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# ResNet101 Example
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## Description
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This is an example of training ResNet101 with ImageNet 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 [ImageNet](http://image-net.org/download).
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> Unzip the ImageNet dataset to any path you want, the folder should include train and eval dataset as follows:
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```
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.
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└─dataset
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├─ilsvrc
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│
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└─validation_preprocess
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```
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## Example structure
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```shell
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.
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├── crossentropy.py # CrossEntropy loss function
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├── var_init.py # weight initial
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├── config.py # parameter configuration
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├── dataset.py # data preprocessing
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├── eval.py # eval net
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├── lr_generator.py # generate learning rate
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├── run_distribute_train.sh # launch distributed training(8p)
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├── run_infer.sh # launch evaluating
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├── run_standalone_train.sh # launch standalone training(1p)
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└── train.py # train net
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```
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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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"class_num": 1001, # dataset class number
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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 optimizer
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"weight_decay": 1e-4, # weight decay
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"epoch_size": 120, # epoch sizes for training
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"buffer_size": 1000, # 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": 500, # 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": 40, # only keep the last keep_checkpoint_max checkpoint
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"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
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"warmup_epochs": 0, # number of warmup epoch
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"lr_decay_mode": "cosine" # decay mode for generating learning rate
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"label_smooth": 1, # label_smooth
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"label_smooth_factor": 0.1, # label_smooth_factor
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"lr": 0.1 # base learning rate
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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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# distributed training
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sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
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# standalone training
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sh run_standalone_train.sh [DATASET_PATH]
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```
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#### Launch
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```bash
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# distributed training example(8p)
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sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
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# standalone training example（1p）
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sh run_standalone_train.sh dataset/ilsvrc
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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". 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: 5004, loss is 4.805483
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epoch: 2 step: 5004, loss is 3.2121816
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epoch: 3 step: 5004, loss is 3.429647
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epoch: 4 step: 5004, loss is 3.3667371
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epoch: 5 step: 5004, loss is 3.1718972
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...
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epoch: 67 step: 5004, loss is 2.2768745
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epoch: 68 step: 5004, loss is 1.7223864
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epoch: 69 step: 5004, loss is 2.0665488
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epoch: 70 step: 5004, loss is 1.8717369
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...
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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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sh run_infer.sh [VALIDATION_DATASET_PATH] [CHECKPOINT_PATH]
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```
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#### Launch
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```bash
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# infer with checkpoint
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sh run_infer.sh dataset/validation_preprocess/ train_parallel0/resnet-120_5004.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: {'top_5_accuracy': 0.9429417413572343, 'top_1_accuracy': 0.7853513124199744} ckpt=train_parallel0/resnet-120_5004.ckpt
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```
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