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