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!3719 resnet101 changes for GPU 

Merge pull request !3719 from RobinGrosman/master
This commit is contained in:
mindspore-ci-bot 2020-07-31 16:09:09 +08:00 committed by Gitee
parent 59aaf56a1e a75228d7c5
commit 6adcb5a859
5 changed files with 216 additions and 41 deletions
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3
model_zoo/official/cv/resnet/eval.py
View File

@ -58,11 +58,8 @@ if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False, device_id=device_id)
# create dataset
if args_opt.net == "resnet50":
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
target=target)
else:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# define net

93
model_zoo/official/cv/resnet/scripts/run_distribute_train_gpu.sh Executable file
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@ -0,0 +1,93 @@
#!/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.
# ============================================================================
if [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: sh run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
then
echo "error: the selected net is neither resnet50 nor resnet101"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet2012" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet2012"
exit 1
fi
if [ $1 == "resnet101" ] && [ $2 == "cifar10" ]
then
echo "error: training resnet101 with cifar10 dataset is unsupported now!"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $3)
if [ $# == 4 ]
then
PATH2=$(get_real_path $4)
fi
if [ ! -d $PATH2 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 5 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
rm -rf ./train_parallel
mkdir ./train_parallel
cp ../*.py ./train_parallel
cp *.sh ./train_parallel
cp -r ../src ./train_parallel
cd ./train_parallel || exit
if [ $# == 3 ]
then
mpirun --allow-run-as-root -n $RANK_SIZE \
python train.py --net=$1 --dataset=$2 --run_distribute=True \
--device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &
fi
if [ $# == 4 ]
then
mpirun --allow-run-as-root -n $RANK_SIZE \
python train.py --net=$1 --dataset=$2 --run_distribute=True \
--device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi

95
model_zoo/official/cv/resnet/scripts/run_standalone_train_gpu.sh Executable file
View File

@ -0,0 +1,95 @@
#!/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.
# ============================================================================
if [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: sh run_standalone_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
then
echo "error: the selected net is neither resnet50 nor resnet101"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet2012" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet2012"
exit 1
fi
if [ $1 == "resnet101" ] && [ $2 == "cifar10" ]
then
echo "error: training resnet101 with cifar10 dataset is unsupported now!"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $3)
if [ $# == 4 ]
then
PATH2=$(get_real_path $4)
fi
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 4 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_ID=0
export RANK_SIZE=1
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp ../*.py ./train
cp *.sh ./train
cp -r ../src ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
if [ $# == 3 ]
then
python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 &> log &
fi
if [ $# == 4 ]
then
python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi
cd ..

44
model_zoo/official/cv/resnet/src/dataset.py
View File

@ -139,7 +139,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
return ds
def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32):
def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval imagenet2012 dataset for resnet101
Args:
@ -158,36 +158,26 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32):
else:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
resize_height = 224
rescale = 1.0 / 255.0
shift = 0.0
image_size = 224
mean = [0.475 * 255, 0.451 * 255, 0.392 * 255]
std = [0.275 * 255, 0.267 * 255, 0.278 * 255]
# define map operations
decode_op = C.Decode()
random_resize_crop_op = C.RandomResizedCrop(resize_height, (0.08, 1.0), (0.75, 1.33), max_attempts=100)
horizontal_flip_op = C.RandomHorizontalFlip(rank_id / (rank_id + 1))
resize_op_256 = C.Resize((256, 256))
center_crop = C.CenterCrop(224)
rescale_op = C.Rescale(rescale, shift)
normalize_op = C.Normalize((0.475, 0.451, 0.392), (0.275, 0.267, 0.278))
changeswap_op = C.HWC2CHW()
if do_train:
trans = [decode_op,
random_resize_crop_op,
horizontal_flip_op,
rescale_op,
normalize_op,
changeswap_op]
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(rank_id/ (rank_id +1)),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [decode_op,
resize_op_256,
center_crop,
rescale_op,
normalize_op,
changeswap_op]
trans = [
C.Decode(),
C.Resize(256),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)

14
model_zoo/official/cv/resnet/train.py
View File

@ -86,12 +86,8 @@ if __name__ == '__main__':
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
# create dataset
if args_opt.net == "resnet50":
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
batch_size=config.batch_size, target=target)
else:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# define net
@ -122,7 +118,7 @@ if __name__ == '__main__':
lr = get_lr(lr_init=config.lr_init, lr_end=0.0, lr_max=config.lr_max, warmup_epochs=config.warmup_epochs,
total_epochs=config.epoch_size, steps_per_epoch=step_size, lr_decay_mode='cosine')
else:
lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, 120,
lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
config.pretrain_epoch_size * step_size)
lr = Tensor(lr)
@ -147,9 +143,13 @@ if __name__ == '__main__':
amp_level="O2", keep_batchnorm_fp32=False)
else:
# GPU target
loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum)
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean", is_grad=False,
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
##Mixed precision
#model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
# amp_level="O2", keep_batchnorm_fp32=True)
# define callbacks
time_cb = TimeMonitor(data_size=step_size)