Parameters for both training and evaluating can be set in config.py.
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
"epoch_size": 120, # epoch sizes for training
"pretrain_epoch_size": 0, # epoch size of pretrain checkpoint
"buffer_size": 1000, # 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_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": 1, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr": 0.1 # base learning rate
```
## Running the example
### Train
#### Usage
```
# distributed training
sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_PATH](optional)
# standalone training
sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_PATH](optional)
```
#### Launch
```bash
# distributed training example(8p)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
If you want to load pretrained ckpt file,
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc ./ckpt/pretrained.ckpt
# standalone training example（1p）
sh run_standalone_train.sh dataset/ilsvrc
If you want to load pretrained ckpt file,
sh run_standalone_train.sh dataset/ilsvrc ./ckpt/pretrained.ckpt
```
> 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 scripts path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in log.
```
# distribute training result(8p)
epoch: 1 step: 5004, loss is 4.805483
epoch: 2 step: 5004, loss is 3.2121816
epoch: 3 step: 5004, loss is 3.429647
epoch: 4 step: 5004, loss is 3.3667371
epoch: 5 step: 5004, loss is 3.1718972
...
epoch: 67 step: 5004, loss is 2.2768745
epoch: 68 step: 5004, loss is 1.7223864
epoch: 69 step: 5004, loss is 2.0665488
epoch: 70 step: 5004, loss is 1.8717369
...
```
### Infer
#### Usage
```
# infer
sh run_eval.sh [VALIDATION_DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```bash
# infer with checkpoint
sh run_eval.sh dataset/validation_preprocess/ train_parallel0/resnet-120_5004.ckpt
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the scripts path, whose folder name is "eval". Under this, you can find result like the followings in log.
