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[NeurIPS 2021 | AIJ 2024] Multi-Objective Meta Learning
Dear author:
FSL的代码能公布吗,想学习一下
Thanks very much for sharing this work. I have two questions when executing this code.
python ./MTL/moml_office.py --gpu_id 1 --model DMTL --dataset office-31 --batchsize 64 --MGDA --dataroot /path/to/office-31
It reported the following error:
TypeError: unsupported operand type(s) for *: 'float' and 'NoneType'
on line 84 p_fast = p - 1e-4 * grads[g_index]
.
It seems that there are some inputs are not used for gradient calculation on line 80 grads = torch.autograd.grad(loss, (meta_model.parameters()), create_graph=True, allow_unused=True)
. What could be that?
python ./MTL/moml_office.py --gpu_id 1 --model DMTL --dataset office-31 --batchsize 64 --MGDA --dataroot /path/to/office-31
,I saw that there are four data modes used: train
, val
, test
, trval
. What are their purposes? It seems that some parts of the network are trained purely by gradient descent with train
, while the head is trained by MOML with val
and trval
. Am I right?
Thanks!
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