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resnext50 readme normalize
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# ResNext50 Example
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# Contents
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## Description
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- [ResNeXt50 Description](#resnext50-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Features](#features)
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- [Mixed Precision](#mixed-precision)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#evaluation-performance)
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- [Inference Performance](#evaluation-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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This is an example of training ResNext50 in MindSpore.
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# [ResNeXt50 Description](#contents)
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## Requirements
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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.
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- Install [Mindspore](http://www.mindspore.cn/install/en).
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[Paper](https://arxiv.org/abs/1611.05431): Xie S, Girshick R, Dollár, Piotr, et al. Aggregated Residual Transformations for Deep Neural Networks. 2016.
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- Downlaod the dataset.
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## Structure
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# [Model architecture](#contents)
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```shell
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The overall network architecture of ResNeXt is show below:
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[Link](https://arxiv.org/abs/1611.05431)
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# [Dataset](#contents)
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Dataset used: [imagenet](http://www.image-net.org/)
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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# [Features](#contents)
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## [Mixed Precision](#contents)
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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.
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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’.
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# [Environment Requirements](#contents)
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- Hardware（Ascend/GPU）
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- 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.
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- Framework
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- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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# [Script description](#contents)
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## [Script and sample code](#contents)
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```python
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.
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.
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└─resnext50
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└─resnext50
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├─README.md
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├─README.md
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├─scripts
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├─scripts
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├─run_standalone_train.sh # launch standalone training(1p)
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├─run_standalone_train.sh # launch standalone training for ascend(1p)
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├─run_distribute_train.sh # launch distributed training(8p)
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├─run_distribute_train.sh # launch distributed training for ascend(8p)
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├─run_standalone_train_for_gpu.sh # launch standalone training for gpu(1p)
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├─run_distribute_train_for_gpu.sh # launch distributed training for gpu(8p)
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└─run_eval.sh # launch evaluating
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└─run_eval.sh # launch evaluating
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├─src
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├─src
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├─backbone
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├─backbone
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@ -44,9 +99,9 @@ This is an example of training ResNext50 in MindSpore.
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```
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```
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## Parameter Configuration
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## [Script Parameters](#contents)
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Parameters for both training and evaluating can be set in config.py
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Parameters for both training and evaluating can be set in config.py.
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```
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```
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"image_height": '224,224' # image size
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"image_height": '224,224' # image size
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@ -74,17 +129,29 @@ Parameters for both training and evaluating can be set in config.py
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"group_size": 1 # world size of distributed
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"group_size": 1 # world size of distributed
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```
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```
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## Running the example
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## [Training Process](#contents)
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### Train
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#### Usage
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#### Usage
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You can start training by python script:
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```
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```
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python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0
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```
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or shell stript:
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```
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Ascend:
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# distribute training example(8p)
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# distribute training example(8p)
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sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
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sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
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# standalone training
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# standalone training
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sh run_standalone_train.sh DEVICE_ID DATA_PATH
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sh run_standalone_train.sh DEVICE_ID DATA_PATH
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GPU:
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# distribute training example(8p)
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sh run_distribute_train_for_gpu.sh DATA_PATH
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# standalone training
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sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_PATH
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```
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```
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#### Launch
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#### Launch
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sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train
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sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train
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```
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```
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#### Result
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You can find checkpoint file together with result in log.
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You can find checkpoint file together with result in log.
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### Evaluation
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## [Evaluation Process](#contents)
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#### Usage
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### Usage
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You can start training by python script:
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```
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python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt
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```
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or shell stript:
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```
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```
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# Evaluation
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# Evaluation
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sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt Ascend
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sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt Ascend
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```
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```
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> checkpoint can be produced in training process.
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#### Result
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#### Result
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Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
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Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
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@ -132,3 +203,44 @@ Evaluation result will be stored in the scripts path. Under this, you can find r
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acc=78.16%(TOP1)
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acc=78.16%(TOP1)
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acc=93.88%(TOP5)
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acc=93.88%(TOP5)
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```
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```
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# [Model description](#contents)
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## [Performance](#contents)
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### Training Performance
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| Parameters | ResNeXt50 | |
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| -------------------------- | ---------------------------------------------------------- | ------------------------- |
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| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G |
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| uploaded Date | 06/30/2020 | 07/23/2020 |
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| MindSpore Version | 0.5.0 | 0.6.0 |
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| Dataset | ImageNet | ImageNet |
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| Training Parameters | src/config.py | src/config.py |
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| Optimizer | Momentum | Momentum |
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| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
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| Loss | 1.76592 | 1.8965 |
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| Accuracy | 78%(TOP1) | 77.8%(TOP1) |
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| Total time | 7.8 h 8ps | 21.5 h 8ps |
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| Checkpoint for Fine tuning | 192 M(.ckpt file) | 192 M(.ckpt file) |
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#### Inference Performance
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| Parameters | | | |
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| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
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| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 |
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| uploaded Date | 06/30/2020 | 07/23/2020 | 07/23/2020 |
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| MindSpore Version | 0.5.0 | 0.6.0 | 0.6.0 |
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| Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W |
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| batch_size | 1 | 1 | 1 |
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| outputs | probability | probability | probability |
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| Accuracy | acc=78.16%(TOP1) | acc=78.05%(TOP1) | |
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# [Description of Random Situation](#contents)
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In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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