Hello,
I suppose that you have a custom dataset class that inherits torch.data.utils.Dataset. In your __getitem__ function, simply return both rgb and gray image. Here is an example:
class CustomDatasetRGB(torch.utils.data.Dataset):
    def __init__(self, images, labels):
        self.images = images
        self.labels = labels

    def __getitem__(self, index):
        rgb_image = cv2.imread(self.images[index], cv2.IMREAD_COLOR)
        gray_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2GRAY)
        # cv2.imshow('gray', gray_image)
        # cv2.imshow('rgb', rgb_image)
        # cv2.waitKey(0)
        rgb_image = np.transpose(rgb_image, axes=(2, 0, 1))
        return torch.from_numpy(rgb_image), torch.from_numpy(gray_image), self.labels[index]

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

# 1000 images in dataset
images = ['im2.png'] * 1000
labels = np.random.randint(low=0, high=10, size=1000)

dataset = CustomDatasetRGB(images, labels)
loader = torch.utils.data.DataLoader(dataset, batch_size=4)

for step, (rgb, gray, labels) in enumerate(loader):
    print(rgb.size())
    print(gray.size())
    print(labels.size())
    '''
    torch.Size([4, 3, 375, 450])
    torch.Size([4, 375, 450])
    torch.Size([4])
    '''