用于加载常用的图像数据集# 导入 torchvision.transforms 中的 ToTensor 和 Lambda 类,用于数据的转换和处理ds=datasets.FashionMNIST(root="data",# 指定数据集的根目录,数据将下载到这个目录下train=True,# 指定要加载的是训练集,如果为 False,则加载测试集download=True...
train=False,download=True,transform=ToTensor())train_dataloader=DataLoader(training_data,batch_size=64...
ToTensor(), # 将图像转换为PyTorch张量,并将像素值从[0, 255]缩放到[0, 1] transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) ## 对每个通道的图像进行标准化,使其均值为0,标准差为1 trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=...
原来,ToTensor函数是在对datasets对象调用__getitem__方法时触发调用的(这里使用了惰性求值(lazy evaluation)的思想),__getitem__方法大致如下所示: def__getitem__(self, idx): x, y = self.dataset[idx]ifself.transform: x = self.transform(x)returnx, y 而我们使用training_data.data[0]获取数据,是在...
参数:transforms (list of Transform objects) #对变换序列进行组合 例子: import torch import torchvision import torchvision.transforms as transforms transforms.Compose([ transforms.CenterCrop(10), transforms.ToTensor(), ]) 1. 2. 3. 4. 5.
transforms.ToTensor()]) unloader = transforms.ToPILImage()# 输入图片地址# 返回tensor变量defimage_loader(image_name): image = Image.open(image_name).convert('RGB') image = loader(image).unsqueeze(0)returnimage.to(device, torch.float)# 输入PIL格式图片# 返回tensor变量defPIL_to_tensor(image):...
data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) 1. 2. 3. 4. 5. 2.torchvision.transforms具体用法
)transform_valid=transforms.Compose([transforms.ToTensor()])# 根据图片目录创建数据集deftransform_...
torchvision.transforms.ToTensor, torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) ]) val_transform = torchvision.transforms.Compose([ torchvision.transforms.Resize(256), torchvision.transforms.CenterCrop(224), ...
transforms.ToTensor]) # transform it into a torch tensor def image_loader(image_name): image = Image.open(image_name) # fake batch dimension required to fit network's input dimensions image = loader(image).unsqueeze(0) return image.to(device, torch.float) ...