add tests and fix existing ones

Project.toml

@@ -33,6 +33,7 @@ julia = "1.9"
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
@@ -42,4 +43,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [targets]
-test = ["CUDA", "cuDNN", "LinearAlgebra", "MLJBase", "Random", "StableRNGs", "StatisticalMeasures", "StatsBase", "Test"]
+test = ["CUDA", "cuDNN", "LinearAlgebra", "Logging", "MLJBase", "Random", "StableRNGs", "StatisticalMeasures", "StatsBase", "Test"]

test/core.jl

@@ -14,23 +14,28 @@ rowvec(y::Vector) = reshape(y, 1, length(y))
 end
 
 @testset "collate" begin
-    # NeuralNetworRegressor:
-    Xmatrix = broadcast(x->round(x, sigdigits=2), rand(stable_rng, 10, 3))
+    Xmatrix = broadcast(x->round(x, sigdigits=2), rand(stable_rng, Float32, 10, 3))
+    Xmat_f64 = Float64.(Xmatrix)
     # convert to a column table:
     X = MLJBase.table(Xmatrix)
+    X_64 = MLJBase.table(Xmat_f64)
 
+    # NeuralNetworRegressor:
     y = rand(stable_rng, Float32, 10)
     model = MLJFlux.NeuralNetworkRegressor()
     model.batch_size= 3
-    @test MLJFlux.collate(model, X, y) ==
+    @test MLJFlux.collate(model, X, y, 1) == MLJFlux.collate(model, X_64, y, 1) ==
         ([Xmatrix'[:,1:3], Xmatrix'[:,4:6], Xmatrix'[:,7:9], Xmatrix'[:,10:10]],
          rowvec.([y[1:3], y[4:6], y[7:9], y[10:10]]))
+    @test_logs (:info,) MLJFlux.collate(model, X_64, y, 1)
+    @test_logs min_level=Logging.Info MLJFlux.collate(model, X, y, 1)
+    @test_logs min_level=Logging.Info MLJFlux.collate(model, X, y, 0)
 
     # NeuralNetworClassifier:
     y = categorical(['a', 'b', 'a', 'a', 'b', 'a', 'a', 'a', 'b', 'a'])
     model = MLJFlux.NeuralNetworkClassifier()
     model.batch_size = 3
-    data = MLJFlux.collate(model, X, y)
+    data = MLJFlux.collate(model, X, y, 1)
 
     @test data == ([Xmatrix'[:,1:3], Xmatrix'[:,4:6],
                     Xmatrix'[:,7:9], Xmatrix'[:,10:10]],
@@ -42,13 +47,13 @@ end
     y = MLJBase.table(ymatrix) # a rowaccess table
     model = MLJFlux.NeuralNetworkRegressor()
     model.batch_size= 3
-    @test MLJFlux.collate(model, X, y) ==
+    @test MLJFlux.collate(model, X, y, 1) ==
         ([Xmatrix'[:,1:3], Xmatrix'[:,4:6], Xmatrix'[:,7:9], Xmatrix'[:,10:10]],
          rowvec.([ymatrix'[:,1:3], ymatrix'[:,4:6], ymatrix'[:,7:9],
                   ymatrix'[:,10:10]]))
 
     y = Tables.columntable(y) # try a columnaccess table
-    @test MLJFlux.collate(model, X, y) ==
+    @test MLJFlux.collate(model, X, y, 1) ==
         ([Xmatrix'[:,1:3], Xmatrix'[:,4:6], Xmatrix'[:,7:9], Xmatrix'[:,10:10]],
          rowvec.([ymatrix'[:,1:3], ymatrix'[:,4:6],
                   ymatrix'[:,7:9], ymatrix'[:,10:10]]))
@@ -58,7 +63,7 @@ end
     y = categorical(['a', 'b', 'a', 'a', 'b', 'a', 'a', 'a', 'b', 'a'])
     model = MLJFlux.ImageClassifier(batch_size=2)
 
-    data = MLJFlux.collate(model, Xmatrix, y)
+    data = MLJFlux.collate(model, Xmatrix, y, 1)
     @test  first.(data) == (Float32.(cat(Xmatrix[1], Xmatrix[2], dims=4)),
                             rowvec.([1 0;0 1]))
 

test/mlj_model_interface.jl

@@ -94,7 +94,7 @@ end
         # (2) manually train for one epoch explicitly adding a loss penalty:
         chain = MLJFlux.build(builder, StableRNG(123), 3, 1);
         penalty = Penalizer(lambda, alpha); # defined in test_utils.jl
-        X, y = MLJFlux.collate(model, Xuser, yuser);
+        X, y = MLJFlux.collate(model, Xuser, yuser, 0);
         loss = model.loss;
         n_batches = div(nobservations, batch_size)
         optimiser_state = Optimisers.setup(optimiser, chain);

test/runtests.jl

@@ -13,6 +13,7 @@ using StableRNGs
 using CUDA, cuDNN
 import StatisticalMeasures
 import Optimisers
+import Logging
 
 using ComputationalResources
 using ComputationalResources: CPU1, CUDALibs