Comments (3)
hi @shwoo93 , the library meant to work with GPU/cuda and cpu, in https://github.com/oeway/pytorch-deform-conv/blob/master/scaled_mnist.py you can see we use it in cuda mode, and when I test it, I use cuda as well, I am sure it will work with GPU.
Plus, I couldn't find torch.range
in my code, perhaps it from your own code?
from pytorch-deform-conv.
@oeway Thanks for reply.
Actually in the deform_conv.py file there is a function called "th_batch_map_coordinates" which uses torch.range.
Anyway, you mean that there is no problem in usage of it and also did not the error occured in torch.range?
from pytorch-deform-conv.
hi, that's right, there is indeed a torch.range:
idx = th_repeat(torch.range(0, batch_size-1), n_coords).long()
idx = Variable(idx, requires_grad=False)
if input.is_cuda:
idx = idx.cuda()
however, if you read the code, you will found that my torch.range
is actually working in cpu mode, and after it may convert to cuda if the input is in cuda mode. So it still doesn't mean it caused by torch.range
.
Come back to your question, yes, we have no problem when running in cuda mode.
from pytorch-deform-conv.
Related Issues (20)
- Question about implementation HOT 1
- Input image w!=h HOT 5
- Gif image visualization HOT 6
- Offset output channel HOT 1
- What is the size of the offset? HOT 3
- A re-train normal CNN on scaled data beats deform-conv. HOT 3
- about the implementation.. view instead of permute ? HOT 5
- Confusion about the shape of offset HOT 7
- Why only fine tuning on deformable convnet HOT 1
- Weighting in deformed kernel
- Fixed
- only for test, cannot be used for training
- The deform-conv layers reduce the detection accuracy
- Offset BUG HOT 1
- How to get the sampling points?
- related to TF_version #4
- related to TF_version #4
- related to TF_version #4
- Process finished with exit code 139 (interrupted by signal 11: SIGSEGV) HOT 1
- Deformable Pooling is not included HOT 1
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from pytorch-deform-conv.