From a5c16fc4acb8b812afc6f9c425ae05ad263153a6 Mon Sep 17 00:00:00 2001 From: zhaoting Date: Sat, 22 Aug 2020 10:56:51 +0800 Subject: [PATCH] resnext50 readme normalize --- model_zoo/official/cv/resnext50/README.md | 164 ++++++++++++++++++---- 1 file changed, 138 insertions(+), 26 deletions(-) diff --git a/model_zoo/official/cv/resnext50/README.md b/model_zoo/official/cv/resnext50/README.md index 6119fd79137..75631126aa7 100644 --- a/model_zoo/official/cv/resnext50/README.md +++ b/model_zoo/official/cv/resnext50/README.md @@ -1,23 +1,78 @@ -# ResNext50 Example +# Contents -## Description +- [ResNeXt50 Description](#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 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) -This is an example of training ResNext50 in MindSpore. +# [ResNeXt50 Description](#contents) -## Requirements +ResNeXt 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 ResNeXt. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. -- Install [Mindspore](http://www.mindspore.cn/install/en). -- Downlaod the dataset. +[Paper](https://arxiv.org/abs/1611.05431): Xie S, Girshick R, Dollár, Piotr, et al. Aggregated Residual Transformations for Deep Neural Networks. 2016. -## Structure +# [Model architecture](#contents) -```shell +The overall network architecture of ResNeXt is show below: + +[Link](https://arxiv.org/abs/1611.05431) + + +# [Dataset](#contents) + +Dataset used: [imagenet](http://www.image-net.org/) + +- Dataset size: ~125G, 1.2W colorful images in 1000 classes + - Train: 120G, 1.2W 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/zh-CN/master/advanced_use/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 ‘reduce precision’. + +# [Environment Requirements](#contents) + +- Hardware（Ascend/GPU） + - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. +- Framework + - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) +- For more information, please check the resources below： + - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) + - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) + +# [Script description](#contents) + +## [Script and sample code](#contents) + +```python . └─resnext50 ├─README.md ├─scripts - ├─run_standalone_train.sh # launch standalone training(1p) - ├─run_distribute_train.sh # launch distributed training(8p) + ├─run_standalone_train.sh # launch standalone training for ascend(1p) + ├─run_distribute_train.sh # launch distributed training for ascend(8p) + ├─run_standalone_train_for_gpu.sh # launch standalone training for gpu(1p) + ├─run_distribute_train_for_gpu.sh # launch distributed training for gpu(8p) └─run_eval.sh # launch evaluating ├─src ├─backbone @@ -44,9 +99,9 @@ This is an example of training ResNext50 in MindSpore. ``` -## Parameter Configuration +## [Script Parameters](#contents) -Parameters for both training and evaluating can be set in config.py +Parameters for both training and evaluating can be set in config.py. ``` "image_height": '224,224' # image size @@ -74,17 +129,29 @@ Parameters for both training and evaluating can be set in config.py "group_size": 1 # world size of distributed ``` -## Running the example - -### Train +## [Training Process](#contents) #### Usage +You can start training by python script: + ``` -# 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 +python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0 +``` + +or shell stript: + +``` +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 +GPU: + # distribute training example(8p) + sh run_distribute_train_for_gpu.sh DATA_PATH + # standalone training + sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_PATH ``` #### Launch @@ -101,13 +168,19 @@ sh scripts/run_distribute_train_for_gpu.sh /dataset/train sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train ``` -#### Result - You can find checkpoint file together with result in log. -### Evaluation +## [Evaluation Process](#contents) -#### Usage +### Usage + +You can start training by python script: + +``` +python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt +``` + +or shell stript: ``` # Evaluation @@ -122,8 +195,6 @@ PLATFORM is Ascend or GPU, default is Ascend. sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt Ascend ``` -> checkpoint can be produced in training process. - #### Result Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log. @@ -131,4 +202,45 @@ Evaluation result will be stored in the scripts path. Under this, you can find r ``` acc=78.16%(TOP1) acc=93.88%(TOP5) -``` \ No newline at end of file +``` + +# [Model description](#contents) + +## [Performance](#contents) + +### Training Performance + +| Parameters | ResNeXt50 | | +| -------------------------- | ---------------------------------------------------------- | ------------------------- | +| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G | +| uploaded Date | 06/30/2020 | 07/23/2020 | +| MindSpore Version | 0.5.0 | 0.6.0 | +| Dataset | ImageNet | ImageNet | +| Training Parameters | src/config.py | src/config.py | +| Optimizer | Momentum | Momentum | +| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy | +| Loss | 1.76592 | 1.8965 | +| Accuracy | 78%(TOP1) | 77.8%(TOP1) | +| Total time | 7.8 h 8ps | 21.5 h 8ps | +| Checkpoint for Fine tuning | 192 M(.ckpt file) | 192 M(.ckpt file) | + +#### Inference Performance + +| Parameters | | | | +| -------------------------- | ----------------------------- | ------------------------- | -------------------- | +| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 | +| uploaded Date | 06/30/2020 | 07/23/2020 | 07/23/2020 | +| MindSpore Version | 0.5.0 | 0.6.0 | 0.6.0 | +| Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W | +| batch_size | 1 | 1 | 1 | +| outputs | probability | probability | probability | +| Accuracy | acc=78.16%(TOP1) | acc=78.05%(TOP1) | | + + +# [Description of Random Situation](#contents) + +In dataset.py, we set the seed inside “create_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).