Hi, in the below code, I normalized the images with a formula. And, I saved images in this format. However, I want to know can I do it with torch.nn.functional.normalize … I don’t want to change images that are in the folder, because I want to visualize predicted images and I can’t see the original images with this way.

import numpy as np
from PIL import Image
files = src
def normalize(files):
k= 0
for i in files:
image = Image.open(i)
new_image = image.resize((600, 600))
pixels = np.asarray(image)
pixels = pixels.astype('float32')
mean, std = pixels.mean(), pixels.std()
# global standardization of pixels
pixels = (pixels - mean) / std
print(pixels)
outfile_name = i.replace(".png", ".npy")
with open(outfile_name, 'wb') as f:
np.save(f, pixels)
np.save(f, np.array([mean, std]))
print(k)
with open(outfile_name, 'rb') as f:
norm_t = np.load(f) # Load normalized array
mean_std = np.load(f) # Load mean and std
mean, std = tuple(mean_std)
k =k+1
t = norm_t*std + mean # Unnormalized (get the original pixels before normalizing)
t = np.round(t.clip(0, 255)).astype(np.uint8) # Convert from float to uint8
img = Image.fromarray(t, 'RGB')
img.save("degis{0}.png".format(k))
img.show()
normalize(files)

I didn’t understand one thing from the documentation. If I used it like that: transforms.Compose([transforms.Normalize()]), and yes it will not change my images, however, I have to give mean and std. And, how I can give your example code logic here? As I got from your code, I have to save new png images as like before.

I hope I explained my question as clearly. Thanks for your help.

You can use it without Compose… Normalize() is like any other module that you can call on an input.

Yes, you will need to give mean and std. In many examples, you’ll see a 3-dimensional mean and std tensor for a 3-channel image. This ensures that the normalization happens channel-wise.

You can save it if you like, I included that line because in your normalize() you are doing that. Here are some ways to visualize without needing to save the tensor to an image: Visualization utilities — Torchvision 0.12 documentation

And, how I can give your example code logic here?

Sorry, I’m not sure what you mean - can you clarify this?

I mean that if I want to use this code, how I can give these normalized images to the model?

Yes, you will need to give mean and std. In many examples, you’ll see a 3-dimensional mean and std tensor for a 3-channel image

I understood better now.
So if use this formula, find these 3-dim values and give them to the module, will it be the same/right way to implement the algorithm that I shared?

mean = 0.0
meansq = 0.0
count = 0
for index, data in enumerate(train_loader):
mean = data.sum()
meansq = meansq + (data**2).sum()
count += np.prod(data.shape)
total_mean = mean/count
total_var = (meansq/count) - (total_mean**2)
total_std = torch.sqrt(total_var)
print("mean: " + str(total_mean))
print("std: " + str(total_std))
cited from (https://discuss.pytorch.org/t/computing-the-mean-and-std-of-dataset/34949/20)

I should actually find the mean and standard deviation for the channel of the image I am interested in. Then, calculate the new value (p-m)/s of the pixel in that channel and use it as input.

But, I couldn’t find a proper way to use it without saving images after applying this algorithm.