PythonCall.jl
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Published
20 hours ago •
JuliaPy
JuliaPy
172407 segmentation fault (core dumped) python
Affects: PythonCall / JuliaCall / Both [delete as appropriate]
Describe the bug
When I use UNet.jl from Python using juliacall I get a segmentation fault (core dumped). When I went to reproduce this error with a minimal example it did work once, but then failed a second time. If I had to guess it might be related to the actual data being passed to the Unet model; maybe its spitting out a type that isn't compatible with Python.
This is a minimal example of the error. I activate my environment that has UNet.jl installed in it, then create some random data to simulate an image and pass it to an untrained model. I get the same results with real images and a trained model.
Python 3.11.7 (main, Dec 4 2023, 18:10:11) [GCC 13.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from juliacall import Main as jl
>>> jl.seval("using Pkg")
>>> jl.Pkg.activate("./model")
Activating project at `~/work-code/sentinal-hub/model`
>>> jl.seval("using UNet")
>>> img_proxy = jl.rand(256,256,3,1)
>>> unet = jl.Unet(3)
>>> unet(img_proxy)
┌ Warning: Slow fallback implementation invoked for conv! You probably don't want this; check your datatypes.
│ yT = Float64
│ T1 = Float64
│ T2 = Float32
└ @ NNlib ~/.config/julia/packages/NNlib/Fg3DQ/src/conv.jl:192
┌ Warning: Slow fallback implementation invoked for ∇conv_data! You probably don't want this; check your datatypes.
│ yT = Float64
│ T1 = Float64
│ T2 = Float32
└ @ NNlib ~/.config/julia/packages/NNlib/Fg3DQ/src/conv.jl:233
Julia:
256×256×3×1 Array{Float64, 4}:
[:, :, 1, 1] =
-0.0010724 -0.00112552 -0.00113706 … -0.00103519 -0.000839464
-0.000795291 -0.00161727 -0.00204406 -0.0009993 -0.000439908
-0.000780577 -0.00116744 -0.00134499 -0.00115543 -0.000573814
-0.000860885 -0.00116414 -0.000965194 -0.00140911 -0.000761458
-0.000755788 -0.00132476 -0.00171172 -0.00114426 -0.000614211
-0.000820267 -0.00138928 -0.00182287 … -0.00135342 -0.000624007
-0.00142297 -0.0017854 -0.00136742 -0.00158695 -0.000791874
-0.00044614 -0.00124075 -0.00200514 -0.00151648 -0.00105216
-0.00127456 -0.00144327 -0.00143971 -0.00160615 -0.000831712
⋮ ⋱ ⋮
-0.00081029 -0.00123124 -0.00143105 -0.00146172 -0.00102908
-0.00142603 -0.00151539 -0.00138619 -0.00166838 -0.0007978
-0.000832417 -0.0018811 -0.0018118 -0.00110241 -0.000492823
-0.0010199 -0.00116831 -0.00139769 … -0.00111267 -0.000343543
-0.00121636 -0.00164644 -0.0013786 -0.0010909 -0.000885556
-0.000991607 -0.00133452 -0.00151132 -0.00168399 -0.000749311
-0.000765725 -0.00120822 -0.00101517 -0.00143129 -0.000483814
-0.000692864 -0.000724345 -0.00136182 -0.00145267 -0.000722094
-0.000375997 -0.000676651 -0.00050234 … -0.000409303 1.38022e-5
[:, :, 2, 1] =
-0.000460067 -0.000107008 5.77905e-6 … -0.000240988 0.000301157
-0.000478966 -0.000925048 -0.000427395 -0.000241288 -0.000264133
-0.000360445 -2.95737e-5 -0.000434373 -0.000289934 -6.69133e-5
-0.000216115 -0.000247443 -0.000172126 -0.000211383 -0.000136124
-0.000232735 -0.000382114 -0.000460042 -8.31166e-5 -0.000159839
-0.000589542 -8.42927e-5 -0.000223337 … -0.000574704 -5.28923e-5
-0.000425194 -0.000388782 -0.000240721 -0.000288628 -0.000253867
-0.000480769 -0.000710803 -0.000844951 -0.000207783 -0.000235723
-0.000341701 0.000267551 -0.000264922 3.98389e-6 -0.000140127
⋮ ⋱ ⋮
-0.0003459 -2.07316e-5 -0.000133522 -0.000437807 -0.000310041
-0.00046411 -0.000290278 -7.01407e-5 -0.000545766 -0.000203747
-0.000626812 -0.000646226 -0.000518441 -0.000184424 -0.000245369
-0.000348504 -0.00016867 -0.000313526 … -0.000363552 -0.000242687
-0.000545295 -0.000355292 -0.000290152 -0.000299346 0.00015746
-0.00025227 -0.000617288 -0.000418469 -0.000420933 -5.88442e-5
-0.000257633 -8.63061e-5 -0.000428485 -8.45088e-5 -0.000438236
-0.000102137 -0.000326709 -0.000400668 -0.000355772 -1.43647e-5
-0.000100777 -0.00015405 -7.97384e-5 … -0.000520814 -0.000936311
[:, :, 3, 1] =
-0.000548159 -0.000477585 -0.000481873 … -0.000418627 -0.000233547
-0.000336213 -0.000695811 -0.000715167 -0.000361649 -0.000291876
-0.000255482 -0.000366544 -0.000544169 -0.000500734 -0.0003229
-0.000300283 -0.000279591 -0.000585925 -0.000335869 -0.000303898
-0.000252053 -0.000579463 -0.000467105 -0.000513136 -0.00031526
-0.000468617 -0.000659257 -0.000710513 … -0.00054457 -0.000389497
-0.000372169 -0.000483613 -0.000773947 -0.000423746 -0.00058391
-0.000217254 -0.000644031 -0.000634734 -0.000646502 -0.000405915
-0.00033467 -0.000407474 -0.000578466 -0.000392831 -0.000199668
⋮ ⋱ ⋮
-0.000340643 -0.000672335 -0.000457055 -0.000829627 -0.000533199
-0.000442569 -0.000660255 -0.000655409 -0.000518397 -0.000289253
-0.000479983 -0.000490807 -0.000667401 -0.000384335 -0.000380235
-0.00028761 -0.000664416 -0.000677983 … -0.000415594 -0.000365336
-0.000468542 -0.000480694 -0.000676793 -0.000697129 -0.000421928
-0.000230119 -0.000454513 -0.000582444 -0.000374222 -0.00037701
-0.000217566 -0.000276869 -0.000450451 -0.000470439 -0.000380189
-5.60605e-5 -0.000273944 -0.000347952 -6.15815e-5 -0.000395992
2.86524e-5 -3.08762e-5 -5.8798e-5 … -0.000350873 -0.000154495
>>> unet(img_proxy)
Julia:
256×256×3×1 Array{Float64, 4}:
[:, :, 1, 1] =
-0.0010724 -0.00112552 -0.00113706 … -0.00103519 -0.000839464
-0.000795291 -0.00161727 -0.00204406 -0.0009993 -0.000439908
-0.000780577 -0.00116744 -0.00134499 -0.00115543 -0.000573814
-0.000860885 -0.00116414 -0.000965194 -0.00140911 -0.000761458
-0.000755788 -0.00132476 -0.00171172 -0.00114426 -0.000614211
-0.000820267 -0.00138928 -0.00182287 … -0.00135342 -0.000624007
-0.00142297 -0.0017854 -0.00136742 -0.00158695 -0.000791874
-0.00044614 -0.00124075 -0.00200514 -0.00151648 -0.00105216
-0.00127456 -0.00144327 -0.00143971 -0.00160615 -0.000831712
⋮ ⋱ ⋮
-0.00081029 -0.00123124 -0.00143105 -0.00146172 -0.00102908
-0.00142603 -0.00151539 -0.00138619 -0.00166838 -0.0007978
-0.000832417 -0.0018811 -0.0018118 -0.00110241 -0.000492823
-0.0010199 -0.00116831 -0.00139769 … -0.00111267 -0.000343543
-0.00121636 -0.00164644 -0.0013786 -0.0010909 -0.000885556
-0.000991607 -0.00133452 -0.00151132 -0.00168399 -0.000749311
-0.000765725 -0.00120822 -0.00101517 -0.00143129 -0.000483814
-0.000692864 -0.000724345 -0.00136182 -0.00145267 -0.000722094
-0.000375997 -0.000676651 -0.00050234 … -0.000409303 1.38022e-5
[:, :, 2, 1] =
-0.000460067 -0.000107008 5.77905e-6 … -0.000240988 0.000301157
-0.000478966 -0.000925048 -0.000427395 -0.000241288 -0.000264133
-0.000360445 -2.95737e-5 -0.000434373 -0.000289934 -6.69133e-5
-0.000216115 -0.000247443 -0.000172126 -0.000211383 -0.000136124
-0.000232735 -0.000382114 -0.000460042 -8.31166e-5 -0.000159839
-0.000589542 -8.42927e-5 -0.000223337 … -0.000574704 -5.28923e-5
-0.000425194 -0.000388782 -0.000240721 -0.000288628 -0.000253867
-0.000480769 -0.000710803 -0.000844951 -0.000207783 -0.000235723
-0.000341701 0.000267551 -0.000264922 3.98389e-6 -0.000140127
⋮ ⋱ ⋮
-0.0003459 -2.07316e-5 -0.000133522 -0.000437807 -0.000310041
-0.00046411 -0.000290278 -7.01407e-5 -0.000545766 -0.000203747
-0.000626812 -0.000646226 -0.000518441 -0.000184424 -0.000245369
-0.000348504 -0.00016867 -0.000313526 … -0.000363552 -0.000242687
-0.000545295 -0.000355292 -0.000290152 -0.000299346 0.00015746
-0.00025227 -0.000617288 -0.000418469 -0.000420933 -5.88442e-5
-0.000257633 -8.63061e-5 -0.000428485 -8.45088e-5 -0.000438236
-0.000102137 -0.000326709 -0.000400668 -0.000355772 -1.43647e-5
-0.000100777 -0.00015405 -7.97384e-5 … -0.000520814 -0.000936311
[:, :, 3, 1] =
-0.000548159 -0.000477585 -0.000481873 … -0.000418627 -0.000233547
-0.000336213 -0.000695811 -0.000715167 -0.000361649 -0.000291876
-0.000255482 -0.000366544 -0.000544169 -0.000500734 -0.0003229
-0.000300283 -0.000279591 -0.000585925 -0.000335869 -0.000303898
-0.000252053 -0.000579463 -0.000467105 -0.000513136 -0.00031526
-0.000468617 -0.000659257 -0.000710513 … -0.00054457 -0.000389497
-0.000372169 -0.000483613 -0.000773947 -0.000423746 -0.00058391
-0.000217254 -0.000644031 -0.000634734 -0.000646502 -0.000405915
-0.00033467 -0.000407474 -0.000578466 -0.000392831 -0.000199668
⋮ ⋱ ⋮
-0.000340643 -0.000672335 -0.000457055 -0.000829627 -0.000533199
-0.000442569 -0.000660255 -0.000655409 -0.000518397 -0.000289253
-0.000479983 -0.000490807 -0.000667401 -0.000384335 -0.000380235
-0.00028761 -0.000664416 -0.000677983 … -0.000415594 -0.000365336
-0.000468542 -0.000480694 -0.000676793 -0.000697129 -0.000421928
-0.000230119 -0.000454513 -0.000582444 -0.000374222 -0.00037701
-0.000217566 -0.000276869 -0.000450451 -0.000470439 -0.000380189
-5.60605e-5 -0.000273944 -0.000347952 -6.15815e-5 -0.000395992
2.86524e-5 -3.08762e-5 -5.8798e-5 … -0.000350873 -0.000154495
>>> img_proxy = jl.rand(256,256,3,1)
>>> unet(img_proxy)
[1] 172407 segmentation fault (core dumped) python
Your system
I am running NixOS 23.11 and below is the Poetry environment that I am using with juliacall.
Julia is version 1.10.0.
───────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
│ File: pyproject.toml
───────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ [tool.poetry]
2 │ name = "scientific_test"
3 │ version = "0.1.0"
4 │ description = ""
5 │ authors = ["Your Name <[email protected]>"]
6 │
7 │ [tool.poetry.dependencies]
8 │ python = "^3.10"
9 │ matplotlib = "*"
10 │ pandas = "*"
11 │ requests = "^2.31.0"
12 │ pillow = "^10.1.0"
13 │ pyjulia = "^0.0.6"
14 │ juliacall = "^0.9.14"
15 │ julia = "^0.6.1"
16 │ mlflow = "*"
17 │ setuptools = "^69.0.3"
18 │ setuptools-scm = "^8.0.4"
19 │ boto3 = "^1.34.33"
20 │ pip = "^23.3.2"
21 │ virtualenv = "^20.25.0"
22 │ gunicorn = "^21.2.0"
23 │ cloudpickle = "^3.0.0"
24 │
25 │ [build-system]
26 │ requires = ["poetry-core>=1.1"]
27 │ build-backend = "poetry.core.masonry.api"
28 │
───────┴──────────────────────────────────────────────────
Status `~/work-code/sentinal-hub/model/Project.toml`
[fbe9abb3] AWS v1.90.4
[1c724243] AWSS3 v0.11.2
⌅ [79e6a3ab] Adapt v3.7.2
[c7e460c6] ArgParse v1.1.5
[02898b10] Augmentor v0.6.6
[fbb218c0] BSON v0.3.9
[6e4b80f9] BenchmarkTools v1.5.0
⌅ [052768ef] CUDA v3.13.1
[3da002f7] ColorTypes v0.11.4
[992eb4ea] CondaPkg v0.2.22
[864edb3b] DataStructures v0.18.18
[44557152] DateFormats v0.1.18
[5789e2e9] FileIO v1.16.2
⌅ [587475ba] Flux v0.12.10
⌃ [28b8d3ca] GR v0.72.8
[4c0ca9eb] Gtk v1.3.0
[cd3eb016] HTTP v1.10.3
[7073ff75] IJulia v1.24.2
⌅ [a09fc81d] ImageCore v0.8.22
[4381153b] ImageDraw v0.2.6
⌅ [6a3955dd] ImageFiltering v0.6.21
⌅ [82e4d734] ImageIO v0.5.9
⌃ [6218d12a] ImageMagick v1.3.0
⌅ [02fcd773] ImageTransformations v0.8.13
⌃ [86fae568] ImageView v0.10.14
⌅ [916415d5] Images v0.24.1
[033835bb] JLD2 v0.4.46
[682c06a0] JSON v0.21.4
[a21d8daa] LabelStudioHelper v1.0.0-DEV `https://gitlab.com/usmcamp0811/labelstudiohelper#main`
[64a0f543] MLFlowClient v0.4.4 `https://github.com/usmcamp0811/MLFlowClient.jl#main`
[4281f0d9] Minio v0.2.1
[e94cdb99] MosaicViews v0.3.4
[6fe1bfb0] OffsetArrays v1.13.0
[5fb14364] OhMyREPL v0.5.24
[91a5bcdd] Plots v1.40.1
[c3e4b0f8] Pluto v0.19.40
[92933f4c] ProgressMeter v1.10.0
[438e738f] PyCall v1.96.4
[6099a3de] PythonCall v0.9.15
⌅ [2913bbd2] StatsBase v0.33.21
[5e47fb64] TestImages v1.8.0
[0d73aaa9] UNet v0.2.1
[104b5d7c] WebSockets v1.6.0
⌃ [da03df04] gdk_pixbuf_jll v2.42.8+0
[ade2ca70] Dates
[4af54fe1] LazyArtifacts
[8dfed614] Test
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`
Cannot reproduce (I'm on macOS). UNet seems to work fine for me.
Did you start Python with PYTHON_JULIACALL_HANDLE_SIGNALS=yes in the environment variable? Otherwise it can have segfaults due to multithreading (since Python has a global interpreter lock).
