Hi, thanks for your wonderful project. There are some questions I want to confirm when I apply the MGDA weighting method, could you please give me an answer, thanks!

1. what's the self.rep_tasks?
2. what's the rep_grad?

For the above two problems, I try to give my answer, the first is the representation generated by the representation layer (sharing parameters), and the second is whether using the gradients of representations.

In this case, my third problem is what is the purpose of the variable rep_grad when it is in MGDA?

It's used to implement the MGDA-UB? I realize that it will save the gradients of self.rep_tasks in the function of _compute_grad() in abstract_weighting.py, so I made such an assumption.

I'm a little bit confused about these technique detail, hope you can help me, thanks again!

when I try to run this trainer , .next() can't run

-> "AttributeError: 'dict_keyiterator' object has no attribute 'next'"

I use: python=3.7 torch=11.3

This method has been deleted in python 3? Why not use next(iter)?

or what should I do to fix it?

I have found someone says set num_workers=0 will work,but it's too slow...My system is Ubuntu

Exception ignored in: <function _MultiProcessingDataLoaderIter.del at 0x7f7c408fce60> Traceback (most recent call last): File "/home/user/miniconda3/envs/IB/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1510, in del self._shutdown_workers() File "/home/user/miniconda3/envs/IB/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1493, in _shutdown_workers if w.is_alive(): File "/home/user/miniconda3/envs/IB/lib/python3.7/multiprocessing/process.py", line 151, in is_alive assert self._parent_pid == os.getpid(), 'can only test a child process' AssertionError: can only test a child process

I ran the demo of office-31and found some errors in this example: The version of pip is not the same version published on github The operating environment is as follows：

• python = 3.8
• pytroch = 1.12

In train.py, .next() can't run

After I modified this syntax, the new problem appeared again data = data.to(self.device, non_blocking=True)

I want to know if this is a version reason or something else

Hi, I know it's kinda OOT but I am curious whether I can apply multi-task learning to graph neural network. What I learn from HPS, we shall share the encoder/decoder across the layer. I am curious should I create an encoder on top of the graph layer? Kinda stuck in this experiment, any suggestion would be helpful. Thanks

Thanks for this great repo, it is very useful!

Could you please add support for the Nash-MTL method described in the paper "Multi-Task Learning as a Bargaining Game"?

Paper: https://arxiv.org/abs/2202.01017 Official code: https://github.com/AvivNavon/nash-mtl

Thanks

I introduced L1Metric class ,I get a error:

AttributeError: 'L1Metric' object has no attribute 'abs_record'

class L1Metric(AbsMetric):
    r"""Calculate the Mean Absolute Error (MAE).
    """
    def __init__(self):
        super(L1Metric, self).__init__()
        
    def update_fun(self, pred, gt):
        r"""
        """
        abs_err = torch.abs(pred - gt)
        self.record.append(abs_err)
        self.bs.append(pred.size()[0])
        
    def score_fun(self):
        r"""
        """
        records = np.array(self.abs_record)
        batch_size = np.array(self.bs)
        return [(records*batch_size).sum()/(sum(batch_size))]

L1Metric class inherit AbsMetric class,but AbsMetric class has no attribute 'abs_record',So I guess there are maybe some problems,of course,this property may also come from other places

class AbsMetric(object):
    r"""An abstract class for the performance metrics of a task. 

    Attributes:
        record (list): A list of the metric scores in every iteration.
        bs (list): A list of the number of data in every iteration.
    """
    def __init__(self):
        self.record = []
        self.bs = []
    
    @property
    def update_fun(self, pred, gt):
        r"""Calculate the metric scores in every iteration and update :attr:`record`.

        Args:
            pred (torch.Tensor): The prediction tensor.
            gt (torch.Tensor): The ground-truth tensor.
        """
        pass
    
    @property
    def score_fun(self):
        r"""Calculate the final score (when an epoch ends).

        Return:
            list: A list of metric scores.
        """
        pass
    
    def reinit(self):
        r"""Reset :attr:`record` and :attr:`bs` (when an epoch ends).
        """
        self.record = []
        self.bs = []

你好，非常感谢您的工作，但在多卡使用时出现了如下问题想请教： https://github.com/median-research-group/LibMTL/blob/main/LibMTL/weighting/abstract_weighting.py#L43

配置：MGDA+hardParam

另外还有个问题想请教一下：

1. 项目中的weighter类型有哪些和半精度amp训练兼容？ 兼容的话应该在scale之前进行weighting还是scale之后weighting？

When I try to save the trained model i.e. full model using the following command -

torch.save(model, "<path>")

It throws this error

AttributeError: Can't pickle local object 'Trainer._prepare_model.<locals>.MTLmodel'

Trainer class use fails with the error "No module named 'torchvision.models.utils'"

Full stack here - Traceback (most recent call last): File "src/main/pipelines/train_nsfw_mtl.py", line 11, in from LibMTL import Trainer File "/azureml-envs/azureml_8a26314e09753d45d0790003a01faf79/lib/python3.8/site-packages/LibMTL/init.py", line 2, in from . import model File "/azureml-envs/azureml_8a26314e09753d45d0790003a01faf79/lib/python3.8/site-packages/LibMTL/model/init.py", line 1, in from LibMTL.model.resnet import resnet18 File "/azureml-envs/azureml_8a26314e09753d45d0790003a01faf79/lib/python3.8/site-packages/LibMTL/model/resnet.py", line 3, in from torchvision.models.utils import load_state_dict_from_url ModuleNotFoundError: No module named 'torchvision.models.utils'

The issue is fixed by using 'torch.hub' instead.

Hi, I found that the value -0.5 was used when initializing the parameter in line 19 of uw.py. My doubt is why this value is not 0, since the variable loss_scale is equivalent to log \sigma in the original paper.

self.loss_scale = nn.Parameter(torch.tensor([-0.5]*self.task_num, device=self.device))