AWS Lambda Python Keras Image Classification

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model = Sequential()
model.add(Dense(1, activation='sigmoid', input_dim=100))
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense
import numpy as np
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
data = np.random.random((1000, 100))
labels = np.random.randint(2, size=(1000, 1))
model.fit(data, labels, epochs=10, batch_size=32)

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from tensorflow.python.keras.applications.xception import Xception
model = Xception(weights='imagenet')

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from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])

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model = Xception(include_top=False, weights='imagenet')

def train(train_data_dir, validation_data_dir, model_path):
base_model = Xception(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(base_model.input, predictions)
transformation_ratio = .05
train_datagen = ImageDataGenerator(rescale=1. / 255,
rotation_range=transformation_ratio,
shear_range=transformation_ratio,
zoom_range=transformation_ratio,
cval=transformation_ratio,
horizontal_flip=True,
vertical_flip=True)

def train(train_data_dir, validation_data_dir, model_path):
...( )...
train_generator = train_datagen.flow_from_directory(train_data_dir,
batch_size=32,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(validation_data_dir,
batch_size=32,
class_mode='categorical')
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
top_weights_path = os.path.join(os.path.abspath(model_path), 'top_model_weights.h5')
callbacks_list = [
ModelCheckpoint(top_weights_path, monitor='val_acc', verbose=1, save_best_only=True),
EarlyStopping(monitor='val_acc', patience=5, verbose=0)
]
model.fit_generator(train_generator,
samples_per_epoch=train_generator.nb_sample,
nb_epoch=nb_epoch / 5,
validation_data=validation_generator,
nb_val_samples=validation_generator.nb_sample,
callbacks=callbacks_list)

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final_weights_path = os.path.join(os.path.abspath(model_path), 'model_weights.h5')
model.save_weights(final_weights_path)
model_json = model.to_json()
json_file = open(os.path.join(os.path.abspath(model_path), 'model.json'), 'w')
json_file.write(model_json)

def inference(trained_model_dir, test_data_dir, results_path):
# load json and create model
json_file = open(os.path.join(trained_model_dir, model_name), 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights(os.path.join(trained_model_dir, model_weights))
# Read Data
test_datagen = ImageDataGenerator(rescale=1. / 255)
test_generator = test_datagen.flow_from_directory(test_data_dir,
batch_size=batch_size,
shuffle=False)
# Calculate class posteriors probabilities
y_probabilities = model.predict_generator(test_generator,
val_samples=test_generator.nb_sample)
# Calculate class labels
y_classes = probas_to_classes(y_probabilities)
filenames = [filename.split('/')[1] for filename in test_generator.filenames]
ids = [filename.split('.')[0] for filename in filenames]

# event
{
'Records': [
{
'eventVersion': '2.0',
'eventSource': 'aws:s3',
'awsRegion': 'ap-northeast-2', #
'eventTime': '2017-12-13T03:28:13.528Z', #
'eventName': 'ObjectCreated:Put',
'userIdentity': {'principalId': 'AFK2RA1O3ML1F'},
'requestParameters': {'sourceIPAddress': '123.24.137.5'},
'responseElements': {
'x-amz-request-id': '1214K424C14C384D',
'x-amz-id-2': 'BOTBfAoB/gKBbn412ITN4t2psTW499iMRKZDK/CQTsjrkeSSzSdsDUMGabcdnvHeYNtbTDHoHKs='
},
's3': {
's3SchemaVersion': '1.0', 'configurationId': 'b249eeda-3d48-4319-a7e2-853f964c1a25',
'bucket': {
'name': 'aws-summit-kr-2018', #
'ownerIdentity': {
'principalId': 'AFK2RA1O3ML1F'
},
'arn': 'arn:aws:s3:::aws-summit-kr-2018'
},
'object': {
'key': 'img/test_img.png', #
'size': 11733, #
'eTag': 'f2d12d123aebda1cc1fk17479207e838',
'sequencer': '125B119E4D7B2A0A48'
}
}
}
]
}

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#
def handler(event, context):
bucket_name = event['Records'][0]['s3']['bucket']['name']
file_path = event['Records'][0]['s3']['object']['key']

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ACCESS_KEY = os.environ.get('ACCESS_KEY')
SECRET_KEY = os.environ.get('SECRET_KEY')
def downloadFromS3(strBucket, s3_path, local_path):
s3_client = boto3.client('s3',
aws_access_key_id=ACCESS_KEY,
aws_secret_access_key=SECRET_KEY)
s3_client.download_file(strBucket, s3_path, local_path)
def uploadToS3(bucket, s3_path, local_path):
s3_client = boto3.client('s3',
aws_access_key_id=ACCESS_KEY,
aws_secret_access_key=SECRET_KEY)
s3_client.upload_file(local_path, bucket, s3_path)

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file_name = file_path.split('/')[-1]
downloadFromS3(bucket_name, file_path, '/tmp/'+file_name)

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downloadFromS3(
'aws-summit-kr-2018',
'xception_weights_tf_dim_ordering_tf_kernels.h5',
'/tmp/.keras/xception_weights_tf_dim_ordering_tf_kernels.h5'
)

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from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
def predict(img_path):
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
return decode_predictions(preds, top=3)[0]

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downloadFromS3(
'aws-summit-kr-2018',
'xception_weights_tf_dim_ordering_tf_kernels.h5',
'/tmp/.keras/xception_weights_tf_dim_ordering_tf_kernels.h5'
)
result = predict('/tmp/'+file_name)
return result

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docker run -v $(pwd):/outputs --name lambdapack -d amazonlinux:latest tail -f /dev/null

dev_install() {
yum -y update
yum -y upgrade
yum install -y
wget
gcc
gcc-c++
cmake
python36-devel
python36-virtualenv
python36-pip
findutils
zlib-devel
zip
unzip
blas-devel lapack-devel atlas-devel
}

mkvirtualenv() {
cd /home/
rm -rf env
python3 -m virtualenv env --python=python3
source env/bin/activate
}
pip_install() {
source /home/env/bin/activate
pip install -U pip wheel
pip install --use-wheel tensorflow==1.7.0 --no-deps
pip install protobuf html5lib bleach --no-deps
pip install --use-wheel pillow==4.0.0
pip install h5py
}

gather_pack() {
cd /home/ && rm -rf pack && mkdir pack && cd pack
cp -R /home/env/lib/python3.6/site-packages/* .
cp -R /home/env/lib64/python3.6/site-packages/* .
cp /outputs/index.py /home/pack/index.py
find . -type d -name "test" -exec rm -rf {} +
find -name "*.so" | xargs strip
find -name "*.so.*" | xargs strip
rm -r pip && rm -r pip-* && rm -r wheel && rm -r wheel-*
find . | grep -E "(__pycache__|.pyc$)" | xargs rm -rf
echo "stripped size $(du -sh /home/pack | cut -f1)"
zip -FS -r1 /outputs/pack.zip * > /dev/null
echo "compressed size $(du -sh /outputs/pack.zip | cut -f1)"
}

import boto3
def upload_to_s3(bucket, s3_path, local_path):
client = boto3.client('s3',
aws_access_key_id=ACCESS_KEY,aws_secret_access_key=SECRET_KEY)
client.upload_file(local_path, bucket, s3_path)
def update_lambda(function_name, bucket, s3_path):
client = boto3.client('lambda',
aws_access_key_id=ACCESS_KEY,aws_secret_access_key=SECRET_KEY)
client.update_function_code(
FunctionName=function_name,
S3Bucket=bucket,
S3Key=s3_path,
)
uploadToS3(' ', ' /pack.zip', './pack.zip')
update_lambda(' ', ' ', ' /pack.zip')

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def download_s3_object(strBucket, s3_path):
import io
file_obj = io.BytesIO()
s3_client = boto3.client(
's3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY
)
s3_client.download_fileobj(strBucket, s3_path, file_obj)
return file_obj
pack2 = download_s3_object('bucket-name', 'pack2.zip')
import zipfile
zip_ref = zipfile.ZipFile(pack2)
zip_ref.extractall('/tmp')
zip_ref.close()
import sys
sys.path.append("/tmp")

© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
docker run --rm -v "$PWD":/var/task lambci/lambda:python3.6 my_module.my_handler
docker run --rm -v "$PWD":/var/task lambci/lambda:python3.6
my_module.my_handler '{"some": "event"}'

AWS Lambda Python Keras Image Classification

AWS Lambda Python Keras Image Classification

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