mirror of https://github.com/kubeflow/examples.git
fixed parentheses syntax errors
This commit is contained in:
Rebecca McFadden
parent
d81d83512a
commit
fdea8cd59e
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@ -16,12 +16,12 @@ def init():
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else:
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model_path = '/model/latest.h5'
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print 'Attempting to load model'
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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('Done!')
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print 'Initialized model "{}" at {}'.format(model_path, datetime.datetime.now())
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print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now()))
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return model
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@ -36,7 +36,7 @@ def run(raw_data, model):
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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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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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@ -45,7 +45,7 @@ def run(raw_data, model):
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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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print('Input ({}), Prediction ({})'.format(post['image'], payload))
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return payload
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@ -65,10 +65,10 @@ def process_image(path, image_size):
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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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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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@ -80,14 +80,14 @@ if __name__ == "__main__":
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my_model = init()
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for k, v in images.items():
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print '{} => {}'.format(k, v)
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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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print(taco)
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run(taco, my_model)
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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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print(burrito)
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run(burrito, my_model)
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@ -15,22 +15,22 @@ def check_dir(path):
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def download(source, target, force_clear=False):
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if force_clear and os.path.exists(target):
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print 'Removing {}...'.format(target)
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print('Removing {}...'.format(target))
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shutil.rmtree(target)
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check_dir(target)
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targt_file = str(Path(target).joinpath('data.zip'))
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if os.path.exists(targt_file) and not force_clear:
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print 'data already exists, skipping download'
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print('data already exists, skipping download')
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return
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if source.startswith('http'):
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print "Downloading from {} to {}".format(source, target)
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print("Downloading from {} to {}".format(source, target))
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wget.download(source, targt_file)
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print "Done!"
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print("Done!")
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else:
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print "Copying from {} to {}".format(source, target)
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print("Copying from {} to {}".format(source, target))
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shutil.copyfile(source, targt_file)
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print 'Unzipping {}'.format(targt_file)
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@ -48,22 +48,22 @@ def process_image(path, image_size=160):
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def walk_images(path, image_size=160):
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imgs = []
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print 'Scanning {}'.format(path)
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print('Scanning {}'.format(path))
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# find subdirectories in base path
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# (they should be the labels)
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labels = []
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for (_, dirs, _) in os.walk(path):
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print 'Found {}'.format(dirs)
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print('Found {}'.format(dirs))
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labels = dirs
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break
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for d in labels:
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path = os.path.join(path, d)
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print 'Processing {}'.format(path)
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print('Processing {}'.format(path))
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# only care about files in directory
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for item in os.listdir(path):
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if not item.lower().endswith('.jpg'):
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print 'skipping {}'.format(item)
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print('skipping {}'.format(item))
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continue
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image = os.path.join(path, item)
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@ -73,7 +73,7 @@ def walk_images(path, image_size=160):
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# write out good images
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imgs.append(image)
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except Exception as e:
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print '{}\n{}\n'.format(e, image)
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print('{}\n{}\n'.format(e, image))
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return imgs
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@ -88,30 +88,30 @@ if __name__ == "__main__":
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parser.add_argument('-f', '--force',
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help='force clear all data', default=False, action='store_true')
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args = parser.parse_args()
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print args
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print(args)
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print 'Using TensorFlow v.{}'.format(tf.__version__)
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print('Using TensorFlow v.{}'.format(tf.__version__))
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base_path = Path(args.base_path).resolve(strict=False)
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print 'Base Path: {}'.format(base_path)
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print('Base Path: {}'.format(base_path))
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data_path = base_path.joinpath(args.data).resolve(strict=False)
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print 'Train Path: {}'.format(data_path)
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print('Train Path: {}'.format(data_path))
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target_path = Path(base_path).resolve(strict=False).joinpath(args.target)
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print 'Train File: {}'.format(target_path)
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print('Train File: {}'.format(target_path))
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zip_path = args.zipfile
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print 'Acquiring data...'
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print('Acquiring data...')
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download('https://aiadvocate.blob.core.windows.net/public/tacodata.zip',
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str(base_path), args.force)
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if os.path.exists(str(target_path)):
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print 'dataset text file already exists, skipping check'
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print('dataset text file already exists, skipping check')
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else:
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print 'Testing images...'
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print('Testing images...')
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images = walk_images(str(data_path), args.img_size)
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# save file
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print 'writing dataset to {}'.format(target_path)
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print('writing dataset to {}'.format(target_path))
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with open(str(target_path), 'w+') as f:
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f.write('\n'.join(images))
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@ -4,26 +4,20 @@ import datetime
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from io import BytesIO
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import requests
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import numpy as np
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from PIL import Image
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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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if Model.get_model_path('tacosandburritos'):
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model_path = Model.get_model_path('tacosandburritos')
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else:
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model_path = '/model/latest.h5'
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print 'Attempting to load model'
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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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print('Done!')
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print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now()))
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return model
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@ -38,7 +32,7 @@ def run(raw_data, model):
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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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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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@ -47,7 +41,7 @@ def run(raw_data, model):
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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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print('Input ({}), Prediction ({})'.format(post['image'], payload))
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return payload
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@ -67,10 +61,10 @@ def process_image(path, image_size):
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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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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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@ -82,14 +76,14 @@ if __name__ == "__main__":
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my_model = init()
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for k, v in images.items():
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print '{} => {}'.format(k, v)
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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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print(taco)
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run(taco, my_model)
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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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print(burrito)
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run(burrito, my_model)
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@ -9,14 +9,14 @@ from azureml.core.authentication import ServicePrincipalAuthentication
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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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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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def get_ws(tenant_id, service_principal_id,
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service_principal_password, subscription_id, resource_group, workspace):
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service_principal_password, subscription_id, resource_group, workspace):
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auth_args = {
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'tenant_id': tenant_id,
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'service_principal_id': service_principal_id,
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@ -31,17 +31,17 @@ def get_ws(tenant_id, service_principal_id,
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ws = Workspace.get(workspace, **ws_args)
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return ws
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def run(mdl_path, model_name, ws, tgs):
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print(ws.get_details())
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print ws.get_details()
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print '\nSaving model {} to {}'.format(mdl_path, model_name)
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print('\nSaving model {} to {}'.format(mdl_path, model_name))
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# Model Path needs to be relative
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mdl_path = relpath(mdl_path, '.')
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Model.register(ws, model_name=model_name, model_path=mdl_path, tags=tgs)
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print 'Done!'
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print('Done!')
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if __name__ == "__main__":
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@ -58,7 +58,7 @@ if __name__ == "__main__":
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parser.add_argument('-w', '--workspace', help='workspace')
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args = parser.parse_args()
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print 'Azure ML SDK Version: {}'.format(azureml.core.VERSION)
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print('Azure ML SDK Version: {}'.format(azureml.core.VERSION))
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args.model = 'model/' + args.model
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model_path = str(Path(args.base_path).resolve(
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strict=False).joinpath(args.model).resolve(strict=False))
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@ -80,16 +80,16 @@ if __name__ == "__main__":
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# printing out args for posterity
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for i in wsrgs:
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if i == 'service_principal_password':
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print '{} => **********'.format(i)
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print('{} => **********'.format(i))
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else:
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print '{} => {}'.format(i, rgs[i])
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print('{} => {}'.format(i, rgs[i]))
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with(open(str(params_path), 'r')) as f:
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tags = json.load(f)
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print '\n\nUsing the following tags:'
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print('\n\nUsing the following tags:')
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for tag in tags:
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print '{} => {}'.format(tag, tags[tag])
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print('{} => {}'.format(tag, tags[tag]))
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rgs['tags'] = tags
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