kuc2477 / pytorch-deep-generative-replay Goto Github PK

Can incremental learning be achieved, that is to say, if a model can recognize a picture as a cat or a dog, it is necessary to add new categories on this basis and only train new categories without retraining all data?

I had problems running the code initially. The first problem that I encountered was "ImportError: cannot import name 'ImageOps' ". I solved it by changing it to "from PIL import ImageOps". The second problem I had has something to do with visdom which I solved it by removing all related codes.
Hopefully the above information help someone who would like to run this code.

If anyone can provide codes that are compatible with newer PyTorch (either 0.4.1 or 1.0), Please share it !

not in real life,
in minecraft

Kindly help as to how to extract more than two variables (x,y) using default_collate in label_squeezing_collate_fn in utils.py.

During the periodic evaluation, the solver model is utilized but the model is still in .train() mode. It might affect the training process.

I found that training loss for generator and critic will quickly explode and the accuracy can't achieve the one represented in the paper. Is it because unfit training hyperparameter? Btw, I modified the code to make it trainable with newer version pytorch.

Hi,
I am facing the foll. issue using the pytorch, torchvision, and PIL versions (because of compatibility requirement with cuda 10.1) as:
torch: 1.4.0
torchvision: 0.5.0
PIL: 5.2.0

Traceback (most recent call last):
File "./main.py", line 186, in
cuda=cuda
File "/home/js/DGR_pytorch/train.py", line 102, in train
collate_fn=collate_fn,
File "/home/js/DGR_pytorch/dgr.py", line 130, in train_with_replay
collate_fn=collate_fn,
File "/home/js/DGR_pytorch/dgr.py", line 205, in _train_batch_trainable_with_replay
callback(trainable, progress, batch_index, result)
File "/home/js/DGR_pytorch/train.py", line 157, in cb
result['g_loss'], 'generator g loss', iteration, env=env
File "/home/js/DGR_pytorch/visual.py", line 87, in visualize_scalar
[name], name, iteration, env=env
File "/home/js/DGR_pytorch/visual.py", line 92, in visualize_scalars
assert len(scalars) == len(names)
File "/home/js/anaconda3/envs/env_con/lib/python3.5/site-packages/torch/tensor.py", line 445, in len
raise TypeError("len() of a 0-d tensor")
TypeError: len() of a 0-d tensor

Kindly suggest an alternative. Thank you.