Tensorflow version of Pytorch Transforms

Here is some pointer, in pytorch you have

from torchvision import transforms
from PIL import Image 
import torch 

def image_loader(transform, image_name):
    image = Image.open(image_name).convert('RGB')
    image = transform(image).float()
    image = torch.tensor(image)
    image = image.unsqueeze(0)
    return image

data_transforms = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# check: visualize 
i = image_loader(data_transforms, '/content/1.png')
i.shape

plt.figure(figsize=(25,10))
subplot(121); imshow(np.array(i[0]).transpose(1, 2, 0)); 

And in tensorflow, you can achieve this as follows

def transform(image, mean, std):
    for channel in range(3):
        image[:, :, channel] = (image[:, :, channel] - mean[channel]) \
            / std[channel]
    return image


def image_loader(image_name):
    image = Image.open(image_name).convert('RGB')
    image = transform(np.array(image) / 255, mean=[0.485, 0.456,
                      0.406], std=[0.229, 0.224, 0.225])
    image = tf.cast(image, tf.float32)
    image = tf.expand_dims(image, 0)
    return image

# check: visualize 
i = image_loader('/content/1.png')
i.shape 

plt.figure(figsize=(25,10))
subplot(121); imshow(i[0]); 

This should output the same. Note, in the second case, we define the transform function, from another OP, here, it's fine, however, you can also check tf. keras...Normalization, see this answer for details.