def __init__(self, name, predictor_host):
super().__init__(name)
self.predictor_host = predictor_host
self.preprocessor = create_preprocessor('xception', target_size=(299, 299))
self.labels = [
'dress',
'hat',
'longsleeve',
'outwear',
'pants',
'shirt',
'shoes',
'shorts',
'skirt',
't-shirt'
]
def main():
parser = configure_arg_parser()
args, _ = parser.parse_known_args()
size = os.environ['MODEL_INPUT_SIZE']
size_h, size_w = size.split(',')
size_h = int(size_h)
size_w = int(size_w)
keras_model = os.environ['KERAS_MODEL_NAME']
labels = os.environ['MODEL_LABELS'].split(',')
preprocessor = create_preprocessor(keras_model,
target_size=(size_w, size_w))
transformer = ImageTransformer(args.model_name,
predictor_host=args.predictor_host,
preprocessor=preprocessor,
labels=labels)
kfserver = kfserving.KFServer()
kfserver.start(models=[transformer])
import grpc
import os
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
from keras_image_helper import create_preprocessor
from flask import Flask, request, jsonify
host = os.getenv('TF_SERVING_HOST', 'localhost:8500')
channel = grpc.insecure_channel(host)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
preprocessor = create_preprocessor('xception', target_size=(299, 299))
labels = [
'dress', 'hat', 'longsleeve', 'outwear', 'pants', 'shirt', 'shoes',
'shorts', 'skirt', 't-shirt'
]
def np_to_protobuf(data):
return tf.make_tensor_proto(data, shape=data.shape)
def make_request(X):
pb_request = predict_pb2.PredictRequest()
pb_request.model_spec.name = 'clothing-model'
pb_request.model_spec.signature_name = 'serving_default'
pb_request.inputs['input_8'].CopyFrom(np_to_protobuf(X))
return pb_request
def lambda_handler(event, context):
url = event['url']
preprocessor = create_preprocessor('xception', target_size=(150, 150))
x = preprocessor.from_url(url)
pred = predict(x)
return {"result": pred}
