Comments (1)
It seems like that the gradient calculation on network is correct, but the hash encoding is not correct. One can do backward multiple times for different losses to get better results, but it slows down the training. Is there any solution for this?
from tiny-cuda-nn.
Related Issues (20)
- initiailization of hash grid
- tinycudann ImportError: tinycudann_bindings/_80_C.cpython-38-x86_64-linux-gnu.so: undefined symbol: HOT 2
- Enable 5D grids HOT 2
- Link Against tiny-cuda-nn in C++ Program HOT 1
- [Question]: can tiny-cuda-nn build a network with layer's bias=0?
- Problems encountered during installation HOT 1
- Already setted the CUDA_HOME but still:CUDA_HOME environment variable is not set. Please set it to your CUDA install root. HOT 1
- Manual installation with torch extension fails: parameter packs not expanding after cmake build success HOT 1
- Tiny cuda nn compilation issue HOT 1
- pt
- pip install of tiny-cuda-nn does not install it in
- tinycudnn not working with conda environment
- pip install:g++ error HOT 1
- Inconsistent number of parameters loading encoding with tiny-cuda-nn pytorch bindings and instant-ngp
- OSError: Could not find compatible tinycudann extension for compute capability 87. HOT 5
- Install inside poetry
- compute compatibility 87 ( jetson orin) HOT 6
- What is the purpose of this software ?
- ERROR: Could not build wheels for tinycudann, which is required to install pyproject.toml-based projects
- error: subprocess-exited-with-error × python setup.py bdist_wheel did not run successfully. │ exit code: 1 ╰─> [186 lines of output] HOT 2
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from tiny-cuda-nn.