use MLUtils

NEWS.md

@@ -7,6 +7,8 @@ been removed in favour of MLDatasets.jl.
 * `flatten` is not exported anymore due to clash with Iterators.flatten.
 * Remove Juno.jl progress bar support as it is now obsolete.
 * `Dropout` gained improved compatibility with Int and Complex arrays and is now twice-differentiable.
+* Many utily functions and the `DataLoader` are [now provided by MLUtils.jl](https://github.com/FluxML/Flux.jl/pull/1874).
+* The DataLoader is now compatible with generic dataset types implementing `MLUtils.numobs` and `MLUtils.getobs`.
 
 ## v0.12.10
 * `Dropout`/`AlphaDropout` now supports [user-specified RNGs](https://github.com/FluxML/Flux.jl/pull/1838)

Project.toml

@@ -8,6 +8,7 @@ ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
@@ -25,6 +26,7 @@ Adapt = "3.0"
 ArrayInterface = "3.1, 4"
 CUDA = "3"
 Functors = "0.2.1"
+MLUtils = "0.1.4"
 MacroTools = "0.5"
 NNlib = "0.8.2"
 NNlibCUDA = "0.2"

docs/src/models/nnlib.md

@@ -76,7 +76,10 @@ NNlib.gather
 NNlib.gather!
 NNlib.scatter
 NNlib.scatter!
+```
+
+## Miscellaneous
 
-## Utilities
+```@docs
 NNlib.logsumexp
 ```

docs/src/utilities.md

@@ -7,15 +7,17 @@ callback functions.
 
 ## Working with Data
 
+Utilities for data processing are provided by [MLUtils.jl](https://github.com/JuliaML/MLUtils.jl). Below is a non-exhaustive list.
+
 ```@docs
-Flux.unsqueeze
-Flux.stack
-Flux.unstack
-Flux.chunk
-Flux.frequencies
-Flux.batch
-Flux.unbatch
-Flux.batchseq
+MLUtils.unsqueeze
+MLUtils.stack
+MLUtils.unstack
+MLUtils.chunk
+MLUtils.group_counts
+MLUtils.batch
+MLUtils.unbatch
+MLUtils.batchseq
 Base.rpad(v::AbstractVector, n::Integer, p)
 ```
 

src/Flux.jl

@@ -7,6 +7,9 @@ using Statistics, Random, LinearAlgebra
 using Zygote, MacroTools, ProgressLogging, Reexport
 using MacroTools: @forward
 @reexport using NNlib
+
+using MLUtils
+
 using Zygote: Params, @adjoint, gradient, pullback, @nograd
 export gradient
 
@@ -50,6 +53,7 @@ include("outputsize.jl")
 include("data/Data.jl")
 using .Data
 
+
 include("losses/Losses.jl")
 using .Losses # TODO: stop importing Losses in Flux's namespace in v0.12
 

src/data/Data.jl

@@ -1,9 +1,6 @@
 module Data
 
-using Random: shuffle!
-using Base: @propagate_inbounds
-
-include("dataloader.jl")
+using MLUtils
 export DataLoader
 
 end#module

src/deprecations.jl

@@ -16,3 +16,4 @@ ones32(::Type, dims...) = throw(ArgumentError("Flux.ones32 is always Float32, us
 zeros32(::Type, dims...) = throw(ArgumentError("Flux.zeros32 is always Float32, use Base.zeros to specify the element type"))
 
 # v0.13 deprecations
+@deprecate frequencies(xs) group_counts(xs)

src/onehot.jl

@@ -87,7 +87,7 @@ Base.hcat(x::T, xs::T...) where {L, T <: OneHotLike{<:Any, L, <:Any, 2}} =
 Base.hcat(x::T, xs::T...) where {L, T <: OneHotLike{<:Any, L, <:Any, 1}} =
   OneHotMatrix(reduce(vcat, _indices.(xs); init = _indices(x)), L)
 
-batch(xs::AbstractArray{<:OneHotVector{<:Any, L}}) where L = OneHotArray(_indices.(xs), L)
+MLUtils.batch(xs::AbstractArray{<:OneHotVector{<:Any, L}}) where L = OneHotMatrix(_indices.(xs), L)
 
 Adapt.adapt_structure(T, x::OneHotArray{<:Any, L}) where L = OneHotArray(adapt(T, _indices(x)), L)