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How do I load custom image based datasets into Pytorch for use with a CNN?

I have searched for hours on the internet to find a good solution to my issue. Here is some relevant background information to help you answer my question.

This is my first ever deep learning project and I have no idea what I am doing. I know the theory but not the practical elements.

The data that I am using can be found on kaggle at this link:
(https://www.kaggle.com/alxmamaev/flowers-recognition)

I am aiming to classify flowers based on the images provided in the dataset using a CNN.

Here is some sample code I have tried to use to load data in so far, this is my best attempt but as I mentioned I am clueless and Pytorch docs didn't offer much help that I could understand at my level.
(https://pastebin.com/fNLVW1UW)

# Loads the images for use with the CNN.
def load_images(image_size=32, batch_size=64, root="../images"):
transform = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_set = datasets.ImageFolder(root=root, train=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2)

return train_loader


# Defining variables for use with the CNN.
classes = ('daisy', 'dandelion', 'rose', 'sunflower', 'tulip')
train_loader_data = load_images()

# Training samples.
n_training_samples = 3394
train_sampler = SubsetRandomSampler(np.arange(n_training_samples, dtype=np.int64))

# Validation samples.
n_val_samples = 424
val_sampler = SubsetRandomSampler(np.arange(n_training_samples, n_training_samples + n_val_samples, dtype=np.int64))

# Test samples.
n_test_samples = 424
test_sampler = SubsetRandomSampler(np.arange(n_test_samples, dtype=np.int64))


Here are my direct questions that I require answers too:

How do I fix my code to load in the dataset in an 80/10/10 split for training/test/validation?

How do i create the required labels/classes for these images which are already divided by folders in /images ?

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