Hi,  I want to predict depth and mask in an image. The depth will be predicted for roads and i want to mask a car in the image. I have implemented UNet for this problem.
The dataloader code is
class DepthDataSet(Dataset):
“”" Dataset for Depth and mask prediction “”"
def init(self, conf, fg_bg_dir, mask_dir, depth_dir, transform=None, scale=1):
“”"
Args:
conf = configuration file
image_dir: Directory to images of depth
mask_dir: Directory to mask images of depth
transformation: transformations applied on that image
“”"
self.conf = conf
self.fg_bg_dir = fg_bg_dir
self.mask_dir = mask_dir
self.depth_dir = depth_dir
self.scale = scale
self.ids = [file for file in listdir(fg_bg_dir) if not file.startswith(’.’)]
def len(self):
return len(self.ids)
@classmethod
def preprocess(cls, pil_img, scale):
w, h = pil_img.size
newW, newH = int(scale * w), int(scale * h)
assert newW > 0 and newH > 0, ‘Scale is too small’
pil_img = pil_img.resize((newW, newH))
  img_nd = np.array(pil_img)

  if len(img_nd.shape) == 2:
      img_nd = np.expand_dims(img_nd, axis=2)

  # HWC to CHW
  img_trans = img_nd.transpose((2, 0, 1))
  if img_trans.max() > 1:
      img_trans = img_trans / 255

  return img_trans

def getitem(self, i):
idx = self.ids[i]
# image_file = glob(self.image_dir + ‘/’+ idx )
# mask_file = glob(self.mask_dir + ‘/’+ idx )
# print(idx)
# print(self.image_dir)
# print(mask_file)
# assert len(mask_file) > 1, "No mask found"
# assert len(image_file) > 1, "No image found"

mask = Image.open(self.mask_dir + '/'+ idx)
fg_bg = Image.open(self.fg_bg_dir + '/'+ idx)
depth = Image.open(self.depth_dir + '/'+ idx)
#if image.size != mask.size:
#assert image.size == mask.size
#img = self.preprocess(image, self.scale)
#mask = self.preprocess(mask, self.scale)

return {
        'image': torch.from_numpy(np.array(fg_bg)), 
        'mask': torch.from_numpy(np.array(mask)), 
        'depth': torch.from_numpy(np.array(depth))
       }

I am feeding the image to the model and checking the loss for mask and depth.
images = images.to(device=self.device, dtype=torch.float)
mask_type = torch.float32 if self.model.n_classes == 1 else torch.long
mask = mask.to(device=device, dtype=mask_type)
depth = depth.to(device=device, dtype=mask_type)
        mask_pred = self.model(images)
        loss_mask = self.criterion(mask_pred, mask)
        loss_depth = self.criterion(mask_pred, depth)
        loss = loss_mask + loss_depth

        train_loss += loss_mask.item() + loss_depth.item()

My result is not proper , Can you please help me to make this solution better.
Or is it the correct approach to add up the loss against the ground truths.
Thanks,