torchvision-transforms 常用函数总结
文章目录
- torchvision-transforms 常用函数总结
-
- 一、概述——为何要用transforms
- 二、函数介绍
-
- 1、ToTensor
- 2、Normalize
- 3、Resize(非常常用)
- 4、Compose
- 5、RandomCrop
- 三、transforms和数据集的结合使用
torchvision-transforms 常用函数总结
一、概述——为何要用transforms
需求是多样的，因此可以通过实例化一个transforms，满足转换的需要。具体的class可以参考transforms.py中的描述
二、函数介绍
1、ToTensor
功能：将PIL.image读取的PIL类型图片或者cv2.imread读取的numpy.ndarray转化为tensor类型
最简单的函数，没什么参数，直接默认构造函数然后调用即可，具体如下：
from torchvision import transforms
from PIL import Image
if __name__ == '__main__':
img_path = "data/hymenoptera_data/train/ants/5650366_e22b7e1065.jpg"
img = Image.open(img_path)
img2tensor = transforms.ToTensor()
img_tensor = img2tensor(img)
print(img_tensor)
2、Normalize
功能：输入RGB三通道的标准差和方差，输出正则化的图像矩阵
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
import cv2
if __name__ == '__main__':
img_path = "data/hymenoptera_data/train/ants/5650366_e22b7e1065.jpg"
img = Image.open(img_path)
img2tensor = transforms.ToTensor()
img_tensor = img2tensor(img)
writer = SummaryWriter("logs")
writer.add_image("original", img_tensor)
trans_norm = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
img_norm = trans_norm(img_tensor)
writer.add_image("Normalize", img_norm)
writer.close()
原图
正则化后的图像
3、Resize(非常常用)
功能：
1、Resize([h, w])——对一个图像进行缩放,虽然会改变长宽比，但图像未发生裁剪，因此可以通过Resize再次还原回来
2、Resize(x) ——对短边缩放到x,长宽比不变
注意
PIL image 的size属性返回的是w, h而Resize参数顺序是h,w，切勿弄错
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
import cv2
if __name__ == '__main__':
img_path = "data/hymenoptera_data/train/ants/5650366_e22b7e1065.jpg"
img = Image.open(img_path)
writer = SummaryWriter("logs")
trans_resize = transforms.Resize((512, 512))
resized_img = trans_resize(img)
img2tensor = transforms.ToTensor()
img_tensor = img2tensor(resized_img)
writer.add_image("resized", img_tensor)
writer.close()
缩放后的结果
4、Compose
功能：组合变换，参数是各种变换组成的列表“[transform1, transform2, …]”
trans_resize = transforms.Resize((512, 512))
img2tensor = transforms.ToTensor()
trans = transforms.Compose([trans_resize, img2tensor])
img_tensor = trans(img)
5、RandomCrop
功能：随机裁剪，和Resize类似
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
import cv2
if __name__ == '__main__':
img_path = "data/hymenoptera_data/train/ants/5650366_e22b7e1065.jpg"
img = Image.open(img_path)
writer = SummaryWriter("logs")
trans_random_crop = transforms.RandomCrop((300, 400))
img2tensor = transforms.ToTensor()
trans = transforms.Compose([trans_random_crop, img2tensor])
for i in range(5):
img_tensor = trans(img)
writer.add_image("random crop", img_tensor, i)
writer.close()
三、transforms和数据集的结合使用
方法：先查看数据集里都有啥(调试)，然后根据需求加transform
import torchvision
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
trans = torchvision.transforms.Compose([
torchvision.transforms.ToTensor()
])
if __name__ == '__main__':
train_set = torchvision.datasets.CIFAR10(root="./dataset", train=True, transform=trans, download=True)
test_set = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=trans, download=True)
print(test_set)
img, target = test_set[1]
writer = SummaryWriter("logs")
writer.add_image("pic1", img, 1)
print(test_set.classes[target])