【tensorflow2.0】数据管道dataset

‘feature_names‘: [‘sepal length (cm)‘,
  ‘sepal width (cm)‘,
  ‘petal length (cm)‘,
  ‘petal width (cm)‘],
 ‘filename‘: ‘/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/iris.csv‘,
 ‘target‘: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),
 ‘target_names‘: array([‘setosa‘, ‘versicolor‘, ‘virginica‘], dtype=‘<U10‘)

# 从Numpy array构建数据管道
 
import tensorflow as tf
import numpy as np 
from sklearn import datasets 
iris = datasets.load_iris()
 
 
ds1 = tf.data.Dataset.from_tensor_slices((iris["data"],iris["target"]))
for features,label in ds1.take(5):
    print(features,label)

# 从 Pandas DataFrame构建数据管道
import tensorflow as tf
from sklearn import datasets 
import pandas as pd
iris = datasets.load_iris()
dfiris = pd.DataFrame(iris["data"],columns = iris.feature_names)
ds2 = tf.data.Dataset.from_tensor_slices((dfiris.to_dict("list"),iris["target"]))
 
for features,label in ds2.take(3):
    print(features,label)

# 从Python generator构建数据管道
import tensorflow as tf
from matplotlib import pyplot as plt 
from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
# 定义一个从文件中读取图片的generator
image_generator = ImageDataGenerator(rescale=1.0/255).flow_from_directory(
                    "./data/cifar2/test/",
                    target_size=(32, 32),
                    batch_size=20,
                    class_mode=‘binary‘)
 
classdict = image_generator.class_indices
print(classdict)

def generator():
    for features,label in image_generator:
        yield (features,label)
 
ds3 = tf.data.Dataset.from_generator(generator,output_types=(tf.float32,tf.int32))
%matplotlib inline
%config InlineBackend.figure_format = ‘svg‘
plt.figure(figsize=(6,6)) 
for i,(img,label) in enumerate(ds3.unbatch().take(9)):
    ax=plt.subplot(3,3,i+1)
    ax.imshow(img.numpy())
    ax.set_title("label = %d"%label)
    ax.set_xticks([])
    ax.set_yticks([]) 
plt.show()

# 从csv文件构建数据管道
ds4 = tf.data.experimental.make_csv_dataset(
      file_pattern = ["./data/titanic/train.csv","./data/titanic/test.csv"],
      batch_size=3, 
      label_name="Survived",
      na_value="",
      num_epochs=1,
      ignore_errors=True)
 
for data,label in ds4.take(2):
    print(data,label)

# 从文本文件构建数据管道
 
ds5 = tf.data.TextLineDataset(
    filenames = ["./data/titanic/train.csv","./data/titanic/test.csv"]
    ).skip(1) #略去第一行header
 
for line in ds5.take(5):
    print(line)

s6 = tf.data.Dataset.list_files("./data/cifar2/train/*/*.jpg")
for file in ds6.take(5):
    print(file)

from matplotlib import pyplot as plt 
def load_image(img_path,size = (32,32)):
    label = 1 if tf.strings.regex_full_match(img_path,".*/automobile/.*") else 0
    img = tf.io.read_file(img_path)
    img = tf.image.decode_jpeg(img) #注意此处为jpeg格式
    img = tf.image.resize(img,size)
    return(img,label)
 
%matplotlib inline
%config InlineBackend.figure_format = ‘svg‘
for i,(img,label) in enumerate(ds6.map(load_image).take(2)):
    plt.figure(i)
    plt.imshow((img/255.0).numpy())
    plt.title("label = %d"%label)
    plt.xticks([])
    plt.yticks([])

import os
import numpy as np
 
# inpath：原始数据路径 outpath:TFRecord文件输出路径
def create_tfrecords(inpath,outpath): 
    writer = tf.io.TFRecordWriter(outpath)
    dirs = os.listdir(inpath)
    for index, name in enumerate(dirs):
        class_path = inpath +"/"+ name+"/"
        for img_name in os.listdir(class_path):
            img_path = class_path + img_name
            img = tf.io.read_file(img_path)
            #img = tf.image.decode_image(img)
            #img = tf.image.encode_jpeg(img) #统一成jpeg格式压缩
            example = tf.train.Example(
               features=tf.train.Features(feature={
                    ‘label‘: tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
                    ‘img_raw‘: tf.train.Feature(bytes_list=tf.train.BytesList(value=[img.numpy()]))
               }))
            writer.write(example.SerializeToString())
    writer.close()
 
create_tfrecords("./data/cifar2/test/","./data/cifar2_test.tfrecords/")
 
from matplotlib import pyplot as plt 
 
def parse_example(proto):
    description ={ ‘img_raw‘ : tf.io.FixedLenFeature([], tf.string),
                   ‘label‘: tf.io.FixedLenFeature([], tf.int64)} 
    example = tf.io.parse_single_example(proto, description)
    img = tf.image.decode_jpeg(example["img_raw"])   #注意此处为jpeg格式
    img = tf.image.resize(img, (32,32))
    label = example["label"]
    return(img,label)
 
ds7 = tf.data.TFRecordDataset("./data/cifar2_test.tfrecords").map(parse_example).shuffle(3000)
 
%matplotlib inline
%config InlineBackend.figure_format = ‘svg‘
plt.figure(figsize=(6,6)) 
for i,(img,label) in enumerate(ds7.take(9)):
    ax=plt.subplot(3,3,i+1)
    ax.imshow((img/255.0).numpy())
    ax.set_title("label = %d"%label)
    ax.set_xticks([])
    ax.set_yticks([]) 
plt.show()

# map:将转换函数映射到数据集每一个元素
 
ds = tf.data.Dataset.from_tensor_slices(["hello world","hello China","hello Beijing"])
ds_map = ds.map(lambda x:tf.strings.split(x," "))
for x in ds_map:
    print(x)

# flat_map:将转换函数映射到数据集的每一个元素，并将嵌套的Dataset压平。
 
ds = tf.data.Dataset.from_tensor_slices(["hello world","hello China","hello Beijing"])
ds_flatmap = ds.flat_map(lambda x:tf.data.Dataset.from_tensor_slices(tf.strings.split(x," ")))
for x in ds_flatmap:
    print(x)

# interleave: 效果类似flat_map,但可以将不同来源的数据夹在一起。
 
ds = tf.data.Dataset.from_tensor_slices(["hello world","hello China","hello Beijing"])
ds_interleave = ds.interleave(lambda x:tf.data.Dataset.from_tensor_slices(tf.strings.split(x," ")))
for x in ds_interleave:
    print(x)

# filter:过滤掉某些元素。
 
ds = tf.data.Dataset.from_tensor_slices(["hello world","hello China","hello Beijing"])
# 找出含有字母a或B的元素
ds_filter = ds.filter(lambda x: tf.strings.regex_full_match(x, ".*[a|B].*"))
for x in ds_filter:
    print(x)

# zip:将两个长度相同的Dataset横向铰合。
 
ds1 = tf.data.Dataset.range(0,3)
ds2 = tf.data.Dataset.range(3,6)
ds3 = tf.data.Dataset.range(6,9)
ds_zip = tf.data.Dataset.zip((ds1,ds2,ds3))
for x,y,z in ds_zip:
    print(x.numpy(),y.numpy(),z.numpy())

# condatenate:将两个Dataset纵向连接。
 
ds1 = tf.data.Dataset.range(0,3)
ds2 = tf.data.Dataset.range(3,6)
ds_concat = tf.data.Dataset.concatenate(ds1,ds2)
for x in ds_concat:
    print(x)

# reduce:执行归并操作。
 
ds = tf.data.Dataset.from_tensor_slices([1,2,3,4,5.0])
result = ds.reduce(0.0,lambda x,y:tf.add(x,y))
result

# batch:构建批次，每次放一个批次。比原始数据增加一个维度。 其逆操作为unbatch。 
 
ds = tf.data.Dataset.range(12)
ds_batch = ds.batch(4)
for x in ds_batch:
    print(x)

# padded_batch:构建批次，类似batch, 但可以填充到相同的形状。
 
elements = [[1, 2],[3, 4, 5],[6, 7],[8]]
ds = tf.data.Dataset.from_generator(lambda: iter(elements), tf.int32)
 
ds_padded_batch = ds.padded_batch(2,padded_shapes = [4,])
for x in ds_padded_batch:
    print(x)

# window:构建滑动窗口，返回Dataset of Dataset.
 
ds = tf.data.Dataset.range(12)
# window返回的是Dataset of Dataset,可以用flat_map压平
ds_window = ds.window(3, shift=1).flat_map(lambda x: x.batch(3,drop_remainder=True)) 
for x in ds_window:
    print(x)

# shuffle:数据顺序洗牌。
 
ds = tf.data.Dataset.range(12)
ds_shuffle = ds.shuffle(buffer_size = 5)
for x in ds_shuffle:
    print(x)

# repeat:重复数据若干次，不带参数时，重复无数次。
 
ds = tf.data.Dataset.range(3)
ds_repeat = ds.repeat(3)
for x in ds_repeat:
    print(x)

# shard:采样，从某个位置开始隔固定距离采样一个元素。
 
ds = tf.data.Dataset.range(12)
ds_shard = ds.shard(3,index = 1)
 
for x in ds_shard:
    print(x)

# take:采样，从开始位置取前几个元素。
 
ds = tf.data.Dataset.range(12)
ds_take = ds.take(3)
 
list(ds_take.as_numpy_iterator())

import tensorflow as tf
 
# 打印时间分割线
@tf.function
def printbar():
    ts = tf.timestamp()
    today_ts = ts%(24*60*60)
 
    hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
    minite = tf.cast((today_ts%3600)//60,tf.int32)
    second = tf.cast(tf.floor(today_ts%60),tf.int32)
 
    def timeformat(m):
        if tf.strings.length(tf.strings.format("{}",m))==1:
            return(tf.strings.format("0{}",m))
        else:
            return(tf.strings.format("{}",m))
 
    timestring = tf.strings.join([timeformat(hour),timeformat(minite),
                timeformat(second)],separator = ":")
    tf.print("=========="*8,end = "")
    tf.print(timestring)
 
import time
 
# 数据准备和参数迭代两个过程默认情况下是串行的。
 
# 模拟数据准备
def generator():
    for i in range(10):
        #假设每次准备数据需要2s
        time.sleep(2) 
        yield i 
ds = tf.data.Dataset.from_generator(generator,output_types = (tf.int32))
 
# 模拟参数迭代
def train_step():
    #假设每一步训练需要1s
    time.sleep(1) 
 
# 训练过程预计耗时 10*2+10*1+ = 30s
printbar()
tf.print(tf.constant("start training..."))
for x in ds:
    train_step()  
printbar()
tf.print(tf.constant("end training..."))
# 使用 prefetch 方法让数据准备和参数迭代两个过程相互并行。
 
# 训练过程预计耗时 max(10*2,10*1) = 20s
printbar()
tf.print(tf.constant("start training with prefetch..."))
 
# tf.data.experimental.AUTOTUNE 可以让程序自动选择合适的参数
for x in ds.prefetch(buffer_size = tf.data.experimental.AUTOTUNE):
    train_step()  
 
printbar()
tf.print(tf.constant("end training..."))

================================================================================16:18:31
start training...
================================================================================16:19:01
end training...
================================================================================16:19:01
start training with prefetch...
================================================================================16:19:23
end training...

ds_files = tf.data.Dataset.list_files("./data/titanic/*.csv")
ds = ds_files.flat_map(lambda x:tf.data.TextLineDataset(x).skip(1))
for line in ds.take(4):
    print(line)

ds_files = tf.data.Dataset.list_files("./data/titanic/*.csv")
ds = ds_files.interleave(lambda x:tf.data.TextLineDataset(x).skip(1))
for line in ds.take(8):
    print(line)

ds = tf.data.Dataset.list_files("./data/cifar2/train/*/*.jpg")
def load_image(img_path,size = (32,32)):
    label = 1 if tf.strings.regex_full_match(img_path,".*/automobile/.*") else 0
    img = tf.io.read_file(img_path)
    img = tf.image.decode_jpeg(img) #注意此处为jpeg格式
    img = tf.image.resize(img,size)
    return(img,label)
# 单进程转换
printbar()
tf.print(tf.constant("start transformation..."))
 
ds_map = ds.map(load_image)
for _ in ds_map:
    pass
 
printbar()
tf.print(tf.constant("end transformation..."))
# 多进程转换
printbar()
tf.print(tf.constant("start parallel transformation..."))
 
ds_map_parallel = ds.map(load_image,num_parallel_calls = tf.data.experimental.AUTOTUNE)
for _ in ds_map_parallel:
    pass
 
printbar()
tf.print(tf.constant("end parallel transformation..."))

import time
 
# 模拟数据准备
def generator():
    for i in range(5):
        #假设每次准备数据需要2s
        time.sleep(2) 
        yield i 
ds = tf.data.Dataset.from_generator(generator,output_types = (tf.int32))
 
# 模拟参数迭代
def train_step():
    #假设每一步训练需要0s
    pass
 
# 训练过程预计耗时 (5*2+5*0)*3 = 30s
printbar()
tf.print(tf.constant("start training..."))
for epoch in tf.range(3):
    for x in ds:
        train_step()  
    printbar()
    tf.print("epoch =",epoch," ended")
printbar()
tf.print(tf.constant("end training..."))
 
import time
 
# 模拟数据准备
def generator():
    for i in range(5):
        #假设每次准备数据需要2s
        time.sleep(2) 
        yield i 
 
# 使用 cache 方法让数据在第一个epoch后缓存到内存中，仅限于数据集不大情形。
ds = tf.data.Dataset.from_generator(generator,output_types = (tf.int32)).cache()
 
# 模拟参数迭代
def train_step():
    #假设每一步训练需要0s
    time.sleep(0) 
 
# 训练过程预计耗时 (5*2+5*0)+(5*0+5*0)*2 = 10s
printbar()
tf.print(tf.constant("start training..."))
for epoch in tf.range(3):
    for x in ds:
        train_step()  
    printbar()
    tf.print("epoch =",epoch," ended")
printbar()
tf.print(tf.constant("end training..."))

================================================================================17:07:57
start training...
================================================================================17:08:07
epoch = 0  ended
================================================================================17:08:17
epoch = 1  ended
================================================================================17:08:27
epoch = 2  ended
================================================================================17:08:27
end training...
================================================================================17:08:27
start training...
================================================================================17:08:37
epoch = 0  ended
================================================================================17:08:37
epoch = 1  ended
================================================================================17:08:37
epoch = 2  ended
================================================================================17:08:37
end training...

# 先map后batch
ds = tf.data.Dataset.range(100000)
ds_map_batch = ds.map(lambda x:x**2).batch(20)
 
printbar()
tf.print(tf.constant("start scalar transformation..."))
for x in ds_map_batch:
    pass
printbar()
tf.print(tf.constant("end scalar transformation..."))
 
# 先batch后map
ds = tf.data.Dataset.range(100000)
ds_batch_map = ds.batch(20).map(lambda x:x**2)
 
printbar()
tf.print(tf.constant("start vector transformation..."))
for x in ds_batch_map:
    pass
printbar()
tf.print(tf.constant("end vector transformation..."))

================================================================================17:09:10
start scalar transformation...
================================================================================17:09:13
end scalar transformation...
================================================================================17:09:13
start vector transformation...
================================================================================17:09:14
end vector transformation...