Comments (10)
from flownet2-pytorch.
OK PyTorch is fixing lots of api inconsistencies, thanks for the notice.
from flownet2-pytorch.
Could someone also explain what is the benefits of using the static method in new Pytorch version and what is ctx?
from flownet2-pytorch.
Hey! Im also interested in running this on python 3+ and pytorch 0.3
At the moment I dont have enough free time to fiddle with it myself so im going to keep an eye on this issue.
I dont know what the benefit of using the static method is as that is the only one I have used.
As for ctx, that is the context. The context you recieve in the forward pass of a function is the same context object that you recieve for the backward pass. You can for example save tensors in the forward pass that you are going to need in your backward pass in the ctx variable.
from flownet2-pytorch.
Hi, I meet a simiar problem in another package, May I ask which line in the commit #35 solve this problem? Thank you! @hellock
from flownet2-pytorch.
Hi @lfz ,
ChannelNorm_cuda_forward
and ChannelNorm_cuda_backward
should accept Tensor
instead of Variable
as arguments.
So the problem is solved by changing to
channelnorm.ChannelNorm_cuda_backward(input1, output, grad_output.data,
grad_input1.data, ctx.norm_deg)
from flownet2-pytorch.
does it influence the final output, I mean, the input is now a tensor, so is its output changed to a tensor too?
from flownet2-pytorch.
The final output of backward function such as grad_input1
should be a Variable. You can refer to #35 for details.
from flownet2-pytorch.
from flownet2-pytorch.
we've added a python3 branch https://github.com/NVIDIA/flownet2-pytorch/tree/python36
from flownet2-pytorch.
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