批次分类时，Tensorflow Inception-V3推理越来越慢

Question

我正在使用TF运行Inception V3图像识别（session.run）。我正在循环播放图像文件夹，并逐个顺序地识别它们。我遇到了奇怪的现象，在〜20-500张图像（取决于机器）之后，每个session.run的运行时间都在增加。在几百张图像之后，持续时间慢了近100倍。

下面是我的代码

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import os

def load_graph(model_file):
    graph = tf.Graph()
    graph_def = tf.GraphDef()

    with open(model_file, "rb") as f:
        graph_def.ParseFromString(f.read())
    with graph.as_default():
        tf.import_graph_def(graph_def)

    return graph


def read_tensor_from_image_file(file_name,
                                input_height=299,
                                input_width=299,
                                input_mean=0,
                                input_std=255):
    input_name = "file_reader"
    output_name = "normalized"
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith(".png"):
        image_reader = tf.image.decode_png(
            file_reader, channels=3, name="png_reader")
    elif file_name.endswith(".gif"):
        image_reader = tf.squeeze(
            tf.image.decode_gif(file_reader, name="gif_reader"))
    elif file_name.endswith(".bmp"):
        image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
    else:
        image_reader = tf.image.decode_jpeg(
            file_reader, channels=3, name="jpeg_reader")
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0)
    resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session() # create session
    result = sess.run(normalized)

    return result


def load_labels(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label


if __name__ == "__main__":
    model_file = \
        "tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb"
    label_file = "tensorflow/examples/label_image/data/imagenet_slim_labels.txt"
    input_height = 299
    input_width = 299
    input_mean = 0
    input_std = 255
    input_layer = "input"
    output_layer = "InceptionV3/Predictions/Reshape_1"

    parser = argparse.ArgumentParser()
    parser.add_argument("--image", help="image to be processed")
    parser.add_argument("--graph", help="graph/model to be executed")
    parser.add_argument("--labels", help="name of file containing labels")
    parser.add_argument("--input_height", type=int, help="input height")
    parser.add_argument("--input_width", type=int, help="input width")
    parser.add_argument("--input_mean", type=int, help="input mean")
    parser.add_argument("--input_std", type=int, help="input std")
    parser.add_argument("--input_layer", help="name of input layer")
    parser.add_argument("--output_layer", help="name of output layer")
    args = parser.parse_args()

    if args.graph:
        model_file = args.graph
    if args.image:
        file_name = args.image
    if args.labels:
        label_file = args.labels
    if args.input_height:
        input_height = args.input_height
    if args.input_width:
        input_width = args.input_width
    if args.input_mean:
        input_mean = args.input_mean
    if args.input_std:
        input_std = args.input_std
    if args.input_layer:
        input_layer = args.input_layer
    if args.output_layer:
        output_layer = args.output_layer

    graph = load_graph(model_file)
    input_name = "import/" + input_layer
    output_name = "import/" + output_layer
    input_operation = graph.get_operation_by_name(input_name)
    output_operation = graph.get_operation_by_name(output_name)
    dir_name ="xxxxx" # images dir
    with tf.Session(graph=graph) as sess:
        for i in os.walk(dir_name):
            sub_dir, _, file_names = i
            for index, file in enumerate(file_names):
                file_name = os.path.join(sub_dir, file)
                t = read_tensor_from_image_file(
                    file_name,
                    input_height=input_height,
                    input_width=input_width,
                    input_mean=input_mean,
                    input_std=input_std)
                # the time of get t increase 
                results = sess.run(output_operation.outputs[0], {
                    input_operation.outputs[0]: t
                })

为了清楚起见，我将图形分为两个不同的部分。如何连接这两个部分？