# Simple way to inverse transform ? Normalization

Hey
A way to reverse the normalization does not seem to exist. However it is pretty straightforward to create a simple class that does so.

``````class UnNormalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std

def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized image.
"""
for t, m, s in zip(tensor, self.mean, self.std):
# The normalize code -> t.sub_(m).div_(s)
return tensor
``````

You instantiate it with the same arguments used for the normalize. and then use it the same way

``````unorm = UnNormalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
unorm(tensor)``````
53 Likes

Most easiest way would be:

``````invTrans = transforms.Compose([ transforms.Normalize(mean = [ 0., 0., 0. ],
std = [ 1/0.229, 1/0.224, 1/0.225 ]),
transforms.Normalize(mean = [ -0.485, -0.456, -0.406 ],
std = [ 1., 1., 1. ]),
])

inv_tensor = invTrans(inp_tensor)
``````
24 Likes

A more concise approach based on Saurabh’s answer:

``````inv_normalize = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.255],
std=[1/0.229, 1/0.224, 1/0.255]
)
inv_tensor = inv_normalize(tensor)
``````
7 Likes

the mean should be
`mean=[-0.485 * 0.229, -0.456 * 0.224, -0.406 * 0.255]`

because the renormalization needs the answer to be (X/sigma - mu) = (X - mu*sigma)/sigma

The inverse normalization should be
x = z*sigma + mean
= (z + mean/sigma) * sigma
= (z - (-mean/sigma)) / (1/sigma),
since the normalization process is actually z = (x - mean) / sigma if you look carefully at the documentation of `transforms.Normalize`. I tested my original code and it worked fine.

7 Likes

Keep in mind, `torchvision.transforms.Normalize`'s operations are in-place. If you want immutable implementation,

``````class NormalizeInverse(torchvision.transforms.Normalize):
"""
Undoes the normalization and returns the reconstructed images in the input domain.
"""

def __init__(self, mean, std):
mean = torch.as_tensor(mean)
std = torch.as_tensor(std)
std_inv = 1 / (std + 1e-7)
mean_inv = -mean * std_inv
super().__init__(mean=mean_inv, std=std_inv)

def __call__(self, tensor):
return super().__call__(tensor.clone())
``````
9 Likes

Thank you.
But there is a typo in the third element of mean.
It’s 0.225 not 0.255

``````inv_normalize = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
std=[1/0.229, 1/0.224, 1/0.255]
)
inv_tensor = inv_normalize(tensor)
``````
2 Likes

There is still a typo in the std

``````inv_normalize = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
std=[1/0.229, 1/0.224, 1/0.225]
)
inv_tensor = inv_normalize(tensor)
``````
2 Likes

you can also use `kornia.denormalise`

1 Like

A slightly modified version of the above code

This suppose a defined mean and std.

``````inv_normalize = transforms.Normalize(
mean= [-m/s for m, s in zip(mean, std)],
std= [1/s for s in std]
)

inv_tensor = inv_normalize(tensor)
``````
1 Like

Sorry to bother, but this doesn’t seem to work for me. As `inv_normalize(tensor)` will raise a `TypeError` complaining `tensor is not a torch image.`

Is this a version issue? Thanks in advance!

Could you check the shape and type of the input you are passing to this method?
I guess the number of dimensions of type might be unexpected and thus `Normalize` might raise this issue.

1 Like

following @joels method, it worked for me.

``````def inverse_normalize(tensor, mean, std):
for t, m, s in zip(tensor, mean, std):
return tensor

input_tensor= inverse_normalize(tensor=input_tensor, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
``````
2 Likes

I found out that even after performing the inverse normalization, the inverse normalized opencv imag is not equivalent to an original opencv via `cv2.imread`. I am sticking to getting a mask from pytorch model output and multiplying it with original frame via `cv2.imread`

like this,

``````def inverse_normalize(tensor, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
mean = torch.as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
std = torch.as_tensor(std, dtype=tensor.dtype, device=tensor.device)
if mean.ndim == 1:
mean = mean.view(-1, 1, 1)
if std.ndim == 1:
std = std.view(-1, 1, 1)
return tensor
``````
3 Likes

Works like charm. Normalization+unnormalization give original image.

this code seems simpler

If you want to reverse the normalization, all you need to do is to use a new normalization, with slight modifications:

``````mean = torch.tensor([0.4915, 0.4823, 0.4468])
std = torch.tensor([0.2470, 0.2435, 0.2616])

normalize = transforms.Normalize(mean.tolist(), std.tolist())

unnormalize = transforms.Normalize((-mean / std).tolist(), (1.0 / std).tolist())
img_unn = unnormalize(img)

plt.imshow(img_unn.permute(1, 2, 0))
plt.show()

``````

Anyway I hate the fact that we don’t have un-normalize (de-normalize), it would be quite helpful for regression neural networks too.

Here is some working code as this is still one of the first Google results in inverse normalization. It follows torchvision.transforms convention so you can compose it.

Code Gist

``````class UnNormalize(torchvision.transforms.Normalize):
def __init__(self,mean,std,*args,**kwargs):
new_mean = [-m/s for m,s in zip(mean,std)]
new_std = [1/s for s in std]
super().__init__(new_mean, new_std, *args, **kwargs)
``````

yes im hving this issue too.
Any method that can fix it?

hi, this is my solution to that problem

#normalize images
image_transforms = Compose([
Resize((256,256)),
ToTensor(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

#unnormalize images
def imshow(image):
npimg = image.numpy()
npimg = np.transpose(npimg, (1,2,0))
npimg = ((npimg * [0.229, 0.224, 0.225]) + [0.485, 0.456, 0.406])
plt.imshow(npimg, interpolation=‘nearest’)