gmvandeven / class-incremental-learning Goto Github PK

In the paper, you mentiond that MNIST and CIFAR10 are both split into 5 tasks, but I can't find the argument for the number of tasks (i.e., something like add_argument("--tasks", type=int....)) in the file options_gen_classifier.py,

but I did find an argument called --tasks in the file options.py .

Could you please tell me the reason~? Thank you.

flow<compare_all.py --experiment=CORe50 --n-seeds=10 --seed=11 --single-epochs --batch=1 --fc-layers=2 --z-dim=200 --fc-units=1024 --lr=0.0001 --c=10 --lambda=10 --omega-max=0.1 --ar1-c=1. --dg-prop=0. --bir-c=0.01 --si-dg-prop=0.6>

According to the code provided in the file, the result of running Core50 dataset is inconsistent with that in the article( BIR Table2)

Got this error:

internals>", line 200, in argmax
File "/home/19mkn1/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 1242, in argmax
return _wrapfunc(a, 'argmax', axis=axis, out=out, **kwds)
File "/home/19mkn1/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 54, in _wrapfunc
return _wrapit(obj, method, *args, **kwds)
File "/home/19mkn1/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 43, in _wrapit
result = getattr(asarray(obj), method)(*args, **kwds)
File "/home/19mkn1/.local/lib/python3.8/site-packages/torch/_tensor.py", line 678, in array
return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
srun: error: aurora: task 0: Exited with exit code 1

This error occurs in a Python script that is attempting to convert a PyTorch tensor to a NumPy array. However, the tensor is located on a CUDA device (i.e., GPU) rather than on the CPU. PyTorch doesn't support directly converting CUDA tensors to NumPy arrays because NumPy operates on CPU memory.

The error message specifically suggests using Tensor.cpu() to copy the tensor to host memory first, before converting it to a NumPy array. Here's how you can fix it:

import torch

# Assuming 'cuda_tensor' is your PyTorch tensor on the CUDA device
cuda_tensor = torch.tensor([1, 2, 3]).cuda()

# Move the tensor to CPU
cpu_tensor = cuda_tensor.cpu()

# Now you can convert it to a NumPy array
numpy_array = cpu_tensor.numpy()

By first moving the tensor to the CPU using cpu(), you can then safely convert it to a NumPy array.

Hi Gido,

Does this method have a drawback of a too large model size for large label space like ImageNet?

Hi,

Nice to meet you.
May I know is this code is applicable to online incremental learning? Because after i read your paper, the paper mentions online incremental learning.

Thanks if you could assist me :D