zhoudw-zdw / cil_survey Goto Github PK

Thanks for your sharing!
I'd be grateful if you could help me with the bug when reproducing the der.json & foster.json on VTab.

Details belike:
File "LAMDA-PILOT-main/models/base.py", line 260, in _construct_exemplar
i = np.argmin(np.sqrt(np.sum((class_mean - mu_p) ** 2, axis=1)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "envs/pilot/lib/python3.11/site-packages/numpy/core/fromnumeric.py", line 1325, in argmin
return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pilot/lib/python3.11/site-packages/numpy/core/fromnumeric.py", line 59, in _wrapfunc
return bound(*args, **kwds)
^^^^^^^^^^^^^^^^^^^^
ValueError: attempt to get argmin of an empty sequence

Hi.

Thank you for this great Library for Continual Learning.

I wanted to ask regarding the label mapping when we are at the task 2 of class incremental training. Let's say I am training a simple finetuning model.

I am training on CIFAR100 on an increment of 10 classes in each task. The first training will have labels [0,1,2,3,4,5,6,7,8,9]. For the second task, the labels are [10,11,12,13,14,15,16,17,18,19]. Do you map these to [0,1,2,3,4,5,6,7,8,9] or use some other loss function instead of cross-entropy? If you map it, can you please point out the code piece. And how do you handle this at inference time?

Thanks in Advance.

I got a problems when using your code in different datasets. The loss reduced to 0 but the training accuracy did not increase. This is because of only using partial data /tensor in the last layers (fake target implementation). That leads to loss equals while the prediction results is not correct. So this affect methods using custom losses (finetune, lwf, memo, ...)

For example, I set number of classes = 2, this is the task 3 (training on classes 4-5)

from torch import Tensor
import torch

from torch.nn import functional as F
tensor = Tensor(
[
    [-3.6737,   3.6739,   7.9670,  -6.8142,   7.5660,  -7.4853],
    [-3.8698,   3.8700,   8.3494,  -7.1354,   7.9662,  -7.8834],
    [3.0694,  -3.0693,  -6.3453,   4.8575, -14.1109,  14.8372],
    [3.0781,  -3.0780,  -6.3650,   4.8734, -14.1494,  14.8778],
    [-3.7971,   3.7972,   8.2072,  -7.0160,   7.8165,  -7.7347],
    [-3.7736,   3.7738,   8.1614,  -6.9775,   7.7684,  -7.6869],
    [-3.7105,   3.7107,   8.0386,  -6.8743,   7.6404,  -7.5594],
    [-3.7717,   3.7719,   8.1577,  -6.9744,   7.7645,  -7.6830],
    [-3.8698,   3.8700,   8.3495,  -7.1355,   7.9662,  -7.8834],
    [3.1876,  -3.1874,  -6.6086,   5.0743, -14.6059,  15.3588],
    [-3.7871,   3.7873,   8.1883,  -7.0001,   7.7980,  -7.7159],
    [3.1329,  -3.1327,  -6.4858,   4.9743, -14.3710,  15.1113],
    [3.1295,  -3.1293,  -6.4794,   4.9677, -14.3636,  15.1034],
    [-3.6428,   3.6429,   7.9025,  -6.7601,   7.4887,  -7.4108],
    [3.0746,  -3.0745,  -6.3574,   4.8670, -14.1357,  14.8634],
    [3.2236,  -3.2235,  -6.6936,   5.1387, -14.7841,  15.5461],
    [3.1713,  -3.1712,  -6.5750,   5.0436, -14.5523,  15.3020],
    [-3.7287,   3.7289,   8.0736,  -6.9037,   7.6759,  -7.5950],
    [3.0622,  -3.0621,  -6.3294,   4.8443, -14.0822,  14.8070],
    [3.2201,  -3.2200,  -6.6833,   5.1333, -14.7547,  15.5154],
    [-3.4313,   3.4315,   7.4917,  -6.4150,   7.0624,  -6.9858],
    [-3.6644,   3.6645,   7.9488,  -6.7989,   7.5468,  -7.4662]
]
)
target = Tensor(
    [4, 4, 5, 5, 4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 5, 5, 5, 4, 5, 5, 4, 4]
)
print(target)
prediction = torch.max(tensor, dim=1)
print(prediction)
target = target.to(dtype=torch.int64)
fake_target = target - 4
print(F.cross_entropy(tensor[:, 4:], fake_target))

Sorry. How do I draw the result graph in the resources folder? I can't find the corresponding code.

Hi, I found that the 'increment' parameter of the 'accuracy' method in 'toolkit.py' is always 10 by default. Shall the actual increment number be passed to it when calling the method in the base model?

My advice code for "base.py":

self._increment = args["increment"]
  
def _evaluate(self, y_pred, y_true):
      ret = {}
      grouped = accuracy(y_pred.T[0], y_true, self._known_classes, self._increment)

Hello, I think your work is very meaningful!
I would like to ask you about the 16 selected methods mentioned in your paper, including the methods using transformer structure, such as dytox, l2p and dualprompt, which have not been included in this project. How can I reproduce the comparison of these parts? Is there any plan to release it later?

Looking forward to your reply, best wishes!