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inference.py
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inference.py
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# Copyright 2019 Google Inc. All Rights Reserved.
#
# 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.
"""Converts images to json file
Converts images to JSON File.
Currently models support 2 formats, tensor or jpg
Depending the model select the correct model type.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ast import literal_eval
from PIL import Image
import base64
import codecs
import json
import logging
import requests
import numpy as np
INPUT_FILE = 'image.jpg'
OUTPUT_FILE = '/tmp/out.json'
LOAD_BALANCER = 'localhost:8888' # Enter your TF Serve IP Address.
URL = 'http://%s/v1/models/default:predict' % LOAD_BALANCER
UPLOAD_FOLDER = '/tmp/'
NUM_REQUESTS = 10
# Wait this long for outgoing HTTP connections to be established.
_CONNECT_TIMEOUT_SECONDS = 90
# Wait this long to read from an HTTP socket.
_READ_TIMEOUT_SECONDS = 120
MODEL_TYPE = 'jpg' # tensor | jpg
ENABLE_PREDICT = True
def get_classes():
url = 'https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw' \
'/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5' \
'/imagenet1000_clsidx_to_labels.txt'
response = requests.get(url)
classes = literal_eval(response.text)
return classes
def convert_to_json(image_file):
"""Open image, convert it to numpy and create JSON request"""
img = Image.open(image_file).resize((240, 240))
img_array = np.array(img)
predict_request = {"instances": [img_array.tolist()]}
json.dump(predict_request, codecs.open(OUTPUT_FILE, 'w', encoding='utf-8'),
separators=(',', ':'), sort_keys=True, indent=4)
return predict_request
def convert_to_base64(image_file):
"""Open image and convert it to base64"""
with open(image_file, 'rb') as f:
jpeg_bytes = base64.b64encode(f.read()).decode('utf-8')
predict_request = '{"instances" : [{"b64": "%s"}]}' % jpeg_bytes
# Write JSON to file
with open(OUTPUT_FILE, 'w') as f:
f.write(predict_request)
return predict_request
def model_predict(predict_request):
"""Sends Image for prediction."""
total_time = 0
session = requests.Session()
try:
for _ in range(0, NUM_REQUESTS):
response = session.post(
URL,
data=predict_request,
timeout=(_CONNECT_TIMEOUT_SECONDS, _READ_TIMEOUT_SECONDS),
allow_redirects=False)
response.raise_for_status()
total_time += response.elapsed.total_seconds()
print('Num requests: {} Avg latency: {} ms'.format(NUM_REQUESTS, (
total_time * 1000) / NUM_REQUESTS))
return response.json()
except requests.exceptions.HTTPError as err:
logging.exception(err)
if err.response.status_code == 400:
logging.exception('Server error %s', URL)
return
if err.response.status_code == 404:
logging.exception('Page not found %s', URL)
return
def main():
if MODEL_TYPE == 'tensor':
predict_request = convert_to_json(INPUT_FILE)
elif MODEL_TYPE == 'jpg':
predict_request = convert_to_base64(INPUT_FILE)
else:
logging.error('Invalid Model Type')
return
if ENABLE_PREDICT:
classes = get_classes()
response = model_predict(predict_request)
if response:
prediction_class = response.get('predictions')[0].get('classes') - 1
prediction_probabilities = response.get('predictions')[0].get('probabilities')
print(
'Prediction: [%d] %s Probability [%.2f] ' % (
prediction_class, classes[prediction_class], max(prediction_probabilities)))
if __name__ == '__main__':
main()