Allow cpu(::DataLoader) (FluxML#2388)

src/functor.jl

@@ -403,8 +403,9 @@ function _metal end
 
 """
     gpu(data::DataLoader)
+    cpu(data::DataLoader)
 
-Transforms a given `DataLoader` to apply `gpu` to each batch of data,
+Transforms a given `DataLoader` to apply `gpu` or `cpu` to each batch of data,
 when iterated over. (If no GPU is available, this does nothing.)
 
 # Example
@@ -456,6 +457,18 @@ function gpu(d::MLUtils.DataLoader)
   )
 end
 
+function cpu(d::MLUtils.DataLoader)
+  MLUtils.DataLoader(MLUtils.mapobs(cpu, d.data),
+    d.batchsize,
+    d.buffer,
+    d.partial,
+    d.shuffle,
+    d.parallel,
+    d.collate,
+    d.rng,
+  )
+end
+
 # Defining device interfaces.
 """
     Flux.AbstractDevice <: Function

test/data.jl

@@ -1,3 +1,4 @@
+using Flux: DataLoader
 using Random
 
 @testset "DataLoader" begin
@@ -14,6 +15,11 @@ using Random
     @test batches[2] == X[:,3:4]
     @test batches[3] == X[:,5:5]
 
+    d_cpu = d |> cpu  # does nothing but shouldn't error
+    @test d_cpu isa DataLoader
+    @test first(d_cpu) == X[:,1:2]
+    @test length(d_cpu) == 3
+
     d = DataLoader(X, batchsize=2, partial=false)
     # @inferred first(d)
     batches = collect(d)

test/ext_cuda/cuda.jl

@@ -182,11 +182,14 @@ end
   X = randn(Float64, 3, 33)
   pre1 = Flux.DataLoader(X |> gpu; batchsize=13, shuffle=false)
   post1 = Flux.DataLoader(X; batchsize=13, shuffle=false) |> gpu
+  rev1 = pre1 |> cpu  # inverse operation
   for epoch in 1:2
-    for (p, q) in zip(pre1, post1)
+    for (p, q, a) in zip(pre1, post1, rev1)
       @test p isa CuArray{Float32}
       @test q isa CuArray{Float32}
       @test p ≈ q
+      @test a isa Array{Float32}
+      @test a ≈ Array(p)
     end
   end