update samples from Release-129 as a part of SDK release

tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py

@@ -1,80 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-import os
-import argparse
-import datetime
-import time
-import tensorflow as tf
-from math import ceil
-import numpy as np
-import shutil
-from tensorflow.contrib.slim.python.slim.nets import inception_v3
-
-from azureml.core import Run
-from azureml.core.model import Model
-from azureml.core.dataset import Dataset
-
-slim = tf.contrib.slim
-
-image_size = 299
-num_channel = 3
-
-
-def get_class_label_dict(labels_dir):
-    label = []
-    labels_path = os.path.join(labels_dir, 'labels.txt')
-    proto_as_ascii_lines = tf.gfile.GFile(labels_path).readlines()
-    for temp in proto_as_ascii_lines:
-        label.append(temp.rstrip())
-    return label
-
-
-def init():
-    global g_tf_sess, probabilities, label_dict, input_images
-
-    parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
-    parser.add_argument('--model_name', dest="model_name", required=True)
-    parser.add_argument('--labels_dir', dest="labels_dir", required=True)
-    args, _ = parser.parse_known_args()
-
-    label_dict = get_class_label_dict(args.labels_dir)
-    classes_num = len(label_dict)
-
-    with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
-        input_images = tf.placeholder(tf.float32, [1, image_size, image_size, num_channel])
-        logits, _ = inception_v3.inception_v3(input_images,
-                                              num_classes=classes_num,
-                                              is_training=False)
-        probabilities = tf.argmax(logits, 1)
-
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    g_tf_sess = tf.Session(config=config)
-    g_tf_sess.run(tf.global_variables_initializer())
-    g_tf_sess.run(tf.local_variables_initializer())
-
-    model_path = Model.get_model_path(args.model_name)
-    saver = tf.train.Saver()
-    saver.restore(g_tf_sess, model_path)
-
-
-def file_to_tensor(file_path):
-    image_string = tf.read_file(file_path)
-    image = tf.image.decode_image(image_string, channels=3)
-
-    image.set_shape([None, None, None])
-    image = tf.image.resize_images(image, [image_size, image_size])
-    image = tf.divide(tf.subtract(image, [0]), [255])
-    image.set_shape([image_size, image_size, num_channel])
-    return image
-
-
-def run(mini_batch):
-    result_list = []
-    for file_path in mini_batch:
-        test_image = file_to_tensor(file_path)
-        out = g_tf_sess.run(test_image)
-        result = g_tf_sess.run(probabilities, feed_dict={input_images: [out]})
-        result_list.append(os.path.basename(file_path) + ": " + label_dict[result[0]])
-    return result_list