parent
53b3d187b9
commit
7321bdb514
@ -0,0 +1,125 @@
|
|||||||
|
# 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
|
||||||
|
```
|
Loading…
Reference in new issue