Package evaluation of InvertibleNetworks on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T21:21:30.192 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.5s ################################################################################ # Installation # Installing InvertibleNetworks... Resolving package versions... Installed PyCall ─ v1.96.4 Installed Conda ── v1.10.2 Updating `~/.julia/environments/v1.11/Project.toml` [b7115f24] + InvertibleNetworks v2.3.0 Updating `~/.julia/environments/v1.11/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [7f219486] + AbstractNFFTs v0.8.2 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.1 [ab4f0b2a] + BFloat16s v0.5.1 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [26cce99e] + BasicInterpolators v0.7.1 [fa961155] + CEnum v0.5.0 [052768ef] + CUDA v5.7.2 [1af6417a] + CUDA_Runtime_Discovery v0.3.5 [082447d4] + ChainRules v1.72.3 [d360d2e6] + ChainRulesCore v1.25.1 [3da002f7] + ColorTypes v0.12.1 [5ae59095] + Colors v0.13.0 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [8f4d0f93] + Conda v1.10.2 [187b0558] + ConstructionBase v1.5.8 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 ⌅ [717857b8] + DSP v0.7.10 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [aaf54ef3] + DistributedArrays v0.6.7 [ffbed154] + DocStringExtensions v0.9.4 [e2ba6199] + ExprTools v0.1.10 [7a1cc6ca] + FFTW v1.8.1 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [1a297f60] + FillArrays v1.13.0 [53c48c17] + FixedPointNumbers v0.8.5 ⌅ [587475ba] + Flux v0.14.25 [f6369f11] + ForwardDiff v1.0.1 ⌅ [d9f16b24] + Functors v0.4.12 [0c68f7d7] + GPUArrays v11.2.2 [46192b85] + GPUArraysCore v0.2.0 [61eb1bfa] + GPUCompiler v1.3.2 [096a3bc2] + GPUToolbox v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [7869d1d1] + IRTools v0.4.14 [22cec73e] + InitialValues v0.3.1 [842dd82b] + InlineStrings v1.4.3 [505f98c9] + InplaceOps v0.3.0 [18e54dd8] + IntegerMathUtils v0.1.2 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [b7115f24] + InvertibleNetworks v2.3.0 [92d709cd] + IrrationalConstants v0.2.4 [c8e1da08] + IterTools v1.10.0 [42fd0dbc] + IterativeSolvers v0.9.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 ⌅ [bb331ad6] + JOLI v0.8.5 [682c06a0] + JSON v0.21.4 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.34 [929cbde3] + LLVM v9.2.0 [8b046642] + LLVMLoopInfo v1.0.0 [b964fa9f] + LaTeXStrings v1.4.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.15 [c03570c3] + Memoize v0.4.4 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [efe261a4] + NFFT v0.13.7 [872c559c] + NNlib v0.9.30 [5da4648a] + NVTX v1.0.0 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [4d1e1d77] + Nullables v1.0.0 [0b1bfda6] + OneHotArrays v0.2.7 ⌅ [3bd65402] + Optimisers v0.3.4 [bac558e1] + OrderedCollections v1.8.0 [69de0a69] + Parsers v2.8.1 [f27b6e38] + Polynomials v4.0.19 [2dfb63ee] + PooledArrays v1.4.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v2.4.0 [27ebfcd6] + Primes v0.5.7 [33c8b6b6] + ProgressLogging v0.1.4 [43287f4e] + PtrArrays v1.3.0 [438e738f] + PyCall v1.96.4 [74087812] + Random123 v1.7.0 [e6cf234a] + RandomNumbers v1.6.0 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [7e506255] + ScopedValues v1.3.0 [6c6a2e73] + Scratch v1.2.1 [91c51154] + SentinelArrays v1.4.8 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.5.0 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [a759f4b9] + TimerOutputs v0.5.28 [28d57a85] + Transducers v0.4.84 [013be700] + UnsafeAtomics v0.3.0 [81def892] + VersionParsing v1.3.0 ⌅ [29a6e085] + Wavelets v0.9.5 ⌅ [e88e6eb3] + Zygote v0.6.76 [700de1a5] + ZygoteRules v0.2.7 [02a925ec] + cuDNN v1.4.2 [4ee394cb] + CUDA_Driver_jll v0.12.1+1 [76a88914] + CUDA_Runtime_jll v0.16.1+0 ⌅ [62b44479] + CUDNN_jll v9.4.0+0 [f5851436] + FFTW_jll v3.3.11+0 [1d5cc7b8] + IntelOpenMP_jll v2025.0.4+0 [9c1d0b0a] + JuliaNVTXCallbacks_jll v0.2.1+0 [dad2f222] + LLVMExtra_jll v0.0.35+0 [856f044c] + MKL_jll v2025.0.1+1 [e98f9f5b] + NVTX_jll v3.1.1+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [1e29f10c] + demumble_jll v1.3.0+0 [1317d2d5] + oneTBB_jll v2022.0.0+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.7.2+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+0 [14a3606d] + MozillaCACerts_jll v2023.12.12 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Building Conda ─→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b19db3927f0db4151cb86d073689f2428e524576/build.log` Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` Installation completed after 77.43s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 678.97s ################################################################################ # Testing # Testing InvertibleNetworks Status `/tmp/jl_yWm2o1/Project.toml` [052768ef] CUDA v5.7.2 [d360d2e6] ChainRulesCore v1.25.1 ⌅ [587475ba] Flux v0.14.25 [b7115f24] InvertibleNetworks v2.3.0 ⌅ [bb331ad6] JOLI v0.8.5 [872c559c] NNlib v0.9.30 [10745b16] Statistics v1.11.1 [a759f4b9] TimerOutputs v0.5.28 ⌅ [29a6e085] Wavelets v0.9.5 [02a925ec] cuDNN v1.4.2 [37e2e46d] LinearAlgebra v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_yWm2o1/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7f219486] AbstractNFFTs v0.8.2 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [a9b6321e] Atomix v1.1.1 [ab4f0b2a] BFloat16s v0.5.1 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [26cce99e] BasicInterpolators v0.7.1 [fa961155] CEnum v0.5.0 [052768ef] CUDA v5.7.2 [1af6417a] CUDA_Runtime_Discovery v0.3.5 [082447d4] ChainRules v1.72.3 [d360d2e6] ChainRulesCore v1.25.1 [3da002f7] ColorTypes v0.12.1 [5ae59095] Colors v0.13.0 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.16.0 [a33af91c] CompositionsBase v0.1.2 [8f4d0f93] Conda v1.10.2 [187b0558] ConstructionBase v1.5.8 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 ⌅ [717857b8] DSP v0.7.10 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [aaf54ef3] DistributedArrays v0.6.7 [ffbed154] DocStringExtensions v0.9.4 [e2ba6199] ExprTools v0.1.10 [7a1cc6ca] FFTW v1.8.1 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [1a297f60] FillArrays v1.13.0 [53c48c17] FixedPointNumbers v0.8.5 ⌅ [587475ba] Flux v0.14.25 [f6369f11] ForwardDiff v1.0.1 ⌅ [d9f16b24] Functors v0.4.12 [0c68f7d7] GPUArrays v11.2.2 [46192b85] GPUArraysCore v0.2.0 [61eb1bfa] GPUCompiler v1.3.2 [096a3bc2] GPUToolbox v0.2.0 [076d061b] HashArrayMappedTries v0.2.0 [7869d1d1] IRTools v0.4.14 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.3 [505f98c9] InplaceOps v0.3.0 [18e54dd8] IntegerMathUtils v0.1.2 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [b7115f24] InvertibleNetworks v2.3.0 [92d709cd] IrrationalConstants v0.2.4 [c8e1da08] IterTools v1.10.0 [42fd0dbc] IterativeSolvers v0.9.4 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.0 ⌅ [bb331ad6] JOLI v0.8.5 [682c06a0] JSON v0.21.4 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.34 [929cbde3] LLVM v9.2.0 [8b046642] LLVMLoopInfo v1.0.0 [b964fa9f] LaTeXStrings v1.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [c2834f40] MLCore v1.0.0 ⌃ [7e8f7934] MLDataDevices v1.5.3 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.15 [c03570c3] Memoize v0.4.4 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [efe261a4] NFFT v0.13.7 [872c559c] NNlib v0.9.30 [5da4648a] NVTX v1.0.0 [77ba4419] NaNMath v1.1.3 [71a1bf82] NameResolution v0.1.5 [4d1e1d77] Nullables v1.0.0 [0b1bfda6] OneHotArrays v0.2.7 ⌅ [3bd65402] Optimisers v0.3.4 [bac558e1] OrderedCollections v1.8.0 [69de0a69] Parsers v2.8.1 [f27b6e38] Polynomials v4.0.19 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v2.4.0 [27ebfcd6] Primes v0.5.7 [33c8b6b6] ProgressLogging v0.1.4 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 [74087812] Random123 v1.7.0 [e6cf234a] RandomNumbers v1.6.0 [c1ae055f] RealDot v0.1.0 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [7e506255] ScopedValues v1.3.0 [6c6a2e73] Scratch v1.2.1 [91c51154] SentinelArrays v1.4.8 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [699a6c99] SimpleTraits v0.9.4 [a2af1166] SortingAlgorithms v1.2.1 [dc90abb0] SparseInverseSubset v0.1.2 [276daf66] SpecialFunctions v2.5.0 [171d559e] SplittablesBase v0.1.15 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.0 [2913bbd2] StatsBase v0.34.4 [892a3eda] StringManipulation v0.4.1 [09ab397b] StructArrays v0.7.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [a759f4b9] TimerOutputs v0.5.28 [28d57a85] Transducers v0.4.84 [013be700] UnsafeAtomics v0.3.0 [81def892] VersionParsing v1.3.0 ⌅ [29a6e085] Wavelets v0.9.5 ⌅ [e88e6eb3] Zygote v0.6.76 [700de1a5] ZygoteRules v0.2.7 [02a925ec] cuDNN v1.4.2 [4ee394cb] CUDA_Driver_jll v0.12.1+1 [76a88914] CUDA_Runtime_jll v0.16.1+0 ⌅ [62b44479] CUDNN_jll v9.4.0+0 [f5851436] FFTW_jll v3.3.11+0 [1d5cc7b8] IntelOpenMP_jll v2025.0.4+0 [9c1d0b0a] JuliaNVTXCallbacks_jll v0.2.1+0 [dad2f222] LLVMExtra_jll v0.0.35+0 [856f044c] MKL_jll v2025.0.1+1 [e98f9f5b] NVTX_jll v3.1.1+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [1e29f10c] demumble_jll v1.3.0+0 [1317d2d5] oneTBB_jll v2022.0.0+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [4af54fe1] LazyArtifacts v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [1a1011a3] SharedArrays v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Gradient test mse loss 211.53394; 23.178146 111.56152; 5.7945175 57.229004; 1.4490166 28.976318; 0.36269188 14.578857; 0.0906477 7.3120117; 0.02274084 Gradient test log likelihood 56.354675; 6.047352 29.689087; 1.5119267 15.222168; 0.3783388 7.7056274; 0.09462595 3.8764038; 0.023722887 1.9438477; 0.0062156916 Gradient test sequential network: input 202.7461; 93.40875 124.763916; 23.313507 68.19702; 5.84169 35.52881; 1.4905472 18.133057; 0.37662125 Gradient test sequential network: parameters 75.61133; 79.49833 21.857422; 23.800924 5.5249023; 6.4966536 1.1948242; 1.6806997 0.18554688; 0.42848462 Jacobian test 120.44234; 22.790457 58.667603; 7.0637865 28.980957; 2.2669199 14.406375; 0.75595796 7.1825814; 0.25975788 Gradient test convolutions 0.0029042512; 1.4587073e-5 0.0014557838; 3.6353013e-6 0.000728786; 9.2358096e-7 0.0003646314; 2.233719e-7 0.00018237531; 5.2081305e-8 9.122491e-5; 1.1212251e-8 Gradient test convolutions 9.886902; 0.04967785 4.9558716; 0.01241827 2.4810486; 0.003096342 1.241272; 0.0008004904 0.6208191; 0.00021713972 0.31048584; 3.2275915e-5 WARNING: Method definition objective(Any, Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_nnlib_convolution.jl:69 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_nnlib_convolution.jl:136. Gradient test convolutions 0.0007138923; 3.594614e-6 0.0003578514; 8.920615e-7 0.00017914921; 2.2251334e-7 8.9630485e-5; 5.5377313e-8 4.483387e-5; 9.062205e-9 2.2418797e-5; 2.6684575e-9 Gradient test convolutions 150.26758; 0.75497437 75.322754; 0.18852234 37.708496; 0.04714203 18.8667; 0.0111198425 9.436523; 0.0023860931 4.7192383; 0.00021648407 Gradient test leaky ReLU 0.14250827; 0.011141017 0.073902786; 0.002921857 0.03766191; 0.0007504113 0.019016504; 0.00018965639 0.009558082; 4.4998713e-5 WARNING: Method definition objective(Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:27 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:69. Gradient test ReLU 0.13978952; 0.010263011 0.072467744; 0.002558522 0.036824167; 0.00068896636 0.018567383; 0.00018918328 0.009328783; 4.9500726e-5 WARNING: Method definition objective(Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:69 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:116. Gradient test Sigmoid 0.03176409; 0.0016666912 0.01629883; 0.0004165601 0.008253574; 0.00010412093 0.0041528493; 2.5998335e-5 0.002082914; 6.509712e-6 WARNING: Method definition objective(Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:116 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:163. Gradient test scaled and shifted Sigmoid 0.008042183; 0.00042197108 0.0041266233; 0.000105453655 0.0020896755; 2.6362948e-5 0.0010514222; 6.5970235e-6 0.00052735955; 1.6500708e-6 WARNING: Method definition objective(Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:163 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:206. Gradient test GaLU 0.062198132; 0.0032479092 0.031911194; 0.00081182644 0.01615858; 0.0002029296 0.008130044; 5.0711446e-5 0.0040777326; 1.2645032e-5 WARNING: Method definition objective(Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:206 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:250. Gradient test Sigmoid2 herehere35.011353; 0.23540115 here17.58255; 0.040826797 here8.803665; 0.008023262 here4.4040985; 0.0017457008 here2.2025452; 0.00037693977 WARNING: Method definition objective(Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:250 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_activations.jl:298. Gradient test ExpClamp 1.0363317; 0.05393648 0.5316529; 0.01348114 0.2691965; 0.0033705235 0.13544083; 0.00084269047 0.0679307; 0.00021106005 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_sequential.jl:67 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_flux.jl:22. ┌ Warning: This type should probably now use `Flux.@layer` instead of `@functor`: ActNorm │ caller = ip:0x0 └ @ Core :-1 ┌ Warning: Assignment to `grads` in soft scope is ambiguous because a global variable by the same name exists: `grads` will be treated as a new local. Disambiguate by using `local grads` to suppress this warning or `global grads` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_flux.jl:41 [ Info: Loss: 0.00034544035 [ Info: Loss: 0.0013099898 [ Info: Loss: 0.0027621435 [ Info: Loss: 0.004498652 [ Info: Loss: 0.006275171 Test Summary: | Pass Total Time Basics | 104 104 14m29.8s Test test_utils/test_objectives.jl | 4 4 43.8s Test test_utils/test_sequential.jl | 13 13 5m24.6s Test test_utils/test_nnlib_convolution.jl | 10 10 41.3s Test test_utils/test_activations.jl | 19 19 1m08.5s Test test_utils/test_squeeze.jl | 19 19 2m12.1s Test test_utils/test_jacobian.jl | 2 2 1.5s Test test_utils/test_chainrules.jl | 2 2 3m10.4s Test test_utils/test_flux.jl | 35 35 1m07.6s ┌ Warning: Assignment to `X` in soft scope is ambiguous because a global variable by the same name exists: `X` will be treated as a new local. Disambiguate by using `local X` to suppress this warning or `global X` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:22 ┌ Warning: Assignment to `X0` in soft scope is ambiguous because a global variable by the same name exists: `X0` will be treated as a new local. Disambiguate by using `local X0` to suppress this warning or `global X0` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:23 ┌ Warning: Assignment to `dX` in soft scope is ambiguous because a global variable by the same name exists: `dX` will be treated as a new local. Disambiguate by using `local dX` to suppress this warning or `global dX` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:24 ┌ Warning: Assignment to `Y` in soft scope is ambiguous because a global variable by the same name exists: `Y` will be treated as a new local. Disambiguate by using `local Y` to suppress this warning or `global Y` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:49 ┌ Warning: Assignment to `f0` in soft scope is ambiguous because a global variable by the same name exists: `f0` will be treated as a new local. Disambiguate by using `local f0` to suppress this warning or `global f0` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:64 ┌ Warning: Assignment to `ΔX` in soft scope is ambiguous because a global variable by the same name exists: `ΔX` will be treated as a new local. Disambiguate by using `local ΔX` to suppress this warning or `global ΔX` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:64 ┌ Warning: Assignment to `h` in soft scope is ambiguous because a global variable by the same name exists: `h` will be treated as a new local. Disambiguate by using `local h` to suppress this warning or `global h` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:65 ┌ Warning: Assignment to `maxiter` in soft scope is ambiguous because a global variable by the same name exists: `maxiter` will be treated as a new local. Disambiguate by using `local maxiter` to suppress this warning or `global maxiter` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:66 ┌ Warning: Assignment to `err1` in soft scope is ambiguous because a global variable by the same name exists: `err1` will be treated as a new local. Disambiguate by using `local err1` to suppress this warning or `global err1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:67 ┌ Warning: Assignment to `err2` in soft scope is ambiguous because a global variable by the same name exists: `err2` will be treated as a new local. Disambiguate by using `local err2` to suppress this warning or `global err2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:68 ┌ Warning: Assignment to `err3` in soft scope is ambiguous because a global variable by the same name exists: `err3` will be treated as a new local. Disambiguate by using `local err3` to suppress this warning or `global err3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:88 ┌ Warning: Assignment to `err4` in soft scope is ambiguous because a global variable by the same name exists: `err4` will be treated as a new local. Disambiguate by using `local err4` to suppress this warning or `global err4` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:89 ┌ Warning: Assignment to `err5` in soft scope is ambiguous because a global variable by the same name exists: `err5` will be treated as a new local. Disambiguate by using `local err5` to suppress this warning or `global err5` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:112 ┌ Warning: Assignment to `err6` in soft scope is ambiguous because a global variable by the same name exists: `err6` will be treated as a new local. Disambiguate by using `local err6` to suppress this warning or `global err6` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:113 ┌ Warning: Assignment to `θ` in soft scope is ambiguous because a global variable by the same name exists: `θ` will be treated as a new local. Disambiguate by using `local θ` to suppress this warning or `global θ` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:141 ┌ Warning: Assignment to `dθ` in soft scope is ambiguous because a global variable by the same name exists: `dθ` will be treated as a new local. Disambiguate by using `local dθ` to suppress this warning or `global dθ` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:145 ┌ Warning: Assignment to `dY` in soft scope is ambiguous because a global variable by the same name exists: `dY` will be treated as a new local. Disambiguate by using `local dY` to suppress this warning or `global dY` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:149 ┌ Warning: Assignment to `dY_` in soft scope is ambiguous because a global variable by the same name exists: `dY_` will be treated as a new local. Disambiguate by using `local dY_` to suppress this warning or `global dY_` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:173 ┌ Warning: Assignment to `dX_` in soft scope is ambiguous because a global variable by the same name exists: `dX_` will be treated as a new local. Disambiguate by using `local dX_` to suppress this warning or `global dX_` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:174 ┌ Warning: Assignment to `dθ_` in soft scope is ambiguous because a global variable by the same name exists: `dθ_` will be treated as a new local. Disambiguate by using `local dθ_` to suppress this warning or `global dθ_` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:174 ┌ Warning: Assignment to `a` in soft scope is ambiguous because a global variable by the same name exists: `a` will be treated as a new local. Disambiguate by using `local a` to suppress this warning or `global a` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:175 ┌ Warning: Assignment to `b` in soft scope is ambiguous because a global variable by the same name exists: `b` will be treated as a new local. Disambiguate by using `local b` to suppress this warning or `global b` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_residual_block.jl:176 Testing activation InvertibleNetworks.ActivationFunction(InvertibleNetworks.ReLU, nothing, InvertibleNetworks.ReLUgrad) Gradient test convolutions 0.045305222; 0.0014301948 0.023012549; 0.00034981035 0.01159358; 9.003002e-5 0.005819738; 2.2596214e-5 0.0029147267; 3.731111e-6 Gradient test convolutions 83.95273; 5.8751755 43.409363; 1.5045891 22.072662; 0.38431358 11.1315; 0.096987724 5.589905; 0.024339199 Gradient test convolutions 29.625458; 0.75168896 14.947876; 0.24069738 7.534088; 0.060198545 3.7821655; 0.014977813 1.8947754; 0.0037962794 Jacobian test 10.056957; 2.4778755 5.0143714; 0.83906484 2.5038686; 0.28022856 1.2519344; 0.096704446 0.6255761; 0.03256906 Testing activation InvertibleNetworks.ActivationFunction(InvertibleNetworks.LeakyReLU, InvertibleNetworks.LeakyReLUinv, InvertibleNetworks.LeakyReLUgrad) Gradient test convolutions 0.04830551; 0.0015602633 0.024541944; 0.00038748607 0.012367517; 9.699445e-5 0.0062078834; 2.4316367e-5 0.0031099916; 6.0677994e-6 Gradient test convolutions 36.513306; 1.2160263 17.952087; 0.30344772 8.9001465; 0.075826645 4.4311523; 0.018992424 2.2107544; 0.0046744347 Gradient test convolutions 32.166077; 1.3773804 15.461304; 0.06695557 7.687866; 0.009307861 3.8441772; 0.00440979 1.9228516; 0.0014419556 Jacobian test 3.7162976; 1.0904312 1.8708103; 0.39809364 0.94053614; 0.14542258 0.4717914; 0.05376677 0.23606046; 0.0219157 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_flux.jl:22 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_flux_block.jl:37. Gradient test convolutions 0.46857333; 0.024661988 0.24045205; 0.0061656088 0.12176728; 0.0015415475 0.061268806; 0.00038560852 0.030730724; 9.6483156e-5 Gradient test convolutions 0.18159056; 0.019344598 0.0956316; 0.004835978 0.04902482; 0.0012089685 0.024814844; 0.00030205026 0.012482882; 7.556565e-5 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_flux_block.jl:37 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_resnet.jl:28. Gradient test 1.3785076; 0.08788419 0.71124697; 0.021948934 0.36101055; 0.005587399 0.18182373; 0.0014752448 0.09127092; 0.000378564 Gradient test convolutions 6.2205367; 3.5361319 2.0176187; 0.67541623 0.8211112; 0.15000999 0.37078714; 0.035236537 0.17642736; 0.008652061 WARNING: Method definition loss(Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_utils/test_chainrules.jl:49 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_layer_conv1x1.jl:97. Gradient test ΔX 126.51367; 0.63648224 63.415527; 0.15954971 31.747559; 0.039979935 15.883301; 0.010468483 7.944336; 0.0025486946 Gradient test Δv1 (dot(Δv1, Δv1), dot(Δv2, Δv2), dot(Δv3, Δv3)) = (212308.27f0, 3.409981f6, 1.8460841f6) 4.126953; 0.13814545 2.0996094; 0.03293991 1.059082; 0.0071926117 0.5317383; 0.0013990402 0.26757812; 0.0010094643 WARNING: Method definition loss(Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_layer_conv1x1.jl:97 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_layer_conv1x1.jl:158. Gradient test ΔX 64.48779; 0.63607025 32.402832; 0.15909958 16.240723; 0.04024315 8.130371; 0.010111809 4.067871; 0.0023703575 Gradient test Δv1 50.98462; 3.2050934 26.303467; 0.79138947 13.351074; 0.19635391 6.7246094; 0.04910469 3.3745117; 0.012345314 Jacobian test 44.710308; 7.9757266 22.381126; 2.0000103 11.170719; 0.49939722 5.5771527; 0.12468282 2.7861166; 0.031139646 1.3923894; 0.0077839247 0.6960279; 0.001945726 Jacobian (inverse) test 44.66122; 7.990711 22.361956; 2.0041766 11.162515; 0.5004927 5.5733986; 0.12496436 2.7843268; 0.031211782 1.391517; 0.0078030773 0.69559705; 0.0019512497 Gradient test coupling layer 305.7273; 17.104866 157.10718; 4.3089027 79.60181; 1.1062336 40.053467; 0.30055332 20.103271; 0.073738575 10.071777; 0.016727686 Gradient test coupling layer 3.204895; 3.907508 0.79730225; 1.1486087 0.1638794; 0.3395326 0.008850098; 0.09667671 WARNING: Method definition loss(Any, Any, Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_basic.jl:93 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_basic.jl:164. Gradient test coupling layer 1053.343; 50.536926 540.34424; 11.595734 273.84082; 2.1291656 137.59912; 0.3858719 68.893555; 0.0989418 34.452637; 0.043611526 Gradient test coupling layer 63.8468; 12.1818695 34.778687; 3.235649 18.250183; 0.7569847 9.398926; 0.10465813 Jacobian test 6.610278; 0.88653594 3.2505078; 0.2946847 1.6107839; 0.0967147 0.79997647; 0.03246505 0.39904407; 0.013399464 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_resnet.jl:28 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_irim.jl:40. Gradient test invertible layer 2411.537; 127.23633 1237.6211; 31.765625 626.74414; 7.9492188 315.36523; 1.9814453 158.17676; 0.49658203 Gradient test invertible layer 9.908207; 2.9137962 5.633812; 0.7771896 3.0043259; 0.20117486 1.5518074; 0.050942957 0.78853226; 0.012842923 Gradient test invertible layer 0.14440155; 0.044800207 0.059761047; 0.03483983 0.027549744; 0.019750696 0.015258789; 0.008391431 Test test_layers/test_coupling_layer_irim.jl: Test Failed at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_irim.jl:126 Expression: isapprox(err6[end] / (err6[1] / 4 ^ (maxiter - 1)), 1.0f0; atol = 10.0f0) Evaluated: isapprox(11.987702f0, 1.0f0; atol = 10.0f0) Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.11/Test/src/Test.jl:679 [inlined] [2] top-level scope @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_irim.jl:515 [3] include(fname::String) @ Main ./sysimg.jl:38 [4] macro expansion @ ~/.julia/packages/InvertibleNetworks/NquDv/test/runtests.jl:64 [inlined] [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.11/Test/src/Test.jl:1704 [inlined] [6] macro expansion @ ~/.julia/packages/InvertibleNetworks/NquDv/test/runtests.jl:64 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.11/Test/src/Test.jl:1704 [inlined] [8] top-level scope @ ~/.julia/packages/InvertibleNetworks/NquDv/test/runtests.jl:62 Jacobian test 11.18342; 0.62514985 5.58518; 0.20327468 2.7906659; 0.07079729 1.3945897; 0.022570875 0.69716054; 0.0068461737 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_irim.jl:40 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:37. Gradient test coupling layer 290.36194; 17.56491 235.03955; 11.301956 189.82983; 7.2433624 153.00708; 4.651474 123.14526; 2.9815826 98.984375; 1.9171066 ┌ Warning: Assignment to `X` in soft scope is ambiguous because a global variable by the same name exists: `X` will be treated as a new local. Disambiguate by using `local X` to suppress this warning or `global X` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:88 ┌ Warning: Assignment to `Y` in soft scope is ambiguous because a global variable by the same name exists: `Y` will be treated as a new local. Disambiguate by using `local Y` to suppress this warning or `global Y` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:89 ┌ Warning: Assignment to `L02` in soft scope is ambiguous because a global variable by the same name exists: `L02` will be treated as a new local. Disambiguate by using `local L02` to suppress this warning or `global L02` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:91 ┌ Warning: Assignment to `Lini` in soft scope is ambiguous because a global variable by the same name exists: `Lini` will be treated as a new local. Disambiguate by using `local Lini` to suppress this warning or `global Lini` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:92 ┌ Warning: Assignment to `dW1` in soft scope is ambiguous because a global variable by the same name exists: `dW1` will be treated as a new local. Disambiguate by using `local dW1` to suppress this warning or `global dW1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:93 ┌ Warning: Assignment to `dW2` in soft scope is ambiguous because a global variable by the same name exists: `dW2` will be treated as a new local. Disambiguate by using `local dW2` to suppress this warning or `global dW2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:94 ┌ Warning: Assignment to `dW3` in soft scope is ambiguous because a global variable by the same name exists: `dW3` will be treated as a new local. Disambiguate by using `local dW3` to suppress this warning or `global dW3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:95 ┌ Warning: Assignment to `f0` in soft scope is ambiguous because a global variable by the same name exists: `f0` will be treated as a new local. Disambiguate by using `local f0` to suppress this warning or `global f0` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `ΔX` in soft scope is ambiguous because a global variable by the same name exists: `ΔX` will be treated as a new local. Disambiguate by using `local ΔX` to suppress this warning or `global ΔX` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `Δv1` in soft scope is ambiguous because a global variable by the same name exists: `Δv1` will be treated as a new local. Disambiguate by using `local Δv1` to suppress this warning or `global Δv1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `Δv2` in soft scope is ambiguous because a global variable by the same name exists: `Δv2` will be treated as a new local. Disambiguate by using `local Δv2` to suppress this warning or `global Δv2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `Δv3` in soft scope is ambiguous because a global variable by the same name exists: `Δv3` will be treated as a new local. Disambiguate by using `local Δv3` to suppress this warning or `global Δv3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `ΔW1` in soft scope is ambiguous because a global variable by the same name exists: `ΔW1` will be treated as a new local. Disambiguate by using `local ΔW1` to suppress this warning or `global ΔW1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `ΔW2` in soft scope is ambiguous because a global variable by the same name exists: `ΔW2` will be treated as a new local. Disambiguate by using `local ΔW2` to suppress this warning or `global ΔW2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `ΔW3` in soft scope is ambiguous because a global variable by the same name exists: `ΔW3` will be treated as a new local. Disambiguate by using `local ΔW3` to suppress this warning or `global ΔW3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:97 ┌ Warning: Assignment to `h` in soft scope is ambiguous because a global variable by the same name exists: `h` will be treated as a new local. Disambiguate by using `local h` to suppress this warning or `global h` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:98 ┌ Warning: Assignment to `maxiter` in soft scope is ambiguous because a global variable by the same name exists: `maxiter` will be treated as a new local. Disambiguate by using `local maxiter` to suppress this warning or `global maxiter` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:99 ┌ Warning: Assignment to `err3` in soft scope is ambiguous because a global variable by the same name exists: `err3` will be treated as a new local. Disambiguate by using `local err3` to suppress this warning or `global err3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:100 ┌ Warning: Assignment to `err4` in soft scope is ambiguous because a global variable by the same name exists: `err4` will be treated as a new local. Disambiguate by using `local err4` to suppress this warning or `global err4` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:101 ┌ Warning: Assignment to `factor1` in soft scope is ambiguous because a global variable by the same name exists: `factor1` will be treated as a new local. Disambiguate by using `local factor1` to suppress this warning or `global factor1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:115 ┌ Warning: Assignment to `factor2` in soft scope is ambiguous because a global variable by the same name exists: `factor2` will be treated as a new local. Disambiguate by using `local factor2` to suppress this warning or `global factor2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:116 Gradient test coupling layer 11.044067; 14.08816 10.432129; 9.673655 9.547302; 6.537324 8.507568; 4.3601327 Gradient test coupling layer 4.968689; 8.04722 4.853821; 5.5589066 4.50354; 3.8266416 4.0770874; 2.5870583 Gradient test coupling layer 3.055664; 3.6918254 2.0699463; 2.578875 1.3570557; 1.7641991 0.8691406; 1.1948553 ┌ Warning: Assignment to `X` in soft scope is ambiguous because a global variable by the same name exists: `X` will be treated as a new local. Disambiguate by using `local X` to suppress this warning or `global X` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:132 ┌ Warning: Assignment to `Y` in soft scope is ambiguous because a global variable by the same name exists: `Y` will be treated as a new local. Disambiguate by using `local Y` to suppress this warning or `global Y` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:133 ┌ Warning: Assignment to `L01` in soft scope is ambiguous because a global variable by the same name exists: `L01` will be treated as a new local. Disambiguate by using `local L01` to suppress this warning or `global L01` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:134 ┌ Warning: Assignment to `Lini` in soft scope is ambiguous because a global variable by the same name exists: `Lini` will be treated as a new local. Disambiguate by using `local Lini` to suppress this warning or `global Lini` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:135 ┌ Warning: Assignment to `dv1` in soft scope is ambiguous because a global variable by the same name exists: `dv1` will be treated as a new local. Disambiguate by using `local dv1` to suppress this warning or `global dv1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:136 ┌ Warning: Assignment to `dv2` in soft scope is ambiguous because a global variable by the same name exists: `dv2` will be treated as a new local. Disambiguate by using `local dv2` to suppress this warning or `global dv2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:137 ┌ Warning: Assignment to `dv3` in soft scope is ambiguous because a global variable by the same name exists: `dv3` will be treated as a new local. Disambiguate by using `local dv3` to suppress this warning or `global dv3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:138 ┌ Warning: Assignment to `f0` in soft scope is ambiguous because a global variable by the same name exists: `f0` will be treated as a new local. Disambiguate by using `local f0` to suppress this warning or `global f0` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `ΔX` in soft scope is ambiguous because a global variable by the same name exists: `ΔX` will be treated as a new local. Disambiguate by using `local ΔX` to suppress this warning or `global ΔX` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `Δv1` in soft scope is ambiguous because a global variable by the same name exists: `Δv1` will be treated as a new local. Disambiguate by using `local Δv1` to suppress this warning or `global Δv1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `Δv2` in soft scope is ambiguous because a global variable by the same name exists: `Δv2` will be treated as a new local. Disambiguate by using `local Δv2` to suppress this warning or `global Δv2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `Δv3` in soft scope is ambiguous because a global variable by the same name exists: `Δv3` will be treated as a new local. Disambiguate by using `local Δv3` to suppress this warning or `global Δv3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `ΔW1` in soft scope is ambiguous because a global variable by the same name exists: `ΔW1` will be treated as a new local. Disambiguate by using `local ΔW1` to suppress this warning or `global ΔW1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `ΔW2` in soft scope is ambiguous because a global variable by the same name exists: `ΔW2` will be treated as a new local. Disambiguate by using `local ΔW2` to suppress this warning or `global ΔW2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `ΔW3` in soft scope is ambiguous because a global variable by the same name exists: `ΔW3` will be treated as a new local. Disambiguate by using `local ΔW3` to suppress this warning or `global ΔW3` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:140 ┌ Warning: Assignment to `h` in soft scope is ambiguous because a global variable by the same name exists: `h` will be treated as a new local. Disambiguate by using `local h` to suppress this warning or `global h` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:141 ┌ Warning: Assignment to `maxiter` in soft scope is ambiguous because a global variable by the same name exists: `maxiter` will be treated as a new local. Disambiguate by using `local maxiter` to suppress this warning or `global maxiter` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:142 ┌ Warning: Assignment to `err5` in soft scope is ambiguous because a global variable by the same name exists: `err5` will be treated as a new local. Disambiguate by using `local err5` to suppress this warning or `global err5` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:143 ┌ Warning: Assignment to `err6` in soft scope is ambiguous because a global variable by the same name exists: `err6` will be treated as a new local. Disambiguate by using `local err6` to suppress this warning or `global err6` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:144 ┌ Warning: Assignment to `factor1` in soft scope is ambiguous because a global variable by the same name exists: `factor1` will be treated as a new local. Disambiguate by using `local factor1` to suppress this warning or `global factor1` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:158 ┌ Warning: Assignment to `factor2` in soft scope is ambiguous because a global variable by the same name exists: `factor2` will be treated as a new local. Disambiguate by using `local factor2` to suppress this warning or `global factor2` to assign to the existing global variable. └ @ ~/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:159 Gradient test coupling layer 57.711426; 6.582144 47.738525; 3.696333 39.13501; 2.0128803 31.749268; 1.1690445 Gradient test coupling layer 43.630127; 278.1223 37.60132; 219.80063 31.664307; 174.25726 26.233643; 138.5036 Gradient test coupling layer 19.088867; 460.93408 19.052002; 364.9664 17.421387; 289.7933 15.175537; 230.59622 Jacobian test 16.307419; 4.657675 13.095585; 3.2077153 10.497308; 2.2147722 8.411005; 1.5292294 6.741121; 1.056131 5.403782; 0.728805 4.327577; 0.5000146 Gradient test coupling layer 11.1106415; 0.66401005 5.720764; 0.1665616 2.9019623; 0.0417006 1.4614029; 0.010428548 0.73332214; 0.002593577 0.36730957; 0.0006482899 Gradient test coupling layer 4.5826683; 0.25963688 2.3563766; 0.064775944 1.1943932; 0.016183138 0.6012497; 0.004038453 Gradient test weights for permute=none, reverse=true, logdet=true 2.4209595; 2.209106 1.8467712; 0.46826148 1.0585022; 0.09901416 0.56015015; 0.018608034 0.28671265; 0.0026664436 Gradient test ΔX for permute=none, reverse=true, logdet=true 14.100098; 11.867797 4.1505737; 3.0344234 1.3552856; 0.7972104 0.47982788; 0.20079026 0.19116211; 0.051643297 Gradient test ΔX for permute=none, reverse=true, logdet=true 7.784546; 11.165292 1.1627197; 2.8530927 0.10144043; 0.7437461 0.23120117; 0.1913921 0.1616211; 0.04967554 Gradient test ΔX for permute=none, reverse=true, logdet=true 12.332336; 11.690671 3.1960144; 2.8751817 0.93362427; 0.7732079 0.2960205; 0.21581233 0.09725952; 0.05715543 Gradient test weights for permute=none, reverse=false, logdet=true 0.44430542; 0.3119377 0.17175293; 0.10556906 0.07897949; 0.045887556 0.028778076; 0.012232108 0.01083374; 0.0025607562 Gradient test ΔX for permute=none, reverse=false, logdet=true 3.64917; 4.3819447 0.75408936; 1.1204767 0.09661865; 0.27981234 0.020202637; 0.0713942 0.027374268; 0.01842415 Gradient test ΔX for permute=none, reverse=false, logdet=true 4.4631653; 3.5825386 1.3358765; 0.89556307 0.44360352; 0.22344682 0.1619873; 0.051908955 0.068481445; 0.01344227 Gradient test ΔX for permute=none, reverse=false, logdet=true 4.204132; 3.9644701 1.0759583; 0.9561273 0.27011108; 0.21019562 0.0736084; 0.043650664 0.025390625; 0.010411757 Gradient test weights for permute=none, reverse=true, logdet=false 8.953348; 9.9907875 7.026106; 2.445962 4.132532; 0.6035018 2.2085457; 0.15947127 1.1428928; 0.04111564 Gradient test ΔX for permute=none, reverse=true, logdet=false 29.098816; 23.755478 8.779419; 6.10775 2.9503174; 1.6144829 1.0770264; 0.40910912 0.43988037; 0.105921745 Gradient test ΔX for permute=none, reverse=true, logdet=false 13.404419; 18.974258 2.0375977; 4.822517 0.12365723; 1.2688024 0.37298584; 0.32324398 0.26348877; 0.08462614 Gradient test ΔX for permute=none, reverse=true, logdet=false 23.749512; 20.967873 6.5480957; 5.157276 2.0905762; 1.3951664 0.73950195; 0.39179704 0.27972412; 0.10587166 Gradient test weights for permute=none, reverse=false, logdet=false 2.385327; 2.6270292 1.8618262; 0.64435196 1.09165; 0.16143906 0.58538723; 0.041157305 0.3030343; 0.010237962 Gradient test ΔX for permute=none, reverse=false, logdet=false 6.399872; 8.744157 1.0300598; 2.2022023 0.046203613; 0.53986764 0.15744019; 0.13559544 0.111846924; 0.03467089 Gradient test ΔX for permute=none, reverse=false, logdet=false 11.090912; 10.521312 2.9595947; 2.6747944 0.8081665; 0.6657664 0.23501587; 0.16381583 0.07757568; 0.04197566 Gradient test ΔX for permute=none, reverse=false, logdet=false 9.323303; 10.342289 1.9953918; 2.5048845 0.31680298; 0.5715493 0.0004272461; 0.12694593 0.03466797; 0.029018618 Gradient test weights for permute=lower, reverse=true, logdet=true 7.325592; 4.854825 5.0294495; 1.0607591 2.796051; 0.24905324 1.4597168; 0.062835336 0.74716187; 0.014114201 Gradient test ΔX for permute=lower, reverse=true, logdet=true 14.100067; 11.867767 4.150543; 3.0343933 1.3553467; 0.7972717 0.47988892; 0.20085144 0.19128418; 0.051765442 Gradient test ΔX for permute=lower, reverse=true, logdet=true 7.784668; 11.165413 1.1626587; 2.8530312 0.10116577; 0.74402046 0.23104858; 0.19154453 0.16159058; 0.049705982 Gradient test ΔX for permute=lower, reverse=true, logdet=true 12.332184; 11.690516 3.1958618; 2.8750284 0.9335022; 0.7730855 0.29580688; 0.21559851 0.09713745; 0.057033263 Gradient test weights for permute=lower, reverse=false, logdet=true 2.492859; 1.2575078 1.6025391; 0.27264428 0.86572266; 0.071869016 0.45108032; 0.017715514 0.22729492; 0.007102996 Gradient test ΔX for permute=lower, reverse=false, logdet=true 4.144104; 4.0511446 1.077301; 1.0308214 0.28912354; 0.26588374 0.07962036; 0.06800046 0.023162842; 0.01735289 Gradient test ΔX for permute=lower, reverse=false, logdet=true 4.625244; 3.9207382 1.3451538; 0.99290085 0.42642212; 0.25029564 0.14685059; 0.05878734 0.059570312; 0.015538689 Gradient test ΔX for permute=lower, reverse=false, logdet=true 2.595459; 2.509299 0.66519165; 0.6221117 0.17071533; 0.14917536 0.044311523; 0.033541538 0.013458252; 0.008073258 Gradient test weights for permute=lower, reverse=true, logdet=false 2.474319; 2.0979023 1.7043066; 0.58180404 0.99484634; 0.14820898 0.5359955; 0.035532176 0.27501392; 0.010749906 Gradient test ΔX for permute=lower, reverse=true, logdet=false 29.098694; 23.755358 8.779419; 6.107751 2.9503174; 1.6144835 1.0770264; 0.4091094 0.43988037; 0.105921894 Gradient test ΔX for permute=lower, reverse=true, logdet=false 13.404419; 18.974257 2.0375977; 4.8225164 0.12365723; 1.2688022 0.37298584; 0.32324386 0.26348877; 0.08462608 Gradient test ΔX for permute=lower, reverse=true, logdet=false 23.749512; 20.96787 6.548218; 5.1573973 2.0906982; 1.3952879 0.73950195; 0.39179677 0.27972412; 0.10587153 Gradient test weights for permute=lower, reverse=false, logdet=false 0.7211319; 0.59562516 0.49473166; 0.16364688 0.2837956; 0.045393676 0.15347719; 0.011117443 0.07921767; 0.0030796453 Gradient test ΔX for permute=lower, reverse=false, logdet=false 7.3898163; 8.082634 1.6764832; 2.022892 0.33869934; 0.51190376 0.042007446; 0.12860967 0.0107421875; 0.032558925 Gradient test ΔX for permute=lower, reverse=false, logdet=false 11.415009; 11.197651 2.9781494; 2.8694704 0.7737732; 0.71943367 0.20462036; 0.17745061 0.0597229; 0.046138026 Gradient test ΔX for permute=lower, reverse=false, logdet=false 6.10614; 7.4321303 1.1741486; 1.8371437 0.11810303; 0.44960055 0.058685303; 0.10706346 0.058532715; 0.024341665 Gradient test weights for permute=both, reverse=true, logdet=true 2.4151306; 2.10353 1.8240356; 0.43529463 1.0346985; 0.09496665 0.53900146; 0.025831103 0.274292; 0.008124292 Gradient test ΔX for permute=both, reverse=true, logdet=true 12.173859; 12.150938 3.0948792; 3.0834186 0.76293945; 0.75720924 0.18191528; 0.17905018 0.05026245; 0.048829895 Gradient test ΔX for permute=both, reverse=true, logdet=true 12.6828; 15.004396 2.59198; 3.752778 0.36904907; 0.94944805 0.05316162; 0.23703787 0.08151245; 0.06358729 Gradient test ΔX for permute=both, reverse=true, logdet=true 13.559601; 9.383762 4.744995; 2.6570761 1.6263123; 0.58235276 0.6442566; 0.12227684 0.29769897; 0.0367091 Gradient test weights for permute=both, reverse=false, logdet=true 1.329895; 1.267164 1.048523; 0.25000656 0.6157837; 0.03348106 0.32333374; 0.0012986362 0.16113281; 0.0011833757 Gradient test ΔX for permute=both, reverse=false, logdet=true 3.0928955; 2.9627934 0.7930908; 0.72803974 0.21673584; 0.18421029 0.062927246; 0.04666447 0.018066406; 0.009935019 Gradient test ΔX for permute=both, reverse=false, logdet=true 5.705017; 3.13612 2.1201782; 0.8357297 0.8708191; 0.22859484 0.38049316; 0.059381038 0.17630005; 0.015743986 Gradient test ΔX for permute=both, reverse=false, logdet=true 3.4647827; 3.6067467 0.81085205; 0.881834 0.19442749; 0.22991845 0.036987305; 0.05473279 0.0031738281; 0.01204657 Gradient test weights for permute=both, reverse=true, logdet=false 1.6433454; 2.1860068 1.3435507; 0.5711254 0.8041215; 0.15321654 0.4347868; 0.04388222 0.22629738; 0.01303713 Gradient test ΔX for permute=both, reverse=true, logdet=false 21.36914; 22.150509 5.231476; 5.6221595 1.1873779; 1.3827198 0.22686768; 0.32453862 0.038726807; 0.08756228 Gradient test ΔX for permute=both, reverse=true, logdet=false 24.77356; 28.538267 5.285095; 7.167449 0.88964844; 1.8308254 0.010498047; 0.46009046 0.11016846; 0.1251258 Gradient test ΔX for permute=both, reverse=true, logdet=false 26.256042; 16.452938 9.6622925; 4.7607408 3.468567; 1.017791 1.4263916; 0.20100367 0.67407227; 0.0613783 Gradient test weights for permute=both, reverse=false, logdet=false 0.5848496; 0.62487864 0.4503528; 0.15451133 0.26216817; 0.04026389 0.14090538; 0.01031065 0.072466135; 0.00314188 Gradient test ΔX for permute=both, reverse=false, logdet=false 9.16452; 8.077109 2.5444336; 2.0007281 0.7723694; 0.50051665 0.26290894; 0.12698258 0.09820557; 0.03024239 Gradient test ΔX for permute=both, reverse=false, logdet=false 12.00235; 7.743035 4.139984; 2.0103266 1.5904236; 0.52559483 0.66574097; 0.13332659 0.30010986; 0.033902675 Gradient test ΔX for permute=both, reverse=false, logdet=false 7.7928925; 9.52825 1.448822; 2.316501 0.17274475; 0.6065842 0.063583374; 0.15333633 0.07249451; 0.035965346 Gradient test weights for permute=full, reverse=true, logdet=true 2.9067688; 2.5906467 2.187561; 0.56114674 1.2306519; 0.14370203 0.6508484; 0.036328554 0.33377075; 0.009817719 Gradient test ΔX for permute=full, reverse=true, logdet=true 14.10025; 11.867949 4.1507874; 3.0346365 1.3554993; 0.79742384 0.480011; 0.20097327 0.19143677; 0.05191791 Gradient test ΔX for permute=full, reverse=true, logdet=true 7.784546; 11.165293 1.1626587; 2.853032 0.10128784; 0.7438988 0.23104858; 0.19154474 0.16140747; 0.049889192 Gradient test ΔX for permute=full, reverse=true, logdet=true 12.332336; 11.69067 3.195984; 2.8751504 0.9336548; 0.77323806 0.29592896; 0.2157206 0.09732056; 0.05721638 Gradient test weights for permute=full, reverse=false, logdet=true 1.329895; 1.267164 1.048523; 0.25000656 0.6157837; 0.03348106 0.32333374; 0.0012986362 0.16113281; 0.0011833757 Gradient test ΔX for permute=full, reverse=false, logdet=true 3.092987; 2.9628847 0.7931824; 0.7281311 0.2168274; 0.18430176 0.06298828; 0.046725467 0.018096924; 0.009965518 Gradient test ΔX for permute=full, reverse=false, logdet=true 5.7052307; 3.1363342 2.1201782; 0.83572996 0.8708801; 0.228656 0.3805542; 0.059442133 0.17642212; 0.015866086 Gradient test ΔX for permute=full, reverse=false, logdet=true 3.4647522; 3.6067164 0.810791; 0.8817732 0.19442749; 0.22991857 0.036956787; 0.054702327 0.0031433105; 0.012016079 Gradient test weights for permute=full, reverse=true, logdet=false 2.3083425; 2.3058472 1.7551966; 0.55189824 1.0049238; 0.14862359 0.53777313; 0.03900057 0.27929783; 0.009089023 Gradient test ΔX for permute=full, reverse=true, logdet=false 29.098633; 23.755295 8.779419; 6.1077495 2.9502563; 1.6144216 1.0768433; 0.4089259 0.43981934; 0.10586065 Gradient test ΔX for permute=full, reverse=true, logdet=false 13.40448; 18.97432 2.0375977; 4.8225174 0.12365723; 1.2688026 0.37298584; 0.3232441 0.26348877; 0.0846262 Gradient test ΔX for permute=full, reverse=true, logdet=false 23.749512; 20.967869 6.548279; 5.157458 2.0907593; 1.3953488 0.739563; 0.3918577 0.27978516; 0.10593252 Gradient test weights for permute=full, reverse=false, logdet=false 0.5848496; 0.6248785 0.4503528; 0.15451127 0.26216817; 0.04026386 0.14090538; 0.010310635 0.072466135; 0.0031418726 Gradient test ΔX for permute=full, reverse=false, logdet=false 9.164459; 8.077047 2.5444336; 2.0007277 0.77233887; 0.5004859 0.26287842; 0.12695195 0.09820557; 0.030242331 Gradient test ΔX for permute=full, reverse=false, logdet=false 12.00235; 7.743035 4.140045; 2.0103877 1.5904236; 0.52559483 0.665802; 0.13338763 0.3001709; 0.03396371 Gradient test ΔX for permute=full, reverse=false, logdet=false 7.7928925; 9.528251 1.448822; 2.3165011 0.17277527; 0.6066148 0.063583374; 0.1533364 0.07249451; 0.035965383 Jacobian test 6.289905; 1.2745527 3.1469064; 0.45107976 1.5712025; 0.16593735 0.78480744; 0.05868296 0.39224765; 0.020267475 Gradient test coupling layer 351.62988; 19.471039 180.698; 4.852463 91.60376; 1.1714706 46.106934; 0.2806816 23.123291; 0.07051659 11.580078; 0.016825676 Gradient test coupling layer 76.519775; 18.441982 42.303833; 5.177046 22.34253; 1.3979101 11.526855; 0.34336424 Gradient test coupling layer 8.700195; 1.0976534 4.088135; 0.2868638 1.9724121; 0.07177663 0.963501; 0.013183236 WARNING: Method definition mean(Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:7 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_conditional_layer_hint.jl:7. WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_glow.jl:37 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_conditional_layer_hint.jl:53. Conditional HINT invertibility test with permute=true, logdet=true, reverse=true Conditional HINT gradient test for input with permute=true, logdet=true, reverse=true 224.5664; 199.86293 132.42578; 112.663 88.05469; 72.24446 57.53125; 44.883068 44.808594; 34.69005 220.54297; 81.94156 148.90234; 38.02121 104.24219; 15.537277 72.74219; 1.7782593 56.08203; 0.6891098 94.22461; 94.11662 98.45703; 52.21597 93.27734; 27.261055 83.13867; 13.292046 72.19141; 4.953169 Conditional HINT gradient test for weights with permute=true, logdet=true, reverse=true 72733.75; 27459.691 27861.734; 5224.705 11623.922; 305.40723 5213.3125; 445.94482 2671.4531; 158.17554 31440.633; 26207.473 7834.367; 5217.787 1159.3672; 148.92273 1129.5391; 1783.6841 1842.3828; 2169.4553 2077.1328; 30369.04 2509.211; 11636.742 2118.711; 4954.2656 2253.5625; 1282.9258 1565.4453; 202.79883 Conditional HINT invertibility test with permute=true, logdet=true, reverse=false Conditional HINT gradient test for input with permute=true, logdet=true, reverse=false 8.193359; 7.830579 5.2993164; 5.009092 3.444336; 3.2121563 2.2539062; 2.0681624 1.4516602; 1.3030651 9.227051; 8.783275 6.046875; 5.6918535 3.9580078; 3.6739907 2.6157227; 2.388509 1.7338867; 1.5521158 13.413574; 8.086923 9.456543; 5.1952214 6.6904297; 3.2813725 4.804199; 2.0769534 3.4658203; 1.2840238 Conditional HINT gradient test for weights with permute=true, logdet=true, reverse=false 3.584961; 5.1590767 1.2397461; 2.026804 0.16552734; 0.5590563 0.008300781; 0.20506525 0.068359375; 0.03002286 1.2456055; 0.054343343 0.59277344; 1.2427478 0.09326172; 0.23172548 0.21240234; 0.049908742 0.20214844; 0.12090164 6.666504; 1.270412 3.6079102; 0.9098642 1.5908203; 0.24179733 0.7421875; 0.06767601 0.43554688; 0.09829113 Conditional HINT invertibility test with permute=false, logdet=true, reverse=true Conditional HINT gradient test for input with permute=false, logdet=true, reverse=true 208.375; 1245.6567 20.765625; 809.05975 97.453125; 566.40717 90.40625; 440.68198 95.484375; 329.3862 418.46875; 1693.0944 18.046875; 1001.65375 206.35938; 609.4011 261.03125; 391.57715 270.46875; 251.61798 1197.4531; 1419.604 764.34375; 942.0645 495.67188; 637.8485 312.5625; 426.30377 196.04688; 287.03992 Conditional HINT gradient test for weights with permute=false, logdet=true, reverse=true 9113.836; 15472.324 7798.8555; 4494.2246 4865.828; 1280.7119 2682.4062; 390.86377 1419.5703; 117.0647 2882.4258; 7623.034 2424.836; 2827.894 822.77344; 1803.5916 184.5664; 1497.7489 760.48047; 1417.0718 18977.504; 21977.766 3571.8477; 5071.978 889.1953; 1639.2605 445.88672; 820.9193 411.71875; 599.23505 Conditional HINT invertibility test with permute=false, logdet=true, reverse=false Conditional HINT gradient test for input with permute=false, logdet=true, reverse=false 3.446289; 9.07667 1.2729492; 5.777254 0.051757812; 3.6552017 0.5961914; 2.2865636 0.86621094; 1.4399931 4.736328; 9.984083 2.3095703; 6.5077744 0.9824219; 4.340985 0.16845703; 2.8553076 0.2836914; 1.8657889 18.746582; 9.47737 13.54248; 6.127111 9.869629; 3.937334 7.213379; 2.4675426 5.355957; 1.559288 Conditional HINT gradient test for weights with permute=false, logdet=true, reverse=false 5.493164; 6.3210135 4.89209; 1.0149989 2.6308594; 0.322685 1.4609375; 0.01583469 0.7475586; 0.009172499 6.7851562; 5.195922 2.7963867; 2.0017695 1.4765625; 1.0792539 0.6899414; 0.4912871 0.41455078; 0.31522363 1.4086914; 0.15566003 0.17089844; 0.45561725 0.3388672; 0.025609344 0.11035156; 0.04627736 0.043945312; 0.12225977 Conditional HINT invertibility test with permute=true, logdet=false, reverse=true Conditional HINT gradient test for input with permute=true, logdet=false, reverse=true 727.9844; 1661.5957 862.34375; 1049.3202 832.78125; 696.5499 776.65625; 446.80878 695.40625; 283.36578 1993.6562; 119.223816 1545.9844; 46.438354 1243.2344; 43.597534 1033.3125; 73.60306 878.53125; 110.76367 1639.9375; 1236.5925 1133.875; 811.199 789.0; 530.8592 567.03125; 360.51862 435.42188; 270.21173 Conditional HINT gradient test for weights with permute=true, logdet=false, reverse=true 2525.2656; 5777.3896 2296.1719; 1855.1558 1462.2812; 613.38257 828.7344; 209.09753 449.95312; 68.96283 17135.86; 13170.162 11394.391; 3758.62 6026.75; 1549.7554 3009.0781; 779.17456 1258.2188; 635.9076 43264.39; 16058.385 18620.344; 5017.341 9879.078; 3077.5767 5636.9062; 2236.1555 3527.75; 1827.3746 Conditional HINT invertibility test with permute=true, logdet=false, reverse=false Conditional HINT gradient test for input with permute=true, logdet=false, reverse=false 20.038208; 19.230225 12.888306; 12.241919 8.303223; 7.7861133 5.3377686; 4.924081 3.4122314; 3.0812814 22.704102; 21.227095 14.922363; 13.740758 9.80188; 8.856596 6.460205; 5.703978 4.2596436; 3.6546617 33.930176; 20.358631 24.044434; 13.187199 17.124878; 8.43909 12.345215; 5.3965845 8.966309; 3.4074042 Conditional HINT gradient test for weights with permute=true, logdet=false, reverse=false 21.590088; 27.384693 3.6914062; 6.588709 0.14135742; 1.5900089 0.45239258; 0.27193314 0.29345703; 0.06870583 31.272705; 31.79325 6.95874; 7.2190123 2.1906738; 2.3208098 0.9880371; 1.0531051 0.77075195; 0.80328596 30.049316; 32.856888 7.5612793; 8.965065 1.6728516; 2.3747444 0.16601562; 0.51696205 0.26123047; 0.08575724 Conditional HINT invertibility test with permute=false, logdet=false, reverse=true Conditional HINT gradient test for input with permute=false, logdet=false, reverse=true 416.96875; 2486.5955 40.15625; 1615.5453 193.15625; 1131.4049 179.21875; 880.4302 189.09375; 658.6254 836.5; 3382.0522 35.625; 2000.8167 411.5; 1217.6534 520.875; 782.44775 539.6875; 502.97064 2395.4688; 2836.3286 1529.875; 1882.563 992.90625; 1275.0566 626.46875; 852.189 393.375; 573.9513 Conditional HINT gradient test for weights with permute=false, logdet=false, reverse=true 18207.68; 30958.785 15584.039; 8999.193 9724.211; 2567.4053 5361.9453; 783.8628 2837.7734; 235.13062 5603.875; 15204.635 4756.9766; 5647.2783 1596.9688; 3605.1587 392.34375; 2993.4075 1531.9922; 2832.524 38192.594; 43870.67 7306.0625; 10145.101 1851.875; 3271.394 920.2344; 1629.9939 830.33594; 1185.2157 Conditional HINT invertibility test with permute=false, logdet=false, reverse=false Conditional HINT gradient test for input with permute=false, logdet=false, reverse=false 19.080566; 24.10881 11.339111; 15.361706 6.550537; 9.768613 3.578125; 6.1525855 1.8671875; 3.926756 8.902588; 25.611847 3.1120605; 16.47947 0.049072266; 10.742998 1.6086426; 6.9464984 2.3806152; 4.463497 43.06714; 25.515175 30.53711; 16.495537 21.793457; 10.5602 15.605957; 6.6193514 11.346924; 4.157639 Conditional HINT gradient test for weights with permute=false, logdet=false, reverse=false 41.991943; 71.501175 3.9753418; 18.72996 2.609375; 4.767934 2.5319824; 1.156672 1.5795898; 0.26473737 46.13623; 59.09933 9.739746; 16.221296 1.9172363; 5.1580114 0.1418457; 1.7622334 0.0026855469; 0.8128794 59.627686; 55.17311 16.5625; 14.335212 4.4157715; 3.3021274 1.119873; 0.56305104 0.0041503906; 0.2742606 Conditional HINT jacobian test with permute=true, logdet=true 19.109127; 6.344054 9.310747; 2.106029 4.602494; 0.72679424 2.2874968; 0.26121035 1.1412746; 0.09357628 Conditional HINT jacobian test with permute=false, logdet=true 15.34473; 3.583938 7.306123; 1.1723208 3.5638502; 0.40605637 1.7624439; 0.14647187 0.8767431; 0.052231863 Conditional HINT jacobian test with permute=true, logdet=false 22.405533; 5.8555574 10.777503; 1.8796129 5.309144; 0.620961 2.6348996; 0.20839946 1.3119167; 0.07052181 Conditional HINT jacobian test with permute=false, logdet=false 21.528118; 4.0568085 10.162814; 1.233521 4.9401565; 0.40281397 2.4358933; 0.13465902 1.2098396; 0.045568068 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_conditional_layer_hint.jl:53 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_conditional_res_block.jl:21. Gradient test convolutions 0.08899951; 0.010183342 0.047017664; 0.002573762 0.024140805; 0.0006549079 0.012229741; 0.00016811583 0.0061559677; 4.2960513e-5 Gradient test convolutions 0.10567814; 0.030157544 0.05981332; 0.008104522 0.031862855; 0.0020960663 0.0164392; 0.00054026116 0.0083527565; 0.0001369738 Jacobian test 14.356366; 4.38959 6.971167; 1.5308284 3.4411378; 0.54324734 1.7106614; 0.19214852 0.8532956; 0.06879022 WARNING: Method definition loss(Any, Any, Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_coupling_layer_basic.jl:164 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_hyperbolic_layer.jl:36. Invertibility test hyperbolic layer with action=1 Gradient test hyperbolic layer input with action=1 517.7544; 517.7544 129.4745; 129.4745 32.37572; 32.37572 8.094469; 8.094469 2.0235574; 2.0235574 0.5058871; 0.5058871 0.12647165; 0.12647165 0.031618208; 0.031618208 0.007904619; 0.007904619 0.0019761915; 0.0019761915 Gradient test hyperbolic layer weights with action=1 762.04474; 762.04474 115.03092; 115.03092 24.25645; 24.25645 5.8050747; 5.8050747 1.4328474; 1.4328474 0.35840997; 0.35840997 0.08969104; 0.08969104 0.022426784; 0.022426784 0.0056068865; 0.0056068865 0.0014024323; 0.0014024323 Invertibility test hyperbolic layer with action=-1 Gradient test hyperbolic layer input with action=-1 551.3571; 551.3571 137.84065; 137.84065 34.46326; 34.46326 8.615988; 8.615988 2.1539612; 2.1539612 0.53847265; 0.53847265 0.1346146; 0.1346146 0.033653446; 0.033653446 0.008413449; 0.008413449 0.002103363; 0.002103363 Gradient test hyperbolic layer weights with action=-1 613.0314; 613.0314 96.17617; 96.17617 20.961626; 20.961626 5.1098375; 5.1098375 1.278382; 1.278382 0.32121748; 0.32121748 0.080876276; 0.080876276 0.020305779; 0.020305779 0.0050841593; 0.0050841593 0.001272662; 0.001272662 Invertibility test hyperbolic layer with action=0 Gradient test hyperbolic layer input with action=0 533.63684; 533.63684 133.3969; 133.3969 33.3476; 33.3476 8.336517; 8.336517 2.0840964; 2.0840964 0.5210494; 0.5210494 0.13026538; 0.13026538 0.03256661; 0.03256661 0.008141698; 0.008141698 0.0020354374; 0.0020354374 Gradient test hyperbolic layer weights with action=0 752.7986; 752.7986 113.75928; 113.75928 24.858624; 24.858624 6.0673456; 6.0673456 1.5117186; 1.5117186 0.37874612; 0.37874612 0.09511244; 0.09511244 0.023813128; 0.023813128 0.005962856; 0.005962856 0.0014911022; 0.0014911022 Jacobian test 32.228825; 0.6400954 16.175524; 0.18791221 8.102398; 0.059375126 4.054631; 0.020306878 2.028103; 0.0070763356 Jacobian test 33.486786; 0.51961106 16.755861; 0.17364225 8.381157; 0.062276162 4.1912265; 0.023776522 2.0958028; 0.008032883 Jacobian test 32.96088; 0.72181296 16.510548; 0.22596815 8.262684; 0.07403006 4.1329374; 0.026499778 2.06692; 0.010224995 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_conditional_res_block.jl:21 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_actnorm.jl:112. Gradient test actnorm 592.95386; 31.207947 304.27905; 7.8018494 154.09009; 1.9503632 77.532715; 0.48751068 38.888428; 0.12168503 19.47461; 0.030447006 Gradient test actnorm 1.2047176; 7.0241947 1.1562271; 1.7535114 1.0167847; 0.4380846 0.61795044; 0.109484196 0.3363266; 0.027390718 0.17503738; 0.0068212748 Gradient test actnorm reverse 583.00024; 30.684326 299.17114; 7.6711426 151.50317; 1.9179688 76.23096; 0.47961426 38.235107; 0.12017822 19.14746; 0.030181885 Gradient test actnorm reverse 7.3618774; 0.11345148 3.654457; 0.030244112 1.8200226; 0.007916093 0.9080887; 0.002035439 0.4535141; 0.0004874766 0.22663116; 0.000117853284 Jacobian test 8.1736965; 0.57996356 4.099835; 0.14499094 2.054116; 0.036247756 1.028226; 0.009061918 0.5144195; 0.0022654731 WARNING: Method definition loss(Any, Any, Any) in module Main at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_actnorm.jl:112 overwritten at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/test_layers/test_layer_affine.jl:45. Gradient test affine layer 0.9560547; 0.035000324 0.4873047; 0.008222818 0.24609375; 0.0016700029 0.12402344; 0.00014156103 0.0625; 0.00055906177 Gradient test affine layer 196.81714; 15.08638 102.180176; 3.7715836 52.032837; 0.94304276 26.252197; 0.23574257 13.185059; 0.058911324 Jacobian test 1.2507468; 0.08473413 0.6138626; 0.021183532 0.3041511; 0.0052958806 0.15139307; 0.0013239649 0.07552752; 0.00033099658 Test Summary: | Pass Fail Total Time Layers | 575 1 576 4m58.4s Test test_layers/test_residual_block.jl | 18 18 2.4s Test test_layers/test_flux_block.jl | 4 4 10.1s Test test_layers/test_resnet.jl | 4 4 39.9s Test test_layers/test_layer_conv1x1.jl | 29 29 2.2s Test test_layers/test_coupling_layer_basic.jl | 29 29 4.2s Test test_layers/test_coupling_layer_irim.jl | 10 1 11 6.0s Test test_layers/test_coupling_layer_glow.jl | 17 17 54.7s Test test_layers/test_coupling_layer_hint.jl | 163 163 5.8s Test test_layers/test_conditional_layer_glow.jl | 8 8 1.4s Test test_layers/test_conditional_layer_hint.jl | 84 84 36.4s Test test_layers/test_conditional_res_block.jl | 7 7 13.2s Test test_layers/test_hyperbolic_layer.jl | 159 159 39.1s Test test_layers/test_actnorm.jl | 32 32 33.0s Test test_layers/test_layer_affine.jl | 11 11 50.3s ERROR: LoadError: Some tests did not pass: 575 passed, 1 failed, 0 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/InvertibleNetworks/NquDv/test/runtests.jl:60 Testing failed after 1230.13s ERROR: LoadError: Package InvertibleNetworks errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2124 [3] test @ /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2007 [inlined] [4] test(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:481 [5] test(pkgs::Vector{Pkg.Types.PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:159 [6] test @ /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:147 [inlined] [7] #test#74 @ /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:146 [inlined] [8] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:219 in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 PkgEval failed after 2066.93s: package has test failures