Where is the code for Global-to-Global matching? / Why not use pretrained model for TinyFlowNet?

[shoutOutYangJie]

I want to reproduce your result, and want to know how many GPUs you train the model on?
Thank you.

[shoutOutYangJie]

Another question. Your tiny flownet is also trained. Why not use pretrained model? I mean first training a tiny flownet, and then fixing it when train RMNet.

[shoutOutYangJie]

Third question. In your code, you still use global-to-global memory matching. It is strange.

[hzxie]

Thanks for your interest in our work.

1. At first, we use 4 1080Ti GPUs. As the memory usage increases (because our method becomes more complex), we use 2 V100 (32GB) GPUs.
2. Actually, the weights of TinyFlowNet are partially borrowed from FlowNet-S and fine-tuned on the precomputed optical flow (See also The problem of the precomputed optical flow #3).
3. The matrix multiplication is highly optimized in PyTorch. We implement an extension for local-to-local matching with PyTorch API and CUBLAS, but the speed is lower than the one in PyTorch. Moreover, we find that PyTorch is optimized for our situation where there are lots of zeros in matrices.