how can I modify Dataset class to make the mask RCNN work with multiple objects?

Question

I am currently working on instance segmentation. I follow these two tutorials:

1. https://haochen23.github.io/2020/06/fine-tune-mask-rcnn-pytorch.html

2. https://colab.research.google.com/github/dlmacedo/starter-academic/blob/master/content/courses/deeplearning/notebooks/pytorch/torchvision_finetuning_instance_segmentation.ipynb#scrollTo=mTgWtixZTs3X

However, these two tutorials work perfectly with one class like person + background. But in my case, I have two classes like a person and car + background. I didn't find any resources about making the Mask RCNN work with multiple objects.

Notice that:

1. I am using PyTorch ( torchvision ), torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0

2. I am using a Pascal VOC annotation

3. i used segmentation class (not the XML file) + the images

and this is my dataset class

class PennFudanDataset(torch.utils.data.Dataset):
def __init__(self, root, transforms=None):
    self.root = root
    self.transforms = transforms
    # load all image files, sorting them to
    # ensure that they are aligned
    self.imgs = list(sorted(os.listdir(os.path.join(root, "img"))))
    self.masks = list(sorted(os.listdir(os.path.join(root, "imgMask"))))

def __getitem__(self, idx):
    # load images ad masks
    img_path = os.path.join(self.root, "img", self.imgs[idx])
    mask_path = os.path.join(self.root, "imgMask", self.masks[idx])
    img = Image.open(img_path).convert("RGB")
    # note that we haven't converted the mask to RGB,
    # because each color corresponds to a different instance
    # with 0 being background
    mask = Image.open(mask_path)

    mask = np.array(mask)
    # instances are encoded as different colors
    obj_ids = np.unique(mask)
    # first id is the background, so remove it
    obj_ids = obj_ids[1:]

    # split the color-encoded mask into a set
    # of binary masks
    masks = mask == obj_ids[:, None, None]

    # get bounding box coordinates for each mask
    num_objs = len(obj_ids)
    boxes = []
    for i in range(num_objs):
        pos = np.where(masks[i])
        xmin = np.min(pos[1])
        xmax = np.max(pos[1])
        ymin = np.min(pos[0])
        ymax = np.max(pos[0])
        boxes.append([xmin, ymin, xmax, ymax])

    boxes = torch.as_tensor(boxes, dtype=torch.float32)
    # there is only one class
    labels = torch.ones((num_objs,), dtype=torch.int64) 
    masks = torch.as_tensor(masks, dtype=torch.uint8)

    image_id = torch.tensor([idx])
    area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
    # suppose all instances are not crowd
    iscrowd = torch.zeros((num_objs,), dtype=torch.int64)

    target = {}
    target["boxes"] = boxes
    target["labels"] = labels
    target["masks"] = masks
    target["image_id"] = image_id
    target["area"] = area
    target["iscrowd"] = iscrowd
    if self.transforms is not None:
        img, target = self.transforms(img, target)

    return img, target

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

anyone can help me?