Remove F in ctc tests; update ctc-gpu test syntax

test/ctc-gpu.jl

@@ -23,32 +23,19 @@ function ctc_ngradient(x, y)
   return grads
 end
 
-function F(A, blank)
-  prev = A[1]
-  z = [prev]
-  for curr in A[2:end]
-    if curr != prev
-      push!(z, curr)
-    end
-    prev = curr
-  end
-  filter!(x -> x != blank, z)
-  return z
-end
-
 @testset "ctc-gpu" begin
   x = rand(10, 50)
-  y = F(rand(1:9, 30), 10)
+  y = rand(1:9, 30)
   x_cu = CuArray(x)
   g1 = gradient(ctc_loss, x_cu, y)[1]
   g1 = g1 |> collect
   g2 = ctc_ngradient(x, y)
-  @test all(isapprox.(g1, g2, rtol=1e-5, atol=1e-5))
+  @test g1 ≈ g2 rtol=1e-5 atol=1e-5
 
   # test that GPU loss matches CPU implementation
   l1 = ctc_loss(x_cu, y)
   l2 = ctc_loss(x, y)
-  @test all(isapprox.(l1, l2, rtol=1e-5, atol=1e-5))
+  @test l1 ≈ l2
 
   # tests using hand-calculated values
   x_cu = [1. 2. 3.; 2. 1. 1.; 3. 3. 2.] |> CuArray
@@ -57,13 +44,13 @@ end
 
   g = [-0.317671 -0.427729 0.665241; 0.244728 -0.0196172 -0.829811; 0.0729422 0.447346 0.16457]
   ghat = gradient(ctc_loss, x_cu, y)[1] |> collect
-  @test all(isapprox.(g, ghat, rtol=1e-5, atol=1e-5))
+  @test g ≈ ghat rtol=1e-5 atol=1e-5
 
   x_cu = [-3. 12. 8. 15.; 4. 20. -2. 20.; 8. -33. 6. 5.] |> CuArray
   y = [1, 2] |> CuArray
   @test ctc_loss(x_cu, y) ≈ 8.02519869363453
 
   g = [-2.29294774655333e-06 -0.999662657278862 1.75500863563993e-06 0.00669284889063; 0.017985914969696 0.999662657278861 -1.9907078755387e-06 -0.006693150917307; -0.01798362202195 -2.52019580677916e-20 2.35699239251042e-07 3.02026677058789e-07]
   ghat = gradient(ctc_loss, x_cu, y)[1] |> collect
-  @test all(isapprox.(g, ghat, rtol=1e-5, atol=1e-5))
+  @test g ≈ ghat rtol=1e-5 atol=1e-5
 end

test/ctc.jl

@@ -22,25 +22,12 @@ function ctc_ngradient(x, y)
   return grads
 end
 
-function F(A, blank)
-  prev = A[1]
-  z = [prev]
-  for curr in A[2:end]
-    if curr != prev
-      push!(z, curr)
-    end
-    prev = curr
-  end
-  filter!(x -> x != blank, z)
-  return z
-end
-
 @testset "ctc_loss" begin
   x = rand(10, 50)
-  y = F(rand(1:9, 30), 10)
+  y = rand(1:9, 30)
   g1 = gradient(ctc_loss, x, y)[1]
   g2 = ctc_ngradient(x, y)
-  @test g1 ≈ g2  rtol=1e-5 atol=1e-5
+  @test g1 ≈ g2 rtol=1e-5 atol=1e-5
 
   # tests using hand-calculated values
   x = [1. 2. 3.; 2. 1. 1.; 3. 3. 2.]
@@ -49,13 +36,13 @@ end
 
   g = [-0.317671 -0.427729 0.665241; 0.244728 -0.0196172 -0.829811; 0.0729422 0.447346 0.16457]
   ghat = gradient(ctc_loss, x, y)[1]
-  @test g ≈ ghat  rtol=1e-5 atol=1e-5
+  @test g ≈ ghat rtol=1e-5 atol=1e-5
 
   x = [-3. 12. 8. 15.; 4. 20. -2. 20.; 8. -33. 6. 5.]
   y = [1, 2]
   @test ctc_loss(x, y) ≈ 8.02519869363453
 
   g = [-2.29294774655333e-06 -0.999662657278862 1.75500863563993e-06 0.00669284889063; 0.017985914969696 0.999662657278861 -1.9907078755387e-06 -0.006693150917307; -0.01798362202195 -2.52019580677916e-20 2.35699239251042e-07 3.02026677058789e-07]
   ghat = gradient(ctc_loss, x, y)[1]
-  @test g ≈ ghat   rtol=1e-5 atol=1e-5
+  @test g ≈ ghat rtol=1e-5 atol=1e-5
 end