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!4917 Modify readme for inceptionv3
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# Inception-v3 Example
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# Contents
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## Description
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- [InceptionV3 Description](#InceptionV3-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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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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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 Inception-v3 in MindSpore.
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# [InceptionV3 Description](#contents)
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## Requirements
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InceptionV3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures.
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- Install [Mindspore](http://www.mindspore.cn/install/en).
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- Downlaod the dataset.
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[Paper](https://arxiv.org/pdf/1512.00567.pdf) Min Sun, Ali Farhadi, Steve Seitz. Ranking Domain-Specific Highlights by Analyzing Edited Videos[J]. 2014.
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## Structure
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# [Model architecture](#contents)
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The overall network architecture of InceptionV3 is show below:
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[Link](https://arxiv.org/pdf/1905.02244)
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# [Dataset](#contents)
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Dataset used can refer to paper.
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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(Ascend)](#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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```shell
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.
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└─Inception-v3
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├─README.md
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├─scripts
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├─run_standalone_train.sh # launch standalone training with ascend platform(1p)
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├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p)
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├─run_distribute_train.sh # launch distributed training with ascend platform(8p)
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├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p)
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├─run_eval.sh # launch evaluating with ascend platform
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└─run_eval_for_gpu.sh # launch evaluating with gpu platform
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├─src
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├─config.py # parameter configuration
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@ -30,12 +84,10 @@ This is an example of training Inception-v3 in MindSpore.
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└─train.py # train net
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```
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## [Script Parameters](#contents)
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## Parameter Configuration
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Parameters for both training and evaluating can be set in config.py
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```
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```python
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Major parameters in train.py and config.py are:
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'random_seed': 1, # fix random seed
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'rank': 0, # local rank of distributed
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'group_size': 1, # world size of distributed
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@ -59,14 +111,22 @@ Parameters for both training and evaluating can be set in config.py
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'is_save_on_master': 1 # save checkpoint on rank0, distributed parameters
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```
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## [Training process](#contents)
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### Usage
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## Running the example
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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### Train
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#### Usage
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- Ascend:
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```
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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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# standalone training
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sh run_standalone_train.sh DEVICE_ID DATA_PATH
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```
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- GPU:
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```
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# distribute training example(8p)
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sh run_distribute_train_for_gpu.sh DATA_DIR
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@ -74,42 +134,94 @@ sh run_distribute_train_for_gpu.sh DATA_DIR
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sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
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```
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#### Launch
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### Launch
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```bash
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# distributed training example(8p) for GPU
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sh scripts/run_distribute_train_for_gpu.sh /dataset/train
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# standalone training example for GPU
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sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train
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```
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# training example
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python:
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Ascend: python train.py --dataset_path /dataset/train --platform Ascend
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GPU: python train.py --dataset_path /dataset/train --platform GPU
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shell:
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# distributed training example(8p) for GPU
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sh scripts/run_distribute_train_for_gpu.sh /dataset/train
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# standalone training example for GPU
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sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train
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```
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#### Result
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### Result
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You can find checkpoint file together with result in log.
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### Evaluation
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#### Usage
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Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./log.txt` like followings.
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```
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epoch: 0 step: 1251, loss is 5.7787247
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Epoch time: 360760.985, per step time: 288.378
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epoch: 1 step: 1251, loss is 4.392868
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Epoch time: 160917.911, per step time: 128.631
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```
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# Evaluation
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sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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## [Eval process](#contents)
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### Usage
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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- Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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- GPU: sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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### Launch
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```
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# eval example
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python:
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Ascend: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform Ascend
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GPU: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform GPU
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shell:
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Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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GPU: sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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```
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#### Launch
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> checkpoint can be produced in training process.
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```bash
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# Evaluation with checkpoint
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sh scripts/run_eval_for_gpu.sh 0 /dataset/val ./checkpoint/inceptionv3-rank3-247_1251.ckpt
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### Result
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Evaluation result will be stored in the example path, you can find result like the followings in `log.txt`.
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```
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metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}
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```
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> checkpoint can be produced in training process.
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# [Model description](#contents)
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#### Result
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## [Performance](#contents)
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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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### Training Performance
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| Parameters | InceptionV3 | |
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| -------------------------- | ---------------------------------------------------------- | ------------------------- |
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| Model Version | | |
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| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G |
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| uploaded Date | 08/21/2020 | 08/21/2020 |
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| MindSpore Version | 0.6.0-beta | 0.6.0-beta |
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| Training Parameters | src/config.py | src/config.py |
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| Optimizer | RMSProp | RMSProp |
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| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
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| outputs | probability | probability |
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| Loss | 1.98 | 1.98 |
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| Accuracy | ACC1[78.8%] ACC5[94.2%] | ACC1[78.7%] ACC5[94.1%] |
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| Total time | 11h | 72h |
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| Params (M) | 103M | 103M |
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| Checkpoint for Fine tuning | 313M | 312.41 |
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| Model for inference | | |
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#### Inference Performance
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To be added.
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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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```
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acc=78.75%(TOP1)
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acc=94.07%(TOP5)
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```
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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@ -64,7 +64,7 @@ config_ascend = edict({
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'weight_decay': 0.00004,
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'momentum': 0.9,
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'opt_eps': 1.0,
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'keep_checkpoint_max': 100,
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'keep_checkpoint_max': 10,
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'ckpt_path': './checkpoint/',
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'is_save_on_master': 0,
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'dropout_keep_prob': 0.8,
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