這篇分類總結比較完整
數據處理是模型訓練之前的必備的一步，在Pytorch的TORCHVISION.TRANSFORMS.TRANSFORMS包含下面一下圖像處理的函數（transform中的函數主要是處理PIL格式圖像）：

Compose

Args:transforms (list of ``Transform`` objects): list of transforms to compose.

"Composes several transforms together” 組合幾種不同的變形方法

ToTensor

Convert a PIL Image or numpy.ndarray to tensor. Converts a PIL Image 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]。將PIL圖像或者numpy.ndarry類型數據轉成tensor.

ToPILImage

Convert a tensor or an ndarray to PIL Image.
Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape H x W x C to a PIL Image while preserving the value range.

Normalize 有參數（mean,std）
處理的數據類型是tensor類型

Normalize a tensor image with mean and standard deviation.

Args:mean (sequence): Sequence of means for each channel.std (sequence): Sequence of standard deviations for each channel.

Given mean: (M1,...,Mn) and std: (S1,..,Sn) for n channels, this transform will normalize each channel of the input torch.*Tensor i.e. input[channel] = (input[channel] - mean[channel]) / std[channel]

Resize

Resize the input PIL Image to the given size,默認采用PIL.Image.BILINEAR插值法。

Args:size (sequence or int): Desired output size. If size is a sequence like(h, w), output size will be matched to this. If size is an int,smaller edge of the image will be matched to this number.i.e, if height > width, then image will be rescaled to(size * height / width, size)interpolation (int, optional): Desired interpolation. Default is``PIL.Image.BILINEAR``

Scale

推薦換成Resize

CenterCrop 參數（目標尺寸）

Crops the given PIL Image at the center
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made

Pad

Pad the given PIL Image on all sides with the given “pad” value
Args:
padding (int or tuple):
fill (int or tuple):
padding_mode (str):

Lambda

Apply a user-defined lambda as a transform
Args:
lambd (function): Lambda/function to be used for transform.

RandomTransforms

Base class for a list of transformations with randomness
Args:
transforms (list or tuple): list of transformations

RandomApply

Apply randomly a list of transformations with a given probability
Args:
transforms (list or tuple): list of transformations
p (float): probability

RandomOrder

Apply a list of transformations in a random order

RandomChoice

Apply single transformation randomly picked from a list

RandomCrop

Crop the given PIL Image at a random location.

Args:size (sequence or int)padding (int or sequence, optional):pad_if_needed (boolean)fillpadding_mode

RandomHorizontalFlip

Horizontally flip the given PIL Image randomly with a given probability.

Args:p (float): probability of the image being flipped. Default value is 0.5

RandomVerticalFlip

Verticallly flip the given PIL Image randomly with a given probability.

Args:p (float): probability of the image being flipped. Default value is 0.5

RandomPerspective

Performs Perspective transformation of the given PIL Image randomly with a given probability.

RandomResizedCrop

Crop the given PIL Image to random size and aspect ratio

RandomSizedCrop

廢棄

FiveCrop

Crop the given PIL Image into four corners and the central crop

TenCrop

Five Crop后翻轉（默認是水平翻轉）

LinearTransformation

Transform a tensor image with a square transformation matrix and a mean_vector computed
offline

ColorJitter

Randomly change the brightness, contrast and saturation of an image.
args:brightness=0, contrast=0, saturation=0, hue=0

RandomRotation

Rotate the image by angle
args:degrees, resample=False, expand=False, center=None

RandomAffine

Random affine transformation of the image keeping center invariant
degrees, translate=None, scale=None, shear=None, resample=False, fillcolor=0

Grayscale

Convert image to grayscale.
args:num_output_channels=1

RandomGrayscale

Randomly convert image to grayscale with a probability of p (default 0.1)

總結

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