10.标准化:transforms.Normalize class torchvision.transforms.Normalize(mean, std) 功能:对数据按通道进行标准化,即先减均值,再除以标准差,注意是 hwc 11.转为tensor:transforms.ToTensor class torchvision.transforms.ToTensor 功能:将PIL Image或者 ndarray 转换为tensor,并且归一化至[0-1] 注意事项:归一化至[0-...
step() #每100个batch计算当前的损失,并在所有进程中进行聚合然后打印 if (batch_idx + 1) % 100 == 0: # 将当前的loss转换为tensor,并在所有进程间进行求和 loss_tensor = torch.tensor([loss.item()]).cuda(rank) dist.all_reduce(loss_tensor) # 计算所有进程的平均损失 mean_loss = loss_tensor...
ToTensor:将PIL Image对象转成Tensor,会自动将[0, 255]归一化至[0, 1] 对Tensor的操作包括: Normalize:标准化,即减均值,除以标准差 ToPILImage:将Tensor转为PIL Image对象 如果要对图片进行多个操作,可通过Compose函数将这些操作拼接起来,类似于nn.Sequential。注意,这些操作定义后是以函数的形式存在,真正使用时需...
]batch_size=64transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307),(0.3081,))])train_dataset=CustomMNIST(root=r'F:\MyPracticeProject\刘二大人\dataset/mnist/',train=True,download=True,transform=transform)train_loader=DataLoader(train_dataset,shuffle=True,batch_size...
import matplotlib.pyplot as plt import numpy as np # Helper function for inline image display def matplotlib_imshow(img, one_channel=False): if one_channel: img = img.mean(dim=0) img = img / 2 + 0.5 # unnormalize npimg = img.numpy() if one_channel: plt.imshow(npimg, cmap="Greys...
img = img / 2 + 0.5 # unnormalizenpimg = img.numpy()if one_channel:plt.imshow(npimg, cmap="Greys")else:plt.imshow(np.transpose(npimg, (1, 2, 0))) 我们将定义一个类似于该教程的模型架构,只需进行轻微修改以适应图像现在是单通道而不是三通道,28x28 而不是 32x32 的事实:...
self.transform = torchvision.transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) self.crop_size = crop_size features, labels = read_voc_images(voc_dir, is_train=is_train) self.features = [self.normalize_image(feature) ...
transforms.Normalize(mean = (0.5,), std = (0.5,))# value of tensor: [0, 1] -> [-1, 1] ]) mnist = datasets.MNIST(root='data', train=True, download=True, transform=transform) transforms.Normalize()用于将图像进行标准化:(x−mean)std,使得处理的数据呈正态分布。
# 将损失从所有进程中收集起来并求平均# 创建一个和loss相同的tensor,用于聚合操作reduced_loss = torch.tensor([loss.item()]).cuda(rank)# all_reduce操作默认是求和dist.all_reduce(reduced_loss)# 求平均reduced_loss = reduced_loss / dist.get_world_size()# 只在rank为0的进程中打印信息ifrank ==0...
( [ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225]), ] ) image = Image.open(image_file) image = data_transforms(image).float() image = torch.tensor(image) image = image.unsqueeze(0)returnimage.numpy...