From 45947f93d875e2cb486925f6d0b93e09b641e67d Mon Sep 17 00:00:00 2001
From: panfengfeng <panfengfeng@huawei.com>
Date: Thu, 29 Oct 2020 23:06:29 +0800
Subject: [PATCH] update scripts
---
model_zoo/official/cv/nasnet/README.md | 146 +++++++++++++-----
.../cv/shufflenetv2/{Readme.md => README.md} | 37 +++++
2 files changed, 147 insertions(+), 36 deletions(-)
mode change 100755 => 100644 model_zoo/official/cv/nasnet/README.md
rename model_zoo/official/cv/shufflenetv2/{Readme.md => README.md} (73%)
diff --git a/model_zoo/official/cv/nasnet/README.md b/model_zoo/official/cv/nasnet/README.md
old mode 100755
new mode 100644
index 6490947155..f65a03a4f0
--- a/model_zoo/official/cv/nasnet/README.md
+++ b/model_zoo/official/cv/nasnet/README.md
@@ -1,39 +1,81 @@
-# NASNet Example
+# Contents
-## Description
+- [NASNet Description](#nasnet-description)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [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)
+- [ModelZoo Homepage](#modelzoo-homepage)
-This is an example of training NASNet-A-Mobile in MindSpore.
+# [NASNet Description](#contents)
-## Requirements
-- Install [Mindspore](http://www.mindspore.cn/install/en).
-- Download the dataset.
+[Paper](https://arxiv.org/abs/1707.07012): Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. Learning Transferable Architectures for Scalable Image Recognition. 2017.
-## Structure
+# [Model architecture](#contents)
-```shell
+The overall network architecture of NASNet is show below:
+
+[Link](https://arxiv.org/abs/1707.07012)
+
+
+# [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
+
+
+# [Environment Requirements](#contents)
+
+- Hardware GPU
+ - Prepare hardware environment with GPU processor.
+- Framework
+ - [MindSpore](https://www.mindspore.cn/install/en)
+- For more information, please check the resources below：
+ - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+ - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
+
+# [Script description](#contents)
+
+## [Script and sample code](#contents)
+
+```python
.
-└─nasnet
+└─nasnet
├─README.md
- ├─scripts
- ├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p)
- ├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p)
- └─run_eval_for_gpu.sh # launch evaluating with gpu platform
+ ├─scripts
+ ├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p)
+ ├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p)
+ └─run_eval_for_gpu.sh # launch evaluating with gpu platform
├─src
├─config.py # parameter configuration
├─dataset.py # data preprocessing
├─loss.py # Customized CrossEntropy loss function
├─lr_generator.py # learning rate generator
- ├─nasnet_a_mobile.py # network definition
- ├─eval.py # eval net
- ├─export.py # convert checkpoint
- └─train.py # train net
-
+├─nasnet_a_mobile.py # network definition
+├─eval.py # eval net
+├─export.py # convert checkpoint
+└─train.py # train net
+
```
-## 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.
```
'random_seed': 1, # fix random seed
@@ -56,22 +98,18 @@ Parameters for both training and evaluating can be set in config.py
'opt_eps': 1.0, # epsilon
'rmsprop_decay': 0.9, # rmsprop decay
'loss_scale': 1, # loss scale
-
```
-
-
-## Running the example
-
-### Train
+## [Training Process](#contents)
#### Usage
```
-# distribute training example(8p)
-sh run_distribute_train_for_gpu.sh DATA_DIR
-# standalone training
-sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
+GPU:
+ # distribute training example(8p)
+ sh run_distribute_train_for_gpu.sh DATA_DIR
+ # standalone training
+ sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
```
#### Launch
@@ -83,13 +121,11 @@ 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
```
# Evaluation
@@ -103,9 +139,47 @@ sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
sh scripts/run_eval_for_gpu.sh 0 /dataset/val ./checkpoint/nasnet-a-mobile-rank0-248_10009.ckpt
```
-> 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.
+```
+acc=73.5%(TOP1)
+```
+
+# [Model description](#contents)
+
+## [Performance](#contents)
+
+### Training Performance
+
+| Parameters | NASNet |
+| -------------------------- | ------------------------- |
+| Resource | NV SMX2 V100-32G |
+| uploaded Date | 09/24/2020 |
+| MindSpore Version | 1.0.0 |
+| Dataset | ImageNet |
+| Training Parameters | src/config.py |
+| Optimizer | Momentum |
+| Loss Function | SoftmaxCrossEntropyWithLogits |
+| Loss | 1.8965 |
+| Accuracy | 73.5%(TOP1) |
+| Total time | 144 h 8ps |
+| Checkpoint for Fine tuning | 89 M(.ckpt file) |
+
+### Inference Performance
+
+| Parameters | |
+| -------------------------- | ------------------------- |
+| Resource | NV SMX2 V100-32G |
+| uploaded Date | 09/24/2020 |
+| MindSpore Version | 1.0.0 |
+| Dataset | ImageNet, 1.2W |
+| batch_size | 32 |
+| outputs | probability |
+| Accuracy | acc=73.5%(TOP1) |
+
+
+# [ModelZoo Homepage](#contents)
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
diff --git a/model_zoo/official/cv/shufflenetv2/Readme.md b/model_zoo/official/cv/shufflenetv2/README.md
similarity index 73%
rename from model_zoo/official/cv/shufflenetv2/Readme.md
rename to model_zoo/official/cv/shufflenetv2/README.md
index cd6aa51483..810cf10aa8 100644
--- a/model_zoo/official/cv/shufflenetv2/Readme.md
+++ b/model_zoo/official/cv/shufflenetv2/README.md
@@ -14,6 +14,8 @@
- [Training Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
+- [ModelZoo Homepage](#modelzoo-homepage)
+
# [ShuffleNetV2 Description](#contents)
ShuffleNetV2 is a much faster and more accurate netowrk than the previous networks on different platforms such as Ascend or GPU.
@@ -117,3 +119,38 @@ You can start evaluation using python or shell scripts. The usage of shell scrip
### Result
Inference result will be stored in the example path, you can find result in `eval.log`.
+
+# [Model description](#contents)
+
+## [Performance](#contents)
+
+### Training Performance
+
+| Parameters | ShuffleNetV2 |
+| -------------------------- | ------------------------- |
+| Resource | NV SMX2 V100-32G |
+| uploaded Date | 09/24/2020 |
+| MindSpore Version | 1.0.0 |
+| Dataset | ImageNet |
+| Training Parameters | src/config.py |
+| Optimizer | Momentum |
+| Loss Function | CrossEntropySmooth |
+| Accuracy | 69.4%(TOP1) |
+| Total time | 49 h 8ps |
+
+### Inference Performance
+
+| Parameters | |
+| -------------------------- | ------------------------- |
+| Resource | NV SMX2 V100-32G |
+| uploaded Date | 09/24/2020 |
+| MindSpore Version | 1.0.0 |
+| Dataset | ImageNet, 1.2W |
+| batch_size | 128 |
+| outputs | probability |
+| Accuracy | acc=69.4%(TOP1) |
+
+
+# [ModelZoo Homepage](#contents)
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).