update samples - test

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

@@ -0,0 +1,83 @@
+# 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():
+    label = []
+    proto_as_ascii_lines = tf.gfile.GFile("labels.txt").readlines()
+    for l in proto_as_ascii_lines:
+        label.append(l.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_name', dest="labels_name", required=True)
+    args, _ = parser.parse_known_args()
+
+    workspace = Run.get_context(allow_offline=False).experiment.workspace
+    label_ds = Dataset.get_by_name(workspace=workspace, name=args.labels_name)
+    label_ds.download(target_path='.', overwrite=True)
+
+    label_dict = get_class_label_dict()
+    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