transforms用途

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms

# 1.transforms.Totensor使用
img_path = "D:/05深度学习笔记/Data/FirstTypeData/train/ants/0013035.jpg"
img = Image.open(img_path)

tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)  # 转化为张量
print(tensor_img)

# 2.Tensor有一些属性，比如反向传播、梯度等属性，它包装了神经网络需要的一些属性。
img_path = "D:/05深度学习笔记/Data/FirstTypeData/train/ants/0013035.jpg"
img = Image.open(img_path)

writer = SummaryWriter("logs")

tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)  # 转化为张量

writer.add_image("Tensor_image", tensor_img)
writer.close()


# 3.transforms的工具主要关注他的输入、输出、作用
# 3.1 __call__魔法方法使用
class Person:
    def __call__(self, name):
        print("__call__" + "hello" + name)

    def hello(self, name):
        print("hello" + name)


# 实例化对象
person = Person()
# 调用__call__方法
person("张三")
# 调用hello方法
person.hello("李四")