Comments (6)
Using sparse convolutions only makes sense if the input is spatially sparse. What is your input?
The output of dense convolutions will not be sparse, so you should not use dense convolutions followed by sparse convolutions.
from sparseconvnet.
Hi, thanks for your reply.
My input is actually sparse. In my network, after some convolutional layers, I apply a binary mask to the pixels having a low probability to be classified correctly, and I propagate only that set of pixels to the deeper layers. I would like to apply the sparse convolution in those layers.
from sparseconvnet.
That might work. But the learning signal is the dense layers will be limited to the sites that are not filtered out, which could be problematic.
Can you not use sparse filters end-to-end?
from sparseconvnet.
I think that this should not be an issue, correct me if I'm wrong. I'll give you some more information about my network, so that you could better understand what I'm trying to do.
After the application of the binary mask to the input, the filtered pixels are not lost, but they are directly connected to a layer where I overlap the results of the sparse convolution (after the application of a SparseToDense module) and the dense convolutive layers. In this way, I could use the last part of my network (the sparse one) to learn difficult features in an efficient way, and the first part (the dense one) to learn both difficult and easy features. Joining the results of the two classification, I should be able to backpropagate the learning signals for both the sites with difficult and easy classification. What do you think about it?
from sparseconvnet.
Could I simply implement a layer where I map my input to an InputBatch (in the updateOutput function), like you do in your example, and the sparse gradOutput to a dense gradInput (in the updateGradInput)?
from sparseconvnet.
Please contact me at [email protected] so we can discuss this in more detail.
Regards
Ben
from sparseconvnet.
Related Issues (20)
- Some questions about operational efficiency
- About the parameters of InputLayer HOT 3
- Dense to Sparse for input is quite slow
- AttributeError: module 'sparseconvnet.SCN' has no attribute 'Metadata_2' HOT 3
- RuntimeError: CUDA error: an illegal memory access was encountered HOT 2
- voxel input HOT 1
- Cloning face an error
- undefined symbol: _ZNSt15__exception_ptr13exception_ptr10_M_releaseEv HOT 2
- How to compute FLOPs for spraseconvnet HOT 3
- RuntimeError: expected scalar type Long but found Float HOT 5
- Building failure related to gcc version
- RuntimeError: expected scalar type Long but found Float
- Dilated convolution HOT 1
- Directly applying convolution HOT 1
- Rewrite for convolution operation
- Output with empty tensor
- setup.py中的C++17要改成C++14
- question from paper HOT 2
- Segment Fault due to resolution
- RuntimeError: Error compiling objects for extension
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