zhoudw-zdw/cil_survey

The code repository for "Deep Class-Incremental Learning: A Survey" in PyTorch.

cil_survey's Issues

Fake target in Loss function problems.

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))

BaseLearner method _evaluate missed the increment attribute

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)

About select methods

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!

Accuracy logging information

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?

Labels for task>0

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.