Custom implementation of Corrleation 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
python setup.py install
,python benchmark.py {cpu, cuda}
,python grad_check.py --backend {cpu, cuda}
.This module is expected to compile for Pytorch 2.1.0
.
Before installation please check compatibility of your GPU and CUDA (Compute Capability) nvidia docs. e.g RTX 6000 is using CC=8.9 so we are setting the environment variable to
export TORCH_CUDA_ARCH_LIST="8.9+PTX"
be reminded this module requires python3-dev
to compile C++ code, e.g. on Ubuntu run:
apt install python3-dev
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
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
API has a few difference with NVIDIA's module
input (B x C x H x W) -> output (B x PatchH x PatchW x oH x oW)
oH
and oW
are no longer dependant of patch size, but only of kernel size and paddingpatch_size
is now the whole patch, and not only the radii.stride1
is now stride
andstride2
is dilation_patch
, which behave like dilated convolutionsmax_displacement
is then dilation_patch * (patch_size - 1) / 2
.dilation
is a new parameter, it acts the same way as dilated convolution regarding the correlation kernelkernel_size=1
patch_size=21,
stride=1,
padding=0,
dilation=1
dilation_patch=2
import torch
from spatial_correlation_sampler import SpatialCorrelationSampler, spatial_correlation_sample
device = "cuda"
batch_size = 1
channel = 1
H = 10
W = 10
dtype = torch.float32
input1 = torch.randint(1, 4, (batch_size, channel, H, W), dtype=dtype, device=device, requires_grad=True)
input2 = torch.randint_like(input1, 1, 4).requires_grad_(True)
#You can either use the function or the module. Note that the module doesn't contain any parameter tensor.
#function
out = spatial_correlation_sample(input1,
input2,
kernel_size=3,
patch_size=1,
stride=2,
padding=0,
dilation=2,
dilation_patch=1)
#module
correlation_sampler = SpatialCorrelationSampler(
kernel_size=3,
patch_size=1,
stride=2,
padding=0,
dilation=2,
dilation_patch=1)
out = correlation_sampler(input1, input2)
benchmark.py
, FlowNetC parameters are same as use in FlowNetC
with a batch size of 4, described in this paper, implemented here and here.CUDA_LAUNCH_BLOCKING
set to 1
.float32
is benchmarked.CUDA_LAUNCH_BLOCKING=1 python benchmark.py --scale ms -k1 --patch 21 -s1 -p0 --patch_dilation 2 -b4 --height 48 --width 64 -c256 cuda -d float
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 |
kernel_size
> 1 during backward needs some investigation, feel free to
dive in the code to improve it !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 |