diff --git a/official/cv/se_resnext50/README.md b/official/cv/se_resnext50/README.md
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+# Contents
+
+- [SE_ResNeXt50 Description](#se_resnext50-description)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [Features](#features)
+- [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 Parameters](#script-parameters)
+ - [Training Process](#training-process)
+ - [Evaluation Process](#evaluation-process)
+ - [Model Export](#model-export)
+ - [Inference Process](#inference-process)
+- [Model Description](#model-description)
+ - [Performance](#performance)
+ - [Training Performance](#evaluation-performance)
+ - [Inference Performance](#evaluation-performance)
+- [Description of Random Situation](#description-of-random-situation)
+- [ModelZoo Homepage](#modelzoo-homepage)
+
+# [SE_ResNeXt50 Description](#contents)
+
+SE-ResNeXt50 is a variant of ResNeXt50 which reference [paper 1](https://arxiv.org/abs/1709.01507) below, ResNeXt50 is a simple, highly modularized network architecture for image classification. It designs results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set in ResNeXt50. This strategy exposes a new dimension, which we call 鈥渃ardinality鈥� (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.ResNeXt50 reference [paper 2](https://arxiv.org/abs/1611.05431) below.
+
+[paper1](https://arxiv.org/abs/1709.01507)锛欽ie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu."Squeeze-and-Excitation Networks"
+
+[paper2](https://arxiv.org/abs/1611.05431)锛歋aining Xie, Ross Girshick, Piotr Doll谩r, Zhuowen Tu, Kaiming He." Aggregated Residual Transformations for Deep Neural Networks"
+
+# [Model architecture](#contents)
+
+The overall network architecture of SE_ResNeXt50 is show below:
+
+[Link](https://arxiv.org/abs/1709.01507)
+
+# [Dataset](#contents)
+
+Dataset used: [imagenet2012](http://www.image-net.org/)
+
+- Dataset size: ~125G, 224*224 colorful images in 1000 classes
+- Train: 120G, 1281167 images
+- Test: 5G, 50000 images
+- Data format: RGB images
+- Note: Data will be processed in src/dataset.py
+
+# [Features](#contents)
+
+## [Mixed Precision](#contents)
+
+The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
+
+For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching 鈥榬educe precision鈥�.
+
+# [Environment Requirements](#contents)
+
+- Hardware锛圓scend锛�
+- Prepare hardware environment with Ascend processor.
+- Framework
+- [MindSpore](https://www.mindspore.cn/install/en)
+- For more information, please check the resources below锛�
+- [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/r1.3/index.html)
+- [MindSpore Python API](https://www.mindspore.cn/docs/api/en/r1.3/index.html)
+
+If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
+
+```bash
+# run distributed training on modelarts example
+# (1) First, Perform a or b.
+# a. Set "enable_modelarts=True" on yaml file.
+# Set other parameters on yaml file you need.
+# b. Add "enable_modelarts=True" on the website UI interface.
+# Add other parameters on the website UI interface.
+# (2) Set the code directory to "/path/SE_ResNeXt50" on the website UI interface.
+# (3) Set the startup file to "train.py" on the website UI interface.
+# (4) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+# (5) Create your job.
+
+# run evaluation on modelarts example
+# (1) Copy or upload your trained model to S3 bucket.
+# (2) Perform a or b.
+# a. Set "enable_modelarts=True" on yaml file.
+# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
+# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
+# b. Add "enable_modelarts=True" on the website UI interface.
+# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+# (3) Set the code directory to "/path/se_resnext50" on the website UI interface.
+# (4) Set the startup file to "eval.py" on the website UI interface.
+# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+# (6) Create your job.
+```
+
+# [Script description](#contents)
+
+## [Script and sample code](#contents)
+
+```python
+.
+鈹斺攢SE_ResNeXt50
+ 鈹溾攢README.md
+ 鈹溾攢README_CN.md
+ 鈹溾攢scripts
+ 鈹溾攢run_standalone_train.sh # launch standalone training for ascend(1p)
+ 鈹溾攢run_distribute_train.sh # launch distributed training for ascend(8p)
+ 鈹斺攢run_eval.sh # launch evaluating
+ 鈹溾攢src
+ 鈹溾攢backbone
+ 鈹溾攢_init_.py # initialize
+ 鈹溾攢resnet.py # SE_ResNeXt50 backbone
+ 鈹溾攢utils
+ 鈹溾攢_init_.py # initialize
+ 鈹溾攢cunstom_op.py # network operation
+ 鈹溾攢logging.py # print log
+ 鈹溾攢optimizers_init_.py # get parameters
+ 鈹溾攢sampler.py # distributed sampler
+ 鈹溾攢var_init_.py # calculate gain value
+ 鈹溾攢_init_.py # initialize
+ 鈹溾攢config.py # parameter configuration
+ 鈹溾攢crossentropy.py # CrossEntropy loss function
+ 鈹溾攢dataset.py # data preprocessing
+ 鈹溾攢head.py # common head
+ 鈹溾攢image_classification.py # get resnet
+ 鈹溾攢linear_warmup.py # linear warmup learning rate
+ 鈹溾攢warmup_cosine_annealing.py # learning rate each step
+ 鈹溾攢warmup_step_lr.py # warmup step learning rate
+ 鈹溾攢鈹€ model_utils
+ 鈹溾攢鈹€config.py # parameter configuration
+ 鈹溾攢鈹€device_adapter.py # device adapter
+ 鈹溾攢鈹€local_adapter.py # local adapter
+ 鈹溾攢鈹€moxing_adapter.py # moxing adapter
+ 鈹溾攢鈹€ default_config.yaml # parameter configuration
+ 鈹溾攢eval.py # eval net
+ 鈹溾攢鈹€train.py # train net
+ 鈹溾攢鈹€export.py # export mindir script
+ 鈹溾攢鈹€mindspore_hub_conf.py # mindspore hub interface
+
+```
+
+## [Script Parameters](#contents)
+
+Parameters for both training and evaluating can be set in config.py.
+
+```python
+image_size: [224,224] # image size
+num_classes: 1000 # dataset class number
+batch_size: 1 # batch size of input
+lr: 0.05 # base learning rate
+lr_scheduler: "cosine_annealing" # learning rate mode
+lr_epochs: [30,60,90,120] # epoch of lr changing
+lr_gamma: 0.1 # decrease lr by a factor of exponential
+eta_min: 0 # eta_min in cosine_annealing scheduler
+T_max: 150 # T-max in cosine_annealing scheduler
+max_epoch: 150 # max epoch num to train the model
+warmup_epochs: 1 # warmup epoch
+weight_decay: 0.0001 # weight decay
+momentum: 0.9 # momentum
+is_dynamic_loss_scale: 0 # dynamic loss scale
+loss_scale: 1024 # loss scale
+label_smooth: 1 # label_smooth
+label_smooth_factor: 0. # label_smooth_factor
+per_batch_size: 128 # batch size of input tensor
+ckpt_interval: 2000 # ckpt_interval
+ckpt_save_max: 5 # max of checkpoint save
+is_save_on_master: 1
+rank_save_ckpt_flag: 0 # local rank of distributed
+outputs_dir: "" # output path
+log_path: "./output_log" # log path
+```
+
+## [Training Process](#contents)
+
+### Usage
+
+You can start training by python script:
+
+```bash
+python train.py --data_dir ~/imagenet/train/ --device_target Ascend --run_distribute 0
+```
+
+or shell script:
+
+```bash
+Ascend:
+ # distribute training example(8p)
+ sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
+ # standalone training
+ sh run_standalone_train.sh DEVICE_ID DATA_PATH
+```
+
+#### Launch
+
+```bash
+# distributed training example(8p) for Ascend
+sh run_distribute_train.sh RANK_TABLE_FILE /dataset/train
+# standalone training example for Ascend
+sh run_standalone_train.sh 0 /dataset/train
+```
+
+You can find checkpoint file together with result in log.
+
+## [Evaluation Process](#contents)
+
+### Usage
+
+You can start executing by python script:
+
+Before execution, modify the configuration item run_distribute of default config.yaml to False.
+
+```bash
+python eval.py --data_path ~/imagenet/val/ --device_target Ascend --checkpoint_file_path se_resnext50.ckpt
+```
+
+or shell script:
+
+```bash
+# Evaluation
+sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH DEVICE_TARGET
+```
+
+DEVICE_TARGET is Ascend, default is Ascend.
+
+#### Launch
+
+```bash
+# Evaluation with checkpoint
+sh run_eval.sh 0 /opt/npu/datasets/classification/val /se_resnext50.ckpt Ascend
+```
+
+#### Result
+
+Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
+
+```log
+acc=78.81%(TOP1)
+acc=94.40%(TOP5)
+```
+
+## [Model Export](#contents)
+
+```bash
+python export.py --device_target [DEVICE_TARGET] --checkpoint_file_path [CKPT_PATH] --file_format [EXPORT_FORMAT]
+```
+
+The `checkpoint_file_path` parameter is required.
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"].
+
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) Perform a or b.
+# a. Set "enable_modelarts=True" on default_config.yaml file.
+# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
+# Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
+# Set "file_name='./resnext50'" on default_config.yaml file.
+# Set "file_format='AIR'" on default_config.yaml file.
+# Set other parameters on default_config.yaml file you need.
+# b. Add "enable_modelarts=True" on the website UI interface.
+# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+# Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+# Add "file_name='./SE_ResNeXt50'" on the website UI interface.
+# Add "file_format='AIR'" on the website UI interface.
+# Add other parameters on the website UI interface.
+# (2) Set the config_path="/path/yaml file" on the website UI interface.
+# (3) Set the code directory to "/path/SE_ResNeXt50" on the website UI interface.
+# (4) Set the startup file to "export.py" on the website UI interface.
+# (5) Set the "Output file path" and "Job log path" to your path on the website UI interface.
+# (6) Create your job.
+```
+
+## [Inference Process](#contents)
+
+### Usage
+
+Before performing inference, the mindir file must be exported by export.py. Currently, only batchsize 1 is supported.
+
+```bash
+# 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.
+
+```log
+Total data:50000, top1 accuracy:0.79174, top5 accuracy:0.94492.
+```
+
+# [Model description](#contents)
+
+## [Performance](#contents)
+
+### Training Performance
+
+| Parameters | SE_ResNeXt50 |
+| -------------------------- | ----------------------------------------------------------- |
+| Resource | Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8 |
+| uploaded Date | 08/31/2021 |
+| MindSpore Version | 1.3.0 |
+| Dataset | ImageNet2012 |
+| Training Parameters | default_config.yaml |
+| Optimizer | Momentum |
+| Loss Function | SoftmaxCrossEntropy |
+| Loss | 1.4159617 |
+| Accuracy | 78%(TOP1) |
+| Total time | 10 h (8ps) |
+| Checkpoint for Fine tuning | 212 M(.ckpt file) |
+
+#### Inference Performance
+
+| Parameters | | |
+| ----------------- | ----------------------- | ------------ |
+| Resource | Ascend 910; OS Euler2.8 | Ascend 310 |
+| uploaded Date | 08/31/2021 | 08/31/2021 |
+| MindSpore Version | 1.3.0 | 1.3.0 |
+| Dataset | ImageNet2012 | ImageNet2012 |
+| batch_size | 128 | 1 |
+| outputs | probability | probability |
+| Accuracy | acc=78.61%(TOP1) | |
+
+# [Description of Random Situation](#contents)
+
+In dataset.py, we set the seed inside 鈥渃reate_dataset" function. We also use random seed in train.py.
+
+# [ModelZoo Homepage](#contents)
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
diff --git a/official/cv/se_resnext50/README_CN.md b/official/cv/se_resnext50/README_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..51de7cd0d82474e7e988201e525b9186337737e7
--- /dev/null
+++ b/official/cv/se_resnext50/README_CN.md
@@ -0,0 +1,340 @@
+# 鐩綍
+
+- [鐩綍](#鐩綍)
+- [SE_ResNeXt50璇存槑](#resnext50璇存槑)
+- [妯″瀷鏋舵瀯](#妯″瀷鏋舵瀯)
+- [鏁版嵁闆哴(#鏁版嵁闆�)
+- [鐗规€(#鐗规€�)
+ - [娣峰悎绮惧害](#娣峰悎绮惧害)
+- [鐜瑕佹眰](#鐜瑕佹眰)
+- [鑴氭湰璇存槑](#鑴氭湰璇存槑)
+ - [鑴氭湰鍙婃牱渚嬩唬鐮乚(#鑴氭湰鍙婃牱渚嬩唬鐮�)
+ - [鑴氭湰鍙傛暟](#鑴氭湰鍙傛暟)
+ - [璁粌杩囩▼](#璁粌杩囩▼)
+ - [鐢ㄦ硶](#鐢ㄦ硶)
+ - [鏍蜂緥](#鏍蜂緥)
+ - [璇勪及杩囩▼](#璇勪及杩囩▼)
+ - [鐢ㄦ硶](#鐢ㄦ硶-1)
+ - [鏍蜂緥](#鏍蜂緥-1)
+ - [缁撴灉](#缁撴灉)
+ - [妯″瀷瀵煎嚭](#妯″瀷瀵煎嚭)
+ - [鎺ㄧ悊杩囩▼](#鎺ㄧ悊杩囩▼)
+ - [鐢ㄦ硶](#鐢ㄦ硶-2)
+ - [缁撴灉](#缁撴灉-2)
+- [妯″瀷鎻忚堪](#妯″瀷鎻忚堪)
+ - [鎬ц兘](#鎬ц兘)
+ - [璁粌鎬ц兘](#璁粌鎬ц兘)
+ - [鎺ㄧ悊鎬ц兘](#鎺ㄧ悊鎬ц兘)
+- [闅忔満鎯呭喌璇存槑](#闅忔満鎯呭喌璇存槑)
+- [ModelZoo涓婚〉](#modelzoo涓婚〉)
+
+# SE_ResNeXt50璇存槑
+
+SE_ResNeXt50鏄疪esNeXt50鐨勪竴涓彉浣擄紝鍙弬鑰冭鏂�1銆俁esNeXt50鏄竴涓畝鍗曘€侀珮搴︽ā鍧楀寲鐨勫浘鍍忓垎绫荤綉缁滄灦鏋勩€俁esNeXt50鐨勮璁′负缁熶竴鐨勩€佸鍒嗘敮鐨勬灦鏋勶紝璇ユ灦鏋勪粎闇€璁剧疆鍑犱釜瓒呭弬鏁般€傛绛栫暐鎻愪緵浜嗕竴涓柊缁村害锛屽嵆鈥滃熀鏁扳€濓紙杞崲闆嗙殑澶у皬锛夛紝瀹冩槸娣卞害鍜屽搴︾淮搴︿箣澶栫殑涓€涓噸瑕佸洜绱狅紝ResNeXt50鍙弬鑰冭鏂�2銆�
+
+璁烘枃1锛欽ie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu."Squeeze-and-Excitation Networks"
+
+璁烘枃2锛歋aining Xie, Ross Girshick, Piotr Doll谩r, Zhuowen Tu, Kaiming He." Aggregated Residual Transformations for Deep Neural Networks"
+
+# 妯″瀷鏋舵瀯
+
+SE_ResNeXt鏁翠綋缃戠粶鏋舵瀯濡備笅锛�
+
+[閾炬帴](https://arxiv.org/abs/1709.01507)
+
+# 鏁版嵁闆�
+
+浣跨敤鐨勬暟鎹泦锛歔ImageNet2012](http://www.image-net.org/)
+
+- 鏁版嵁闆嗗ぇ灏忥細绾�125G, 鍏�1000涓被锛�224*224褰╄壊鍥惧儚
+ - 璁粌闆嗭細120G锛屽叡1281167寮犲浘鍍�
+ - 娴嬭瘯闆嗭細5G锛�50000寮犲浘鍍�
+- 鏁版嵁鏍煎紡锛歊GB鍥惧儚銆�
+ - 娉細鏁版嵁鍦╯rc/dataset.py涓鐞嗐€�
+
+# 鐗规€�
+
+## 娣峰悎绮惧害
+
+閲囩敤[娣峰悎绮惧害](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)鐨勮缁冩柟娉曚娇鐢ㄦ敮鎸佸崟绮惧害鍜屽崐绮惧害鏁版嵁鏉ユ彁楂樻繁搴﹀涔犵缁忕綉缁滅殑璁粌閫熷害锛屽悓鏃朵繚鎸佸崟绮惧害璁粌鎵€鑳借揪鍒扮殑缃戠粶绮惧害銆傛贩鍚堢簿搴﹁缁冩彁楂樿绠楅€熷害銆佸噺灏戝唴瀛樹娇鐢ㄧ殑鍚屾椂锛屾敮鎸佸湪鐗瑰畾纭欢涓婅缁冩洿澶х殑妯″瀷鎴栧疄鐜版洿澶ф壒娆＄殑璁粌銆�
+
+浠P16绠楀瓙涓轰緥锛屽鏋滆緭鍏ユ暟鎹被鍨嬩负FP32锛孧indSpore鍚庡彴浼氳嚜鍔ㄩ檷浣庣簿搴︽潵澶勭悊鏁版嵁銆傜敤鎴峰彲鎵撳紑INFO鏃ュ織锛屾悳绱⑩€渞educe precision鈥濇煡鐪嬬簿搴﹂檷浣庣殑绠楀瓙銆�
+
+# 鐜瑕佹眰
+
+- 纭欢锛圓scend锛�
+ - 鍑嗗Ascend澶勭悊鍣ㄦ惌寤虹‖浠剁幆澧冦€�
+- 妗嗘灦
+ - [MindSpore](https://www.mindspore.cn/install)
+- 濡傞渶鏌ョ湅璇︽儏锛岃鍙傝濡備笅璧勬簮锛�
+ - [MindSpore鏁欑▼](https://www.mindspore.cn/tutorials/zh-CN/r1.3/index.html)
+ - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/r1.3/index.html)
+
+濡傛灉瑕佸湪modelarts涓婅繘琛屾ā鍨嬬殑璁粌锛屽彲浠ュ弬鑰僲odelarts鐨勫畼鏂规寚瀵兼枃妗�(https://support.huaweicloud.com/modelarts/)
+寮€濮嬭繘琛屾ā鍨嬬殑璁粌鍜屾帹鐞嗭紝鍏蜂綋鎿嶄綔濡備笅锛�
+
+```bash
+# 鍦╩odelarts涓婁娇鐢ㄥ垎甯冨紡璁粌鐨勭ず渚嬶細
+# (1) 閫夋嫨a鎴栬€卋鍏朵腑涓€绉嶆柟寮忋€�
+# a. 璁剧疆 "enable_modelarts=True" 銆�
+# 鍦▂aml鏂囦欢涓婅缃綉缁滄墍闇€鐨勫弬鏁般€�
+# b. 澧炲姞 "enable_modelarts=True" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 鍦╩odelarts鐨勭晫闈笂璁剧疆缃戠粶鎵€闇€鐨勫弬鏁般€�
+# (2) 鍦╩odelarts鐨勭晫闈笂璁剧疆浠ｇ爜鐨勮矾寰� "/path/SE_ResNeXt50"銆�
+# (3) 鍦╩odelarts鐨勭晫闈笂璁剧疆妯″瀷鐨勫惎鍔ㄦ枃浠� "train.py" 銆�
+# (4) 鍦╩odelarts鐨勭晫闈笂璁剧疆妯″瀷鐨勬暟鎹矾寰� "Dataset path" ,
+# 妯″瀷鐨勮緭鍑鸿矾寰�"Output file path" 鍜屾ā鍨嬬殑鏃ュ織璺緞 "Job log path" 銆�
+# (5) 寮€濮嬫ā鍨嬬殑璁粌銆�
+
+# 鍦╩odelarts涓婁娇鐢ㄦā鍨嬫帹鐞嗙殑绀轰緥
+# (1) 鎶婅缁冨ソ鐨勬ā鍨嬪湴鏂瑰埌妗剁殑瀵瑰簲浣嶇疆銆�
+# (2) 閫夊潃a鎴栬€卋鍏朵腑涓€绉嶆柟寮忋€�
+# a. 璁剧疆 "enable_modelarts=True"
+# 璁剧疆 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 鍦� yaml 鏂囦欢.
+# 璁剧疆 "checkpoint_url=/The path of checkpoint in S3/" 鍦� yaml 鏂囦欢.
+# b. 澧炲姞 "enable_modelarts=True" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 澧炲姞 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 澧炲姞 "checkpoint_url=/The path of checkpoint in S3/" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# (3) 鍦╩odelarts鐨勭晫闈笂璁剧疆浠ｇ爜鐨勮矾寰� "/path/SE_ResNeXt50"銆�
+# (4) 鍦╩odelarts鐨勭晫闈笂璁剧疆妯″瀷鐨勫惎鍔ㄦ枃浠� "eval.py" 銆�
+# (5) 鍦╩odelarts鐨勭晫闈笂璁剧疆妯″瀷鐨勬暟鎹矾寰� "Dataset path" ,
+# 妯″瀷鐨勮緭鍑鸿矾寰�"Output file path" 鍜屾ā鍨嬬殑鏃ュ織璺緞 "Job log path" 銆�
+# (6) 寮€濮嬫ā鍨嬬殑鎺ㄧ悊銆�
+```
+
+# 鑴氭湰璇存槑
+
+## 鑴氭湰鍙婃牱渚嬩唬鐮�
+
+```python
+.
+鈹斺攢SE_ResNeXt50
+ 鈹溾攢README.md
+ 鈹溾攢README_CN.md
+ 鈹溾攢scripts
+ 鈹溾攢run_standalone_train.sh # 鍚姩Ascend鍗曟満璁粌锛堝崟鍗★級
+ 鈹溾攢run_distribute_train.sh # 鍚姩Ascend鍒嗗竷寮忚缁冿紙8鍗★級
+ 鈹斺攢run_eval.sh # 鍚姩璇勪及
+ 鈹溾攢src
+ 鈹溾攢backbone
+ 鈹溾攢_init_.py # 鍒濆鍖�
+ 鈹溾攢resnet.py # SE_ResNeXt50楠ㄥ共
+ 鈹溾攢utils
+ 鈹溾攢_init_.py # 鍒濆鍖�
+ 鈹溾攢cunstom_op.py # 缃戠粶鎿嶄綔
+ 鈹溾攢logging.py # 鎵撳嵃鏃ュ織
+ 鈹溾攢optimizers_init_.py # 鑾峰彇鍙傛暟
+ 鈹溾攢sampler.py # 鍒嗗竷寮忛噰鏍峰櫒
+ 鈹溾攢var_init_.py # 璁＄畻澧炵泭鍊�
+ 鈹溾攢_init_.py # 鍒濆鍖�
+ 鈹溾攢config.py # 鍙傛暟閰嶇疆
+ 鈹溾攢crossentropy.py # 浜ゅ弶鐔垫崯澶卞嚱鏁�
+ 鈹溾攢dataset.py # 鏁版嵁棰勫鐞�
+ 鈹溾攢head.py # 甯歌澶�
+ 鈹溾攢image_classification.py # 鑾峰彇ResNet
+ 鈹溾攢linear_warmup.py # 绾挎€х儹韬涔犵巼
+ 鈹溾攢warmup_cosine_annealing.py # 姣忔杩唬鐨勫涔犵巼
+ 鈹溾攢warmup_step_lr.py # 鐑韩杩唬瀛︿範鐜�
+ 鈹溾攢model_utils
+ 鈹溾攢鈹€config.py # 鍙傛暟閰嶇疆
+ 鈹溾攢鈹€device_adapter.py # 璁惧閰嶇疆
+ 鈹溾攢鈹€local_adapter.py # 鏈湴璁惧閰嶇疆
+ 鈹溾攢鈹€moxing_adapter.py # modelarts璁惧閰嶇疆
+ 鈹溾攢鈹€ default_config.yaml # 鍙傛暟閰嶇疆椤�
+ 鈹溾攢eval.py # 璇勪及缃戠粶
+ 鈹溾攢鈹€train.py # 璁粌缃戠粶
+ 鈹溾攢鈹€mindspore_hub_conf.py # MindSpore Hub鎺ュ彛
+```
+
+## 鑴氭湰鍙傛暟
+
+鍦╠efault_config.yaml 涓彲浠ュ悓鏃堕厤缃缁冨拰璇勪及鍙傛暟銆�
+
+```python
+# Training options
+image_size: [224,224] # 鍥惧儚澶у皬
+num_classes: 1000 # 鏁版嵁闆嗙被鏁�
+batch_size: 1 # 杈撳叆鏁版嵁鎵规
+lr: 0.05 # 鍩虹瀛︿範鐜�
+lr_scheduler: "cosine_annealing" # 瀛︿範鐜囨ā寮�
+lr_epochs: [30,60,90,120] # 瀛︿範鐜囧彉鍖栬疆娆�
+lr_gamma: 0.1 # 鍑忓皯LR鐨別xponential lr_scheduler鍥犲瓙
+eta_min: 0 # cosine_annealing璋冨害鍣ㄤ腑鐨別ta_min
+T_max: 150 # cosine_annealing璋冨害鍣ㄤ腑鐨凾-max
+max_epoch: 150 # 璁粌妯″瀷鐨勬渶澶ц疆娆℃暟閲�
+warmup_epochs: 1 # 鐑韩杞
+weight_decay: 0.0001 # 鏉冮噸琛板噺
+momentum: 0.9 # 鍔ㄩ噺
+is_dynamic_loss_scale: 0 # 鍔ㄦ€佹崯澶辨斁澶�
+loss_scale: 1024 # 鎹熷け鏀惧ぇ
+label_smooth: 1 # 鏍囩骞虫粦
+label_smooth_factor: 0. # 鏍囩骞虫粦鍥犲瓙
+per_batch_size: 128 # 杈撳叆寮犻噺鐨勬壒娆″ぇ灏�
+ckpt_interval: 2000 # 妫€鏌ョ偣闂撮殧
+ckpt_save_max: 5 # 妫€鏌ョ偣鏈€澶т繚瀛樻暟
+is_save_on_master: 1
+rank_save_ckpt_flag: 0 # 鍒嗗竷寮忔湰鍦拌繘绋嬪簭鍙�
+outputs_dir: "" # 杈撳嚭璺緞
+log_path: "./output_log" # 鏃ュ織璺緞
+```
+
+## 璁粌杩囩▼
+
+### 鐢ㄦ硶
+
+鎮ㄥ彲浠ラ€氳繃python鑴氭湰寮€濮嬭缁冿細
+
+```bash
+python train.py --data_dir ~/imagenet/train/ --device_target Ascend --run_distribute 0
+```
+
+鎴栭€氳繃shell鑴氭湰寮€濮嬭缁冿細
+
+```bash
+Ascend:
+ # 鍒嗗竷寮忚缁冪ず渚嬶紙8鍗★級
+ sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
+ # 鍗曟満璁粌
+ sh run_standalone_train.sh DEVICE_ID DATA_PATH
+```
+
+### 鏍蜂緥
+
+```bash
+# Ascend鍒嗗竷寮忚缁冪ず渚嬶紙8鍗★級
+sh run_distribute_train.sh RANK_TABLE_FILE /dataset/train
+# Ascend鍗曟満璁粌绀轰緥
+sh run_standalone_train.sh 0 /dataset/train
+```
+
+鎮ㄥ彲浠ュ湪鏃ュ織涓壘鍒版鏌ョ偣鏂囦欢鍜岀粨鏋溿€�
+
+## 璇勪及杩囩▼
+
+### 鐢ㄦ硶
+
+鎮ㄥ彲浠ラ€氳繃python鑴氭湰鎵ц锛�
+
+鎵ц鍓嶏紝淇敼default_config.yaml鐨勯厤缃」run_distribute涓篎alse銆�
+
+```bash
+python eval.py --data_path ~/imagenet/val/ --device_target Ascend --checkpoint_file_path se_resnext50.ckpt
+```
+
+鎴栭€氳繃shell鑴氭湰鎵ц锛�
+
+```bash
+# 璇勪及
+sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH DEVICE_TARGET
+```
+
+DEVICE_TARGET is Ascend锛� default is Ascend.
+
+#### 鏍蜂緥
+
+```bash
+# 妫€鏌ョ偣璇勪及
+sh run_eval.sh 0 /opt/npu/datasets/classification/val /se_resnext50.ckpt Ascend
+```
+
+#### 缁撴灉
+
+璇勪及缁撴灉淇濆瓨鍦ㄨ剼鏈矾寰勪笅銆傛偍鍙互鍦ㄦ棩蹇椾腑鎵惧埌绫讳技浠ヤ笅鐨勭粨鏋溿€�
+
+```log
+acc=78.81%(TOP1)
+acc=94.40%(TOP5)
+```
+
+## 妯″瀷瀵煎嚭
+
+```bash
+python export.py --device_target [DEVICE_TARGET] --checkpoint_file_path [CKPT_PATH] --file_format [EXPORT_FORMAT]
+```
+
+`checkpoint_file_path` 鍙傛暟涓哄繀濉」銆�
+`EXPORT_FORMAT` 鍙€� ["AIR", "MINDIR"]銆�
+
+ModelArts瀵煎嚭mindir
+
+```python
+# (1) 鎶婅缁冨ソ鐨勬ā鍨嬪湴鏂瑰埌妗剁殑瀵瑰簲浣嶇疆銆�
+# (2) 閫夊潃a鎴栬€卋鍏朵腑涓€绉嶆柟寮忋€�
+# a. 璁剧疆 "enable_modelarts=True"
+# 璁剧疆 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 鍦� yaml 鏂囦欢銆�
+# 璁剧疆 "checkpoint_url=/The path of checkpoint in S3/" 鍦� yaml 鏂囦欢銆�
+# 璁剧疆 "file_name='./SE_ResNeXt50'"鍙傛暟鍦▂aml鏂囦欢銆�
+# 璁剧疆 "file_format='AIR'" 鍙傛暟鍦▂aml鏂囦欢銆�
+# b. 澧炲姞 "enable_modelarts=True" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 澧炲姞 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 澧炲姞 "checkpoint_url=/The path of checkpoint in S3/" 鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 璁剧疆 "file_name='./resnext50'"鍙傛暟鍦╩odearts鐨勭晫闈笂銆�
+# 璁剧疆 "file_format='AIR'" 鍙傛暟鍦╩odelarts鐨勭晫闈笂銆�
+# (3) 璁剧疆缃戠粶閰嶇疆鏂囦欢鐨勮矾寰� "config_path=/The path of config in S3/"
+# (4) 鍦╩odelarts鐨勭晫闈笂璁剧疆浠ｇ爜鐨勮矾寰� "/path/SE_ResNeXt50"銆�
+# (5) 鍦╩odelarts鐨勭晫闈笂璁剧疆妯″瀷鐨勫惎鍔ㄦ枃浠� "export.py" 銆�
+# 妯″瀷鐨勮緭鍑鸿矾寰�"Output file path" 鍜屾ā鍨嬬殑鏃ュ織璺緞 "Job log path" 銆�
+# (6) 寮€濮嬪鍑簃indir銆�
+```
+
+## [鎺ㄧ悊杩囩▼](#contents)
+
+### 鐢ㄦ硶
+
+鍦ㄦ墽琛屾帹鐞嗕箣鍓嶏紝闇€瑕侀€氳繃export.py瀵煎嚭mindir鏂囦欢銆�
+鐩墠浠呭彲澶勭悊batch_Size涓�1銆�
+
+```bash
+#Ascend310 鎺ㄧ悊
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+```
+
+`DEVICE_ID` 鍙€夛紝榛樿鍊间负 0銆�
+
+### 缁撴灉
+
+鎺ㄧ悊缁撴灉淇濆瓨鍦ㄥ綋鍓嶈矾寰勶紝鍙湪acc.log涓湅鍒版渶缁堢簿搴︾粨鏋溿€�
+
+```log
+Total data:50000, top1 accuracy:0.79174, top5 accuracy:0.94492.
+```
+
+# 妯″瀷鎻忚堪
+
+## 鎬ц兘
+
+### 璁粌鎬ц兘
+
+| 鍙傛暟 | SE_ResNeXt50 |
+| -------------------------- | ---------------------------------------------------------- |
+| 璧勬簮 | Ascend 910锛汣PU 2.60GHz锛�192鏍革紱鍐呭瓨 755GB锛涚郴缁� Euler2.8 |
+| 涓婁紶鏃ユ湡 | 2021-8-31 |
+| MindSpore鐗堟湰 | 1.3.0 |
+| 鏁版嵁闆� | ImageNet2012 |
+| 璁粌鍙傛暟 | default_config.yaml |
+| 浼樺寲鍣� | Momentum |
+| 鎹熷け鍑芥暟 | Softmax浜ゅ弶鐔� |
+| 鎹熷け | 1.4159617 |
+| 鍑嗙‘鐜� | 78%(TOP1) |
+| 鎬绘椂闀� | 10灏忔椂 锛�8鍗★級 |
+| 璋冧紭妫€鏌ョ偣 | 212 M锛�.ckpt鏂囦欢锛� |
+
+#### 鎺ㄧ悊鎬ц兘
+
+| 鍙傛暟 | | |
+| -------------------------- | ----------------------------- | -------------------- |
+| 璧勬簮 | Ascend 910锛涚郴缁� Euler2.8 | Ascend 310 |
+| 涓婁紶鏃ユ湡 | 2021-8-31 | 2021-8-31 |
+| MindSpore鐗堟湰 | 1.3.0 | 1.3.0 |
+| 鏁版嵁闆� | ImageNet2012 | ImageNet2012 |
+| batch_size | 128 | 1 |
+| 杈撳嚭 | 姒傜巼 | 姒傜巼 |
+| 鍑嗙‘鐜� | acc=78.61%(TOP1) | |
+
+# 闅忔満鎯呭喌璇存槑
+
+dataset.py涓缃簡鈥渃reate_dataset鈥濆嚱鏁板唴鐨勭瀛愶紝鍚屾椂杩樹娇鐢ㄤ簡train.py涓殑闅忔満绉嶅瓙銆�
+
+# ModelZoo涓婚〉
+
+璇锋祻瑙堝畼缃慬涓婚〉](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)銆�
diff --git a/official/cv/se_resnext50/ascend310_infer/inc/utils.h b/official/cv/se_resnext50/ascend310_infer/inc/utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8ae1e5b473d869b77af8d725a280d7c7665527c
--- /dev/null
+++ b/official/cv/se_resnext50/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/official/cv/se_resnext50/ascend310_infer/src/CMakeLists.txt b/official/cv/se_resnext50/ascend310_infer/src/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0397995b0e0b37c4fa7c39c93ebd41011d5bd936
--- /dev/null
+++ b/official/cv/se_resnext50/ascend310_infer/src/CMakeLists.txt
@@ -0,0 +1,14 @@
+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)
diff --git a/official/cv/se_resnext50/ascend310_infer/src/build.sh b/official/cv/se_resnext50/ascend310_infer/src/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7fac9cff3a98c83bce7e8f66053fab2ecebab86d
--- /dev/null
+++ b/official/cv/se_resnext50/ascend310_infer/src/build.sh
@@ -0,0 +1,18 @@
+#!/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="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make
\ No newline at end of file
diff --git a/official/cv/se_resnext50/ascend310_infer/src/main.cc b/official/cv/se_resnext50/ascend310_infer/src/main.cc
new file mode 100644
index 0000000000000000000000000000000000000000..d85816925229fde1d1eb784295ba57ba985d6c31
--- /dev/null
+++ b/official/cv/se_resnext50/ascend310_infer/src/main.cc
@@ -0,0 +1,145 @@
+/**
+ * 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);
+ 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/official/cv/se_resnext50/ascend310_infer/src/utils.cc b/official/cv/se_resnext50/ascend310_infer/src/utils.cc
new file mode 100644
index 0000000000000000000000000000000000000000..d71f388b83d23c2813d8bfc883dbcf2e7e0e4ef0
--- /dev/null
+++ b/official/cv/se_resnext50/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/official/cv/se_resnext50/create_imagenet2012_label.py b/official/cv/se_resnext50/create_imagenet2012_label.py
new file mode 100644
index 0000000000000000000000000000000000000000..773855964154b5558a863b7a309efaa4d68efad7
--- /dev/null
+++ b/official/cv/se_resnext50/create_imagenet2012_label.py
@@ -0,0 +1,43 @@
+# 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
+#
+# less 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):
+ 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/official/cv/se_resnext50/default_config.yaml b/official/cv/se_resnext50/default_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..10696db17283b45847350687345992d5f787fc15
--- /dev/null
+++ b/official/cv/se_resnext50/default_config.yaml
@@ -0,0 +1,73 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+network: "se_resnext50"
+# 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
+is_save_on_master: 1
+rank_save_ckpt_flag: 0
+outputs_dir: ""
+log_path: "./output_log"
+
+# Export options
+device_id: 0
+width: 224
+height: 224
+file_name: "se_resnext50"
+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, 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/official/cv/se_resnext50/eval.py b/official/cv/se_resnext50/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..e339710b4b4400cc96fb32033b0dc5109002385d
--- /dev/null
+++ b/official/cv/se_resnext50/eval.py
@@ -0,0 +1,207 @@
+# 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.
+# ============================================================================
+"""Eval"""
+import os
+import time
+import datetime
+import glob
+import numpy as np
+import mindspore.nn as nn
+
+from mindspore import Tensor, context
+from mindspore.context import ParallelMode
+from mindspore.communication.management import init, get_rank, get_group_size, release
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.common import dtype as mstype
+
+from src.utils.logging import get_logger
+from src.utils.auto_mixed_precision import auto_mixed_precision
+from src.utils.var_init import load_pretrain_model
+from src.image_classification import get_network
+from src.dataset import classification_dataset
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+
+
+class ParameterReduce(nn.Cell):
+ """ParameterReduce"""
+ def __init__(self):
+ super(ParameterReduce, self).__init__()
+ self.cast = P.Cast()
+ self.reduce = P.AllReduce()
+
+ def construct(self, x):
+ one = self.cast(F.scalar_to_array(1.0), mstype.float32)
+ out = x * one
+ ret = self.reduce(out)
+ return ret
+
+
+def set_parameters():
+ """set_parameters"""
+ if config.run_distribute:
+ init()
+ config.rank = get_rank()
+ config.group_size = get_group_size()
+ else:
+ config.rank = 0
+ config.group_size = 1
+
+ config.outputs_dir = os.path.join(config.log_path,
+ datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+
+ config.logger = get_logger(config.outputs_dir, config.rank)
+ return config
+
+
+def get_top5_acc(top5_arg, gt_class):
+ sub_count = 0
+ for top5, gt in zip(top5_arg, gt_class):
+ if gt in top5:
+ sub_count += 1
+ return sub_count
+
+
+def get_result(model, top1_correct, top5_correct, img_tot):
+ """calculate top1 and top5 value."""
+ results = [[top1_correct], [top5_correct], [img_tot]]
+ config.logger.info('before results=%s', results)
+ if config.run_distribute:
+ model_md5 = model.replace('/', '')
+ tmp_dir = '/cache'
+ if not os.path.exists(tmp_dir):
+ os.mkdir(tmp_dir)
+ top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(config.rank, model_md5)
+ top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(config.rank, model_md5)
+ img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(config.rank, model_md5)
+ np.save(top1_correct_npy, top1_correct)
+ np.save(top5_correct_npy, top5_correct)
+ np.save(img_tot_npy, img_tot)
+ while True:
+ rank_ok = True
+ for other_rank in range(config.group_size):
+ top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
+ top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
+ img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
+ if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \
+ not os.path.exists(img_tot_npy):
+ rank_ok = False
+ if rank_ok:
+ break
+
+ top1_correct_all = 0
+ top5_correct_all = 0
+ img_tot_all = 0
+ for other_rank in range(config.group_size):
+ top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
+ top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
+ img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
+ top1_correct_all += np.load(top1_correct_npy)
+ top5_correct_all += np.load(top5_correct_npy)
+ img_tot_all += np.load(img_tot_npy)
+ results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
+ results = np.array(results)
+ else:
+ results = np.array(results)
+
+ config.logger.info('after results=%s', results)
+ return results
+
+@moxing_wrapper()
+def test():
+ """test"""
+ set_parameters()
+ context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+ device_target=config.device_target, save_graphs=False)
+ if os.getenv('DEVICE_ID', "not_set").isdigit():
+ context.set_context(device_id=int(os.getenv('DEVICE_ID')))
+
+ # init distributed
+ if config.run_distribute:
+ parallel_mode = ParallelMode.DATA_PARALLEL
+ context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=config.group_size,
+ gradients_mean=True)
+
+ config.logger.save_args(config)
+
+ # network
+ config.logger.important_info('start create network')
+ if os.path.isdir(config.checkpoint_file_path):
+ models = list(glob.glob(os.path.join(config.checkpoint_file_path, '*.ckpt')))
+ print(models)
+ if config.checkpoint_file_path:
+ f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
+ else:
+ f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
+ config.models = sorted(models, key=f)
+ else:
+ config.models = [config.checkpoint_file_path,]
+
+ for model in config.models:
+ de_dataset = classification_dataset(config.data_path, image_size=config.image_size,
+ per_batch_size=config.per_batch_size,
+ max_epoch=1, rank=config.rank, group_size=config.group_size,
+ mode='eval')
+ eval_dataloader = de_dataset.create_tuple_iterator(output_numpy=True, num_epochs=1)
+ network = get_network(network=config.network, num_classes=config.num_classes, platform=config.device_target)
+
+ load_pretrain_model(model, network, config)
+
+ img_tot = 0
+ top1_correct = 0
+ top5_correct = 0
+ if config.device_target == "Ascend":
+ network.to_float(mstype.float16)
+ else:
+ auto_mixed_precision(network)
+ network.set_train(False)
+ t_end = time.time()
+ it = 0
+ for data, gt_classes in eval_dataloader:
+ output = network(Tensor(data, mstype.float32))
+ output = output.asnumpy()
+
+ top1_output = np.argmax(output, (-1))
+ top5_output = np.argsort(output)[:, -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 += config.per_batch_size
+
+ if config.rank == 0 and it == 0:
+ t_end = time.time()
+ it = 1
+ if config.rank == 0:
+ time_used = time.time() - t_end
+ fps = (img_tot - config.per_batch_size) * config.group_size / time_used
+ config.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
+ results = get_result(model, top1_correct, top5_correct, img_tot)
+ top1_correct = results[0, 0]
+ top5_correct = results[1, 0]
+ img_tot = results[2, 0]
+ acc1 = 100.0 * top1_correct / img_tot
+ acc5 = 100.0 * top5_correct / img_tot
+ config.logger.info('after allreduce eval: top1_correct={}, tot={},'
+ 'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
+ config.logger.info('after allreduce eval: top5_correct={}, tot={},'
+ 'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
+ if config.run_distribute:
+ release()
+
+
+if __name__ == "__main__":
+ test()
diff --git a/official/cv/se_resnext50/export.py b/official/cv/se_resnext50/export.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec7699f46b51f091fac9ffb66b4c44805f1d5972
--- /dev/null
+++ b/official/cv/se_resnext50/export.py
@@ -0,0 +1,53 @@
+# 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.
+# ============================================================================
+"""
+se_resnext50 export mindir.
+"""
+import os
+import numpy as np
+from mindspore.common import dtype as mstype
+from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.image_classification import get_network
+from src.utils.auto_mixed_precision import auto_mixed_precision
+
+
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+if config.device_target == "Ascend":
+ context.set_context(device_id=config.device_id)
+
+def modelarts_pre_process():
+ '''modelarts pre process function.'''
+ config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+ """run export."""
+ network = get_network(network=config.network, num_classes=config.num_classes, platform=config.device_target)
+
+ param_dict = load_checkpoint(config.checkpoint_file_path)
+ load_param_into_net(network, param_dict)
+ if config.device_target == "Ascend":
+ network.to_float(mstype.float16)
+ else:
+ auto_mixed_precision(network)
+ network.set_train(False)
+ input_shp = [config.batch_size, 3, config.height, config.width]
+ input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
+ export(network, input_array, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+ run_export()
diff --git a/official/cv/se_resnext50/mindspore_hub_conf.py b/official/cv/se_resnext50/mindspore_hub_conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..20028866cb29e1f1002c13d4a1a326a6a264a7ac
--- /dev/null
+++ b/official/cv/se_resnext50/mindspore_hub_conf.py
@@ -0,0 +1,22 @@
+# 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.
+# ============================================================================
+"""hub config."""
+from src.image_classification import get_network
+
+def create_network(name, *args, **kwargs):
+ if name != "renext50":
+ raise NotImplementedError(f"{name} is not implemented in the repo")
+ net = get_network(*args, **kwargs)
+ return net
diff --git a/official/cv/se_resnext50/postprocess.py b/official/cv/se_resnext50/postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..62c84645a8a0f3eb92a59067d6416424e86077a3
--- /dev/null
+++ b/official/cv/se_resnext50/postprocess.py
@@ -0,0 +1,51 @@
+# 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
+#
+# less 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
+
+batch_size = 1
+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()
+
+num_classes = 1000
+
+def get_result(result_path, label_path):
+ 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/official/cv/se_resnext50/requirements.txt b/official/cv/se_resnext50/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2238567851e149895552b0629989b89dd3a03690
--- /dev/null
+++ b/official/cv/se_resnext50/requirements.txt
@@ -0,0 +1,2 @@
+pillow
+pyyaml
diff --git a/official/cv/se_resnext50/scripts/run_distribute_train.sh b/official/cv/se_resnext50/scripts/run_distribute_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ebb768e160e0f7b86754a3eba3709cdafeeadcf0
--- /dev/null
+++ b/official/cv/se_resnext50/scripts/run_distribute_train.sh
@@ -0,0 +1,74 @@
+#!/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 [ $# != 2 ] && [ $# != 3 ]
+then
+ echo "Usage: bash run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)"
+ exit 1
+fi
+
+DATA_DIR=$2
+export RANK_TABLE_FILE=$1
+export RANK_SIZE=8
+export HCCL_CONNECT_TIMEOUT=600
+echo "hccl connect time out has changed to 600 second"
+PATH_CHECKPOINT=""
+if [ $# == 3 ]
+then
+ PATH_CHECKPOINT=$3
+fi
+
+if [ ! -d $2 ];
+then echo "DATA_DIR Does Not Exist!"
+fi
+
+if [ ! -f $1 ];
+then echo "RANK_TABLE_FILE Does Not Exist!"
+fi
+
+cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
+echo "the number of logical core" $cores
+avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
+core_gap=`expr $avg_core_per_rank \- 1`
+echo "avg_core_per_rank" $avg_core_per_rank
+echo "core_gap" $core_gap
+for((i=0;i<RANK_SIZE;i++))
+do
+ start=`expr $i \* $avg_core_per_rank`
+ export DEVICE_ID=$i
+ export RANK_ID=$i
+ export DEPLOY_MODE=0
+ export GE_USE_STATIC_MEMORY=1
+ end=`expr $start \+ $core_gap`
+ cmdopt=$start"-"$end
+
+ rm -rf LOG$i
+ mkdir ./LOG$i
+ cp *.py ./LOG$i
+ cp *.yaml ./LOG$i
+ cp -r ./src ./LOG$i
+ cd ./LOG$i || exit
+ echo "start training for rank $i, device $DEVICE_ID"
+
+ env > env.log
+ taskset -c $cmdopt python ../train.py \
+ --run_distribute=1 \
+ --device_id=$DEVICE_ID \
+ --checkpoint_file_path=$PATH_CHECKPOINT \
+ --data_path=$DATA_DIR \
+ --output_path './output' > log.txt 2>&1 &
+ cd ../
+done
diff --git a/official/cv/se_resnext50/scripts/run_eval.sh b/official/cv/se_resnext50/scripts/run_eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ac1f9e4980d37675c0d843aa08af0033bccdbb30
--- /dev/null
+++ b/official/cv/se_resnext50/scripts/run_eval.sh
@@ -0,0 +1,35 @@
+#!/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.
+# ============================================================================
+
+export DEVICE_ID=$1
+DATA_DIR=$2
+PATH_CHECKPOINT=$3
+PLATFORM=$4
+
+if [ ! -d $2 ];
+then echo "DATA_DIR Does Not Exist!"
+fi
+
+if [ ! -f $3 ];
+then echo "PATH_CHECKPOINT Does Not Exist!"
+fi
+
+
+python eval.py \
+ --checkpoint_file_path=$PATH_CHECKPOINT \
+ --device_target=$PLATFORM \
+ --data_path=$DATA_DIR \
+ --device_target=$PLATFORM > log.txt 2>&1 &
diff --git a/official/cv/se_resnext50/scripts/run_infer_310.sh b/official/cv/se_resnext50/scripts/run_infer_310.sh
new file mode 100644
index 0000000000000000000000000000000000000000..60584182677a2b6e5e2261d28fb1c37b1d259779
--- /dev/null
+++ b/official/cv/se_resnext50/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()
+{
+ python3.7 ../create_imagenet2012_label.py --img_path=$data_path
+ python3.7 ../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/official/cv/se_resnext50/scripts/run_standalone_train.sh b/official/cv/se_resnext50/scripts/run_standalone_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..19000ef870b60b18349e40f34c93cbec4f4311f6
--- /dev/null
+++ b/official/cv/se_resnext50/scripts/run_standalone_train.sh
@@ -0,0 +1,31 @@
+#!/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.
+# ============================================================================
+
+export DEVICE_ID=$1
+DATA_DIR=$2
+PATH_CHECKPOINT=""
+if [ $# == 3 ]
+then
+ PATH_CHECKPOINT=$3
+fi
+
+python train.py \
+ --run_distribute=0 \
+ --device_id=$DEVICE_ID \
+ --checkpoint_file_path=$PATH_CHECKPOINT \
+ --data_path=$DATA_DIR \
+ --output_path './output' > log.txt 2>&1 &
+
diff --git a/official/cv/se_resnext50/src/__init__.py b/official/cv/se_resnext50/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/official/cv/se_resnext50/src/backbone/__init__.py b/official/cv/se_resnext50/src/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b29269af6feeafe91688523fc2a110102389aca1
--- /dev/null
+++ b/official/cv/se_resnext50/src/backbone/__init__.py
@@ -0,0 +1,16 @@
+# 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.
+# ============================================================================
+"""resnet"""
+from .resnet import *
diff --git a/official/cv/se_resnext50/src/backbone/resnet.py b/official/cv/se_resnext50/src/backbone/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..72373c2c328833e4693f08732bb2873db5d95324
--- /dev/null
+++ b/official/cv/se_resnext50/src/backbone/resnet.py
@@ -0,0 +1,248 @@
+# 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.
+# ============================================================================
+"""
+ResNet based ResNext
+"""
+import mindspore.nn as nn
+from mindspore.ops.operations import Add, Split, Concat
+from mindspore.ops import operations as P
+from mindspore.common.initializer import TruncatedNormal
+
+from src.utils.cunstom_op import SEBlock, GroupConv
+
+
+__all__ = ['ResNet', 'se_resnext50']
+
+
+def weight_variable(shape, factor=0.1):
+ return TruncatedNormal(0.02)
+
+def conv7x7(in_channels, out_channels, stride=1, padding=3, has_bias=False, groups=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=7, stride=stride, has_bias=has_bias,
+ padding=padding, pad_mode="pad", group=groups)
+
+def conv3x3(in_channels, out_channels, stride=1, padding=1, has_bias=False, groups=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, has_bias=has_bias,
+ padding=padding, pad_mode="pad", group=groups)
+
+def conv1x1(in_channels, out_channels, stride=1, padding=0, has_bias=False, groups=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, has_bias=has_bias,
+ padding=padding, pad_mode="pad", group=groups)
+
+class _DownSample(nn.Cell):
+ """
+ Downsample for ResNext-ResNet.
+
+ Args:
+ in_channels (int): Input channels.
+ out_channels (int): Output channels.
+ stride (int): Stride size for the 1*1 convolutional layer.
+
+ Returns:
+ Tensor, output tensor.
+
+ Examples:
+ >>>DownSample(32, 64, 2)
+ """
+ def __init__(self, in_channels, out_channels, stride):
+ super(_DownSample, self).__init__()
+ self.conv = conv1x1(in_channels, out_channels, stride=stride, padding=0)
+ self.bn = nn.BatchNorm2d(out_channels)
+
+ def construct(self, x):
+ out = self.conv(x)
+ out = self.bn(out)
+ return out
+
+class BasicBlock(nn.Cell):
+ """
+ ResNet basic block definition.
+
+ Args:
+ in_channels (int): Input channels.
+ out_channels (int): Output channels.
+ stride (int): Stride size for the first convolutional layer. Default: 1.
+
+ Returns:
+ Tensor, output tensor.
+
+ Examples:
+ >>>BasicBlock(32, 256, stride=2)
+ """
+ expansion = 1
+
+ def __init__(self, in_channels, out_channels, stride=1, down_sample=None, use_se=True,
+ platform="Ascend", **kwargs):
+ super(BasicBlock, self).__init__()
+ self.conv1 = conv3x3(in_channels, out_channels, stride=stride)
+ self.bn1 = nn.BatchNorm2d(out_channels)
+ self.relu = P.ReLU()
+ self.conv2 = conv3x3(out_channels, out_channels, stride=1)
+ self.bn2 = nn.BatchNorm2d(out_channels)
+ self.use_se = use_se
+ if self.use_se:
+ self.se = SEBlock(out_channels)
+ self.down_sample_flag = False
+ if down_sample is not None:
+ self.down_sample = down_sample
+ self.down_sample_flag = True
+ self.add = Add()
+
+ def construct(self, x):
+ identity = x
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+ out = self.conv2(out)
+ out = self.bn2(out)
+ if self.use_se:
+ out = self.se(out)
+ if self.down_sample_flag:
+ identity = self.down_sample(x)
+ out = self.add(out, identity)
+ out = self.relu(out)
+ return out
+
+class Bottleneck(nn.Cell):
+ """
+ ResNet Bottleneck block definition.
+
+ Args:
+ in_channels (int): Input channels.
+ out_channels (int): Output channels.
+ stride (int): Stride size for the initial convolutional layer. Default: 1.
+
+ Returns:
+ Tensor, the ResNet unit's output.
+
+
+ >>>Bottleneck(3, 256, stride=2)
+ """
+ expansion = 4
+
+ def __init__(self, in_channels, out_channels, stride=1, down_sample=None,
+ base_width=64, groups=1, use_se=True, platform="Ascend", **kwargs):
+ super(Bottleneck, self).__init__()
+
+ width = int(out_channels * (base_width / 64.0)) * groups
+ self.groups = groups
+ self.conv1 = conv1x1(in_channels, width, stride=1)
+ self.bn1 = nn.BatchNorm2d(width)
+ self.relu = P.ReLU()
+ self.conv3x3s = nn.CellList()
+ self.conv2 = GroupConv(width, width, 3, stride, pad=1, groups=groups)
+ self.op_split = Split(axis=1, output_num=self.groups)
+ self.op_concat = Concat(axis=1)
+ self.bn2 = nn.BatchNorm2d(width)
+ self.conv3 = conv1x1(width, out_channels * self.expansion, stride=1)
+ self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
+ self.use_se = use_se
+ if self.use_se:
+ self.se = SEBlock(out_channels * self.expansion)
+ self.down_sample_flag = False
+ if down_sample is not None:
+ self.down_sample = down_sample
+ self.down_sample_flag = True
+ self.cast = P.Cast()
+ self.add = Add()
+
+ def construct(self, x):
+ identity = x
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = self.relu(out)
+ out = self.conv3(out)
+ out = self.bn3(out)
+ if self.use_se:
+ out = self.se(out)
+ if self.down_sample_flag:
+ identity = self.down_sample(x)
+ out = self.add(out, identity)
+ out = self.relu(out)
+ return out
+
+class ResNet(nn.Cell):
+ """
+ ResNet architecture.
+
+ Args:
+ block (cell): Block for network.
+ layers (list): Numbers of block in different layers.
+ width_per_group (int): Width of every group.
+ groups (int): Groups number.
+
+ Returns:
+ Tuple, output tensor tuple.
+
+ Examples:
+ >>>ResNet()
+ """
+ def __init__(self, block, layers, width_per_group=64, groups=1, use_se=True, platform="Ascend"):
+ super(ResNet, self).__init__()
+ self.in_channels = 64
+ self.groups = groups
+ self.base_width = width_per_group
+ self.conv = conv7x7(3, self.in_channels, stride=2, padding=3)
+ self.bn = nn.BatchNorm2d(self.in_channels)
+ self.relu = P.ReLU()
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
+ self.layer1 = self._make_layer(block, 64, layers[0], use_se=use_se, platform=platform)
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2, use_se=use_se, platform=platform)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2, use_se=use_se, platform=platform)
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2, use_se=use_se, platform=platform)
+ self.out_channels = 512 * block.expansion
+ self.cast = P.Cast()
+
+ def construct(self, x):
+ x = self.conv(x)
+ x = self.bn(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+ x = self.layer4(x)
+ return x
+
+ def _make_layer(self, block, out_channels, blocks_num, stride=1, use_se=True, platform="Ascend"):
+ """_make_layer"""
+ down_sample = None
+ if stride != 1 or self.in_channels != out_channels * block.expansion:
+ down_sample = _DownSample(self.in_channels,
+ out_channels * block.expansion,
+ stride=stride)
+ layers = []
+ layers.append(block(self.in_channels,
+ out_channels,
+ stride=stride,
+ down_sample=down_sample,
+ base_width=self.base_width,
+ groups=self.groups,
+ use_se=use_se,
+ platform=platform))
+ self.in_channels = out_channels * block.expansion
+ for _ in range(1, blocks_num):
+ layers.append(block(self.in_channels, out_channels, base_width=self.base_width,
+ groups=self.groups, use_se=use_se, platform=platform))
+ return nn.SequentialCell(layers)
+
+ def get_out_channels(self):
+ return self.out_channels
+
+def se_resnext50(platform="Ascend"):
+ return ResNet(Bottleneck, [3, 4, 6, 3], width_per_group=4, groups=32, platform=platform)
diff --git a/official/cv/se_resnext50/src/crossentropy.py b/official/cv/se_resnext50/src/crossentropy.py
new file mode 100644
index 0000000000000000000000000000000000000000..01039e39ea3e4fe4a7fa353a738a6d96ada2809b
--- /dev/null
+++ b/official/cv/se_resnext50/src/crossentropy.py
@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+"""
+define loss function for network.
+"""
+from mindspore.nn.loss.loss import LossBase
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+import mindspore.nn as nn
+
+class CrossEntropy(LossBase):
+ """
+ the redefined loss function with SoftmaxCrossEntropyWithLogits.
+ """
+ def __init__(self, smooth_factor=0., num_classes=1000):
+ super(CrossEntropy, self).__init__()
+ self.onehot = P.OneHot()
+ self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
+ self.off_value = Tensor(1.0 * smooth_factor / (num_classes -1), mstype.float32)
+ self.ce = nn.SoftmaxCrossEntropyWithLogits()
+ self.mean = P.ReduceMean(False)
+
+ def construct(self, logit, label):
+ one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
+ loss = self.ce(logit, one_hot_label)
+ loss = self.mean(loss, 0)
+ return loss
diff --git a/official/cv/se_resnext50/src/dataset.py b/official/cv/se_resnext50/src/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce7b43c00efac7641a909d4ec4bdeb2feb72a76
--- /dev/null
+++ b/official/cv/se_resnext50/src/dataset.py
@@ -0,0 +1,158 @@
+# 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.
+# ============================================================================
+"""
+dataset processing.
+"""
+import os
+from PIL import Image, ImageFile
+from mindspore.common import dtype as mstype
+import mindspore.dataset as de
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision.c_transforms as V_C
+from src.utils.sampler import DistributedSampler
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class TxtDataset():
+ """
+ create txt dataset.
+
+ Args:
+ Returns:
+ de_dataset.
+ """
+
+ def __init__(self, root, txt_name):
+ super(TxtDataset, self).__init__()
+ self.imgs = []
+ self.labels = []
+ fin = open(txt_name, "r")
+ for line in fin:
+ img_name, label = line.strip().split(' ')
+ self.imgs.append(os.path.join(root, img_name))
+ self.labels.append(int(label))
+ fin.close()
+
+ def __getitem__(self, index):
+ img = Image.open(self.imgs[index]).convert('RGB')
+ return img, self.labels[index]
+
+ def __len__(self):
+ return len(self.imgs)
+
+
+def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
+ mode='train',
+ input_mode='folder',
+ root='',
+ num_parallel_workers=None,
+ shuffle=None,
+ sampler=None,
+ class_indexing=None,
+ drop_remainder=True,
+ transform=None,
+ target_transform=None):
+ """
+ A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
+ If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
+ are written into a textfile.
+
+ Args:
+ data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
+ Or path of the textfile that contains every image's path of the dataset.
+ image_size (Union(int, sequence)): Size of the input images.
+ per_batch_size (int): the batch size of evey step during training.
+ max_epoch (int): the number of epochs.
+ rank (int): The shard ID within num_shards (default=None).
+ group_size (int): Number of shards that the dataset should be divided
+ into (default=None).
+ mode (str): "train" or others. Default: " train".
+ input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
+ root (str): the images path for "input_mode="txt"". Default: " ".
+ num_parallel_workers (int): Number of workers to read the data. Default: None.
+ shuffle (bool): Whether or not to perform shuffle on the dataset
+ (default=None, performs shuffle).
+ sampler (Sampler): Object used to choose samples from the dataset. Default: None.
+ class_indexing (dict): A str-to-int mapping from folder name to index
+ (default=None, the folder names will be sorted
+ alphabetically and each class will be given a
+ unique index starting from 0).
+
+ Examples:
+ >>> from src.dataset import classification_dataset
+ >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
+ >>> data_dir = "/path/to/imagefolder_directory"
+ >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
+ >>> per_batch_size=64, max_epoch=100,
+ >>> rank=0, group_size=4)
+ >>> # Path of the textfile that contains every image's path of the dataset.
+ >>> data_dir = "/path/to/dataset/images/train.txt"
+ >>> images_dir = "/path/to/dataset/images"
+ >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
+ >>> per_batch_size=64, max_epoch=100,
+ >>> rank=0, group_size=4,
+ >>> input_mode="txt", root=images_dir)
+ """
+
+ mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+ std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+ if transform is None:
+ if mode == 'train':
+ transform_img = [
+ V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ V_C.RandomHorizontalFlip(prob=0.5),
+ V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
+ V_C.Normalize(mean=mean, std=std),
+ V_C.HWC2CHW()
+ ]
+ else:
+ transform_img = [
+ V_C.Decode(),
+ V_C.Resize((256, 256)),
+ V_C.CenterCrop(image_size),
+ V_C.Normalize(mean=mean, std=std),
+ V_C.HWC2CHW()
+ ]
+ else:
+ transform_img = transform
+
+ if target_transform is None:
+ transform_label = [C.TypeCast(mstype.int32)]
+ else:
+ transform_label = target_transform
+
+ if input_mode == 'folder':
+ de_dataset = de.ImageFolderDataset(data_dir, num_parallel_workers=num_parallel_workers,
+ shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
+ num_shards=group_size, shard_id=rank)
+ else:
+ dataset = TxtDataset(root, data_dir)
+ sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
+ de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
+
+ de_dataset = de_dataset.map(operations=transform_img, input_columns="image",
+ num_parallel_workers=num_parallel_workers)
+ de_dataset = de_dataset.map(operations=transform_label, input_columns="label",
+ num_parallel_workers=num_parallel_workers)
+
+ columns_to_project = ["image", "label"]
+ de_dataset = de_dataset.project(columns=columns_to_project)
+
+ de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
+ de_dataset = de_dataset.repeat(max_epoch)
+
+ return de_dataset
diff --git a/official/cv/se_resnext50/src/head.py b/official/cv/se_resnext50/src/head.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfc63befc310ce7b355cde7dc288fb7cc5ed25ab
--- /dev/null
+++ b/official/cv/se_resnext50/src/head.py
@@ -0,0 +1,42 @@
+# 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.
+# ============================================================================
+"""
+common architecture.
+"""
+import mindspore.nn as nn
+from src.utils.cunstom_op import GlobalAvgPooling
+
+__all__ = ['CommonHead']
+
+class CommonHead(nn.Cell):
+ """
+ common architecture definition.
+
+ Args:
+ num_classes (int): Number of classes.
+ out_channels (int): Output channels.
+
+ Returns:
+ Tensor, output tensor.
+ """
+ def __init__(self, num_classes, out_channels):
+ super(CommonHead, self).__init__()
+ self.avgpool = GlobalAvgPooling()
+ self.fc = nn.Dense(out_channels, num_classes, has_bias=True).add_flags_recursive(fp16=True)
+
+ def construct(self, x):
+ x = self.avgpool(x)
+ x = self.fc(x)
+ return x
diff --git a/official/cv/se_resnext50/src/image_classification.py b/official/cv/se_resnext50/src/image_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfacbf9e8f66beb734362842e953c728ac94a30a
--- /dev/null
+++ b/official/cv/se_resnext50/src/image_classification.py
@@ -0,0 +1,100 @@
+# 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.
+# ============================================================================
+"""
+Image classifiation.
+"""
+import math
+import mindspore.nn as nn
+from mindspore.common import initializer as init
+import src.backbone as backbones
+import src.head as heads
+from src.utils.var_init import default_recurisive_init, KaimingNormal
+
+
+class ImageClassificationNetwork(nn.Cell):
+ """
+ architecture of image classification network.
+
+ Args:
+ Returns:
+ Tensor, output tensor.
+ """
+ def __init__(self, backbone, head, include_top=True, activation="None"):
+ super(ImageClassificationNetwork, self).__init__()
+ self.backbone = backbone
+ self.include_top = include_top
+ self.need_activation = False
+ if self.include_top:
+ self.head = head
+ if activation != "None":
+ self.need_activation = True
+ if activation == "Sigmoid":
+ self.activation = P.Sigmoid()
+ elif activation == "Softmax":
+ self.activation = P.Softmax()
+ else:
+ raise NotImplementedError(f"The activation {activation} not in [Sigmoid, Softmax].")
+
+ def construct(self, x):
+ x = self.backbone(x)
+ if self.include_top:
+ x = self.head(x)
+ if self.need_activation:
+ x = self.activation(x)
+ return x
+
+
+class Resnet(ImageClassificationNetwork):
+ """
+ Resnet architecture.
+ Args:
+ backbone_name (string): backbone.
+ num_classes (int): number of classes, Default is 1000.
+ Returns:
+ Resnet.
+ """
+ def __init__(self, backbone_name, num_classes=1000, platform="Ascend", include_top=True, activation="None"):
+ self.backbone_name = backbone_name
+ backbone = backbones.__dict__[self.backbone_name](platform=platform)
+ out_channels = backbone.get_out_channels()
+ head = heads.CommonHead(num_classes=num_classes, out_channels=out_channels)
+ super(Resnet, self).__init__(backbone, head, include_top, activation)
+
+ default_recurisive_init(self)
+
+ for cell in self.cells_and_names():
+ if isinstance(cell, nn.Conv2d):
+ cell.weight.set_data(init.initializer(
+ KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
+ cell.weight.shape, cell.weight.dtype))
+ elif isinstance(cell, nn.BatchNorm2d):
+ cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
+ cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
+
+ # Zero-initialize the last BN in each residual branch,
+ # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+ # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+ for cell in self.cells_and_names():
+ if isinstance(cell, backbones.resnet.Bottleneck):
+ cell.bn3.gamma.set_data(init.initializer('zeros', cell.bn3.gamma.shape))
+ elif isinstance(cell, backbones.resnet.BasicBlock):
+ cell.bn2.gamma.set_data(init.initializer('zeros', cell.bn2.gamma.shape))
+
+
+
+def get_network(network, **kwargs):
+ if network not in ['se_resnext50']:
+ raise NotImplementedError(f"The network {network} not in [se_resnext50].")
+ return Resnet('se_resnext50', **kwargs)
diff --git a/official/cv/se_resnext50/src/lr_generator.py b/official/cv/se_resnext50/src/lr_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fcca9ad252f43e848f8af7af424eef127ac3b4f
--- /dev/null
+++ b/official/cv/se_resnext50/src/lr_generator.py
@@ -0,0 +1,142 @@
+# 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.
+# ============================================================================
+"""
+learning rate generator.
+"""
+import math
+from collections import Counter
+import numpy as np
+
+
+def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
+ """
+ Applies liner Increasing to generate learning rate array in warmup stage.
+
+ Args:
+ current_step(int): current step in warmup stage.
+ warmup_steps(int): all steps in warmup stage.
+ base_lr(float): init learning rate.
+ init_lr(float): end learning rate
+
+ Returns:
+ float, learning rate.
+ """
+ lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
+ lr = float(init_lr) + lr_inc * current_step
+ return lr
+
+
+def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
+ """
+ Applies cosine decay to generate learning rate array with warmup.
+
+ Args:
+ lr(float): init learning rate
+ steps_per_epoch(int): steps of one epoch
+ warmup_epochs(int): number of warmup epochs
+ max_epoch(int): total epoch of training
+ T_max(int): max epoch in decay.
+ eta_min(float): end learning rate
+
+ Returns:
+ np.array, learning rate array.
+ """
+ base_lr = lr
+ warmup_init_lr = 0
+ total_steps = int(max_epoch * steps_per_epoch)
+ warmup_steps = int(warmup_epochs * steps_per_epoch)
+
+ lr_each_step = []
+ for i in range(total_steps):
+ last_epoch = i // steps_per_epoch
+ if i < warmup_steps:
+ lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+ else:
+ lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2
+ lr_each_step.append(lr)
+
+ return np.array(lr_each_step).astype(np.float32)
+
+
+def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
+ """
+ Applies step decay to generate learning rate array with warmup.
+
+ Args:
+ lr(float): init learning rate
+ lr_epochs(list): learning rate decay epoches list
+ steps_per_epoch(int): steps of one epoch
+ warmup_epochs(int): number of warmup epochs
+ max_epoch(int): total epoch of training
+ gamma(float): attenuation constants.
+
+ Returns:
+ np.array, learning rate array.
+ """
+ base_lr = lr
+ warmup_init_lr = 0
+ total_steps = int(max_epoch * steps_per_epoch)
+ warmup_steps = int(warmup_epochs * steps_per_epoch)
+ milestones = lr_epochs
+ milestones_steps = []
+ for milestone in milestones:
+ milestones_step = milestone * steps_per_epoch
+ milestones_steps.append(milestones_step)
+
+ lr_each_step = []
+ lr = base_lr
+ milestones_steps_counter = Counter(milestones_steps)
+ for i in range(total_steps):
+ if i < warmup_steps:
+ lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+ else:
+ lr = lr * gamma**milestones_steps_counter[i]
+ lr_each_step.append(lr)
+
+ return np.array(lr_each_step).astype(np.float32)
+
+
+def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
+ return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
+
+
+def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
+ lr_epochs = []
+ for i in range(1, max_epoch):
+ if i % epoch_size == 0:
+ lr_epochs.append(i)
+ return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
+
+
+def get_lr(args):
+ """generate learning rate array."""
+ if args.lr_scheduler == 'exponential':
+ lr = warmup_step_lr(args.lr,
+ args.lr_epochs,
+ args.steps_per_epoch,
+ args.warmup_epochs,
+ args.max_epoch,
+ gamma=args.lr_gamma,
+ )
+ elif args.lr_scheduler == 'cosine_annealing':
+ lr = warmup_cosine_annealing_lr(args.lr,
+ args.steps_per_epoch,
+ args.warmup_epochs,
+ args.max_epoch,
+ args.T_max,
+ args.eta_min)
+ else:
+ raise NotImplementedError(args.lr_scheduler)
+ return lr
diff --git a/official/cv/se_resnext50/src/model_utils/config.py b/official/cv/se_resnext50/src/model_utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..1611eb76d3bd15a0d5a1097ad1f98f61400e2a81
--- /dev/null
+++ b/official/cv/se_resnext50/src/model_utils/config.py
@@ -0,0 +1,125 @@
+# 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
+
+
+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)
+ pprint(default)
+ args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+ final_config = merge(args, default)
+ return Config(final_config)
+
+config = get_config()
diff --git a/official/cv/se_resnext50/src/model_utils/device_adapter.py b/official/cv/se_resnext50/src/model_utils/device_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c3d21d5e47c22617170887df9da97beff668495
--- /dev/null
+++ b/official/cv/se_resnext50/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/official/cv/se_resnext50/src/model_utils/local_adapter.py b/official/cv/se_resnext50/src/model_utils/local_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..769fa6dc78e59eb66dbc8e6773accdc1d08b649e
--- /dev/null
+++ b/official/cv/se_resnext50/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/official/cv/se_resnext50/src/model_utils/moxing_adapter.py b/official/cv/se_resnext50/src/model_utils/moxing_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..aabd5ac6cf1bde3ca20f3d6ea9cf3d5310169f1e
--- /dev/null
+++ b/official/cv/se_resnext50/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
diff --git a/official/cv/se_resnext50/src/utils/__init__.py b/official/cv/se_resnext50/src/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/official/cv/se_resnext50/src/utils/auto_mixed_precision.py b/official/cv/se_resnext50/src/utils/auto_mixed_precision.py
new file mode 100644
index 0000000000000000000000000000000000000000..6be124658a2cf3df2bcec766c981d6bdbee1e225
--- /dev/null
+++ b/official/cv/se_resnext50/src/utils/auto_mixed_precision.py
@@ -0,0 +1,53 @@
+# 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.
+# ============================================================================
+"""Auto mixed precision."""
+import mindspore.nn as nn
+from mindspore.ops import functional as F
+from mindspore._checkparam import Validator as validator
+from mindspore.common import dtype as mstype
+
+
+class OutputTo(nn.Cell):
+ "Cast cell output back to float16 or float32"
+
+ def __init__(self, op, to_type=mstype.float16):
+ super(OutputTo, self).__init__(auto_prefix=False)
+ self._op = op
+ validator.check_type_name('to_type', to_type, [mstype.float16, mstype.float32], None)
+ self.to_type = to_type
+
+ def construct(self, x):
+ return F.cast(self._op(x), self.to_type)
+
+
+def auto_mixed_precision(network):
+ """Do keep batchnorm fp32."""
+ cells = network.name_cells()
+ change = False
+ network.to_float(mstype.float16)
+ for name in cells:
+ subcell = cells[name]
+ if subcell == network:
+ continue
+ elif name == 'fc':
+ network.insert_child_to_cell(name, OutputTo(subcell, mstype.float32))
+ change = True
+ elif isinstance(subcell, (nn.BatchNorm2d, nn.BatchNorm1d)):
+ network.insert_child_to_cell(name, OutputTo(subcell.to_float(mstype.float32), mstype.float16))
+ change = True
+ else:
+ auto_mixed_precision(subcell)
+ if isinstance(network, nn.SequentialCell) and change:
+ network.cell_list = list(network.cells())
diff --git a/official/cv/se_resnext50/src/utils/cunstom_op.py b/official/cv/se_resnext50/src/utils/cunstom_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7567c28f56420cb5bf05f06c7ba9c9fed74c0a7
--- /dev/null
+++ b/official/cv/se_resnext50/src/utils/cunstom_op.py
@@ -0,0 +1,103 @@
+# 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.
+# ============================================================================
+"""
+network operations
+"""
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+
+class GlobalAvgPooling(nn.Cell):
+ """
+ global average pooling feature map.
+
+ Args:
+ mean (tuple): means for each channel.
+ """
+ def __init__(self):
+ super(GlobalAvgPooling, self).__init__()
+ self.mean = P.ReduceMean(False)
+
+ def construct(self, x):
+ x = self.mean(x, (2, 3))
+ return x
+
+
+class SEBlock(nn.Cell):
+ """
+ squeeze and excitation block.
+
+ Args:
+ channel (int): number of feature maps.
+ reduction (int): weight.
+ """
+ def __init__(self, channel, reduction=16):
+ super(SEBlock, self).__init__()
+
+ self.avg_pool = GlobalAvgPooling()
+ self.fc1 = nn.Dense(channel, channel // reduction)
+ self.relu = P.ReLU()
+ self.fc2 = nn.Dense(channel // reduction, channel)
+ self.sigmoid = P.Sigmoid()
+ self.reshape = P.Reshape()
+ self.shape = P.Shape()
+ self.cast = P.Cast()
+
+ def construct(self, x):
+ b, c = self.shape(x)
+ y = self.avg_pool(x)
+
+ y = self.reshape(y, (b, c))
+ y = self.fc1(y)
+ y = self.relu(y)
+ y = self.fc2(y)
+ y = self.sigmoid(y)
+ y = self.reshape(y, (b, c, 1, 1))
+ return x * y
+
+class GroupConv(nn.Cell):
+ """
+ group convolution operation.
+
+ Args:
+ in_channels (int): Input channels of feature map.
+ out_channels (int): Output channels of feature map.
+ kernel_size (int): Size of convolution kernel.
+ stride (int): Stride size for the group convolution layer.
+
+ Returns:
+ tensor, output tensor.
+ """
+ def __init__(self, in_channels, out_channels, kernel_size, stride, pad_mode="pad", pad=0, groups=1, has_bias=False):
+ super(GroupConv, self).__init__()
+ assert in_channels % groups == 0 and out_channels % groups == 0
+ self.groups = groups
+ self.convs = nn.CellList()
+ self.op_split = P.Split(axis=1, output_num=self.groups)
+ self.op_concat = P.Concat(axis=1)
+ self.cast = P.Cast()
+ for _ in range(groups):
+ self.convs.append(nn.Conv2d(in_channels//groups, out_channels//groups,
+ kernel_size=kernel_size, stride=stride, has_bias=has_bias,
+ padding=pad, pad_mode=pad_mode, group=1))
+
+ def construct(self, x):
+ features = self.op_split(x)
+ outputs = ()
+ for i in range(self.groups):
+ outputs = outputs + (self.convs[i](self.cast(features[i], mstype.float32)),)
+ out = self.op_concat(outputs)
+ return out
diff --git a/official/cv/se_resnext50/src/utils/logging.py b/official/cv/se_resnext50/src/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..c17befd265b04d2c820c8ec71bf361273eacc31e
--- /dev/null
+++ b/official/cv/se_resnext50/src/utils/logging.py
@@ -0,0 +1,82 @@
+# 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.
+# ============================================================================
+"""
+get logger.
+"""
+import logging
+import os
+import sys
+from datetime import datetime
+
+class LOGGER(logging.Logger):
+ """
+ set up logging file.
+
+ Args:
+ logger_name (string): logger name.
+ log_dir (string): path of logger.
+
+ Returns:
+ string, logger path
+ """
+ def __init__(self, logger_name, rank=0):
+ super(LOGGER, self).__init__(logger_name)
+ if rank % 8 == 0:
+ console = logging.StreamHandler(sys.stdout)
+ console.setLevel(logging.INFO)
+ formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+ console.setFormatter(formatter)
+ self.addHandler(console)
+
+ def setup_logging_file(self, log_dir, rank=0):
+ """set up log file"""
+ self.rank = rank
+ if not os.path.exists(log_dir):
+ os.makedirs(log_dir, exist_ok=True)
+ log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
+ self.log_fn = os.path.join(log_dir, log_name)
+ fh = logging.FileHandler(self.log_fn)
+ fh.setLevel(logging.INFO)
+ formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+ fh.setFormatter(formatter)
+ self.addHandler(fh)
+
+ def info(self, msg, *args, **kwargs):
+ if self.isEnabledFor(logging.INFO):
+ self._log(logging.INFO, msg, args, **kwargs)
+
+ def save_args(self, args):
+ self.info('Args:')
+ args_dict = vars(args)
+ for key in args_dict.keys():
+ self.info('--> %s: %s', key, args_dict[key])
+ self.info('')
+
+ def important_info(self, msg, *args, **kwargs):
+ if self.isEnabledFor(logging.INFO) and self.rank == 0:
+ line_width = 2
+ important_msg = '\n'
+ important_msg += ('*'*70 + '\n')*line_width
+ important_msg += ('*'*line_width + '\n')*2
+ important_msg += '*'*line_width + ' '*8 + msg + '\n'
+ important_msg += ('*'*line_width + '\n')*2
+ important_msg += ('*'*70 + '\n')*line_width
+ self.info(important_msg, *args, **kwargs)
+
+
+def get_logger(path, rank):
+ logger = LOGGER("mindversion", rank)
+ logger.setup_logging_file(path, rank)
+ return logger
diff --git a/official/cv/se_resnext50/src/utils/optimizers__init__.py b/official/cv/se_resnext50/src/utils/optimizers__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c70a530f2c7c51fee15ba8949bd0c1c80f296008
--- /dev/null
+++ b/official/cv/se_resnext50/src/utils/optimizers__init__.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.
+# ============================================================================
+"""
+optimizer parameters.
+"""
+def get_param_groups(network):
+ """get param groups"""
+ decay_params = []
+ no_decay_params = []
+ for x in network.trainable_params():
+ parameter_name = x.name
+ if parameter_name.endswith('.bias'):
+ # all bias not using weight decay
+ no_decay_params.append(x)
+ elif parameter_name.endswith('.gamma'):
+ # bn weight bias not using weight decay, be carefully for now x not include BN
+ no_decay_params.append(x)
+ elif parameter_name.endswith('.beta'):
+ # bn weight bias not using weight decay, be carefully for now x not include BN
+ no_decay_params.append(x)
+ else:
+ decay_params.append(x)
+
+ return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
diff --git a/official/cv/se_resnext50/src/utils/sampler.py b/official/cv/se_resnext50/src/utils/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd8e3f61ac09f2fa6e8148c1c56d20ffee8adee0
--- /dev/null
+++ b/official/cv/se_resnext50/src/utils/sampler.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.
+# ============================================================================
+"""
+choose samples from the dataset
+"""
+import math
+import numpy as np
+
+class DistributedSampler():
+ """
+ sampling the dataset.
+
+ Args:
+ Returns:
+ num_samples, number of samples.
+ """
+ def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
+ self.dataset = dataset
+ self.rank = rank
+ self.group_size = group_size
+ self.dataset_length = len(self.dataset)
+ self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
+ self.total_size = self.num_samples * self.group_size
+ self.shuffle = shuffle
+ self.seed = seed
+
+ def __iter__(self):
+ if self.shuffle:
+ self.seed = (self.seed + 1) & 0xffffffff
+ np.random.seed(self.seed)
+ indices = np.random.permutation(self.dataset_length).tolist()
+ else:
+ indices = list(range(len(self.dataset_length)))
+
+ indices += indices[:(self.total_size - len(indices))]
+ indices = indices[self.rank::self.group_size]
+ return iter(indices)
+
+ def __len__(self):
+ return self.num_samples
diff --git a/official/cv/se_resnext50/src/utils/var_init.py b/official/cv/se_resnext50/src/utils/var_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2954978269a5b26bd286ff40e93b46448551b08
--- /dev/null
+++ b/official/cv/se_resnext50/src/utils/var_init.py
@@ -0,0 +1,228 @@
+# 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.
+# ============================================================================
+"""
+Initialize.
+"""
+import os
+import math
+from functools import reduce
+import numpy as np
+import mindspore.nn as nn
+from mindspore.common import initializer as init
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+def _calculate_gain(nonlinearity, param=None):
+ r"""
+ Return the recommended gain value for the given nonlinearity function.
+
+ The values are as follows:
+ ================= ====================================================
+ nonlinearity gain
+ ================= ====================================================
+ Linear / Identity :math:`1`
+ Conv{1,2,3}D :math:`1`
+ Sigmoid :math:`1`
+ Tanh :math:`\frac{5}{3}`
+ ReLU :math:`\sqrt{2}`
+ Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
+ ================= ====================================================
+
+ Args:
+ nonlinearity: the non-linear function
+ param: optional parameter for the non-linear function
+
+ Examples:
+ >>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
+ """
+ linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
+ if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
+ return 1
+ if nonlinearity == 'tanh':
+ return 5.0 / 3
+ if nonlinearity == 'relu':
+ return math.sqrt(2.0)
+ if nonlinearity == 'leaky_relu':
+ if param is None:
+ negative_slope = 0.01
+ elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
+ negative_slope = param
+ else:
+ raise ValueError("negative_slope {} not a valid number".format(param))
+ return math.sqrt(2.0 / (1 + negative_slope ** 2))
+
+ raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
+
+def _assignment(arr, num):
+ """Assign the value of `num` to `arr`."""
+ if arr.shape == ():
+ arr = arr.reshape((1))
+ arr[:] = num
+ arr = arr.reshape(())
+ else:
+ if isinstance(num, np.ndarray):
+ arr[:] = num[:]
+ else:
+ arr[:] = num
+ return arr
+
+def _calculate_in_and_out(arr):
+ """
+ Calculate n_in and n_out.
+
+ Args:
+ arr (Array): Input array.
+
+ Returns:
+ Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
+ """
+ dim = len(arr.shape)
+ if dim < 2:
+ raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
+
+ n_in = arr.shape[1]
+ n_out = arr.shape[0]
+
+ if dim > 2:
+ counter = reduce(lambda x, y: x * y, arr.shape[2:])
+ n_in *= counter
+ n_out *= counter
+ return n_in, n_out
+
+def _select_fan(array, mode):
+ mode = mode.lower()
+ valid_modes = ['fan_in', 'fan_out']
+ if mode not in valid_modes:
+ raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
+
+ fan_in, fan_out = _calculate_in_and_out(array)
+ return fan_in if mode == 'fan_in' else fan_out
+
+class KaimingInit(init.Initializer):
+ r"""
+ Base Class. Initialize the array with He kaiming algorithm.
+
+ Args:
+ a: the negative slope of the rectifier used after this layer (only
+ used with ``'leaky_relu'``)
+ mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
+ preserves the magnitude of the variance of the weights in the
+ forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
+ backwards pass.
+ nonlinearity: the non-linear function, recommended to use only with
+ ``'relu'`` or ``'leaky_relu'`` (default).
+ """
+ def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
+ super(KaimingInit, self).__init__()
+ self.mode = mode
+ self.gain = _calculate_gain(nonlinearity, a)
+ def _initialize(self, arr):
+ pass
+
+
+class KaimingUniform(KaimingInit):
+ r"""
+ Initialize the array with He kaiming uniform algorithm. The resulting tensor will
+ have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
+
+ .. math::
+ \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
+
+ Input:
+ arr (Array): The array to be assigned.
+
+ Returns:
+ Array, assigned array.
+
+ Examples:
+ >>> w = np.empty(3, 5)
+ >>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
+ """
+
+ def _initialize(self, arr):
+ fan = _select_fan(arr, self.mode)
+ bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
+ data = np.random.uniform(-bound, bound, arr.shape)
+
+ _assignment(arr, data)
+
+
+class KaimingNormal(KaimingInit):
+ r"""
+ Initialize the array with He kaiming normal algorithm. The resulting tensor will
+ have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
+
+ .. math::
+ \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
+
+ Input:
+ arr (Array): The array to be assigned.
+
+ Returns:
+ Array, assigned array.
+
+ Examples:
+ >>> w = np.empty(3, 5)
+ >>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
+ """
+
+ def _initialize(self, arr):
+ fan = _select_fan(arr, self.mode)
+ std = self.gain / math.sqrt(fan)
+ data = np.random.normal(0, std, arr.shape)
+
+ _assignment(arr, data)
+
+
+def default_recurisive_init(custom_cell):
+ """default_recurisive_init"""
+ for _, cell in custom_cell.cells_and_names():
+ if isinstance(cell, nn.Conv2d):
+ cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
+ cell.weight.shape,
+ cell.weight.dtype))
+ if cell.bias is not None:
+ fan_in, _ = _calculate_in_and_out(cell.weight)
+ bound = 1 / math.sqrt(fan_in)
+ cell.bias.set_data(init.initializer(init.Uniform(bound),
+ cell.bias.shape,
+ cell.bias.dtype))
+ elif isinstance(cell, nn.Dense):
+ cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
+ cell.weight.shape,
+ cell.weight.dtype))
+ if cell.bias is not None:
+ fan_in, _ = _calculate_in_and_out(cell.weight)
+ bound = 1 / math.sqrt(fan_in)
+ cell.bias.set_data(init.initializer(init.Uniform(bound),
+ cell.bias.shape,
+ cell.bias.dtype))
+ elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
+ pass
+
+
+def load_pretrain_model(ckpt_file, network, args):
+ """load pretrain model."""
+ if os.path.isfile(ckpt_file):
+ param_dict = load_checkpoint(ckpt_file)
+ param_dict_new = {}
+ for key, values in param_dict.items():
+ if key.startswith('moments.'):
+ continue
+ elif key.startswith('network.'):
+ param_dict_new[key[8:]] = values
+ else:
+ param_dict_new[key] = values
+ load_param_into_net(network, param_dict_new)
+ args.logger.info('load model {} success'.format(ckpt_file))
diff --git a/official/cv/se_resnext50/train.py b/official/cv/se_resnext50/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..97ebb71fc89d67e96e69fd075e082e17a7e20c27
--- /dev/null
+++ b/official/cv/se_resnext50/train.py
@@ -0,0 +1,208 @@
+# 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.
+# ============================================================================
+"""train ImageNet."""
+import os
+import time
+import datetime
+
+import mindspore.nn as nn
+from mindspore import Tensor, context
+from mindspore.context import ParallelMode
+from mindspore.nn.optim import Momentum
+from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.train.callback import ModelCheckpoint
+from mindspore.train.callback import CheckpointConfig, Callback
+from mindspore.train.model import Model
+from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
+from mindspore.common import set_seed
+
+from src.dataset import classification_dataset
+from src.crossentropy import CrossEntropy
+from src.lr_generator import get_lr
+from src.utils.logging import get_logger
+from src.utils.optimizers__init__ import get_param_groups
+from src.utils.var_init import load_pretrain_model
+from src.image_classification import get_network
+from src.model_utils.config import config
+
+from src.model_utils.moxing_adapter import moxing_wrapper
+
+
+set_seed(1)
+
+
+class BuildTrainNetwork(nn.Cell):
+ """build training network"""
+ def __init__(self, network, criterion):
+ super(BuildTrainNetwork, self).__init__()
+ self.network = network
+ self.criterion = criterion
+
+ def construct(self, input_data, label):
+ output = self.network(input_data)
+ loss = self.criterion(output, label)
+ return loss
+
+
+class ProgressMonitor(Callback):
+ """monitor loss and time"""
+ def __init__(self, args):
+ super(ProgressMonitor, self).__init__()
+ self.me_epoch_start_time = 0
+ self.me_epoch_start_step_num = 0
+ self.args = args
+ self.ckpt_history = []
+
+ def begin(self, run_context):
+ self.args.logger.info('start network train...')
+
+ def epoch_begin(self, run_context):
+ pass
+
+ def epoch_end(self, run_context, *me_args):
+ cb_params = run_context.original_args()
+ me_step = cb_params.cur_step_num - 1
+
+ real_epoch = me_step // self.args.steps_per_epoch
+ time_used = time.time() - self.me_epoch_start_time
+ fps_mean = self.args.per_batch_size * (me_step-self.me_epoch_start_step_num) * self.args.group_size / time_used
+ self.args.logger.info('epoch[{}], iter[{}], loss:{}, mean_fps:{:.2f}'
+ 'imgs/sec'.format(real_epoch, me_step, cb_params.net_outputs, fps_mean))
+
+ if self.args.rank_save_ckpt_flag:
+ import glob
+ ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
+ for ckpt in ckpts:
+ ckpt_fn = os.path.basename(ckpt)
+ if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
+ continue
+ if ckpt in self.ckpt_history:
+ continue
+ self.ckpt_history.append(ckpt)
+ self.args.logger.info('epoch[{}], iter[{}], loss:{}, ckpt:{},'
+ 'ckpt_fn:{}'.format(real_epoch, me_step, cb_params.net_outputs, ckpt, ckpt_fn))
+
+
+ self.me_epoch_start_step_num = me_step
+ self.me_epoch_start_time = time.time()
+
+ def step_begin(self, run_context):
+ pass
+
+ def step_end(self, run_context, *me_args):
+ pass
+
+ def end(self, run_context):
+ self.args.logger.info('end network train...')
+
+
+def set_parameters():
+ """parameters"""
+ context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+ device_target=config.device_target, save_graphs=False)
+ # init distributed
+ if config.run_distribute:
+ init()
+ config.rank = get_rank()
+ config.group_size = get_group_size()
+ else:
+ config.rank = 0
+ config.group_size = 1
+
+ if config.is_dynamic_loss_scale == 1:
+ config.loss_scale = 1 # for dynamic loss scale can not set loss scale in momentum opt
+
+ # select for master rank save ckpt or all rank save, compatible for model parallel
+ config.rank_save_ckpt_flag = 0
+ if config.is_save_on_master:
+ if config.rank == 0:
+ config.rank_save_ckpt_flag = 1
+ else:
+ config.rank_save_ckpt_flag = 1
+
+ # logger
+ config.outputs_dir = os.path.join(config.output_path,
+ datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+ config.logger = get_logger(config.outputs_dir, config.rank)
+ return config
+
+@moxing_wrapper()
+def train():
+ """training process"""
+ set_parameters()
+ if int(os.getenv('DEVICE_ID', "0")):
+ context.set_context(device_id=int(os.getenv('DEVICE_ID')))
+
+ # init distributed
+ if config.run_distribute:
+ parallel_mode = ParallelMode.DATA_PARALLEL
+ context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=config.group_size,
+ gradients_mean=True)
+ # dataloader
+ de_dataset = classification_dataset(config.data_path, config.image_size,
+ config.per_batch_size, 1,
+ config.rank, config.group_size, num_parallel_workers=8)
+ config.steps_per_epoch = de_dataset.get_dataset_size()
+
+ config.logger.save_args(config)
+
+ # network
+ config.logger.important_info('start create network')
+ # get network and init
+ network = get_network(network=config.network, num_classes=config.num_classes, platform=config.device_target)
+
+ load_pretrain_model(config.checkpoint_file_path, network, config)
+
+ # lr scheduler
+ lr = get_lr(config)
+
+ # optimizer
+ opt = Momentum(params=get_param_groups(network),
+ learning_rate=Tensor(lr),
+ momentum=config.momentum,
+ weight_decay=config.weight_decay,
+ loss_scale=config.loss_scale)
+
+
+ # loss
+ if not config.label_smooth:
+ config.label_smooth_factor = 0.0
+ loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.num_classes)
+
+ if config.is_dynamic_loss_scale == 1:
+ loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
+ else:
+ loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
+
+ model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager,
+ metrics={'acc'}, amp_level="O3")
+
+ # checkpoint save
+ progress_cb = ProgressMonitor(config)
+ callbacks = [progress_cb,]
+ if config.rank_save_ckpt_flag:
+ ckpt_config = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval * config.steps_per_epoch,
+ keep_checkpoint_max=config.ckpt_save_max)
+ save_ckpt_path = os.path.join(config.outputs_dir, 'ckpt_' + str(config.rank) + '/')
+ ckpt_cb = ModelCheckpoint(config=ckpt_config,
+ directory=save_ckpt_path,
+ prefix='{}'.format(config.rank))
+ callbacks.append(ckpt_cb)
+
+ model.train(config.max_epoch, de_dataset, callbacks=callbacks, dataset_sink_mode=True)
+
+
+if __name__ == "__main__":
+ train()