Remove NNlib

Author: pxl-th

Project.toml

@@ -1,6 +1,6 @@
 name = "GaussianSplatting"
 uuid = "991e6b22-92f0-46ac-8b70-8a93e7beee5d"
-version = "1.0.2"
+version = "1.1.0"
 authors = ["Anton Smirnov <[email protected]>"]
 
 [deps]
@@ -22,7 +22,6 @@ ImageTransformations = "02fcd773-0e25-5acc-982a-7f6622650795"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 ModernGL = "66fc600b-dfda-50eb-8b99-91cfa97b1301"
-NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 NerfUtils = "99c1d5ce-7c61-4a25-a107-a5ade2e2a8e4"
 NeuralGraphicsGL = "263f7e6d-e369-49af-a86e-c85638573b76"
@@ -63,7 +62,6 @@ ImageMagick = "1.3"
 ImageTransformations = "0.10"
 KernelAbstractions = "0.9.34"
 ModernGL = "1.1"
-NNlib = "0.9"
 NearestNeighbors = "0.4"
 NerfUtils = "0.2"
 NeuralGraphicsGL = "0.5"
@@ -73,5 +71,5 @@ Rotations = "1.7"
 SIMD = "3.6"
 StaticArrays = "1.9"
 VideoIO = "1.1"
-Zygote = "=0.7.3"
+Zygote = "0.7"
 julia = "1.10"

benchmark/pipeline.jl

@@ -1,7 +1,6 @@
 import GaussianSplatting as GSP
 
-function main(dataset_path::String; scale::Int)
-    kab = GSP.gpu_backend()
+function benchmark(kab, dataset_path::String; scale::Int)
     @info "Using `$kab` GPU backend."
 
     dataset = GSP.ColmapDataset(kab, dataset_path; scale,
@@ -31,4 +30,5 @@ function main(dataset_path::String; scale::Int)
     end
     return
 end
-main("/home/pxlth/Downloads/360_v2/bicycle"; scale=4)
+benchmark(ROCBackend(), "/home/pxlth/Downloads/360_v2/bicycle"; scale=4)
+# benchmark(CUDABackend(), "/home/pxl-th/Downloads/360_v2/bicycle"; scale=4)

src/GaussianSplatting.jl

@@ -33,7 +33,6 @@ import CImGui.lib as iglib
 
 import BSON
 import ChainRulesCore as CRC
-import NNlib
 import ImageFiltering
 import KernelAbstractions as KA
 import NerfUtils as NU

src/rasterization/rasterizer.jl

@@ -149,7 +149,7 @@ function (rast::GaussianRasterizer)(
     uncertainties::Maybe{AbstractMatrix{Float32}} = nothing,
 )
     # If rendering outside AD, use non-allocating path.
-    within_AD = NNlib.within_gradient(means_3d)
+    within_AD = within_gradient(means_3d)
 
     shs = if within_AD
         isempty(sh_remainder) ? sh_color : hcat(sh_color, sh_remainder)