Comments (8)
I successfully coded and decoded in pytorch1.7, and the weight trained in pytorch1.7 appeared noise in the images after encoding and decoding.The weight trained on pytorch1.1 is no problem after encoding and decoding with pytorch1.7. Do you have any suggestions for me to find the problem? Thank you very much.
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AT_CHECK
is deprecated, use TORCH_CHECK
.
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Is there any hope of training on Pytorch1.7? My video card is a series 30
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Probably easier to just create an environment with PyTorch 1.1, or is this not possible?
from l3c-pytorch.
I successfully coded and decoded in pytorch1.7, and the weight trained in pytorch1.7 appeared noise in the images after encoding and decoding.The weight trained on pytorch1.1 is no problem after encoding and decoding with pytorch1.7. Do you have any suggestions for me to find the problem? Thank you very much.
It seems I encountered same problems with you, I have to use pytorch 1.7 with RTX 3090. The decoded PNG files seems to be noisy.
Do you have any solution to this?
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It seems I encountered same problems with you, I have to use pytorch 1.7 with RTX 3090. The decoded PNG files seems to be noisy. Do you have any solution to this?
I tried to find it but couldn't find it. There is probably a problem with the weights saved during training, because it is correct to run forward images on Pytorch1.7 using the weights published by the author.
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I successfully coded and decoded in pytorch1.7, and the weight trained in pytorch1.7 appeared noise in the images after encoding and decoding.The weight trained on pytorch1.1 is no problem after encoding and decoding with pytorch1.7. Do you have any suggestions for me to find the problem? Thank you very much.
It seems I encountered same problems with you, I have to use pytorch 1.7 with RTX 3090. The decoded PNG files seems to be noisy. Do you have any solution to this?
![]()
@fab-jul Please Dr. Fabian, Would you please give some comments how to debug this problem(noisy decoded images)?
Should it be a problem with torchac, the encoder/decoder part, or save/restore part?
Thanks a lot.
from l3c-pytorch.
It seems I encountered same problems with you, I have to use pytorch 1.7 with RTX 3090. The decoded PNG files seems to be noisy. Do you have any solution to this?
I tried to find it but couldn't find it. There is probably a problem with the weights saved during training, because it is correct to run forward images on Pytorch1.7 using the weights published by the author.
I made some experiments between 2 servers, A with pytorch 1.1, RTX 2060, B with pytroch 1.7, RTX 3090.
model A generated from server A
model B generated from server B using same source code with A
model A can be used to encoding/decoding in both server A and B.
model B can not perform encoding/decoding, even in server B, the decoded images are noisy.
I tried to downgrade pytorch to 1.1 in server B, but problem still not solved.
I have no idea how to debug this problem anymore, so there is no hope to run this code in 3090 GPU?
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Related Issues (20)
- AssertionError: Expected a base dir for every component, got ['0524_0001'] and ['configs/ms', 'configs/dl'] HOT 8
- The loss value remained around 5 HOT 4
- questions about the probability prediction part in the paper HOT 1
- Question about the computation of cdf in the code HOT 13
- torchac: how to find <EOM> to separate bytes strings (channels) ? HOT 1
- get errors when compress image HOT 2
- run train.py error HOT 7
- questions about AC code
- weird problem when encoding HOT 1
- Question about CDF function and AC code HOT 2
- CDF function and model p HOT 3
- Error while decompressing HOT 4
- AttributeError: module 'torchac.torchac' has no attribute 'CUDA_SUPPORTED' HOT 2
- Size of compressed file HOT 1
- Evaluation Sets
- How to modify this code to enable compression of 16Bit Images HOT 1
- Running time
- python .\setup.py install faill HOT 1
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