ACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
add_executable(main AclProcess.cpp
DvppCommon.cpp
ModelProcess.cpp
main.cpp)
target_link_libraries(main ${acl} gflags ${acl_dvpp} pthread)

735
model_zoo/official/cv/maskrcnn/ascend310_infer/src/DvppCommon.cpp
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@ -1,735 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 <iostream>
#include <memory>
#include "../inc/DvppCommon.h"
#include "../inc/CommonDataType.h"
static auto g_resizeConfigDeleter = [](acldvppResizeConfig *p) { acldvppDestroyResizeConfig(p); };
static auto g_picDescDeleter = [](acldvppPicDesc *picDesc) { acldvppDestroyPicDesc(picDesc); };
static auto g_roiConfigDeleter = [](acldvppRoiConfig *p) { acldvppDestroyRoiConfig(p); };
DvppCommon::DvppCommon(aclrtStream dvppStream):dvppStream_(dvppStream) {}
/*
* @description: Create a channel for processing image data,
* the channel description is created by acldvppCreateChannelDesc
* @return: OK if success, other values if failure
*/
int DvppCommon::Init(void) {
dvppChannelDesc_ = acldvppCreateChannelDesc();
if (dvppChannelDesc_ == nullptr) {
return -1;
}
int ret = acldvppCreateChannel(dvppChannelDesc_);
if (ret != 0) {
std::cout << "Failed to create dvpp channel, ret = " << ret << "." << std::endl;
acldvppDestroyChannelDesc(dvppChannelDesc_);
dvppChannelDesc_ = nullptr;
return ret;
}
return OK;
}
/*
* @description: destroy the channel and the channel description used by image.
* @return: OK if success, other values if failure
*/
int DvppCommon::DeInit(void) {
int ret = aclrtSynchronizeStream(dvppStream_); // int ret
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppDestroyChannel(dvppChannelDesc_);
if (ret != OK) {
std::cout << "Failed to destroy dvpp channel, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppDestroyChannelDesc(dvppChannelDesc_);
if (ret != OK) {
std::cout << "Failed to destroy dvpp channel description, ret = " << ret << "." << std::endl;
return ret;
}
return OK;
}
/*
* @description: Release the memory that is allocated in the interfaces which are started with "Combine"
*/
void DvppCommon::ReleaseDvppBuffer() {
if (resizedImage_ != nullptr) {
RELEASE_DVPP_DATA(resizedImage_->data);
}
if (decodedImage_ != nullptr) {
RELEASE_DVPP_DATA(decodedImage_->data);
}
if (inputImage_ != nullptr) {
RELEASE_DVPP_DATA(inputImage_->data);
}
}
/*
* @description: Get the size of buffer used to save image for VPC according to width, height and format
* @param width specifies the width of the output image
* @param height specifies the height of the output image
* @param format specifies the format of the output image
* @param: vpcSize is used to save the result size
* @return: OK if success, other values if failure
*/
int DvppCommon::GetVpcDataSize(uint32_t width, uint32_t height, acldvppPixelFormat format, uint32_t *vpcSize) {
if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
std::cout << "Format[" << format << "] for VPC is not supported, just support NV12 or NV21." << std::endl;
return INVALID_PARAM;
}
uint32_t widthStride = DVPP_ALIGN_UP(width, VPC_WIDTH_ALIGN);
uint32_t heightStride = DVPP_ALIGN_UP(height, VPC_HEIGHT_ALIGN);
*vpcSize = widthStride * heightStride * YUV_BGR_SIZE_CONVERT_3 / YUV_BGR_SIZE_CONVERT_2;
return OK;
}
/*
* @description: Get the aligned width and height of the input image according to the image format
* @param: width specifies the width before alignment
* @param: height specifies the height before alignment
* @param: format specifies the image format
* @param: widthStride is used to save the width after alignment
* @param: heightStride is used to save the height after alignment
* @return: OK if success, other values if failure
*/
int DvppCommon::GetVpcInputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride) {
uint32_t inputWidthStride;
if (format >= PIXEL_FORMAT_YUV_400 && format <= PIXEL_FORMAT_YVU_SEMIPLANAR_444) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH);
} else if (format >= PIXEL_FORMAT_YUYV_PACKED_422 && format <= PIXEL_FORMAT_VYUY_PACKED_422) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH) * YUV422_WIDTH_NU;
} else if (format >= PIXEL_FORMAT_YUV_PACKED_444 && format <= PIXEL_FORMAT_BGR_888) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH) * YUV444_RGB_WIDTH_NU;
} else if (format >= PIXEL_FORMAT_ARGB_8888 && format <= PIXEL_FORMAT_BGRA_8888) {
inputWidthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH) * XRGB_WIDTH_NU;
} else {
std::cout << "Input format[" << format << "] for VPC is invalid, please check it." << std::endl;
return INVALID_PARAM;
}
uint32_t inputHeightStride = DVPP_ALIGN_UP(height, VPC_STRIDE_HEIGHT);
if (inputWidthStride > MAX_RESIZE_WIDTH || inputWidthStride < MIN_RESIZE_WIDTH) {
std::cout << "Input width stride " << inputWidthStride << " is invalid, not in [" << MIN_RESIZE_WIDTH \
<< ", " << MAX_RESIZE_WIDTH << "]." << std::endl;
return INVALID_PARAM;
}
if (inputHeightStride > MAX_RESIZE_HEIGHT || inputHeightStride < MIN_RESIZE_HEIGHT) {
std::cout << "Input height stride " << inputHeightStride << " is invalid, not in [" << MIN_RESIZE_HEIGHT \
<< ", " << MAX_RESIZE_HEIGHT << "]." << std::endl;
return INVALID_PARAM;
}
*widthStride = inputWidthStride;
*heightStride = inputHeightStride;
return OK;
}
/*
* @description: Get the aligned width and height of the output image according to the image format
* @param: width specifies the width before alignment
* @param: height specifies the height before alignment
* @param: format specifies the image format
* @param: widthStride is used to save the width after alignment
* @param: heightStride is used to save the height after alignment
* @return: OK if success, other values if failure
*/
int DvppCommon::GetVpcOutputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
uint32_t *widthStride, uint32_t *heightStride) {
if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
std::cout << "Output format[" << format << "] is not supported, just support NV12 or NV21." << std::endl;
return INVALID_PARAM;
}
*widthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH);
*heightStride = DVPP_ALIGN_UP(height, VPC_STRIDE_HEIGHT);
return OK;
}
/*
* @description: Set picture description information and execute resize function
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: withSynchronize specifies whether to execute synchronously
* @param: processType specifies whether to perform proportional scaling, default is non-proportional resize
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::VpcResize(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
bool withSynchronize, VpcProcessType processType) {
acldvppPicDesc *inputDesc = acldvppCreatePicDesc();
acldvppPicDesc *outputDesc = acldvppCreatePicDesc();
resizeInputDesc_.reset(inputDesc, g_picDescDeleter);
resizeOutputDesc_.reset(outputDesc, g_picDescDeleter);
// Set dvpp picture descriptin info of input image
int ret = SetDvppPicDescData(input, resizeInputDesc_);
if (ret != OK) {
std::cout << "Failed to set dvpp input picture description, ret = " << ret << "." << std::endl;
return ret;
}
// Set dvpp picture descriptin info of output image
ret = SetDvppPicDescData(output, resizeOutputDesc_);
if (ret != OK) {
std::cout << "Failed to set dvpp output picture description, ret = " << ret << "." << std::endl;
return ret;
}
if (processType == VPC_PT_DEFAULT) {
return ResizeProcess(resizeInputDesc_, resizeOutputDesc_, withSynchronize);
}
// Get crop area according to the processType
CropRoiConfig cropRoi = {0};
GetCropRoi(input, output, processType, &cropRoi);
// The width and height of the original image will be resized by the same ratio
CropRoiConfig pasteRoi = {0};
GetPasteRoi(input, output, processType, &pasteRoi);
return ResizeWithPadding(resizeInputDesc_, resizeOutputDesc_, cropRoi, pasteRoi, withSynchronize);
}
/*
* @description: Set image description information
* @param: dataInfo specifies the image information
* @param: picsDesc specifies the picture description information to be set
* @return: OK if success, other values if failure
*/
int DvppCommon::SetDvppPicDescData(std::shared_ptr<DvppDataInfo> dataInfo, std::shared_ptr<acldvppPicDesc>picDesc) {
int ret = acldvppSetPicDescData(picDesc.get(), dataInfo->data);
if (ret != OK) {
std::cout << "Failed to set data for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescSize(picDesc.get(), dataInfo->dataSize);
if (ret != OK) {
std::cout << "Failed to set size for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescFormat(picDesc.get(), dataInfo->format);
if (ret != OK) {
std::cout << "Failed to set format for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescWidth(picDesc.get(), dataInfo->width);
if (ret != OK) {
std::cout << "Failed to set width for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescHeight(picDesc.get(), dataInfo->height);
if (ret != OK) {
std::cout << "Failed to set height for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescWidthStride(picDesc.get(), dataInfo->widthStride);
if (ret != OK) {
std::cout << "Failed to set aligned width for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
ret = acldvppSetPicDescHeightStride(picDesc.get(), dataInfo->heightStride);
if (ret != OK) {
std::cout << "Failed to set aligned height for dvpp picture description, ret = " << ret << "." << std::endl;
return ret;
}
return OK;
}
/*
* @description: Check whether the image format and zoom ratio meet the requirements
* @param: input specifies the input image information
* @param: output specifies the output image information
* @return: OK if success, other values if failure
*/
int DvppCommon::CheckResizeParams(const DvppDataInfo &input, const DvppDataInfo &output) {
if (output.format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && output.format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
std::cout << "Output format[" << output.format << "]is not supported, just support NV12 or NV21." << std::endl;
return INVALID_PARAM;
}
float heightScale = static_cast<float>(output.height) / input.height;
if (heightScale < MIN_RESIZE_SCALE || heightScale > MAX_RESIZE_SCALE) {
std::cout << "Resize scale should be in range [1/16, 32], which is " << heightScale << "." << std::endl;
return INVALID_PARAM;
}
float widthScale = static_cast<float>(output.width) / input.width;
if (widthScale < MIN_RESIZE_SCALE || widthScale > MAX_RESIZE_SCALE) {
std::cout << "Resize scale should be in range [1/16, 32], which is " << widthScale << "." << std::endl;
return INVALID_PARAM;
}
return OK;
}
/*
* @description: Scale the input image to the size specified by the output image and
* saves the result to the output image (non-proportionate scaling)
* @param: inputDesc specifies the description information of the input image
* @param: outputDesc specifies the description information of the output image
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
*/
int DvppCommon::ResizeProcess(std::shared_ptr<acldvppPicDesc>inputDesc,
std::shared_ptr<acldvppPicDesc>outputDesc,
bool withSynchronize) {
acldvppResizeConfig *resizeConfig = acldvppCreateResizeConfig();
if (resizeConfig == nullptr) {
std::cout << "Failed to create dvpp resize config." << std::endl;
return INVALID_POINTER;
}
resizeConfig_.reset(resizeConfig, g_resizeConfigDeleter);
int ret = acldvppVpcResizeAsync(dvppChannelDesc_, inputDesc.get(), outputDesc.get(),
resizeConfig_.get(), dvppStream_);
if (ret != OK) {
std::cout << "Failed to resize asynchronously, ret = " << ret << "." << std::endl;
return ret;
}
if (withSynchronize) {
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
return ret;
}
}
return OK;
}
/*
* @description: Crop the image from the input image based on the specified area and
* paste the cropped image to the specified position of the target image
* as the output image
* @param: inputDesc specifies the description information of the input image
* @param: outputDesc specifies the description information of the output image
* @param: cropRoi specifies the cropped area
* @param: pasteRoi specifies the pasting area
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
* @attention: If the width and height of the crop area are different from those of the
* paste area, the image is scaled again
*/
int DvppCommon::ResizeWithPadding(std::shared_ptr<acldvppPicDesc> inputDesc,
std::shared_ptr<acldvppPicDesc> outputDesc,
const CropRoiConfig &cropRoi, const CropRoiConfig &pasteRoi, bool withSynchronize) {
acldvppRoiConfig *cropRoiCfg = acldvppCreateRoiConfig(cropRoi.left, cropRoi.right, cropRoi.up, cropRoi.down);
if (cropRoiCfg == nullptr) {
std::cout << "Failed to create dvpp roi config for corp area." << std::endl;
return INVALID_POINTER;
}
cropAreaConfig_.reset(cropRoiCfg, g_roiConfigDeleter);
acldvppRoiConfig *pastRoiCfg = acldvppCreateRoiConfig(pasteRoi.left, pasteRoi.right, pasteRoi.up, pasteRoi.down);
if (pastRoiCfg == nullptr) {
std::cout << "Failed to create dvpp roi config for paster area." << std::endl;
return INVALID_POINTER;
}
pasteAreaConfig_.reset(pastRoiCfg, g_roiConfigDeleter);
int ret = acldvppVpcCropAndPasteAsync(dvppChannelDesc_, inputDesc.get(), outputDesc.get(), cropAreaConfig_.get(),
pasteAreaConfig_.get(), dvppStream_);
if (ret != OK) {
// release resource.
std::cout << "Failed to crop and paste asynchronously, ret = " << ret << "." << std::endl;
return ret;
}
if (withSynchronize) {
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed tp synchronize stream, ret = " << ret << "." << std::endl;
return ret;
}
}
return OK;
}
/*
* @description: Get crop area
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: processType specifies whether to perform proportional scaling
* @param: cropRoi is used to save the info of the crop roi area
* @return: OK if success, other values if failure
*/
void DvppCommon::GetCropRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *cropRoi) {
// When processType is not VPC_PT_FILL, crop area is the whole input image
if (processType != VPC_PT_FILL) {
cropRoi->right = CONVERT_TO_ODD(input->width - ODD_NUM_1);
cropRoi->down = CONVERT_TO_ODD(input->height - ODD_NUM_1);
return;
}
bool widthRatioSmaller = true;
// The scaling ratio is based on the smaller ratio to ensure the smallest edge to fill the targe edge
float resizeRatio = static_cast<float>(input->width) / output->width;
if (resizeRatio > (static_cast<float>(input->height) / output->height)) {
resizeRatio = static_cast<float>(input->height) / output->height;
widthRatioSmaller = false;
}
const int halfValue = 2;
// The left and up must be even, right and down must be odd which is required by acl
if (widthRatioSmaller) {
cropRoi->left = 0;
cropRoi->right = CONVERT_TO_ODD(input->width - ODD_NUM_1);
cropRoi->up = CONVERT_TO_EVEN(static_cast<uint32_t>((input->height - output->height * resizeRatio) /
halfValue));
cropRoi->down = CONVERT_TO_ODD(input->height - cropRoi->up - ODD_NUM_1);
return;
}
cropRoi->up = 0;
cropRoi->down = CONVERT_TO_ODD(input->height - ODD_NUM_1);
cropRoi->left = CONVERT_TO_EVEN(static_cast<uint32_t>((input->width - output->width * resizeRatio) / halfValue));
cropRoi->right = CONVERT_TO_ODD(input->width - cropRoi->left - ODD_NUM_1);
return;
}
/*
* @description: Get paste area
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: processType specifies whether to perform proportional scaling
* @param: pasteRio is used to save the info of the paste area
* @return: OK if success, other values if failure
*/
void DvppCommon::GetPasteRoi(std::shared_ptr<DvppDataInfo> input, std::shared_ptr<DvppDataInfo> output,
VpcProcessType processType, CropRoiConfig *pasteRoi) {
if (processType == VPC_PT_FILL) {
pasteRoi->right = CONVERT_TO_ODD(output->width - ODD_NUM_1);
pasteRoi->down = CONVERT_TO_ODD(output->height - ODD_NUM_1);
return;
}
bool widthRatioLarger = true;
// The scaling ratio is based on the larger ratio to ensure the largest edge to fill the targe edge
float resizeRatio = static_cast<float>(input->width) / output->width;
if (resizeRatio < (static_cast<float>(input->height) / output->height)) {
resizeRatio = static_cast<float>(input->height) / output->height;
widthRatioLarger = false;
}
// Left and up is 0 when the roi paste on the upper left corner
if (processType == VPC_PT_PADDING) {
pasteRoi->right = (input->width / resizeRatio) - ODD_NUM_1;
pasteRoi->down = (input->height / resizeRatio) - ODD_NUM_1;
pasteRoi->right = CONVERT_TO_ODD(pasteRoi->right);
pasteRoi->down = CONVERT_TO_ODD(pasteRoi->down);
return;
}
const int halfValue = 2;
// Left and up is 0 when the roi paste on the middler location
if (widthRatioLarger) {
pasteRoi->left = 0;
pasteRoi->right = output->width - ODD_NUM_1;
pasteRoi->up = (output->height - (input->height / resizeRatio)) / halfValue;
pasteRoi->down = output->height - pasteRoi->up - ODD_NUM_1;
} else {
pasteRoi->up = 0;
pasteRoi->down = output->height - ODD_NUM_1;
pasteRoi->left = (output->width - (input->width / resizeRatio)) / halfValue;
pasteRoi->right = output->width - pasteRoi->left - ODD_NUM_1;
}
// The left must be even and align to 16, up must be even, right and down must be odd which is required by acl
pasteRoi->left = DVPP_ALIGN_UP(CONVERT_TO_EVEN(pasteRoi->left), VPC_WIDTH_ALIGN);
pasteRoi->right = CONVERT_TO_ODD(pasteRoi->right);
pasteRoi->up = CONVERT_TO_EVEN(pasteRoi->up);
pasteRoi->down = CONVERT_TO_ODD(pasteRoi->down);
return;
}
/*
* @description: Resize the image specified by input and save the result to member variable resizedImage_
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: withSynchronize specifies whether to execute synchronously
* @param: processType specifies whether to perform proportional scaling, default is non-proportional resize
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::CombineResizeProcess(std::shared_ptr<DvppDataInfo> input, const DvppDataInfo &output,
bool withSynchronize, VpcProcessType processType) {
int ret = CheckResizeParams(*input, output);
if (ret != OK) {
return ret;
}
// Get widthStride and heightStride for input and output image according to the format
ret = GetVpcInputStrideSize(input->widthStride, input->heightStride, input->format,
&(input->widthStride), &(input->heightStride));
if (ret != OK) {
return ret;
}
resizedImage_ = std::make_shared<DvppDataInfo>();
resizedImage_->width = output.width;
resizedImage_->height = output.height;
resizedImage_->format = output.format;
ret = GetVpcOutputStrideSize(output.width, output.height, output.format, &(resizedImage_->widthStride),
&(resizedImage_->heightStride));
if (ret != OK) {
return ret;
}
// Get output buffer size for resize output
ret = GetVpcDataSize(output.width, output.height, output.format, &(resizedImage_->dataSize));
if (ret != OK) {
return ret;
}
// Malloc buffer for output of resize module
// Need to pay attention to release of the buffer
ret = acldvppMalloc(reinterpret_cast<void **>(&(resizedImage_->data)), resizedImage_->dataSize);
if (ret != OK) {
std::cout << "Failed to malloc " << resizedImage_->dataSize << " bytes on dvpp for resize" << std::endl;
return ret;
}
aclrtMemset(resizedImage_->data, resizedImage_->dataSize, YUV_GREYER_VALUE, resizedImage_->dataSize);
resizedImage_->frameId = input->frameId;
ret = VpcResize(input, resizedImage_, withSynchronize, processType);
if (ret != OK) {
// Release the output buffer when resize failed, otherwise release it after use
RELEASE_DVPP_DATA(resizedImage_->data);
}
return ret;
}
/*
* @description: Set the description of the output image and decode
* @param: input specifies the input image information
* @param: output specifies the output image information
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::JpegDecode(std::shared_ptr<DvppDataInfo> input,
std::shared_ptr<DvppDataInfo> output,
bool withSynchronize) {
acldvppPicDesc *outputDesc = acldvppCreatePicDesc();
decodeOutputDesc_.reset(outputDesc, g_picDescDeleter);
int ret = SetDvppPicDescData(output, decodeOutputDesc_);
if (ret != OK) {
return ret;
}
ret = acldvppJpegDecodeAsync(dvppChannelDesc_, input->data, input->dataSize, decodeOutputDesc_.get(), dvppStream_);
if (ret != OK) {
std::cout << "Failed to decode jpeg, ret = " << ret << "." << std::endl;
return ret;
}
if (withSynchronize) {
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
return DECODE_FAIL;
}
}
return OK;
}
/*
* @description: Get the aligned width and height of the image after decoding
* @param: width specifies the width before alignment
* @param: height specifies the height before alignment
* @param: widthStride is used to save the width after alignment
* @param: heightStride is used to save the height after alignment
* @return: OK if success, other values if failure
*/
void DvppCommon::GetJpegDecodeStrideSize(uint32_t width, uint32_t height,
uint32_t *widthStride, uint32_t *heightStride) {
*widthStride = DVPP_ALIGN_UP(width, JPEGD_STRIDE_WIDTH);
*heightStride = DVPP_ALIGN_UP(height, JPEGD_STRIDE_HEIGHT);
}
/*
* @description: Get picture width and height and number of channels from image data
* @param: data specifies the memory to store the image data
* @param: dataSize specifies the size of the image data
* @param: width is used to save the image width
* @param: height is used to save the image height
* @param: components is used to save the number of channels
* @return: OK if success, other values if failure
*/
int DvppCommon::GetJpegImageInfo(const void *data, uint32_t dataSize, uint32_t *width, uint32_t *height,
int32_t *components) {
uint32_t widthTmp;
uint32_t heightTmp;
int32_t componentsTmp;
int ret = acldvppJpegGetImageInfo(data, dataSize, &widthTmp, &heightTmp, &componentsTmp);
if (ret != OK) {
std::cout << "Failed to get image info of jpeg, ret = " << ret << "." << std::endl;
return ret;
}
if (widthTmp > MAX_JPEGD_WIDTH || widthTmp < MIN_JPEGD_WIDTH) {
std::cout << "Input width is invalid, not in [" << MIN_JPEGD_WIDTH << ", "
<< MAX_JPEGD_WIDTH << "]." << std::endl;
return INVALID_PARAM;
}
if (heightTmp > MAX_JPEGD_HEIGHT || heightTmp < MIN_JPEGD_HEIGHT) {
std::cout << "Input height is invalid, not in [" << MIN_JPEGD_HEIGHT << ", "
<< MAX_JPEGD_HEIGHT << "]." << std::endl;
return INVALID_PARAM;
}
*width = widthTmp;
*height = heightTmp;
*components = componentsTmp;
return OK;
}
/*
* @description: Get the size of the buffer for storing decoded images based on the image data, size, and format
* @param: data specifies the memory to store the image data
* @param: dataSize specifies the size of the image data
* @param: format specifies the image format
* @param: decSize is used to store the result size
* @return: OK if success, other values if failure
*/
int DvppCommon::GetJpegDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
uint32_t *decSize) {
uint32_t outputSize;
int ret = acldvppJpegPredictDecSize(data, dataSize, format, &outputSize);
if (ret != OK) {
std::cout << "Failed to predict decode size of jpeg image, ret = " << ret << "." << std::endl;
return ret;
}
*decSize = outputSize;
return OK;
}
/*
* @description: Decode the image specified by imageInfo and save the result to member variable decodedImage_
* @param: imageInfo specifies image information
* @param: format specifies the image format
* @param: withSynchronize specifies whether to execute synchronously
* @return: OK if success, other values if failure
* @attention: This function can be called only when the DvppCommon object is initialized with Init
*/
int DvppCommon::CombineJpegdProcess(const RawData& imageInfo, acldvppPixelFormat format, bool withSynchronize) {
int32_t components;
inputImage_ = std::make_shared<DvppDataInfo>();
inputImage_->format = format;
int ret = GetJpegImageInfo(imageInfo.data.get(), imageInfo.lenOfByte, &(inputImage_->width), &(inputImage_->height),
&components);
if (ret != OK) {
std::cout << "Failed to get input image info, ret = " << ret << "." << std::endl;
return ret;
}
// Get the buffer size of decode output according to the input data and output format
uint32_t outBuffSize;
ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, &outBuffSize);
if (ret != OK) {
std::cout << "Failed to get size of decode output buffer, ret = " << ret << "." << std::endl;
return ret;
}
// In TransferImageH2D function, device buffer will be allocated to store the input image
// Need to pay attention to release of the buffer
ret = TransferImageH2D(imageInfo, inputImage_);
if (ret != OK) {
return ret;
}
decodedImage_ = std::make_shared<DvppDataInfo>();
decodedImage_->format = format;
decodedImage_->width = inputImage_->width;
decodedImage_->height = inputImage_->height;
GetJpegDecodeStrideSize(inputImage_->width, inputImage_->height, &(decodedImage_->widthStride),
&(decodedImage_->heightStride));
decodedImage_->dataSize = outBuffSize;
// Need to pay attention to release of the buffer
ret = acldvppMalloc(reinterpret_cast<void **>(&decodedImage_->data), decodedImage_->dataSize);
if (ret != OK) {
std::cout << "Failed to malloc memory on dvpp, ret = " << ret << "." << std::endl;
RELEASE_DVPP_DATA(inputImage_->data);
return ret;
}
ret = JpegDecode(inputImage_, decodedImage_, withSynchronize);
if (ret != OK) {
RELEASE_DVPP_DATA(inputImage_->data);
inputImage_->data = nullptr;
RELEASE_DVPP_DATA(decodedImage_->data);
decodedImage_->data = nullptr;
return ret;
}
return OK;
}
/*
* @description: Transfer data from host to device
* @param: imageInfo specifies the image data on the host
* @param: jpegInput is used to save the buffer and its size which is allocate on the device
* @return: OK if success, other values if failure
*/
int DvppCommon::TransferImageH2D(const RawData& imageInfo, const std::shared_ptr<DvppDataInfo>& jpegInput) {
if (imageInfo.lenOfByte == 0) {
std::cout << "The input buffer size on host should not be empty." << std::endl;
return INVALID_PARAM;
}
uint8_t* inDevBuff = nullptr;
int ret = acldvppMalloc(reinterpret_cast<void **>(&inDevBuff), imageInfo.lenOfByte);
if (ret != OK) {
std::cout << "Failed to malloc " << imageInfo.lenOfByte << " bytes on dvpp, ret = " << ret << "." << std::endl;
return ret;
}
// Copy the image data from host to device
ret = aclrtMemcpyAsync(inDevBuff, imageInfo.lenOfByte, imageInfo.data.get(), imageInfo.lenOfByte,
ACL_MEMCPY_HOST_TO_DEVICE, dvppStream_);
if (ret != OK) {
std::cout << "Failed to copy " << imageInfo.lenOfByte << " bytes from host to device" << std::endl;
RELEASE_DVPP_DATA(inDevBuff);
return ret;
}
// Attention: We must call the aclrtSynchronizeStream to ensure the task of memory replication has been completed
// after calling aclrtMemcpyAsync
ret = aclrtSynchronizeStream(dvppStream_);
if (ret != OK) {
std::cout << "Failed to synchronize stream, ret = " << ret << "." << std::endl;
RELEASE_DVPP_DATA(inDevBuff);
return ret;
}
jpegInput->data = inDevBuff;
jpegInput->dataSize = imageInfo.lenOfByte;
return OK;
}
std::shared_ptr<DvppDataInfo> DvppCommon::GetInputImage() {
return inputImage_;
}
std::shared_ptr<DvppDataInfo> DvppCommon::GetDecodedImage() {
return decodedImage_;
}
std::shared_ptr<DvppDataInfo> DvppCommon::GetResizedImage() {
return resizedImage_;
}
DvppCommon::~DvppCommon() {}

226
model_zoo/official/cv/maskrcnn/ascend310_infer/src/ModelProcess.cpp
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@ -1,226 +0,0 @@
/*
* Copyright(C) 2020. Huawei Technologies Co.,Ltd. All rights reserved.
*
* 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 <fstream>
#include "../inc/ModelProcess.h"
ModelProcess::ModelProcess(const int deviceId) {
deviceId_ = deviceId;
}
ModelProcess::ModelProcess() {}
ModelProcess::~ModelProcess() {
if (!isDeInit_) {
DeInit();
}
}
void ModelProcess::DestroyDataset(aclmdlDataset *dataset) {
// Just release the DataBuffer object and DataSet object, remain the buffer, because it is managerd by user
if (dataset != nullptr) {
for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(dataset); i++) {
aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(dataset, i);
if (dataBuffer != nullptr) {
aclDestroyDataBuffer(dataBuffer);
dataBuffer = nullptr;
}
}
aclmdlDestroyDataset(dataset);
}
}
aclmdlDesc *ModelProcess::GetModelDesc() {
return modelDesc_.get();
}
int ModelProcess::ModelInference(const std::vector<void *> &inputBufs,
const std::vector<size_t> &inputSizes,
const std::vector<void *> &ouputBufs,
const std::vector<size_t> &outputSizes,
std::map<double, double> *costTime_map) {
std::cout << "ModelProcess:Begin to inference." << std::endl;
aclmdlDataset *input = nullptr;
input = CreateAndFillDataset(inputBufs, inputSizes);
if (input == nullptr) {
return INVALID_POINTER;
}
int ret;
aclmdlDataset *output = nullptr;
output = CreateAndFillDataset(ouputBufs, outputSizes);
if (output == nullptr) {
DestroyDataset(input);
input = nullptr;
return INVALID_POINTER;
}
struct timeval start;
struct timeval end;
double startTime_ms;
double endTime_ms;
mtx_.lock();
gettimeofday(&start, NULL);
ret = aclmdlExecute(modelId_, input, output);
gettimeofday(&end, NULL);
startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
costTime_map->insert(std::pair<double, double>(startTime_ms, endTime_ms));
mtx_.unlock();
if (ret != OK) {
std::cout << "aclmdlExecute failed, ret[" << ret << "]." << std::endl;
return ret;
}
DestroyDataset(input);
DestroyDataset(output);
return OK;
}
int ModelProcess::DeInit() {
isDeInit_ = true;
int ret = aclmdlUnload(modelId_);
if (ret != OK) {
std::cout << "aclmdlUnload failed, ret["<< ret << "]." << std::endl;
return ret;
}
if (modelDevPtr_ != nullptr) {
ret = aclrtFree(modelDevPtr_);
if (ret != OK) {
std::cout << "aclrtFree failed, ret[" << ret << "]." << std::endl;
return ret;
}
modelDevPtr_ = nullptr;
}
if (weightDevPtr_ != nullptr) {
ret = aclrtFree(weightDevPtr_);
if (ret != OK) {
std::cout << "aclrtFree failed, ret[" << ret << "]." << std::endl;
return ret;
}
weightDevPtr_ = nullptr;
}
return OK;
}
/**
* Read a binary file, store the data into a uint8_t array
*
* @param fileName the file for reading
* @param buffShared a shared pointer to a uint8_t array for storing file
* @param buffLength the length of the array
* @return OK if create success, error code otherwise
*/
int ModelProcess::ReadBinaryFile(const std::string &fileName, uint8_t **buffShared, int *buffLength) {
std::ifstream inFile(fileName, std::ios::in | std::ios::binary);
if (!inFile) {
std::cout << "FaceFeatureLib: read file " << fileName << " fail." <<std::endl;
return READ_FILE_FAIL;
}
inFile.seekg(0, inFile.end);
*buffLength = inFile.tellg();
inFile.seekg(0, inFile.beg);
uint8_t *tempShared = reinterpret_cast<uint8_t *>(malloc(*buffLength));
inFile.read(reinterpret_cast<char *>(tempShared), *buffLength);
inFile.close();
*buffShared = tempShared;
std::cout << "read file: fileName=" << fileName << ", size=" << *buffLength << "." << std::endl;
return OK;
}
int ModelProcess::Init(const std::string &modelPath) {
std::cout << "ModelProcess:Begin to init instance." << std::endl;
int modelSize = 0;
uint8_t *modelData = nullptr;
int ret = ReadBinaryFile(modelPath, &modelData, &modelSize);
if (ret != OK) {
std::cout << "read model file failed, ret[" << ret << "]." << std::endl;
return ret;
}
ret = aclmdlQuerySizeFromMem(modelData, modelSize, &modelDevPtrSize_, &weightDevPtrSize_);
if (ret != OK) {
std::cout << "aclmdlQuerySizeFromMem failed, ret[" << ret << "]." << std::endl;
return ret;
}
std::cout << "modelDevPtrSize_[" << modelDevPtrSize_ << "]" << std::endl;
std::cout << " weightDevPtrSize_[" << weightDevPtrSize_ << "]." << std::endl;
ret = aclrtMalloc(&modelDevPtr_, modelDevPtrSize_, ACL_MEM_MALLOC_HUGE_FIRST);
if (ret != OK) {
std::cout << "aclrtMalloc dev_ptr failed, ret[" << ret << "]." << std::endl;
return ret;
}
ret = aclrtMalloc(&weightDevPtr_, weightDevPtrSize_, ACL_MEM_MALLOC_HUGE_FIRST);
if (ret != OK) {
std::cout << "aclrtMalloc weight_ptr failed, ret[" << ret << "] " << std::endl;
return ret;
}
ret = aclmdlLoadFromMemWithMem(modelData, modelSize, &modelId_, modelDevPtr_, modelDevPtrSize_,
weightDevPtr_, weightDevPtrSize_);
if (ret != OK) {
std::cout << "aclmdlLoadFromMemWithMem failed, ret[" << ret << "]." << std::endl;
return ret;
}
ret = aclrtGetCurrentContext(&contextModel_);
if (ret != OK) {
std::cout << "aclrtMalloc weight_ptr failed, ret[" << ret << "]." << std::endl;
return ret;
}
aclmdlDesc *modelDesc = aclmdlCreateDesc();
if (modelDesc == nullptr) {
std::cout << "aclmdlCreateDesc failed." << std::endl;
return ret;
}
ret = aclmdlGetDesc(modelDesc, modelId_);
if (ret != OK) {
std::cout << "aclmdlGetDesc ret fail, ret:" << ret << "." << std::endl;
return ret;
}
modelDesc_.reset(modelDesc, aclmdlDestroyDesc);
free(modelData);
return OK;
}
aclmdlDataset *ModelProcess::CreateAndFillDataset(const std::vector<void *> &bufs, const std::vector<size_t> &sizes) {
aclmdlDataset *dataset = aclmdlCreateDataset();
if (dataset == nullptr) {
std::cout << "ACL_ModelInputCreate failed." << std::endl;
return nullptr;
}
for (size_t i = 0; i < bufs.size(); ++i) {
aclDataBuffer *data = aclCreateDataBuffer(bufs[i], sizes[i]);
if (data == nullptr) {
DestroyDataset(dataset);
std::cout << "aclCreateDataBuffer failed." << std::endl;
return nullptr;
}
int ret = aclmdlAddDatasetBuffer(dataset, data);
if (ret != OK) {
DestroyDataset(dataset);
std::cout << "ACL_ModelInputDataAdd failed, ret[" << ret << "]." << std::endl;
return nullptr;
}
}
return dataset;
}

56
model_zoo/official/cv/maskrcnn/ascend310_infer/src/build.sh
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@ -1,56 +0,0 @@
#!/bin/bash
# Copyright 2020 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.
# ============================================================================
path_cur=$(cd "`dirname $0`" || exit; pwd)
build_type="Release"
function preparePath() {
rm -rf $1
mkdir -p $1
cd $1 || exit
}
function buildA300() {
if [ ! "${ARCH_PATTERN}" ]; then
# set ARCH_PATTERN to acllib when it was not specified by user
export ARCH_PATTERN=acllib
echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
else
echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
export ARCH_PATTERN=${ARCH_PATTERN}/acllib
fi
path_build=$path_cur/build
preparePath $path_build
cmake -DCMAKE_BUILD_TYPE=$build_type ..
make -j
ret=$?
cd ..
return ${ret}
}
# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
if [ ! "${ASCEND_VERSION}" ]; then
export ASCEND_VERSION=ascend-toolkit/latest
echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
else
echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
fi
buildA300
if [ $? -ne 0 ]; then
exit 1
fi

235
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@ -0,0 +1,235 @@
/**
* 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/context.h"
#include "include/api/model.h"
#include "include/api/types.h"
#include "include/api/serialization.h"
#include "include/minddata/dataset/include/vision.h"
#include "include/minddata/dataset/include/transforms.h"
#include "include/minddata/dataset/include/execute.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::DataType;
using mindspore::dataset::Execute;
using mindspore::dataset::TensorTransform;
using mindspore::dataset::vision::Resize;
using mindspore::dataset::vision::Pad;
using mindspore::dataset::vision::HWC2CHW;
using mindspore::dataset::vision::Normalize;
using mindspore::dataset::vision::SwapRedBlue;
using mindspore::dataset::vision::Decode;
using mindspore::dataset::transforms::TypeCast;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
const int IMAGEWIDTH = 1280;
const int IMAGEHEIGHT = 768;
int PadImage(const MSTensor &input, MSTensor *output) {
std::shared_ptr<TensorTransform> normalize(new Normalize({103.53, 116.28, 123.675},
{57.375, 57.120, 58.395}));
Execute composeNormalize({normalize});
std::vector<int64_t> shape = input.Shape();
auto imgResize = MSTensor();
auto imgPad = MSTensor();
float widthScale, heightScale;
widthScale = static_cast<float>(IMAGEWIDTH) / shape[1];
heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0];
Status ret;
if (widthScale < heightScale) {
int heightSize = shape[0]*widthScale;
std::shared_ptr<TensorTransform> resize(new Resize({heightSize, IMAGEWIDTH}));
Execute composeResizeWidth({resize});
ret = composeResizeWidth(input, &imgResize);
if (ret != kSuccess) {
std::cout << "ERROR: Resize Width failed." << std::endl;
return 1;
}
int paddingSize = IMAGEHEIGHT - heightSize;
std::shared_ptr<TensorTransform> pad(new Pad({0, 0, 0, paddingSize}));
Execute composePad({pad});
ret = composePad(imgResize, &imgPad);
if (ret != kSuccess) {
std::cout << "ERROR: Height Pad failed." << std::endl;
return 1;
}
ret = composeNormalize(imgPad, output);
if (ret != kSuccess) {
std::cout << "ERROR: Normalize failed." << std::endl;
return 1;
}
} else {
int widthSize = shape[1]*heightScale;
std::shared_ptr<TensorTransform> resize(new Resize({IMAGEHEIGHT, widthSize}));
Execute composeResizeHeight({resize});
ret = composeResizeHeight(input, &imgResize);
if (ret != kSuccess) {
std::cout << "ERROR: Resize Height failed." << std::endl;
return 1;
}
int paddingSize = IMAGEWIDTH - widthSize;
std::shared_ptr<TensorTransform> pad(new Pad({0, 0, paddingSize, 0}));
Execute composePad({pad});
ret = composePad(imgResize, &imgPad);
if (ret != kSuccess) {
std::cout << "ERROR: Width Pad failed." << std::endl;
return 1;
}
ret = composeNormalize(imgPad, output);
if (ret != kSuccess) {
std::cout << "ERROR: Normalize failed." << std::endl;
return 1;
}
}
return 0;
}
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->SetPrecisionMode("allow_fp32_to_fp16");
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;
}
std::vector<MSTensor> model_inputs = model.GetInputs();
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();
std::shared_ptr<TensorTransform> decode(new Decode());
Execute composeDecode({decode});
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
Execute composeTranspose({hwc2chw});
std::shared_ptr<TensorTransform> typeCast(new TypeCast(DataType::kNumberTypeFloat16));
Execute transformCast(typeCast);
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;
auto imgDecode = MSTensor();
auto image = ReadFileToTensor(all_files[i]);
composeDecode(image, &imgDecode);
auto imgPad = MSTensor();
PadImage(imgDecode, &imgPad);
auto img = MSTensor();
composeTranspose(imgPad, &img);
transformCast(img, &img);
std::vector<int64_t> shape = imgDecode.Shape();
float widthScale = static_cast<float>(IMAGEWIDTH) / shape[1];
float heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0];
float resizeScale = widthScale < heightScale ? widthScale : heightScale;
float imgInfo[4];
imgInfo[0] = shape[0];
imgInfo[1] = shape[1];
imgInfo[2] = resizeScale;
imgInfo[3] = resizeScale;
MSTensor imgMeta("imgMeta", DataType::kNumberTypeFloat32, {static_cast<int64_t>(4)}, imgInfo, 16);
transformCast(imgMeta, &imgMeta);
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
img.Data().get(), img.DataSize());
inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
imgMeta.Data().get(), imgMeta.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;
}

132
model_zoo/official/cv/maskrcnn/ascend310_infer/src/main.cpp
View File

@ -1,132 +0,0 @@
/*
* Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
* 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 <dirent.h>
#include <sys/stat.h>
#include <gflags/gflags.h>
#include <unistd.h>
#include <cstring>
#include <fstream>
#include <sstream>
#include "../inc/AclProcess.h"
#include "../inc/CommonDataType.h"
DEFINE_string(om_path, "./maskrcnn.om", "om model path.");
DEFINE_string(data_path, "./test.jpg", "om model path.");
DEFINE_int32(width, 1280, "width");
DEFINE_int32(height, 768, "height");
DEFINE_int32(device_id, 0, "height");
static bool is_file(const std::string &filename) {
struct stat buffer;
return (stat(filename.c_str(), &buffer) == 0 && S_ISREG(buffer.st_mode));
}
static bool is_dir(const std::string &filefodler) {
struct stat buffer;
return (stat(filefodler.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode));
}
/*
* @description Initialize and run AclProcess module
* @param resourceInfo resource info of deviceIds, model info, single Operator Path, etc
* @param file the absolute path of input file
* @return int int code
*/
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
std::cout << "OM File Path :" << FLAGS_om_path << std::endl;
std::cout << "data Path :" << FLAGS_data_path << std::endl;
std::cout << "width :" << FLAGS_width << std::endl;
std::cout << "height :" << FLAGS_height << std::endl;
std::cout << "deviceId :" << FLAGS_device_id << std::endl;
char omAbsPath[PATH_MAX];
if (realpath(FLAGS_om_path.c_str(), omAbsPath) == nullptr) {
std::cout << "Failed to get the om real path." << std::endl;
return INVALID_PARAM;
}
if (access(omAbsPath, R_OK) == -1) {
std::cout << "ModelPath " << omAbsPath << " doesn't exist or read failed." << std::endl;
return INVALID_PARAM;
}
char dataAbsPath[PATH_MAX];
if (realpath(FLAGS_data_path.c_str(), dataAbsPath) == nullptr) {
std::cout << "Failed to get the data real path." << std::endl;
return INVALID_PARAM;
}
if (access(dataAbsPath, R_OK) == -1) {
std::cout << "data paeh " << dataAbsPath << " doesn't exist or read failed." << std::endl;
return INVALID_PARAM;
}
std::map<double, double> costTime_map;
AclProcess aclProcess(FLAGS_device_id, FLAGS_om_path, FLAGS_width, FLAGS_height);
int ret = aclProcess.InitResource();
if (ret != OK) {
aclProcess.Release();
return ret;
}
if (is_file(FLAGS_data_path)) {
ret = aclProcess.Process(FLAGS_data_path, &costTime_map);
if (ret != OK) {
std::cout << "model process failed, errno = " << ret << std::endl;
return ret;
}
} else if (is_dir(FLAGS_data_path)) {
struct dirent *filename;
DIR *dir;
dir = opendir(FLAGS_data_path.c_str());
if (dir == nullptr) {
return ERROR;
}
while ((filename = readdir(dir)) != nullptr) {
if (strcmp(filename->d_name, ".") == 0 || strcmp(filename->d_name, "..") == 0) {
continue;
}
std::string wholePath = FLAGS_data_path + "/" + filename->d_name;
ret = aclProcess.Process(wholePath, &costTime_map);
if (ret != OK) {
std::cout << "model process failed, errno = " << ret << std::endl;
return ret;
}
}
} else {
std::cout << " input image path error" << std::endl;
}
double average = 0.0;
int infer_cnt = 0;
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
double diff = 0.0;
diff = iter->second - iter->first;
average += diff;
infer_cnt++;
}
average = average / infer_cnt;
std::stringstream timeCost;
timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
file_stream << timeCost.str();
file_stream.close();
costTime_map.clear();
aclProcess.Release();
return OK;
}

129
model_zoo/official/cv/maskrcnn/ascend310_infer/src/utils.cc Normal file
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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;
}
MSTensor ReadFileToTensor(const std::string &file) {
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return MSTensor();
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return MSTensor();
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return MSTensor();
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
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;
}

18
model_zoo/official/cv/maskrcnn/postprocess.py
View File

@ -13,6 +13,7 @@
# limitations under the License.
# ============================================================================
"""post process for 310 inference"""
import os
import argparse
import numpy as np
from PIL import Image
@ -27,6 +28,7 @@ dst_height = 768
parser = argparse.ArgumentParser(description="maskrcnn inference")
parser.add_argument("--ann_file", type=str, required=True, help="ann file.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
parser.add_argument("--result_path", type=str, required=True, help="result file path.")
args = parser.parse_args()
def get_img_size(file_name):
@ -41,10 +43,10 @@ def get_resize_ratio(img_size):
return resize_ratio
def get_eval_result(ann_file, img_path):
def get_eval_result(ann_file, img_path, result_path):
""" Get metrics result according to the annotation file and result file"""
max_num = 128
result_path = "./result_Files/"
result_path = result_path
outputs = []
dataset_coco = COCO(ann_file)
@ -52,16 +54,16 @@ def get_eval_result(ann_file, img_path):
for img_id in img_ids:
file_id = str(img_id).zfill(12)
file = img_path + "/" + file_id + ".jpg"
file = os.path.join(img_path, file_id + ".jpg")
img_size = get_img_size(file)
resize_ratio = get_resize_ratio(img_size)
img_metas = np.array([img_size[1], img_size[0]] + [resize_ratio, resize_ratio])
bbox_result_file = result_path + file_id + "_0.bin"
label_result_file = result_path + file_id + "_1.bin"
mask_result_file = result_path + file_id + "_2.bin"
mask_fb_result_file = result_path + file_id + "_3.bin"
bbox_result_file = os.path.join(result_path, file_id + "_0.bin")
label_result_file = os.path.join(result_path, file_id + "_1.bin")
mask_result_file = os.path.join(result_path, file_id + "_2.bin")
mask_fb_result_file = os.path.join(result_path, file_id + "_3.bin")
all_bbox = np.fromfile(bbox_result_file, dtype=np.float16).reshape(80000, 5)
all_label = np.fromfile(label_result_file, dtype=np.int32).reshape(80000, 1)
@ -94,4 +96,4 @@ def get_eval_result(ann_file, img_path):
coco_eval(result_files, eval_types, dataset_coco, single_result=False)
if __name__ == '__main__':
get_eval_result(args.ann_file, args.img_path)
get_eval_result(args.ann_file, args.img_path, args.result_path)

56
model_zoo/official/cv/maskrcnn/scripts/run_infer_310.sh Executable file → Normal file
View File

@ -14,60 +14,51 @@
# limitations under the License.
# ============================================================================
if [[ $# -lt 3 || $# -gt 4 ]]; then
echo "Usage: sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DEVICE_ID]
if [[ $# -lt 3 || $# -gt 4 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)
ann_file=$(get_real_path $3)
device_id=0
if [ $# == 4 ]; then
device_id=$4
elif [ $# == 3 ]; then
if [ -z $device_id ]; then
device_id=0
else
device_id=$device_id
fi
fi
echo $model
echo $data_path
echo $ann_file
echo $device_id
echo "mindir name: "$model
echo "dataset path: "$data_path
echo "ann file: "$ann_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/acllib/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ones:$LD_LIBRARY_PATH
export TBE_IMPL_PATH=${ASCEND_HOME}/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/acllib/lib64:$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:$ASCEND_HOME/add-ones:$LD_LIBRARY_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 air_to_om()
{
atc --input_format=NCHW --framework=1 --model=$model --input_shape="x:1, 3, 768, 1280; im_info: 1, 4" --output=maskrcnn --insert_op_conf=../src/aipp.cfg --precision_mode=allow_fp32_to_fp16 --soc_version=Ascend310 &>atc.log
}
function compile_app()
{
cd ../ascend310_infer/src || exit
sh build.sh &> build.log
cd ../ascend310_infer || exit
bash build.sh &> build.log
cd - || exit
}
@ -76,24 +67,19 @@ function infer()
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/src/out/main --om_path=./maskrcnn.om --data_path=$data_path --device_id=$device_id &> infer.log
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
}
function cal_acc()
{
python ../postprocess.py --ann_file=$ann_file --img_path=$data_path &> acc.log &
python3.7 ../postprocess.py --ann_file=$ann_file --img_path=$data_path --result_path=./result_Files &> acc.log &
}
air_to_om
if [ $? -ne 0 ]; then
echo "air to om failed"
exit 1
fi
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"

26
model_zoo/official/cv/maskrcnn/src/aipp.cfg
View File

@ -1,26 +0,0 @@
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
}