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Hi, thanks for the code you provided, I solved a lot of things in this code. However, the following problem occurred when I used my own D-bit data set. I checked many parts of the code that could be modified, but still couldn't find a solution, so please help me to answer this problem, thank you very much.
Traceback (most recent call last):
File "/home/root/FSGS/train.py", line 282, in
training(lp.extract(args), op.extract(args), pp.extract(args), args)
File "/home/root/FSGS/train.py", line 139, in training
loss.backward()
File "/opt/anaconda3/envs/3dgs/lib/python3.9/site-packages/torch/_tensor.py", line 522, in backward
torch.autograd.backward(
File "/opt/anaconda3/envs/3dgs/lib/python3.9/site-packages/torch/autograd/init.py", line 266, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Function _RasterizeGaussiansBackward returned an invalid gradient at index 2 - got [0, 0, 3] but expected shape compatible with [0, 16, 3]
Training progress: 0%| | 0/10000 [00:00<?, ?it/s]
The text was updated successfully, but these errors were encountered:
Loading Training Cameras [02/12 15:53:13]
24it [00:00, 44.71it/s]
Loading Test Cameras [02/12 15:53:13]
35it [00:00, 64.50it/s]
Number of points at initialisation : 0 [02/12 15:53:16]
Hi, thanks for the code you provided, I solved a lot of things in this code. However, the following problem occurred when I used my own D-bit data set. I checked many parts of the code that could be modified, but still couldn't find a solution, so please help me to answer this problem, thank you very much.
Traceback (most recent call last):
File "/home/root/FSGS/train.py", line 282, in
training(lp.extract(args), op.extract(args), pp.extract(args), args)
File "/home/root/FSGS/train.py", line 139, in training
loss.backward()
File "/opt/anaconda3/envs/3dgs/lib/python3.9/site-packages/torch/_tensor.py", line 522, in backward
torch.autograd.backward(
File "/opt/anaconda3/envs/3dgs/lib/python3.9/site-packages/torch/autograd/init.py", line 266, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Function _RasterizeGaussiansBackward returned an invalid gradient at index 2 - got [0, 0, 3] but expected shape compatible with [0, 16, 3]
Training progress: 0%| | 0/10000 [00:00<?, ?it/s]
The text was updated successfully, but these errors were encountered: