前言：本文为学习 PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】时记录的 Jupyter 笔记，部分截图来自视频中的课件。
本文主要通过 transform.ToTensor 解决两个问题：
- transform如何使用
- tensor数据类型的特色
| from torchvision import transforms |
| from PIL import Image |
| img_path = "D:/work/StudyCode/jupyter/dataset_for_pytorch_dataloading/train/ants/0013035.jpg" |
| img = Image.open(img_path) |
| print(img) |
| <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=768x512 at 0x1FE4AA30940> |
| tensor_trans = transforms.ToTensor() |
| tensor_img = tensor_trans(img) |
| tensor_img.shape |
| torch.Size([3, 512, 768]) |
可以看到Tensor数据类型中有很多属性，除了data即数据属性外，还有一些比较重要的属性：
backward_hooks 用于反向传播_grad 记录梯度device 记录数据存储在什么设备上（GPU or CPU）dtype 记录数据类型requires_grad 表示是否跟踪梯度
可以看到这些属性都是与神经网络关系密切的，所以tensor在纯数据的基础上，可以看成是一个针对神经网络所需参数打包后的一个数据类型。
| import cv2 |
| cv_img = cv2.imread(img_path) |
| type(cv_img) |
使用OpenCV读取图片可以发现是ndarray类型的数据，而ToTensor方法支持ndarray类型和PIL类型，刚好对应了两种主要的图片读取方法。
下面介绍一个Python对象中的内置的实例方法：
call方法：
可以看到 内置方法 __call__ 本质就是在类中重载 () 运算符，使得类实例对象可以像调用普通函数那样执行 __call__ 中的函数
| |
| class Person: |
| def __call__(self, name): |
| print("__call__ "+"Hello "+name) |
| def hello(self, name): |
| print("hello " + name) |
| person = Person() |
| person("Here_SDUT") |
| person.hello("lisi") |
| __call__ Hello Here_SDUT |
| hello lisi |
用于将多种transform方法打包起来，具体用法可以看Example
| class Compose(builtins.object) |
| | Compose(transforms) |
| | |
| | Composes several transforms together. This transform does not support torchscript. |
| | Please, see the note below. |
| | |
| | Args: |
| | transforms (list of ``Transform`` objects): list of transforms to compose. |
| | |
| | Example: |
| | >>> transforms.Compose([ |
| | >>> transforms.CenterCrop(10), |
| | >>> transforms.PILToTensor(), |
| | >>> transforms.ConvertImageDtype(torch.float), |
| | >>> ]) |
用于将PIL类型或者ndarray类型的数据转换成Tensor类型，具体可以见前文
输入为 Tensor 数据类型，进行归一化，缩小数据的范围
| class Normalize(torch.nn.modules.module.Module) |
| | Normalize(mean, std, inplace=False) |
| | |
| | Normalize a tensor image with mean and standard deviation. |
| | This transform does not support PIL Image. |
| | Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n`` |
| | channels, this transform will normalize each channel of the input |
| | ``torch.*Tensor`` i.e., |
| | ``output[channel] = (input[channel] - mean[channel]) / std[channel]`` |
| | |
| | .. note:: |
| | This transform acts out of place, i.e., it does not mutate the input tensor. |
| | |
| | Args: |
| | mean (sequence): Sequence of means for each channel. |
| | std (sequence): Sequence of standard deviations for each channel. |
| | inplace(bool,optional): Bool to make this operation in-place. |
| trans_norm = transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]) |
| img_norm = trans_norm(tensor_img) |
| img_norm[0][0][0] |
| |
| |
| class Resize(torch.nn.modules.module.Module) |
| | Resize(size, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, max_size=None, antialias=None) |
| | |
| | Resize the input image to the given size. |
| | If the image is torch Tensor, it is expected |
| | to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions |
| | |
| | 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). |
| type(img) |
| img.size |
| trans_resize = transforms.Resize((512,512)) |
| img_resize = trans_resize(img) |
| img_resize.size |
| type(img_resize) |
| PIL.JpegImagePlugin.JpegImageFile |
| |
| trans_resize_2 = transforms.Resize((512,512)) |
| trans_compos = transforms.Compose([trans_resize_2, transforms.ToTensor()]) |
| img_resize_2 = trans_compos(img) |
| img_resize_2.shape |
| type(img_resize_2) |
| torch.Size([3, 512, 512]) |
随机裁剪操作
| class RandomCrop(torch.nn.modules.module.Module) |
| | RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant') |
| | |
| | Crop the given image at a random location. |
| | If the image is torch Tensor, it is expected |
| | to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions, |
| | but if non-constant padding is used, the input is expected to have at most 2 leading dimensions |
| | |
| | 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. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). |
| | padding (int or sequence, optional): Optional padding on each border |
| | of the image. Default is None. If a single int is provided this |
| | is used to pad all borders. If sequence of length 2 is provided this is the padding |
| | on left/right and top/bottom respectively. If a sequence of length 4 is provided |
| | this is the padding for the left, top, right and bottom borders respectively. |
| | |
| trans_random = transforms.RandomCrop((200,300)) |
| |
| img_random = trans_random(img) |
| img_random |
| |
对于其他transform中的工具，可以按照以下步骤自行探索：
- 使用help命令查看用法或者翻阅官方文档
- 看函数的输入和输出是什么
- 关注方法需要什么参数以及参数的意义
- 不知道返回值的时候：
- print一下
- print(type())
- debug
- 最后查阅网上资料
