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gpu update example resnet

This commit is contained in:
VectorSL 2020-05-25 21:18:07 +08:00
parent e5c7ecfd46
commit ceebbd01f4
8 changed files with 122 additions and 56 deletions
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12
example/resnet50_cifar10/README.md
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@ -123,3 +123,15 @@ Inference result will be stored in the example path, whose folder name is "infer
```
result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
```
### Running on GPU
```
# distributed training example
mpirun -n 8 python train.py --dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True
# standalone training example
python train.py --dataset_path=~/cifar-10-batches-bin --device_target="GPU"
# infer example
python eval.py --dataset_path=~/cifar10-10-verify-bin --device_target="GPU" --checkpoint_path=resnet-90_195.ckpt
```

12
example/resnet50_cifar10/dataset.py
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@ -20,10 +20,11 @@ import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.transforms.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
from mindspore.communication.management import get_rank, get_group_size
from config import config
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval dataset
@ -32,12 +33,17 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
target(str): the device target. Default: Ascend
Returns:
dataset
"""
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
if target == "Ascend":
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
else:
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)

28
example/resnet50_cifar10/eval.py
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@ -25,7 +25,7 @@ from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.model import Model, ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.communication.management import init
from mindspore.communication.management import init, get_group_size
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
@ -34,26 +34,32 @@ parser.add_argument('--do_train', type=bool, default=False, help='Do train or no
parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
context.set_context(device_id=device_id)
if __name__ == '__main__':
target = args_opt.device_target
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
if not args_opt.do_eval and args_opt.run_distribute:
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
init()
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=device_id)
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
init()
elif target == "GPU":
init("nccl")
context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
epoch_size = config.epoch_size
net = resnet50(class_num=config.class_num)
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
if args_opt.do_eval:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
target=target)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:

43
example/resnet50_cifar10/train.py
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@ -29,7 +29,7 @@ from mindspore.train.model import Model, ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.communication.management import init
from mindspore.communication.management import init, get_rank, get_group_size
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
@ -37,28 +37,37 @@ parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
enable_auto_mixed_precision=True)
if __name__ == '__main__':
target = args_opt.device_target
if not args_opt.do_eval and args_opt.run_distribute:
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
init()
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
enable_auto_mixed_precision=True)
init()
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
ckpt_save_dir = config.save_checkpoint_path
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
elif target == "GPU":
context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False)
init("nccl")
context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')
epoch_size = config.epoch_size
net = resnet50(class_num=config.class_num)
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
if args_opt.do_train:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
repeat_num=epoch_size, batch_size=config.batch_size)
repeat_num=epoch_size, batch_size=config.batch_size, target=target)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
@ -67,9 +76,11 @@ if __name__ == '__main__':
lr_decay_mode='poly'))
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2",
keep_batchnorm_fp32=False)
if target == 'GPU':
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
else:
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=True)
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
@ -77,6 +88,6 @@ if __name__ == '__main__':
if config.save_checkpoint:
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)

15
example/resnet50_imagenet2012/README.md
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@ -133,3 +133,18 @@ Inference result will be stored in the example path, whose folder name is "infer
```
result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt
```
### Running on GPU
```
# distributed training example
mpirun -n 8 python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --run_distribute=True
# standalone training example
python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU"
# standalone training example with pretrained checkpoint
python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --pre_trained=pretrained.ckpt
# infer example
python eval.py --dataset_path=dataset/ilsvrc/val --device_target="GPU" --checkpoint_path=resnet-90_5004ss.ckpt
```

13
example/resnet50_imagenet2012/dataset.py
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@ -20,9 +20,9 @@ import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.transforms.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
from mindspore.communication.management import get_rank, get_group_size
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval dataset
@ -31,12 +31,17 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
target(str): the device target. Default: Ascend
Returns:
dataset
"""
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
if target == "Ascend":
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
else:
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)

14
example/resnet50_imagenet2012/eval.py
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@ -32,12 +32,13 @@ parser.add_argument('--do_train', type=bool, default=False, help='Do train or no
parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
context.set_context(device_id=device_id)
target = args_opt.device_target
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=device_id)
if __name__ == '__main__':
@ -47,7 +48,8 @@ if __name__ == '__main__':
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
if args_opt.do_eval:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
target=target)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:

41
example/resnet50_imagenet2012/train.py
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@ -29,7 +29,7 @@ from mindspore.train.model import Model, ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.communication.management import init
from mindspore.communication.management import init, get_rank, get_group_size
import mindspore.nn as nn
import mindspore.common.initializer as weight_init
from crossentropy import CrossEntropy
@ -40,21 +40,28 @@ parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
enable_auto_mixed_precision=True)
if __name__ == '__main__':
target = args_opt.device_target
if not args_opt.do_eval and args_opt.run_distribute:
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True, parameter_broadcast=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
init()
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
enable_auto_mixed_precision=True)
init()
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
ckpt_save_dir = config.save_checkpoint_path
elif target == "GPU":
context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False)
init("nccl")
context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
epoch_size = config.epoch_size
net = resnet50(class_num=config.class_num)
@ -81,7 +88,7 @@ if __name__ == '__main__':
if args_opt.do_train:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
repeat_num=epoch_size, batch_size=config.batch_size)
repeat_num=epoch_size, batch_size=config.batch_size, target=target)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
@ -93,9 +100,11 @@ if __name__ == '__main__':
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2",
keep_batchnorm_fp32=False)
if target == "Ascend":
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False)
elif target == "GPU":
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
time_cb = TimeMonitor(data_size=step_size)
@ -104,6 +113,6 @@ if __name__ == '__main__':
if config.save_checkpoint:
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)