Switch asserts to tests for PnP

JuliaImageRecon · May 28, 2024 · 119a463 · 119a463
1 parent 2497651
commit 119a463
Showing 1 changed file with 22 additions and 17 deletions.
diff --git a/test/testRegularization.jl b/test/testRegularization.jl
@@ -3,17 +3,17 @@
     model(x) = x
     # reduced constructor, checking defaults
     pnp_reg = PnPRegularization(model, [2])
-    @assert pnp_reg.λ == 1.0
-    @assert pnp_reg.model == model
-    @assert pnp_reg.shape == [2]
-    @assert pnp_reg.input_transform == RegularizedLeastSquares.MinMaxTransform
-    @assert pnp_reg.ignoreIm == false
+    @test pnp_reg.λ == 1.0
+    @test pnp_reg.model == model
+    @test pnp_reg.shape == [2]
+    @test pnp_reg.input_transform == RegularizedLeastSquares.MinMaxTransform
+    @test pnp_reg.ignoreIm == false
     # full constructor
     pnp_reg = PnPRegularization(0.1; model=model, shape=[2], input_transform=x -> x, ignoreIm=true)
     # full constructor defaults
     pnp_reg = PnPRegularization(0.1; model=model, shape=[2])
-    @assert pnp_reg.input_transform == RegularizedLeastSquares.MinMaxTransform
-    @assert pnp_reg.ignoreIm == false
+    @test pnp_reg.input_transform == RegularizedLeastSquares.MinMaxTransform
+    @test pnp_reg.ignoreIm == false
     # unnecessary kwargs are ignored
     pnp_reg = PnPRegularization(0.1; model=model, shape=[2], input_transform=x -> x, ignoreIm=true, sMtHeLsE=1)
 end
@@ -27,9 +27,14 @@ end
     b = A * x
 
     for solver in supported_solvers
-        S = createLinearSolver(solver, A, iterations=2; reg=[pnp_reg])
-        x_approx = solve!(S, b)
-        @info "PnP Regularization and $solver Compatibility"
+        @test try 
+            S = createLinearSolver(solver, A, iterations=2; reg=[pnp_reg])
+            x_approx = solve!(S, b)
+            @info "PnP Regularization and $solver Compatibility"
+            true
+        catch ex
+            false
+        end
     end
 end
 
@@ -38,7 +43,7 @@ end
     pnp_reg = PnPRegularization(0.1; model=x -> zeros(eltype(x), size(x)), shape=[2], input_transform=RegularizedLeastSquares.IdentityTransform)
     out = prox!(pnp_reg, [1.0, 2.0], 0.1)
     @info out
-    @assert out == [0.9, 1.8]
+    @test out == [0.9, 1.8]
 end
 
 
@@ -49,24 +54,24 @@ end
         input_transform=RegularizedLeastSquares.IdentityTransform
     )
     out = prox!(pnp_reg, [1.0 + 1.0im, 2.0 + 2.0im], 0.1)
-    @assert real(out) == [0.9, 1.8]
-    @assert imag(out) == [0.9, 1.8]
+    @test real(out) == [0.9, 1.8]
+    @test imag(out) == [0.9, 1.8]
     # ignoreIm = true
     pnp_reg = PnPRegularization(
         0.1; model=x -> zeros(eltype(x), size(x)), shape=[2],
         input_transform=RegularizedLeastSquares.IdentityTransform,
         ignoreIm=true
     )
     out = prox!(pnp_reg, [1.0 + 1.0im, 2.0 + 2.0im], 0.1)
-    @assert real(out) == [0.9, 1.8]
-    @assert imag(out) == [1.0, 2.0]
+    @test real(out) == [0.9, 1.8]
+    @test imag(out) == [1.0, 2.0]
 end
 
 
 @testset "PnP Prox λ clipping" begin
     pnp_reg = PnPRegularization(0.1; model=x -> zeros(eltype(x), size(x)), shape=[2], input_transform=RegularizedLeastSquares.IdentityTransform)
     out = @test_warn "$(typeof(pnp_reg)) was given λ with value 1.5. Valid range is [0, 1]. λ changed to temp" prox!(pnp_reg, [1.0, 2.0], 1.5)
-    @assert out == [0.0, 0.0]
+    @test out == [0.0, 0.0]
     out = @test_warn "$(typeof(pnp_reg)) was given λ with value -1.5. Valid range is [0, 1]. λ changed to temp" prox!(pnp_reg, [1.0, 2.0], -1.5)
-    @assert out == [1.0, 2.0]
+    @test out == [1.0, 2.0]
 end