torchvision库

torchvision是独立于pytorch的关于图像操作的一些方便工具库。

torchvision主要包括一下几个包:

vision.datasets: 几个常用视觉数据集,可以下载和加载,这里主要的高级用法就是可以看源码如何自己写自己的Dataset的子类

vision.models: 流行的模型,例如 AlexNet, VGG, ResNet 和 Densenet 以及 与训练好的参数。

vision.transforms: 常用的图像操作,例如:随机切割,旋转,数据类型转换,图像到tensor ,numpy 数组到tensor , tensor 到 图像等。

vision.utils: 用于把形似 (3 x H x W) 的张量保存到硬盘中,给一个mini-batch的图像可以产生一个图像格网。


数据集 torchvision.datasets

包括以下数据集:

MNIST

Fashion-MNIST

KMNIST

EMNIST

FakeData

COCO

Captions

Detection

LSUN

ImageFolder

DatasetFolder

Imagenet-12

CIFAR

STL10

SVHN

PhotoTour

SBU

Flickr

VOC

Cityscapes

SBD

数据集有 API: -__getitem__-__len__他们都是torch.utils.data.Dataset的子类。这样我们在实现我们自己的Dataset数据集的时候至少要实现上边两个方法。

因此,他们可以使用torch.utils.data.DataLoader里的多线程 (python multiprocessing) 。

MNIST

dset.MNIST(root, train=True, transform=None, target_transform=None, download=False)

root:数据的目录,里边有processed/training.pt和processed/test.pt的内容

train:True-使用训练集,False-使用测试集.

transform: 给输入图像施加变换

target_transform:给目标值(类别标签)施加的变换

download: 是否下载mnist数据集

COCO

This requires theCOCO API to be installed

Captions:

dset.CocoCaptions(root="dir where images are",annFile="json annotation file", [transform, target_transform])

Example:

importtorchvision.datasetsasdsetimporttorchvision.transformsastransformscap=dset.CocoCaptions(root='dir where images are',annFile='json annotation file',transform=transforms.ToTensor())print('Number of samples: ',len(cap))img,target=cap[3]# load 4th sampleprint("Image Size: ",img.size())print(target)

Output:

Number of samples: 82783

Image Size: (3L, 427L, 640L)

[u'A plane emitting smoke stream flying over a mountain.',

u'A plane darts across a bright blue sky behind a mountain covered in snow',

u'A plane leaves a contrail above the snowy mountain top.',

u'A mountain that has a plane flying overheard in the distance.',

u'A mountain view with a plume of smoke in the background']

Detection:

dset.CocoDetection(root="dir where images are",annFile="json annotation file", [transform, target_transform])

LSUN

dset.LSUN(db_path,classes='train', [transform, target_transform])

db_path= root directory for the database files

classes=

'train'- all categories, training set

'val'- all categories, validation set

'test'- all categories, test set

['bedroom_train','church_train', …] : a list of categories to load

CIFAR

dset.CIFAR10(root, train=True, transform=None, target_transform=None, download=False)

dset.CIFAR100(root, train=True, transform=None, target_transform=None, download=False)

root: root directory of dataset where there is foldercifar-10-batches-py

train:True= Training set,False= Test set

download:True= downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, does not do anything.

STL10

dset.STL10(root,split='train', transform=None, target_transform=None, download=False)

root: root directory of dataset where there is folderstl10_binary

split:'train'= Training set,'test'= Test set,'unlabeled'= Unlabeled set,

'train+unlabeled'= Training + Unlabeled set (missing label marked as-1)

download:True= downloads the dataset from the internet and

puts it in root directory. If dataset is already downloaded, does not do anything.

SVHN

dset.SVHN(root,split='train', transform=None, target_transform=None, download=False)

root: root directory of dataset where there is folderSVHN

split:'train'= Training set,'test'= Test set,'extra'= Extra training set

download:True= downloads the dataset from the internet and

puts it in root directory. If dataset is already downloaded, does not do anything.

ImageFolder

一个通用的数据加载器,图像应该按照以下方式放置:

root/dog/xxx.png

root/dog/xxy.png

root/dog/xxz.png

root/cat/123.png

root/cat/nsdf3.png

root/cat/asd932_.png

dset.ImageFolder(root="root folder path", [transform, target_transform])

ImageFolder有以下成员:

self.classes- 类别名列表

self.class_to_idx- 类别名到标签,例如 “狗”-->[1,0,0]

self.imgs- 一个包括 (image path, class-index) 元组的列表。

Imagenet-12

This is simply implemented with an ImageFolder dataset.

The data is preprocessedas described here

Here is an example.

PhotoTour

Learning Local Image Descriptors Datahttp://phototour.cs.washington.edu/patches/default.htm

importtorchvision.datasetsasdsetimporttorchvision.transformsastransformsdataset=dset.PhotoTour(root='dir where images are',name='name of the dataset to load',transform=transforms.ToTensor())print('Loaded PhotoTour: {} with {} images.'.format(dataset.name,len(dataset.data)))



模型

models 子包含了以下的模型框架:

AlexNet 

VGG

ResNet

SqueezeNet

DenseNet

Inceptionv3

GoogLeNet

这里对于每种模型里可能包含很多子模型,比如Resnet就有 34,51,101,152不同层数。

这些成熟的模型的意义就是你可以在torchvision的安装路径下找到 可以通过命令

print(torchvision.models.__file__)  

#'d:\\Anaconda3\\lib\\site-packages\\torchvision\\models\\__init__.py'

学习这些优秀的模型是如何搭建的。

你可以用随机参数初始化一个模型:

importtorchvision.modelsasmodelsresnet18=models.resnet18()alexnet=models.alexnet()vgg16=models.vgg16()squeezenet=models.squeezenet1_0()

我们提供了预训练的ResNet的模型参数,以及 SqueezeNet 1.0 and 1.1, and AlexNet, 使用 PyTorchmodel zoo. 可以在构造函数里添加pretrained=True:

importtorchvision.modelsasmodelsresnet18=models.resnet18(pretrained=True)alexnet=models.alexnet(pretrained=True)squeezenet=models.squeezenet1_0(pretrained=True)

所有的预训练模型期待输入同样标准化的数据,例如mini-baches 包括形似(3*H*W)的3通道的RGB图像,H,W最少是224。

图像的范围必须在[0,1]之间,然后使用mean=[0.485, 0.456, 0.406]andstd=[0.229, 0.224, 0.225]  进行标准化。

相关的例子在:the imagenet example here




变换

变换(Transforms)是常用的图像变换。可以通过transforms.Compose进行连续操作:

transforms.Compose

你可以组合几个变换在一起,例如:

transform=transforms.Compose([transforms.RandomSizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225]),])

PIL.Image支持的变换

Scale(size, interpolation=Image.BILINEAR)

缩放输入的 PIL.Image到给定的“尺寸”。 ‘尺寸’ 指的是较短边的尺寸.

例如,如果 height > width, 那么图像将被缩放为 (size * height / width, size) - size: 图像较短边的尺寸- interpolation: Default: PIL.Image.BILINEAR

CenterCrop(size)- 从中间裁剪图像到指定大小

从中间裁剪一个 PIL.Image 到给定尺寸. 尺寸可以是一个元组 (target_height, target_width) 或一个整数,整数将被认为是正方形的尺寸 (size, size)

RandomCrop(size, padding=0)

Crops the given PIL.Image at a random location to have a region of the given size. size can be a tuple (target_height, target_width) or an integer, in which case the target will be of a square shape (size, size) Ifpaddingis non-zero, then the image is first zero-padded on each side withpaddingpixels.

RandomHorizontalFlip()

随机进行PIL.Image图像的水平翻转,概率是0.5.

RandomSizedCrop(size, interpolation=Image.BILINEAR)

Random crop the given PIL.Image to a random size of (0.08 to 1.0) of

the original size and and a random aspect ratio of 3/4 to 4/3 of the

original aspect ratio

This is popularly used to train the Inception networks - size: size

of the smaller edge - interpolation: Default: PIL.Image.BILINEAR

Pad(padding, fill=0)

Pads the given image on each side withpaddingnumber of pixels, and the padding pixels are filled with pixel valuefill. If a5x5image is padded withpadding=1then it becomes7x7

对于 torch.*Tensor 的变换

Normalize(mean, std)

Given mean: (R, G, B) and std: (R, G, B), will normalize each channel

of the torch.*Tensor, i.e. channel = (channel - mean) / std

转换变换

ToTensor()- Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]

ToPILImage()- Converts a torch.*Tensor of range [0, 1] and shape C x H x W or numpy ndarray of dtype=uint8, range[0, 255] and shape H x W x C to a PIL.Image of range [0, 255]

广义变换

Lambda(lambda)

Given a Python lambda, applies it to the inputimgand returns it. For example:

transforms.Lambda(lambdax:x.add(10))

便利函数

make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False)

Given a 4D mini-batch Tensor of shape (B x C x H x W), or a list of images all of the same size, makes a grid of images

normalize=True will shift the image to the range (0, 1), by subtracting the minimum and dividing by the maximum pixel value.

if range=(min, max) where min and max are numbers, then these numbers are used to normalize the image.

scale_each=True will scale each image in the batch of images separately rather than computing the (min, max) over all images.

Example usage is given in this notebook

save_image(tensor, filename, nrow=8, padding=2, normalize=False, range=None, scale_each=False)

Saves a given Tensor into an image file.

If given a mini-batch tensor, will save the tensor as a grid of images.

All options afterfilenameare passed through tomake_grid. Refer to it’s documentation for more details

用以输出图像的拼接,很方便。

没想到这篇文章阅读量这么大,考虑跟新下。

图像引擎:由于需要读取处理图片所以需要相关的图像库。现在torchvision可以支持多个图像读取库,可以切换。

使用的函数是:

torchvision.get_image_backend()#获取图像存取引擎

torchvision.set_image_backend(backend)   #改变图像读取引擎

#backend(string) –图像引擎的名字:是  {‘PIL’, ‘accimage’}其中之一。accimage包使用的是因特尔(Intel) IPP 库。它的速度快于PIL,但是并不支持很多的图像操作。

由于这个是后边的,普通用处不大,知道即可。

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