一、为什么使用TFRecord?
正常情况下我们训练文件夹经常会生成 train, test 或者val文件夹,这些文件夹内部往往会存着成千上万的图片或文本等文件,这些文件被散列存着,这样不仅占用磁盘空间,并且再被一个个读取的时候会非常慢,繁琐。占用大量内存空间(有的大型数据不足以一次性加载)。此时我们TFRecord格式的文件存储形式会很合理的帮我们存储数据。TFRecord内部使用了“Protocol Buffer”二进制数据编码方案,它只占用一个内存块,只需要一次性加载一个二进制文件的方式即可,简单,快速,尤其对大型训练数据很友好。而且当我们的训练数据量比较大的时候,可以将数据分成多个TFRecord文件,来提高处理效率。
二、 生成TFRecord简单实现方式
我们可以分成两个部分来介绍如何生成TFRecord,分别是TFRecord生成器以及样本Example模块。
- TFRecord生成器
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writer = tf.python_io.TFRecordWriter(record_path) writer.write(tf_example.SerializeToString()) writer.close() |
这里面writer
就是我们TFrecord生成器。接着我们就可以通过writer.write(tf_example.SerializeToString())
来生成我们所要的tfrecord文件了。这里需要注意的是我们TFRecord生成器在写完文件后需要关闭writer.close()
。这里tf_example.SerializeToString()
是将Example中的map压缩为二进制文件,更好的节省空间。那么tf_example是如何生成的呢?那就是下面所要介绍的样本Example模块了。
-
Example模块
首先们来看一下Example协议块是什么样子的。
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message Example { Features features = 1 ; }; message Features { map <string, Feature> feature = 1 ; }; message Feature { oneof kind { BytesList bytes_list = 1 ; FloatList float_list = 2 ; Int64List int64_list = 3 ; } }; |
我们可以看出上面的tf_example可以写入的数据形式有三种,分别是BytesList, FloatList以及Int64List的类型。那我们如何写一个tf_example呢?下面有一个简单的例子。
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def int64_feature(value): return tf.train.Feature(int64_list = tf.train.Int64List(value = [value])) def bytes_feature(value): return tf.train.Feature(bytes_list = tf.train.BytesList(value = [value])) tf_example = tf.train.Example( features = tf.train.Features(feature = { 'image/encoded' : bytes_feature(encoded_jpg), 'image/format' : bytes_feature( 'jpg' .encode()), 'image/class/label' : int64_feature(label), 'image/height' : int64_feature(height), 'image/width' : int64_feature(width)})) |
下面我们来好好从外部往内部分解来解释一下上面的内容。
(1)tf.train.Example(features = None)
这里的features是tf.train.Features类型的特征实例。
(2)tf.train.Features(feature = None)
这里的feature是以字典的形式存在,*key:要保存数据的名字 value:要保存的数据,但是格式必须符合tf.train.Feature实例要求。
三、 生成TFRecord文件完整代码实例
首先我们需要提供数据集
图片文件夹
通过图片文件夹我们可以知道这里面总共有七种分类图片,类别的名称就是每个文件夹名称,每个类别文件夹存储各自的对应类别的很多图片。下面我们通过一下代码(generate_annotation_json.py
和generate_tfrecord.py
)生成train.record。
- generate_annotation_json.py
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# -*- coding: utf-8 -*- # @Time : 2018/11/22 22:12 # @Author : MaochengHu # @Email : wojiaohumaocheng@gmail.com # @File : generate_annotation_json.py # @Software: PyCharm import os import json def get_annotation_dict(input_folder_path, word2number_dict): label_dict = {} father_file_list = os.listdir(input_folder_path) for father_file in father_file_list: full_father_file = os.path.join(input_folder_path, father_file) son_file_list = os.listdir(full_father_file) for image_name in son_file_list: label_dict[os.path.join(full_father_file, image_name)] = word2number_dict[father_file] return label_dict def save_json(label_dict, json_path): with open (json_path, 'w' ) as json_path: json.dump(label_dict, json_path) print ( "label json file has been generated successfully!" ) |
- generate_tfrecord.py
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# -*- coding: utf-8 -*- # @Time : 2018/11/23 0:09 # @Author : MaochengHu # @Email : wojiaohumaocheng@gmail.com # @File : generate_tfrecord.py # @Software: PyCharm import os import tensorflow as tf import io from PIL import Image from generate_annotation_json import get_annotation_dict flags = tf.app.flags flags.DEFINE_string( 'images_dir' , '/data2/raycloud/jingxiong_datasets/six_classes/images' , 'Path to image(directory)' ) flags.DEFINE_string( 'annotation_path' , '/data1/humaoc_file/classify/data/annotations/annotations.json' , 'Path to annotation' ) flags.DEFINE_string( 'record_path' , '/data1/humaoc_file/classify/data/train_tfrecord/train.record' , 'Path to TFRecord' ) FLAGS = flags.FLAGS def int64_feature(value): return tf.train.Feature(int64_list = tf.train.Int64List(value = [value])) def bytes_feature(value): return tf.train.Feature(bytes_list = tf.train.BytesList(value = [value])) def process_image_channels(image): process_flag = False # process the 4 channels .png if image.mode = = 'RGBA' : r, g, b, a = image.split() image = Image.merge( "RGB" , (r,g,b)) process_flag = True # process the channel image elif image.mode ! = 'RGB' : image = image.convert( "RGB" ) process_flag = True return image, process_flag def process_image_reshape(image, resize): width, height = image.size if resize is not None : if width > height: width = int (width * resize / height) height = resize else : width = resize height = int (height * resize / width) image = image.resize((width, height), Image.ANTIALIAS) return image def create_tf_example(image_path, label, resize = None ): with tf.gfile.GFile(image_path, 'rb' ) as fid: encode_jpg = fid.read() encode_jpg_io = io.BytesIO(encode_jpg) image = Image. open (encode_jpg_io) # process png pic with four channels image, process_flag = process_image_channels(image) # reshape image image = process_image_reshape(image, resize) if process_flag = = True or resize is not None : bytes_io = io.BytesIO() image.save(bytes_io, format = 'JPEG' ) encoded_jpg = bytes_io.getvalue() width, height = image.size tf_example = tf.train.Example( features = tf.train.Features( feature = { 'image/encoded' : bytes_feature(encode_jpg), 'image/format' : bytes_feature(b 'jpg' ), 'image/class/label' : int64_feature(label), 'image/height' : int64_feature(height), 'image/width' : int64_feature(width) } )) return tf_example def generate_tfrecord(annotation_dict, record_path, resize = None ): num_tf_example = 0 writer = tf.python_io.TFRecordWriter(record_path) for image_path, label in annotation_dict.items(): if not tf.gfile.GFile(image_path): print ( "{} does not exist" . format (image_path)) tf_example = create_tf_example(image_path, label, resize) writer.write(tf_example.SerializeToString()) num_tf_example + = 1 if num_tf_example % 100 = = 0 : print ( "Create %d TF_Example" % num_tf_example) writer.close() print ( "{} tf_examples has been created successfully, which are saved in {}" . format (num_tf_example, record_path)) def main(_): word2number_dict = { "combinations" : 0 , "details" : 1 , "sizes" : 2 , "tags" : 3 , "models" : 4 , "tileds" : 5 , "hangs" : 6 } images_dir = FLAGS.images_dir #annotation_path = FLAGS.annotation_path record_path = FLAGS.record_path annotation_dict = get_annotation_dict(images_dir, word2number_dict) generate_tfrecord(annotation_dict, record_path) if __name__ = = '__main__' : tf.app.run() |
* 这里需要说明的是generate_annotation_json.py是为了得到图片标注的label_dict。通过这个代码块可以获得我们需要的图片标注字典,key是图片具体地址, value是图片的类别,具体实例如下:
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{ "/images/hangs/862e67a8-5bd9-41f1-8c6d-876a3cb270df.JPG" : 6, "/images/tags/adc264af-a76b-4477-9573-ac6c435decab.JPG" : 3, "/images/tags/fd231f5a-b42c-43ba-9e9d-4abfbaf38853.JPG" : 3, "/images/hangs/2e47d877-1954-40d6-bfa2-1b8e3952ebf9.jpg" : 6, "/images/tileds/a07beddc-4b39-4865-8ee2-017e6c257e92.png" : 5, "/images/models/642015c8-f29d-4930-b1a9-564f858c40e5.png" : 4 } |
- 如何运行代码
(1)首先我们的文件夹构成形式是如下结构,其中images_root
是图片根文件夹,combinations, details, sizes, tags, models, tileds, hangs
分别存放不同类别的图片文件夹。
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-<images_root> -<combinations> -图片.jpg -<details> -图片.jpg -<sizes> -图片.jpg -<tags> -图片.jpg -<models> -图片.jpg -<tileds> -图片.jpg -<hangs> -图片.jpg |
(2)建立文件夹TFRecord
,并将generate_tfrecord.py
和generate_annotation_json.py
这两个python文件放入文件夹内,需要注意的是我们需要将 generate_tfrecord.py
文件中字典word2number_dict换成自己的字典(即key是放不同类别的图片文件夹名称,value是对应的分类number)
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word2number_dict = { "combinations" : 0, "details" : 1, "sizes" : 2, "tags" : 3, "models" : 4, "tileds" : 5, "hangs" : 6 } |
(3)直接执行代码 python3/python2 ./TFRecord/generate_tfrecord.py --image_dir="images_root地址" --record_path="你想要保存record地址(.record文件全路径)"
即可。如下是一个实例:
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python3 generate_tfrecord.py - - image_dir / images / - - record_path / classify / data / train_tfrecord / train.record |
TFRecord读取
上面我们介绍了如何生成TFRecord,现在我们尝试如何通过使用队列读取读取我们的TFRecord。
读取TFRecord可以通过tensorflow两个个重要的函数实现,分别是tf.train.string_input_producer
和 tf.TFRecordReader
的tf.parse_single_example
解析器。如下图
AnimatedFileQueues.gif
四、 读取TFRecord的简单实现方式
解析TFRecord有两种解析方式一种是利用tf.parse_single_example
, 另一种是通过tf.contrib.slim
(* 推荐使用)。
第一种方式(tf.parse_single_example)解析步骤如下:
(1).第一步,我们将train.record
文件读入到队列中,如下所示:
filename_queue = tf.train.string_input_producer([tfrecords_filename])
(2) 第二步,我们需要通过TFRecord将生成的队列读入
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reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) #返回文件名和文件 |
(3)第三步, 通过解析器tf.parse_single_example
将我们的example解析出来。
第二种方式(tf.contrib.slim)解析步骤如下:
(1) 第一步, 我们要设置decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)
, 其中key_to_features
这个字典需要和TFrecord文件中定义的字典项匹配,items_to_handlers
中的关键字可以是任意值,但是它的handler的初始化参数必须要来自于keys_to_features中的关键字。
(2) 第二步, 我们要设定dataset = slim.dataset.Dataset(params)
, 其中params包括:
a. data_source
: 为tfrecord文件地址
b. reader
: 一般设置为tf.TFRecordReader阅读器
c. decoder
: 为第一步设置的decoder
d. num_samples
: 样本数量
e. items_to_description
: 对样本及标签的描述
f. num_classes
: 分类的数量
(3) 第三步, 我们设置provider = slim.dataset_data_provider.DatasetDataProvider(params)
, 其中params包括 :
a. dataset
: 第二步骤我们生成的数据集
b. num_reader
: 并行阅读器数量
c. shuffle
: 是否打乱
d. num_epochs
:每个数据源被读取的次数,如果设为None数据将会被无限循环的读取
e. common_queue_capacity
:读取数据队列的容量,默认为256
f. scope
:范围
g. common_queue_min
:读取数据队列的最小容量。
(4) 第四步, 我们可以通过provider.get
得到我们需要的数据了。
3. 对不同图片大小的TFRecord读取并resize成相同大小reshape_same_size
函数来对图片进行resize,这样我们可以对我们的图片进行batch操作了,因为有的神经网络训练需要一个batch一个batch操作,不同大小的图片在组成一个batch的时候会报错,因此我们我通过后期处理可以更好的对图片进行batch操作。
或者直接通过resized_image = tf.squeeze(tf.image.resize_bilinear([image], size=[FLAG.resize_height, FLAG.resize_width]))
即可。
五、tf.contrib.slim模块读取TFrecord文件完整代码实例
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# -*- coding: utf-8 -*- # @Time : 2018/12/1 11:06 # @Author : MaochengHu # @Email : wojiaohumaocheng@gmail.com # @File : read_tfrecord.py # @Software: PyCharm import os import tensorflow as tf flags = tf.app.flags flags.DEFINE_string( 'tfrecord_path' , '/data1/humaoc_file/classify/data/train_tfrecord/train.record' , 'path to tfrecord file' ) flags.DEFINE_integer( 'resize_height' , 800 , 'resize height of image' ) flags.DEFINE_integer( 'resize_width' , 800 , 'resize width of image' ) FLAG = flags.FLAGS slim = tf.contrib.slim def print_data(image, resized_image, label, height, width): with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord = coord) for i in range ( 10 ): print ( "______________________image({})___________________" . format (i)) print_image, print_resized_image, print_label, print_height, print_width = sess.run([image, resized_image, label, height, width]) print ( "resized_image shape is: " , print_resized_image.shape) print ( "image shape is: " , print_image.shape) print ( "image label is: " , print_label) print ( "image height is: " , print_height) print ( "image width is: " , print_width) coord.request_stop() coord.join(threads) def reshape_same_size(image, output_height, output_width): """Resize images by fixed sides. Args: image: A 3-D image `Tensor`. output_height: The height of the image after preprocessing. output_width: The width of the image after preprocessing. Returns: resized_image: A 3-D tensor containing the resized image. """ output_height = tf.convert_to_tensor(output_height, dtype = tf.int32) output_width = tf.convert_to_tensor(output_width, dtype = tf.int32) image = tf.expand_dims(image, 0 ) resized_image = tf.image.resize_nearest_neighbor( image, [output_height, output_width], align_corners = False ) resized_image = tf.squeeze(resized_image) return resized_image def read_tfrecord(tfrecord_path, num_samples = 14635 , num_classes = 7 , resize_height = 800 , resize_width = 800 ): keys_to_features = { 'image/encoded' : tf.FixedLenFeature([], default_value = '', dtype = tf.string,), 'image/format' : tf.FixedLenFeature([], default_value = 'jpeg' , dtype = tf.string), 'image/class/label' : tf.FixedLenFeature([], tf.int64, default_value = 0 ), 'image/height' : tf.FixedLenFeature([], tf.int64, default_value = 0 ), 'image/width' : tf.FixedLenFeature([], tf.int64, default_value = 0 ) } items_to_handlers = { 'image' : slim.tfexample_decoder.Image(image_key = 'image/encoded' , format_key = 'image/format' , channels = 3 ), 'label' : slim.tfexample_decoder.Tensor( 'image/class/label' , shape = []), 'height' : slim.tfexample_decoder.Tensor( 'image/height' , shape = []), 'width' : slim.tfexample_decoder.Tensor( 'image/width' , shape = []) } decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) labels_to_names = None items_to_descriptions = { 'image' : 'An image with shape image_shape.' , 'label' : 'A single integer between 0 and 9.' } dataset = slim.dataset.Dataset( data_sources = tfrecord_path, reader = tf.TFRecordReader, decoder = decoder, num_samples = num_samples, items_to_descriptions = None , num_classes = num_classes, ) provider = slim.dataset_data_provider.DatasetDataProvider(dataset = dataset, num_readers = 3 , shuffle = True , common_queue_capacity = 256 , common_queue_min = 128 , seed = None ) image, label, height, width = provider.get([ 'image' , 'label' , 'height' , 'width' ]) resized_image = tf.squeeze(tf.image.resize_bilinear([image], size = [resize_height, resize_width])) return resized_image, label, image, height, width def main(): resized_image, label, image, height, width = read_tfrecord(tfrecord_path = FLAG.tfrecord_path, resize_height = FLAG.resize_height, resize_width = FLAG.resize_width) #resized_image = reshape_same_size(image, FLAG.resize_height, FLAG.resize_width) #resized_image = tf.squeeze(tf.image.resize_bilinear([image], size=[FLAG.resize_height, FLAG.resize_width])) print_data(image, resized_image, label, height, width) if __name__ = = '__main__' : main() |
代码运行方式
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python3 read_tfrecord.py - - tfrecord_path / data1 / humaoc_file / classify / data / train_tfrecord / train.record - - resize_height 800 - - resize_width 800 |
最终我们可以看到我们读取文件的部分内容:
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______________________image(0)___________________ resized_image shape is: (800, 800, 3) image shape is: (2000, 1333, 3) image label is: 5 image height is: 2000 image width is: 1333 ______________________image(1)___________________ resized_image shape is: (800, 800, 3) image shape is: (667, 1000, 3) image label is: 0 image height is: 667 image width is: 1000 ______________________image(2)___________________ resized_image shape is: (800, 800, 3) image shape is: (667, 1000, 3) image label is: 3 image height is: 667 image width is: 1000 ______________________image(3)___________________ resized_image shape is: (800, 800, 3) image shape is: (800, 800, 3) image label is: 5 image height is: 800 image width is: 800 ______________________image(4)___________________ resized_image shape is: (800, 800, 3) image shape is: (1424, 750, 3) image label is: 0 image height is: 1424 image width is: 750 ______________________image(5)___________________ resized_image shape is: (800, 800, 3) image shape is: (1196, 1000, 3) image label is: 6 image height is: 1196 image width is: 1000 ______________________image(6)___________________ resized_image shape is: (800, 800, 3) image shape is: (667, 1000, 3) image label is: 5 image height is: 667 image width is: 1000 |
参考:
[1] TensorFlow 自定义生成 .record 文件
[2] TensorFlow基础5:TFRecords文件的存储与读取讲解及代码实现
[3] Slim读取TFrecord文件
[4] Tensorflow针对不定尺寸的图片读写tfrecord文件总结
到此这篇关于Tensorflow中TFRecord生成与读取的实现的文章就介绍到这了,更多相关Tensorflow TFRecord生成与读取内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家!
原文链接:https://www.jianshu.com/p/b480e5fcb638