forked from mindspore-Ecosystem/mindspore
!18411 se_net add 310 infer
Merge pull request !18411 from chenweitao_295/se-net
This commit is contained in:
Gitee
commit
dab3b656a2
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@ -12,10 +12,15 @@
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- [Script Parameters](#script-parameters)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Inference Process](#inference-process)
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- [Export MindIR](#export-mindir)
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- [Infer on Ascend310](#infer-on-ascend310)
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- [result](#result)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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- [Inference Performance](#inference-performance)
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- [310Inference Performance](#310inference-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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@ -91,19 +96,22 @@ python eval.py --net=se-resnet50 --dataset=imagenet2012 --checkpoint_path=[CHECK
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.
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└──SE-Net
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├── README.md
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├── ascend310_infer # application for 310 inference
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├── scripts
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├── run_distribute_train.sh # launch ascend distributed training(8 pcs)
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├── run_eval.sh # launch ascend evaluation
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├── run_standalone_train.sh # launch ascend standalone training(1 pcs)
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└─ run_infer_310.sh # shell script for 310inference on ascend
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├── src
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├── config.py # parameter configuration
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├── CrossEntropySmooth.py # loss definition for ImageNet2012 dataset
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├── dataset.py # data preprocessing
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├── lr_generator.py # generate learning rate for each step
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├── resnet.py # resnet50 backbone
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└── se.py # se-block definition
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└── se.py # se-block definition
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├── export.py # export model for inference
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├── eval.py # eval net
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├── postprocess.py # postprocess scripts
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└── train.py # train net
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```
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@ -195,6 +203,39 @@ bash run_eval.sh /imagenet/val/ /path/to/resnet-90_625.ckpt
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result: {'top_5_accuracy': 0.9385269007731959, 'top_1_accuracy': 0.7774645618556701}
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```
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## [Inference Process](#contents)
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### Export MindIR
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```shell
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python export.py --ckpt_file [CKPT_PATH] --batch_size [BATCH_SIZE] --file_format [FILE_FORMAT]
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```
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The ckpt_file parameter is required,
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`FILE_FORMAT` should be in ["AIR", "MINDIR"]
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`BATCH_SIZE` current batch_size can only be set to 1.
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### Infer on Ascend310
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Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_FILE] [DVPP] [DEVICE_ID]
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```
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- `LABEL_FILE` label.txt path. Write a py script to sort the category under the dataset, map the file names under the categories and category sort values,Such as[file name : sort value], and write the mapping results to the labe.txt file.
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- `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. SE-net only support CPU mode.
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- `DEVICE_ID` is optional, default value is 0.
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### result
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Inference result is saved in current path, you can find result like this in acc.log file.
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```bash
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result: {'top_5_accuracy': 93.86%, 'top_1_accuracy': 77.80%}
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```
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# [Model Description](#contents)
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## [Performance](#contents)
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@ -236,6 +277,22 @@ result: {'top_5_accuracy': 0.9385269007731959, 'top_1_accuracy': 0.7774645618556
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| Accuracy | 77.74% |
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| Model for inference | # (.air file) |
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### 310Inference Performance
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#### SE-ResNet50 on ImageNet2012
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| Parameters | Ascend |
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| ------------------- | --------------------------- |
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| Model Version | SE-ResNet50 |
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| Resource | Ascend 310; OS Euler2.8 |
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| Uploaded Date | 06/16/2021 (month/day/year) |
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| MindSpore Version | 1.2.0 |
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| Dataset | ImageNet2012 |
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| batch_size | 1 |
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| outputs | Accuracy |
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| Accuracy | 77.80% |
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| Model for inference | 285M(.ckpt file) |
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# [Description of Random Situation](#contents)
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In dataset.py, we set the seed inside "create_dataset" function. We also use random seed in train.py.
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@ -0,0 +1,14 @@
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cmake_minimum_required(VERSION 3.14.1)
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project(Ascend310Infer)
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add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
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set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
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option(MINDSPORE_PATH "mindspore install path" "")
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include_directories(${MINDSPORE_PATH})
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include_directories(${MINDSPORE_PATH}/include)
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include_directories(${PROJECT_SRC_ROOT})
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find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
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file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
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add_executable(main src/main.cc src/utils.cc)
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target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
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@ -0,0 +1,23 @@
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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 [ ! -d out ]; then
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mkdir out
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fi
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cd out || exit
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cmake .. \
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-DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
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make
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@ -0,0 +1,32 @@
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/**
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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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#ifndef MINDSPORE_INFERENCE_UTILS_H_
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#define MINDSPORE_INFERENCE_UTILS_H_
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#include <sys/stat.h>
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#include <dirent.h>
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#include <vector>
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#include <string>
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#include <memory>
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#include "include/api/types.h"
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std::vector<std::string> GetAllFiles(std::string_view dirName);
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DIR *OpenDir(std::string_view dirName);
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std::string RealPath(std::string_view path);
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mindspore::MSTensor ReadFileToTensor(const std::string &file);
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int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
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#endif
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@ -0,0 +1,158 @@
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/**
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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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#include <sys/time.h>
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#include <gflags/gflags.h>
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#include <dirent.h>
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#include <iostream>
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#include <string>
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#include <algorithm>
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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/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::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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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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using mindspore::kSuccess;
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using mindspore::MSTensor;
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using mindspore::dataset::Execute;
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using mindspore::dataset::MapTargetDevice;
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using mindspore::dataset::TensorTransform;
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using mindspore::dataset::vision::Resize;
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using mindspore::dataset::vision::HWC2CHW;
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using mindspore::dataset::vision::Normalize;
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using mindspore::dataset::vision::Decode;
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using mindspore::dataset::vision::CenterCrop;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(dataset_path, ".", "dataset path");
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DEFINE_int32(device_id, 0, "device id");
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DEFINE_string(aipp_path, "../../scripts/aipp.cfg", "aipp path");
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DEFINE_string(cpu_dvpp, "", "cpu or dvpp process");
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DEFINE_int32(image_height, 224, "image height");
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DEFINE_int32(image_width, 224, "image width");
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int main(int argc, char **argv) {
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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if (RealPath(FLAGS_mindir_path).empty()) {
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std::cout << "Invalid mindir" << std::endl;
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return 1;
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}
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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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ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
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}
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}
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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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auto decode = Decode();
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auto resize = Resize({256});
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auto center_crop = CenterCrop({FLAGS_image_height, FLAGS_image_width});
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auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375});
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auto hwc2chw = HWC2CHW();
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Execute dvpptransform({decode, resize, center_crop, normalize}, MapTargetDevice::kAscend310);
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Execute transform({decode, resize, center_crop, normalize, hwc2chw});
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auto all_files = GetAllFiles(FLAGS_dataset_path);
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std::map<double, double> costTime_map;
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size_t size = all_files.size();
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for (size_t i = 0; i < size; ++i) {
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struct timeval start = {0};
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struct timeval end = {0};
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double startTimeMs;
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double endTimeMs;
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std::vector<MSTensor> inputs;
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std::vector<MSTensor> outputs;
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std::cout << "Start predict input files:" << all_files[i] << std::endl;
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if (FLAGS_cpu_dvpp == "DVPP") {
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auto imgDvpp = std::make_shared<MSTensor>();
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dvpptransform(ReadFileToTensor(all_files[i]), imgDvpp.get());
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inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
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imgDvpp->Data().get(), imgDvpp->DataSize());
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} else {
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auto img = MSTensor();
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auto image = ReadFileToTensor(all_files[i]);
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transform(image, &img);
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std::vector<MSTensor> model_inputs = model.GetInputs();
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inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
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img.Data().get(), img.DataSize());
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}
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gettimeofday(&start, nullptr);
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ret = model.Predict(inputs, &outputs);
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gettimeofday(&end, nullptr);
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if (ret != kSuccess) {
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std::cout << "Predict " << all_files[i] << " failed." << std::endl;
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return 1;
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}
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startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
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endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
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costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
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WriteResult(all_files[i], outputs);
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}
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double average = 0.0;
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int inferCount = 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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average += diff;
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inferCount++;
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}
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average = 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 << 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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}
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@ -0,0 +1,130 @@
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/**
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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");
|
||||
* 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.
|
||||
*/
|
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|
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#include "inc/utils.h"
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#include <fstream>
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#include <algorithm>
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#include <iostream>
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using mindspore::MSTensor;
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using mindspore::DataType;
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std::vector<std::string> GetAllFiles(std::string_view dirName) {
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struct dirent *filename;
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DIR *dir = OpenDir(dirName);
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if (dir == nullptr) {
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return {};
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}
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std::vector<std::string> res;
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while ((filename = readdir(dir)) != nullptr) {
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std::string dName = std::string(filename->d_name);
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if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
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continue;
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}
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res.emplace_back(std::string(dirName) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
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for (auto &f : res) {
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std::cout << "image file: " << f << std::endl;
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}
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return res;
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}
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int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
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std::string homePath = "./result_Files";
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for (size_t i = 0; i < outputs.size(); ++i) {
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size_t outputSize;
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std::shared_ptr<const void> netOutput;
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netOutput = outputs[i].Data();
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outputSize = outputs[i].DataSize();
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int pos = imageFile.rfind('/');
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std::string fileName(imageFile, pos + 1);
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fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
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std::string outFileName = homePath + "/" + fileName;
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FILE * outputFile = fopen(outFileName.c_str(), "wb");
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fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
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fclose(outputFile);
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outputFile = nullptr;
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}
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return 0;
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}
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mindspore::MSTensor ReadFileToTensor(const std::string &file) {
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if (file.empty()) {
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std::cout << "Pointer file is nullptr" << std::endl;
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return mindspore::MSTensor();
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}
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std::ifstream ifs(file);
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if (!ifs.good()) {
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std::cout << "File: " << file << " is not exist" << std::endl;
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return mindspore::MSTensor();
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}
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||||
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if (!ifs.is_open()) {
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std::cout << "File: " << file << "open failed" << std::endl;
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return mindspore::MSTensor();
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}
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ifs.seekg(0, std::ios::end);
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size_t size = ifs.tellg();
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mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
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||||
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ifs.seekg(0, std::ios::beg);
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||||
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
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||||
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;
|
||||
}
|
||||
|
|
@ -32,7 +32,7 @@ parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint fil
|
|||
parser.add_argument("--file_name", type=str, default="resnet", help="output file name.")
|
||||
parser.add_argument('--width', type=int, default=224, help='input width')
|
||||
parser.add_argument('--height', type=int, default=224, help='input height')
|
||||
parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
|
||||
parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="AIR", help="file format")
|
||||
parser.add_argument("--device_target", type=str, default="Ascend",
|
||||
choices=["Ascend", "GPU", "CPU"], help="device target(default: Ascend)")
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,73 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""post process for 310 inference"""
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
|
||||
parser = argparse.ArgumentParser(description='SE_net calcul acc')
|
||||
parser.add_argument("--result_path", type=str, required=True, default='', help="result file path")
|
||||
parser.add_argument("--label_file", type=str, required=True, default='', help="label file")
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
def get_top5_acc(top_arg, gt_class):
|
||||
sub_count = 0
|
||||
for top5, gt in zip(top_arg, gt_class):
|
||||
if gt in top5:
|
||||
sub_count += 1
|
||||
return sub_count
|
||||
|
||||
|
||||
def read_label(label_file):
|
||||
with open(label_file, 'r') as f:
|
||||
lines = f.readlines()
|
||||
img_dict = {}
|
||||
for line in lines:
|
||||
img_id = line.split(':')[0]
|
||||
label = line.split(':')[1]
|
||||
img_dict[img_id] = label
|
||||
return img_dict
|
||||
|
||||
|
||||
def cal_acc_imagenet(result_path, label_file):
|
||||
""" calcul acc """
|
||||
img_label = read_label(label_file)
|
||||
img_tot = 0
|
||||
top1_correct = 0
|
||||
top5_correct = 0
|
||||
result_shape = (1, 1001)
|
||||
files = os.listdir(result_path)
|
||||
for file in files:
|
||||
full_file_path = os.path.join(result_path, file)
|
||||
if os.path.isfile(full_file_path):
|
||||
result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
|
||||
gt_classes = int(img_label[file.split('.')[0][:-2]])
|
||||
|
||||
top1_output = np.argmax(result, (-1))
|
||||
top5_output = np.argsort(result)[:, -5:]
|
||||
|
||||
t1_correct = np.equal(top1_output, gt_classes).sum()
|
||||
top1_correct += t1_correct
|
||||
top5_correct += get_top5_acc(top5_output, [gt_classes])
|
||||
img_tot += 1
|
||||
acc1 = 100 * top1_correct / img_tot
|
||||
acc5 = 100 * top5_correct / img_tot
|
||||
print('total={}, top1_correct={}, acc={:.2f}%'.format(img_tot, top1_correct, acc1))
|
||||
print('total={}, top5_correct={}, acc={:.2f}%'.format(img_tot, top5_correct, acc5))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
cal_acc_imagenet(args.result_path, args.label_file)
|
||||
|
|
@ -0,0 +1,107 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [[ $# -lt 4 || $# -gt 5 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_FILE] [DVPP] [DEVICE_ID]
|
||||
DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive,the net only support CPU mode.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
data_path=$(get_real_path $2)
|
||||
label_file=$(get_real_path $3)
|
||||
DVPP=${4^^}
|
||||
|
||||
device_id=0
|
||||
if [ $# == 5 ]; then
|
||||
device_id=$5
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$data_path
|
||||
echo "label file: "$label_file
|
||||
echo "image process mode: "$DVPP
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
if [ "$DVPP" == "DVPP" ];then
|
||||
echo "only support CPU mode"
|
||||
elif [ "$DVPP" == "CPU" ]; then
|
||||
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --cpu_dvpp=$DVPP --device_id=$device_id --image_height=224 --image_width=224 &> infer.log
|
||||
else
|
||||
echo "image process mode must be in [DVPP|CPU]"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --label_file=$label_file --result_path=./result_Files &> acc.log &
|
||||
}
|
||||
|
||||
compile_app
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "compile app code failed"
|
||||
exit 1
|
||||
fi
|
||||
infer
|
||||
if [ $? -ne 0 ]; then
|
||||
echo " execute inference failed"
|
||||
exit 1
|
||||
fi
|
||||
cal_acc
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
||||
Loading…
Reference in New Issue