I have to add the following code in renderocc.py's function 'simple_test' and 'forward_train' to run the training process without error:
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor
code:
def forward_train(self,
points=None,
img_metas=None,
img_inputs=None,
**kwargs):
for i in range(len(img_inputs)):
img_inputs[i] = img_inputs[i].to('cuda')
...
But i got the exact same code as the git repo.
there is another issue in swin-transformer which is also confused:
/home/lkshpc/miniconda3/envs/renderocc/lib/python3.8/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
I have to add the following code in renderocc.py's function 'simple_test' and 'forward_train' to run the training process without error: RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor
code:
But i got the exact same code as the git repo.
there is another issue in swin-transformer which is also confused:
/home/lkshpc/miniconda3/envs/renderocc/lib/python3.8/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None