基于深度学习迁移学习的端到端图像分类器

深度学习中需要大量的数据和计算资源且需花费大量时间来训练模型,但在实际中难以满足这些需求,而使用迁移学习则能有效降低数据量、计算量和计算时间,并能定制在新场景的业务需求,可谓一大利器。

此解决方案基于 Amazon SageMaker完全托管的机器学习服务,使用自己的数据来微调一个预训练的图像分类模型并且达到较高的准确率来构建一个车型号分类器。Amazon SageMaker 是一项完全托管的模块化机器学习服务,可帮助开发人员和数据科学家大规模地构建、训练和部署机器学习模型。

 

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使用Amazon SageMaker训练汽车型号图像识别的模型


1. 浏览器打开 Amazon AWS 官网,登陆控制台,搜索 ‘SageMaker’ 进入服务页面

 


2. 创建一个 Amazon SageMaker 笔记本实例

 

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3. 在控制台搜索”S3”进入服务页面,然后创建存储桶用来存放本次实验数据

a) 注意S3存储桶需要跟Amazon SageMaker实例在同一个region

 

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本文使用了斯坦福大学提供的开源数据集cars dataset,该数据集包含了196种不同汽车品牌车型的16185张图片。其中我们将使用三种车型号组成训练集。您可以访问该数据集主页查看完整的数据说明和进行下载。

 


1. 在Jupyter Notebook处新建一个命令窗口,可以通过wget指令下载对应数据集,然后存储在Notebook实例上

 

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2. 在SageMaker notbook的命令窗口运行下载的数据处理脚本process.sh

 

#!/usr/bin/env bash
## The process file for image classification data transform to the recordio format
# current directory contains only one zip file : my_traindataset.zip
#image to record tool
git clone https://github.com/apache/incubator-mxnet.git
source activate amazonei_mxnet_p36
#unzip your own dataset
unzip *.zip
mkdir train
mkdir validation

#need to fill with your own data path
data_path=/home/ec2-user/Sagemaker/yourowndatafile/
echo "data_path: ${data_path}"
train_path=train/
echo "train_path: ${train_path}"
val_path=validation/
echo "val_path: ${val_path}"

python incubator-mxnet/tools/im2rec.py \
  --list \
  --train-ratio 0.8 \
  --recursive \
  $data_path/data $data_path

python incubator-mxnet/tools/im2rec.py \
    --resize 224 \
    --center-crop \
    --num-thread 4 \
$data_path/data $data_path

#move the xxx.rec file to train & test file
mv ${data_path}data_train.rec $train_path
mv ${data_path}data_val.rec $val_path
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3. 将处理之后的train_data.rec/val_data.rec上传至已创建的S3存储桶内供后续模型训练使用

 

import os 
import boto3

     
def upload_to_s3(file):
    s3 = boto3.resource('s3')
    data = open(file, "rb")
    key = file
    s3.Bucket(bucket).put_object(Key=key, Body=data)


# caltech-256
s3_train_key = "car_data_sample/train"
s3_validation_key = "car_data_sample/validation"
s3_train = 's3://{}/{}/'.format(bucket, s3_train_key)
s3_validation = 's3://{}/{}/'.format(bucket, s3_validation_key)

upload_to_s3('car_data_sample/train/data_train.rec')

upload_to_s3('car_data_sample/validation/data_val.rec')
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1. 在SageMaker Notebook中创建环境

    a) 训练模型所需的权限

    b) 指定训练数据集和模型存储的S3存储桶

    c) Amazon SageMaker中预训练好的图像分类模型

 

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2.  在SageMaker Notebook中配置模型训练的一系列超参数

# The algorithm supports multiple network depth (number of layers). They are 18, 34, 50, 101, 152 and 200
# For this training, we will use 18 layers
num_layers = 18
# we need to specify the input image shape for the training data
image_shape = "3,224,224"
# we also need to specify the number of training samples in the training set
# for caltech it is 15420
num_training_samples = 96
# specify the number of output classes
num_classes = 3
# batch size for training
mini_batch_size =  30
# number of epochs
epochs = 100
# learning rate
learning_rate = 0.01
top_k=2
# Since we are using transfer learning, we set use_pretrained_model to 1 so that weights can be 
# initialized with pre-trained weights
use_pretrained_model = 1

3. 在Amazon SageMaker Notebook中创建SageMaker API : 构建对应的训练任务

     a) 指定训练的输入与输出

     b) 指定训练的计算实例配置

 

%%time
import time
import boto3
from time import gmtime, strftime


s3 = boto3.client('s3')
# create unique job name 
job_name_prefix = 'cars-imageclassification'
timestamp = time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
job_name = job_name_prefix + timestamp
training_params = \
{
    # specify the training docker image
    "AlgorithmSpecification": {
        "TrainingImage": training_image,
        "TrainingInputMode": "File"
    },
    "RoleArn": role,
    "OutputDataConfig": {
        "S3OutputPath": 's3://{}/{}/output'.format(bucket, job_name_prefix)
    },
    "ResourceConfig": {
        "InstanceCount": 1,
        "InstanceType": "ml.p2.xlarge",
        "VolumeSizeInGB": 50
    },
    "TrainingJobName": job_name,
    "HyperParameters": {
        "image_shape": image_shape,
        "num_layers": str(num_layers),
        "num_training_samples": str(num_training_samples),
        "num_classes": str(num_classes),
        "mini_batch_size": str(mini_batch_size),
        "epochs": str(epochs),
        "learning_rate": str(learning_rate),
        "use_pretrained_model": str(use_pretrained_model)
    },
    "StoppingCondition": {
        "MaxRuntimeInSeconds": 360000
    },
#Training data should be inside a subdirectory called "train"
#Validation data should be inside a subdirectory called "validation"
#The algorithm currently only supports fullyreplicated model (where data is copied onto each machine)
    "InputDataConfig": [
        {
            "ChannelName": "train",
            "DataSource": {
                "S3DataSource": {
                    "S3DataType": "S3Prefix",
                    "S3Uri": s3_train,
                    "S3DataDistributionType": "FullyReplicated"
                }
            },
            "ContentType": "application/x-recordio",
            "CompressionType": "None"
        },
        {
            "ChannelName": "validation",
            "DataSource": {
                "S3DataSource": {
                    "S3DataType": "S3Prefix",
                    "S3Uri": s3_validation,
                    "S3DataDistributionType": "FullyReplicated"
                }
            },
            "ContentType": "application/x-recordio",
            "CompressionType": "None"
        }
    ]
}
print('Training job name: {}'.format(job_name))
print('\nInput Data Location: {}'.format(training_params['InputDataConfig'][0]['DataSource']['S3DataSource']))
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4. 在Amazon SageMaker Console中可以查询训练任务进程,训练时间由训练实例类型和训练epoch决定

 

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5. 在训练过程中,您还可以通过监控cloudwatch logs来查看训练过程中的loss变化


 

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1. 训练完成后在之前配置的S3存储桶里的output文件夹可以查看最新的模型文件


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2.  通过Amazon SageMaker Notebook创建线上部署模型,配置接口以及推理使用的实例类型

 

%%time
import boto3
from time import gmtime, strftime

sage = boto3.Session().client(service_name='sagemaker') 

model_name="cars-imageclassification-" + time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
print(model_name)
info = sage.describe_training_job(TrainingJobName=job_name)
model_data = info['ModelArtifacts']['S3ModelArtifacts']
print(model_data)

hosting_image = get_image_uri(boto3.Session().region_name, 'image-classification')

primary_container = {
    'Image': hosting_image,
    'ModelDataUrl': model_data,
}

create_model_response = sage.create_model(
    ModelName = model_name,
    ExecutionRoleArn = role,
    PrimaryContainer = primary_container)

print(create_model_response['ModelArn'])

from time import gmtime, strftime

timestamp = time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
endpoint_config_name = job_name_prefix + '-epc-' + timestamp
endpoint_config_response = sage.create_endpoint_config(
    EndpointConfigName = endpoint_config_name,
    ProductionVariants=[{
        'InstanceType':'ml.m4.xlarge',
        'InitialInstanceCount':1,
        'ModelName':model_name,
        'VariantName':'AllTraffic'}])

print('Endpoint configuration name: {}'.format(endpoint_config_name))
print('Endpoint configuration arn:  {}'.format(endpoint_config_response['EndpointConfigArn']))

3. 使用上面配置的模型和端口在Amazon SageMaker Notebook中配置创建endpoint,大约需要10分钟左右的时间,当以下代码运行结果为Endpoint creation ended with EndpointStatus = InService,那么代表以及成功创建了一个托管在Amazon SageMaker的endpoint。

 

%%time
import time

timestamp = time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
endpoint_name = job_name_prefix + '-ep-' + timestamp
print('Endpoint name: {}'.format(endpoint_name))

endpoint_params = {
    'EndpointName': endpoint_name,
    'EndpointConfigName': endpoint_config_name,
}
endpoint_response = sagemaker.create_endpoint(**endpoint_params)
print('EndpointArn = {}'.format(endpoint_response['EndpointArn']))
# get the status of the endpoint
response = sagemaker.describe_endpoint(EndpointName=endpoint_name)
status = response['EndpointStatus']
print('EndpointStatus = {}'.format(status))
# wait until the status has changed
sagemaker.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)
# print the status of the endpoint
endpoint_response = sagemaker.describe_endpoint(EndpointName=endpoint_name)
status = endpoint_response['EndpointStatus']
print('Endpoint creation ended with EndpointStatus = {}'.format(status))

4. 通过Amazon SageMaker Console查看到已经创建好的的endpoint

 

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1. 下载acura的汽车型号Acura RL Sedan 2012,图片如下


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2. 在Amazon SageMaker Notebook中直接调用之前创建的endpoint进行推理,返回训练模型预测的结果与概率。如果你想要得到更高的准确率,请使用完整数据集进行更多轮次的训练。

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以上就是一个完整的使用Amazon SageMaker构建图像分类模型,训练,部署的过程。您可以将它进行修改,完成不同场景下自己的图像分类任务,同时,我们后续还会推出,使用Amazon SageMaker进行目标检测等不同场景的图像任务应用的实现方式,敬请关注。