@ -8,7 +8,9 @@ This example is for VGG16 model training and evaluation.
- Install [MindSpore ](https://www.mindspore.cn/install/en ).
- Download the CIFAR-10 binary version dataset.
- Download the dataset CIFAR-10 or ImageNet2012.
CIFAR-10
> Unzip the CIFAR-10 dataset to any path you want and the folder structure should be as follows:
> ```
@ -17,16 +19,89 @@ This example is for VGG16 model training and evaluation.
> └── cifar-10-verify-bin # infer dataset
> ```
ImageNet2012
> Unzip the ImageNet2012 dataset to any path you want and the folder should include train and eval dataset as follows:
>
> ```
> .
> └─dataset
> ├─ilsvrc # train dataset
> └─validation_preprocess # evaluate dataset
> ```
## Parameter configuration
Parameters for both training and evaluation can be set in config.py.
- config for vgg16, CIFAR-10 dataset
```
"num_classes": 10, # dataset class num
"lr": 0.01, # learning rate
"lr_init": 0.01, # initial learning rate
"lr_max": 0.1, # max learning rate
"lr_epochs": '30,60,90,120', # lr changing based epochs
"lr_scheduler": "step", # learning rate mode
"warmup_epochs": 5, # number of warmup epoch
"batch_size": 64, # batch size of input tensor
"max_epoch": 70, # only valid for taining, which is always 1 for inference
"momentum": 0.9, # momentum
"weight_decay": 5e-4, # weight decay
"loss_scale": 1.0, # loss scale
"label_smooth": 0, # label smooth
"label_smooth_factor": 0, # label smooth factor
"buffer_size": 10, # shuffle buffer size
"image_size": '224,224', # image size
"pad_mode": 'same', # pad mode for conv2d
"padding": 0, # padding value for conv2d
"has_bias": False, # whether has bias in conv2d
"batch_norm": True, # wether has batch_norm in conv2d
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"initialize_mode": "XavierUniform", # conv2d init mode
"has_dropout": True # wether using Dropout layer
```
- config for vgg16, ImageNet2012 dataset
```
"num_classes": 1000, # dataset class num
"lr": 0.01, # learning rate
"lr_init": 0.01, # initial learning rate
"lr_max": 0.1, # max learning rate
"lr_epochs": '30,60,90,120', # lr changing based epochs
"lr_scheduler": "cosine_annealing", # learning rate mode
"warmup_epochs": 0, # number of warmup epoch
"batch_size": 32, # batch size of input tensor
"max_epoch": 150, # only valid for taining, which is always 1 for inference
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
"loss_scale": 1024, # loss scale
"label_smooth": 1, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"buffer_size": 10, # shuffle buffer size
"image_size": '224,224', # image size
"pad_mode": 'pad', # pad mode for conv2d
"padding": 1, # padding value for conv2d
"has_bias": True, # whether has bias in conv2d
"batch_norm": False, # wether has batch_norm in conv2d
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"initialize_mode": "KaimingNormal", # conv2d init mode
"has_dropout": True # wether using Dropout layer
```
## Running the Example
### Training
**Run vgg16, using CIFAR-10 dataset**
- Training using single device(1p)
```
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.
After training, you'll get some checkpoint files in specified ckpt_path, default in ./output directory .
You will get the loss value as following:
```
@ -36,22 +111,7 @@ epcoh: 2 step: 781, loss is 1.827582
...
```
### Evaluation
- Do eval as follows, need to specify dataset type as "cifar10" or "imagenet2012"
```
python eval.py --data_path=your_data_path --dataset="cifar10" --pre_trained=./train_vgg_cifar10-70-781.ckpt > out.eval.log 2>& 1 &
```
- If the using dataset is
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': 0.92}
```
### Distribute Training
- Distribute Training
```
sh run_distribute_train.sh rank_table.json your_data_path
```
@ -70,18 +130,63 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579
```
> About rank_table.json, you can refer to the [distributed training tutorial ](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html ).
**Run vgg16, using imagenet2012 dataset**
- Training using single device(1p)
```
python train.py --device_target="GPU" --dataset="imagenet2012" --is_distributed=0 --data_path=$DATA_PATH > output.train.log 2>& 1 &
```
- Distribute Training
```
# distributed training(8p)
bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train"
```
### Evaluation
- Do eval as follows, need to specify dataset type as "cifar10" or "imagenet2012"
```
# when using cifar10 dataset
python eval.py --data_path=your_data_path --dataset="cifar10" --device_target="Ascend" --pre_trained=./*-70-781.ckpt > out.eval.log 2>& 1 &
# when using imagenet2012 dataset
python eval.py --data_path=your_data_path --dataset="imagenet2012" --device_target="GPU" --pre_trained=./*-150-5004.ckpt > out.eval.log 2>& 1 &
```
- If the using dataset is
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:
```
# when using cifar10 dataset
# grep "result: " out.eval.log
result: {'acc': 0.92}
# when using the imagenet2012 dataset
after allreduce eval: top1_correct=36636, tot=50000, acc=73.27%
after allreduce eval: top5_correct=45582, tot=50000, acc=91.16%
```
## Usage:
### Training
```
usage: train.py [--device_target TARGET][--data_path DATA_PATH]
[--dataset DATASET_TYPE][--is_distributed VALUE]
[--device_id DEVICE_ID][--pre_trained PRE_TRAINED]
[--ckpt_path CHECKPOINT_PATH][--ckpt_interval INTERVAL_STEP]
parameters/options:
--device_target the training backend type, default is Ascend.
--device_target the training backend type, Ascend or GPU, default is Ascend.
--dataset the dataset type, cifar10 or imagenet2012.
--is_distributed the way of traing, whether do distribute traing, value can be 0 or 1.
--data_path the storage path of dataset
--device_id the device which used to train model.
--pre_trained the pretrained checkpoint file path.
--ckpt_path the path to save checkpoint.
--ckpt_interval the epoch interval for saving checkpoint.
```
@ -89,16 +194,19 @@ parameters/options:
```
usage: eval.py [--device_target TARGET][--data_path DATA_PATH]
[--device_id DEVICE_ID][--checkpoint_path CKPT_PATH]
[--dataset DATASET_TYPE][--pre_trained PRE_TRAINED]
[--device_id DEVICE_ID]
parameters/options:
--device_target the evaluation backend type, default is Ascend.
--data_path the storage path of datasetd
--device_target the evaluation backend type, Ascend or GPU, default is Ascend.
--dataset the dataset type, cifar10 or imagenet2012.
--data_path the storage path of dataset.
--device_id the device which used to evaluate model.
--pre_trained the checkpoint file path used to evaluate model.
--pre_trained the checkpoint file path used to evaluate model.
```
### Distribute Training
- Train on Ascend.
```
Usage: sh script/run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]
@ -107,3 +215,11 @@ parameters/options:
MINDSPORE_HCCL_CONFIG_PATH HCCL configuration file path.
DATA_PATH the storage path of dataset.
```
- Train on GPU.
```
Usage: bash run_distribute_train_gpu.sh [DATA_PATH]
parameters/options:
DATA_PATH the storage path of dataset.
```
ms_yan
5 years ago