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create_predict_request.py
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create_predict_request.py
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import base64
import json
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.python.saved_model.signature_constants import PREDICT_INPUTS
import os
import sys
sys.path.append((os.path.normpath(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
'..'))))
from image_utils import create_tensorflow_image_loader
from image_utils import create_yahoo_image_loader
from model import InputType
IMAGE_LOADER_TENSORFLOW = "tensorflow"
IMAGE_LOADER_YAHOO = "yahoo"
# Thanks to https://stackoverflow.com/a/47626762
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
"""Generates a json prediction request suitable for consumption by a model
generated with 'export-model.py' and deployed on either ml-engine or tensorflow-serving
"""
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="Path to the input image file")
parser.add_argument("-i", "--input_type", required=True,
default=InputType.TENSOR.name.lower(),
help="Input type",
choices=[InputType.TENSOR.name.lower(),
InputType.BASE64_JPEG.name.lower()])
parser.add_argument("-l", "--image_loader", required=False,
default=IMAGE_LOADER_YAHOO,
help="Image loading mechanism. Only relevant when using input_type 'tensor'",
choices=[IMAGE_LOADER_YAHOO, IMAGE_LOADER_TENSORFLOW])
parser.add_argument("-t", "--target", required=True,
choices=['ml-engine', 'tf-serving'],
help="Create json request for ml-engine or tensorflow-serving")
args = parser.parse_args()
target = args.target
input_type = InputType[args.input_type.upper()]
image_data = None
if input_type == InputType.TENSOR:
fn_load_image = None
if args.image_loader == IMAGE_LOADER_TENSORFLOW:
with tf.Session() as sess:
fn_load_image = create_tensorflow_image_loader(sess)
sess.run(tf.global_variables_initializer())
image_data = fn_load_image(args.input_file)[0]
else:
image_data = create_yahoo_image_loader(tf.Session(graph=tf.Graph()))(args.input_file)[0]
elif input_type == InputType.BASE64_JPEG:
import base64
image_data = base64.urlsafe_b64encode(open(args.input_file, "rb").read()).decode("ascii")
if target == "ml-engine":
print(json.dumps({PREDICT_INPUTS: image_data}, cls=NumpyEncoder))
elif target == "tf-serving":
print(json.dumps({"instances": [image_data]}, cls=NumpyEncoder))

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