From d29ce081d3048c5d2ec05879671a1ae70bdb2f1f Mon Sep 17 00:00:00 2001
From: yuanAIhan <zhanggy9@mail2.sysu.edu.cn>
Date: Thu, 23 Dec 2021 18:18:57 +0800
Subject: [PATCH] feat: the code of 310

---
 research/cv/resnext152_64x4d/README.md        | 120 ++++++++----
 research/cv/resnext152_64x4d/README_CN.md     | 103 +++++++---
 .../ascend310_infer/inc/utils.h               |  35 ++++
 .../ascend310_infer/src/CMakeLists.txt        |  16 ++
 .../ascend310_infer/src/build.sh              |  17 ++
 .../ascend310_infer/src/main.cc               | 146 ++++++++++++++
 .../ascend310_infer/src/main_preprocess.cc    | 126 ++++++++++++
 .../ascend310_infer/src/utils.cc              | 185 ++++++++++++++++++
 .../create_imagenet2012_label.py              |  49 +++++
 .../cv/resnext152_64x4d/default_config.yaml   |  76 +++++++
 research/cv/resnext152_64x4d/postprocess.py   |  52 +++++
 research/cv/resnext152_64x4d/requirements.txt |   3 +
 .../resnext152_64x4d/scripts/run_infer_310.sh |  99 ++++++++++
 .../src/model_utils/config.py                 | 130 ++++++++++++
 .../src/model_utils/device_adapter.py         |  27 +++
 .../src/model_utils/local_adapter.py          |  36 ++++
 .../src/model_utils/moxing_adapter.py         | 115 +++++++++++
 17 files changed, 1276 insertions(+), 59 deletions(-)
 create mode 100644 research/cv/resnext152_64x4d/ascend310_infer/inc/utils.h
 create mode 100644 research/cv/resnext152_64x4d/ascend310_infer/src/CMakeLists.txt
 create mode 100644 research/cv/resnext152_64x4d/ascend310_infer/src/build.sh
 create mode 100644 research/cv/resnext152_64x4d/ascend310_infer/src/main.cc
 create mode 100644 research/cv/resnext152_64x4d/ascend310_infer/src/main_preprocess.cc
 create mode 100644 research/cv/resnext152_64x4d/ascend310_infer/src/utils.cc
 create mode 100644 research/cv/resnext152_64x4d/create_imagenet2012_label.py
 create mode 100644 research/cv/resnext152_64x4d/default_config.yaml
 create mode 100644 research/cv/resnext152_64x4d/postprocess.py
 create mode 100644 research/cv/resnext152_64x4d/requirements.txt
 create mode 100644 research/cv/resnext152_64x4d/scripts/run_infer_310.sh
 create mode 100644 research/cv/resnext152_64x4d/src/model_utils/config.py
 create mode 100644 research/cv/resnext152_64x4d/src/model_utils/device_adapter.py
 create mode 100644 research/cv/resnext152_64x4d/src/model_utils/local_adapter.py
 create mode 100644 research/cv/resnext152_64x4d/src/model_utils/moxing_adapter.py

diff --git a/research/cv/resnext152_64x4d/README.md b/research/cv/resnext152_64x4d/README.md
index ce2a5e0d5..3320bcf35 100644
--- a/research/cv/resnext152_64x4d/README.md
+++ b/research/cv/resnext152_64x4d/README.md
@@ -1,22 +1,30 @@
 # Contents
 
+- [Contents](#contents)
 - [ResNeXt152 Description](#resnext152-description)
-- [Model Architecture](#model-architecture)
+- [Model architecture](#model-architecture)
 - [Dataset](#dataset)
 - [Features](#features)
-- [Mixed Precision](#mixed-precision)
+    - [Mixed Precision](#mixed-precision)
 - [Environment Requirements](#environment-requirements)
-- [Quick Start](#quick-start)
-- [Script Description](#script-description)
-    - [Script and Sample Code](#script-and-sample-code)
+- [Script description](#script-description)
+    - [Script and sample code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
+        - [Usage](#usage)
+            - [Launch](#launch)
     - [Evaluation Process](#evaluation-process)
+        - [Usage](#usage-1)
+            - [Launch](#launch-1)
+            - [Result](#result)
     - [Model Export](#model-export)
-- [Model Description](#model-description)
-    - [Performance](#performance)  
-        - [Training Performance](#evaluation-performance)
-        - [Inference Performance](#evaluation-performance)
+    - [Inference Process](#inference-process)
+        - [Usage](#usage-2)
+        - [result](#result-1)
+- [Model description](#model-description)
+    - [Performance](#performance)
+        - [Training Performance](#training-performance)
+            - [Inference Performance](#inference-performance)
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 
@@ -67,19 +75,35 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 ```python
 .
 └─resnext152_64x4d
+  ├─ascend310_infer                   # 310 inference code
+    ├─inc
+      ├─utils.h                       # Tool library header file
+    ├─src
+      ├─build.sh                      # Run script
+      ├─CMakeLists.txt                # cmake file
+      ├─main_preprocess.cc            # pre process
+      ├─main.cc                       # the entry of main function
+      ├─utils.cc                      # Tool library function implementation
+  ├─README.md
   ├─scripts
     ├─run_standalone_train.sh         # launch standalone training for ascend(1p)
     ├─run_standalone_train_gpu.sh     # launch standalone training for gpu (1p)
     ├─run_distribute_train.sh         # launch distributed training for ascend(8p)
     ├─run_distribute_train_gpu.sh     # launch distributed training for gpu (8p)
-    ├─run_eval.sh                     # launch evaluating
+    ├─run_eval.sh                     # launch evaluate
     └─run_eval_gpu.sh                 # launch evaluating for gpu
   ├─src
     ├─backbone
       ├─_init_.py                     # initialize
       ├─resnet.py                     # resnext152 backbone
+    ├─model_utils
+      ├─config.py                     # Related parameters
+      ├─device_adapter.py             # Device adapter for ModelArts
+      ├─local_adapter.py              # Local adapter
+      ├─moxing_adapter.py             # Moxing adapter for ModelArts
     ├─utils
       ├─_init_.py                     # initialize
+      ├─auto_mixed_precision.py       # Mixed precision
       ├─cunstom_op.py                 # network operation
       ├─logging.py                    # print log
       ├─optimizers_init_.py           # get parameters
@@ -91,15 +115,20 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     ├─dataset.py                      # data preprocessing
     ├─eval_callback.py                # Inference during training
     ├─head.py                         # common head
-    ├─image_classification.py         # get resnet
-    ├─lr_generator.py                 # Learning rate scheduler
+    ├─image_classification.py         # get ResNet
     ├─metric.py                       # Inference
+    ├─linear_warmup.py                # linear warmup learning rate
+    ├─warmup_cosine_annealing.py      # learning rate each step
+    ├─warmup_step_lr.py               # warmup step learning rate
+  ├─create_imagenet2012_label.py      # create label
+  ├─default_config.yaml               # parameters
   ├─eval.py                           # eval net
   ├─export.py                         # export mindir script
+  ├─postprocess.py                    # 310 post-processing
   ├─train.py                          # train net
+  ├─requirements.txt                  # Required python libraries
   ├─README.md                         # Documentation in English
   ├─README_CN.md                      # Documentation in Chinese
-
 ```
 
 ## [Script Parameters](#contents)
@@ -221,37 +250,58 @@ python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_forma
 
 `EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
 
+## [Inference Process](#contents)
+
+### Usage
+
+Before performing inference, the mindir file must be exported by export.py. Currently, only batchsize 1 is supported.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+```
+
+`DEVICE_ID` is optional, default value is 0.
+
+### result
+
+Inference result is saved in current path, you can find result in acc.log file.
+
+```shell
+Total data: 50000, top1 accuracy: 0.79174, top5 accuracy: 0.94178.
+```
+
 # [Model description](#contents)
 
 ## [Performance](#contents)
 
 ### Training Performance
 
-| Parameters                 | ResNeXt152                                    | ResNeXt152                                    |
-| -------------------------- | --------------------------------------------- | --------------------------------------------- |
-| Resource                   | Ascend 910, cpu:2.60GHz 192cores, memory:755G | 8x V100, Intel Xeon Gold 6226R CPU @ 2.90GHz  |
-| uploaded Date              | 06/30/2021                                    | 06/30/2021                                    |
-| MindSpore Version          | 1.2                                           | 1.5.0 (docker build, CUDA 11.1)               |
-| Dataset                    | ImageNet                                      | ImageNet                                      |
-| Training Parameters        | src/config.py                                 | src/config.py; lr=0.05, per_batch_size=16     |
-| Optimizer                  | Momentum                                      | Momentum                                      |
-| Loss Function              | SoftmaxCrossEntropy                           | SoftmaxCrossEntropy                           |
-| Loss                       | 1.28923                                       | 2.172222                                      |
-| Accuracy                   | 80.08%(TOP1)                                  | 79.36%(TOP1) (148 epoch, early stopping)      |
-| Total time                 | 7.8 h 8ps                                     | 2 days 45 minutes (8P, processes)             |
-| Checkpoint for Fine tuning | 192 M(.ckpt file)                             | -                                             |
+| Parameters                 | ResNeXt152                                    | ResNeXt152                                   |
+| -------------------------- | --------------------------------------------- | -------------------------------------------- |
+| Resource                   | Ascend 910, cpu:2.60GHz 192cores, memory:755G | 8x V100, Intel Xeon Gold 6226R CPU @ 2.90GHz |
+| uploaded Date              | 06/30/2021                                    | 06/30/2021                                   |
+| MindSpore Version          | 1.2                                           | 1.5.0 (docker build, CUDA 11.1)              |
+| Dataset                    | ImageNet                                      | ImageNet                                     |
+| Training Parameters        | src/config.py                                 | src/config.py; lr=0.05, per_batch_size=16    |
+| Optimizer                  | Momentum                                      | Momentum                                     |
+| Loss Function              | SoftmaxCrossEntropy                           | SoftmaxCrossEntropy                          |
+| Loss                       | 1.28923                                       | 2.172222                                     |
+| Accuracy                   | 80.08%(TOP1)                                  | 79.36%(TOP1) (148 epoch, early stopping)     |
+| Total time                 | 7.8 h 8ps                                     | 2 days 45 minutes (8P, processes)            |
+| Checkpoint for Fine tuning | 192 M(.ckpt file)                             | -                                            |
 
 #### Inference Performance
 
-| Parameters        |                  |                  |
-| ----------------- | ---------------- | ---------------- |
-| Resource          | Ascend 910       | GPU V100         |
-| uploaded Date     | 06/20/2021       | -                |
-| MindSpore Version | 1.2              | 1.5.0, CUDA 11.1 |
-| Dataset           | ImageNet, 1.2W   | ImageNet, 1.2W   |
-| batch_size        | 1                | 32               |
-| outputs           | probability      | probability      |
-| Accuracy          | acc=80.08%(TOP1) | acc=79.36%(TOP1) |
+| Parameters        |                  |                  |                  |
+| ----------------- | ---------------- | ---------------- | ---------------- |
+| Resource          | Ascend 910       | GPU V100         | Ascend 310       |
+| uploaded Date     | 06/20/2021       | 2021-10-27       | 2021-10-27       |
+| MindSpore Version | 1.2              | 1.5.0, CUDA 11.1 | 1.3.0            |
+| Dataset           | ImageNet, 1.2W   | ImageNet, 1.2W   | ImageNet, 1.2W   |
+| batch_size        | 1                | 32               | 1                |
+| outputs           | probability      | probability      | probability      |
+| Accuracy          | acc=80.08%(TOP1) | acc=79.36%(TOP1) | acc=79.34%(TOP1) |
 
 # [Description of Random Situation](#contents)
 
diff --git a/research/cv/resnext152_64x4d/README_CN.md b/research/cv/resnext152_64x4d/README_CN.md
index 3b49eb517..8b6a05b0e 100644
--- a/research/cv/resnext152_64x4d/README_CN.md
+++ b/research/cv/resnext152_64x4d/README_CN.md
@@ -18,6 +18,9 @@
             - [样例](#样例-1)
             - [结果](#结果)
     - [模型导出](#模型导出)
+    - [推理过程](#推理过程)
+        - [用法](#用法-2)
+        - [结果](#结果-1)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [训练性能](#训练性能)
@@ -69,9 +72,18 @@ ResNeXt整体网络架构如下:
 
 ## 脚本及样例代码
 
-```path
+```python
 .
 └─resnext152_64x4d
+  ├─ascend310_infer                   # 310的推理代码
+    ├─inc
+      ├─utils.h                       # 工具库头文件
+    ├─src
+      ├─build.sh                      # 运行脚本
+      ├─CMakeLists.txt                # cmake文件
+      ├─main_preprocess.cc            # 预处理
+      ├─main.cc                       # 主函数入口
+      ├─utils.cc                      # 工具库函数实现
   ├─README.md
   ├─scripts
     ├─run_standalone_train.sh         # 启动Ascend单机训练(单卡)
@@ -84,8 +96,14 @@ ResNeXt整体网络架构如下:
     ├─backbone
       ├─_init_.py                     # 初始化
       ├─resnet.py                     # ResNeXt152骨干
+    ├─model_utils
+      ├─config.py                     # 相关参数
+      ├─device_adapter.py             # Device adapter for ModelArts
+      ├─local_adapter.py              # Local adapter
+      ├─moxing_adapter.py             # Moxing adapter for ModelArts
     ├─utils
       ├─_init_.py                     # 初始化
+      ├─auto_mixed_precision.py       # 混合精度
       ├─cunstom_op.py                 # 网络操作
       ├─logging.py                    # 打印日志
       ├─optimizers_init_.py           # 获取参数
@@ -102,9 +120,13 @@ ResNeXt整体网络架构如下:
     ├─linear_warmup.py                # 线性热身学习率
     ├─warmup_cosine_annealing.py      # 每次迭代的学习率
     ├─warmup_step_lr.py               # 热身迭代学习率
+  ├─create_imagenet2012_label.py      # 创建标签
+  ├─default_config.yaml               # 参数
   ├─eval.py                           # 评估网络
   ├─export.py                         # export mindir script
+  ├─postprocess.py                    # 310的后期处理
   ├─train.py                          # 训练网络
+  ├─requirements.txt                  # 需要的python库
   ├─README.md                         # Documentation in English
   ├─README_CN.md                      # Documentation in Chinese
 ```
@@ -113,7 +135,7 @@ ResNeXt整体网络架构如下:
 
 在config.py中可以同时配置训练和评估参数。
 
-```python
+```config
 "image_size": '224,224'                   # 图像大小
 "num_classes": 1000,                      # 数据集类数
 "per_batch_size": 128,                    # 输入张量的批次大小
@@ -208,6 +230,18 @@ acc=80.08%(TOP1)
 acc=94.71%(TOP5)
 ```
 
+Example for the GPU evaluation:
+
+```text
+...
+[DATE/TIME]:INFO:load model /path/to/checkpoints/ckpt_0/0-148_10009.ckpt success
+[DATE/TIME]:INFO:Inference Performance: 218.14 img/sec
+[DATE/TIME]:INFO:before results=[[39666], [46445], [49984]]
+[DATE/TIME]:INFO:after results=[[39666] [46445] [49984]]
+[DATE/TIME]:INFO:after allreduce eval: top1_correct=39666, tot=49984,acc=79.36%(TOP1)
+[DATE/TIME]:INFO:after allreduce eval: top5_correct=46445, tot=49984,acc=92.92%(TOP5)
+```
+
 ## 模型导出
 
 ```shell
@@ -216,37 +250,58 @@ python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_forma
 
 `EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"].
 
+## 推理过程
+
+### 用法
+
+在执行推理之前,需要通过export.py导出mindir文件。目前仅可处理batch_size为1。
+
+```shell
+#Ascend310 推理
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+```
+
+`MINDIR_PATH`为生成的mindir的路径,`DATA_PATH`为imagenet的数据集路径,`DEVICE_ID`可选,默认值为0。
+
+### 结果
+
+推理结果保存在当前路径,可在acc.log中看到最终精度结果。
+
+```shell
+Total data: 50000, top1 accuracy: 0.79174, top5 accuracy: 0.94178.
+```
+
 # 模型描述
 
 ## 性能
 
 ### 训练性能
 
-| 参数 | ResNeXt152 |
-| -------------------------- | ---------------------------------------------------------- |
-| 资源                   | Ascend 910;CPU:2.60GHz,192核;内存:755GB              |
-| 上传日期              | 2021-6-30                                          |
-| MindSpore版本          | 1.2                                                    |
-| 数据集 | ImageNet |
-| 训练参数        | src/config.py                                           |
-| 优化器                  | Momentum                                                        |
-| 损失函数             | Softmax交叉熵 |
-| 损失                       | 1.2892 |
-| 准确率 | 80.08%(TOP1)                                          |
-| 总时长                 | 7.8小时 (8卡) |
-| 调优检查点 | 192 M(.ckpt文件) |
+| 参数       | ResNeXt152                                    | ResNeXt152                                   |
+| ---------- | --------------------------------------------- | -------------------------------------------- |
+| 资源       | Ascend 910, cpu:2.60GHz 192cores, memory:755G | 8x V100, Intel Xeon Gold 6226R CPU @ 2.90GHz |
+| 上传日期   | 06/30/2021                                    | 06/30/2021                                   |
+| 版本信息   | 1.3                                           | 1.5.0 (docker build, CUDA 11.1)              |
+| 数据集     | ImageNet                                      | ImageNet                                     |
+| 训练参数   | src/config.py                                 | src/config.py; lr=0.05, per_batch_size=16    |
+| 优化器     | Momentum                                      | Momentum                                     |
+| 损失函数   | SoftmaxCrossEntropy                           | SoftmaxCrossEntropy                          |
+| 损失       | 1.28923                                       | 2.172222                                     |
+| 准确率     | 80.08%(TOP1)                                  | 79.36%(TOP1) (148 epoch, early stopping)     |
+| 总时长     | 7.8 h 8ps                                     | 2 days 45 minutes (8P, processes)            |
+| 调优检查点 | 192 M(.ckpt file)                             | -                                            |
 
 #### 推理性能
 
-| 参数                 |                      |
-| -------------------------- | -------------------- |
-| 资源                   | Ascend 910          |
-| 上传日期              | 2021-6-20 |
-| MindSpore版本         | 1.2             |
-| 数据集 | ImageNet, 1.2万 |
-| batch_size                 | 1                    |
-| 输出 | 概率 |
-| 准确率 | acc=80.08%(TOP1) |
+| 参数       |                  |                  |                  |
+| ---------- | ---------------- | ---------------- | ---------------- |
+| 资源       | Ascend 910       | GPU V100         | Ascend 310       |
+| 上传日期   | 06/20/2021       | 2021-10-27       | 2021-10-27       |
+| 版本信息   | 1.2              | 1.5.0, CUDA 11.1 | 1.3.0            |
+| 数据集     | ImageNet, 1.2W   | ImageNet, 1.2W   | ImageNet, 1.2W   |
+| batch_size | 1                | 32               | 1                |
+| outputs    | probability      | probability      | probability      |
+| 准确率     | acc=80.08%(TOP1) | acc=79.36%(TOP1) | acc=79.34%(TOP1) |
 
 # 随机情况说明
 
diff --git a/research/cv/resnext152_64x4d/ascend310_infer/inc/utils.h b/research/cv/resnext152_64x4d/ascend310_infer/inc/utils.h
new file mode 100644
index 000000000..f8ae1e5b4
--- /dev/null
+++ b/research/cv/resnext152_64x4d/ascend310_infer/inc/utils.h
@@ -0,0 +1,35 @@
+/**
+ * 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);
+std::vector<std::string> GetAllFiles(std::string dir_name);
+std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name);
+
+#endif
diff --git a/research/cv/resnext152_64x4d/ascend310_infer/src/CMakeLists.txt b/research/cv/resnext152_64x4d/ascend310_infer/src/CMakeLists.txt
new file mode 100644
index 000000000..9fbb83330
--- /dev/null
+++ b/research/cv/resnext152_64x4d/ascend310_infer/src/CMakeLists.txt
@@ -0,0 +1,16 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(MindSporeCxxTestcase[CXX])
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT}/../)
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main main.cc utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
+# add_executable(main_preprocess main_preprocess.cc utils.cc)
+# target_link_libraries(main_preprocess ${MS_LIB} gflags)
diff --git a/research/cv/resnext152_64x4d/ascend310_infer/src/build.sh b/research/cv/resnext152_64x4d/ascend310_infer/src/build.sh
new file mode 100644
index 000000000..5a080d259
--- /dev/null
+++ b/research/cv/resnext152_64x4d/ascend310_infer/src/build.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+cmake . -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make
\ No newline at end of file
diff --git a/research/cv/resnext152_64x4d/ascend310_infer/src/main.cc b/research/cv/resnext152_64x4d/ascend310_infer/src/main.cc
new file mode 100644
index 000000000..60d3cdc0b
--- /dev/null
+++ b/research/cv/resnext152_64x4d/ascend310_infer/src/main.cc
@@ -0,0 +1,146 @@
+/**
+ * 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/model.h"
+#include "include/api/context.h"
+#include "include/api/types.h"
+#include "include/api/serialization.h"
+#include "include/dataset/vision_ascend.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/transforms.h"
+#include "include/dataset/vision.h"
+#include "inc/utils.h"
+
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::CenterCrop;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+using mindspore::dataset::TensorTransform;
+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::dataset::Execute;
+
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_int32(device_id, 0, "device id");
+
+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;
+  }
+
+  auto all_files = GetAllInputData(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());
+  std::shared_ptr<TensorTransform> resize(new Resize({256, 256}));
+  std::shared_ptr<TensorTransform> centercrop(new CenterCrop({224, 224}));
+  std::shared_ptr<TensorTransform> normalize(new Normalize({123.675, 116.28, 103.53},
+                                                           {58.395, 57.12, 57.375}));
+  std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
+
+  std::vector<std::shared_ptr<TensorTransform>> trans_list;
+  trans_list = {decode, resize, centercrop, normalize, hwc2chw};
+
+  mindspore::dataset::Execute SingleOp(trans_list);
+
+  for (size_t i = 0; i < size; ++i) {
+    for (size_t j = 0; j < all_files[i].size(); ++j) {
+      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][j] <<std::endl;
+      auto imgDvpp = std::make_shared<MSTensor>();
+      SingleOp(ReadFileToTensor(all_files[i][j]), imgDvpp.get());
+
+      inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
+                          imgDvpp->Data().get(), imgDvpp->DataSize());
+      gettimeofday(&start, nullptr);
+      ret = model.Predict(inputs, &outputs);
+      gettimeofday(&end, nullptr);
+      if (ret != kSuccess) {
+        std::cout << "Predict " << all_files[i][j] << " 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][j], 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;
+}
diff --git a/research/cv/resnext152_64x4d/ascend310_infer/src/main_preprocess.cc b/research/cv/resnext152_64x4d/ascend310_infer/src/main_preprocess.cc
new file mode 100644
index 000000000..59e8099a1
--- /dev/null
+++ b/research/cv/resnext152_64x4d/ascend310_infer/src/main_preprocess.cc
@@ -0,0 +1,126 @@
+/**
+ * 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/model.h"
+#include "include/api/context.h"
+#include "include/api/types.h"
+#include "include/api/serialization.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::GraphCell;
+using mindspore::Model;
+using mindspore::ModelType;
+using mindspore::MSTensor;
+using mindspore::Serialization;
+using mindspore::Status;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_string(image_path, ".", "image path");
+DEFINE_int32(device_id, 0, "device id");
+
+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.IsError()) {
+    std::cout << "ERROR: Build failed." << std::endl;
+    return 1;
+  }
+
+  std::cout << "Check if data preprocess exists: " << model.HasPreprocess() << std::endl;
+
+  // way 1, construct a common MSTensor
+  std::vector<MSTensor> inputs1 = {ReadFileToTensor(FLAGS_image_path)};
+  std::vector<MSTensor> outputs1;
+
+  ret = model.PredictWithPreprocess(inputs1, &outputs1);
+  if (ret.IsError()) {
+    std::cout << "ERROR: Predict failed." << std::endl;
+    return 1;
+  }
+
+  std::ofstream o1("result1.txt", std::ios::out);
+  o1.write(reinterpret_cast<const char *>(outputs1[0].MutableData()), std::streamsize(outputs1[0].DataSize()));
+
+  // way 2, construct a pointer of MSTensor, be careful of destroy
+  MSTensor *tensor = MSTensor::CreateImageTensor(FLAGS_image_path);
+  std::vector<MSTensor> inputs2 = {*tensor};
+  MSTensor::DestroyTensorPtr(tensor);
+  std::vector<MSTensor> outputs2;
+
+  ret = model.PredictWithPreprocess(inputs2, &outputs2);
+  if (ret.IsError()) {
+    std::cout << "ERROR: Predict failed." << std::endl;
+    return 1;
+  }
+
+  std::ofstream o2("result2.txt", std::ios::out);
+  o2.write(reinterpret_cast<const char *>(outputs2[0].MutableData()), std::streamsize(outputs2[0].DataSize()));
+
+  // way 3, split preprocess and predict
+  std::vector<MSTensor> inputs3 = {ReadFileToTensor(FLAGS_image_path)};
+  std::vector<MSTensor> outputs3;
+
+  ret = model.Preprocess(inputs3, &outputs3);
+  if (ret.IsError()) {
+    std::cout << "ERROR: Preprocess failed." << std::endl;
+    return 1;
+  }
+
+  std::vector<MSTensor> outputs4;
+  ret = model.Predict(outputs3, &outputs4);
+  if (ret.IsError()) {
+    std::cout << "ERROR: Preprocess failed." << std::endl;
+    return 1;
+  }
+
+  std::ofstream o3("result3.txt", std::ios::out);
+  o3.write(reinterpret_cast<const char *>(outputs4[0].MutableData()), std::streamsize(outputs4[0].DataSize()));
+
+  // check shape
+  auto shape = outputs1[0].Shape();
+  std::cout << "Output Shape: " << std::endl;
+  for (auto s : shape) {
+    std::cout << s << ", ";
+  }
+  std::cout << std::endl;
+
+  return 0;
+}
diff --git a/research/cv/resnext152_64x4d/ascend310_infer/src/utils.cc b/research/cv/resnext152_64x4d/ascend310_infer/src/utils.cc
new file mode 100644
index 000000000..d71f388b8
--- /dev/null
+++ b/research/cv/resnext152_64x4d/ascend310_infer/src/utils.cc
@@ -0,0 +1,185 @@
+/**
+ * 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::vector<std::string>> GetAllInputData(std::string dir_name) {
+  std::vector<std::vector<std::string>> ret;
+
+  DIR *dir = OpenDir(dir_name);
+  if (dir == nullptr) {
+    return {};
+  }
+  struct dirent *filename;
+  /* read all the files in the dir ~ */
+  std::vector<std::string> sub_dirs;
+  while ((filename = readdir(dir)) != nullptr) {
+    std::string d_name = std::string(filename->d_name);
+    // get rid of "." and ".."
+    if (d_name == "." || d_name == ".." || d_name.empty()) {
+      continue;
+    }
+    std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name);
+    struct stat s;
+    lstat(dir_path.c_str(), &s);
+    if (!S_ISDIR(s.st_mode)) {
+      continue;
+    }
+
+    sub_dirs.emplace_back(dir_path);
+  }
+  std::sort(sub_dirs.begin(), sub_dirs.end());
+
+  (void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret),
+                       [](const std::string &d) { return GetAllFiles(d); });
+
+  return ret;
+}
+
+
+std::vector<std::string> GetAllFiles(std::string dir_name) {
+  struct dirent *filename;
+  DIR *dir = OpenDir(dir_name);
+  if (dir == nullptr) {
+    return {};
+  }
+
+  std::vector<std::string> res;
+  while ((filename = readdir(dir)) != nullptr) {
+    std::string d_name = std::string(filename->d_name);
+    if (d_name == "." || d_name == ".." || d_name.size() <= 3) {
+      continue;
+    }
+    res.emplace_back(std::string(dir_name) + "/" + filename->d_name);
+  }
+  std::sort(res.begin(), res.end());
+
+  return res;
+}
+
+
+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;
+}
+
+mindspore::MSTensor ReadFileToTensor(const std::string &file) {
+  if (file.empty()) {
+    std::cout << "Pointer file is nullptr" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  std::ifstream ifs(file);
+  if (!ifs.good()) {
+    std::cout << "File: " << file << " is not exist" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  if (!ifs.is_open()) {
+    std::cout << "File: " << file << "open failed" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  ifs.seekg(0, std::ios::end);
+  size_t size = ifs.tellg();
+  mindspore::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;
+}
diff --git a/research/cv/resnext152_64x4d/create_imagenet2012_label.py b/research/cv/resnext152_64x4d/create_imagenet2012_label.py
new file mode 100644
index 000000000..72d5c563f
--- /dev/null
+++ b/research/cv/resnext152_64x4d/create_imagenet2012_label.py
@@ -0,0 +1,49 @@
+# 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.
+# ============================================================================
+"""create_imagenet2012_label"""
+import os
+import json
+import argparse
+
+parser = argparse.ArgumentParser(description="resnet imagenet2012 label")
+parser.add_argument("--img_path", type=str, required=True, help="imagenet2012 file path.")
+args = parser.parse_args()
+
+
+def create_label(file_path):
+    """create label of imagenet."""
+    print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!")
+    dirs = os.listdir(file_path)
+    file_list = []
+    for file in dirs:
+        file_list.append(file)
+    file_list = sorted(file_list)
+
+    total = 0
+    img_label = {}
+    for i, file_dir in enumerate(file_list):
+        files = os.listdir(os.path.join(file_path, file_dir))
+        for f in files:
+            img_label[f] = i
+        total += len(files)
+
+    with open("imagenet_label.json", "w+") as label:
+        json.dump(img_label, label)
+
+    print("[INFO] Completed! Total {} data.".format(total))
+
+
+if __name__ == '__main__':
+    create_label(args.img_path)
diff --git a/research/cv/resnext152_64x4d/default_config.yaml b/research/cv/resnext152_64x4d/default_config.yaml
new file mode 100644
index 000000000..53381fcb9
--- /dev/null
+++ b/research/cv/resnext152_64x4d/default_config.yaml
@@ -0,0 +1,76 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+network: "resnext152"
+# Url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# Path for local
+run_distribute: False
+enable_profiling: False
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path/"
+device_target: "Ascend"
+checkpoint_path: "./checkpoint/"
+checkpoint_file_path: ""
+
+# ==============================================================================
+# Training options
+image_size: [224, 224]
+num_classes: 1000
+batch_size: 1
+
+lr: 0.4
+lr_scheduler: "cosine_annealing"
+lr_epochs: [30, 60, 90, 120]
+lr_gamma: 0.1
+eta_min: 0
+T_max: 150
+max_epoch: 150
+warmup_epochs: 1
+
+weight_decay: 0.0001
+momentum: 0.9
+is_dynamic_loss_scale: 0
+loss_scale: 1024
+label_smooth: 1
+label_smooth_factor: 0.1
+per_batch_size: 128
+
+ckpt_interval: 5
+ckpt_save_max: 5
+ckpt_path: "output_demo/"
+is_save_on_master: 1
+rank: 0
+group_size: 1
+rank_save_ckpt_flag: 0
+outputs_dir: ""
+log_path: "./output_log"
+
+# Export options
+device_id: 0
+width: 224
+height: 224
+file_name: "resnext152"
+file_format: "AIR"
+result_path: ""
+label_path: ""
+
+---
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Dataset url for obs"
+train_url: "Training output url for obs"
+checkpoint_url: "The location of checkpoint for obs"
+data_path: "Dataset path for local"
+output_path: "Training output path for local"
+load_path: "The location of checkpoint for obs"
+device_target: "Target device type, available: [Ascend, GPU, CPU]"
+enable_profiling: "Whether enable profiling while training, default: False"
+num_classes: "Class for dataset"
+batch_size: "Batch size for training and evaluation"
+epoch_size: "Total training epochs."
+keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
+checkpoint_path: "The location of the checkpoint file."
+checkpoint_file_path: "The location of the checkpoint file."
diff --git a/research/cv/resnext152_64x4d/postprocess.py b/research/cv/resnext152_64x4d/postprocess.py
new file mode 100644
index 000000000..79045f85c
--- /dev/null
+++ b/research/cv/resnext152_64x4d/postprocess.py
@@ -0,0 +1,52 @@
+# 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 json
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description="resnet inference")
+parser.add_argument("--result_path", type=str, required=True, help="result files path.")
+parser.add_argument("--label_path", type=str, required=True, help="image file path.")
+args = parser.parse_args()
+
+batch_size = 1
+num_classes = 1000
+
+def get_result(result_path, label_path):
+    """calculate the result"""
+    files = os.listdir(result_path)
+    with open(label_path, "r") as label:
+        labels = json.load(label)
+
+    top1 = 0
+    top5 = 0
+    total_data = len(files)
+    for file in files:
+        img_ids_name = file.split('_0.')[0]
+        data_path = os.path.join(result_path, img_ids_name + "_0.bin")
+        result = np.fromfile(data_path, dtype=np.float16).reshape(batch_size, num_classes)
+        for batch in range(batch_size):
+            predict = np.argsort(-result[batch], axis=-1)
+            if labels[img_ids_name+".JPEG"] == predict[0]:
+                top1 += 1
+            if labels[img_ids_name+".JPEG"] in predict[:5]:
+                top5 += 1
+    print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.")
+
+
+if __name__ == '__main__':
+    get_result(args.result_path, args.label_path)
diff --git a/research/cv/resnext152_64x4d/requirements.txt b/research/cv/resnext152_64x4d/requirements.txt
new file mode 100644
index 000000000..e18c54395
--- /dev/null
+++ b/research/cv/resnext152_64x4d/requirements.txt
@@ -0,0 +1,3 @@
+numpy
+pillow
+pyyaml
diff --git a/research/cv/resnext152_64x4d/scripts/run_infer_310.sh b/research/cv/resnext152_64x4d/scripts/run_infer_310.sh
new file mode 100644
index 000000000..b11b0fb76
--- /dev/null
+++ b/research/cv/resnext152_64x4d/scripts/run_infer_310.sh
@@ -0,0 +1,99 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [[ $# -lt 2 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo "mindir name: "$model
+echo "dataset path: "$data_path
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/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=$ASCEND_HOME/opp
+fi
+
+function compile_app()
+{
+    cd ../ascend310_infer/src/ || exit
+    if [ -f "Makefile" ]; then
+        make clean
+    fi
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id  &> infer.log
+}
+
+function cal_acc()
+{
+    python ../create_imagenet2012_label.py  --img_path=$data_path
+    python ../postprocess.py --result_path=./result_Files --label_path=./imagenet_label.json  &> 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
\ No newline at end of file
diff --git a/research/cv/resnext152_64x4d/src/model_utils/config.py b/research/cv/resnext152_64x4d/src/model_utils/config.py
new file mode 100644
index 000000000..2fc78d23c
--- /dev/null
+++ b/research/cv/resnext152_64x4d/src/model_utils/config.py
@@ -0,0 +1,130 @@
+# 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.
+# ============================================================================
+
+"""Parse arguments"""
+
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+import yaml
+
+_config_path = "./default_config.yaml"
+
+class Config:
+    """
+    Configuration namespace. Convert dictionary to members.
+    """
+    def __init__(self, cfg_dict):
+        for k, v in cfg_dict.items():
+            if isinstance(v, (list, tuple)):
+                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+            else:
+                setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+    def __str__(self):
+        return pformat(self.__dict__)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
+    """
+    Parse command line arguments to the configuration according to the default yaml.
+
+    Args:
+        parser: Parent parser.
+        cfg: Base configuration.
+        helper: Helper description.
+        cfg_path: Path to the default yaml config.
+    """
+    parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
+                                     parents=[parser])
+    helper = {} if helper is None else helper
+    choices = {} if choices is None else choices
+    for item in cfg:
+        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+            help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
+            choice = choices[item] if item in choices else None
+            if isinstance(cfg[item], bool):
+                parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+                                    help=help_description)
+            else:
+                parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+                                    help=help_description)
+    args = parser.parse_args()
+    return args
+
+
+def parse_yaml(yaml_path):
+    """
+    Parse the yaml config file.
+
+    Args:
+        yaml_path: Path to the yaml config.
+    """
+    with open(yaml_path, 'r') as fin:
+        try:
+            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+            cfgs = [x for x in cfgs]
+            if len(cfgs) == 1:
+                cfg_helper = {}
+                cfg = cfgs[0]
+                cfg_choices = {}
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+                cfg_choices = {}
+            elif len(cfgs) == 3:
+                cfg, cfg_helper, cfg_choices = cfgs
+            else:
+                raise ValueError("At most 3 docs (config description for help, choices) are supported in config yaml")
+            print(cfg_helper)
+        except:
+            raise ValueError("Failed to parse yaml")
+    return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+    """
+    Merge the base config from yaml file and command line arguments.
+
+    Args:
+        args: Command line arguments.
+        cfg: Base configuration.
+    """
+    args_var = vars(args)
+    for item in args_var:
+        cfg[item] = args_var[item]
+    return cfg
+
+
+def get_config():
+    """
+    Get Config according to the yaml file and cli arguments.
+    """
+    parser = argparse.ArgumentParser(description="default name", add_help=False)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"),
+                        help="Config file path")
+    path_args, _ = parser.parse_known_args()
+    default, helper, choices = parse_yaml(path_args.config_path)
+    args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+    final_config = merge(args, default)
+    pprint(final_config)
+    print("Please check the above information for the configurations", flush=True)
+    return Config(final_config)
+
+config = get_config()
diff --git a/research/cv/resnext152_64x4d/src/model_utils/device_adapter.py b/research/cv/resnext152_64x4d/src/model_utils/device_adapter.py
new file mode 100644
index 000000000..9c3d21d5e
--- /dev/null
+++ b/research/cv/resnext152_64x4d/src/model_utils/device_adapter.py
@@ -0,0 +1,27 @@
+# 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.
+# ============================================================================
+
+"""Device adapter for ModelArts"""
+
+from src.model_utils.config import config
+
+if config.enable_modelarts:
+    from src.model_utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from src.model_utils.local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+
+__all__ = [
+    "get_device_id", "get_device_num", "get_rank_id", "get_job_id"
+]
diff --git a/research/cv/resnext152_64x4d/src/model_utils/local_adapter.py b/research/cv/resnext152_64x4d/src/model_utils/local_adapter.py
new file mode 100644
index 000000000..769fa6dc7
--- /dev/null
+++ b/research/cv/resnext152_64x4d/src/model_utils/local_adapter.py
@@ -0,0 +1,36 @@
+# 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.
+# ============================================================================
+
+"""Local adapter"""
+
+import os
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    return "Local Job"
diff --git a/research/cv/resnext152_64x4d/src/model_utils/moxing_adapter.py b/research/cv/resnext152_64x4d/src/model_utils/moxing_adapter.py
new file mode 100644
index 000000000..aabd5ac6c
--- /dev/null
+++ b/research/cv/resnext152_64x4d/src/model_utils/moxing_adapter.py
@@ -0,0 +1,115 @@
+# 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.
+# ============================================================================
+
+"""Moxing adapter for ModelArts"""
+
+import os
+import functools
+from mindspore import context
+from src.model_utils.config import config
+
+_global_sync_count = 0
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    job_id = os.getenv('JOB_ID')
+    job_id = job_id if job_id != "" else "default"
+    return job_id
+
+def sync_data(from_path, to_path):
+    """
+    Download data from remote obs to local directory if the first url is remote url and the second one is local path
+    Upload data from local directory to remote obs in contrast.
+    """
+    import moxing as mox
+    import time
+    global _global_sync_count
+    sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
+    _global_sync_count += 1
+
+    # Each server contains 8 devices as most.
+    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+        print("from path: ", from_path)
+        print("to path: ", to_path)
+        mox.file.copy_parallel(from_path, to_path)
+        print("===finish data synchronization===")
+        try:
+            os.mknod(sync_lock)
+        except IOError:
+            pass
+        print("===save flag===")
+
+    while True:
+        if os.path.exists(sync_lock):
+            break
+        time.sleep(1)
+
+    print("Finish sync data from {} to {}.".format(from_path, to_path))
+
+
+def moxing_wrapper(pre_process=None, post_process=None):
+    """
+    Moxing wrapper to download dataset and upload outputs.
+    """
+    def wrapper(run_func):
+        @functools.wraps(run_func)
+        def wrapped_func(*args, **kwargs):
+            # Download data from data_url
+            if config.enable_modelarts:
+                if config.data_url:
+                    sync_data(config.data_url, config.data_path)
+                    print("Dataset downloaded: ", os.listdir(config.data_path))
+                if config.checkpoint_url:
+                    sync_data(config.checkpoint_url, config.load_path)
+                    print("Preload downloaded: ", os.listdir(config.load_path))
+                if config.train_url:
+                    sync_data(config.train_url, config.output_path)
+                    print("Workspace downloaded: ", os.listdir(config.output_path))
+
+                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
+                config.device_num = get_device_num()
+                config.device_id = get_device_id()
+                if not os.path.exists(config.output_path):
+                    os.makedirs(config.output_path)
+
+                if pre_process:
+                    pre_process()
+
+            run_func(*args, **kwargs)
+
+            # Upload data to train_url
+            if config.enable_modelarts:
+                if post_process:
+                    post_process()
+
+                if config.train_url:
+                    print("Start to copy output directory")
+                    sync_data(config.output_path, config.train_url)
+        return wrapped_func
+    return wrapper
-- 
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