#Reading data
https://zhuanlan.zhihu.com/p/27238630
很好的讲解文章，了解了文件名队列
https://hk.saowen.com/a/ba8b012d8c6505b401c36a12c9cbd6d63db4d2a7d3d97abda4ca18133a6bf72b
讲解了decode_raw和FixedLengthRecordReader搭配使用
##所有方式
第二和第三种的区别方式在于前一种需要自己每次循环中设置数据，第三种无需指定。
而第一种方式是最新的方法，代码中使用的则是第三种方式。
There are four methods of getting data into a TensorFlow program:
tf.dataAPI: Easily construct a complex input pipeline. (preferred method)- Feeding: Python code provides the data when running each step.
QueueRunner: a queue-based input pipeline reads the data from files at the beginning of a TensorFlow graph.- Preloaded data: a constant or variable in the TensorFlow graph holds all the data (for small data sets).
数据写入 .tfrecords 文件，需要将每一个样本数据封装为tf.train.Example格式，再将Example逐个写入文件。Example格式中的数据基础类型是tf.train.Feature。
def file_based_convert_examples_to_features(
examples, label_list, max_seq_length, tokenizer, output_file):
"""Convert a set of `InputExample`s to a TFRecord file."""
writer = tf.python_io.TFRecordWriter(output_file)
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
feature = convert_single_example(ex_index, example, label_list,
max_seq_length, tokenizer)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["label_ids"] = create_int_feature([feature.label_id])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())###基础数据格式
三种基础数据类型：bytes，float，int64
对应tf.train中三种类型：BytesList (字符串列表), FloatList (浮点数列表), Int64List (64位整数列表)，构造它们三个依照下面的方式传入相对应的value。
tf.train.BytesList(value=[context_idxs.tostring()]
tf.train.Int64List(value=[1,2])
tf.train.FloatList(value=[0.1,0.2])
###example 协议格式
要将我们的数据写入 .tfrecords 文件，需要将每一个样本数据封装为tf.train.Example格式，再将Example逐个写入文件。Example格式中的数据基础类型是tf.train.Feature
tf.train.Feature(): 它的参数是BytesList, FloatList, Int64List三种
tf.train.Feature(bytes_list=tf.train.BytesList(value=)
tf.train.Feature(int64_list=tf.train.Int64List(value=)
tf.train.Feature(float_list=tf.train.FloatList(value=)
tf.train.Features: 它的参数是一个字典，k-v对中 v 的类型是Feature，对应每一个字段。
tf.train.Features(feature={
"k1": tf.train.Feature(bytes_list=tf.train.BytesList(value=])),
"k2": tf.train.Feature(bytes_list=tf.train.BytesList(value=)),
"k3": tf.train.Feature(float_list=tf.train.FloatList(value=)),
})
tf.train.FeatureList: 它的参数是一个Feature的list, [Feature1, Feature2,...]
"context_idxs": tf.train.FeatureList(feature=
[tf.train.Feature(int64_list=tf.train.Int64List(value=[])])
tf.train.FeatureLists: 它的参数是一个字典，k-v对中 v 的类型是FeatureList。
feature_lists=tf.train.FeatureLists(feature_list={
"k1": tf.train.FeatureList(feature=
[tf.train.Feature(int64_list=tf.train.Int64List(value=[])]),
"k2": tf.train.FeatureList(feature=
[tf.train.Feature(int64_list=tf.train.Int64List(value=v))])
})
我们需要根据我们的数据，找到每一个字段应该映射为 Feature或FeatureList, 多个Feature组成Features，多个FeatureList组成FeatureLists, 然后我们就定义了我们的一个训练数据对应的 Features，FeatureLists, 再将其封装为 tf.train.Example 就可以写入 tfrecords二进制文件了。
tf.train.Example(features=): 传入的features对应一个 tf.train.Features
tf.train.SequenceExample(context=, featurelists=): 传入的context对应一个 tf.train.Features, features_lists对应一个tf.train.FeatureLists
这样就需要选择使用Example还是SequenceExample, SequenceExample多了一个featurelists, 也就是说如果数据中存在字段，我们把它映射为了FeatureList而不是Feature, 那么就要用SequenceExample, 否则用Example。
那么什么样的数据需要映射为FeatureList或Feature？
我的理解是对于长度固定的字段类型，映射为Feature， 比如分类问题中的类别这个字段一般用一个数字表示，二分类就是0或1，那么就class=0映射为tf.train.Feature(tf.train.Int64List(value=[0])), 只要这个字段包含的数据维度是固定的，就可以封装为 Feature。
对于长度不固定的字段类型，映射为FeatureList。比如NLP样本有一个特征是一句话，那么一句话的长度是不固定的，NLP中一般是先分词，然后把每个词对应为该词在字典中的索引，一句话就用一个一维整形数组来表示 [2, 3, 5, 20, ...]，这个数组的长度是不固定的，我们就映射为
tf.train.FeatureList(feature=[tf.train.Feature(value=[v]) for v in [2, 3, 5, 20,...] ] )
- 读入所有的tfrecord文件，组成列表传入dataset = tf.data.TFRecordDataset(filename)
- 利用函数的方式进行处理dataset = dataset.map(decode)，对其中每一个图片文件名进行处理，返回的也只是一个样本。注意这里虽然没有对所有样本循环，我猜测是流式隐循环； 然后设置shuffle（不用考虑min_after_dequeue和num_threads），repeat和batch或apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
- 定义解析函数：
- parse_single_example将之前的A scalar string Tensor, a single serialized Example转变成张量。返回：A
dictmapping feature keys toTensorandSparseTensorvalues. - 其中FixedLenFeature表示Configuration for parsing a fixed-length input feature，J理解为怎么从protocol buffer中以怎样的格式结束数据。
def file_based_input_fn_builder(input_file, seq_length, is_training,
drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
"label_ids": tf.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fnJ总结一下该方式的整体思路：
先用string_input_producer生成一个文件名队列，注意这里虽然传入的是tfrecord格式的地址，但我猜测其会解析成具体图片，那么就是说根据epoch数，生成epoch遍的图片文件名；
然后采用read和parse_single_example对其中每一个图片文件名进行处理，返回的也只是一个样本。注意这里虽然没有对所有样本循环，我猜测是流式隐循环；
最后将单一个样本符号送入shuffle_batch，他会根据batch_size等到输入足够的样本数，就返回一个batch，然后不断这么返回，注意这个函数返回的是batch数据！！！见下面演示代码，调用一次run后的数据看效果。
###查看具体数据的演示代码
如果需要查看具体的数据可使用下列代码
import tensorflow as tf
# output file name string to a queue
filename_queue = tf.train.string_input_producer(['./data/train/train-001.tfrecords'], num_epochs=None)
# create a reader from file queue
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
# get feature from serialized example
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'age': tf.FixedLenFeature([], tf.int64),
'gender': tf.FixedLenFeature([], tf.int64),
'file_name': tf.FixedLenFeature([], tf.string)
})
#preprocessing
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([160 * 160 * 3])
image = tf.reshape(image, [160, 160, 3])
image = tf.reverse_v2(image, [-1])
image = tf.image.per_image_standardization(image)
age = features['age']
gender = features['gender']
file_path = features['file_name']
print (image)
print (age)
print (gender)
print (file_path)
# Tensor("div:0", shape=(160, 160, 3), dtype=float32)
# Tensor("ParseSingleExample/Squeeze_age:0", shape=(), dtype=int64)
# Tensor("ParseSingleExample/Squeeze_gender:0", shape=(), dtype=int64)
# Tensor("ParseSingleExample/Squeeze_file_name:0", shape=(), dtype=string)
#batch
images_batch, sparse_labels_batch, genders_batch, file_paths_batch = tf.train.shuffle_batch([image, age, gender, file_path], batch_size=3,
capacity=200, min_after_dequeue=100, num_threads=2)
#excuate
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
images_val, sparse_labels_val, genders_val = sess.run([images_batch, sparse_labels_batch, genders_batch])
# print(a_val, b_val, c_val)
print ('first batch:')
print (' image_val:',images_val)
print (' label_val:',sparse_labels_val)
print (' gender_val:',genders_val)
# label_val: [51 31 17]
# gender_val: [1 0 0]
images_val, sparse_labels_val, genders_val = sess.run([images_batch, sparse_labels_batch, genders_batch])
print ('second batch:')
print (' image_val:',images_val)
print (' label_val:',sparse_labels_val)
print (' gender_val:',genders_val)
# label_val: [35 69 32]
# gender_val: [0 1 1]Warning: This section discusses implementing input pipelines using the queue-based APIs which can be cleanly replaced by the tf.data API.
A typical queue-based pipeline for reading records from files has the following stages:
- The list of filenames
- Optional filename shuffling
- Optional epoch limit
- Filename queue
- A Reader for the file format
- A decoder for a record read by the reader
- Optional preprocessing
- Example queue
For the list of filenames, use either a constant string Tensor (like ["file0", "file1"] or [("file%d" % i) for i in range(2)]).
def get_files_name(path):
list = os.listdir(path)
result = []
for line in list:
file_path = os.path.join(path, line)
if os.path.isfile(file_path):
result.append(file_path)
return resultPass the list of filenames to the tf.train.string_input_producer function.string_input_producer creates a FIFO queue for holding the filenames until the reader needs them.
def inputs(path, batch_size, num_epochs, allow_smaller_final_batch=False):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
# filename = os.path.join(FLAGS.train_dir,
# TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
filename_queue = tf.train.string_input_producer(
path, num_epochs=num_epochs)Select the reader that matches your input file format and pass the filename queue to the reader's read method. The read method outputs a key identifying the file and record (useful for debugging if you have some weird records), and a scalar string value. Use one (or more) of the decoder and conversion ops to decode this string into the tensors that make up an example.
J即返回(key, value)，其中value就是serialized_example，因为是scalar string（字符串），所以需要进一步解析成为tensor。
J对于tfrecord格式，单独一个则用tf.data.TFRecordDataset，多个文件的话，使用队列，则用tf.TFRecordReader。J我认为这里我搞错了，其实tfrecorddataset也可以，而且输入的也不是多文件（不是多个tfrecord文件）。
Another approach is to convert whatever data you have into a supported format. This approach makes it easier to mix and match data sets and network architectures. The recommended format for TensorFlow is a TFRecords file containing tf.train.Example protocol buffers (which containFeatures as a field). You write a little program that gets your data, stuffs it in an Example protocol buffer, serializes the protocol buffer to a string, and then writes the string to a TFRecords file using the tf.python_io.TFRecordWriter. For example,tensorflow/examples/how_tos/reading_data/convert_to_records.py converts MNIST data to this format.
The recommended way to read a TFRecord file is with a tf.data.TFRecordDataset, as in this example:
dataset = tf.data.TFRecordDataset(filename)
dataset = dataset.repeat(num_epochs)
# map takes a python function and applies it to every sample
dataset = dataset.map(decode)To accomplish the same task with a queue based input pipeline requires the following code (using the same decode function from the above example):
其中的reader方法：Returns the next record (key, value) pair produced by a reader.
Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file).
J也就是意思说read每次产生一个新的样本，而非新的tfrecord file文件。见原文The read method outputs a key identifying the file and record (useful for debugging if you have some weird records), and a scalar string value.
这里最大的误会可能就在于没有循环，因而以为是读取了整个tfrecord的file文件，J实际上是隐循环，因为下面的decode肯定是对每一个图像进行解析的，因而读取的是样本级，而非文件级。
filename_queue = tf.train.string_input_producer([filename], num_epochs=num_epochs)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
image,label = decode(serialized_example)def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)Jparse_single_example将之前的A scalar string Tensor, a single serialized Example转变成张量。返回：A dict mapping feature keys to Tensor and SparseTensor values.
其中FixedLenFeature表示Configuration for parsing a fixed-length input feature，J理解为怎么从protocol buffer中以怎样的格式结束数据。
而decode_raw表示Reinterpret the bytes of a string as a vector of numbers，就是将字符串解析为数值，J猜测上一步只是按照什么格式去读，但是读出来还是字符串，需要转换。
To read a file of TFRecords, use tf.TFRecordReader with the tf.parse_single_example decoder. The parse_single_example op decodes the example protocol buffers into tensors.
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'age': tf.FixedLenFeature([], tf.int64),
'gender': tf.FixedLenFeature([], tf.int64),
'file_name': tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
#<tf.Tensor 'ParseSingleExample/Squeeze_image_raw:0' shape=() dtype=string>
image.set_shape([160 * 160 * 3]) #人脸时设置的像素值
image = tf.reshape(image, [160, 160, 3]) #将其展开
image = tf.reverse_v2(image, [-1]) #opencv默认bgr，转换成rgb
image = tf.image.per_image_standardization(image)
# <tf.Tensor 'div:0' shape=(160, 160, 3) dtype=float32>
# 因为需要转成0，255范围，而对于dense tensor的可转类型只有tfloat32, int64, string，所以无法在上一步直接转，所以需要先转成string，然后调用decode_raw转成uint8
# 这里是因为age和gender可以直接int64位，所以无需调用decode_raw转换。
age = features['age']
#<tf.Tensor 'ParseSingleExample/Squeeze_age:0' shape=() dtype=int64>
gender = features['gender']
file_path = features['file_name']
return image, age, gender, file_pathtf.image.per_image_standardization：Linearly scales image to have zero mean and unit norm.
At the end of the pipeline we use another queue to batch together examples for training, evaluation, or inference. For this we use a queue that randomizes the order of examples, using thetf.train.shuffle_batch.
J这里有等待前面样本凑够了一批的含义。之前read是每个样本读出来后操作，虽然这里batch传入的参数还是单个样本的符号，比如下面的[example, label]，但是这个函数会负责后面生成batch的操作，所以不用担心。
tf.train.shuffle_batch是将队列中数据打乱后，再读取出来，因此队列中剩下的数据也是乱序的，队头也是一直在补充（我猜也是按顺序补充），比如batch_size=5,capacity=10,min_after_dequeue=5,
初始是有序的0,1，..,9(10条记录)，
然后打乱8,2,6,4,3,7,9,2,0,1(10条记录),
队尾取出5条，剩下7,9,2,0,1(5条记录),
然后又按顺序补充进来，变成7,9,2,0,1,10,11,12,13,14(10条记录)，
再打乱13,10,2,7,0,12...1(10条记录)，
再出队...
capacity可以看成是局部数据的范围，读取的数据是基于这个范围的，
在这个范围内，min_after_dequeue越大，数据越乱。
这样按batch读取的话，最后会自动在前面添加一个维度，比如数据的维度是[1],batch_size是10，那么读取出来的shape就是[10,1] 。J就是第一个是batch_size的维度。
def read_my_file_format(filename_queue):
reader = tf.SomeReader()
key, record_string = reader.read(filename_queue)
example, label = tf.some_decoder(record_string)
processed_example = some_processing(example)
return processed_example, label
def input_pipeline(filenames, batch_size, num_epochs=None):
filename_queue = tf.train.string_input_producer(
filenames, num_epochs=num_epochs, shuffle=True)
example, label = read_my_file_format(filename_queue)
# min_after_dequeue defines how big a buffer we will randomly sample
# from -- bigger means better shuffling but slower start up and more
# memory used.
# capacity must be larger than min_after_dequeue and the amount larger
# determines the maximum we will prefetch. Recommendation:
# min_after_dequeue + (num_threads + a small safety margin) * batch_size
min_after_dequeue = 10000
capacity = min_after_dequeue + 3 * batch_size
example_batch, label_batch = tf.train.shuffle_batch(
[example, label], batch_size=batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch###Creating threads to prefetch using QueueRunner objects
上面的tf.train的函数都会产生QueueRunner对象，其存储了对于队列的相关操作。Holds a list of enqueue operations for a queue, each to be run in a thread.需要调用start_queue_runners启动这些QueueRunner对象才能操作。而QueueRunner需要配合Coordinator处理其队列可能出现的error。
示例代码为：
# Create the graph, etc.
init_op = tf.global_variables_initializer()
# Create a session for running operations in the Graph.
sess = tf.Session()
# Initialize the variables (like the epoch counter).
sess.run(init_op)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
# Run training steps or whatever
sess.run(train_op)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()实例代码为：
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = sess.run(global_step) #初始化一次step值方便后面的第一次summary
start_time = time.time()
while not coord.should_stop(): #在此循环，等待是否有任何队列所关联的线程发出停止的消息，因为需要不断对批量数据进行迭代，之前设置的allow_smaller_final_batch=False也是这个道理保证只有最后批次才不满足batch，直到末尾触发了outofrange的error，才会退出while
_, summary = sess.run([train_op, merged], {train_mode: True})
train_writer.add_summary(summary, step)
if step % 100 == 0: #因为一开始就是0，所以会对第一次进行统计
duration = time.time() - start_time
print('%.3f sec' % duration)
start_time = time.time()
if step % 1000 == 0:
save_path = new_saver.save(sess, os.path.join(model_path, "model.ckpt"), global_step=global_step)
print("Model saved in file: %s" % save_path)
step = sess.run(global_step)
except tf.errors.OutOfRangeError:#到了末尾后，就会到这里
print('Done training for %d epochs, %d steps.' % (epoch, step))
finally:#最后退出try模块，就会执行此步骤
# When done, ask the threads to stop.
save_path = new_saver.save(sess, os.path.join(model_path, "model.ckpt"), global_step=global_step)
print("Model saved in file: %s" % save_path)
coord.request_stop() #要求停止所有队列
# Wait for threads to finish.
coord.join(threads) #等待队列全部停止
sess.close() #关闭sess即可在一个input函数中：
先读入所有的tfrecord文件，组成列表传入dataset = tf.data.TFRecordDataset(filename)
然后利用函数的方式进行处理dataset = dataset.map(decode)，对其中每一个图片文件名进行处理，返回的也只是一个样本。注意这里虽然没有对所有样本循环，我猜测是流式隐循环；
然后设置shuffle（不用考虑min_after_dequeue和num_threads），repeat和batch或apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
最后生成迭代器返回iterator = dataset.make_one_shot_iterator()，get_next()
而在input函数外
用try进行训练sess.run，捕获tf.errors.OutOfRangeError，然后关闭sess.close即可
可以考虑像上一个一样，设置较小的batch，然后sess.run就可以打印出。
def decode(serialized_example):
"""Parses an image and label from the given `serialized_example`."""
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'age': tf.FixedLenFeature([], tf.int64),
'gender': tf.FixedLenFeature([], tf.int64),
'file_name': tf.FixedLenFeature([], tf.string),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([160 * 160 * 3])
image = tf.reshape(image, [160, 160, 3])
image = tf.reverse_v2(image, [-1])
image = tf.image.per_image_standardization(image)
# Convert label from a scalar uint8 tensor to an int32 scalar.
age = features['age']
gender = features['gender']
file_path = features['file_name']
return image, age, gender, file_path
def inputs(path, batch_size, num_epochs):
"""Reads input data
Args:
path: the path where tfrecord file is stored
Returns:
A tuple (images, age_labels, gender_labels, file_paths), where:
This function creates a one_shot_iterator, meaning that it will only iterate
over the dataset once. On the other hand there is no special initialization
required.
"""
filename = glob.glob(path + "/*.tfrecords")
with tf.name_scope('input'):
# TFRecordDataset opens a binary file and reads one record at a time.
# `filename` could also be a list of filenames, which will be read in order.
dataset = tf.data.TFRecordDataset(filename)
# The map transformation takes a function and applies it to every element
# of the dataset.
dataset = dataset.map(decode)
# The shuffle transformation uses a finite-sized buffer to shuffle elements
# in memory. The parameter is the number of elements in the buffer. For
# completely uniform shuffling, set the parameter to be the same as the
# number of elements in the dataset.
dataset = dataset.shuffle(1000 + 3 * batch_size)
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
#dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
#the final batch contain smaller tensors with shape N % batch_size in the batch dimension.
#If your program depends on the batches having the same shape,
#consider using the tf.contrib.data.batch_and_drop_remainder transformation instead.
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()
def test_once(image_path, batch_size, model_checkpoint_path):
with tf.Graph().as_default():
sess = tf.Session()
images, age_labels, gender_labels, file_paths = inputs(
path=image_path,
batch_size=batch_size,
num_epochs=1)
train_mode = tf.placeholder(tf.bool)
age_logits, gender_logits, _ = inception_resnet_v1.inference(images, keep_probability=0.8,
phase_train=train_mode,
weight_decay=1e-5)
age_cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=age_labels, logits=age_logits)
age_cross_entropy_mean = tf.reduce_mean(age_cross_entropy)
gender_cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=gender_labels,
logits=gender_logits)
gender_cross_entropy_mean = tf.reduce_mean(gender_cross_entropy)
total_loss = tf.add_n(
[gender_cross_entropy_mean, age_cross_entropy_mean] + tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES),
name="total_loss")
age_ = tf.cast(tf.constant([i for i in range(0, 101)]), tf.float32)
prob_age = tf.reduce_sum(tf.multiply(tf.nn.softmax(age_logits), age_), axis=1)
abs_age_error = tf.losses.absolute_difference(prob_age, age_labels)
prob_gender = tf.argmax(tf.nn.softmax(gender_logits), 1)
gender_acc = tf.reduce_mean(tf.cast(tf.nn.in_top_k(gender_logits, gender_labels, 1), tf.float32))
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
mean_error_age, mean_gender_acc, mean_loss = [], [], []
try:
while True: # Train until OutOfRangeError
prob_gender_val, real_gender, prob_age_val, real_age, image_val, gender_acc_val, abs_age_error_val, cross_entropy_mean_val, file_names = sess.run(
[prob_gender, gender_labels, prob_age, age_labels, images, gender_acc, abs_age_error, total_loss,
file_paths], {train_mode: False})
mean_error_age.append(abs_age_error_val)
mean_gender_acc.append(gender_acc_val)
mean_loss.append(cross_entropy_mean_val)
print("Age_MAE:%.2f,Gender_Acc:%.2f%%,Loss:%.2f" % (
abs_age_error_val, gender_acc_val * 100, cross_entropy_mean_val))
except tf.errors.OutOfRangeError:
print('!!!TESTING DONE!!!')
sess.close()
return prob_age_val, real_age, prob_gender_val, real_gender, image_val, np.mean(
mean_error_age), np.mean(mean_gender_acc), np.mean(mean_loss), file_names###基本代码
def decode(serialized_example):
"""Parses an image and label from the given `serialized_example`."""
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'age': tf.FixedLenFeature([], tf.int64),
'gender': tf.FixedLenFeature([], tf.int64),
'file_name': tf.FixedLenFeature([], tf.string),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([160 * 160 * 3])
image = tf.reshape(image, [160, 160, 3])
image = tf.reverse_v2(image, [-1])
# image = tf.image.per_image_standardization(image)
# Convert label from a scalar uint8 tensor to an int32 scalar.
age = features['age']
gender = features['gender']
file_path = features['file_name']
return image, age, gender, file_path
def augment(image, age, gender, file_path):
image = tf.image.random_flip_left_right(image)
image = tf.image.random_brightness(image, max_delta=32.0 / 255.0)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
return image, age, gender, file_path
def normalize(image, age, gender, file_path):
image = tf.image.per_image_standardization(image)
return image, age, gender, file_path
def inputs(path, batch_size, num_epochs, allow_smaller_final_batch=False):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
# filename = os.path.join(FLAGS.train_dir,
# TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
# TFRecordDataset opens a binary file and reads one record at a time.
# `filename` could also be a list of filenames, which will be read in order.
dataset = tf.data.TFRecordDataset(path)
# The map transformation takes a function and applies it to every element
# of the dataset.
dataset = dataset.map(decode)
dataset = dataset.map(augment)
dataset = dataset.map(normalize)
# The shuffle transformation uses a finite-sized buffer to shuffle elements
# in memory. The parameter is the number of elements in the buffer. For
# completely uniform shuffling, set the parameter to be the same as the
# number of elements in the dataset.
dataset = dataset.shuffle(1000 + 3 * batch_size)
dataset = dataset.repeat(num_epochs)
dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
#the final batch contain smaller tensors with shape N % batch_size in the batch dimension.
#If your program depends on the batches having the same shape,
#consider using the tf.contrib.data.batch_and_drop_remainder transformation instead.
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()###基础知识
J因而根据分析，一般色相不去动（颜色就变了），明度和饱和度去动（因为可能环境会有灯光影响），对比度就不动了（默认最亮颜色和最暗间的阶层是不变的）。
对比度，则表示指图像的最亮部分与最暗部分亮度的比值。
色相：色彩的相貌。我们常说的 红色黄色橙色绿色蓝色。。这些就是对色相的一个描述。
明度：色彩的明亮程度。我们可以通俗的理解成，这个颜色加了多少白颜料，加了多少黑颜料。
纯度：色彩的饱和度。从左至右，纯度由高变低。纯度越高，颜色越正，越容易分辨。所以我们在日常生活中看到的那些 一眼看不出是什么颜色的颜色大致就是纯度低的颜色。
#!/usr/bin/python
# coding:utf-8
# 图像预处理
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# 调整亮度.对比度.饱和度.色相的顺序可以得到不同的结果
# 预处理时随机选择的一种,降低无关因素对模型的影响
def distort_color(image, color_ordering=0):
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32./255)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
elif color_ordering ==1:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32./255)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
elif color_ordering ==2:
image = tf.image.random_brightness(image, max_delta=32./255)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
elif color_ordering ==3:
image = tf.image.random_brightness(image, max_delta=32./255)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
elif color_ordering ==4:
image = tf.image.random_brightness(image, max_delta=32./255)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
elif color_ordering ==5:
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32./255)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
return tf.clip_by_value(image, 0.0, 1.0)
# 给定解码后的图像.目标图像的尺寸以及图像上的标注框
def preprocess(image, height, width, bbox):
# 若没有提供标注框则默认为关注区域为整个图像
if bbox is None:
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
# 转换图像数据类型
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# 随机截取图像减小识别物体大小对模型的影响
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(tf.shape(image), bounding_boxes=bbox)
distorted_image = tf.slice(image, bbox_begin, bbox_size)
# 随机调整图像的大小
distorted_image = tf.image.resize_images(distorted_image, (height, width), method=np.random.randint(4))
# 随机左右翻转图像
distorted_image = tf.image.random_flip_left_right(distorted_image)
# 使用一种随机的顺序调整图像色彩
distorted_image = distort_color(distorted_image, np.random.randint(2))
return distorted_image
# 获取图像
image_raw_data = tf.gfile.FastGFile('daibola.jpg', 'r').read()
with tf.Session() as sess:
img_data = tf.image.decode_jpeg(image_raw_data)
boxes = tf.constant([[[0.1, 0.32, 0.8, 0.7]]])
# 获得9种不同的图像并显示结果
for i in range(9):
# 将图像大小调整为200*200
result = preprocess(img_data, 200, 200, boxes)
plt.subplot(331+i), plt.imshow(result.eval()), plt.title(str(i+1))
plt.show()#coding=utf-8
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
image_raw_data = tf.gfile.FastGFile('cat.jpg','rb').read()
with tf.Session() as sess:
img_data = tf.image.decode_jpeg(image_raw_data)
plt.subplot(331)
plt.title("Original")
plt.imshow(img_data.eval())
#plt.show()
resized = tf.image.resize_images(img_data, [100, 100], method=0)
# TensorFlow的函数处理图片后存储的数据是float32格式的，需要转换成uint8才能正确打印图片。
print("Digital type: ", resized.dtype)
resized = np.asarray(resized.eval(), dtype='uint8')
# tf.image.convert_image_dtype(rgb_image, tf.float32)
plt.subplot(332)
plt.title("100*100")
plt.imshow(resized)
#plt.show()
croped = tf.image.resize_image_with_crop_or_pad(img_data, 500, 500)
padded = tf.image.resize_image_with_crop_or_pad(img_data, 1500, 1500)
plt.subplot(333)
plt.title("500*500")
plt.imshow(croped.eval())
# plt.show()
plt.subplot(334)
plt.title("1500*1500")
plt.imshow(padded.eval())
#plt.show()
central_cropped = tf.image.central_crop(img_data, 0.5)
plt.subplot(335)
plt.title("*0.5")
plt.imshow(central_cropped.eval())
# plt.show()
# 上下翻转
flipped1 = tf.image.flip_up_down(img_data)
plt.subplot(336)
plt.title("up-down")
plt.imshow(flipped1.eval())
#plt.show()
# 左右翻转
flipped2 = tf.image.flip_left_right(img_data)
plt.subplot(337)
plt.title("left-right")
plt.imshow(flipped2.eval())
#plt.show()
# 对角线翻转
transposed = tf.image.transpose_image(img_data)
plt.subplot(338)
plt.title("transpose")
plt.imshow(transposed.eval())
# plt.show()
flipped3 = tf.image.random_flip_up_down(img_data)
plt.subplot(339)
plt.title("flip-up-down")
plt.imshow(flipped3.eval())
plt.show()
#————————————————————————————————————————————#
# 将图片的亮度-0.5。
adjusted = tf.image.adjust_brightness(img_data, -0.5)
plt.subplot(331)
plt.imshow(adjusted.eval())
plt.title("bright-0.5")
#plt.show()
# 将图片的亮度0.5
adjusted = tf.image.adjust_brightness(img_data, 0.5)
plt.subplot(332)
plt.imshow(adjusted.eval())
plt.title("bright+0.5")
#plt.show()
# 在[-max_delta, max_delta)的范围随机调整图片的亮度。
adjusted = tf.image.random_brightness(img_data, max_delta=0.5)
plt.subplot(333)
plt.imshow(adjusted.eval())
plt.title("bright-random")
#plt.show()
# 将图片的对比度-5
adjusted = tf.image.adjust_contrast(img_data, -5)
plt.subplot(334)
plt.imshow(adjusted.eval())
plt.title("contrast-5")
#plt.show()
# 将图片的对比度+5
adjusted = tf.image.adjust_contrast(img_data, 5)
plt.subplot(335)
plt.imshow(adjusted.eval())
plt.title("contrast+5")
#plt.show()
# 在[lower, upper]的范围随机调整图的对比度。
adjusted = tf.image.random_contrast(img_data, 0.1, 0.6)
plt.subplot(336)
plt.imshow(adjusted.eval())
plt.title("contrast-random")
#plt.show()
# 调整图片的色相
adjusted = tf.image.adjust_hue(img_data, 0.1)
plt.subplot(337)
plt.imshow(adjusted.eval())
plt.title("hue_0.1")
#plt.show()
# 在[-max_delta, max_delta]的范围随机调整图片的色相。max_delta的取值在[0, 0.5]之间。
adjusted = tf.image.random_hue(img_data, 0.5)
plt.subplot(338)
plt.imshow(adjusted.eval())
plt.title("hue-random_0.5")
#plt.show()
# 将图片的饱和度-5。
adjusted = tf.image.adjust_saturation(img_data, -2)
plt.subplot(339)
plt.title("saturation-2")
plt.imshow(adjusted.eval())
plt.show()
# 在[lower, upper]的范围随机调整图的饱和度。
#adjusted = tf.image.random_saturation(img_data, 0, 5)
# 将代表一张图片的三维矩阵中的数字均值变为0，方差变为1。
#adjusted = tf.image.per_image_standardization(img_data)