Write your own dataset class:
class CustomDataSet(Dataset):
    def __init__(self, main_dir, transform):
        self.main_dir = main_dir
        self.transform = transform
        all_imgs = os.listdir(main_dir)
        self.total_imgs = natsort.natsorted(all_imgs)

    def __len__(self):
        return len(self.total_imgs)

    def __getitem__(self, idx):
        img_loc = os.path.join(self.main_dir, self.total_imgs[idx])
        image = Image.open(img_loc).convert("RGB")
        tensor_image = self.transform(image)
        return tensor_image

Then call the Dataset class and Dataloader:
my_dataset = CustomDataSet(img_folder_path, transform=trsfm)
train_loader = data.DataLoader(my_dataset , batch_size=batch_size, shuffle=False, 
                               num_workers=4, drop_last=True)

Then iterate:
for idx, img in enumerate(train_loader):
    #do your training now