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!5922 vgg16 support imagenet dataset on Ascend 

Merge pull request !5922 from caojian05/ms_r0.5_vgg16_support_imagenet_on_ascend
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model_zoo/vgg16/README.md
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# VGG16 Example
# Contents
## Description
- [VGG Description](#vgg-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Features](#features)
- [Mixed Precision](#mixed-precision)
- [Environment Requirements](#environment-requirements)
- [Quick Start](#quick-start)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- [Parameter configuration](#parameter-configuration)
- [Training Process](#training-process)
- [Training](#training)
- [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#training-performance)
- [Evaluation Performance](#evaluation-performance)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
This example is for VGG16 model training and evaluation.
## Requirements
# [VGG Description](#contents)
- Install [MindSpore](https://www.mindspore.cn/install/en).
VGG, a very deep convolutional networks for large-scale image recognition, was proposed in 2014 and won the 1th place in object localization and 2th place in image classification task in ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
- Download the CIFAR-10 binary version dataset.
[Paper](): Simonyan K, zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
> 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
> ```
# [Model Architecture](#contents)
VGG 16 network is mainly consisted by several basic modules (including convolution and pooling layer) and three continuous Dense layer.
here basic modules mainly include basic operation like: **3×3 conv** and **2×2 max pooling**.
## Running the Example
# [Dataset](#contents)
#### Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
- CIFAR-10 Dataset size：175M，60,000 32*32 colorful images in 10 classes
- Train：146M，50,000 images
- Test：29.3M，10,000 images
- Data format: binary files
- Note: Data will be processed in src/dataset.py
#### Dataset used: [ImageNet2012](http://www.image-net.org/)
- Dataset size: ~146G, 1.28 million colorful images in 1000 classes
- Train: 140G, 1,281,167 images
- Test: 6.4G, 50, 000 images
- Data format: RGB images
- Note: Data will be processed in src/dataset.py
#### Dataset organize way
CIFAR-10
> 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
> ```
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
> ```
# [Features](#contents)
## Mixed Precision
The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
# [Environment Requirements](#contents)
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Framework
- [MindSpore](https://www.mindspore.cn/install/en)
- For more information, please check the resources below：
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
# [Quick Start](#contents)
After installing MindSpore via the official website, you can start training and evaluation as follows:
- Running on Ascend
```python
# run training example
python train.py --data_path=[DATA_PATH] --device_id=[DEVICE_ID] > output.train.log 2>&1 &
# run distributed training example
sh run_distribute_train.sh [RANL_TABLE_JSON] [DATA_PATH]
# run evaluation example
python eval.py --data_path=[DATA_PATH] --pre_trained=[PRE_TRAINED] > output.eval.log 2>&1 &
```
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link below:
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools
- Running on GPU
```
# run training example
python train.py --device_target="GPU" --device_id=[DEVICE_ID] --dataset=[DATASET_TYPE] --data_path=[DATA_PATH] > output.train.log 2>&1 &
# run distributed training example
sh run_distribute_train_gpu.sh [DATA_PATH]
# run evaluation example
python eval.py --device_target="GPU" --device_id=[DEVICE_ID] --dataset=[DATASET_TYPE] --data_path=[DATA_PATH] --pre_trained=[PRE_TRAINED] > output.eval.log 2>&1 &
```
# [Script Description](#contents)
## [Script and Sample Code](#contents)
```
├── model_zoo
├── README.md // descriptions about all the models
├── vgg16
├── README.md // descriptions about googlenet
├── scripts
│ ├── run_distribute_train.sh // shell script for distributed training on Ascend
│ ├── run_distribute_train_gpu.sh // shell script for distributed training on GPU
├── src
│ ├── utils
│ │ ├── logging.py // logging format setting
│ │ ├── sampler.py // create sampler for dataset
│ │ ├── util.py // util function
│ │ ├── var_init.py // network parameter init method
│ ├── config.py // parameter configuration
│ ├── crossentropy.py // loss caculation
│ ├── dataset.py // creating dataset
│ ├── linear_warmup.py // linear leanring rate
│ ├── warmup_cosine_annealing_lr.py // consine anealing learning rate
│ ├── warmup_step_lr.py // step or multi step learning rate
│ ├──vgg.py // vgg architecture
├── train.py // training script
├── eval.py // evaluation script
```
## [Script Parameters](#contents)
### 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`.
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]
After training, you'll get some checkpoint files under the script folder by default.
parameters/options:
--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.
You will get the loss value as following:
```
# grep "loss is " out.train.log
epoch: 1 step: 781, loss is 2.093086
epcoh: 2 step: 781, loss is 1.827582
...
```
### Evaluation
```
python eval.py --data_path=your_data_path --device_id=6 --checkpoint_path=./train_vgg_cifar10-70-781.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`.
usage: eval.py [--device_target TARGET][--data_path DATA_PATH]
[--dataset DATASET_TYPE][--pre_trained PRE_TRAINED]
[--device_id DEVICE_ID]
You will get the accuracy as following:
```
# grep "result: " out.eval.log
result: {'acc': 0.92}
parameters/options:
--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.
```
### Distribute Training
## [Parameter configuration](#contents)
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
```
## [Training Process](#contents)
### Training
#### Run vgg16 on Ascend
- Training using single device(1p), using CIFAR-10 dataset in default
```
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 in specified ckpt_path, default in ./output directory.
You will get the loss value as following:
```
# grep "loss is " output.train.log
epoch: 1 step: 781, loss is 2.093086
epcoh: 2 step: 781, loss is 1.827582
...
```
- Distributed Training
```
sh run_distribute_train.sh rank_table.json your_data_path
```
@ -68,40 +292,83 @@ 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).
## Usage:
### Training
#### Run vgg16 on GPU
- Training using single device(1p)
```
usage: train.py [--device_target TARGET][--data_path DATA_PATH]
[--device_id DEVICE_ID][--pre_trained PRE_TRAINED]
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.
--pre_trained the pretrained checkpoint file path.
python train.py --device_target="GPU" --dataset="imagenet2012" --is_distributed=0 --data_path=$DATA_PATH > output.train.log 2>&1 &
```
- Distributed Training
```
# distributed training(8p)
bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train"
```
## [Evaluation Process](#contents)
### Evaluation
- Do eval as follows, need to specify dataset type as "cifar10" or "imagenet2012"
```
usage: eval.py [--device_target TARGET][--data_path DATA_PATH]
[--device_id DEVICE_ID][--checkpoint_path CKPT_PATH]
# when using cifar10 dataset
python eval.py --data_path=your_data_path --dataset="cifar10" --device_target="Ascend" --pre_trained=./*-70-781.ckpt > output.eval.log 2>&1 &
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.
# when using imagenet2012 dataset
python eval.py --data_path=your_data_path --dataset="imagenet2012" --device_target="GPU" --pre_trained=./*-150-5004.ckpt > output.eval.log 2>&1 &
```
- The above python command will run in the background, you can view the results through the file `output.eval.log`. You will get the accuracy as following:
```
# when using cifar10 dataset
# grep "result: " output.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%
```
### Distribute Training
```
Usage: sh script/run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]
# [Model Description](#contents)
## [Performance](#contents)
parameters/options:
MINDSPORE_HCCL_CONFIG_PATH HCCL configuration file path.
DATA_PATH the storage path of dataset.
```
### Training Performance
| Parameters | VGG16(Ascend) | VGG16(GPU) |
| -------------------------- | ---------------------------------------------- |------------------------------------|
| Model Version | VGG16 | VGG16 |
| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G |NV SMX2 V100-32G |
| uploaded Date | 08/20/2020 |08/20/2020 |
| MindSpore Version | 0.5.0-alpha |0.5.0-alpha |
| Dataset | CIFAR-10 |ImageNet2012 |
| Training Parameters | epoch=70, steps=781, batch_size = 64, lr=0.1 |epoch=150, steps=40036, batch_size = 32, lr=0.1 |
| Optimizer | Momentum |Momentum |
| Loss Function | SoftmaxCrossEntropy |SoftmaxCrossEntropy |
| outputs | probability |probability |
| Loss | 0.01 |1.5~2.0 |
| Speed | 1pc: 79 ms/step; 8pcs: 104 ms/step |1pc: 81 ms/step; 8pcs 94.4ms/step |
| Total time | 1pc: 72 mins; 8pcs: 11.8 mins |8pcs: 19.7 hours |
| Checkpoint for Fine tuning | 1.1G(.ckpt file) |1.1G(.ckpt file) |
| Scripts |[vgg16](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/vgg16) | |
### Evaluation Performance
| Parameters | VGG16(Ascend) | VGG16(GPU)
| ------------------- | --------------------------- |---------------------
| Model Version | VGG16 | VGG16 |
| Resource | Ascend 910 | GPU |
| Uploaded Date | 08/20/2020 | 08/20/2020 |
| MindSpore Version | 0.5.0-alpha |0.5.0-alpha |
| Dataset | CIFAR-10, 10,000 images |ImageNet2012, 5000 images |
| batch_size | 64 | 32 |
| outputs | probability | probability |
| Accuracy | 1pc: 93.4% |1pc: 73.0%; |
# [Description of Random Situation](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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@ -12,42 +12,201 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
##############test vgg16 example on cifar10#################
python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
"""
"""Eval"""
import os
import time
import argparse
import datetime
import glob
import numpy as np
import mindspore.nn as nn
from mindspore import context
from mindspore import Tensor, context
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.config import cifar_cfg as cfg
from src.dataset import vgg_create_dataset
from src.vgg import vgg16
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common import dtype as mstype
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Cifar10 classification')
from src.utils.logging import get_logger
from src.vgg import vgg16
from src.dataset import vgg_create_dataset
from src.dataset import classification_dataset
class ParameterReduce(nn.Cell):
"""ParameterReduce"""
def __init__(self):
super(ParameterReduce, self).__init__()
self.cast = P.Cast()
self.reduce = P.AllReduce()
def construct(self, x):
one = self.cast(F.scalar_to_array(1.0), mstype.float32)
out = x * one
ret = self.reduce(out)
return ret
def parse_args(cloud_args=None):
"""parse_args"""
parser = argparse.ArgumentParser('mindspore classification test')
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
help='device where the code will be implemented. (Default: Ascend)')
parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
# dataset related
parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
parser.add_argument('--data_path', type=str, default='', help='eval data dir')
parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
# network related
parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
parser.add_argument('--pre_trained', default='', type=str, help='fully path of pretrained model to load. '
'If it is a direction, it will test all ckpt')
# logging related
parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
args_opt = parser.parse_args()
args_opt = merge_args(args_opt, cloud_args)
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
context.set_context(device_id=args_opt.device_id)
if args_opt.dataset == "cifar10":
from src.config import cifar_cfg as cfg
else:
from src.config import imagenet_cfg as cfg
net = vgg16(num_classes=cfg.num_classes)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
args_opt.image_size = cfg.image_size
args_opt.num_classes = cfg.num_classes
args_opt.per_batch_size = cfg.batch_size
args_opt.momentum = cfg.momentum
args_opt.weight_decay = cfg.weight_decay
args_opt.buffer_size = cfg.buffer_size
args_opt.pad_mode = cfg.pad_mode
args_opt.padding = cfg.padding
args_opt.has_bias = cfg.has_bias
args_opt.batch_norm = cfg.batch_norm
args_opt.initialize_mode = cfg.initialize_mode
args_opt.has_dropout = cfg.has_dropout
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
dataset = vgg_create_dataset(args_opt.data_path, 1, False)
res = model.eval(dataset)
print("result: ", res)
args_opt.image_size = list(map(int, args_opt.image_size.split(',')))
return args_opt
def get_top5_acc(top5_arg, gt_class):
sub_count = 0
for top5, gt in zip(top5_arg, gt_class):
if gt in top5:
sub_count += 1
return sub_count
def merge_args(args, cloud_args):
"""merge_args"""
args_dict = vars(args)
if isinstance(cloud_args, dict):
for key in cloud_args.keys():
val = cloud_args[key]
if key in args_dict and val:
arg_type = type(args_dict[key])
if arg_type is not type(None):
val = arg_type(val)
args_dict[key] = val
return args
def test(cloud_args=None):
"""test"""
args = parse_args(cloud_args)
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
device_target=args.device_target, save_graphs=False)
if os.getenv('DEVICE_ID', "not_set").isdigit():
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
args.outputs_dir = os.path.join(args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
args.logger = get_logger(args.outputs_dir, args.rank)
args.logger.save_args(args)
if args.dataset == "cifar10":
net = vgg16(num_classes=args.num_classes, args=args)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, args.momentum,
weight_decay=args.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
param_dict = load_checkpoint(args.pre_trained)
load_param_into_net(net, param_dict)
net.set_train(False)
dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, training=False)
res = model.eval(dataset)
print("result: ", res)
else:
# network
args.logger.important_info('start create network')
if os.path.isdir(args.pre_trained):
models = list(glob.glob(os.path.join(args.pre_trained, '*.ckpt')))
print(models)
if args.graph_ckpt:
f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
else:
f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
args.models = sorted(models, key=f)
else:
args.models = [args.pre_trained,]
for model in args.models:
dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size, mode='eval')
eval_dataloader = dataset.create_tuple_iterator()
network = vgg16(args.num_classes, args, phase="test")
# pre_trained
load_param_into_net(network, load_checkpoint(model))
network.add_flags_recursive(fp16=True)
img_tot = 0
top1_correct = 0
top5_correct = 0
network.set_train(False)
t_end = time.time()
it = 0
for data, gt_classes in eval_dataloader:
output = network(Tensor(data, mstype.float32))
output = output.asnumpy()
top1_output = np.argmax(output, (-1))
top5_output = np.argsort(output)[:, -5:]
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
top5_correct += get_top5_acc(top5_output, gt_classes)
img_tot += args.per_batch_size
if args.rank == 0 and it == 0:
t_end = time.time()
it = 1
if args.rank == 0:
time_used = time.time() - t_end
fps = (img_tot - args.per_batch_size) * args.group_size / time_used
args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
results = [[top1_correct], [top5_correct], [img_tot]]
args.logger.info('before results={}'.format(results))
results = np.array(results)
args.logger.info('after results={}'.format(results))
top1_correct = results[0, 0]
top5_correct = results[1, 0]
img_tot = results[2, 0]
acc1 = 100.0 * top1_correct / img_tot
acc5 = 100.0 * top5_correct / img_tot
args.logger.info('after allreduce eval: top1_correct={}, tot={},'
'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
args.logger.info('after allreduce eval: top5_correct={}, tot={},'
'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
if __name__ == "__main__":
test()

27
model_zoo/vgg16/scripts/run_distribute_train.sh Executable file → Normal file
View File

@ -14,15 +14,15 @@
# limitations under the License.
# ============================================================================
if [ $# != 2 ]
if [ $# != 2 ] && [ $# != 3 ]
then
echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]"
echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH] [cifar10|imagenet2012]"
exit 1
fi
if [ ! -f $1 ]
then
echo "error: MINDSPORE_HCCL_CONFIG_PATH=$1 is not a file"
echo "error: RANK_TABLE_FILE=$1 is not a file"
exit 1
fi
@ -32,9 +32,22 @@ then
exit 1
fi
dataset_type='cifar10'
if [ $# == 3 ]
then
if [ $3 != "cifar10" ] && [ $3 != "imagenet2012" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet2012"
exit 1
fi
dataset_type=$3
fi
export DEVICE_NUM=8
export RANK_SIZE=8
export MINDSPORE_HCCL_CONFIG_PATH=$1
export RANK_TABLE_FILE=$1
for((i=0;i<RANK_SIZE;i++))
do
@ -45,8 +58,8 @@ do
cp *.py ./train_parallel$i
cp -r src ./train_parallel$i
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
echo "start training for rank $RANK_ID, device $DEVICE_ID, $dataset_type"
env > env.log
python train.py --data_path=$2 --device_id=$i &> log &
python train.py --data_path=$2 --device_target="Ascend" --device_id=$i --is_distributed=1 --dataset=$dataset_type &> log &
cd ..
done
done

29
model_zoo/vgg16/scripts/run_distribute_train_gpu.sh Normal file
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@ -0,0 +1,29 @@
#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the script as: "
echo "bash run_distribute_train_gpu.sh DATA_PATH"
echo "for example: bash run_distribute_train_gpu.sh /path/ImageNet2012/train"
echo "=============================================================================================================="
DATA_PATH=$1
mpirun -n 8 python train.py \
--device_target="GPU" \
--dataset="imagenet2012" \
--is_distributed=1 \
--data_path=$DATA_PATH > output.train.log 2>&1 &

32
model_zoo/vgg16/scripts/run_eval.sh Normal file
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@ -0,0 +1,32 @@
#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the script as: "
echo "bash run_eval.sh DATA_PATH DATASET_TYPE DEVICE_TYPE CHECKPOINT_PATH"
echo "for example: bash run_eval.sh /path/ImageNet2012/train cifar10 Ascend /path/a.ckpt "
echo "=============================================================================================================="
DATA_PATH=&1
DATASET_TYPE=$2
DEVICE_TYPE=$3
CHECKPOINT_PATH=$4
python eval.py \
--data_path=$DATA_PATH \
--dataset=$DATASET_TYPE \
--device_target=$DEVICE_TYPE \
--pre_trained=$CHECKPOINT_PATH > output.eval.log 2>&1 &

65
model_zoo/vgg16/src/config.py
View File

@ -13,21 +13,60 @@
# limitations under the License.
# ============================================================================
"""
network config setting, will be used in main.py
network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as edict
# config for vgg16, cifar10
cifar_cfg = edict({
'num_classes': 10,
'lr_init': 0.01,
'lr_max': 0.1,
'warmup_epochs': 5,
'batch_size': 64,
'epoch_size': 70,
'momentum': 0.9,
'weight_decay': 5e-4,
'buffer_size': 10,
'image_height': 224,
'image_width': 224,
'keep_checkpoint_max': 10
"num_classes": 10,
"lr": 0.01,
"lr_init": 0.01,
"lr_max": 0.1,
"lr_epochs": '30,60,90,120',
"lr_scheduler": "step",
"warmup_epochs": 5,
"batch_size": 64,
"max_epoch": 70,
"momentum": 0.9,
"weight_decay": 5e-4,
"loss_scale": 1.0,
"label_smooth": 0,
"label_smooth_factor": 0,
"buffer_size": 10,
"image_size": '224,224',
"pad_mode": 'same',
"padding": 0,
"has_bias": False,
"batch_norm": True,
"keep_checkpoint_max": 10,
"initialize_mode": "XavierUniform",
"has_dropout": False
})
# config for vgg16, imagenet2012
imagenet_cfg = edict({
"num_classes": 1000,
"lr": 0.01,
"lr_init": 0.01,
"lr_max": 0.1,
"lr_epochs": '30,60,90,120',
"lr_scheduler": 'cosine_annealing',
"warmup_epochs": 0,
"batch_size": 32,
"max_epoch": 150,
"momentum": 0.9,
"weight_decay": 1e-4,
"loss_scale": 1024,
"label_smooth": 1,
"label_smooth_factor": 0.1,
"buffer_size": 10,
"image_size": '224,224',
"pad_mode": 'pad',
"padding": 1,
"has_bias": False,
"batch_norm": False,
"keep_checkpoint_max": 10,
"initialize_mode": "XavierUnifor",
"has_dropout": True
})

39
model_zoo/vgg16/src/crossentropy.py Normal file
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@ -0,0 +1,39 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""define loss function for network"""
from mindspore.nn.loss.loss import _Loss
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore import Tensor
from mindspore.common import dtype as mstype
import mindspore.nn as nn
class CrossEntropy(_Loss):
"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
def __init__(self, smooth_factor=0., num_classes=1001):
super(CrossEntropy, self).__init__()
self.onehot = P.OneHot()
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
self.ce = nn.SoftmaxCrossEntropyWithLogits()
self.mean = P.ReduceMean(False)
def construct(self, logit, label):
one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
loss = self.ce(logit, one_hot_label)
loss = self.mean(loss, 0)
return loss

154
model_zoo/vgg16/src/dataset.py
View File

@ -13,37 +13,35 @@
# limitations under the License.
# ============================================================================
"""
Data operations, will be used in train.py and eval.py
dataset processing.
"""
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
from mindspore.common import dtype as mstype
import mindspore.dataset as de
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as vision
from .config import cifar_cfg as cfg
from PIL import Image, ImageFile
from src.utils.sampler import DistributedSampler
ImageFile.LOAD_TRUNCATED_IMAGES = True
def vgg_create_dataset(data_home, repeat_num=1, training=True):
def vgg_create_dataset(data_home, image_size, batch_size, rank_id=0, rank_size=1, repeat_num=1, training=True):
"""Data operations."""
ds.config.set_seed(1)
de.config.set_seed(1)
data_dir = os.path.join(data_home, "cifar-10-batches-bin")
if not training:
data_dir = os.path.join(data_home, "cifar-10-verify-bin")
rank_size = int(os.environ.get("RANK_SIZE")) if os.environ.get("RANK_SIZE") else None
rank_id = int(os.environ.get("RANK_ID")) if os.environ.get("RANK_ID") else None
data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
data_set = de.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
resize_height = cfg.image_height
resize_width = cfg.image_width
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
resize_op = vision.Resize(image_size) # interpolation default BILINEAR
rescale_op = vision.Rescale(rescale, shift)
normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
changeswap_op = vision.HWC2CHW()
@ -66,6 +64,134 @@ def vgg_create_dataset(data_home, repeat_num=1, training=True):
data_set = data_set.shuffle(buffer_size=10)
# apply batch operations
data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
return data_set
def classification_dataset(data_dir, image_size, per_batch_size, rank=0, group_size=1,
mode='train',
input_mode='folder',
root='',
num_parallel_workers=None,
shuffle=None,
sampler=None,
repeat_num=1,
class_indexing=None,
drop_remainder=True,
transform=None,
target_transform=None):
"""
A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
are written into a textfile.
Args:
data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
Or path of the textfile that contains every image's path of the dataset.
image_size (str): Size of the input images.
per_batch_size (int): the batch size of evey step during training.
rank (int): The shard ID within num_shards (default=None).
group_size (int): Number of shards that the dataset should be divided
into (default=None).
mode (str): "train" or others. Default: " train".
input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
root (str): the images path for "input_mode="txt"". Default: " ".
num_parallel_workers (int): Number of workers to read the data. Default: None.
shuffle (bool): Whether or not to perform shuffle on the dataset
(default=None, performs shuffle).
sampler (Sampler): Object used to choose samples from the dataset. Default: None.
repeat_num (int): the num of repeat dataset.
class_indexing (dict): A str-to-int mapping from folder name to index
(default=None, the folder names will be sorted
alphabetically and each class will be given a
unique index starting from 0).
Examples:
>>> from mindvision.common.datasets.classification import classification_dataset
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
>>> per_batch_size=64, rank=0, group_size=4)
>>> # Path of the textfile that contains every image's path of the dataset.
>>> dataset_dir = "/path/to/dataset/images/train.txt"
>>> images_dir = "/path/to/dataset/images"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
>>> per_batch_size=64, rank=0, group_size=4,
>>> input_mode="txt", root=images_dir)
"""
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
if transform is None:
if mode == 'train':
transform_img = [
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0)),
vision.RandomHorizontalFlip(prob=0.5),
vision.Normalize(mean=mean, std=std),
vision.HWC2CHW()
]
else:
transform_img = [
vision.Decode(),
vision.Resize((256, 256)),
vision.CenterCrop(image_size),
vision.Normalize(mean=mean, std=std),
vision.HWC2CHW()
]
else:
transform_img = transform
if target_transform is None:
transform_label = [C.TypeCast(mstype.int32)]
else:
transform_label = target_transform
if input_mode == 'folder':
de_dataset = de.ImageFolderDatasetV2(data_dir, num_parallel_workers=num_parallel_workers,
shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
num_shards=group_size, shard_id=rank)
else:
dataset = TxtDataset(root, data_dir)
sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
de_dataset.set_dataset_size(len(sampler))
de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
columns_to_project = ["image", "label"]
de_dataset = de_dataset.project(columns=columns_to_project)
de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
de_dataset = de_dataset.repeat(repeat_num)
return de_dataset
class TxtDataset:
"""
create txt dataset.
Args:
Returns:
de_dataset.
"""
def __init__(self, root, txt_name):
super(TxtDataset, self).__init__()
self.imgs = []
self.labels = []
fin = open(txt_name, "r")
for line in fin:
img_name, label = line.strip().split(' ')
self.imgs.append(os.path.join(root, img_name))
self.labels.append(int(label))
fin.close()
def __getitem__(self, index):
img = Image.open(self.imgs[index]).convert('RGB')
return img, self.labels[index]
def __len__(self):
return len(self.imgs)

23
model_zoo/vgg16/src/linear_warmup.py Normal file
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@ -0,0 +1,23 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
linear warm up learning rate.
"""
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
lr = float(init_lr) + lr_inc * current_step
return lr

82
model_zoo/vgg16/src/utils/logging.py Normal file
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@ -0,0 +1,82 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
get logger.
"""
import logging
import os
import sys
from datetime import datetime
class LOGGER(logging.Logger):
"""
set up logging file.
Args:
logger_name (string): logger name.
log_dir (string): path of logger.
Returns:
string, logger path
"""
def __init__(self, logger_name, rank=0):
super(LOGGER, self).__init__(logger_name)
if rank % 8 == 0:
console = logging.StreamHandler(sys.stdout)
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
console.setFormatter(formatter)
self.addHandler(console)
def setup_logging_file(self, log_dir, rank=0):
"""set up log file"""
self.rank = rank
if not os.path.exists(log_dir):
os.makedirs(log_dir, exist_ok=True)
log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
self.log_fn = os.path.join(log_dir, log_name)
fh = logging.FileHandler(self.log_fn)
fh.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
fh.setFormatter(formatter)
self.addHandler(fh)
def info(self, msg, *args, **kwargs):
if self.isEnabledFor(logging.INFO):
self._log(logging.INFO, msg, args, **kwargs)
def save_args(self, args):
self.info('Args:')
args_dict = vars(args)
for key in args_dict.keys():
self.info('--> %s: %s', key, args_dict[key])
self.info('')
def important_info(self, msg, *args, **kwargs):
if self.isEnabledFor(logging.INFO) and self.rank == 0:
line_width = 2
important_msg = '\n'
important_msg += ('*'*70 + '\n')*line_width
important_msg += ('*'*line_width + '\n')*2
important_msg += '*'*line_width + ' '*8 + msg + '\n'
important_msg += ('*'*line_width + '\n')*2
important_msg += ('*'*70 + '\n')*line_width
self.info(important_msg, *args, **kwargs)
def get_logger(path, rank):
logger = LOGGER("mindversion", rank)
logger.setup_logging_file(path, rank)
return logger

53
model_zoo/vgg16/src/utils/sampler.py Normal file
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@ -0,0 +1,53 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
choose samples from the dataset
"""
import math
import numpy as np
class DistributedSampler():
"""
sampling the dataset.
Args:
Returns:
num_samples, number of samples.
"""
def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
self.dataset = dataset
self.rank = rank
self.group_size = group_size
self.dataset_length = len(self.dataset)
self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
self.total_size = self.num_samples * self.group_size
self.shuffle = shuffle
self.seed = seed
def __iter__(self):
if self.shuffle:
self.seed = (self.seed + 1) & 0xffffffff
np.random.seed(self.seed)
indices = np.random.permutation(self.dataset_length).tolist()
else:
indices = list(range(len(self.dataset_length)))
indices += indices[:(self.total_size - len(indices))]
indices = indices[self.rank::self.group_size]
return iter(indices)
def __len__(self):
return self.num_samples

36
model_zoo/vgg16/src/utils/util.py Normal file
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@ -0,0 +1,36 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Util class or function."""
def get_param_groups(network):
"""Param groups for optimizer."""
decay_params = []
no_decay_params = []
for x in network.trainable_params():
parameter_name = x.name
if parameter_name.endswith('.bias'):
# all bias not using weight decay
no_decay_params.append(x)
elif parameter_name.endswith('.gamma'):
# bn weight bias not using weight decay, be carefully for now x not include BN
no_decay_params.append(x)
elif parameter_name.endswith('.beta'):
# bn weight bias not using weight decay, be carefully for now x not include BN
no_decay_params.append(x)
else:
decay_params.append(x)
return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]

214
model_zoo/vgg16/src/utils/var_init.py Normal file
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@ -0,0 +1,214 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Initialize.
"""
import math
from functools import reduce
import numpy as np
import mindspore.nn as nn
from mindspore.common import initializer as init
def _calculate_gain(nonlinearity, param=None):
r"""
Return the recommended gain value for the given nonlinearity function.
The values are as follows:
================= ====================================================
nonlinearity gain
================= ====================================================
Linear / Identity :math:`1`
Conv{1,2,3}D :math:`1`
Sigmoid :math:`1`
Tanh :math:`\frac{5}{3}`
ReLU :math:`\sqrt{2}`
Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
================= ====================================================
Args:
nonlinearity: the non-linear function
param: optional parameter for the non-linear function
Examples:
>>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
"""
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
return 1
if nonlinearity == 'tanh':
return 5.0 / 3
if nonlinearity == 'relu':
return math.sqrt(2.0)
if nonlinearity == 'leaky_relu':
if param is None:
negative_slope = 0.01
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
negative_slope = param
else:
raise ValueError("negative_slope {} not a valid number".format(param))
return math.sqrt(2.0 / (1 + negative_slope ** 2))
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
def _assignment(arr, num):
"""Assign the value of `num` to `arr`."""
if arr.shape == ():
arr = arr.reshape((1))
arr[:] = num
arr = arr.reshape(())
else:
if isinstance(num, np.ndarray):
arr[:] = num[:]
else:
arr[:] = num
return arr
def _calculate_in_and_out(arr):
"""
Calculate n_in and n_out.
Args:
arr (Array): Input array.
Returns:
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
"""
dim = len(arr.shape)
if dim < 2:
raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
n_in = arr.shape[1]
n_out = arr.shape[0]
if dim > 2:
counter = reduce(lambda x, y: x * y, arr.shape[2:])
n_in *= counter
n_out *= counter
return n_in, n_out
def _select_fan(array, mode):
mode = mode.lower()
valid_modes = ['fan_in', 'fan_out']
if mode not in valid_modes:
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
fan_in, fan_out = _calculate_in_and_out(array)
return fan_in if mode == 'fan_in' else fan_out
class KaimingInit(init.Initializer):
r"""
Base Class. Initialize the array with He kaiming algorithm.
Args:
a: the negative slope of the rectifier used after this layer (only
used with ``'leaky_relu'``)
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
preserves the magnitude of the variance of the weights in the
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
backwards pass.
nonlinearity: the non-linear function, recommended to use only with
``'relu'`` or ``'leaky_relu'`` (default).
"""
def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
super(KaimingInit, self).__init__()
self.mode = mode
self.gain = _calculate_gain(nonlinearity, a)
def _initialize(self, arr):
pass
class KaimingUniform(KaimingInit):
r"""
Initialize the array with He kaiming uniform algorithm. The resulting tensor will
have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
.. math::
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
Input:
arr (Array): The array to be assigned.
Returns:
Array, assigned array.
Examples:
>>> w = np.empty(3, 5)
>>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
"""
def _initialize(self, arr):
fan = _select_fan(arr, self.mode)
bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
np.random.seed(0)
data = np.random.uniform(-bound, bound, arr.shape)
_assignment(arr, data)
class KaimingNormal(KaimingInit):
r"""
Initialize the array with He kaiming normal algorithm. The resulting tensor will
have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
.. math::
\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
Input:
arr (Array): The array to be assigned.
Returns:
Array, assigned array.
Examples:
>>> w = np.empty(3, 5)
>>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
"""
def _initialize(self, arr):
fan = _select_fan(arr, self.mode)
std = self.gain / math.sqrt(fan)
np.random.seed(0)
data = np.random.normal(0, std, arr.shape)
_assignment(arr, data)
def default_recurisive_init(custom_cell):
"""default_recurisive_init"""
for _, cell in custom_cell.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
cell.weight.shape,
cell.weight.dtype)
if cell.bias is not None:
fan_in, _ = _calculate_in_and_out(cell.weight)
bound = 1 / math.sqrt(fan_in)
np.random.seed(0)
cell.bias.default_input = init.initializer(init.Uniform(bound),
cell.bias.shape,
cell.bias.dtype)
elif isinstance(cell, nn.Dense):
cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
cell.weight.shape,
cell.weight.dtype)
if cell.bias is not None:
fan_in, _ = _calculate_in_and_out(cell.weight)
bound = 1 / math.sqrt(fan_in)
np.random.seed(0)
cell.bias.default_input = init.initializer(init.Uniform(bound),
cell.bias.shape,
cell.bias.dtype)
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
pass

66
model_zoo/vgg16/src/vgg.py
View File

@ -12,12 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""VGG."""
"""
Image classifiation.
"""
import math
import mindspore.nn as nn
from mindspore.common.initializer import initializer
import mindspore.common.dtype as mstype
from mindspore.common import initializer as init
from mindspore.common.initializer import initializer
from .utils.var_init import default_recurisive_init, KaimingNormal
def _make_layer(base, batch_norm):
def _make_layer(base, args, batch_norm):
"""Make stage network of VGG."""
layers = []
in_channels = 3
@ -25,13 +31,17 @@ def _make_layer(base, batch_norm):
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
weight_shape = (v, in_channels, 3, 3)
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
weight = 'ones'
if args.initialize_mode == "XavierUniform":
weight_shape = (v, in_channels, 3, 3)
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
conv2d = nn.Conv2d(in_channels=in_channels,
out_channels=v,
kernel_size=3,
padding=0,
pad_mode='same',
padding=args.padding,
pad_mode=args.pad_mode,
has_bias=args.has_bias,
weight_init=weight)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
@ -59,17 +69,25 @@ class Vgg(nn.Cell):
>>> num_classes=1000, batch_norm=False, batch_size=1)
"""
def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, phase="train"):
super(Vgg, self).__init__()
_ = batch_size
self.layers = _make_layer(base, batch_norm=batch_norm)
self.layers = _make_layer(base, args, batch_norm=batch_norm)
self.flatten = nn.Flatten()
dropout_ratio = 0.5
if not args.has_dropout or phase == "test":
dropout_ratio = 1.0
self.classifier = nn.SequentialCell([
nn.Dense(512 * 7 * 7, 4096),
nn.ReLU(),
nn.Dropout(dropout_ratio),
nn.Dense(4096, 4096),
nn.ReLU(),
nn.Dropout(dropout_ratio),
nn.Dense(4096, num_classes)])
if args.initialize_mode == "KaimingNormal":
default_recurisive_init(self)
self.custom_init_weight()
def construct(self, x):
x = self.layers(x)
@ -77,6 +95,25 @@ class Vgg(nn.Cell):
x = self.classifier(x)
return x
def custom_init_weight(self):
"""
Init the weight of Conv2d and Dense in the net.
"""
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.default_input = init.initializer(
KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
cell.weight.shape, cell.weight.dtype)
if cell.bias is not None:
cell.bias.default_input = init.initializer(
'zeros', cell.bias.shape, cell.bias.dtype)
elif isinstance(cell, nn.Dense):
cell.weight.default_input = init.initializer(
init.Normal(0.01), cell.weight.shape, cell.weight.dtype)
if cell.bias is not None:
cell.bias.default_input = init.initializer(
'zeros', cell.bias.shape, cell.bias.dtype)
cfg = {
'11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
@ -86,19 +123,24 @@ cfg = {
}
def vgg16(num_classes=1000):
def vgg16(num_classes=1000, args=None, phase="train"):
"""
Get Vgg16 neural network with batch normalization.
Args:
num_classes (int): Class numbers. Default: 1000.
args(namespace): param for net init.
phase(str): train or test mode.
Returns:
Cell, cell instance of Vgg16 neural network with batch normalization.
Examples:
>>> vgg16(num_classes=1000)
>>> vgg16(num_classes=1000, args=args)
"""
net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True)
if args is None:
from .config import cifar_cfg
args = cifar_cfg
net = Vgg(cfg['16'], num_classes=num_classes, args=args, batch_norm=args.batch_norm, phase=phase)
return net

40
model_zoo/vgg16/src/warmup_cosine_annealing_lr.py Normal file
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@ -0,0 +1,40 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
warm up cosine annealing learning rate.
"""
import math
import numpy as np
from .linear_warmup import linear_warmup_lr
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
"""warm up cosine annealing learning rate."""
base_lr = lr
warmup_init_lr = 0
total_steps = int(max_epoch * steps_per_epoch)
warmup_steps = int(warmup_epochs * steps_per_epoch)
lr_each_step = []
for i in range(total_steps):
last_epoch = i // steps_per_epoch
if i < warmup_steps:
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
else:
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)

84
model_zoo/vgg16/src/warmup_step_lr.py Normal file
View File

@ -0,0 +1,84 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
warm up step learning rate.
"""
from collections import Counter
import numpy as np
from .linear_warmup import linear_warmup_lr
def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
"""Set learning rate."""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
if warmup_steps != 0:
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
else:
inc_each_step = 0
for i in range(total_steps):
if i < warmup_steps:
lr_value = float(lr_init) + inc_each_step * float(i)
else:
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
lr_value = float(lr_max) * base * base
if lr_value < 0.0:
lr_value = 0.0
lr_each_step.append(lr_value)
current_step = global_step
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[current_step:]
return learning_rate
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
"""warmup_step_lr"""
base_lr = lr
warmup_init_lr = 0
total_steps = int(max_epoch * steps_per_epoch)
warmup_steps = int(warmup_epochs * steps_per_epoch)
milestones = lr_epochs
milestones_steps = []
for milestone in milestones:
milestones_step = milestone * steps_per_epoch
milestones_steps.append(milestones_step)
lr_each_step = []
lr = base_lr
milestones_steps_counter = Counter(milestones_steps)
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
else:
lr = lr * gamma**milestones_steps_counter[i]
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
lr_epochs = []
for i in range(1, max_epoch):
if i % epoch_size == 0:
lr_epochs.append(i)
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)

237
model_zoo/vgg16/train.py
View File

@ -17,6 +17,7 @@
python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
"""
import argparse
import datetime
import os
import random
@ -25,83 +26,213 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.communication.management import init
from mindspore import ParallelMode
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.model import Model, ParallelMode
from mindspore.train.model import Model
from mindspore.train.serialization import load_param_into_net, load_checkpoint
from src.config import cifar_cfg as cfg
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from src.dataset import vgg_create_dataset
from src.dataset import classification_dataset
from src.crossentropy import CrossEntropy
from src.warmup_step_lr import warmup_step_lr
from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
from src.warmup_step_lr import lr_steps
from src.utils.logging import get_logger
from src.utils.util import get_param_groups
from src.vgg import vgg16
random.seed(1)
np.random.seed(1)
def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
"""Set learning rate."""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
if warmup_steps != 0:
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
def parse_args(cloud_args=None):
"""parameters"""
parser = argparse.ArgumentParser('mindspore classification training')
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
help='device where the code will be implemented. (Default: Ascend)')
parser.add_argument('--device_id', type=int, default=1, help='device id of GPU or Ascend. (Default: None)')
# dataset related
parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
parser.add_argument('--data_path', type=str, default='', help='train data dir')
# network related
parser.add_argument('--pre_trained', default='', type=str, help='model_path, local pretrained model to load')
parser.add_argument('--lr_gamma', type=float, default=0.1,
help='decrease lr by a factor of exponential lr_scheduler')
parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
parser.add_argument('--T_max', type=int, default=150, help='T-max in cosine_annealing scheduler')
# logging and checkpoint related
parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
parser.add_argument('--ckpt_interval', type=int, default=5, help='ckpt_interval')
parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
# distributed related
parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
args_opt = parser.parse_args()
args_opt = merge_args(args_opt, cloud_args)
if args_opt.dataset == "cifar10":
from src.config import cifar_cfg as cfg
else:
inc_each_step = 0
for i in range(total_steps):
if i < warmup_steps:
lr_value = float(lr_init) + inc_each_step * float(i)
else:
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
lr_value = float(lr_max) * base * base
if lr_value < 0.0:
lr_value = 0.0
lr_each_step.append(lr_value)
from src.config import imagenet_cfg as cfg
current_step = global_step
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[current_step:]
args_opt.label_smooth = cfg.label_smooth
args_opt.label_smooth_factor = cfg.label_smooth_factor
args_opt.lr_scheduler = cfg.lr_scheduler
args_opt.loss_scale = cfg.loss_scale
args_opt.max_epoch = cfg.max_epoch
args_opt.warmup_epochs = cfg.warmup_epochs
args_opt.lr = cfg.lr
args_opt.lr_init = cfg.lr_init
args_opt.lr_max = cfg.lr_max
args_opt.momentum = cfg.momentum
args_opt.weight_decay = cfg.weight_decay
args_opt.per_batch_size = cfg.batch_size
args_opt.num_classes = cfg.num_classes
args_opt.buffer_size = cfg.buffer_size
args_opt.ckpt_save_max = cfg.keep_checkpoint_max
args_opt.pad_mode = cfg.pad_mode
args_opt.padding = cfg.padding
args_opt.has_bias = cfg.has_bias
args_opt.batch_norm = cfg.batch_norm
args_opt.initialize_mode = cfg.initialize_mode
args_opt.has_dropout = cfg.has_dropout
return learning_rate
args_opt.lr_epochs = list(map(int, cfg.lr_epochs.split(',')))
args_opt.image_size = list(map(int, cfg.image_size.split(',')))
return args_opt
def merge_args(args_opt, cloud_args):
"""dictionary"""
args_dict = vars(args_opt)
if isinstance(cloud_args, dict):
for key_arg in cloud_args.keys():
val = cloud_args[key_arg]
if key_arg in args_dict and val:
arg_type = type(args_dict[key_arg])
if arg_type is not None:
val = arg_type(val)
args_dict[key_arg] = val
return args_opt
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Cifar10 classification')
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
help='device where the code will be implemented. (Default: Ascend)')
parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
parser.add_argument('--pre_trained', type=str, default=None, help='the pretrained checkpoint file path.')
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
context.set_context(device_id=args_opt.device_id)
args = parse_args()
device_num = int(os.environ.get("DEVICE_NUM", 1))
if device_num > 1:
if args.is_distributed:
if args.device_target == "Ascend":
init()
context.set_context(device_id=args.device_id)
elif args.device_target == "GPU":
init("nccl")
args.rank = get_rank()
args.group_size = get_group_size()
device_num = args.group_size
context.reset_auto_parallel_context()
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
init()
parameter_broadcast=True, mirror_mean=True)
else:
context.set_context(device_id=args.device_id)
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
# select for master rank save ckpt or all rank save, compatible for model parallel
args.rank_save_ckpt_flag = 0
if args.is_save_on_master:
if args.rank == 0:
args.rank_save_ckpt_flag = 1
else:
args.rank_save_ckpt_flag = 1
# logger
args.outputs_dir = os.path.join(args.ckpt_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
args.logger = get_logger(args.outputs_dir, args.rank)
if args.dataset == "cifar10":
dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, args.rank, args.group_size,
repeat_num=args.max_epoch)
else:
dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size,
args.rank, args.group_size, repeat_num=args.max_epoch)
dataset = vgg_create_dataset(args_opt.data_path, cfg.epoch_size)
batch_num = dataset.get_dataset_size()
args.steps_per_epoch = dataset.get_dataset_size()
args.logger.save_args(args)
# network
args.logger.important_info('start create network')
# get network and init
network = vgg16(args.num_classes, args)
net = vgg16(num_classes=cfg.num_classes)
# pre_trained
if args_opt.pre_trained:
load_param_into_net(net, load_checkpoint(args_opt.pre_trained))
if args.pre_trained:
load_param_into_net(network, load_checkpoint(args.pre_trained))
lr = lr_steps(0, lr_init=cfg.lr_init, lr_max=cfg.lr_max, warmup_epochs=cfg.warmup_epochs,
total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
# lr scheduler
if args.lr_scheduler == 'exponential':
lr = warmup_step_lr(args.lr,
args.lr_epochs,
args.steps_per_epoch,
args.warmup_epochs,
args.max_epoch,
gamma=args.lr_gamma,
)
elif args.lr_scheduler == 'cosine_annealing':
lr = warmup_cosine_annealing_lr(args.lr,
args.steps_per_epoch,
args.warmup_epochs,
args.max_epoch,
args.T_max,
args.eta_min)
elif args.lr_scheduler == 'step':
lr = lr_steps(0, lr_init=args.lr_init, lr_max=args.lr_max, warmup_epochs=args.warmup_epochs,
total_epochs=args.max_epoch, steps_per_epoch=batch_num)
else:
raise NotImplementedError(args.lr_scheduler)
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
# optimizer
opt = Momentum(params=get_param_groups(network),
learning_rate=Tensor(lr),
momentum=args.momentum,
weight_decay=args.weight_decay,
loss_scale=args.loss_scale)
if args.dataset == "cifar10":
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
model = Model(network, loss_fn=loss, optimizer=opt, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
else:
if not args.label_smooth:
args.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
# define callbacks
time_cb = TimeMonitor(data_size=batch_num)
ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck)
loss_cb = LossMonitor()
model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("train success")
loss_cb = LossMonitor(per_print_times=batch_num)
callbacks = [time_cb, loss_cb]
if args.rank_save_ckpt_flag:
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
keep_checkpoint_max=args.ckpt_save_max)
ckpt_cb = ModelCheckpoint(config=ckpt_config,
directory=args.outputs_dir,
prefix='{}'.format(args.rank))
callbacks.append(ckpt_cb)
model.train(args.max_epoch, dataset, callbacks=callbacks)