How to perform similar normalization as tf.image.per_image_standardization in pytorch?

Any way to achieve this?

Could you explain, what this method is doing, as the docs don’t seem to give much information:

```
Linearly scales each image in `image` to have mean 0 and variance 1.
For each 3-D image `x` in `image` , computes `(x - mean) / adjusted_stddev` , where
* `mean` is the average of all values in `x`
* `adjusted_stddev = max(stddev, 1.0/sqrt(N))` is capped away from 0 to protect against division by 0 when handling uniform images
* `N` is the number of elements in `x`
* `stddev` is the standard deviation of all values in `x`
```

I don’t understand what the “per image” part of this normalization is, if the mean is the "average of all values in `x`

and the `stddev`

also seems to use all elements.

This is a replica that we created in PyTorch to use as a lambda function in our transforms. We compared these results with the tensorflow implementation and it seems to work the same.

I believe that this calculates the mean of the pixels in a single image and ‘x’ here refers to each pixel of the same image.

```
def per_image_standardization(image):
"""
This function creates a custom per image standardization
transform which is used for data augmentation.
params:
- image (torch Tensor): Image Tensor that needs to be standardized.
returns:
- image (torch Tensor): Image Tensor post standardization.
"""
# get original data type
orig_dtype = image.dtype
# compute image mean
image_mean = torch.mean(image, dim=(-1, -2, -3))
# compute image standard deviation
stddev = torch.std(image, axis=(-1, -2, -3))
# compute number of pixels
num_pixels = torch.tensor(torch.numel(image), dtype=torch.float32)
# compute minimum standard deviation
min_stddev = torch.rsqrt(num_pixels)
# compute adjusted standard deviation
adjusted_stddev = torch.max(stddev, min_stddev)
# normalize image
image -= image_mean
image = torch.div(image, adjusted_stddev)
# make sure that image output dtype == input dtype
assert image.dtype == orig_dtype
return image
```

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