mirror of https://github.com/kubeflow/examples.git
90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
import json
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import time
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import requests
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import datetime
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import numpy as np
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from PIL import Image
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from io import BytesIO
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import tensorflow as tf
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from azureml.core.model import Model
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def init():
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global model
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try:
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model_path = Model.get_model_path('tacosandburritos')
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except:
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model_path = '/model/latest.h5'
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print('Attempting to load model')
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model = tf.keras.models.load_model(model_path)
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model.summary()
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print('Done!')
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print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now()))
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def run(raw_data):
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global model
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prev_time = time.time()
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post = json.loads(raw_data)
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img_path = post['image']
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current_time = time.time()
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tensor = process_image(img_path, 160)
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t = tf.reshape(tensor, [-1, 160, 160, 3])
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o = model.predict(t, steps=1)#[0][0]
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print(o)
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o = o[0][0]
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inference_time = datetime.timedelta(seconds=current_time - prev_time)
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payload = {
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'time': inference_time.total_seconds(),
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'prediction': 'burrito' if o > 0.5 else 'tacos',
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'scores': str(o)
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}
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print('Input ({}), Prediction ({})'.format(post['image'], payload))
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return payload
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def process_image(path, image_size):
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# Extract image (from web or path)
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if(path.startswith('http')):
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response = requests.get(path)
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img = np.array(Image.open(BytesIO(response.content)))
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else:
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img = np.array(Image.open(path))
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img_tensor = tf.convert_to_tensor(img, dtype=tf.float32)
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#tf.image.decode_jpeg(img_raw, channels=3)
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img_final = tf.image.resize(img_tensor, [image_size, image_size]) / 255
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return img_final
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def info(msg, char = "#", width = 75):
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print("")
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print(char * width)
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print(char + " %0*s" % ((-1*width)+5, msg) + char)
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print(char * width)
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if __name__ == "__main__":
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images = {
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'tacos': 'https://c1.staticflickr.com/5/4022/4401140214_f489c708f0_b.jpg',
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'burrito': 'https://www.exploreveg.org/files/2015/05/sofritas-burrito.jpeg'
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}
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init()
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for k, v in images.items():
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print('{} => {}'.format(k, v))
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info('Taco Test')
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taco = json.dumps({ 'image': images['tacos'] })
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print(taco)
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run(taco)
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info('Burrito Test')
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burrito = json.dumps({ 'image': images['burrito'] })
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print(burrito)
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run(burrito) |