70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
# import torch
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# import torchvision
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# import torchvision.transforms as transforms
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# # 加载CIFAR-10数据集
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# transform = transforms.Compose([transforms.ToTensor()])
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# trainset = torchvision.datasets.CIFAR10(root='./datasets', train=True, download=True, transform=transform)
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# trainloader = torch.utils.data.DataLoader(trainset, batch_size=10000, shuffle=False, num_workers=2)
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# # 将所有数据加载到内存中
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# data = next(iter(trainloader))
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# images, _ = data
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# # 计算每个通道的均值和标准差
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# mean = images.mean([0, 2, 3])
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# std = images.std([0, 2, 3])
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# print(f'Mean: {mean}')
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# print(f'Std: {std}')
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# results:
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# Mean: tensor([0.4935, 0.4834, 0.4472])
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# Std: tensor([0.2476, 0.2446, 0.2626])
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import torch
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from torchvision import datasets, transforms
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from torch.utils.data import DataLoader
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import argparse
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parser = argparse.ArgumentParser(description='Calculate mean and std of dataset')
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parser.add_argument('--dataset', type=str, default='cifar10', help='dataset name')
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parser.add_argument('--data_path', type=str, default='./datasets/cifar-10-batches-py', help='path to dataset image folder')
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args = parser.parse_args()
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# 设置数据集路径
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dataset_path = args.data_path
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dataset_name = args.dataset
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# 设置数据集的transform（这里只使用了ToTensor）
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor()
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])
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# 使用ImageFolder加载数据集
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dataset = datasets.ImageFolder(root=dataset_path, transform=transform)
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dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)
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# 初始化变量来累积均值和标准差
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mean = torch.zeros(3)
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std = torch.zeros(3)
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nb_samples = 0
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count = 0
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for data in dataloader:
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count += 1
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print(f'Processing batch {count}/{len(dataloader)}', end='\r')
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batch_samples = data[0].size(0)
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data = data[0].view(batch_samples, data[0].size(1), -1)
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mean += data.mean(2).sum(0)
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std += data.std(2).sum(0)
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nb_samples += batch_samples
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mean /= nb_samples
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std /= nb_samples
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print(f'Mean: {mean}')
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print(f'Std: {std}')
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