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Correlation module for pytorch

Project description

PyPI

Pytorch Correlation module

this is a custom C++/Cuda implementation of Correlation module, used e.g. in FlowNetC

This tutorial was used as a basis for implementation, as well as NVIDIA's cuda code

  • Build and Install C++ and CUDA extensions by executing python setup.py install,
  • Benchmark C++ vs. CUDA by running python benchmark.py {cpu, cuda},
  • Run gradient checks on the code by running python grad_check.py --backend {cpu, cuda}.

Requirements

This module is expected to compile for Pytorch 1.2, on Python > 3.5 and Python 2.7.

Installation

this module is available on pip

pip install spatial-correlation-sampler

For a cpu-only version, you can install from source with

python setup_cpu.py install

Known Problems

This module needs compatible gcc version and CUDA to be compiled. Namely, CUDA 9.1 and below will need gcc5, while CUDA 9.2 and 10.0 will need gcc7 See this issue for more information

Usage

API has a few difference with NVIDIA's module

  • output is now a 5D tensor, which reflects the shifts horizontal and vertical.
input (B x C x H x W) -> output (B x PatchH x PatchW x oH x oW)
  • Output sizes oH and oW are no longer dependant of patch size, but only of kernel size and padding
  • Patch size patch_size is now the whole patch, and not only the radii.
  • stride1 is now stride andstride2 is dilation_patch, which behave like dilated convolutions
  • equivalent max_displacement is then dilation_patch * (patch_size - 1) / 2.
  • to get the right parameters for FlowNetC, you would have
kernel_size=1
patch_size=21,
stride=1,
padding=0,
dilation_patch=2

Benchmark

  • default parameters are from benchmark.py, FlowNetC parameters are same as use in FlowNetC with a batch size of 4, described in this paper, implemented here and here.
  • Feel free to file an issue to add entries to this with your hardware !

CUDA Benchmark

  • See here for a benchmark script working with NVIDIA's code, and Pytorch.
  • Benchmark are launched with environment variable CUDA_LAUNCH_BLOCKING set to 1.
  • Only float32 is benchmarked.
  • FlowNetC correlation parameters where launched with the following command:
CUDA_LAUNCH_BLOCKING=1 python benchmark.py --scale ms -k1 --patch 21 -s1 -p0 --patch_dilation 2 -b4 --height 48 --width 64 -c256 cuda

CUDA_LAUNCH_BLOCKING=1 python NV_correlation_benchmark.py --scale ms -k1 --patch 21 -s1 -p0 --patch_dilation 2 -b4 --height 48 --width 64 -c256
implementation Correlation parameters device pass min time avg time
ours default 980 GTX forward 5.745 ms 5.851 ms
ours default 980 GTX backward 77.694 ms 77.957 ms
NVIDIA default 980 GTX forward 13.779 ms 13.853 ms
NVIDIA default 980 GTX backward 73.383 ms 73.708 ms
ours FlowNetC 980 GTX forward 26.102 ms 26.179 ms
ours FlowNetC 980 GTX backward 208.091 ms 208.510 ms
NVIDIA FlowNetC 980 GTX forward 35.363 ms 35.550 ms
NVIDIA FlowNetC 980 GTX backward 283.748 ms 284.346 ms

Notes

  • The overhead of our implementation regarding kernel_size > 1 during backward needs some investigation, feel free to dive in the code to improve it !
  • The backward pass of NVIDIA is not entirely correct when stride1 > 1 and kernel_size > 1, because not everything is computed, see here.

CPU Benchmark

  • No other implementation is avalaible on CPU.
  • It is obviously not recommended to run it on CPU if you have a GPU.
Correlation parameters device pass min time avg time
default E5-2630 v3 @ 2.40GHz forward 159.616 ms 188.727 ms
default E5-2630 v3 @ 2.40GHz backward 282.641 ms 294.194 ms
FlowNetC E5-2630 v3 @ 2.40GHz forward 2.138 s 2.144 s
FlowNetC E5-2630 v3 @ 2.40GHz backward 7.006 s 7.075 s

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