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# 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)
```
```
# [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).