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4-th modification based on retest info

This commit is contained in:
Dai Surong 2021-08-30 15:50:00 +08:00
parent c3dd03f694
commit 9805140507
4 changed files with 46 additions and 49 deletions
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7
model_zoo/official/cv/dpn/infer/mxbase/src/DPN.cpp
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@ -15,10 +15,11 @@
*/
#include "DPN.h"
#include "MxBase/DeviceManager/DeviceManager.h"
#include "MxBase/Log/Log.h"
#include <iostream>
#include "MxBase/DeviceManager/DeviceManager.h"
#include <opencv2/dnn.hpp>
#include "MxBase/Log/Log.h"
using namespace MxBase;
using namespace cv::dnn;
@ -27,7 +28,7 @@ namespace {
const uint32_t YUV_BYTE_DE = 2;
const uint32_t VPC_H_ALIGN = 2;
const uint32_t BATCH_SIZE = 32;
} // namespace
} // namespace
APP_ERROR DPN::Init(const InitParam &initParam)
{

47
model_zoo/official/cv/dpn/infer/mxbase/src/DPN.h
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@ -35,31 +35,30 @@ struct InitParam {
};
class DPN {
public:
APP_ERROR Init(const InitParam &initParam);
APP_ERROR DeInit();
APP_ERROR ReadImage(const std::string &imgPath, cv::Mat &imageMat);
APP_ERROR ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat);
APP_ERROR CenterCropImage(const cv::Mat &img, cv::Mat &crop_im);
APP_ERROR CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase);
APP_ERROR Inference(const std::vector<MxBase::TensorBase> &inputs, std::vector<MxBase::TensorBase> &outputs);
APP_ERROR PostProcess(const std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
APP_ERROR Process(const std::vector<std::string> &batchImgPaths);
// get infer time
double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
private:
APP_ERROR SaveResult(const std::vector<std::string> &batchImgPaths,
public:
APP_ERROR Init(const InitParam &initParam);
APP_ERROR DeInit();
APP_ERROR ReadImage(const std::string &imgPath, cv::Mat &imageMat);
APP_ERROR ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat);
APP_ERROR CenterCropImage(const cv::Mat &img, cv::Mat &crop_im);
APP_ERROR CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase);
APP_ERROR Inference(const std::vector<MxBase::TensorBase> &inputs, std::vector<MxBase::TensorBase> &outputs);
APP_ERROR PostProcess(const std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
APP_ERROR Process(const std::vector<std::string> &batchImgPaths);
// get infer time
double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
private:
APP_ERROR SaveResult(const std::vector<std::string> &batchImgPaths,
const std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos);
private:
std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_;
std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
std::shared_ptr<MxBase::Resnet50PostProcess> post_;
MxBase::ModelDesc modelDesc_;
uint32_t deviceId_ = 0;
// infer time
double inferCostTimeMilliSec = 0.0;
private:
std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_;
std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
std::shared_ptr<MxBase::Resnet50PostProcess> post_;
MxBase::ModelDesc modelDesc_;
uint32_t deviceId_ = 0;
// infer time
double inferCostTimeMilliSec = 0.0;
};
#endif

18
model_zoo/official/cv/dpn/infer/mxbase/src/main.cpp
View File

@ -14,17 +14,16 @@
* limitations under the License.
*/
#include "DPN.h"
#include "MxBase/Log/Log.h"
#include <dirent.h>
#include "MxBase/Log/Log.h"
#include "DPN.h"
namespace {
const uint32_t CLASS_NUM = 1000;
const uint32_t BATCH_SIZE = 32;
} // namespace
const uint32_t CLASS_NUM = 1000;
const uint32_t BATCH_SIZE = 32;
} // namespace
APP_ERROR ScanImages(const std::string &path, std::vector<std::string> &imgFiles)
{
APP_ERROR ScanImages(const std::string &path, const std::vector<std::string> &imgFiles){
DIR *dirPtr = opendir(path.c_str());
if (dirPtr == nullptr) {
LogError << "opendir failed. dir:" << path;
@ -43,8 +42,7 @@ APP_ERROR ScanImages(const std::string &path, std::vector<std::string> &imgFiles
return APP_ERR_OK;
}
int main(int argc, char* argv[])
{
int main(int argc, char* argv[]){
if (argc <= 1) {
LogWarn << "Please input image path, such as '../../data/images'.";
return APP_ERR_OK;
@ -74,7 +72,7 @@ int main(int argc, char* argv[])
auto startTime = std::chrono::high_resolution_clock::now();
int inferImgsCount = 0;
LogInfo << "Number of total images load from input data path: " << imgFilePaths.size();
for(uint32_t i = 0; i <= imgFilePaths.size() - BATCH_SIZE; i += BATCH_SIZE){
for( uint32_t i = 0; i <= imgFilePaths.size() - BATCH_SIZE; i += BATCH_SIZE) {
std::vector<std::string>batchImgFilePaths(imgFilePaths.begin()+i, imgFilePaths.begin()+(i+BATCH_SIZE));
ret = dpn->Process(batchImgFilePaths);
if (ret != APP_ERR_OK) {

23
model_zoo/official/cv/dpn/modelarts/train_start.py
View File

@ -87,8 +87,8 @@ def _parse_args():
help='number of parallel workers')
parser.add_argument('--dataset', type=str, default='imagenet-1K',
help='train dataset')
parser.add_argument('--keep_checkpoint_max', type=int, default=3,
help='keep checkpoint max')
parser.add_argument('--keep_checkpoint_max', type=int, default=3,
help='keep checkpoint max')
parser.add_argument('--num_classes', type=int, default=1000,
help='number of classes')
parser.add_argument('--label_smooth', action='store_true',
@ -107,13 +107,13 @@ def _parse_args():
help='pretrained checkpoint file name')
parser.add_argument("--filter_weight", type=ast.literal_eval, default=False,
help="Filter head weight parameters, default is False.")
parser.add_argument('--eval_each_epoch', type=int, default=0,
parser.add_argument('--eval_each_epoch', type=int, default=0,
help='evaluate on each epoch')
parser.add_argument('--global_step', type=int, default=0,
parser.add_argument('--global_step', type=int, default=0,
help='global step')
parser.add_argument('--epoch_size', type=int, default=1,
parser.add_argument('--epoch_size', type=int, default=1,
help='epoch size')
parser.add_argument('--loss_scale_num', type=int, default=1024,
parser.add_argument('--loss_scale_num', type=int, default=1024,
help='loss scale num')
parser.add_argument('--momentum', type=float, default=0.5,
help='momentum')
@ -131,21 +131,20 @@ def _parse_args():
help='max learning rate')
parser.add_argument('--factor', type=float, default=0.1,
help='decay factor')
parser.add_argument('--warmup_epochs', type=int, default=5,
parser.add_argument('--warmup_epochs', type=int, default=5,
help='warmup epochs')
# export configs
parser.add_argument('--export_dir', type=str, default='',
help='dircetory to save exported model, frozen model if not None')
parser.add_argument('--width', type=int, default=224,
parser.add_argument('--width', type=int, default=224,
help='export width')
parser.add_argument('--height', type=int, default=224,
parser.add_argument('--height', type=int, default=224,
help='export height')
parser.add_argument('--file_name', type=str, default='dpn92',
help='export file name')
parser.add_argument('--file_format', type=str, default='AIR',
help='export file format')
# args, _ = parser.parse_known_args()
_args = parser.parse_args()
return _args
@ -162,7 +161,7 @@ def filter_weight_by_list(origin_dict, param_filter):
def dpn_train(config_args, ma_config):
ma_config["training_data"] = config_args.data_path + "/train"
ma_config["image_size"] = [config_args.image_size_height, config_args.image_size_width]
train_dataset = classification_dataset(ma_config["training_data"],
train_dataset = classification_dataset(ma_config["training_data"],
image_size=ma_config["image_size"],
per_batch_size=config_args.batch_size,
max_epoch=1,
@ -329,7 +328,7 @@ def main():
ma_config["rank_save_ckpt_flag"] = 1
# data sync
mox.file.copy_parallel(config_args.data_url, config_args.data_path)
# train
# train
dpn_train(config_args, ma_config)
print('DPN training success!')
# export