How do they know mean and std, the input value of transforms.Normalize

How do they know mean and std, the input value of transforms.Normalize

By : user3099683
Date : January 11 2021, 03:34 PM
it should still fix some issue For normalization input[channel] = (input[channel] - mean[channel]) / std[channel], the mean and standard deviation values are to be taken from the training dataset.
Here, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] are the mean and std of Imagenet dataset.
code :

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CTM transforms vs Affine Transforms in iOS (for translate, rotate, scale)

CTM transforms vs Affine Transforms in iOS (for translate, rotate, scale)

By : user3778662
Date : March 29 2020, 07:55 AM
To fix this issue CTM is a current transformation matrix and the CTM methods will make operations on the current matrix.
The other version of functions will make the transformation on a given matrix which means you need to specify which matrix you are trying to transform. After you did so you may apply the transform to the CTM any way you want or use it for any other purpose.
code :
CGContextTranslateCTM(context, 10, 10);
CGAffineTransform transform = CGAffineTransformIdentity;
transform = CGAffineTransformTranslate(transform, 10, 10);
CGContextConcatCTM(context, transform);
Torchvision 0.2.1 transforms.Normalize does not work as expected

Torchvision 0.2.1 transforms.Normalize does not work as expected

By : Mitch3000
Date : March 29 2020, 07:55 AM
I wish this helpful for you So, in the code you have laid out a plan that how you want to process your data. You have created a data pipeline through which your data will flow and multiple transforms will be applied.
However, You forgot to call torch.utils.data.DataLoader. Until this is called, transformations on your data won't be applied. You can read more about it here.
code :
trainTransform  = tv.transforms.Compose([tv.transforms.ToTensor(), 
                  tv.transforms.Normalize((0.4914, 0.4822, 0.4466), (0.247, 0.243, 0.261))])

trainSet = tv.datasets.CIFAR10(root=dataDir, train=True,
                                    download=False, transform=trainTransform)

dataloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=False, num_workers=4)
images, labels = iter(dataloader).next()
print images
print images.max()
print images.min()
[[ 1.8649,  1.8198,  1.8348,  ...,  0.3924,  0.3774,  0.2572],
      [ 1.9701,  1.9550,  1.9851,  ...,  0.7230,  0.6929,  0.6629],
      [ 2.0001,  1.9550,  2.0001,  ...,  0.7831,  0.7530,  0.7079],
      [-0.8096, -1.0049, -1.0350,  ..., -1.3355, -1.3655, -1.4256],
      [-0.7796, -0.8697, -0.9749,  ..., -1.2754, -1.4557, -1.5609],
      [-0.7645, -0.7946, -0.9298,  ..., -1.4106, -1.5308, -1.5909]]]])
trainTransform  = tv.transforms.Compose([tv.transforms.ToTensor(), 
                  tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
How to normalize PIL image between -1 and 1 in pytorch for transforms.Compose?

How to normalize PIL image between -1 and 1 in pytorch for transforms.Compose?

By : user3417390
Date : March 29 2020, 07:55 AM
Does that help So I have been trying to find a way to normalize some PIL image pixel values between -1 and 1. I searched through documentation and didn't find solution. Only normalization in documentation is transforms.Normalize which normalizes with mean and std. So I am stuck on how to do it. This is my code: , Try:
code :
transform = transforms.Compose([
    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
How to type a function that transforms some properties of an input object

How to type a function that transforms some properties of an input object

By : user3445949
Date : March 29 2020, 07:55 AM
may help you . Function transform has conditional behavior. Looking on the control flow it can return or object which contains lineData or not. Therefor you cannot assign the return which is a union IHaveLineData | IDoNotHaveLineData to IHaveLineData as the return is just wider type. Imagine you want to assign smth which is string | number to number, because of the same reason you can't.
To be sure you get the wanted structure you need to append additional control flow to ensure the type. So just:
code :
// at this level test is infered as with or without lineData
const test= akpiDTOtoAkpiData({
  id: 1,
  name: 'my name',
  startDate: '2019',
  periodData: [] as PeriodDto[],

if ('lineData' in test) {
  // here test is precisely the type with lineData

transforms.Normalize() between 0 and 1 when using Lab

transforms.Normalize() between 0 and 1 when using Lab

By : user3452092
Date : March 29 2020, 07:55 AM
hop of those help? As you can see, PyTorch complains about the Tensor size, since you lack a channel.
Additionally, the "usual" mean and std values are computed on ImageNet dataset, and are useful if the statistics of your data match the ones of that dataset.
code :
transform = transforms.Compose([
   transforms.Normalize(mean=[0.5, 0.5],
                        std=[0.5, 0.5])
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