从字符串列表创建TfRecords并在解码后在tensorflow中提供图 [英] Creating TfRecords from a list of strings and feeding a Graph in tensorflow after decoding
问题描述
目标是创建一个TfRecords数据库. 给出:我有23个文件夹，每个文件夹包含7500张图像，以及23个文本文件，每个文件都有7500行，分别描述了7500图像在单独文件夹中的功能.
The aim was to create a database of TfRecords. Given: I have 23 folders each contain 7500 image, and 23 text file, each with 7500 line describing features for the 7500 images in separate folders.
我通过以下代码创建了数据库:
I created the database through this code:
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并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，我尝试通过使用带有读取器的filename_queue来加倍努力(目的是为图形提供一批数据，而不是一次提供和平数据；此外，目的是通过不同的线程使示例队列入队和出队，从而使网络训练更快.
Therefore, after converting images and text into tfRecords and after being able to read them and convert images into numpy and the (binary text) into string in python, I tried to go the extra mile by using a filename_queue with a reader (The purpose was to provide the graph with batch of data rather one peace of data at a time. Additionally, the aim was to enqueue and dequeue the queue of examples through different threads, therefore, making training the network faster)
因此，我使用了以下代码:
Therefore, I used the following code:
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，出现以下错误: OutOfRangeError(请参阅上面的回溯):FIFOQueue'_0_input_producer'已关闭并且元素不足(请求1，当前大小为0) [[节点:ReaderReadV2 = ReaderReadV2 [_device ="/job:localhost/副本:0/task:0/cpu:0"] [(TFRecordReaderV2，input_producer)]]
Finally, after running the above code, I got the following error: OutOfRangeError (see above for traceback): FIFOQueue '_0_input_producer' is closed and has insufficient elements (requested 1, current size 0) [[Node: ReaderReadV2 = ReaderReadV2[_device="/job:localhost/replica:0/task:0/cpu:0"](TFRecordReaderV2, input_producer)]]
另一个问题:
- 如何解码二进制数据库(tfrecords)以取回作为python字符串数据结构"存储的功能.
- 如何使用 tf.train.batch 创建一批示例以供网络使用.
- How to decode binary database (tfrecords) to retrieve back the features stored "as python string data structure".
- How to use the tf.train.batch to create a batch of examples to feed the network.
谢谢！！ 非常感谢您的帮助.
Thank you!! Any help is much appreciated.
推荐答案
为解决此问题，coordinator和queue runner都必须在Session中初始化.另外，由于纪元数是内部控制的，因此它不是global variable，而是考虑local variable.因此，我们需要在告诉queue_runner开始将file_names排队到Queue中之前，先初始化该局部变量.因此，下面是以下代码:
In order to solve this problem, the coordinator along with the queue runner both had to be initialized within a Session. Additionally, since the number of epoch is controlled internally, it is not a global variable, instead, consider a local variable. Therefore, we need to initialize that local variable before telling the queue_runner to start the enqueuing the file_names into the Queue. Therefore, here is the following code:
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)
现在应该可以工作了.
And now should work.
现在要收集一批图像，然后再将其输入网络，我们可以使用tf.train.shuffle_batch或tf.train.batch.两者都可以.区别很简单.一个随机播放图像，另一个不随机播放.但是请注意，由于在要加入file_names的线程之间进行竞争，因此为线程定义一个数字并使用tf.train.batch可能会使数据样本混洗.无论如何，在初始化Queue之后，应如下所示直接插入以下代码:
Now to gather a batch of images before feeding them into the network, we can use tf.train.shuffle_batch or tf.train.batch. Both works. And the difference is simple. One shuffles the images and the other not. But note that, defining a number a threads and using tf.train.batch might shuffle the data samples because of the race that takes part between the threads that are enqueuing file_names. Anyways, the following code should be inserted directly after initializing the Queue as follows:
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，此处tensors的形状可能有所不同.碰巧读者正在解码大小为[112, 112, 3]的彩色图像.注释中有一个[](没有理由，这是一种特殊情况).
Note that here the shape of the tensors could be different. It happened that the reader was decoding a colored image of size [112, 112, 3]. And the annotation has a [] (there is no reason, that was a particular case).
最后，我们可以将tf.string数据类型视为字符串.实际上，在评估注释张量后，我们可以意识到将张量视为binary string(这是在张量流中真正对待它的方式).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，为了提取特定特征，下面是一个示例:
Finally, we can treat the tf.string datatype as a string. In reality, after evaluating the annotation tensor, we can realize that the tensor is treated as a binary string (This is how it is really treated in tensorflow). Therefore, in my case that string was just a set of features related to that particular image. Therefore, in order to extract specific features, here is an example:
# 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])
希望这个答案会有所帮助.
Hope this answer helps.
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