How to view torch transformed images?

vision
1

Hi
I’m using three torchvision transforms

transform_list = [transforms.RandomHorizontalFlip(p=1),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]

and I want to view that horizontal flip IS indeed done, so in the training loop I did this

    for i, (images, _) in tqdm(enumerate(trainloader)):
        imshow(torchvision.utils.make_grid(images))

but it would only show only original images
how can I view those augmented images?

one more question… (if I may…)
I want to receive a ‘flag’ variable when a certain transformation (or augmentation) is done to that data…
Any possible ideas? I will re-code <torch.utils.data> if I have to

2

Did you apply the transform in in the trainloader ?

3

yes, as you see in the code above
I did apply random horizontal flip with probability of 1

4

I meant if you passed the transformation to the Dataset object as shown in the Transforms section for eg. transformed_dataset = FaceLandmarksDataset(csv_file='data/faces/face_landmarks.csv', root_dir='data/faces/', transform=transforms.Compose([ Rescale(256), RandomCrop(224), ToTensor() ]))
as shown in more detail here

5

Yes it was
The list was made only for my convenience but it was wrapped woth compose and given to dataloader
There no problem with it

6

OK… I thought horizontal flip was flipping on horizontal axis… hence the name horizontal flip
should have noticed though, since horizontal flip was used ubiquitously… nobody wants his/her network to learn from upside down images…
Random horizontal flip is flipping horizontally… (so in my sense, it is flipping on vertical axis)
many peop might know… but I just figured out by comparing original images… haha