我正尝试使用Tensorflow Inception v3对一组照片进行分类（＆lt; 10000）。
我正在Macbook Air上使用TensorFlow Docker installation CPU Binary image plus source code和4gb DDR3。
我编写了一个python脚本来生成一个子进程来对每个图像进行分类：
"""
classifier_spawner.py
Runs custom_classify_image.py in subprocess
"""
if __name__ == "__main__":
import sqlite3
import subprocess as sub
from ast import literal_eval
conn = sqlite3.connect("database_name.db")
c = conn.cursor()
c.execute("SELECT * FROM images")
images = c.fetchall()
for image in images:
p = sub.Popen(["python", "custom_classify_image.py", "--image_file=image_dir/" + str(image[0]) + ".jpg"], stdout=sub.PIPE, stderr=sub.PIPE)
output, errors = p.communicate()
categories = literal_eval(output)
for cat in categories:
c.execute("""INSERT OR IGNORE INTO classification (image_id, class_id, probability) VALUES(?,?,?)""", (str(image[0]), str(cat[0]), float(cat[1])))
conn.commit()
print("Classification complete, exiting.")
conn.close()
我修改了classify_image.py as provided with TensorFlow中的run_inference_on_image（image）来打印前5个分类的元组的python列表，如图所示。
"""My custom run_inference_on_image(image) """
def run_inference_on_image(image):
"""Runs inference on an image.
Args:
image: Image file name.
Returns:
Nothing
"""
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(os.path.join(
FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
top_k_tup_list = []
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
top_k_tup_list.append((human_string, score))
sess.close()
print(top_k_tup_list)
我的问题是，当我运行classifier_spawner.py时，在子进程完成后，它所使用的内存不会被释放，在~10分类之后我的磁盘已满（在写入~15gb到磁盘之后）。然后我必须删除我的虚拟机。
我不明白为什么会发生这种情况，在（1）TensorFlow会话结束和（2）进程退出后，是否应该释放子进程使用的虚拟内存？
提前致谢，如果您需要任何澄清，请告诉我。