my dataset have 32 x 32 images, what is the error in it?

class Genv2(nn.Module):
    def __init__(self, *args, **kwargs):
        super(Genv2, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)

        self.fc1 = nn.Linear(in_features=16*32*32, out_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=84)
        self.out = nn.Linear(in_features=84, out_features=2)

    def forward(self, t):

        print("in forward 1")
        print(t.shape)    # [1,3,224,224]

        # hidden layer
        t = f.relu(self.conv1(t))
        t = f.max_pool2d(t, kernel_size=2, stride=2)
        print("in forward 2")
        print(t.shape)    # [1,9,224,224]
        # hidden layer
        t = f.relu(self.conv2(t))
        t = f.max_pool2d(t, kernel_size=2, stride=2)

        # hidden layer
        t = f.relu(self.fc1(t.view(-1, 16*32*32)))
        t = f.relu(self.fc2(t))

        # output layer
        t = self.out(t)

        return t


if __name__ == "__main__":
    plt.ion()   # interactive mode
    # Data augmentation and normalization for training
    # Just normalization for validation
    data_transforms = {
        'train': transforms.Compose([
            # transforms.Resize((32, 32)),
            # transforms.RandomResizedCrop(224),
            # transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
        'val': transforms.Compose([
            # transforms.Resize((32, 32)),
            # transforms.Resize(256),
            # transforms.CenterCrop(224),
            transforms.ToTensor(),
            # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
    }

    data_dir = 'data/generator'
    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                              data_transforms[x])
                      for x in ['train', 'val']}

    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=1,
                                                  shuffle=True, num_workers=4)
                   for x in ['train', 'val']}

    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}

    network = Genv2()

    class_names = image_datasets['train'].classes
    print(class_names)

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    batch = next(iter(dataloaders['train']))
    image, label = batch

    preds = network(image)