I think those are the mean and std deviation of the MNIST dataset.
@avijit_dasgupta is right. This is the mean and std computed on the training set.
But the PyTorch Tutorial https://github.com/pytorch/tutorials/blob/master/Deep%20Learning%20with%20PyTorch.ipynb says we should always use 0.5 since we are getting PIL images:
# The output of torchvision datasets are PILImage images of range [0, 1]. # We transform them to Tensors of normalized range [-1, 1] transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
Why should be any different for MNIST dataset?
Thanks in advance,
MNIST is not natural images, it’s data distribution is quite different.
What an honor to be replied by you, smth.
But the pytorch imagenet example is also very different from 0.5, 0.5, 0.5.
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.RandomSizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
I guess in the pytorch tutorial we are getting a normalization from a range 0 to 1 to -1 to 1 for each image, not considering the mean-std of the whole dataset.
Yes. On Imagenet, we’ve done a pass on the dataset and calculated per-channel mean/std. In CIFAR10, I thought that this was unncessary to be introduced to the reader, and we quite often just use
0.5, 0.5, 0.5 on many datasets to rerange them to
[-1, +1]. Sorry if this was confusing
Thank you very much for your answer.
Any way you could share the code with which you compute mean/std on the dataset? Do you use the dataset class and iterate over it?
Did you figure out the code for calculating the mean and std within pytorch ?
Should just be able to use the ImageFolder or some other dataloader to iterate over imagenet and then use the standard formulas to compute mean and std. at the channel level E.g., for mean keep 3 running sums, one for the R, G, and B channel values as well as a total pixel count (if you are using Python2 watch for int overflow on the pixel count, could need a different strategy). Then simply divide the running sums by the pixel count
import argparse import os import numpy as np import torchvision import torchvision.transforms as transforms dataset_names = ('cifar10','cifar100','mnist') parser = argparse.ArgumentParser(description='PyTorchLab') parser.add_argument('-d', '--dataset', metavar='DATA', default='cifar10', choices=dataset_names, help='dataset to be used: ' + ' | '.join(dataset_names) + ' (default: cifar10)') args = parser.parse_args() data_dir = os.path.join('.', args.dataset) print(args.dataset) if args.dataset == "cifar10": train_transform = transforms.Compose([transforms.ToTensor()]) train_set = torchvision.datasets.CIFAR10(root=data_dir, train=True, download=True, transform=train_transform) #print(vars(train_set)) print(train_set.train_data.shape) print(train_set.train_data.mean(axis=(0,1,2))/255) print(train_set.train_data.std(axis=(0,1,2))/255) elif args.dataset == "cifar100": train_transform = transforms.Compose([transforms.ToTensor()]) train_set = torchvision.datasets.CIFAR100(root=data_dir, train=True, download=True, transform=train_transform) #print(vars(train_set)) print(train_set.train_data.shape) print(np.mean(train_set.train_data, axis=(0,1,2))/255) print(np.std(train_set.train_data, axis=(0,1,2))/255) elif args.dataset == "mnist": train_transform = transforms.Compose([transforms.ToTensor()]) train_set = torchvision.datasets.MNIST(root=data_dir, train=True, download=True, transform=train_transform) #print(vars(train_set)) print(list(train_set.train_data.size())) print(train_set.train_data.float().mean()/255) print(train_set.train_data.float().std()/255)
So How should I know what mean and std should I use to transfer my images to? it is different for MNIST, CIFAR10, and ImageNEt…
Any role that I need to stick with?
Just caculate them on the whole datasets like @dlmacedo did.
The code is not widely applicable, if the training images are not the same size and in image format, you can not use the code to calculate per channel mean and std
I tired, using
transforms.Lambda(), to even try to normalize data per pixel from the whole data set.
For some reason it made results worse though I’d think it would be better strategy.
I wonder about something, Let’s say the first layer is Linear Layer (Fully Connected).
What’s the point in removing the mean from the data, as there is a Bias term is is optimized, wouldn’t it calculate the best term to begin with?
By normalizing the input, SGD algorithm will work better. If the feature scale is not approximately the same, it will takes longer time to find the minimum.
@jdhao, I wasn’t talking about the scaling, I was talking about the bias term.
Moreover, in the case of images all pixels are within the same range so stuff like normalizing different features units doesn’t apply here.
Put my question differently, after this “Centering” does the Bias of the first layer filter is around 0?
Training is more stable and faster when parameters are small. As a fact, none of these first order optimization method guarantees finding minimum for arbitrary network (in fact, they can’t even find it for the simple ones). Therefore, although scaling & offsetting is equivalent to scaling the weights and offsetting bias at first linear layer, normalization proves to often give better results.
Moreover, you shouldn’t normalize using every pixel’s mean and std. Since conv is an operation on channels, you should just use each channel’s mean and std.
Do we need tensors to be in the range of [-1,1] or is [0,1] okay? I have my own dataset of RGB images with a range of [0,1]. I manually normalized the dataset but the tensors are still in the range of [0,1]. What is the benefit of transforming the range to [-1,1]?
why you guys said [-1,1]? From the document, I just see [0,1]
Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
So if I do the normalization on each channel by myself, to convert [a,b] to [0,1], I don’t need transforms.ToTensor anymore, right?
But what if my data has a different range of each channel, such as x: -10 ~ 10, y: 1 -100, z: 20 -25 (actually they have some hidden correlation between each other), how to normalization? It doesn’t make sense to normalize them to the same range.