From fdea8cd59e67a5ad258969ac4644ea9bdd2b3765 Mon Sep 17 00:00:00 2001 From: Rebecca McFadden Date: Fri, 16 Aug 2019 01:22:46 -0400 Subject: [PATCH] fixed parentheses syntax errors --- pipelines/azurepipeline/code/deploy/score.py | 24 ++++++------ .../azurepipeline/code/preprocess/data.py | 38 +++++++++---------- pipelines/azurepipeline/code/profile/score.py | 30 ++++++--------- .../azurepipeline/code/register/register.py | 28 +++++++------- 4 files changed, 57 insertions(+), 63 deletions(-) diff --git a/pipelines/azurepipeline/code/deploy/score.py b/pipelines/azurepipeline/code/deploy/score.py index 6b21691b..bafdcf4a 100644 --- a/pipelines/azurepipeline/code/deploy/score.py +++ b/pipelines/azurepipeline/code/deploy/score.py @@ -16,12 +16,12 @@ def init(): else: model_path = '/model/latest.h5' - print 'Attempting to load model' + print('Attempting to load model') model = tf.keras.models.load_model(model_path) model.summary() - print 'Done!' + print('Done!') - print 'Initialized model "{}" at {}'.format(model_path, datetime.datetime.now()) + print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now())) return model @@ -36,7 +36,7 @@ def run(raw_data, model): tensor = process_image(img_path, 160) t = tf.reshape(tensor, [-1, 160, 160, 3]) o = model.predict(t, steps=1) # [0][0] - print o + print(o) o = o[0][0] inference_time = datetime.timedelta(seconds=current_time - prev_time) payload = { @@ -45,7 +45,7 @@ def run(raw_data, model): 'scores': str(o) } - print 'Input ({}), Prediction ({})'.format(post['image'], payload) + print('Input ({}), Prediction ({})'.format(post['image'], payload)) return payload @@ -65,10 +65,10 @@ def process_image(path, image_size): def info(msg, char="#", width=75): - print "" - print char * width - print char + " %0*s" % ((-1 * width) + 5, msg) + char - print char * width + print("") + print(char * width) + print(char + " %0*s" % ((-1 * width) + 5, msg) + char) + print(char * width) if __name__ == "__main__": @@ -80,14 +80,14 @@ if __name__ == "__main__": my_model = init() for k, v in images.items(): - print '{} => {}'.format(k, v) + print('{} => {}'.format(k, v)) info('Taco Test') taco = json.dumps({'image': images['tacos']}) - print taco + print(taco) run(taco, my_model) info('Burrito Test') burrito = json.dumps({'image': images['burrito']}) - print burrito + print(burrito) run(burrito, my_model) diff --git a/pipelines/azurepipeline/code/preprocess/data.py b/pipelines/azurepipeline/code/preprocess/data.py index 88fcc812..148e7f67 100644 --- a/pipelines/azurepipeline/code/preprocess/data.py +++ b/pipelines/azurepipeline/code/preprocess/data.py @@ -15,22 +15,22 @@ def check_dir(path): def download(source, target, force_clear=False): if force_clear and os.path.exists(target): - print 'Removing {}...'.format(target) + print('Removing {}...'.format(target)) shutil.rmtree(target) check_dir(target) targt_file = str(Path(target).joinpath('data.zip')) if os.path.exists(targt_file) and not force_clear: - print 'data already exists, skipping download' + print('data already exists, skipping download') return if source.startswith('http'): - print "Downloading from {} to {}".format(source, target) + print("Downloading from {} to {}".format(source, target)) wget.download(source, targt_file) - print "Done!" + print("Done!") else: - print "Copying from {} to {}".format(source, target) + print("Copying from {} to {}".format(source, target)) shutil.copyfile(source, targt_file) print 'Unzipping {}'.format(targt_file) @@ -48,22 +48,22 @@ def process_image(path, image_size=160): def walk_images(path, image_size=160): imgs = [] - print 'Scanning {}'.format(path) + print('Scanning {}'.format(path)) # find subdirectories in base path # (they should be the labels) labels = [] for (_, dirs, _) in os.walk(path): - print 'Found {}'.format(dirs) + print('Found {}'.format(dirs)) labels = dirs break for d in labels: path = os.path.join(path, d) - print 'Processing {}'.format(path) + print('Processing {}'.format(path)) # only care about files in directory for item in os.listdir(path): if not item.lower().endswith('.jpg'): - print 'skipping {}'.format(item) + print('skipping {}'.format(item)) continue image = os.path.join(path, item) @@ -73,7 +73,7 @@ def walk_images(path, image_size=160): # write out good images imgs.append(image) except Exception as e: - print '{}\n{}\n'.format(e, image) + print('{}\n{}\n'.format(e, image)) return imgs @@ -88,30 +88,30 @@ if __name__ == "__main__": parser.add_argument('-f', '--force', help='force clear all data', default=False, action='store_true') args = parser.parse_args() - print args + print(args) - print 'Using TensorFlow v.{}'.format(tf.__version__) + print('Using TensorFlow v.{}'.format(tf.__version__)) base_path = Path(args.base_path).resolve(strict=False) - print 'Base Path: {}'.format(base_path) + print('Base Path: {}'.format(base_path)) data_path = base_path.joinpath(args.data).resolve(strict=False) - print 'Train Path: {}'.format(data_path) + print('Train Path: {}'.format(data_path)) target_path = Path(base_path).resolve(strict=False).joinpath(args.target) - print 'Train File: {}'.format(target_path) + print('Train File: {}'.format(target_path)) zip_path = args.zipfile - print 'Acquiring data...' + print('Acquiring data...') download('https://aiadvocate.blob.core.windows.net/public/tacodata.zip', str(base_path), args.force) if os.path.exists(str(target_path)): - print 'dataset text file already exists, skipping check' + print('dataset text file already exists, skipping check') else: - print 'Testing images...' + print('Testing images...') images = walk_images(str(data_path), args.img_size) # save file - print 'writing dataset to {}'.format(target_path) + print('writing dataset to {}'.format(target_path)) with open(str(target_path), 'w+') as f: f.write('\n'.join(images)) diff --git a/pipelines/azurepipeline/code/profile/score.py b/pipelines/azurepipeline/code/profile/score.py index 5f04501a..f94fdd71 100644 --- a/pipelines/azurepipeline/code/profile/score.py +++ b/pipelines/azurepipeline/code/profile/score.py @@ -4,26 +4,20 @@ import datetime from io import BytesIO import requests import numpy as np - from PIL import Image - import tensorflow as tf - from azureml.core.model import Model - def init(): if Model.get_model_path('tacosandburritos'): model_path = Model.get_model_path('tacosandburritos') else: model_path = '/model/latest.h5' - - print 'Attempting to load model' + print('Attempting to load model') model = tf.keras.models.load_model(model_path) model.summary() - print 'Done!' - - print 'Initialized model "{}" at {}'.format(model_path, datetime.datetime.now()) + print('Done!') + print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now())) return model @@ -38,7 +32,7 @@ def run(raw_data, model): tensor = process_image(img_path, 160) t = tf.reshape(tensor, [-1, 160, 160, 3]) o = model.predict(t, steps=1) # [0][0] - print o + print(o) o = o[0][0] inference_time = datetime.timedelta(seconds=current_time - prev_time) payload = { @@ -47,7 +41,7 @@ def run(raw_data, model): 'scores': str(o) } - print 'Input ({}), Prediction ({})'.format(post['image'], payload) + print('Input ({}), Prediction ({})'.format(post['image'], payload)) return payload @@ -67,10 +61,10 @@ def process_image(path, image_size): def info(msg, char="#", width=75): - print "" - print char * width - print char + " %0*s" % ((-1 * width) + 5, msg) + char - print char * width + print("") + print(char * width) + print(char + " %0*s" % ((-1 * width) + 5, msg) + char) + print(char * width) if __name__ == "__main__": @@ -82,14 +76,14 @@ if __name__ == "__main__": my_model = init() for k, v in images.items(): - print '{} => {}'.format(k, v) + print('{} => {}'.format(k, v)) info('Taco Test') taco = json.dumps({'image': images['tacos']}) - print taco + print(taco) run(taco, my_model) info('Burrito Test') burrito = json.dumps({'image': images['burrito']}) - print burrito + print(burrito) run(burrito, my_model) diff --git a/pipelines/azurepipeline/code/register/register.py b/pipelines/azurepipeline/code/register/register.py index 3e1b5112..1947084f 100644 --- a/pipelines/azurepipeline/code/register/register.py +++ b/pipelines/azurepipeline/code/register/register.py @@ -9,14 +9,14 @@ from azureml.core.authentication import ServicePrincipalAuthentication def info(msg, char="#", width=75): - print "" - print char * width - print char + " %0*s" % ((-1 * width) + 5, msg) + char - print char * width + print("") + print(char * width) + print(char + " %0*s" % ((-1 * width) + 5, msg) + char) + print(char * width) def get_ws(tenant_id, service_principal_id, - service_principal_password, subscription_id, resource_group, workspace): + service_principal_password, subscription_id, resource_group, workspace): auth_args = { 'tenant_id': tenant_id, 'service_principal_id': service_principal_id, @@ -31,17 +31,17 @@ def get_ws(tenant_id, service_principal_id, ws = Workspace.get(workspace, **ws_args) return ws + def run(mdl_path, model_name, ws, tgs): + print(ws.get_details()) - print ws.get_details() - - print '\nSaving model {} to {}'.format(mdl_path, model_name) + print('\nSaving model {} to {}'.format(mdl_path, model_name)) # Model Path needs to be relative mdl_path = relpath(mdl_path, '.') Model.register(ws, model_name=model_name, model_path=mdl_path, tags=tgs) - print 'Done!' + print('Done!') if __name__ == "__main__": @@ -58,7 +58,7 @@ if __name__ == "__main__": parser.add_argument('-w', '--workspace', help='workspace') args = parser.parse_args() - print 'Azure ML SDK Version: {}'.format(azureml.core.VERSION) + print('Azure ML SDK Version: {}'.format(azureml.core.VERSION)) args.model = 'model/' + args.model model_path = str(Path(args.base_path).resolve( strict=False).joinpath(args.model).resolve(strict=False)) @@ -80,16 +80,16 @@ if __name__ == "__main__": # printing out args for posterity for i in wsrgs: if i == 'service_principal_password': - print '{} => **********'.format(i) + print('{} => **********'.format(i)) else: - print '{} => {}'.format(i, rgs[i]) + print('{} => {}'.format(i, rgs[i])) with(open(str(params_path), 'r')) as f: tags = json.load(f) - print '\n\nUsing the following tags:' + print('\n\nUsing the following tags:') for tag in tags: - print '{} => {}'.format(tag, tags[tag]) + print('{} => {}'.format(tag, tags[tag])) rgs['tags'] = tags