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:
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
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：
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<https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>
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<https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>
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。
