forked from mindspore-Ecosystem/mindspore
ascend 310 inference for ssd-resnet50
This commit is contained in:
parent
43c61ff4f0
commit
9b564c70ae
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@ -16,7 +16,10 @@
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- [Evaluation Process](#evaluation-process)
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- [Evaluation on Ascend](#evaluation-on-ascend)
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- [Performance](#performance)
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- [Export MindIR](#export-mindir)
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- [Export Process](#Export-process)
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- [Export](#Export)
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- [Inference Process](#Inference-process)
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- [Inference](#Inference)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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@ -150,9 +153,11 @@ Then you can run everything just like on ascend.
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└─ cv
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└─ ssd
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├─ README.md # descriptions about SSD
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├─ ascend310_infer # application for 310 inference
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├─ scripts
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├─ run_distribute_train.sh # shell script for distributed on ascend
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└─ run_eval.sh # shell script for eval on ascend
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├─ run_eval.sh # shell script for eval on ascend
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└─ run_infer_310.sh # shell script for 310 inference
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├─ src
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├─ __init__.py # init file
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├─ box_utils.py # bbox utils
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@ -165,6 +170,7 @@ Then you can run everything just like on ascend.
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├─ eval.py # eval scripts
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├─ train.py # train scripts
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├─ export.py # export mindir script
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├─ postprogress.py # post process for 310 inference
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└─ mindspore_hub_conf.py # mindspore hub interface
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```
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@ -275,6 +281,47 @@ mAP: 0.32719216721918915
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```
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## [Export Process](#contents)
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### [Export](#content)
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```shell
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python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
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```
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`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
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## [Inference Process](#contents)
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### [Inference](#content)
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Before performing inference, we need to export model first. Air model can only be exported in Ascend 910 environment, mindir model can be exported in any environment.
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Current batch_ Size can only be set to 1.
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
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```
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Inference result will be stored in the example path, you can find result like the followings in acc.log.
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```shell
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.475
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.358
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.353
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.314
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.485
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.692
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mAP: 0.3266651054070853
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```
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### [Performance](#contents)
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| 参数 | Ascend |
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@ -16,7 +16,10 @@
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- [评估过程](#评估过程)
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- [Ascend处理器环境评估](#ascend处理器环境评估)
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- [性能](#性能)
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- [导出MindIR](#导出MindIR)
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- [导出过程](#导出过程)
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- [导出](#导出)
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- [推理过程](#推理过程)
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- [推理](#推理)
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- [随机情况说明](#随机情况说明)
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- [ModelZoo主页](#modelzoo主页)
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@ -112,8 +115,10 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
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└─ cv
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└─ ssd
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├─ README.md ## SSD相关说明
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├─ ascend310_infer ## 实现310推理源代码
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├─ scripts
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├─ run_distribute_train.sh ## Ascend分布式shell脚本
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├─ run_infer_310.sh ## Ascend推理shell脚本
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└─ run_eval.sh ## Ascend评估shell脚本
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├─ src
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├─ __init__.py ## 初始化文件
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@ -125,6 +130,8 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
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├─ lr_schedule.py ## 学习率生成器
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└─ ssd.py ## SSD架构
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├─ eval.py ## 评估脚本
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├─ export.py ## 导出 AIR,MINDIR模型的脚本
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├─ postprogress.py ## 310推理后处理脚本
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├─ train.py ## 训练脚本
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└─ mindspore_hub_conf.py ## MindSpore Hub接口
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```
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@ -233,6 +240,39 @@ mAP: 0.32719216721918915
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```
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## 导出过程
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### 导出
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```shell
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python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
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```
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`EXPORT_FORMAT`可选 ["AIR", "MINDIR"]
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## 推理过程
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### 推理
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在还行推理之前我们需要先导出模型。Air模型只能在昇腾910环境上导出，mindir可以在任意环境上导出。batch_size只支持1。
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```shell
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.475
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.358
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.353
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.314
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.485
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.692
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mAP: 0.3266651054070853
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```
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### 性能
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| 参数 | Ascend |
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@ -245,12 +285,6 @@ mAP: 0.32719216721918915
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| mAP | IoU=0.50: 32.7% |
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| 模型大小 | 281M（.ckpt文件） |
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## 导出MindIR
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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参数ckpt_file为必填项，
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`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
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@ -22,20 +22,20 @@
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#include <iosfwd>
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#include <vector>
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#include <fstream>
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#include <sstream>
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#include "include/api/model.h"
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#include "include/api/context.h"
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#include "include/api/types.h"
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#include "include/api/serialization.h"
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#include "include/minddata/dataset/include/vision_ascend.h"
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#include "include/minddata/dataset/include/execute.h"
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#include "include/minddata/dataset/include/vision.h"
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#include "include/dataset/vision_ascend.h"
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#include "include/dataset/execute.h"
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#include "include/dataset/vision.h"
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#include "inc/utils.h"
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using mindspore::GlobalContext;
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::ModelContext;
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using mindspore::Status;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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@ -64,21 +64,24 @@ int main(int argc, char **argv) {
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return 1;
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}
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GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
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GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
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auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
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auto model_context = std::make_shared<mindspore::ModelContext>();
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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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ascend310->SetBufferOptimizeMode("off_optimize");
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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if (FLAGS_cpu_dvpp == "DVPP") {
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if (RealPath(FLAGS_aipp_path).empty()) {
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std::cout << "Invalid aipp path" << std::endl;
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return 1;
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} else {
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ModelContext::SetInsertOpConfigPath(model_context, FLAGS_aipp_path);
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ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
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}
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}
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Model model(GraphCell(graph), model_context);
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Status ret = model.Build();
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Model model;
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Status ret = model.Build(GraphCell(graph), context);
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if (ret != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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}
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double average = 0.0;
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int inferCount = 0;
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char tmpCh[256] = {0};
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for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
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double diff = 0.0;
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diff = iter->second - iter->first;
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inferCount++;
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}
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average = average / inferCount;
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snprintf(tmpCh, sizeof(tmpCh), \
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"NN inference cost average time: %4.3f ms of infer_count %d \n", average, inferCount);
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std::stringstream timeCost;
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timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
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std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
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fileStream << tmpCh;
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fileStream << timeCost.str();
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fileStream.close();
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costTime_map.clear();
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return 0;
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@ -0,0 +1,105 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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if [[ $# -lt 3 || $# -gt 4 ]]; then
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echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
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DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
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DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
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exit 1
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fi
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get_real_path(){
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if [ "${1:0:1}" == "/" ]; then
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echo "$1"
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else
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echo "$(realpath -m $PWD/$1)"
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fi
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}
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model=$(get_real_path $1)
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data_path=$(get_real_path $2)
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DVPP=${3^^}
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device_id=0
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if [ $# == 4 ]; then
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device_id=$4
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fi
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echo "mindir name: "$model
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echo "dataset path: "$data_path
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echo "image process mode: "$DVPP
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echo "device id: "$device_id
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export ASCEND_HOME=/usr/local/Ascend/
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if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
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export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
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export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
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export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
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export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
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export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
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else
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export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
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export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
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export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
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export ASCEND_OPP_PATH=$ASCEND_HOME/opp
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fi
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function compile_app()
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{
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cd ../ascend310_infer || exit
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bash build.sh &> build.log
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}
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function infer()
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{
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cd - || exit
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if [ -d result_Files ]; then
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rm -rf ./result_Files
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fi
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if [ -d time_Result ]; then
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rm -rf ./time_Result
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fi
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mkdir result_Files
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mkdir time_Result
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if [ "$DVPP" == "DVPP" ];then
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../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --cpu_dvpp=$DVPP --aipp_path=../ascend310_infer/aipp.cfg --image_height=640 --image_width=640 &> infer.log
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elif [ "$DVPP" == "CPU" ]; then
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../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --cpu_dvpp=$DVPP --device_id=$device_id --image_height=300 --image_width=300 &> infer.log
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else
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echo "image process mode must be in [DVPP|CPU]"
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exit 1
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fi
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}
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function cal_acc()
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{
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python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --drop &> acc.log &
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}
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compile_app
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if [ $? -ne 0 ]; then
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echo "compile app code failed"
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exit 1
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fi
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infer
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if [ $? -ne 0 ]; then
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echo " execute inference failed"
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exit 1
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fi
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cal_acc
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if [ $? -ne 0 ]; then
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echo "calculate accuracy failed"
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exit 1
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fi
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