I am creating a classifier using PyTorch for classifying a dog and cat. My question is that I only have 10000 images for cats and dogs, 8000 for training and 2000 for testing. So to prevent overfitting I want to use keras’s ImageDataGenerator function to generator augmented images. This is what I did for that portion
   train_datagen = ImageDataGenerator(rescale = 1./255, 
                                      shear_range = 0.2, #corresponds to shearing to the images
                                      zoom_range = 0.2, #corresponds to a random zoom on the images
                                      horizontal_flip = True) #flips the images horizontally

   test_datagen = ImageDataGenerator(rescale = 1./255) #preprocessing the images of the test set

   #since our images are formatted in a certain way we use the flow_from_directory() function
   #Creating the training set
   training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                     target_size = (64, 64), 
                                                     batch_size = 32,
                                                     class_mode = 'binary')

   #Creating the test set
   test_set = test_datagen.flow_from_directory('dataset/test_set',
                                                target_size = (64, 64),
                                                batch_size = 32,
                                                class_mode ='binary')

Now the only problem is that I need the training_set and test_set to be converted to Torch Variables, but right now there DirectoryIterators. So is there a way to convert the training_set and test_set to Torch Variables. Or can I do the same thing that I did here but using pytorch.
Thank you