mindspore/example/googlenet_cifar10

Googlenet Example

Description

This example is for Googlenet model training and evaluation.

Requirements

• Install MindSpore.

• Download the CIFAR-10 binary version dataset.

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

Running the Example

Training

python train.py --data_path=your_data_path --device_id=6 > out.train.log 2>&1 & 

The python command above will run in the background, you can view the results through the file out.train.log.

After training, you'll get some checkpoint files under the script folder by default.

You will get the loss value as following:

# grep "loss is " out.train.log
epoch: 1 step: 390, loss is 1.4842823
epcoh: 2 step: 390, loss is 1.0897788
...

Evaluation

python eval.py --data_path=your_data_path --device_id=6 --checkpoint_path=./train_googlenet_cifar10-125-390.ckpt > out.eval.log 2>&1 & 

The above python command will run in the background, you can view the results through the file out.eval.log.

You will get the accuracy as following:

# grep "result: " out.eval.log
result: {'acc': 0.934}

Distribute Training

sh run_distribute_train.sh rank_table.json your_data_path

The above shell script will run distribute training in the background, you can view the results through the file train_parallel[X]/log.

You will get the loss value as following:

# grep "result: " train_parallel*/log
train_parallel0/log:epoch: 1 step: 48, loss is 1.4302931
train_parallel0/log:epcoh: 2 step: 48, loss is 1.4023874
...
train_parallel1/log:epoch: 1 step: 48, loss is 1.3458025
train_parallel1/log:epcoh: 2 step: 48, loss is 1.3729336
...
...

About rank_table.json, you can refer to the distributed training tutorial.

Usage:

Training

usage: train.py [--device_target TARGET][--data_path DATA_PATH]
                [--device_id DEVICE_ID]

parameters/options:
  --device_target       the training backend type, default is Ascend.
  --data_path           the storage path of dataset
  --device_id           the device which used to train model.

Evaluation

usage: eval.py [--device_target TARGET][--data_path DATA_PATH]
                [--device_id DEVICE_ID][--checkpoint_path CKPT_PATH]

parameters/options:
  --device_target       the evaluation backend type, default is Ascend.
  --data_path           the storage path of datasetd 
  --device_id           the device which used to evaluate model.
  --checkpoint_path     the checkpoint file path used to evaluate model.

Distribute Training

Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]

parameters/options:
  MINDSPORE_HCCL_CONFIG_PATH   HCCL configuration file path.
  DATA_PATH                    the storage path of dataset.