- 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.
