目的是创建一个TfRecords数据库。 鉴于：我有23个文件夹，每个文件夹包含7500个图像，23个文本文件，每个文件夹有7500行，用于描述单独文件夹中7500图像的功能。
我通过以下代码创建了数据库：
import tensorflow as tf
import numpy as np
from PIL import Image
def _Float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def create_image_annotation_data():
# Code to read images and features.
# images represent a list of numpy array of images, and features_labels represent a list of strings
# where each string represent the whole set of features for each image.
return images, features_labels
# This is the starting point of the program.
# Now I have the images stored as list of numpy array, and the features as list of strings.
images, annotations = create_image_annotation_data()
tfrecords_filename = "database.tfrecords"
writer = tf.python_io.TFRecordWriter(tfrecords_filename)
for img, ann in zip(images, annotations):
# Note that the height and width are needed to reconstruct the original image.
height = img.shape[0]
width = img.shape[1]
# This is how data is converted into binary
img_raw = img.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(height),
'width': _int64_feature(width),
'image_raw': _bytes_feature(img_raw),
'annotation_raw': _bytes_feature(tf.compat.as_bytes(ann))
}))
writer.write(example.SerializeToString())
writer.close()
reconstructed_images = []
record_iterator = tf.python_io.tf_record_iterator(path=tfrecords_filename)
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
height = int(example.features.feature['height']
.int64_list
.value[0])
width = int(example.features.feature['width']
.int64_list
.value[0])
img_string = (example.features.feature['image_raw']
.bytes_list
.value[0])
annotation_string = (example.features.feature['annotation_raw']
.bytes_list
.value[0])
img_1d = np.fromstring(img_string, dtype=np.uint8)
reconstructed_img = img_1d.reshape((height, width, -1))
annotation_reconstructed = annotation_string.decode('utf-8')
因此，在将图像和文本转换为tfRecords之后，在能够读取它们并将图像转换为numpy并将（二进制文本）转换为python中的字符串之后，我试图通过使用带有读取器的filename_queue来加倍努力（目的是为图表提供一批数据而不是一次一个数据的平衡。此外，目的是通过不同的线程将示例队列排队并出列，因此，使网络训练更快）
因此，我使用了以下代码：
import tensorflow as tf
import numpy as np
import time
image_file_list = ["database.tfrecords"]
batch_size = 16
# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(image_file_list, num_epochs=1, shuffle=False)
reader = tf.TFRecordReader()
# Read a whole file from the queue, the first returned value in the tuple is the
# filename which we are ignoring.
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
annotation = tf.decode_raw(features['annotation_raw'], tf.float32)
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
image = tf.reshape(image, [height, width, 3])
# Note that the minimum after dequeue is needed to make sure that the queue is not empty after dequeuing so that
# we don't run into errors
'''
min_after_dequeue = 100
capacity = min_after_dequeue + 3 * batch_size
ann, images_batch = tf.train.batch([annotation, image],
shapes=[[1], [112, 112, 3]],
batch_size=batch_size,
capacity=capacity,
num_threads=1)
'''
# Start a new session to show example output.
with tf.Session() as sess:
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('C:/Users/user/Documents/tensorboard_logs/New_Runs', sess.graph)
# Required to get the filename matching to run.
tf.global_variables_initializer().run()
# Coordinate the loading of image files.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for steps in range(16):
t1 = time.time()
annotation_string, batch, summary = sess.run([annotation, image, merged])
t2 = time.time()
print('time to fetch 16 faces:', (t2 - t1))
print(annotation_string)
tf.summary.image("image_batch", image)
train_writer.add_summary(summary, steps)
# Finish off the filename queue coordinator.
coord.request_stop()
coord.join(threads)
最后，运行上面的代码后，我收到以下错误： OutOfRangeError（参见上面的回溯）：FIFOQueue'_0_input_producer'已关闭且元素不足（请求1，当前大小为0） [[Node：ReaderReadV2 = ReaderReadV2 [_device =“/ job：localhost / replica：0 / task：0 / cpu：0”]（TFRecordReaderV2，input_producer）]]
另一个问题：
谢谢!! 非常感谢任何帮助。
答案 0 :(得分：1)
为了解决此问题，coordinator和queue runner都必须在Session内进行初始化。此外，由于纪元的数量是在内部控制的，因此它不是global variable，而是考虑local variable。因此，在告知queue_runner开始将file_names加入Queue之前，我们需要初始化该局部变量。因此，这是以下代码：
filename_queue = tf.train.string_input_producer(tfrecords_filename, num_epochs=num_epoch, shuffle=False, name='queue')
reader = tf.TFRecordReader()
key, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
...
init_op = tf.group(tf.local_variables_initializer(),
tf.global_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
现在应该工作了。
现在，为了在将图像投入网络之前收集一批图像，我们可以使用tf.train.shuffle_batch或tf.train.batch。两者都有效。差别很简单。一个人洗牌，另一个没洗。但请注意，定义一个数字线程并使用tf.train.batch可能会混乱数据样本，因为在排队file_names的线程之间存在竞争。无论如何，在初始化Queue之后，应该直接插入以下代码，如下所示：
min_after_dequeue = 100
num_threads = 1
capacity = min_after_dequeue + num_threads * batch_size
label_batch, images_batch = tf.train.batch([annotation, image],
shapes=[[], [112, 112, 3]],
batch_size=batch_size,
capacity=capacity,
num_threads=num_threads)
请注意，tensors的形状可能不同。碰巧读者正在解码大小为[112, 112, 3]的彩色图像。注释有一个[]（没有理由，这是一个特例）。
最后，我们可以将tf.string数据类型视为字符串。实际上，在评估了注释张量之后，我们可以认识到张量被视为binary string（这就是它在张量流中的实际处理方式）。因此，在我的情况下，字符串只是与该特定图像相关的一组功能。因此，为了提取特定功能，这里有一个例子：
# The output of string_split is not a tensor, instead, it is a SparseTensorValue. Therefore, it has a property value that stores the actual values. as a tensor.
label_batch_splitted = tf.string_split(label_batch, delimiter=', ')
label_batch_values = tf.reshape(label_batch_splitted.values, [batch_size, -1])
# string_to_number will convert the feature's numbers into float32 as I need them.
label_batch_numbers = tf.string_to_number(label_batch_values, out_type=tf.float32)
# the tf.slice would extract the necessary feature which I am looking.
confidences = tf.slice(label_batch_numbers, begin=[0, 3], size=[-1, 1])
希望这个答案有所帮助。