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resnet152模型

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# Resnet152描述
## 概述
ResNet系列模型是在2015年提出的，通过ResNet单元，成功训练152层神经网络，一举在ILSVRC2015比赛中取得冠军。该网络创新性的提出了残差结构，通过堆叠多个残差结构从而构建了ResNet网络。传统的卷积网络或全连接网络或多或少存在信息丢失的问题，还会造成梯度消失或爆炸，导致深度网络训练失败，ResNet则在一定程度上解决了这个问题。通过将输入信息传递给输出，确保信息完整性。整个网络只需要学习输入和输出的差异部分，简化了学习目标和难度。正因如此，ResNet十分受欢迎，甚至可以直接用于ConceptNet网络。
如下为MindSpore使用ImageNet2012数据集对ResNet152进行训练的示例。
## 论文
1. [论文](https://arxiv.org/pdf/1512.03385.pdf): Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun."Deep Residual Learning for Image Recognition"
# 模型架构
ResNet152的总体网络架构如下：[链接](https://arxiv.org/pdf/1512.03385.pdf)
# 数据集
使用的数据集：[ImageNet2012](http://www.image-net.org/)
- 数据集大小：共1000个类、224*224彩色图像
- 训练集：共1,281,167张图像
- 测试集：共50,000张图像
- 数据格式：JPEG
- 注：数据在dataset.py中处理。
- 下载数据集，目录结构如下：
```text
└─dataset
├─ilsvrc # 训练数据集
└─validation_preprocess # 评估数据集
```
# 环境要求
- 硬件
- 准备Ascend处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
- 框架
- [MindSpore](https://www.mindspore.cn/install/en)
- 如需查看详情，请参见如下资源：
- [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
# 快速入门
通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
- Ascend处理器环境运行
```Shell
# 分布式训练
用法：sh run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
# 单机训练
用法：sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
# 运行评估示例
用法：sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
```
# 脚本说明
## 脚本及样例代码
```text
└──resnet
├── README.md
├── scripts
├── run_distribute_train.sh # 启动Ascend分布式训练（8卡）
├── run_eval.sh # 启动Ascend评估
└── run_standalone_train.sh # 启动Ascend单机训练（单卡）
├── src
├── config.py # 参数配置
├── dataset.py # 数据预处理
├── CrossEntropySmooth.py # ImageNet2012数据集的损失定义
├── lr_generator.py # 生成每个步骤的学习率
└── resnet.py # ResNet骨干网络，包括ResNet50、ResNet101、SE-ResNet50和Resnet152
├── eval.py # 评估网络
└── train.py # 训练网络
```
# 脚本参数
在config.py中可以同时配置训练参数和评估参数。
- 配置ResNet152和ImageNet2012数据集。
```Python
"class_num":1001, # 数据集类数
"batch_size":32, # 输入张量的批次大小
"loss_scale":1024, # 损失等级
"momentum":0.9, # 动量优化器
"weight_decay":1e-4, # 权重衰减
"epoch_size":140, # 训练周期大小
"save_checkpoint":True, # 是否保存检查点
"save_checkpoint_epochs":5, # 两个检查点之间的周期间隔；默认情况下，最后一个检查点将在最后一个周期完成后保存
"keep_checkpoint_max":10, # 只保存最后一个keep_checkpoint_max检查点
"save_checkpoint_path":"./", # 检查点相对于执行路径的保存路径
"warmup_epochs":0, # 热身周期数
"lr_decay_mode":"steps", # 用于生成学习率的衰减模式
"use_label_smooth":True, # 标签平滑
"label_smooth_factor":0.1, # 标签平滑因子
"lr":0.1 # 基础学习率
"lr_end":0.0001, # 最终学习率
```
# 训练过程
## 用法
## Ascend处理器环境运行
```Shell
# 分布式训练
用法：sh run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
# 单机训练
用法：sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
```
分布式训练需要提前创建JSON格式的HCCL配置文件。
具体操作，参见[hccn_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)中的说明。
训练结果保存在示例路径中，文件夹名称以“train”或“train_parallel”开头。您可在此路径下的日志中找到检查点文件以及结果，如下所示。
## 结果
- 使用ImageNet2012数据集训练ResNet50
```text
# 分布式训练结果（8P）
epoch: 1 step: 5004, loss is 4.184874
epoch: 2 step: 5004, loss is 4.013571
epoch: 3 step: 5004, loss is 3.695777
epoch: 4 step: 5004, loss is 3.3244863
epoch: 5 step: 5004, loss is 3.4899402
...
```
# 评估过程
## 用法
### Ascend处理器环境运行
```Shell
# 评估
Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
```
```Shell
# 评估示例
sh run_eval.sh /data/dataset/ImageNet/imagenet_original Resnet152-140_5004.ckpt
```
训练过程中可以生成检查点。
## 结果
评估结果保存在示例路径中，文件夹名为“eval”。您可在此路径下的日志找到如下结果：
- 使用ImageNet2012数据集评估ResNet152
```text
result: {'top_5_accuracy': 0.9438420294494239, 'top_1_accuracy': 0.78817221518} ckpt= resnet152-140_5004.ckpt
```
# 模型描述
## 性能
### 评估性能
#### ImageNet2012上的ResNet152
| 参数 | Ascend 910 |
|---|---|
| 模型版本 | ResNet152 |
| 资源 | Ascend 910；CPU：2.60GHz，192核；内存：755G |
| 上传日期 |2021-02-10 ; |
| MindSpore版本 | 1.0.1 |
| 数据集 | ImageNet2012 |
| 训练参数 | epoch=140, steps per epoch=5004, batch_size = 32 |
| 优化器 | Momentum |
| 损失函数 |Softmax交叉熵 |
| 输出 | 概率 |
| 损失 | 1.7375104 |
|速度|47.47毫秒/步（8卡） |
|总时长 | 577分钟 |
|参数(M) | 60.19 |
| 微调检查点 | 462M（.ckpt文件） |
| 脚本 | [链接](https://gitee.com/panpanrui/mindspore/tree/master/model_zoo/official/cv/resnet152) |
# 随机情况说明
dataset.py中设置了“create_dataset”函数内的种子，同时还使用了train.py中的随机种子。
# ModelZoo主页
请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。

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# Copyright 2021 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 resnet."""
import argparse
from mindspore import context
from mindspore.common import set_seed
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.CrossEntropySmooth import CrossEntropySmooth
from src.resnet import resnet152 as resnet
from src.config import config5 as config
from src.dataset import create_dataset2 as create_dataset
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
args_opt = parser.parse_args()
set_seed(1)
if __name__ == '__main__':
target = "Ascend"
# init context
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
# create dataset
local_data_path = args_opt.data_url
print('Download data.')
dataset = create_dataset(dataset_path=local_data_path, do_train=False, batch_size=config.batch_size,
target=target)
step_size = dataset.get_dataset_size()
# define net
net = resnet(class_num=config.class_num)
ckpt_name = args_opt.checkpoint_path
param_dict = load_checkpoint(ckpt_name)
load_param_into_net(net, param_dict)
net.set_train(False)
# define loss, model
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropySmooth(sparse=True, reduction='mean',
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
# 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=", ckpt_name)

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#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the script as: "
echo "bash run_distribute_train.sh RANK_TABLE_FILE DATA_PATH PRETRAINED_CKPT_PATH](optional)"
echo "For example: bash run_distribute_train.sh hccl_8p_01234567_127.0.0.1.json /path/dataset"
echo "It is better to use the absolute path."
echo "=============================================================================================================="
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 $5)
fi
if [ ! -f $PATH1 ]
then
echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
exit 1
fi
if [ ! -d $PATH2 ]
then
echo "error: DATA_PATH=$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 RANK_TABLE_FILE=$PATH1
DATA_PATH=$2
export DATA_PATH=${DATA_PATH}
for((i=0;i<${RANK_SIZE};i++))
do
rm -rf device$i
mkdir device$i
cp ../*.py ./device$i
cp *.sh ./device$i
cp -r ../src ./device$i
cd ./device$i
export DEVICE_ID=$i
export RANK_ID=$i
echo "start training for device $i"
env > env$i.log
if [ $# == 2 ]
then
python train.py --run_distribute=True --data_url=$PATH2 &> train.log &
fi
if [ $# == 3 ]
then
python train.py --run_distribute=True --data_url=$PATH2 --pre_trained=$PATH3 &> train.log &
fi
cd ../
done

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#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the script as: "
echo "bash run_eval.sh DATA_PATH CHECKPOINT_PATH "
echo "For example: bash run.sh /path/dataset Resnet152-140_5004.ckpt"
echo "It is better to use the absolute path."
echo "=============================================================================================================="
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 "eval" ];
then
rm -rf ./eval
fi
mkdir ./eval
cp ../*.py ./eval
cp *.sh ./eval
cp -r ../src ./eval
cd ./eval
env > env.log
echo "start evaluation for device $DEVICE_ID"
python eval.py --data_url=$PATH1 --checkpoint_path=$PATH2 &> eval.log &
cd ..

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#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the script as: "
echo "bash run_standalone_train.sh DATA_PATH PRETRAINED_CKPT_PATH(optional)"
echo "For example: bash run_standalone_train.sh /path/dataset"
echo "It is better to use the absolute path."
echo "=============================================================================================================="
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=6
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp ../*.py ./train
cp *.sh ./train
cp -r ../src ./train
cd ./train
echo "start training for device $DEVICE_ID"
env > env.log
if [ $# == 1 ]
then
python train.py --data_url=$PATH1 &> train.log &
fi
if [ $# == 2 ]
then
python train.py --data_url=$PATH1 --pre_trained=$PATH2 &> train.log &
fi
cd ..

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# Copyright 2021 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"""
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 CrossEntropySmooth(_Loss):
"""CrossEntropy"""
def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
super(CrossEntropySmooth, self).__init__()
self.onehot = P.OneHot()
self.sparse = sparse
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(reduction=reduction)
def construct(self, logit, label):
if self.sparse:
label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
loss = self.ce(logit, label)
return loss

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# Copyright 2021 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 for resent50, cifar10
config1 = ed({
"class_num": 10,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 90,
"pretrain_epoch_size": 0,
"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": 256,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 90,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 0,
"lr_decay_mode": "linear",
"use_label_smooth": True,
"label_smooth_factor": 0.1,
"lr_init": 0,
"lr_max": 0.8,
"lr_end": 0.0
})
# 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
})
# config for se-resnet50, imagenet2012
config4 = ed({
"class_num": 1001,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 28,
"train_epoch_size": 24,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 4,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 3,
"lr_decay_mode": "cosine",
"use_label_smooth": True,
"label_smooth_factor": 0.1,
"lr_init": 0.0,
"lr_max": 0.3,
"lr_end": 0.0001
})
# config for resnet152, imagenet2012
config5 = ed({
"class_num": 1001,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 140,
"save_checkpoint": True,
"save_checkpoint_epochs": 5,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 0,
"lr_decay_mode": "steps",
"use_label_smooth": True,
"label_smooth_factor": 0.1,
"lr_init": 0.0,
"lr_max": 0.1,
"lr_end": 0.0001
})

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# Copyright 2021 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.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", distribute=False):
"""
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
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if not do_train:
dataset_path = os.path.join(dataset_path, 'eval')
else:
dataset_path = os.path.join(dataset_path, 'train')
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
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(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
ds = ds.map(operations=trans, input_columns="image", 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
def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
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
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if not do_train:
dataset_path = os.path.join(dataset_path, 'val')
else:
dataset_path = os.path.join(dataset_path, 'train')
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDataset(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),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=8)
ds = ds.map(operations=type_cast_op, input_columns="label", 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
def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
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
target(str): the device target. Default: Ascend
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if not do_train:
dataset_path = os.path.join(dataset_path, 'val')
else:
dataset_path = os.path.join(dataset_path, 'train')
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
rank_id = 1
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [0.475 * 255, 0.451 * 255, 0.392 * 255]
std = [0.275 * 255, 0.267 * 255, 0.278 * 255]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(rank_id / (rank_id + 1)),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize(256),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=8)
ds = ds.map(operations=type_cast_op, input_columns="label", 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
def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
create a train or eval imagenet2012 dataset for se-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
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True)
else:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [123.68, 116.78, 103.94]
std = [1.0, 1.0, 1.0]
# 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(292),
C.CenterCrop(256),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=12)
ds = ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def _get_rank_info():
"""
get rank size and rank id
"""
rank_size = int(os.environ.get("RANK_SIZE", 1))
if rank_size > 1:
rank_size = get_group_size()
rank_id = get_rank()
else:
rank_size = 1
rank_id = 0
return rank_size, rank_id

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# Copyright 2021 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 _generate_steps_lr(lr_init, lr_max, total_steps, warmup_steps):
"""
Applies three steps decay to generate learning rate array.
Args:
lr_init(float): init learning rate.
lr_max(float): max learning rate.
total_steps(int): all steps in training.
warmup_steps(int): all steps in warmup epochs.
Returns:
np.array, learning rate array.
"""
decay_epoch_index = [0.2 * total_steps, 0.5 * total_steps, 0.7 * total_steps, 0.9 * total_steps]
lr_each_step = []
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
elif i < decay_epoch_index[3]:
lr = lr_max * 0.001
else:
lr = 0.00005
lr_each_step.append(lr)
return lr_each_step
def _generate_poly_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
"""
Applies polynomial decay to generate learning rate array.
Args:
lr_init(float): init learning rate.
lr_end(float): end learning rate
lr_max(float): max learning rate.
total_steps(int): all steps in training.
warmup_steps(int): all steps in warmup epochs.
Returns:
np.array, learning rate array.
"""
lr_each_step = []
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)
return lr_each_step
def _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
"""
Applies cosine decay to generate learning rate array.
Args:
lr_init(float): init learning rate.
lr_end(float): end learning rate
lr_max(float): max learning rate.
total_steps(int): all steps in training.
warmup_steps(int): all steps in warmup epochs.
Returns:
np.array, learning rate array.
"""
decay_steps = total_steps - warmup_steps
lr_each_step = []
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)
return lr_each_step
def _generate_liner_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
"""
Applies liner decay to generate learning rate array.
Args:
lr_init(float): init learning rate.
lr_end(float): end learning rate
lr_max(float): max learning rate.
total_steps(int): all steps in training.
warmup_steps(int): all steps in warmup epochs.
Returns:
np.array, learning rate array.
"""
lr_each_step = []
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)
return lr_each_step
def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
"""
generate learning rate array
Args:
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, cosine or liner(default)
Returns:
np.array, learning rate array
"""
lr_each_step = []
total_steps = int(steps_per_epoch * total_epochs)
# warmup_steps = steps_per_epoch * warmup_epochs
warmup_steps = warmup_epochs
if lr_decay_mode == 'steps':
lr_each_step = _generate_steps_lr(lr_init, lr_max, total_steps, warmup_steps)
elif lr_decay_mode == 'poly':
lr_each_step = _generate_poly_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
elif lr_decay_mode == 'cosine':
lr_each_step = _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
else:
lr_each_step = _generate_liner_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
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

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"""ResNet"""
import numpy as np
import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.tensor import Tensor
from scipy.stats import truncnorm
def _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size):
fan_in = in_channel * kernel_size * kernel_size
scale = 1.0
scale /= max(1., fan_in)
stddev = (scale ** 0.5)
mu, sigma = 0, stddev
weight = truncnorm(-2, 2, loc=mu, scale=sigma).rvs(out_channel * in_channel * kernel_size * kernel_size)
weight = np.reshape(weight, (out_channel, in_channel, kernel_size, kernel_size))
return Tensor(weight, dtype=mstype.float32)
def _weight_variable(shape, factor=0.01):
init_value = np.random.randn(*shape).astype(np.float32) * factor
return Tensor(init_value)
def _conv3x3(in_channel, out_channel, stride=1, use_se=False):
if use_se:
weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=3)
else:
weight_shape = (out_channel, in_channel, 3, 3)
weight = _weight_variable(weight_shape)
return nn.Conv2d(in_channel, out_channel,
kernel_size=3, stride=stride, padding=0, pad_mode='same', weight_init=weight)
def _conv1x1(in_channel, out_channel, stride=1, use_se=False):
if use_se:
weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=1)
else:
weight_shape = (out_channel, in_channel, 1, 1)
weight = _weight_variable(weight_shape)
return nn.Conv2d(in_channel, out_channel,
kernel_size=1, stride=stride, padding=0, pad_mode='same', weight_init=weight)
def _conv7x7(in_channel, out_channel, stride=1, use_se=False):
if use_se:
weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=7)
else:
weight_shape = (out_channel, in_channel, 7, 7)
weight = _weight_variable(weight_shape)
return nn.Conv2d(in_channel, out_channel,
kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)
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=0, beta_init=0, moving_mean_init=0, moving_var_init=1)
def _fc(in_channel, out_channel, use_se=False):
if use_se:
weight = np.random.normal(loc=0, scale=0.01, size=out_channel*in_channel)
weight = Tensor(np.reshape(weight, (out_channel, in_channel)), dtype=mstype.float32)
else:
weight_shape = (out_channel, in_channel)
weight = _weight_variable(weight_shape)
return nn.Dense(in_channel, out_channel, has_bias=True, weight_init=weight, bias_init=0)
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.
use_se (bool): enable SE-ResNet50 net. Default: False.
se_block(bool): use se block in SE-ResNet50 net. Default: False.
Returns:
Tensor, output tensor.
Examples:
# >>> ResidualBlock(3, 256, stride=2)
"""
expansion = 4
def __init__(self,
in_channel,
out_channel,
stride=1,
use_se=False,
se_block=False):
super(ResidualBlock, self).__init__()
self.stride = stride
self.use_se = use_se
self.se_block = se_block
channel = out_channel // self.expansion
self.conv1 = _conv1x1(in_channel, channel, stride=1, use_se=self.use_se)
self.bn1 = _bn(channel)
if self.use_se and self.stride != 1:
self.e2 = nn.SequentialCell([_conv3x3(channel, channel, stride=1, use_se=True), _bn(channel),
nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same')])
else:
self.conv2 = _conv3x3(channel, channel, stride=stride, use_se=self.use_se)
self.bn2 = _bn(channel)
self.conv3 = _conv1x1(channel, out_channel, stride=1, use_se=self.use_se)
self.bn3 = _bn_last(out_channel)
if self.se_block:
self.se_global_pool = P.ReduceMean(keep_dims=False)
self.se_dense_0 = _fc(out_channel, int(out_channel/4), use_se=self.use_se)
self.se_dense_1 = _fc(int(out_channel/4), out_channel, use_se=self.use_se)
self.se_sigmoid = nn.Sigmoid()
self.se_mul = P.Mul()
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:
if self.use_se:
if stride == 1:
self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel,
stride, use_se=self.use_se), _bn(out_channel)])
else:
self.down_sample_layer = nn.SequentialCell([nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same'),
_conv1x1(in_channel, out_channel, 1,
use_se=self.use_se), _bn(out_channel)])
else:
self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride,
use_se=self.use_se), _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)
if self.use_se and self.stride != 1:
out = self.e2(out)
else:
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.se_block:
out_se = out
out = self.se_global_pool(out, (2, 3))
out = self.se_dense_0(out)
out = self.relu(out)
out = self.se_dense_1(out)
out = self.se_sigmoid(out)
out = F.reshape(out, F.shape(out) + (1, 1))
out = self.se_mul(out, out_se)
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.
use_se (bool): enable SE-ResNet50 net. Default: False.
# se_block(bool): use se block in SE-ResNet50 net in layer 3 and layer 4. Default: False.
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,
use_se=False):
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.use_se = use_se
self.se_block = False
if self.use_se:
self.se_block = True
if self.use_se:
self.conv1_0 = _conv3x3(3, 32, stride=2, use_se=self.use_se)
self.bn1_0 = _bn(32)
self.conv1_1 = _conv3x3(32, 32, stride=1, use_se=self.use_se)
self.bn1_1 = _bn(32)
self.conv1_2 = _conv3x3(32, 64, stride=1, use_se=self.use_se)
else:
self.conv1 = _conv7x7(3, 64, stride=2) # (224, 224, 3) --> (112, 112, 64)
self.bn1 = _bn(64)
self.relu = P.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
out_channel=out_channels[0],
stride=strides[0],
use_se=self.use_se)
self.layer2 = self._make_layer(block,
layer_nums[1],
in_channel=in_channels[1],
out_channel=out_channels[1],
stride=strides[1],
use_se=self.use_se)
self.layer3 = self._make_layer(block,
layer_nums[2],
in_channel=in_channels[2],
out_channel=out_channels[2],
stride=strides[2],
use_se=self.use_se,
se_block=self.se_block)
self.layer4 = self._make_layer(block,
layer_nums[3],
in_channel=in_channels[3],
out_channel=out_channels[3],
stride=strides[3],
use_se=self.use_se,
se_block=self.se_block)
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.end_point = _fc(out_channels[3], num_classes, use_se=self.use_se)
def _make_layer(self, block, layer_num, in_channel, out_channel, stride, use_se=False, se_block=False):
"""
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.
se_block(bool): use se block in SE-ResNet50 net. Default: False.
Returns:
SequentialCell, the output layer.
Examples:
# >>> _make_layer(ResidualBlock, 3, 128, 256, 2)
"""
layers = []
resnet_block = block(in_channel, out_channel, stride=stride, use_se=use_se)
layers.append(resnet_block)
if se_block:
for _ in range(1, layer_num - 1):
resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se)
layers.append(resnet_block)
resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se, se_block=se_block)
layers.append(resnet_block)
else:
for _ in range(1, layer_num):
resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se)
layers.append(resnet_block)
return nn.SequentialCell(layers)
def construct(self, x):
if self.use_se:
x = self.conv1_0(x)
x = self.bn1_0(x)
x = self.relu(x)
x = self.conv1_1(x)
x = self.bn1_1(x)
x = self.relu(x)
x = self.conv1_2(x)
else:
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):
"""
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 se_resnet50(class_num=1001):
"""
Get SE-ResNet50 neural network.
Args:
class_num (int): Class number.
Returns:
Cell, cell instance of SE-ResNet50 neural network.
Examples:
# >>> net = se-resnet50(1001)
"""
return ResNet(ResidualBlock,
[3, 4, 6, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
[1, 2, 2, 2],
class_num,
use_se=True)
def resnet101(class_num=1001):
"""
Get ResNet101 neural network.
Args:
class_num (int): Class number.
Returns:
Cell, cell instance of ResNet101 neural network.
Examples:
# >>> net = resnet101(1001)
"""
return ResNet(ResidualBlock,
[3, 4, 23, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
[1, 2, 2, 2],
class_num)
def resnet152(class_num=1001):
"""
Get ResNet152 neural network.
Args:
class_num (int): Class number.
Returns:
Cell, cell instance of ResNet152 neural network.
Examples:
# >>> net = resnet152(1001)
"""
return ResNet(ResidualBlock,
[3, 8, 36, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
[1, 2, 2, 2],
class_num)

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# Copyright 2021 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 argparse
import ast
from mindspore import context
from mindspore import Tensor
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model
from mindspore.context import 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
from mindspore.common import set_seed
import mindspore.nn as nn
import mindspore.common.initializer as weight_init
from src.lr_generator import get_lr
from src.CrossEntropySmooth import CrossEntropySmooth
from src.resnet import resnet152 as resnet
from src.config import config5 as config
from src.dataset import create_dataset2 as create_dataset # imagenet2012
parser = argparse.ArgumentParser(description='Image classification--resnet152')
parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
parser.add_argument('--is_save_on_master', type=ast.literal_eval, default=True, help='save ckpt on master or all rank')
args_opt = parser.parse_args()
set_seed(1)
if __name__ == '__main__':
ckpt_save_dir = config.save_checkpoint_path
# init context
print(args_opt.run_distribute)
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
if args_opt.run_distribute:
device_id = int(os.getenv('DEVICE_ID'))
rank_size = int(os.environ.get("RANK_SIZE", 1))
print(rank_size)
device_num = rank_size
context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True, all_reduce_fusion_config=[180, 313])
init()
args_opt.rank = get_rank()
print(args_opt.rank)
# select for master rank save ckpt or all rank save, compatible for model parallel
args_opt.rank_save_ckpt_flag = 0
if args_opt.is_save_on_master:
if args_opt.rank == 0:
args_opt.rank_save_ckpt_flag = 1
else:
args_opt.rank_save_ckpt_flag = 1
local_data_path = args_opt.data_url
local_data_path = args_opt.data_url
print('Download data:')
# create dataset
dataset = create_dataset(dataset_path=local_data_path, do_train=True, repeat_num=1,
batch_size=config.batch_size, target="Ascend", distribute=args_opt.run_distribute)
step_size = dataset.get_dataset_size()
print("step"+str(step_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.set_data(weight_init.initializer(weight_init.HeUniform(),
cell.weight.shape,
cell.weight.dtype))
if isinstance(cell, nn.Dense):
cell.weight.set_data(weight_init.initializer(weight_init.HeNormal(),
cell.weight.shape,
cell.weight.dtype))
# init lr
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=config.lr_decay_mode)
lr = Tensor(lr)
# define opt
decayed_params = []
no_decayed_params = []
for param in net.trainable_params():
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
decayed_params.append(param)
else:
no_decayed_params.append(param)
group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
{'params': no_decayed_params},
{'order_params': net.trainable_params()}]
opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
# define loss, model
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropySmooth(sparse=True, reduction="mean",
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale,
metrics={'top_1_accuracy', 'top_5_accuracy'},
amp_level="O3", keep_batchnorm_fp32=False)
# define callbacks
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
if config.save_checkpoint:
if args_opt.rank_save_ckpt_flag:
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="resnet152", directory=ckpt_save_dir, config=config_ck)
cb += [ckpt_cb]
# train model
dataset_sink_mode = True
print(dataset.get_dataset_size())
model.train(config.epoch_size, dataset, callbacks=cb,
sink_size=dataset.get_dataset_size(), dataset_sink_mode=dataset_sink_mode)