swap-nas/calculate_datasets_statistics.py

# import torch
# import torchvision
# import torchvision.transforms as transforms

# # 加载CIFAR-10数据集
# transform = transforms.Compose([transforms.ToTensor()])
# trainset = torchvision.datasets.CIFAR10(root='./datasets', train=True, download=True, transform=transform)
# trainloader = torch.utils.data.DataLoader(trainset, batch_size=10000, shuffle=False, num_workers=2)

# # 将所有数据加载到内存中
# data = next(iter(trainloader))
# images, _ = data

# # 计算每个通道的均值和标准差
# mean = images.mean([0, 2, 3])
# std = images.std([0, 2, 3])

# print(f'Mean: {mean}')
# print(f'Std: {std}')

# results:
# Mean: tensor([0.4935, 0.4834, 0.4472])
# Std: tensor([0.2476, 0.2446, 0.2626])  

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import argparse

parser = argparse.ArgumentParser(description='Calculate mean and std of dataset')
parser.add_argument('--dataset', type=str, default='cifar10', help='dataset name')
parser.add_argument('--data_path', type=str, default='./datasets/cifar-10-batches-py', help='path to dataset image folder')

args = parser.parse_args()

# 设置数据集路径
dataset_path = args.data_path
dataset_name = args.dataset

# 设置数据集的transform（这里只使用了ToTensor）
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# 使用ImageFolder加载数据集
dataset = datasets.ImageFolder(root=dataset_path, transform=transform)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)

# 初始化变量来累积均值和标准差
mean = torch.zeros(3)
std = torch.zeros(3)
nb_samples = 0

count = 0
for data in dataloader:
    count += 1
    print(f'Processing batch {count}/{len(dataloader)}', end='\r')
    batch_samples = data[0].size(0)
    data = data[0].view(batch_samples, data[0].size(1), -1)
    mean += data.mean(2).sum(0)
    std += data.std(2).sum(0)
    nb_samples += batch_samples

mean /= nb_samples
std /= nb_samples

print(f'Mean: {mean}')
print(f'Std: {std}')