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