Merge pull request #921 from SciML/test

Fix test infrastructure
SciML · May 16, 2024 · ad80fe5 · ad80fe5
2 parents d427c7c + aecc7c7
commit ad80fe5
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Showing 6 changed files with 37 additions and 90 deletions.
diff --git a/Project.toml b/Project.toml
@@ -51,3 +51,36 @@ Tracker = "0.2.29"
 Zygote = "0.6"
 ZygoteRules = "0.2"
 julia = "1.9"
+
+[extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
+DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
+DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
+MLDataUtils = "cc2ba9b6-d476-5e6d-8eaf-a92d5412d41d"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
+OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Aqua", "BenchmarkTools", "CUDA", "DataInterpolations", "DelayDiffEq", "DiffEqCallbacks", "Distances", "Distributed", "Flux", "LuxCUDA", "MLDataUtils", "MLDatasets", "NLopt", "NNlib", "OneHotArrays", "Optimisers", "Optimization",  "OptimizationOptimJL",  "OptimizationOptimisers", "OrdinaryDiffEq", "Printf", "RecursiveArrayTools", "ReverseDiff", "SafeTestsets", "StaticArrays", "Statistics", "StochasticDiffEq", "Test"]
diff --git a/README.md b/README.md
@@ -25,7 +25,6 @@ For information on using the package,
 [in-development documentation](https://docs.sciml.ai/DiffEqFlux/dev/) for the version of
 the documentation, which contains the unreleased features.
 
-
 ## Problem Domain
 
 DiffEqFlux.jl is for implicit layer machine learning.

diff --git a/test/Project.toml b/test/Project.toml
diff --git a/test/neural_gde.jl b/test/neural_gde.jl
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -18,9 +18,6 @@ const is_CI = haskey(ENV, "CI")
         @safetestset "Neural DE Tests" begin
             include("neural_de.jl")
         end
-        @safetestset "Neural Graph DE" begin
-            include("neural_gde.jl")
-        end
         @safetestset "Tensor Product Layer" begin
             include("tensor_product_test.jl")
         end

diff --git a/test/tensor_product_test.jl b/test/tensor_product_test.jl
@@ -23,9 +23,11 @@ function run_test(f, layer, atol, N)
     optfunc = Optimization.OptimizationFunction((x, p) -> loss_function(x),
         Optimization.AutoZygote())
     optprob = Optimization.OptimizationProblem(optfunc, ps)
-    res = Optimization.solve(optprob, OptimizationOptimisers.Adam(0.1); callback = cb, maxiters = 100)
+    res = Optimization.solve(
+        optprob, OptimizationOptimisers.Adam(0.1); callback = cb, maxiters = 100)
     optprob = Optimization.OptimizationProblem(optfunc, res.minimizer)
-    res = Optimization.solve(optprob, OptimizationOptimisers.Adam(0.01); callback = cb, maxiters = 100)
+    res = Optimization.solve(
+        optprob, OptimizationOptimisers.Adam(0.01); callback = cb, maxiters = 100)
     optprob = Optimization.OptimizationProblem(optfunc, res.minimizer)
     res = Optimization.solve(optprob, BFGS(); callback = cb, maxiters = 200)
     opt = res.minimizer