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!2628 move resnet series from example to model_zoo 

Merge pull request !2628 from gengdongjie/r0.5
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mindspore-ci-bot 2020-06-29 09:45:09 +08:00 committed by Gitee
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137
example/resnet50_cifar10/README.md
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# ResNet-50 Example
## Description
This is an example of training ResNet-50 with CIFAR-10 dataset in MindSpore.
## Requirements
- Install [MindSpore](https://www.mindspore.cn/install/en).
- Download the dataset CIFAR-10
> Unzip the CIFAR-10 dataset to any path you want and the folder structure should include train and eval dataset as follows:
> ```
> .
> ├── cifar-10-batches-bin # train dataset
> └── cifar-10-verify-bin # infer dataset
> ```
## Example structure
```shell
.
├── config.py # parameter configuration
├── dataset.py # data preprocessing
├── eval.py # infer script
├── lr_generator.py # generate learning rate for each step
├── run_distribute_train.sh # launch distributed training(8 pcs)
├── run_infer.sh # launch infering
├── run_standalone_train.sh # launch standalone training(1 pcs)
└── train.py # train script
```
## Parameter configuration
Parameters for both training and inference can be set in config.py.
```
"class_num": 10, # dataset class num
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
"epoch_size": 90, # only valid for taining, which is always 1 for inference
"buffer_size": 100, # number of queue size in data preprocessing
"image_height": 224, # image height
"image_width": 224, # image width
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_steps": 195, # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 5, # number of warmup epoch
"lr_decay_mode": "poly" # decay mode can be selected in steps, ploy and default
"lr_init": 0.01, # initial learning rate
"lr_end": 0.00001, # final learning rate
"lr_max": 0.1, # maximum learning rate
```
## Running the example
### Train
#### Usage
```
# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
# standalone training
Usage: sh run_standalone_train.sh [DATASET_PATH]
```
#### Launch
```
# distribute training example
sh run_distribute_train.sh rank_table.json ~/cifar-10-batches-bin
# standalone training example
sh run_standalone_train.sh ~/cifar-10-batches-bin
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
#### Result
Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
```
# distribute training result(8 pcs)
epoch: 1 step: 195, loss is 1.9601055
epoch: 2 step: 195, loss is 1.8555021
epoch: 3 step: 195, loss is 1.6707983
epoch: 4 step: 195, loss is 1.8162166
epoch: 5 step: 195, loss is 1.393667
```
### Infer
#### Usage
```
# infer
Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```
# infer example
sh run_infer.sh ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
```
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
```

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example/resnet50_cifar10/config.py
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# 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.
# ============================================================================
"""
network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as ed
config = ed({
"class_num": 10,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 90,
"buffer_size": 100,
"image_height": 224,
"image_width": 224,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 5,
"lr_decay_mode": "poly",
"lr_init": 0.01,
"lr_end": 0.00001,
"lr_max": 0.1
})

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example/resnet50_cifar10/dataset.py
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# 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
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 init, get_rank, get_group_size
from config import config
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
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
"""
if target == "Ascend":
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
else:
init("nccl")
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
# define map operations
trans = []
if do_train:
trans += [
C.RandomCrop((32, 32), (4, 4, 4, 4)),
C.RandomHorizontalFlip(prob=0.5)
]
trans += [
C.Resize((config.image_height, config.image_width)),
C.Rescale(1.0 / 255.0, 0.0),
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds

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example/resnet50_cifar10/eval.py
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# 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.
# ============================================================================
"""
eval.
"""
import os
import argparse
from dataset import create_dataset
from config import config
from mindspore import context
from mindspore.model_zoo.resnet import resnet50
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, get_group_size
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
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()
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:
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,
target=target)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)

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example/resnet50_cifar10/lr_generator.py
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# 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.
# ============================================================================
"""learning rate generator"""
import numpy as np
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
"""
generate learning rate array
Args:
global_step(int): total steps of the training
lr_init(float): init learning rate
lr_end(float): end learning rate
lr_max(float): max learning rate
warmup_epochs(int): number of warmup epochs
total_epochs(int): total epoch of training
steps_per_epoch(int): steps of one epoch
lr_decay_mode(string): learning rate decay mode, including steps, poly or default
Returns:
np.array, learning rate array
"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
if lr_decay_mode == 'steps':
decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
for i in range(total_steps):
if i < decay_epoch_index[0]:
lr = lr_max
elif i < decay_epoch_index[1]:
lr = lr_max * 0.1
elif i < decay_epoch_index[2]:
lr = lr_max * 0.01
else:
lr = lr_max * 0.001
lr_each_step.append(lr)
elif lr_decay_mode == 'poly':
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 = float(lr_init) + inc_each_step * float(i)
else:
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
lr = float(lr_max) * base * base
if lr < 0.0:
lr = 0.0
lr_each_step.append(lr)
else:
for i in range(total_steps):
if i < warmup_steps:
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
else:
lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
lr_each_step.append(lr)
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

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example/resnet50_cifar10/run_distribute_train.sh
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#!/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 [ $# != 2 ]
then
echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
if [ ! -f "$PATH1" ]
then
echo "error: MINDSPORE_HCCL_CONFIG_PATH=$PATH1 is not a file"
exit 1
fi
if [ ! -d "$PATH2" ]
then
echo "error: DATASET_PATH=$PATH2 is not a directory"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
export MINDSPORE_HCCL_CONFIG_PATH=$PATH1
for((i=0; i<${DEVICE_NUM}; i++))
do
export DEVICE_ID=$i
export RANK_ID=$i
rm -rf ./train_parallel$i
mkdir ./train_parallel$i
cp *.py ./train_parallel$i
cp *.sh ./train_parallel$i
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
cd ..
done

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example/resnet50_cifar10/run_infer.sh
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#!/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 [ $# != 2 ]
then
echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]
then
echo "error: CHECKPOINT_PATH=$2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "infer" ];
then
rm -rf ./infer
fi
mkdir ./infer
cp *.py ./infer
cp *.sh ./infer
cd ./infer || exit
env > env.log
echo "start infering for device $DEVICE_ID"
python eval.py --do_eval=True --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
cd ..

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example/resnet50_cifar10/run_standalone_train.sh
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#!/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 [ $# != 1 ]
then
echo "Usage: sh run_standalone_train.sh [DATASET_PATH]"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
if [ ! -d "$PATH1" ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_ID=0
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp *.py ./train
cp *.sh ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
python train.py --do_train=True --dataset_path=$PATH1 &> log &
cd ..

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example/resnet50_cifar10/train.py
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# 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.
# ============================================================================
"""train_imagenet."""
import os
import argparse
import numpy as np
from dataset import create_dataset
from lr_generator import get_lr
from config import config
from mindspore import context
from mindspore import Tensor
from mindspore.model_zoo.resnet import resnet50
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.optim.momentum import Momentum
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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, get_rank, get_group_size
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
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()
if __name__ == '__main__':
target = args_opt.device_target
ckpt_save_dir = config.save_checkpoint_path
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
np.random.seed(1)
if not args_opt.do_eval and args_opt.run_distribute:
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)
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, target=target)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
lr = Tensor(get_lr(global_step=0, lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
lr_decay_mode='poly'))
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
if target == 'GPU':
loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
else:
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False)
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
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=ckpt_save_dir, config=config_ck)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)

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example/resnet50_imagenet2012/README.md
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# ResNet-50 Example
## Description
This is an example of training ResNet-50 with ImageNet2012 dataset in MindSpore.
## Requirements
- Install [MindSpore](https://www.mindspore.cn/install/en).
- Download the dataset ImageNet2012
> Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
> ```
> .
> ├── ilsvrc # train dataset
> └── ilsvrc_eval # infer dataset
> ```
## Example structure
```shell
.
├── crossentropy.py # CrossEntropy loss function
├── config.py # parameter configuration
├── dataset.py # data preprocessing
├── eval.py # infer script
├── lr_generator.py # generate learning rate for each step
├── run_distribute_train.sh # launch distributed training(8 pcs)
├── run_infer.sh # launch infering
├── run_standalone_train.sh # launch standalone training(1 pcs)
└── train.py # train script
```
## Parameter configuration
Parameters for both training and inference can be set in config.py.
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
"epoch_size": 90, # only valid for taining, which is always 1 for inference
"pretrained_epoch_size": 1, # epoch size that model has been trained before load pretrained checkpoint
"buffer_size": 1000, # number of queue size in data preprocessing
"image_height": 224, # image height
"image_width": 224, # image width
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine", # decay mode for generating learning rate
"label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_max": 0.1, # maximum learning rate
```
## Running the example
### Train
#### Usage
```
# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# standalone training
Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
```
#### Launch
```bash
# distributed training example(8 pcs)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
# If you want to load pretrained ckpt file
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc ./pretrained.ckpt
# standalone training example(1 pcs)
sh run_standalone_train.sh dataset/ilsvrc
# If you want to load pretrained ckpt file
sh run_standalone_train.sh dataset/ilsvrc ./pretrained.ckpt
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
#### Result
Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
```
# distribute training result(8 pcs)
epoch: 1 step: 5004, loss is 4.8995576
epoch: 2 step: 5004, loss is 3.9235563
epoch: 3 step: 5004, loss is 3.833077
epoch: 4 step: 5004, loss is 3.2795618
epoch: 5 step: 5004, loss is 3.1978393
```
### Infer
#### Usage
```
# infer
Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```bash
# infer with checkpoint
sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-90_5004.ckpt
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
```
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
```

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example/resnet50_imagenet2012/crossentropy.py
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# 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

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example/resnet50_imagenet2012/dataset.py
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# 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
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 init, get_rank, get_group_size
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
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
"""
if target == "Ascend":
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
else:
init("nccl")
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(prob=0.5),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize((256, 256)),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds

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example/resnet50_imagenet2012/eval.py
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# 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.
# ============================================================================
"""
eval.
"""
import os
import argparse
from dataset import create_dataset
from config import config
from mindspore import context
from mindspore.model_zoo.resnet import resnet50
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from crossentropy import CrossEntropy
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
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()
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__':
net = resnet50(class_num=config.class_num)
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
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,
target=target)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)

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example/resnet50_imagenet2012/run_distribute_train.sh
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#!/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 [ $# != 2 ] && [ $# != 3 ]
then
echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
if [ $# == 3 ]
then
PATH3=$(get_real_path $3)
fi
if [ ! -f "$PATH1" ]
then
echo "error: MINDSPORE_HCCL_CONFIG_PATH=$PATH1 is not a file"
exit 1
fi
if [ ! -d "$PATH2" ]
then
echo "error: DATASET_PATH=$PATH2 is not a directory"
exit 1
fi
if [ $# == 3 ] && [ ! -f "$PATH3" ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
export MINDSPORE_HCCL_CONFIG_PATH=$PATH1
export RANK_TABLE_FILE=$PATH1
for((i=0; i<${DEVICE_NUM}; i++))
do
export DEVICE_ID=$i
export RANK_ID=$i
rm -rf ./train_parallel$i
mkdir ./train_parallel$i
cp *.py ./train_parallel$i
cp *.sh ./train_parallel$i
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
if [ $# == 2 ]
then
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
else
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
fi
cd ..
done

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example/resnet50_imagenet2012/run_infer.sh
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#!/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 [ $# != 2 ]
then
echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]
then
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "infer" ];
then
rm -rf ./infer
fi
mkdir ./infer
cp *.py ./infer
cp *.sh ./infer
cd ./infer || exit
env > env.log
echo "start infering for device $DEVICE_ID"
python eval.py --do_eval=True --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
cd ..

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example/resnet50_imagenet2012/run_standalone_train.sh
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#!/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 [ $# != 1 ] && [ $# != 2 ]
then
echo "Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
if [ $# == 2 ]
then
PATH2=$(get_real_path $2)
fi
if [ ! -d "$PATH1" ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 2 ] && [ ! -f "$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
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp *.py ./train
cp *.sh ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
if [ $# == 1 ]
then
python train.py --do_train=True --dataset_path=$PATH1 &> log &
else
python train.py --do_train=True --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi
cd ..

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example/resnet50_imagenet2012/train.py
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# 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.
# ============================================================================
"""train_imagenet."""
import os
import argparse
import numpy as np
from dataset import create_dataset
from lr_generator import get_lr
from config import config
from mindspore import context
from mindspore import Tensor
from mindspore.model_zoo.resnet import resnet50
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.optim.momentum import Momentum
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, get_rank, get_group_size
import mindspore.nn as nn
import mindspore.common.initializer as weight_init
from crossentropy import CrossEntropy
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
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()
if __name__ == '__main__':
target = args_opt.device_target
ckpt_save_dir = config.save_checkpoint_path
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
np.random.seed(1)
if not args_opt.do_eval and args_opt.run_distribute:
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)
# weight init
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
epoch_size = config.epoch_size - config.pretrained_epoch_size
else:
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
cell.weight.default_input.shape,
cell.weight.default_input.dtype).to_tensor()
if isinstance(cell, nn.Dense):
cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
cell.weight.default_input.shape,
cell.weight.default_input.dtype).to_tensor()
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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, target=target)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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')
if args_opt.pre_trained:
lr = lr[config.pretrained_epoch_size * step_size:]
lr = Tensor(lr)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
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)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
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=ckpt_save_dir, config=config_ck)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)

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example/resnet50_imagenet2012_THOR/README.md
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# ResNet-50-THOR Example
## Description
This is an example of training ResNet-50 V1.5 with ImageNet2012 dataset by second-order optimizer THOR. THOR is a novel approximate seond-order optimization method in MindSpore. With fewer iterations, THOR can finish ResNet-50 V1.5 training in 72 minutes to top-1 accuracy of 75.9% using 8 Ascend 910, which is much faster than SGD with Momentum.
## Requirements
- Install [MindSpore](https://www.mindspore.cn/install/en).
- Download the dataset ImageNet2012
> Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
> ```
> .
> ├── ilsvrc # train dataset
> └── ilsvrc_eval # infer dataset
> ```
## Example structure
```shell
.
├── crossentropy.py # CrossEntropy loss function
├── config.py # parameter configuration
├── dataset_imagenet.py # data preprocessing
├── eval.py # infer script
├── model # include model file of the optimizer
├── run_distribute_train.sh # launch distributed training(8 pcs)
├── run_infer.sh # launch infering
└── train.py # train script
```
## Parameter configuration
Parameters for both training and inference can be set in config.py.
```
"class_num": 1000, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 128, # loss scale
"momentum": 0.9, # momentum of THOR optimizer
"weight_decay": 5e-4, # weight decay
"epoch_size": 45, # only valid for taining, which is always 1 for inference
"buffer_size": 1000, # number of queue size in data preprocessing
"image_height": 224, # image height
"image_width": 224, # image width
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_steps": 5004, # the step interval between two checkpoints. By default, the checkpoint will be saved every epoch
"keep_checkpoint_max": 20, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"frequency": 834, # the step interval to update second-order information matrix
```
## Running the example
### Train
#### Usage
```
# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [DEVICE_NUM]
```
#### Launch
```bash
# distributed training example(8 pcs)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
#### Result
Training result will be stored in the example path, whose folder name begins with "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
```
# distribute training result(8 pcs)
epoch: 1 step: 5004, loss is 4.4182425
epoch: 2 step: 5004, loss is 3.740064
epoch: 3 step: 5004, loss is 4.0546017
epoch: 4 step: 5004, loss is 3.7598825
epoch: 5 step: 5004, loss is 3.3744206
......
```
### Infer
#### Usage
```
# infer
Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```bash
# infer with checkpoint
sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-42_5004.ckpt
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
```
result: {'acc': 0.759503041} ckpt=train_parallel0/resnet-42_5004.ckpt
```

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# 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.
# ============================================================================
"""
network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as ed
config = ed({
"class_num": 1000,
"batch_size": 32,
"loss_scale": 128,
"momentum": 0.9,
"weight_decay": 5e-4,
"epoch_size": 45,
"buffer_size": 1000,
"image_height": 224,
"image_width": 224,
"save_checkpoint": True,
"save_checkpoint_steps": 5004,
"keep_checkpoint_max": 20,
"save_checkpoint_path": "./",
"label_smooth": 1,
"label_smooth_factor": 0.1,
"frequency": 834
})

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example/resnet50_imagenet2012_THOR/crossentropy.py
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# 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.
# ============================================================================
"""CrossEntropy"""
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.nn.loss.loss import _Loss
from mindspore.ops import functional as F
from mindspore.ops import operations as P
class CrossEntropy(_Loss):
"""CrossEntropy"""
def __init__(self, smooth_factor=0., num_classes=1000):
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.cast = P.Cast()
self.ce = nn.SoftmaxCrossEntropyWithLogits()
self.mean = P.ReduceMean(False)
def construct(self, logit, label):
# one_hot_label = self.onehot(self.cast(label, mstype.int32),
# F.shape(logit)[1], self.on_value, self.off_value)、
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

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example/resnet50_imagenet2012_THOR/dataset_imagenet.py
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# 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.transforms.c_transforms as C2
import mindspore.dataset.transforms.vision.c_transforms as V_C
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
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
Returns:
dataset
"""
device_num = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False)
else:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
if do_train:
transform_img = [
V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
V_C.RandomHorizontalFlip(prob=0.5),
V_C.Normalize(mean=mean, std=std),
V_C.HWC2CHW()
]
else:
transform_img = [
V_C.Decode(),
V_C.Resize((256, 256)),
V_C.CenterCrop(image_size),
V_C.Normalize(mean=mean, std=std),
V_C.HWC2CHW()
]
# type_cast_op = C2.TypeCast(mstype.float16)
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image", operations=transform_img, num_parallel_workers=8)
ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
# apply shuffle operations
# ds = ds.shuffle(buffer_size=config.buffer_size)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds

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example/resnet50_imagenet2012_THOR/eval.py
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# 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.
# ============================================================================
"""
eval.
"""
import os
import argparse
from dataset_imagenet import create_dataset
from config import config
from mindspore import context
from mindspore.model_zoo.resnet import resnet50
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from crossentropy import CrossEntropy
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
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')
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__':
net = resnet50(class_num=config.class_num)
if not config.label_smooth:
config.label_smooth_factor = 0.0
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)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)

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example/resnet50_imagenet2012_THOR/model/dataset_helper.py
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# 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.
# ============================================================================
"""Dataset help for minddata dataset"""
from mindspore._checkparam import check_bool
from mindspore.parallel._utils import _get_device_num, _get_parallel_mode
from mindspore.train.dataset_helper import _send_data
from mindspore.train._utils import _exec_datagraph, _get_types_and_shapes, \
_to_full_shapes
from mindspore.train.parallel_utils import ParallelMode
class DatasetHelper:
"""
Help function to use the Minddata dataset.
According to different context, change the iter of dataset, to use the same for loop in different context.
Note:
The iter of DatasetHelper will give one epoch data.
Args:
dataset (DataSet): The dataset.
dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data from host.
Default: True.
Examples:
>>> dataset_helper = DatasetHelper(dataset)
>>> for inputs in dataset_helper:
>>> outputs = network(*inputs)
"""
def __init__(self, dataset, dataset_sink_mode=True, iter_first_order=0):
check_bool(dataset_sink_mode)
self.iter = _DatasetIterMSLoopSink(dataset, iter_first_order)
def __iter__(self):
return self.iter.__iter__()
# A temp solution for loop sink. Delete later
def types_shapes(self):
"""Get the types and shapes from dataset on current config."""
return self.iter.types_shapes()
def loop_size(self):
"""Get loop_size for every iteration."""
return self.iter.loop_size
class _DatasetIter:
"""Base iter for dataset help"""
def __init__(self, dataset):
self.loop_size = 1
if not hasattr(dataset, '__ME_INITED__'):
if not hasattr(dataset, '__loop_size__'):
self.loop_size = dataset.get_dataset_size()
else:
self.loop_size = dataset.__loop_size__
dataset.__TRANSFER_DATASET__ = _exec_datagraph(dataset, self.loop_size)
dataset.__ME_INITED__ = dataset.__TRANSFER_DATASET__.queue_name
if not hasattr(dataset, '__no_send__'):
_send_data(dataset)
else:
_send_data(dataset)
self.ind = 0
self.dataset = dataset
dataset_types, dataset_shapes = _get_types_and_shapes(dataset)
self.dataset_types, self.dataset_shapes = dataset_types, dataset_shapes
def __iter__(self):
self.ind = 0
return self
def __next__(self):
if self.ind >= self.loop_count:
raise StopIteration()
self.ind += 1
return self.op()
def types_shapes(self):
return self.dataset_types, self.dataset_shapes
def get_loop_count(self, dataset):
loop_count = 1
if hasattr(dataset, '__loop_size__'):
loop_size = dataset.__loop_size__
if dataset.get_dataset_size() % loop_size != 0:
raise ValueError(f'Dataset size {dataset.get_dataset_size()} and '
f'loop_size {loop_size} are not matched.')
loop_count = int(dataset.get_dataset_size() / loop_size)
return loop_count
class _DatasetIterMSLoopSink(_DatasetIter):
"""Iter for context (device_target=Ascend)"""
def __init__(self, dataset, iter_first_order):
super(_DatasetIterMSLoopSink, self).__init__(dataset)
loop_size = dataset.__loop_size__ + iter_first_order
self.loop_count = int(dataset.get_dataset_size() / loop_size) * 2
# for self._parallel_mode equal to semi_auto_parallel or auto_parallel, use a complete tensor to
# compile, and slice tensor to run. The batch dimension of tensors for compile is device_number
# times the batch dimension of tensors for run. Now only support LoopSink.
if _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
device_num = _get_device_num()
self.dataset_shapes = _to_full_shapes(self.dataset_shapes, device_num)
def op():
return tuple()
self.op = op

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# 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.
# ============================================================================
"""grad_reducer_thor"""
import mindspore.common.dtype as mstype
from mindspore.communication.management import GlobalComm, get_group_size
from mindspore.nn.cell import Cell
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.ops.operations.comm_ops import AllReduce, ReduceOp
reduce_opt = C.MultitypeFuncGraph("reduce_opt")
_all_reduce_A = AllReduce()
def _init_optimizer_allreduce(group):
global _all_reduce_A
_all_reduce_A = AllReduce(ReduceOp.SUM, GlobalComm.WORLD_COMM_GROUP)
_all_reduce_A.add_prim_attr('fusion', group)
@reduce_opt.register("Function", "Number", "Tensor")
def _tensors_allreduce_mean(mul, degree, grad):
degree = F.scalar_cast(degree, F.dtype(grad))
grad = _all_reduce_A(grad)
cast_op = P.Cast()
return mul(grad, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(grad)))
@reduce_opt.register("Bool", "Tensor")
def _tensors_allreduce(allreduce_filter, grad):
if allreduce_filter:
return _all_reduce_A(grad)
return grad
_get_datatype = C.MultitypeFuncGraph("_get_datatype")
@_get_datatype.register("Tensor")
def _tensors_get_datatype(grad):
"""
Acquire gradient datatype.
Args:
grad (Tensor): The gradient tensor before operation.
Returns:
mstype, the datatype of gradient.
"""
return F.dtype(grad)
_cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@_cast_datatype.register("TypeType", "Tensor")
def _tensors_cast_datatype(datatype, grad):
"""
Cast gradient to datatype.
Args:
datatype (mstype): the destination datatype of gradient.
grad (Tensor): The gradient tensor before operation.
Returns:
Tensor, the gradient tensor after operation.
"""
return F.cast(grad, datatype)
class DistributedGradReducerThor(Cell):
"""
A distributed optimizer.
Constructs a gradient reducer Cell, which applies communication and average operations on
single-process gradient values.
Args:
parameters (list): the parameters to be updated.
mean (bool): When mean is true, the mean coefficient (degree) would apply on gradients. Default: False.
degree (int): The mean coefficient. Usually it equals to device number. Default: None.
Raises:
ValueError: If degree is not a int or less than 0.
Examples:
>>> from mindspore.communication import init, get_group_size
>>> from mindspore.ops import composite as C
>>> from mindspore.ops import operations as P
>>> from mindspore.ops import functional as F
>>> from mindspore import context
>>> from mindspore import nn
>>> from mindspore import ParallelMode, ParameterTuple
>>>
>>> device_id = int(os.environ["DEVICE_ID"])
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
>>> device_id=int(device_id), enable_hccl=True)
>>> init()
>>> context.reset_auto_parallel_context()
>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
>>>
>>>
>>> class TrainingWrapper(nn.Cell):
>>> def __init__(self, network, optimizer, sens=1.0):
>>> super(TrainingWrapper, self).__init__(auto_prefix=False)
>>> self.network = network
>>> self.network.add_flags(defer_inline=True)
>>> self.weights = ParameterTuple(network.trainable_params())
>>> self.optimizer = optimizer
>>> self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
>>> self.sens = sens
>>> self.reducer_flag = False
>>> self.grad_reducer = None
>>> self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
>>> if self.parallel_mode in [ParallelMode.DATA_PARALLEL,
>>> ParallelMode.HYBRID_PARALLEL]:
>>> self.reducer_flag = True
>>> if self.reducer_flag:
>>> mean = context.get_auto_parallel_context("mirror_mean")
>>> if mean.get_device_num_is_set():
>>> degree = context.get_auto_parallel_context("device_num")
>>> else:
>>> degree = get_group_size()
>>> self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
>>>
>>> def construct(self, *args):
>>> weights = self.weights
>>> loss = self.network(*args)
>>> sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
>>> grads = self.grad(self.network, weights)(*args, sens)
>>> if self.reducer_flag:
>>> # apply grad reducer on grads
>>> grads = self.grad_reducer(grads)
>>> return F.depend(loss, self.optimizer(grads))
>>>
>>> network = Net()
>>> optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> train_cell = TrainingWrapper(network, optimizer)
>>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
>>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
>>> grads = train_cell(inputs, label)
"""
def __init__(self, parameters, group, mean=True, degree=None):
super(DistributedGradReducerThor, self).__init__(auto_prefix=False)
self.hyper_map = C.HyperMap()
self.mul = P.Mul()
if degree is None:
self.degree = get_group_size()
else:
if not isinstance(degree, int) or degree <= 0:
raise ValueError("Parameter 'degree' in DistributedGradReducer should large than 0 and be int")
self.degree = degree
self.mean = mean
self.allreduce_filter = tuple(x.layerwise_parallel is False for x in parameters)
_init_optimizer_allreduce(group)
def construct(self, grads):
# In some circumstances, the data precision of grads could be mixed with float16 and float32. Thus, the
# result of AllReduce is unreliable. To solve the problem, grads should be cast to float32 before AllReduce,
# and cast back after the operation.
datatypes = self.hyper_map(F.partial(_get_datatype), grads)
grads = self.hyper_map(F.partial(_cast_datatype, mstype.float32), grads)
if self.mean:
new_grad = self.hyper_map(F.partial(reduce_opt, self.mul, self.degree), grads)
else:
new_grad = self.hyper_map(F.partial(reduce_opt), self.allreduce_filter, grads)
new_grad = self.hyper_map(F.partial(_cast_datatype), datatypes, new_grad)
return new_grad

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example/resnet50_imagenet2012_THOR/model/model_thor.py
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# 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.
# ============================================================================
"""Model."""
import numpy as np
from mindspore import context
from mindspore import log as logger
from mindspore import nn
from mindspore._c_expression import init_exec_dataset
from mindspore._checkparam import check_input_data, check_output_data, check_int_positive, check_bool
from mindspore.common import dtype as mstype
from mindspore.common.dtype import pytype_to_dtype
from mindspore.common.tensor import Tensor
from mindspore.nn.metrics import Loss
from mindspore.nn.metrics import get_metrics
from mindspore.nn.wrap.cell_wrapper import _VirtualDatasetCell
from mindspore.parallel._utils import _get_parallel_mode, _get_device_num, _get_global_rank, \
_get_parameter_broadcast, _device_number_check, _parameter_broadcast_check
from mindspore.train import amp
from mindspore.train.callback import _InternalCallbackParam, RunContext, _CallbackManager
from mindspore.train.parallel_utils import ParallelMode
from model.dataset_helper import DatasetHelper
def _convert_type(types):
"""
Convert from numpy type to tensor type.
Args:
types (list): Numpy type list of element in dataset.
Returns:
list, list of element in dataset.
"""
ms_types = []
for np_type in types:
ms_type = pytype_to_dtype(np_type)
ms_types.append(ms_type)
return ms_types
def _get_types_and_shapes(dataset):
"""Get dataset types and shapes."""
dataset_types = _convert_type(dataset.output_types())
dataset_shapes = dataset.output_shapes()
return dataset_types, dataset_shapes
def _exec_datagraph(exec_dataset, dataset_size, phase='dataset'):
"""Initialize and execute the dataset graph."""
batch_size = exec_dataset.get_batch_size()
input_indexs = exec_dataset.input_indexs
# transform data format
dataset_types, dataset_shapes = _get_types_and_shapes(exec_dataset)
init_exec_dataset(exec_dataset.__ME_INITED__,
dataset_size,
batch_size,
dataset_types,
dataset_shapes,
input_indexs,
phase=phase,
need_run=False)
class Model:
"""
High-Level API for Training or Testing.
`Model` groups layers into an object with training and inference features.
Args:
network (Cell): The training or testing network.
loss_fn (Cell): Objective function, if loss_fn is None, the
network should contain the logic of loss and grads calculation, and the logic
of parallel if needed. Default: None.
optimizer (Cell): Optimizer for updating the weights. Default: None.
metrics (Union[dict, set]): Dict or set of metrics to be evaluated by the model during
training and testing. eg: {'accuracy', 'recall'}. Default: None.
eval_network (Cell): Network for evaluation. If not defined, `network` and `loss_fn` would be wrapped as
`eval_network`. Default: None.
eval_indexes (list): In case of defining the `eval_network`, if `eval_indexes` is None, all outputs of
`eval_network` would be passed to metrics, otherwise `eval_indexes` must contain three
elements, representing the positions of loss value, predict value and label, the loss
value would be passed to `Loss` metric, predict value and label would be passed to other
metric. Default: None.
amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network`, level for mixed
precision training. Supports [O0, O2]. Default: "O0".
- O0: Do not change.
- O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale.
loss_scale_manager (Union[None, LossScaleManager]): If None, not scale the loss, or else
scale the loss by LossScaleManager. If it is set, overwrite the level setting. It's a eyword argument.
e.g. Use `loss_scale_manager=None` to set the value.
keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32`. If set, overwrite the level setting. Default: True.
Examples:
>>> class Net(nn.Cell):
>>> def __init__(self):
>>> super(Net, self).__init__()
>>> self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')
>>> self.bn = nn.BatchNorm2d(64)
>>> self.relu = nn.ReLU()
>>> self.flatten = nn.Flatten()
>>> self.fc = nn.Dense(64*224*224, 12) # padding=0
>>>
>>> def construct(self, x):
>>> x = self.conv(x)
>>> x = self.bn(x)
>>> x = self.relu(x)
>>> x = self.flatten(x)
>>> out = self.fc(x)
>>> return out
>>>
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
>>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
>>> dataset = get_dataset()
>>> model.train(2, dataset)
"""
def __init__(self, network, loss_fn=None, optimizer=None, metrics=None, eval_network=None,
eval_indexes=None, amp_level="O0", frequency=278, stop_epoch=100, **kwargs):
self._network = network
self._loss_fn = loss_fn
self._optimizer = optimizer
self._loss_scale_manager = None
self._loss_scale_manager_set = False
self._keep_bn_fp32 = True
self._check_kwargs(kwargs)
self._amp_level = amp_level
self._process_amp_args(kwargs)
self._parallel_mode = _get_parallel_mode()
self._device_number = _get_device_num()
self._global_rank = _get_global_rank()
self._parameter_broadcast = _get_parameter_broadcast()
self._frequency = frequency
self._stop_epoch = stop_epoch
self._train_network = self._build_train_network()
self._build_eval_network(metrics, eval_network, eval_indexes)
self._build_predict_network()
def _process_amp_args(self, kwargs):
if self._amp_level == "O0":
self._keep_bn_fp32 = False
if 'keep_batchnorm_fp32' in kwargs:
self._keep_bn_fp32 = kwargs['keep_batchnorm_fp32']
if 'loss_scale_manager' in kwargs:
self._loss_scale_manager = kwargs['loss_scale_manager']
self._loss_scale_manager_set = True
def _check_kwargs(self, kwargs):
for arg in kwargs:
if arg not in ['loss_scale_manager', 'keep_batchnorm_fp32']:
raise ValueError(f"Unsupport arg '{arg}'")
def _build_train_network(self):
"""Build train network"""
network = self._network
if self._optimizer:
if self._loss_scale_manager_set:
network = amp.build_train_network(network,
self._optimizer,
self._loss_fn,
level=self._amp_level,
loss_scale_manager=self._loss_scale_manager,
keep_batchnorm_fp32=self._keep_bn_fp32)
else:
network = amp.build_train_network(network,
self._optimizer,
self._loss_fn,
level=self._amp_level,
keep_batchnorm_fp32=self._keep_bn_fp32)
elif self._loss_fn:
network = nn.WithLossCell(network, self._loss_fn)
# If need to check if loss_fn is not None, but optimizer is None
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
network.set_auto_parallel()
return network
def _build_eval_network(self, metrics, eval_network, eval_indexes):
"""Build the network for evaluation."""
self._metric_fns = get_metrics(metrics)
if not self._metric_fns:
return
if eval_network is not None:
if eval_indexes is not None and not (isinstance(eval_indexes, list) and len(eval_indexes) == 3):
raise ValueError("Eval_indexes must be a list or None. If eval_indexes is a list, length of it \
must be three. But got {}".format(eval_indexes))
self._eval_network = eval_network
self._eval_indexes = eval_indexes
else:
if self._loss_fn is None:
raise ValueError("loss_fn can not be None.")
self._eval_network = nn.WithEvalCell(self._network, self._loss_fn, self._amp_level == "O2")
self._eval_indexes = [0, 1, 2]
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
self._eval_network.set_auto_parallel()
def _build_predict_network(self):
"""Build the network for prediction."""
self._predict_network = self._network
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
self._predict_network = _VirtualDatasetCell(self._network)
self._predict_network.set_auto_parallel()
def _clear_metrics(self):
"""Clear metrics local values."""
for metric in self._metric_fns.values():
metric.clear()
def _update_metrics(self, outputs):
"""Update metrics local values."""
if not isinstance(outputs, tuple):
raise ValueError("The `outputs` is not tuple.")
if self._eval_indexes is not None and len(outputs) < 3:
raise ValueError("The length of `outputs` must be greater than or equal to 3, \
but got {}".format(len(outputs)))
for metric in self._metric_fns.values():
if self._eval_indexes is None:
metric.update(*outputs)
else:
if isinstance(metric, Loss):
metric.update(outputs[self._eval_indexes[0]])
else:
metric.update(outputs[self._eval_indexes[1]], outputs[self._eval_indexes[2]])
def _get_metrics(self):
"""Get metrics local values."""
metrics = dict()
for key, value in self._metric_fns.items():
metrics[key] = value.eval()
return metrics
def _get_scaling_sens(self):
"""get the scaling sens"""
scaling_sens = 1
if self._loss_scale_manager is not None:
scaling_sens = self._loss_scale_manager.get_loss_scale()
if self._parallel_mode == ParallelMode.DATA_PARALLEL:
scaling_sens /= self._device_number
return scaling_sens
def _exec_preprocess(self, network, is_train, phase, dataset, dataset_sink_mode, iter_first_order):
"""Initializes dataset."""
need_wrap = False
if dataset_sink_mode:
# remove later to deal with loop sink
if not hasattr(dataset, '__ME_INITED__') and context.get_context("device_target") == "Ascend" \
and not context.get_context("enable_ge"):
need_wrap = True
if not is_train:
dataset.__loop_size__ = 1
dataset_helper = DatasetHelper(dataset, dataset_sink_mode, iter_first_order)
# remove later to deal with loop sink
if need_wrap:
network = nn.DataWrapper(network, *(dataset_helper.types_shapes()), dataset.__ME_INITED__)
network.set_train(is_train)
network.phase = phase
return dataset_helper, network
def init(self, train_dataset=None, valid_dataset=None):
"""
Initializes compute graphs and data graphs with sink mode.
Note:
Pre-init process only supports `GRAPH_MODE` and `Ascend` target currently.
Args:
train_dataset (Dataset): A training dataset iterator. If define `train_dataset`, training graphs will be
initialized. Default: None.
valid_dataset (Dataset): A evaluating dataset iterator. If define `valid_dataset`, evaluation graphs will
be initialized, and `metrics` in `Model` can not be None. Default: None.
Examples:
>>> train_dataset = get_train_dataset()
>>> valid_dataset = get_valid_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
>>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics={'acc'})
>>> model.init(train_dataset, valid_dataset)
>>> model.train(2, train_dataset)
>>> model.eval(valid_dataset)
"""
if context.get_context("mode") != context.GRAPH_MODE or context.get_context("device_target") != "Ascend":
raise RuntimeError('Pre-init process only supports GRAPH MODE and Ascend target currently.')
if not train_dataset and not valid_dataset:
raise ValueError('Both train_dataset and valid_dataset can not be None or empty.')
_device_number_check(self._parallel_mode, self._device_number)
if train_dataset:
_parameter_broadcast_check(self._parallel_mode, self._parameter_broadcast)
self._train_network.set_train()
self._train_network.phase = 'train'
if self._parameter_broadcast:
self._train_network.set_broadcast_flag()
train_dataset_helper, train_network = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True)
self._train_network = train_network
for inputs in train_dataset_helper:
self._train_network.compile(*inputs)
break
if valid_dataset:
if not self._metric_fns:
raise RuntimeError('If define `valid_dataset`, metric fn can not be None or empty.')
self._eval_network.set_train(False)
self._eval_network.phase = 'eval'
valid_dataset_helper, eval_network = self._exec_preprocess(self._eval_network,
is_train=False,
phase='eval',
dataset=valid_dataset,
dataset_sink_mode=True)
self._eval_network = eval_network
for inputs in valid_dataset_helper:
self._eval_network.compile(*inputs)
break
def _train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True):
"""
Training.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) will be
returned and passed to the network. Otherwise, a tuple (data, label) will
be returned, and the data and label are passed to the network and loss
function respectively.
callbacks (list): List of callback object. Callbacks which should be executed while training. Default: None.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
Configure pynative mode, the training process will be performed with
dataset not sink.
"""
epoch = check_int_positive(epoch)
self._train_network.set_train()
if self._parameter_broadcast:
self._train_network.set_broadcast_flag()
# build callback list
cb_params = _InternalCallbackParam()
cb_params.train_network = self._train_network
cb_params.epoch_num = epoch
cb_params.batch_num = train_dataset.get_dataset_size()
cb_params.mode = "train"
cb_params.loss_fn = self._loss_fn
cb_params.optimizer = self._optimizer
cb_params.parallel_mode = self._parallel_mode
cb_params.device_number = self._device_number
cb_params.train_dataset = train_dataset
cb_params.list_callback = callbacks
with _CallbackManager(callbacks) as list_callback:
if not dataset_sink_mode:
self._train_process(epoch, train_dataset, list_callback, cb_params)
elif context.get_context("mode") == context.PYNATIVE_MODE:
logger.warning("The pynative mode cannot support dataset sink mode currently."
"So the training process will be performed with dataset not sink.")
self._train_process(epoch, train_dataset, list_callback, cb_params)
else:
self._train_dataset_sink_process(epoch, train_dataset, list_callback, cb_params)
def _train_dataset_sink_process(self, epoch, train_dataset, list_callback=None, cb_params=None):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
iter_first_order = self._frequency - 1
iter_second_order = 1
train_dataset.__loop_size__ = iter_second_order
dataset_helper, train_network = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
iter_first_order=iter_first_order)
self._train_network = train_network
cb_params.train_network = self._train_network
cb_params.cur_step_num = 0
loop_size = dataset_helper.loop_size()
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
has_do_dataset_init = False
switch_branch_one = True
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:
list_callback.step_begin(run_context)
if switch_branch_one:
cb_params.cur_step_num += loop_size
self._train_network.add_flags_recursive(thor=True)
self._train_network.phase = 'train0'
else:
cb_params.cur_step_num += iter_first_order
self._train_network.add_flags_recursive(thor=False)
self._train_network.phase = 'train1'
if not has_do_dataset_init:
_exec_datagraph(train_dataset, iter_first_order, phase='train1_dataset')
has_do_dataset_init = True
switch_branch_one = not switch_branch_one
outputs = self._train_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)
def _train_process(self, epoch, train_dataset, list_callback=None, cb_params=None):
"""
Training process. The data would be passed to network directly.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
dataset_helper, _ = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=False)
cb_params.cur_step_num = 0
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
for next_element in dataset_helper:
len_element = len(next_element)
if self._loss_fn and len_element != 2:
raise ValueError("when loss_fn is not None, train_dataset should"
"return two elements, but got {}".format(len_element))
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
overflow = False
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update():
scaling_sens = self._get_scaling_sens()
next_element = tuple(next_element) + (Tensor(scaling_sens, mstype.float32),)
outputs = self._train_network(*next_element)
cb_params.net_outputs = outputs
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update():
_, overflow, _ = outputs
overflow = np.all(overflow.asnumpy())
self._loss_scale_manager.update_loss_scale(overflow)
list_callback.step_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
train_dataset.reset()
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)
def train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True):
"""
Training API where the iteration is controlled by python front-end.
When setting pynative mode, the training process will be performed with dataset not sink.
Note:
CPU is not supported when dataset_sink_mode is true.
If dataset_sink_mode is True, epoch of training should be equal to the count of repeat
operation in dataset processing. Otherwise, errors could occur since the amount of data
is not the amount training requires.
If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
of data will be transferred one by one. The limitation of data transmission per time is 256M.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
callbacks (list): List of callback object. Callbacks which should be excuted while training. Default: None.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
Configure pynative mode, the training process will be performed with
dataset not sink.
Examples:
>>> dataset = get_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
>>> loss_scale_manager = FixedLossScaleManager()
>>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager)
>>> model.train(2, dataset)
"""
repeat_count = train_dataset.get_repeat_count()
if epoch != repeat_count and dataset_sink_mode is True:
logger.warning(f"The epoch_size {epoch} is not the same with dataset repeat_count {repeat_count}")
check_bool(dataset_sink_mode)
_device_number_check(self._parallel_mode, self._device_number)
_parameter_broadcast_check(self._parallel_mode, self._parameter_broadcast)
self._train(epoch,
train_dataset,
callbacks=callbacks,
dataset_sink_mode=dataset_sink_mode)
def _eval_dataset_sink_process(self, valid_dataset, list_callback=None, cb_params=None):
"""
Evaluation. The data would be passed to network through dataset channel.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
Returns:
Dict, returns the loss value & metrics values for the model in test mode.
"""
run_context = RunContext(cb_params)
dataset_helper, eval_network = self._exec_preprocess(self._eval_network,
is_train=False,
phase='eval',
dataset=valid_dataset,
dataset_sink_mode=True)
self._eval_network = eval_network
cb_params.eval_network = self._eval_network
list_callback.begin(run_context)
for inputs in dataset_helper:
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
outputs = self._eval_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
self._update_metrics(outputs)
metrics = self._get_metrics()
cb_params.metrics = metrics
list_callback.end(run_context)
return metrics
def _eval_process(self, valid_dataset, list_callback=None, cb_params=None):
"""
Evaluation. The data would be passed to network directly.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
Returns:
Dict, returns the loss value & metrics values for the model in test mode.
"""
run_context = RunContext(cb_params)
list_callback.begin(run_context)
dataset_helper, _ = self._exec_preprocess(self._eval_network,
is_train=False,
phase='eval',
dataset=valid_dataset,
dataset_sink_mode=False)
for next_element in dataset_helper:
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
outputs = self._eval_network(*next_element)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
self._update_metrics(outputs)
metrics = self._get_metrics()
cb_params.metrics = metrics
list_callback.end(run_context)
return metrics
def eval(self, valid_dataset, callbacks=None, dataset_sink_mode=True):
"""
Evaluation API where the iteration is controlled by python front-end.
Configure to pynative mode, the evaluation will be performed with dataset non-sink mode.
Note:
CPU is not supported when dataset_sink_mode is true.
If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
of data will be transferred one by one. The limitation of data transmission per time is 256M.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
callbacks (list): List of callback object. Callbacks which should be excuted
while training. Default: None.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
Returns:
Dict, returns the loss value & metrics values for the model in test mode.
Examples:
>>> dataset = get_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
>>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'})
>>> model.eval(dataset)
"""
check_bool(dataset_sink_mode)
_device_number_check(self._parallel_mode, self._device_number)
if not self._metric_fns:
raise ValueError("metric fn can not be None or empty.")
cb_params = _InternalCallbackParam()
cb_params.eval_network = self._eval_network
cb_params.valid_dataset = valid_dataset
cb_params.batch_num = valid_dataset.get_dataset_size()
cb_params.mode = "eval"
cb_params.cur_step_num = 0
self._eval_network.set_train(mode=False)
self._eval_network.phase = 'eval'
self._clear_metrics()
with _CallbackManager(callbacks) as list_callback:
if dataset_sink_mode:
return self._eval_dataset_sink_process(valid_dataset, list_callback, cb_params)
return self._eval_process(valid_dataset, list_callback, cb_params)
def predict(self, *predict_data):
"""
Generates output predictions for the input samples.
Data could be single tensor, or list of tensor, tuple of tensor.
Note:
Batch data should be put together in one tensor.
Args:
predict_data (Tensor): Tensor of predict data. can be array, list or tuple.
Returns:
Tensor, array(s) of predictions.
Examples:
>>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
>>> model = Model(Net())
>>> model.predict(input_data)
"""
self._predict_network.set_train(False)
check_input_data(*predict_data, data_class=Tensor)
result = self._predict_network(*predict_data)
check_output_data(result)
return result
__all__ = ["Model"]

359
example/resnet50_imagenet2012_THOR/model/resnet.py
View File

@ -1,359 +0,0 @@
# 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.
# ============================================================================
"""ResNet."""
import math
import numpy as np
import mindspore.nn as nn
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from model.thor_layer import Conv2d_Thor, Dense_Thor
def calculate_gain(nonlinearity, param=None):
"""calculate_gain"""
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
res = 0
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
res = 1
elif nonlinearity == 'tanh':
res = 5.0 / 3
elif nonlinearity == 'relu':
res = math.sqrt(2.0)
elif nonlinearity == 'leaky_relu':
if param is None:
negative_slope = 0.01
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
# True/False are instances of int, hence check above
negative_slope = param
else:
raise ValueError("negative_slope {} not a valid number".format(param))
res = math.sqrt(2.0 / (1 + negative_slope ** 2))
else:
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
return res
def _calculate_fan_in_and_fan_out(tensor):
"""_calculate_fan_in_and_fan_out"""
dimensions = len(tensor)
if dimensions < 2:
raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
if dimensions == 2: # Linear
fan_in = tensor[1]
fan_out = tensor[0]
else:
num_input_fmaps = tensor[1]
num_output_fmaps = tensor[0]
receptive_field_size = 1
if dimensions > 2:
receptive_field_size = tensor[2] * tensor[3]
fan_in = num_input_fmaps * receptive_field_size
fan_out = num_output_fmaps * receptive_field_size
return fan_in, fan_out
def _calculate_correct_fan(tensor, 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_fan_in_and_fan_out(tensor)
return fan_in if mode == 'fan_in' else fan_out
def kaiming_normal(inputs_shape, a=0, mode='fan_in', nonlinearity='leaky_relu'):
fan = _calculate_correct_fan(inputs_shape, mode)
gain = calculate_gain(nonlinearity, a)
std = gain / math.sqrt(fan)
return np.random.normal(0, std, size=inputs_shape).astype(np.float32)
def kaiming_uniform(inputs_shape, a=0, mode='fan_in', nonlinearity='leaky_relu'):
fan = _calculate_correct_fan(inputs_shape, mode)
gain = calculate_gain(nonlinearity, a)
std = gain / math.sqrt(fan)
bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation
return np.random.uniform(-bound, bound, size=inputs_shape).astype(np.float32)
def _conv3x3(in_channel, out_channel, stride=1, damping=0.03, loss_scale=1, frequency=278):
weight_shape = (out_channel, in_channel, 3, 3)
weight = Tensor(kaiming_normal(weight_shape, mode="fan_out", nonlinearity='relu'))
return Conv2d_Thor(in_channel, out_channel,
kernel_size=3, stride=stride, padding=0, pad_mode='same', weight_init=weight,
damping=damping, loss_scale=loss_scale, frequency=frequency)
def _conv1x1(in_channel, out_channel, stride=1, damping=0.03, loss_scale=1, frequency=278):
weight_shape = (out_channel, in_channel, 1, 1)
weight = Tensor(kaiming_normal(weight_shape, mode="fan_out", nonlinearity='relu'))
return Conv2d_Thor(in_channel, out_channel,
kernel_size=1, stride=stride, padding=0, pad_mode='same', weight_init=weight,
damping=damping, loss_scale=loss_scale, frequency=frequency)
def _conv7x7(in_channel, out_channel, stride=1, damping=0.03, loss_scale=1, frequency=278):
weight_shape = (out_channel, in_channel, 7, 7)
weight = Tensor(kaiming_normal(weight_shape, mode="fan_out", nonlinearity='relu'))
return Conv2d_Thor(in_channel, out_channel,
kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight,
damping=damping, loss_scale=loss_scale, frequency=frequency)
def _bn(channel):
return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
def _bn_last(channel):
return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
def _fc(in_channel, out_channel, damping, loss_scale, frequency):
weight_shape = (out_channel, in_channel)
weight = Tensor(kaiming_uniform(weight_shape, a=math.sqrt(5)))
return Dense_Thor(in_channel, out_channel, has_bias=False, weight_init=weight,
bias_init=0, damping=damping, loss_scale=loss_scale, frequency=frequency)
class ResidualBlock(nn.Cell):
"""
ResNet V1 residual block definition.
Args:
in_channel (int): Input channel.
out_channel (int): Output channel.
stride (int): Stride size for the first convolutional layer. Default: 1.
Returns:
Tensor, output tensor.
Examples:
>>> ResidualBlock(3, 256, stride=2)
"""
expansion = 4
def __init__(self,
in_channel,
out_channel,
stride=1,
damping=0.03,
loss_scale=1,
frequency=278):
super(ResidualBlock, self).__init__()
channel = out_channel // self.expansion
self.conv1 = _conv1x1(in_channel, channel, stride=1, damping=damping, loss_scale=loss_scale,
frequency=frequency)
self.bn1 = _bn(channel)
self.conv2 = _conv3x3(channel, channel, stride=stride, damping=damping, loss_scale=loss_scale,
frequency=frequency)
self.bn2 = _bn(channel)
self.conv3 = _conv1x1(channel, out_channel, stride=1, damping=damping, loss_scale=loss_scale,
frequency=frequency)
self.bn3 = _bn_last(out_channel)
self.relu = nn.ReLU()
self.down_sample = False
if stride != 1 or in_channel != out_channel:
self.down_sample = True
self.down_sample_layer = None
if self.down_sample:
self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride,
damping=damping, loss_scale=loss_scale,
frequency=frequency),
_bn(out_channel)])
self.add = P.TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.down_sample:
identity = self.down_sample_layer(identity)
out = self.add(out, identity)
out = self.relu(out)
return out
class ResNet(nn.Cell):
"""
ResNet architecture.
Args:
block (Cell): Block for network.
layer_nums (list): Numbers of block in different layers.
in_channels (list): Input channel in each layer.
out_channels (list): Output channel in each layer.
strides (list): Stride size in each layer.
num_classes (int): The number of classes that the training images are belonging to.
Returns:
Tensor, output tensor.
Examples:
>>> ResNet(ResidualBlock,
>>> [3, 4, 6, 3],
>>> [64, 256, 512, 1024],
>>> [256, 512, 1024, 2048],
>>> [1, 2, 2, 2],
>>> 10)
"""
def __init__(self,
block,
layer_nums,
in_channels,
out_channels,
strides,
num_classes,
damping,
loss_scale,
frequency):
super(ResNet, self).__init__()
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
self.conv1 = _conv7x7(3, 64, stride=2, damping=damping, loss_scale=loss_scale, frequency=frequency)
self.bn1 = _bn(64)
self.relu = P.ReLU()
self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
out_channel=out_channels[0],
stride=strides[0],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.layer2 = self._make_layer(block,
layer_nums[1],
in_channel=in_channels[1],
out_channel=out_channels[1],
stride=strides[1],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.layer3 = self._make_layer(block,
layer_nums[2],
in_channel=in_channels[2],
out_channel=out_channels[2],
stride=strides[2], damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.layer4 = self._make_layer(block,
layer_nums[3],
in_channel=in_channels[3],
out_channel=out_channels[3],
stride=strides[3],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.end_point = _fc(out_channels[3], num_classes, damping=damping, loss_scale=loss_scale, frequency=frequency)
def _make_layer(self, block, layer_num, in_channel, out_channel, stride,
damping, loss_scale, frequency):
"""
Make stage network of ResNet.
Args:
block (Cell): Resnet block.
layer_num (int): Layer number.
in_channel (int): Input channel.
out_channel (int): Output channel.
stride (int): Stride size for the first convolutional layer.
Returns:
SequentialCell, the output layer.
Examples:
>>> _make_layer(ResidualBlock, 3, 128, 256, 2)
"""
layers = []
resnet_block = block(in_channel, out_channel, stride=stride,
damping=damping, loss_scale=loss_scale, frequency=frequency)
layers.append(resnet_block)
for _ in range(1, layer_num):
resnet_block = block(out_channel, out_channel, stride=1,
damping=damping, loss_scale=loss_scale, frequency=frequency)
layers.append(resnet_block)
return nn.SequentialCell(layers)
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
c1, _ = self.maxpool(x)
c2 = self.layer1(c1)
c3 = self.layer2(c2)
c4 = self.layer3(c3)
c5 = self.layer4(c4)
out = self.mean(c5, (2, 3))
out = self.flatten(out)
out = self.end_point(out)
return out
def resnet50(class_num=10, damping=0.03, loss_scale=1, frequency=278):
"""
Get ResNet50 neural network.
Args:
class_num (int): Class number.
Returns:
Cell, cell instance of ResNet50 neural network.
Examples:
>>> net = resnet50(10)
"""
return ResNet(ResidualBlock,
[3, 4, 6, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
[1, 2, 2, 2],
class_num,
damping,
loss_scale,
frequency)

199
example/resnet50_imagenet2012_THOR/model/thor.py
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@ -1,199 +0,0 @@
# 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.
# ============================================================================
"""momentum"""
import mindspore.common.dtype as mstype
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.common.parameter import ParameterTuple
from mindspore.common.tensor import Tensor
from mindspore.nn.optim.optimizer import Optimizer
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.parallel._utils import _get_device_num, _get_mirror_mean
from model.grad_reducer_thor import DistributedGradReducerThor
momentum_opt = C.MultitypeFuncGraph("momentum_opt")
@momentum_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_ext(opt, learning_rate, momentum, gradient, weight, moment):
"""Apply momentum optimizer to the weight parameter using Tensor."""
success = True
success = F.depend(success, opt(weight, moment, learning_rate, gradient, momentum))
return success
op_add = P.AddN()
apply_decay = C.MultitypeFuncGraph("apply_decay")
@apply_decay.register("Number", "Bool", "Tensor", "Tensor")
def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
"""Get grad with weight_decay."""
if if_apply:
return op_add((weight * weight_decay, gradient))
return gradient
class THOR(Optimizer):
"""THOR"""
def __init__(self, params, learning_rate, momentum, matrix_A, matrix_G, A_inv_max, G_inv_max, weight_decay=0.0,
loss_scale=1.0,
decay_filter=lambda x: x.name not in []):
super(THOR, self).__init__(learning_rate, params, weight_decay, loss_scale)
if isinstance(momentum, float) and momentum < 0.0:
raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum")
self.params = self.parameters
self.moments = self.params.clone(prefix="moments", init='zeros')
self.hyper_map = C.HyperMap()
self.opt = P.ApplyMomentum()
self.matrix_A = ParameterTuple(matrix_A)
self.matrix_G = ParameterTuple(matrix_G)
self.A_inv_max = ParameterTuple(A_inv_max)
self.G_inv_max = ParameterTuple(G_inv_max)
self.cube_matmul_left = P.CusMatMulCubeFraczLeftCast()
self.cube_matmul_left_fc = P.CusMatMulCubeDenseLeft()
self.cube_matmul_right_fc = P.CusMatMulCubeDenseRight()
self.cube_matmul_right_mul = P.CusMatMulCubeFraczRightMul()
self.transpose = P.Transpose()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.mul = P.Mul()
self.weight_idx = []
for i in range(len(self.params)):
if "conv" in self.params[i].name or "end_point" in self.params[i].name:
self.weight_idx.append(i)
self.weight_idx.append(len(self.params))
self.feature_map = [1.0 / 12544, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136,
1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136,
1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784,
1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784,
1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196,
1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196,
1.0 / 196, 1.0 / 196, 1.0 / 196,
1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49,
1.0]
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer_Amax = DistributedGradReducerThor(self.parameters, 2, mean, degree)
self.grad_reducer_Gmax = DistributedGradReducerThor(self.parameters, 5, mean, degree)
self.grad_reducer_A = DistributedGradReducerThor(self.parameters, 3, mean, degree)
self.grad_reducer_G = DistributedGradReducerThor(self.parameters, 4, mean, degree)
self.matrix_A_inv = ()
self.matrix_G_inv = ()
self.matrix_max_inv = ()
for i in range(54):
self.matrix_max_inv = self.matrix_max_inv + (
Parameter(initializer(1, [1], mstype.float32), name="matrix_max" + str(i), requires_grad=False),)
self.log = P.Log()
self.exp = P.Exp()
self.sqrt = P.Sqrt()
self.matrix_max_inv = ParameterTuple(self.matrix_max_inv)
self.assign = P.Assign()
self.cast = P.Cast()
self.thor = True
self.weight_decay = weight_decay * loss_scale
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
def construct(self, gradients):
params = self.params
moments = self.moments
if self.thor:
matrix_A_allreduce = ()
matrix_G_allreduce = ()
matrix_A_max_allreduce = ()
matrix_G_max_allreduce = ()
for i in range(54):
g = gradients[i * 3]
matrix_A = self.matrix_A[i]
matrix_G = self.matrix_G[i]
A_max = self.A_inv_max[i]
G_max = self.G_inv_max[i]
matrix_A = F.depend(matrix_A, g)
matrix_G = F.depend(matrix_G, g)
A_max = F.depend(A_max, g)
G_max = F.depend(G_max, g)
matrix_A_allreduce = matrix_A_allreduce + (matrix_A,)
matrix_G_allreduce = matrix_G_allreduce + (matrix_G,)
matrix_A_max_allreduce = matrix_A_max_allreduce + (A_max,)
matrix_G_max_allreduce = matrix_G_max_allreduce + (G_max,)
matrix_A_allreduce = self.grad_reducer_A(matrix_A_allreduce)
matrix_G_allreduce = self.grad_reducer_G(matrix_G_allreduce)
matrix_A_max_allreduce = self.grad_reducer_Amax(matrix_A_max_allreduce)
matrix_G_max_allreduce = self.grad_reducer_Gmax(matrix_G_max_allreduce)
new_grads = ()
for i in range(54):
g = gradients[i * 3]
temp_a = matrix_A_allreduce[i]
temp_g = matrix_G_allreduce[i]
temp_a = self.cast(temp_a, mstype.float32)
temp_g = self.cast(temp_g, mstype.float32)
matrix_A_inv_max = self.log(matrix_A_max_allreduce[i])
matrix_A_inv_max = self.mul(matrix_A_inv_max, -1)
matrix_A_inv_max = self.exp(matrix_A_inv_max)
temp_a = self.mul(temp_a, matrix_A_inv_max)
matrix_G_inv_max = self.log(matrix_G_max_allreduce[i])
matrix_G_inv_max = self.mul(matrix_G_inv_max, -1)
matrix_G_inv_max = self.exp(matrix_G_inv_max)
temp_g = self.mul(temp_g, matrix_G_inv_max)
temp_max = self.mul(matrix_A_max_allreduce[i], matrix_G_max_allreduce[i])
temp_max = self.mul(temp_max, self.feature_map[i])
temp_a = self.cast(temp_a, mstype.float16)
temp_g = self.cast(temp_g, mstype.float16)
if i == 53:
g = self.cube_matmul_left_fc(temp_g, g)
g = self.cube_matmul_right_fc(g, temp_a, temp_max)
else:
g = self.cube_matmul_left(temp_g, g)
g = self.cube_matmul_right_mul(g, temp_a, temp_max)
fake_A = self.assign(self.matrix_A[i], temp_a)
fake_G = self.assign(self.matrix_G[i], temp_g)
fake_max = self.assign(self.matrix_max_inv[i], temp_max)
g = F.depend(g, fake_A)
g = F.depend(g, fake_G)
g = F.depend(g, fake_max)
if i == 53:
new_grads = new_grads + (g,)
else:
new_grads = new_grads + (g, gradients[i * 3 + 1], gradients[i * 3 + 2])
gradients = new_grads
else:
new_grads = ()
for i in range(54):
g = gradients[i * 3]
matrix_A = self.matrix_A[i]
matrix_G = self.matrix_G[i]
matrix_max = self.matrix_max_inv[i]
matrix_A = F.depend(matrix_A, g)
matrix_G = F.depend(matrix_G, g)
matrix_max = F.depend(matrix_max, g)
if i == 53:
g = self.cube_matmul_left_fc(matrix_G, g)
g = self.cube_matmul_right_fc(g, matrix_A, matrix_max)
new_grads = new_grads + (g,)
else:
g = self.cube_matmul_left(matrix_G, g)
g = self.cube_matmul_right_mul(g, matrix_A, matrix_max)
new_grads = new_grads + (g, gradients[i * 3 + 1], gradients[i * 3 + 2])
gradients = new_grads
if self.weight_decay > 0:
gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_flags,
params, gradients)
gradients = self.scale_grad(gradients)
lr = self.get_lr()
success = self.hyper_map(F.partial(momentum_opt, self.opt, lr, self.momentum), gradients, params, moments)
return success

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example/resnet50_imagenet2012_THOR/model/thor_layer.py
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@ -1,477 +0,0 @@
# 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.
# ============================================================================
"""thor_layer"""
import numpy as np
import mindspore as ms
import mindspore.common.dtype as mstype
from mindspore._checkparam import check_bool, twice, check_int_positive
from mindspore._extends import cell_attr_register
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore.nn.cell import Cell
from mindspore.nn.layer.activation import get_activation
from mindspore.ops import operations as P
C0 = 16
def caculate_device_shape(matrix_dim, channel, is_A):
ll = (0)
if is_A:
if channel // C0 == 0:
matrix_dim = (matrix_dim / channel) * C0
ll = (int(matrix_dim // C0), int(matrix_dim // C0), C0, C0), int(matrix_dim)
else:
ll = (int(matrix_dim // C0), int(matrix_dim // C0), C0, C0), int(matrix_dim)
return ll
class _Conv(Cell):
r"""Applies a N-D convolution over an input signal composed of several input
planes.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
data_format,
has_bias,
weight_init,
bias_init,
):
super(_Conv, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.pad_mode = pad_mode
self.padding = padding
self.dilation = dilation
self.group = group
self.data_format = data_format
self.has_bias = has_bias
if not (isinstance(in_channels, int) and in_channels > 0):
raise ValueError('Attr \'in_channels\' of \'Conv2D\' Op passed '
+ str(in_channels) + ', should be a int and greater than 0.')
if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \
(not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
kernel_size[0] < 1 or kernel_size[1] < 1:
raise ValueError('Attr \'kernel_size\' of \'Conv2D\' Op passed '
+ str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
if in_channels % group != 0:
raise ValueError('Attr \'in_channels\' of \'Conv2D\' Op must be divisible by '
'attr \'group\' of \'Conv2D\' Op.')
if out_channels % group != 0:
raise ValueError('Attr \'out_channels\' of \'Conv2D\' Op must be divisible by '
'attr \'group\' of \'Conv2D\' Op.')
self.weight = Parameter(initializer(
weight_init, [out_channels, in_channels // group, *kernel_size]), name='weight')
if check_bool(has_bias):
self.bias = Parameter(_initializer(
bias_init, [out_channels]), name='bias')
else:
if bias_init != 'zeros':
logger.warning("Value of 'has_bias' is False, value of 'bias_init' will be ignored.")
self.bias = None
def construct(self, *inputs):
raise NotImplementedError
class Conv2d_Thor(_Conv):
"""Conv2d_Thor"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
data_format='NCHW',
has_bias=False,
weight_init='normal',
damping=0.03,
loss_scale=1,
frequency=278,
bias_init='zeros'):
self.thor = True
ksizes = (1, kernel_size, kernel_size, 1)
self.hw = kernel_size * kernel_size
strides = (1, stride, stride, 1)
kernel_size = twice(kernel_size)
super(Conv2d_Thor, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
data_format,
has_bias,
weight_init,
bias_init,
)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group
)
self.img2col = P.CusImg2Col(ksizes=ksizes, strides=strides)
self.cube_matmul = P.CusMatMulCube(transpose_a=True)
self.matrix_combine = P.CusMatrixCombine()
self.cholesky = P.CusCholeskyTrsm()
self.transpose02314 = P.CusTranspose02314()
self.matrix_A_dim = self.in_channels * self.kernel_size[0] * self.kernel_size[1]
self.matrix_G_dim = self.out_channels
self.matrix_A_device_shape, self.matrix_A_device_dim = caculate_device_shape(self.matrix_A_dim,
self.in_channels, True)
self.matrix_G_device_shape, self.matrix_G_device_dim = caculate_device_shape(self.matrix_G_dim,
self.in_channels, False)
self.matrix_A_device_temp_shape = (
self.matrix_A_device_shape[0], self.matrix_A_device_shape[2], self.matrix_A_device_shape[1],
self.matrix_A_device_shape[3])
self.matrix_G_device_temp_shape = (
self.matrix_G_device_shape[0], self.matrix_G_device_shape[2], self.matrix_G_device_shape[1],
self.matrix_G_device_shape[3])
self.matrix_A_inv = Parameter(
Tensor(np.reshape(np.identity(self.matrix_A_device_dim).astype(np.float16), self.matrix_A_device_shape)),
name='matrix_A_inv', requires_grad=False)
self.A_inv_max = Parameter(initializer(0, [1], mstype.float32), name="A_inv_max", requires_grad=False)
self.matrix_G_inv = Parameter(
Tensor(np.reshape(np.identity(self.matrix_G_device_dim).astype(np.float16), self.matrix_G_device_shape)),
name="matrix_G_inv", requires_grad=False)
self.G_inv_max = Parameter(initializer(0, [1], mstype.float32), name="G_inv_max", requires_grad=False)
self.fake_G = Tensor(
np.reshape(np.identity(self.matrix_G_device_dim).astype(np.float16), self.matrix_G_device_shape))
self.shape = P.Shape()
self.reshape = P.Reshape()
self.transpose = P.Transpose()
self.cov_step = Parameter(initializer(0, [1], mstype.int32), name="cov_step", requires_grad=False)
self.mul = P.Mul()
self.cast = P.Cast()
self.damping = Tensor(damping)
self.vector_matmul = P.CusBatchMatMul()
self.diag_block_dim = 128
self.channels_slice_flag = False
if self.in_channels % C0 != 0:
self.channels_slice_flag = True
self.padA_flag = False
if (self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim \
and self.matrix_A_dim > self.diag_block_dim:
self.padA_flag = True
pad_dim = self.diag_block_dim - self.matrix_A_dim % self.diag_block_dim
self.padA = P.Pad(((0, pad_dim), (0, pad_dim)))
self.device_shape_pad_flag = False
if self.matrix_A_dim != self.matrix_A_device_dim:
self.device_shape_pad_flag = True
self.device_shape_pad = P.Pad(((0, 0), (0, C0 - self.in_channels), (0, 0), (0, C0 - self.in_channels)))
self.slice = P.Slice()
self.gather = P.GatherV2()
self.freq = Tensor(frequency, mstype.int32)
self.loss_scale = Tensor(1 / loss_scale, mstype.float16)
self.axis = 0
dampingA_dim = self.matrix_A_dim
if (self.matrix_A_dim % self.diag_block_dim) != 0 and self.matrix_A_dim > self.diag_block_dim:
dampingA_dim = (self.matrix_A_dim // self.diag_block_dim + 1) * self.diag_block_dim
dampingG_dim = self.matrix_G_dim
if (self.matrix_G_dim % self.diag_block_dim) != 0 and self.matrix_G_dim > self.diag_block_dim:
dampingG_dim = (self.matrix_G_dim // self.diag_block_dim + 1) * self.diag_block_dim
self.dampingA = Tensor(np.identity(dampingA_dim), mstype.float32)
self.dampingG = Tensor(np.identity(dampingG_dim), mstype.float32)
self.fused_abs_max1 = P.CusFusedAbsMax1([self.matrix_A_dim, self.matrix_A_dim])
self.fused_abs_max2 = P.CusFusedAbsMax1()
self.log = P.Log()
self.exp = P.Exp()
self.sqrt = P.Sqrt()
self.getG = P.InsertGradientOf(self.save_gradient)
def save_gradient(self, dout):
"""save_gradient"""
out = dout
dout = self.mul(dout, self.loss_scale)
dout = self.mul(dout, 32.0)
dout = self.transpose02314(dout)
dout_shape = self.shape(dout)
normalizer = dout_shape[0]
matrix_G = self.cube_matmul(dout, dout)
normalizer = self.cast(normalizer, ms.float32)
matrix_G = self.mul(matrix_G, 1.0 / normalizer)
damping_step = self.gather(self.damping, self.cov_step, 0)
self.cov_step = self.cov_step + self.freq
damping_step = self.cast(damping_step, mstype.float32)
damping = self.mul(damping_step, 32.0 / normalizer)
damping = self.sqrt(damping)
dampingG = self.cast(self.dampingG, mstype.float32)
matrix_G = matrix_G + damping * dampingG
matrix_G_inv = self.cholesky(matrix_G)
matrix_G_inv = self.vector_matmul(matrix_G_inv, matrix_G_inv)
matrix_G_inv_max = self.fused_abs_max2(matrix_G_inv)
matrix_G_inv_max = self.fused_abs_max2(matrix_G_inv_max)
self.G_inv_max = matrix_G_inv_max
matrix_G_inv = self.matrix_combine(matrix_G_inv)
matrix_G_inv = self.reshape(matrix_G_inv, self.matrix_G_device_temp_shape)
matrix_G_inv = self.transpose(matrix_G_inv, (2, 0, 1, 3))
matrix_G = self.cast(matrix_G_inv, mstype.float16)
self.matrix_G_inv = matrix_G
return out
def construct(self, x):
if self.thor:
matrix_A = self.img2col(x)
matrix_A_shape = self.shape(matrix_A)
normalizer = matrix_A_shape[0]
matrix_A = self.cube_matmul(matrix_A, matrix_A)
if self.channels_slice_flag:
matrix_A = self.reshape(matrix_A, (self.hw, C0, self.hw, C0))
matrix_A = self.slice(matrix_A, (0, 0, 0, 0), (self.hw, self.in_channels, self.hw, self.in_channels))
matrix_A = self.reshape(matrix_A, (self.matrix_A_dim, self.matrix_A_dim))
normalizer = self.cast(normalizer, ms.float32)
matrix_A = self.mul(matrix_A, 1.0 / normalizer)
if self.padA_flag:
matrix_A = self.padA(matrix_A)
damping_step = self.gather(self.damping, self.cov_step, self.axis)
damping_step = self.cast(damping_step, mstype.float32)
damping = self.mul(damping_step, 32.0 / normalizer)
damping = self.sqrt(damping)
damping_A = self.cast(self.dampingA, mstype.float32)
matrix_A = matrix_A + damping * damping_A
matrix_A_inv = self.cholesky(matrix_A)
matrix_A_inv = self.vector_matmul(matrix_A_inv, matrix_A_inv)
matrix_A_inv_max = self.fused_abs_max1(matrix_A_inv)
matrix_A_inv_max = self.fused_abs_max2(matrix_A_inv_max)
self.A_inv_max = matrix_A_inv_max
matrix_A_inv = self.matrix_combine(matrix_A_inv)
matrix_A_inv = self.cast(matrix_A_inv, mstype.float16)
if self.padA_flag:
matrix_A_inv = self.slice(matrix_A_inv, (0, 0), (self.matrix_A_dim, self.matrix_A_dim))
if self.device_shape_pad_flag:
matrix_A_inv = self.reshape(matrix_A_inv, (self.hw, self.in_channels, self.hw, self.in_channels))
matrix_A_inv = self.device_shape_pad(matrix_A_inv)
matrix_A_inv = self.reshape(matrix_A_inv, self.matrix_A_device_temp_shape)
matrix_A_inv = self.transpose(matrix_A_inv, (2, 0, 1, 3))
self.matrix_A_inv = matrix_A_inv
self.matrix_G_inv = self.fake_G
out = self.conv2d(x, self.weight)
out = self.getG(out)
else:
out = self.conv2d(x, self.weight)
return out
def extra_repr(self):
"""extra_repr"""
s = 'input_channels={}, output_channels={}, kernel_size={},' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, data_format={}, has_bias={},' \
'weight_init={}, bias_init={}'.format(
self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.data_format,
self.has_bias,
self.weight,
self.bias)
if self.has_bias:
s += ', bias={}'.format(self.bias)
return s
class Dense_Thor(Cell):
"""Dense_Thor"""
@cell_attr_register(attrs=['has_bias', 'activation'])
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
damping=0.03,
loss_scale=1,
frequency=278,
has_bias=True,
activation=None):
super(Dense_Thor, self).__init__()
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.has_bias = check_bool(has_bias)
self.thor = True
if isinstance(weight_init, Tensor):
if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("weight_init shape error")
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
self.activation = get_activation(activation)
self.activation_flag = self.activation is not None
self.matrix_A_inv = Parameter(Tensor(np.zeros([128, 128, 16, 16]).astype(np.float16)), name='matrix_A_inv',
requires_grad=False)
self.matrix_G_inv = Parameter(Tensor(np.zeros([63, 63, 16, 16]).astype(np.float16)), name="matrix_G_inv",
requires_grad=False)
self.fake_G = Tensor(np.zeros([63, 63, 16, 16]).astype(np.float16))
self.matmul = P.MatMul(transpose_b=True)
self.cube_matmul = P.CusMatMulCube(transpose_a=True)
self.matrix_combine = P.CusMatrixCombine()
self.cholesky = P.CusCholeskyTrsm()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.transpose = P.Transpose()
self.cov_step = Parameter(initializer(0, [1], mstype.int32), name="cov_step", requires_grad=False)
self.mul = P.Mul()
self.cast = P.Cast()
self.damping = Tensor(damping)
self.loss_scale = Tensor(1 / loss_scale, mstype.float16)
self.vector_matmul = P.CusBatchMatMul()
self.pad = P.Pad(((0, 24), (0, 24)))
self.pad1 = P.Pad(((0, 8), (0, 8)))
self.slice = P.Slice()
self.gather = P.GatherV2()
self.assignadd = P.AssignAdd()
self.freq = Tensor(frequency, mstype.int32)
self.axis = 0
self.A_inv_max = Parameter(initializer(0, [1], mstype.float32), name="A_inv_max", requires_grad=False)
self.G_inv_max = Parameter(initializer(0, [1], mstype.float32), name="G_inv_max", requires_grad=False)
self.fused_abs_max1 = P.CusFusedAbsMax1([1000, 1000])
self.fused_abs_max2 = P.CusFusedAbsMax1()
self.log = P.Log()
self.exp = P.Exp()
self.dampingA = Tensor(np.identity(2048), mstype.float32)
self.dampingG = Tensor(np.identity(1024), mstype.float32)
self.add = P.TensorAdd()
self.sqrt = P.Sqrt()
self.getG = P.InsertGradientOf(self.save_gradient)
def save_gradient(self, dout):
"""save_gradient"""
out = dout
dout = self.mul(dout, self.loss_scale)
dout = self.mul(dout, 32.0)
normalizer = 32
matrix_G = self.cube_matmul(dout, dout)
normalizer = self.cast(normalizer, ms.float32)
matrix_G = self.mul(matrix_G, 1.0 / normalizer)
matrix_G = self.pad(matrix_G)
damping_step = self.gather(self.damping, self.cov_step, 0)
damping_step = self.cast(damping_step, mstype.float32)
self.cov_step = self.cov_step + self.freq
damping = self.sqrt(damping_step)
dampingG = self.cast(self.dampingG, mstype.float32)
matrix_G = matrix_G + damping * dampingG
matrix_G_inv = self.cholesky(matrix_G)
matrix_G_inv = self.vector_matmul(matrix_G_inv, matrix_G_inv)
matrix_G_inv_max = self.fused_abs_max1(matrix_G_inv)
matrix_G_inv_max = self.fused_abs_max2(matrix_G_inv_max)
self.G_inv_max = matrix_G_inv_max
matrix_G_inv = self.matrix_combine(matrix_G_inv)
matrix_G_inv = self.slice(matrix_G_inv, (0, 0), (1000, 1000))
matrix_G_inv = self.pad1(matrix_G_inv)
matrix_G_inv_shape = self.shape(matrix_G_inv)
matrix_G_inv = self.reshape(matrix_G_inv, (matrix_G_inv_shape[0] / 16, 16, matrix_G_inv_shape[0] / 16, 16))
matrix_G_inv = self.transpose(matrix_G_inv, (2, 0, 1, 3))
matrix_G_inv = self.cast(matrix_G_inv, mstype.float16)
self.matrix_G_inv = matrix_G_inv
return out
def construct(self, x):
"""construct"""
if self.thor:
inputs = self.cube_matmul(x, x)
normalizer = 32
normalizer = self.cast(normalizer, ms.float32)
matrix_A = self.mul(inputs, 1.0 / normalizer)
damping_step = self.gather(self.damping, self.cov_step, self.axis)
damping_step = self.cast(damping_step, mstype.float32)
damping = self.sqrt(damping_step)
dampingA = self.cast(self.dampingA, mstype.float32)
matrix_A = matrix_A + damping * dampingA
matrix_A_inv = self.cholesky(matrix_A)
matrix_A_inv = self.vector_matmul(matrix_A_inv, matrix_A_inv)
matrix_A_inv_max = self.fused_abs_max2(matrix_A_inv)
matrix_A_inv_max = self.fused_abs_max2(matrix_A_inv_max)
self.A_inv_max = matrix_A_inv_max
matrix_A_inv = self.matrix_combine(matrix_A_inv)
matrix_A_inv_shape = self.shape(matrix_A_inv)
matrix_A_inv = self.reshape(matrix_A_inv, (matrix_A_inv_shape[0] / 16, 16, matrix_A_inv_shape[0] / 16, 16))
matrix_A_inv = self.transpose(matrix_A_inv, (2, 0, 1, 3))
matrix_A_inv = self.cast(matrix_A_inv, mstype.float16)
self.matrix_A_inv = matrix_A_inv
self.matrix_G_inv = self.fake_G
output = self.matmul(x, self.weight)
output = self.getG(output)
else:
output = self.matmul(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
if self.activation_flag:
return self.activation(output)
return output
def extend_repr(self):
"""extend_repr"""
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info

55
example/resnet50_imagenet2012_THOR/run_distribute_train.sh
View File

@ -1,55 +0,0 @@
#!/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 ]
then
echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [DEVICE_NUM]"
exit 1
fi
if [ ! -f $1 ]
then
echo "error: DMINDSPORE_HCCL_CONFIG_PATH=$1 is not a file"
exit 1
fi
if [ ! -d $2 ]
then
echo "error: DATASET_PATH=$2 is not a directory"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=$3
export RANK_SIZE=$3
export MINDSPORE_HCCL_CONFIG_PATH=$1
for((i=0; i<${DEVICE_NUM}; i++))
do
export DEVICE_ID=$i
export RANK_ID=$i
rm -rf ./train_parallel$i
mkdir ./train_parallel$i
cp *.py ./train_parallel$i
cp *.sh ./train_parallel$i
cp -r model ./train_parallel$i
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$2 > log 2>&1 &
cd ..
done

64
example/resnet50_imagenet2012_THOR/run_infer.sh
View File

@ -1,64 +0,0 @@
#!/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 [ $# != 2 ]
then
echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]
then
echo "error: CHECKPOINT_PATH=$2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "infer" ];
then
rm -rf ./infer
fi
mkdir ./infer
cp *.py ./infer
cp *.sh ./infer
cd ./infer || exit
env > env.log
echo "start infering for device $DEVICE_ID"
python eval.py --do_eval=True --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
cd ..

133
example/resnet50_imagenet2012_THOR/train.py
View File

@ -1,133 +0,0 @@
# 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.
# ============================================================================
"""train_imagenet."""
import argparse
import os
import random
import numpy as np
from mindspore import Tensor
from mindspore import context
from mindspore.communication.management import init
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.train.model import ParallelMode
from model.model_thor import Model
from model.resnet import resnet50
from model.thor import THOR
from config import config
from crossentropy import CrossEntropy
from dataset_imagenet import create_dataset
random.seed(1)
np.random.seed(1)
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
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')
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)
def get_model_lr(global_step, lr_init, decay, total_epochs, steps_per_epoch):
"""get_model_lr"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
for i in range(total_steps):
epoch = (i + 1) / steps_per_epoch
base = (1.0 - float(epoch) / total_epochs) ** decay
lr_local = lr_init * base
if epoch >= 39:
lr_local = lr_local * 0.5
if epoch >= 40:
lr_local = lr_local * 0.5
lr_each_step.append(lr_local)
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 get_model_damping(global_step, damping_init, decay_rate, total_epochs, steps_per_epoch):
"""get_model_damping"""
damping_each_step = []
total_steps = steps_per_epoch * total_epochs
for step in range(total_steps):
epoch = (step + 1) / steps_per_epoch
damping_here = damping_init * (decay_rate ** (epoch / 10))
damping_each_step.append(damping_here)
current_step = global_step
damping_each_step = np.array(damping_each_step).astype(np.float32)
damping_now = damping_each_step[current_step:]
return damping_now
if __name__ == '__main__':
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], "hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum2")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum3")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum4")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum5")
init()
epoch_size = config.epoch_size
damping = get_model_damping(0, 0.03, 0.87, 50, 5004)
net = resnet50(class_num=config.class_num, damping=damping, loss_scale=config.loss_scale,
frequency=config.frequency)
if not config.label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
lr = Tensor(get_model_lr(0, 0.045, 6, 70, 5004))
opt = THOR(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
filter(lambda x: 'matrix_A' in x.name, net.get_parameters()),
filter(lambda x: 'matrix_G' in x.name, net.get_parameters()),
filter(lambda x: 'A_inv_max' in x.name, net.get_parameters()),
filter(lambda x: 'G_inv_max' in x.name, net.get_parameters()),
config.weight_decay, config.loss_scale)
model = Model(net, loss_fn=loss, optimizer=opt, amp_level='O2', loss_scale_manager=loss_scale,
keep_batchnorm_fp32=False, metrics={'acc'}, frequency=config.frequency)
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
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)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)

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@ -0,0 +1,251 @@
# ResNet Example
## Description
These are examples of training ResNet-50/ResNet-101 with CIFAR-10/ImageNet2012 dataset in MindSpore.
(Training ResNet-101 with dataset CIFAR-10 is unsupported now.)
## Requirements
- Install [MindSpore](https://www.mindspore.cn/install/en).
- Download the dataset CIFAR-10 or ImageNet2012
CIFAR-10
> Unzip the CIFAR-10 dataset to any path you want and the folder structure should include train and eval dataset as follows:
> ```
> .
> └─dataset
> ├─ cifar-10-batches-bin # train dataset
> └─ cifar-10-verify-bin # evaluate 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
> ```
## Structure
```shell
.
└──resnet
├── README.md
├── script
├── run_distribute_train.sh # launch distributed training(8 pcs)
├── run_eval.sh # launch evaluation
└── run_standalone_train.sh # launch standalone training(1 pcs)
├── src
├── config.py # parameter configuration
├── dataset.py # data preprocessing
├── crossentropy.py # loss definition for ImageNet2012 dataset
├── lr_generator.py # generate learning rate for each step
└── resnet.py # resnet backbone, including resnet50 and resnet101
├── eval.py # eval net
└── train.py # train net
```
## Parameter configuration
Parameters for both training and evaluation can be set in config.py.
- config for ResNet-50, CIFAR-10 dataset
```
"class_num": 10, # dataset class num
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
"epoch_size": 90, # only valid for taining, which is always 1 for inference
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_steps": 195, # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 5, # number of warmup epoch
"lr_decay_mode": "poly" # decay mode can be selected in steps, ploy and default
"lr_init": 0.01, # initial learning rate
"lr_end": 0.00001, # final learning rate
"lr_max": 0.1, # maximum learning rate
```
- config for ResNet-50, ImageNet2012 dataset
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
"epoch_size": 90, # only valid for taining, which is always 1 for inference
"pretrained_epoch_size": 1, # epoch size that model has been trained before load pretrained checkpoint
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine", # decay mode for generating learning rate
"label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_max": 0.1, # maximum learning rate
```
- config for ResNet-101, ImageNet2012 dataset
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
"epoch_size": 120, # epoch sizes for training
"pretrain_epoch_size": 0, # epoch size of pretrain checkpoint
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": 1, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr": 0.1 # base learning rate
```
## Running the example
### Train
#### Usage
```
# distributed training
Usage: sh run_distribute_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
[PRETRAINED_CKPT_PATH](optional)
# standalone training
Usage: sh run_standalone_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH]
[PRETRAINED_CKPT_PATH](optional)
```
#### Launch
```
# distribute training example
sh run_distribute_train.sh resnet50 cifar10 rank_table.json ~/cifar-10-batches-bin
# standalone training example
sh run_standalone_train.sh resnet50 cifar10 ~/cifar-10-batches-bin
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
#### Result
Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
- training ResNet-50 with CIFAR-10 dataset
```
# distribute training result(8 pcs)
epoch: 1 step: 195, loss is 1.9601055
epoch: 2 step: 195, loss is 1.8555021
epoch: 3 step: 195, loss is 1.6707983
epoch: 4 step: 195, loss is 1.8162166
epoch: 5 step: 195, loss is 1.393667
...
```
- training ResNet-50 with ImageNet2012 dataset
```
# distribute training result(8 pcs)
epoch: 1 step: 5004, loss is 4.8995576
epoch: 2 step: 5004, loss is 3.9235563
epoch: 3 step: 5004, loss is 3.833077
epoch: 4 step: 5004, loss is 3.2795618
epoch: 5 step: 5004, loss is 3.1978393
...
```
- training ResNet-101 with ImageNet2012 dataset
```
# distribute training result(8p)
epoch: 1 step: 5004, loss is 4.805483
epoch: 2 step: 5004, loss is 3.2121816
epoch: 3 step: 5004, loss is 3.429647
epoch: 4 step: 5004, loss is 3.3667371
epoch: 5 step: 5004, loss is 3.1718972
...
epoch: 67 step: 5004, loss is 2.2768745
epoch: 68 step: 5004, loss is 1.7223864
epoch: 69 step: 5004, loss is 2.0665488
epoch: 70 step: 5004, loss is 1.8717369
...
```
### Evaluation
#### Usage
```
# evaluation
Usage: sh run_eval.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```
# evaluation example
sh run_eval.sh resnet50 cifar10 ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
```
> checkpoint can be produced in training process.
#### Result
Evaluation result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
- evaluating ResNet-50 with CIFAR-10 dataset
```
result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
```
- evaluating ResNet-50 with ImageNet2012 dataset
```
result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt
```
- evaluating ResNet-101 with ImageNet2012 dataset
```
result: {'top_5_accuracy': 0.9429417413572343, 'top_1_accuracy': 0.7853513124199744} ckpt=train_parallel0/resnet-120_5004.ckpt
```
### Running on GPU
```
# distributed training example
mpirun -n 8 python train.py ---net=resnet50 --dataset=cifar10 -dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True
# standalone training example
python train.py --net=resnet50 --dataset=cifar10 --dataset_path=~/cifar-10-batches-bin --device_target="GPU"
# infer example
python eval.py --net=resnet50 --dataset=cifar10 --dataset_path=~/cifar10-10-verify-bin --device_target="GPU" --checkpoint_path=resnet-90_195.ckpt
```

90
model_zoo/resnet/eval.py Executable file
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@ -0,0 +1,90 @@
# 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.
# ============================================================================
"""train resnet."""
import os
import random
import argparse
import numpy as np
from mindspore import context
from mindspore import dataset as de
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.crossentropy import CrossEntropy
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet50 or resnet101')
parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
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()
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
if args_opt.net == "resnet50":
from src.resnet import resnet50 as resnet
if args_opt.dataset == "cifar10":
from src.config import config1 as config
from src.dataset import create_dataset1 as create_dataset
else:
from src.config import config2 as config
from src.dataset import create_dataset2 as create_dataset
else:
from src.resnet import resnet101 as resnet
from src.config import config3 as config
from src.dataset import create_dataset3 as create_dataset
if __name__ == '__main__':
target = args_opt.device_target
# init context
device_id = int(os.getenv('DEVICE_ID'))
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
net = resnet(class_num=config.class_num)
# load checkpoint
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
# define loss, model
if args_opt.dataset == "imagenet2012":
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
else:
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
# define model
model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
# eval model
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)

48
model_zoo/resnet101/scripts/run_distribute_train.sh → model_zoo/resnet/scripts/run_distribute_train.sh
View File

@ -14,12 +14,31 @@
# limitations under the License.
# ============================================================================
if [ $# != 2 ] && [ $# != 3 ]
if [ $# != 4 ] && [ $# != 5 ]
then
echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_PATH](optional)"
echo "Usage: sh run_distribute_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [MINDSPORE_HCCL_CONFIG_PATH] [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 "$1"
@ -27,14 +46,13 @@ get_real_path(){
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
echo $PATH1
echo $PATH2
if [ $# == 3 ]
PATH1=$(get_real_path $3)
PATH2=$(get_real_path $4)
if [ $# == 5 ]
then
PATH3=$(get_real_path $3)
echo $PATH3
PATH3=$(get_real_path $5)
fi
if [ ! -f $PATH1 ]
@ -49,9 +67,9 @@ then
exit 1
fi
if [ $# == 3 ] && [ ! -f $PATH3 ]
if [ $# == 5 ] && [ ! -f $PATH3 ]
then
echo "error: PRETRAINED_PATH=$PATH3 is not a file"
echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
exit 1
fi
@ -73,14 +91,14 @@ do
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
if [ $# == 2 ]
if [ $# == 4 ]
then
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
fi
if [ $# == 3 ]
if [ $# == 5 ]
then
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
fi
cd ..

35
model_zoo/resnet101/scripts/run_eval.sh → model_zoo/resnet/scripts/run_eval.sh
View File

@ -14,12 +14,31 @@
# limitations under the License.
# ============================================================================
if [ $# != 2 ]
if [ $# != 4 ]
then
echo "Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]"
echo "Usage: sh run_eval.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
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" ] && [ $2 == "cifar10" ]
then
echo "error: evaluating resnet101 with cifar10 dataset is unsupported now!"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
@ -27,10 +46,10 @@ get_real_path(){
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
echo $PATH1
echo $PATH2
PATH1=$(get_real_path $3)
PATH2=$(get_real_path $4)
if [ ! -d $PATH1 ]
then
@ -60,6 +79,6 @@ cp *.sh ./eval
cp -r ../src ./eval
cd ./eval || exit
env > env.log
echo "start infering for device $DEVICE_ID"
python eval.py --do_eval=True --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
echo "start evaluation for device $DEVICE_ID"
python eval.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
cd ..

45
model_zoo/resnet101/scripts/run_standalone_train.sh → model_zoo/resnet/scripts/run_standalone_train.sh
View File

@ -14,12 +14,31 @@
# limitations under the License.
# ============================================================================
if [ $# != 1 ] && [ $# != 2 ]
if [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_PATH](optional)"
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 "$1"
@ -27,12 +46,12 @@ get_real_path(){
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $1)
echo $PATH1
if [ $# == 2 ]
PATH1=$(get_real_path $3)
if [ $# == 4 ]
then
PATH2=$(get_real_path $2)
echo $PATH2
PATH2=$(get_real_path $4)
fi
if [ ! -d $PATH1 ]
@ -41,9 +60,9 @@ then
exit 1
fi
if [ $# == 2 ] && [ ! -f $PATH2 ]
if [ $# == 4 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_PATH=$PATH2 is not a file"
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
@ -64,13 +83,13 @@ cp -r ../src ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
if [ $# == 1 ]
if [ $# == 3 ]
then
python train.py --do_train=True --dataset_path=$PATH1 &> log &
python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 &> log &
fi
if [ $# == 2 ]
if [ $# == 4 ]
then
python train.py --do_train=True --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi
cd ..

47
example/resnet50_imagenet2012/config.py → model_zoo/resnet/src/config.py
View File

@ -17,17 +17,34 @@ network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as ed
config = ed({
# config for resent50, cifar10
config1 = ed({
"class_num": 10,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 90,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 5,
"lr_decay_mode": "poly",
"lr_init": 0.01,
"lr_end": 0.00001,
"lr_max": 0.1
})
# config for resnet50, imagenet2012
config2 = ed({
"class_num": 1001,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 90,
"pretrained_epoch_size": 1,
"buffer_size": 1000,
"image_height": 224,
"image_width": 224,
"pretrain_epoch_size": 1,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
@ -40,3 +57,23 @@ config = ed({
"lr_max": 0.1
})
# config for resent101, imagenet2012
config3 = ed({
"class_num": 1001,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 120,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 0,
"lr_decay_mode": "cosine",
"use_label_smooth": True,
"label_smooth_factor": 0.1,
"lr": 0.1
})

3
model_zoo/resnet101/src/crossentropy.py → model_zoo/resnet/src/crossentropy.py
View File

@ -20,8 +20,10 @@ 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()
@ -29,6 +31,7 @@ class CrossEntropy(_Loss):
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)

205
model_zoo/resnet/src/dataset.py Executable file
View File

@ -0,0 +1,205 @@
# 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
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 init, get_rank, get_group_size
def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or evaluate cifar10 dataset for resnet50
Args:
dataset_path(string): the path of dataset.
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
"""
if target == "Ascend":
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
else:
init("nccl")
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
# define map operations
trans = []
if do_train:
trans += [
C.RandomCrop((32, 32), (4, 4, 4, 4)),
C.RandomHorizontalFlip(prob=0.5)
]
trans += [
C.Resize((224, 224)),
C.Rescale(1.0 / 255.0, 0.0),
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval imagenet2012 dataset for resnet50
Args:
dataset_path(string): the path of dataset.
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
"""
if target == "Ascend":
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
else:
init("nccl")
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(prob=0.5),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize((256, 256)),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32):
"""
create a train or eval imagenet2012 dataset for resnet101
Args:
dataset_path(string): the path of dataset.
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
Returns:
dataset
"""
device_num = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
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
# 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]
else:
trans = [decode_op,
resize_op_256,
center_crop,
rescale_op,
normalize_op,
changeswap_op]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds

57
example/resnet50_imagenet2012/lr_generator.py → model_zoo/resnet/src/lr_generator.py
View File

@ -28,7 +28,7 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
warmup_epochs(int): number of warmup epochs
total_epochs(int): total epoch of training
steps_per_epoch(int): steps of one epoch
lr_decay_mode(string): learning rate decay mode, including steps, poly, cosine or default
lr_decay_mode(string): learning rate decay mode, including steps, poly or default
Returns:
np.array, learning rate array
@ -62,18 +62,6 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
if lr < 0.0:
lr = 0.0
lr_each_step.append(lr)
elif lr_decay_mode == 'cosine':
decay_steps = total_steps - warmup_steps
for i in range(total_steps):
if i < warmup_steps:
lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
lr = float(lr_init) + lr_inc * (i + 1)
else:
linear_decay = (total_steps - i) / decay_steps
cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
decayed = linear_decay * cosine_decay + 0.00001
lr = lr_max * decayed
lr_each_step.append(lr)
else:
for i in range(total_steps):
if i < warmup_steps:
@ -82,6 +70,47 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
lr_each_step.append(lr)
learning_rate = np.array(lr_each_step).astype(np.float32)
lr_each_step = np.array(lr_each_step).astype(np.float32)
return lr_each_step
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
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch=120, global_step=0):
"""
generate learning rate array with cosine
Args:
lr(float): base learning rate
steps_per_epoch(int): steps size of one epoch
warmup_epochs(int): number of warmup epochs
max_epoch(int): total epochs of training
global_step(int): the current start index of lr array
Returns:
np.array, learning rate array
"""
base_lr = lr
warmup_init_lr = 0
total_steps = int(max_epoch * steps_per_epoch)
warmup_steps = int(warmup_epochs * steps_per_epoch)
decay_steps = total_steps - warmup_steps
lr_each_step = []
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
else:
linear_decay = (total_steps - i) / decay_steps
cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
decayed = linear_decay * cosine_decay + 0.00001
lr = base_lr * decayed
lr_each_step.append(lr)
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[global_step:]
return learning_rate

24
model_zoo/resnet101/src/resnet101.py → model_zoo/resnet/src/resnet.py
View File

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""ResNet101."""
"""ResNet."""
import numpy as np
import mindspore.nn as nn
from mindspore.ops import operations as P
@ -240,6 +240,28 @@ class ResNet(nn.Cell):
return out
def resnet50(class_num=10):
"""
Get ResNet50 neural network.
Args:
class_num (int): Class number.
Returns:
Cell, cell instance of ResNet50 neural network.
Examples:
>>> net = resnet50(10)
"""
return ResNet(ResidualBlock,
[3, 4, 6, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
[1, 2, 2, 2],
class_num)
def resnet101(class_num=1001):
"""
Get ResNet101 neural network.

162
model_zoo/resnet/train.py Executable file
View File

@ -0,0 +1,162 @@
# 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.
# ============================================================================
"""train resnet."""
import os
import random
import argparse
import numpy as np
from mindspore import context
from mindspore import Tensor
from mindspore import dataset as de
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model, ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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, get_rank, get_group_size
import mindspore.nn as nn
import mindspore.common.initializer as weight_init
from src.lr_generator import get_lr, warmup_cosine_annealing_lr
from src.crossentropy import CrossEntropy
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet50 or resnet101')
parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
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()
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
if args_opt.net == "resnet50":
from src.resnet import resnet50 as resnet
if args_opt.dataset == "cifar10":
from src.config import config1 as config
from src.dataset import create_dataset1 as create_dataset
else:
from src.config import config2 as config
from src.dataset import create_dataset2 as create_dataset
else:
from src.resnet import resnet101 as resnet
from src.config import config3 as config
from src.dataset import create_dataset3 as create_dataset
if __name__ == '__main__':
target = args_opt.device_target
ckpt_save_dir = config.save_checkpoint_path
# init context
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
if args_opt.run_distribute:
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
if args_opt.net == "resnet50":
auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
else:
auto_parallel_context().set_all_reduce_fusion_split_indices([180, 313])
init()
# GPU target
else:
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()) + "/"
# create dataset
if args_opt.net == "resnet50":
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=config.epoch_size,
batch_size=config.batch_size, target=target)
else:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=config.epoch_size,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# define net
net = resnet(class_num=config.class_num)
# init weight
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
else:
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
cell.weight.default_input.shape,
cell.weight.default_input.dtype).to_tensor()
if isinstance(cell, nn.Dense):
cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
cell.weight.default_input.shape,
cell.weight.default_input.dtype).to_tensor()
# init lr
if args_opt.net == "resnet50":
if args_opt.dataset == "cifar10":
lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
lr_decay_mode='poly')
else:
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,
config.pretrain_epoch_size * step_size)
lr = Tensor(lr)
# define opt
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
# define loss, model
if target == "Ascend":
if args_opt.dataset == "imagenet2012":
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
else:
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
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)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
# define callbacks
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
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=ckpt_save_dir, config=config_ck)
cb += [ckpt_cb]
# train model
model.train(config.epoch_size, dataset, callbacks=cb)

147
model_zoo/resnet101/README.md
View File

@ -1,147 +0,0 @@
# ResNet101 Example
## Description
This is an example of training ResNet101 with ImageNet dataset in MindSpore.
## Requirements
- Install [MindSpore](https://www.mindspore.cn/install/en).
- Download the dataset ImageNet2012.
> Unzip the ImageNet2012 dataset to any path you want, the folder should include train and eval dataset as follows:
```
.
└─dataset
├─ilsvrc
│
└─validation_preprocess
```
## Structure
```shell
.
└─resnet101
├─README.md
├─scripts
├─run_standalone_train.sh # launch standalone training(1p)
├─run_distribute_train.sh # launch distributed training(8p)
└─run_eval.sh # launch evaluating
├─src
├─config.py # parameter configuration
├─crossentropy.py # CrossEntropy loss function
├─dataset.py # data preprocessin
├─lr_generator.py # generate learning rate
├─resnet101.py # resnet101 backbone
├─eval.py # eval net
└─train.py # train net
```
## Parameter configuration
Parameters for both training and evaluating can be set in config.py.
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
"epoch_size": 120, # epoch sizes for training
"pretrain_epoch_size": 0, # epoch size of pretrain checkpoint
"buffer_size": 1000, # number of queue size in data preprocessing
"image_height": 224, # image height
"image_width": 224, # image width
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": 1, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr": 0.1 # base learning rate
```
## Running the example
### Train
#### Usage
```
# distributed training
sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_PATH](optional)
# standalone training
sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_PATH](optional)
```
#### Launch
```bash
# distributed training example(8p)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
If you want to load pretrained ckpt file,
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc ./ckpt/pretrained.ckpt
# standalone training example（1p）
sh run_standalone_train.sh dataset/ilsvrc
If you want to load pretrained ckpt file,
sh run_standalone_train.sh dataset/ilsvrc ./ckpt/pretrained.ckpt
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
#### Result
Training result will be stored in the scripts path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in log.
```
# distribute training result(8p)
epoch: 1 step: 5004, loss is 4.805483
epoch: 2 step: 5004, loss is 3.2121816
epoch: 3 step: 5004, loss is 3.429647
epoch: 4 step: 5004, loss is 3.3667371
epoch: 5 step: 5004, loss is 3.1718972
...
epoch: 67 step: 5004, loss is 2.2768745
epoch: 68 step: 5004, loss is 1.7223864
epoch: 69 step: 5004, loss is 2.0665488
epoch: 70 step: 5004, loss is 1.8717369
...
```
### Infer
#### Usage
```
# infer
sh run_eval.sh [VALIDATION_DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```bash
# infer with checkpoint
sh run_eval.sh dataset/validation_preprocess/ train_parallel0/resnet-120_5004.ckpt
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the scripts path, whose folder name is "eval". Under this, you can find result like the followings in log.
```
result: {'top_5_accuracy': 0.9429417413572343, 'top_1_accuracy': 0.7853513124199744} ckpt=train_parallel0/resnet-120_5004.ckpt
```

75
model_zoo/resnet101/eval.py
View File

@ -1,75 +0,0 @@
# 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.
# ============================================================================
"""
eval.
"""
import os
import argparse
import random
import numpy as np
from mindspore import context
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.train.model import Model, ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset.engine as de
from mindspore.communication.management import init
from src.resnet101 import resnet101
from src.dataset import create_dataset
from src.config import config
from src.crossentropy import CrossEntropy
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
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')
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)
if __name__ == '__main__':
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([180, 313])
init()
epoch_size = config.epoch_size
net = resnet101(class_num=config.class_num)
if not config.label_smooth:
config.label_smooth_factor = 0.0
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)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)

40
model_zoo/resnet101/src/config.py
View File

@ -1,40 +0,0 @@
# 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.
# ============================================================================
"""
network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as ed
config = ed({
"class_num": 1001,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 120,
"pretrain_epoch_size": 0,
"buffer_size": 1000,
"image_height": 224,
"image_width": 224,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 0,
"lr_decay_mode": "cosine",
"label_smooth": 1,
"label_smooth_factor": 0.1,
"lr": 0.1
})

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model_zoo/resnet101/src/dataset.py
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# 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
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 src.config import config
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
"""
create a train or evaluate dataset
Args:
dataset_path(string): the path of dataset.
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
Returns:
dataset
"""
device_num = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
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
# 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()
trans = []
if do_train:
trans = [decode_op,
random_resize_crop_op,
horizontal_flip_op,
rescale_op,
normalize_op,
changeswap_op]
else:
trans = [decode_op,
resize_op_256,
center_crop,
rescale_op,
normalize_op,
changeswap_op]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
# apply shuffle operations
ds = ds.shuffle(buffer_size=config.buffer_size)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds

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model_zoo/resnet101/src/lr_generator.py
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# 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.
# ============================================================================
"""learning rate generator"""
import math
import numpy as np
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
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch=120, global_step=0):
"""
generate learning rate array with cosine
Args:
lr(float): base learning rate
steps_per_epoch(int): steps size of one epoch
warmup_epochs(int): number of warmup epochs
max_epoch(int): total epochs of training
global_step(int): the current start index of lr array
Returns:
np.array, learning rate array
"""
base_lr = lr
warmup_init_lr = 0
total_steps = int(max_epoch * steps_per_epoch)
warmup_steps = int(warmup_epochs * steps_per_epoch)
decay_steps = total_steps - warmup_steps
lr_each_step = []
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
else:
linear_decay = (total_steps - i) / decay_steps
cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
decayed = linear_decay * cosine_decay + 0.00001
lr = base_lr * decayed
lr_each_step.append(lr)
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[global_step:]
return learning_rate

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model_zoo/resnet101/train.py
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# 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.
# ============================================================================
"""train_imagenet."""
import os
import argparse
import random
import numpy as np
from mindspore import context
from mindspore import Tensor
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.optim.momentum import Momentum
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
import mindspore.dataset.engine as de
from mindspore.communication.management import init
import mindspore.nn as nn
import mindspore.common.initializer as weight_init
from src.resnet101 import resnet101
from src.dataset import create_dataset
from src.lr_generator import warmup_cosine_annealing_lr
from src.config import config
from src.crossentropy import CrossEntropy
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
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('--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__':
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([180, 313])
init()
epoch_size = config.epoch_size
net = resnet101(class_num=config.class_num)
# weight init
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
cell.weight.default_input.shape,
cell.weight.default_input.dtype).to_tensor()
if isinstance(cell, nn.Dense):
cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
cell.weight.default_input.shape,
cell.weight.default_input.dtype).to_tensor()
if not config.label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
# learning rate strategy with cosine
lr = Tensor(warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, 120,
config.pretrain_epoch_size*step_size))
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, amp_level='O2', keep_batchnorm_fp32=False,
loss_scale_manager=loss_scale, metrics={'acc'})
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
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)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)