From 7321bdb51431de5785a39020d8ad44107c63efca Mon Sep 17 00:00:00 2001
From: gengdongjie <gengdongjie@huawei.com>
Date: Wed, 22 Apr 2020 13:02:57 +0800
Subject: [PATCH] add readme in resnet50_cifar10 example

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+# ResNet-50 Example
+
+## Description
+
+This is an example of training ResNet-50 with CIFAR-10 dataset in MindSpore.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the dataset [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz).
+
+> Unzip the CIFAR-10 dataset to any path you want and the folder structure should be as follows:
+> ```
+> .  
+> ├── cifar-10-batches-bin  # train dataset
+> └── cifar-10-verify-bin   # infer dataset
+> ```
+
+
+## Example structure
+
+```shell
+.
+├── config.py                       # parameter configuration
+├── dataset.py                      # data preprocessing
+├── eval.py                         # infer script
+├── lr_generator.py                 # generate learning rate for each step
+├── run_distribute_train.sh         # launch distributed training
+├── run_infer.sh                    # launch infering
+├── run_standalone_train.sh         # launch standalone training 
+└── train.py                        # train script
+```
+
+
+## Parameter configuration
+
+Parameters for both training and inference can be set in config.py.
+
+```
+"class_num": 10,                  # dataset class num
+"batch_size": 32,                 # batch size of input tensor
+"loss_scale": 1024,               # loss scale
+"momentum": 0.9,                  # momentum
+"weight_decay": 1e-4,             # weight decay 
+"epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
+"buffer_size": 100,               # number of queue size in data preprocessing
+"image_height": 224,              # image height
+"image_width": 224,               # image width
+"save_checkpoint": True,          # whether save checkpoint or not
+"save_checkpoint_steps": 195,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
+"keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
+"save_checkpoint_path": "./",     # path to save checkpoint
+"lr_init": 0.01,                  # initial learning rate
+"lr_end": 0.00001,                # final learning rate
+"lr_max": 0.1,                    # maximum learning rate
+"warmup_epochs": 5,               # number of warmup epoch
+"lr_decay_mode": "poly"           # decay mode can be selected in steps, ploy and default
+```
+
+## Running the example
+
+### Train
+
+#### Usage
+
+```
+# distribute training
+Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
+
+# standalone training
+Usage: sh run_standalone_train.sh [DATASET_PATH]
+```
+
+
+#### Launch
+
+```
+# distribute training example
+sh run_distribute_train.sh rank_table.json ~/cifar-10-batches-bin
+
+# standalone training example
+sh run_standalone_train.sh ~/cifar-10-batches-bin
+```
+
+> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+
+#### Result
+
+Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
+
+```
+# distribute training result(8p)
+epoch: 1 step: 195, loss is 1.9601055
+epoch: 2 step: 195, loss is 1.8555021
+epoch: 3 step: 195, loss is 1.6707983
+epoch: 4 step: 195, loss is 1.8162166
+epoch: 5 step: 195, loss is 1.393667
+```
+
+### Infer
+
+#### Usage
+
+```
+# infer
+Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
+```
+
+#### Launch
+
+```
+# infer example
+sh run_infer.sh ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
+```
+
+> checkpoint can be produced in training process.
+
+#### Result
+
+Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
+
+```
+result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
+```
-- 
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