PyTorch之九—cifar-10数据集图像分类
⽂章⽬录
这是⼀个⼊门级的图像分类模型，主要是⽤来熟悉模型。
⼀、准备
cifar-10 数据集
数据集下载：o.edu/~kriz/cifar.html
若是下载太慢了，
数据集组成：本数据及包含了6万张分辨率为32x32的图⽚，⼀共10类，分别为：飞机、汽车、鸟、猫、⿅、狗、青蛙、马、船、货车。
其中，5万张作为训练集，1万张作为测试集。
训练集被分为了5批训练和1批测试。每⼀批都是1万张。
测试集是从每⼀种分类中随机抽取出来1000张组成。
训练集从10个分类中各⾃随机抽取5000张，⼀共5万张。
数据的结构
⾸先看⼀下数据集解压之后的样⼦：
那些没有后缀名的⽂件，其实都是⽤python的cpickle库打包好的，这个库就是⽤来将python中的变量原封不动地存到本地使⽤的，当再次使⽤的时候，可以读取出来。
读取⽅式：
def unpickle(file):
import pickle
with open(file,'rb')as fo:
dict= pickle.load(fo, encoding='bytes')
return dict
如：label_names[0] == "airplane", label_names[1] == "automobile"
键值
data⼀个10000x3072的numpy数组，每⼀⾏都存储着⼀个分辨率为32x32的图⽚
labels标签，长度是10000，每个都是0-9的数字，是⼀个列表。其索引和data⾥⾯的索引相互对应。
可视化保存
import pickle as p
import numpy as np
import matplotlib.image as plimg
from PIL import Image
def load_CIFAR_batch(filename):
with open(filename,'rb')as f:
datadict = p.load(f, encoding='latin1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000,3,32,32)
Y = np.array(Y)
return X, Y
def load_CIFAR_Labels(filename):
with open(filename,'rb')as f:
lines =[x for x adlines()]
print(lines)
if __name__ =="__main__":
load_CIFAR_Labels("E:/pytorch_AI/data/a")
imgX, imgY = load_CIFAR_batch("E:/pytorch_AI/data/cifar-10-batches-py/data_batch_1")
print(imgX.shape)
print("正在保存图⽚:")
for i in range(30):
imgs = imgX[i]# 遍历获取图像
i0 = Image.fromarray(imgs[0])# 通道1
i1 = Image.fromarray(imgs[1])# 通道2
i2 = Image.fromarray(imgs[2])# 通道3
img = ("RGB",(i0, i1, i2))# 从数据，合并三个通道，⽣成image对象
name ="img"+str(i)+'.png'
img.save("./data/cifar-10-batches-py_img/"+ name,"png")# ⽂件夹下是RGB融合后的图像for j in range(imgs.shape[0]):
img = imgs[j]
name ="img"+str(i)+str(j)+".png"
print("正在保存图⽚"+ name)
plimg.imsave("./data/cifar-10-batches-py_img_0/"+ name, img)# ⽂件夹下是RGB分离的图像print("保存完毕.")
查看数据结构
def unpickle(file):
import pickle
with open(file,'rb')as fo:
dict= pickle.load(fo, encoding='bytes')
return dict
if __name__ =='__main__':
file="E:/pytorch_AI/data/cifar-10-batches-py/data_batch_1"
dict= unpickle(file)
dict_keys =dict.keys()
print("dict_keys:          \t",dict_keys)# dict.keys()
print("dict[b'batch_label']:\t",dict[b'batch_label'])# key=b'batch_label'的内容print("dict[b'labels']_type:\t
",type(dict[b'labels']))# key=b'labels'的数据类型print("dict[b'filenames'][0]:\t",dict[b'filenames'][0])# 查看第⼀个图⽚名
print("dict[b'data'].shape():\t",dict[b'data'].shape)# 查看 b'data' 的数据形状print("dict[b'data'][0]    :\t",dict[b'data'][0])# 查看 b'data'的第⼀个数据
输出：
dict_keys:            dict_keys([b'batch_label', b'labels', b'data', b'filenames'])
dict[b'batch_label']:  b'training batch 1 of 5'
dict[b'labels']_type:<class'list'>
dict[b'filenames'][0]:  b'leptodactylus_pentadactylus_s_000004.png'
dict[b'data'].shape():(10000,3072)
dict[b'data'][0]:[1408472]
读取并保存所有数据为png图⽚，完整代码：
cifar2image.py
# !/usr/bin/env python
# -*- coding: utf-8 -*-
import pickle as p
import pickle
import os
import numpy as np
from PIL import Image
def load_CIFAR_batch(filename):
with open(filename,'rb')as f:
datadict = p.load(f, encoding='latin1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000,3,32,32)
Y = np.array(Y)
return X, Y
def load_CIFAR_Labels(filename):
import pickle
with open(filename,'rb')as fo:
dict2idx = pickle.load(fo, encoding='bytes')
return dict2idx
# 所有图⽚放置⼀个⽂件夹内
def main(root_path, files,save_path):
for file in files:
temp_path = os.path.join(root_path ,file)
images, labels = load_CIFAR_batch(temp_path)
print(images.shape)# (10000, 3, 32, 32)
image_num = images.shape[0]# 图⽚总数
for i in range(image_num):
imgs = images[i]
label = labels[i]
label_name = dict2idx[b'label_names'][label].decode()
label_name = dict2idx[b'label_names'][label].decode()
i0 = Image.fromarray(imgs[0])# 通道1
i1 = Image.fromarray(imgs[1])# 通道2
i2 = Image.fromarray(imgs[2])# 通道3
img = ("RGB",(i0, i1, i2))# 合并三个通道，⽣成image对象
name =str(label_name)+"_"+str(file)+"_"+str(i)+'.png'
img.save(os.path.join(save_path , name),"png")
print("done!.")
# 每类图⽚放置⼀个⽂件夹内
def main2(root_path, files,save_path):
for file in files:
temp_path = os.path.join(root_path ,file)
images, labels = load_CIFAR_batch(temp_path)
print(images.shape)# (10000, 3, 32, 32)
image_num = images.shape[0]# 图⽚总数
for i in range(image_num):
imgs = images[i]
label = labels[i]
label_name = dict2idx[b'label_names'][label].decode()
i0 = Image.fromarray(imgs[0])# 通道1
i1 = Image.fromarray(imgs[1])# 通道2
i2 = Image.fromarray(imgs[2])# 通道3
img = ("RGB",(i0, i1, i2))# 合并三个通道，⽣成image对象
save_path_temp = os.path.join(save_path,label_name)
if not ists(save_path_temp):
os.mkdir(save_path_temp)
name =str(file)+"_"+str(i)+'.png'
img.save(os.path.join(save_path_temp,name),"png")
print("done!.")
if __name__ =="__main__":
root_path = r"E:\data\cifar-10-batches-py"
save_path = r"E:\data\all_class"
files =["data_batch_1","data_batch_2","data_batch_3","data_batch_4","data_batch_5"]    dict2idx = load_CIFAR_Labels(os.path.join(root_path,"a"))
# main(root_path, files,save_path)  # 所有图⽚放置⼀个⽂件夹内
main2(root_path, files,save_path)# 每类图⽚放置⼀个⽂件夹内

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