Add preliminary Metalhead.jl integration

Project.toml

@@ -9,6 +9,7 @@ ColorTypes = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
+Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -18,15 +19,15 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 CategoricalArrays = "0.10"
 ColorTypes = "0.10.3, 0.11"
 ComputationalResources = "0.3.2"
-Flux = "0.10.4, 0.11, 0.12, 0.13"
+Flux = "0.13"
+Metalhead = "0.7"
 MLJModelInterface = "1.1.1"
 ProgressMeter = "1.7.1"
 Tables = "1.0"
 julia = "1.6"
 
 [extras]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
@@ -35,4 +36,4 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["LinearAlgebra", "MLDatasets", "MLJBase", "Random", "StableRNGs", "Statistics", "StatsBase", "Test"]
+test = ["LinearAlgebra", "MLJBase", "Random", "StableRNGs", "Statistics", "StatsBase", "Test"]

src/MLJFlux.jl

@@ -13,12 +13,15 @@ using Statistics
 using ColorTypes
 using ComputationalResources
 using Random
+import Metalhead
 
+include("utilities.jl")
 const MMI=MLJModelInterface
 
 include("penalizers.jl")
 include("core.jl")
 include("builders.jl")
+include("metalhead.jl")
 include("types.jl")
 include("regressor.jl")
 include("classifier.jl")
@@ -27,6 +30,7 @@ include("mlj_model_interface.jl")
 
 export NeuralNetworkRegressor, MultitargetNeuralNetworkRegressor
 export NeuralNetworkClassifier, ImageClassifier
+export CUDALibs, CPU1
 
 
 

src/builders.jl

@@ -1,4 +1,4 @@
-## BUILDING CHAINS A FROM HYPERPARAMETERS + INPUT/OUTPUT SHAPE
+# # BUILDING CHAINS A FROM HYPERPARAMETERS + INPUT/OUTPUT SHAPE
 
 # We introduce chain builders as a way of exposing neural network
 # hyperparameters (describing, architecture, dropout rates, etc) to
@@ -9,7 +9,7 @@
 # input/output dimensions/shape.
 
 # Below n or (n1, n2) etc refers to network inputs, while m or (m1,
-# m2) etc refers to outputs. 
+# m2) etc refers to outputs.
 
 abstract type Builder <: MLJModelInterface.MLJType end
 
@@ -38,7 +38,7 @@ using `n_hidden` nodes in the hidden layer and the specified `dropout`
 (defaulting to 0.5). An activation function `σ` is applied between the
 hidden and final layers. If `n_hidden=0` (the default) then `n_hidden`
 is the geometric mean of the number of input and output nodes.  The
-number of input and output nodes is determined from the data. 
+number of input and output nodes is determined from the data.
 
 The each layer is initialized using `Flux.glorot_uniform(rng)`. If
 `rng` is an integer, it is instead used as the seed for a
@@ -96,6 +96,8 @@ function MLJFlux.build(mlp::MLP, rng, n_in, n_out)
 end
 
 
+# # BUILER MACRO
+
 struct GenericBuilder{F} <: Builder
     apply::F
 end

src/core.jl

@@ -1,30 +1,8 @@
 ## EXPOSE OPTIMISERS TO MLJ (for eg, tuning)
 
-# Here we make the optimiser structs "transparent" so that their
-# field values are exposed by calls to MLJ.params
-
-for opt in (:Descent,
-            :Momentum,
-            :Nesterov,
-            :RMSProp,
-            :ADAM,
-            :RADAM,
-            :AdaMax,
-            :OADAM,
-            :ADAGrad,
-            :ADADelta,
-            :AMSGrad,
-            :NADAM,
-            :AdaBelief,
-            :Optimiser,
-            :InvDecay, :ExpDecay, :WeightDecay,
-            :ClipValue,
-            :ClipNorm) # last updated: Flux.jl 0.12.3
-
-    @eval begin
-        MLJModelInterface.istransparent(m::Flux.$opt) = true
-    end
-end
+# make the optimiser structs "transparent" so that their field values
+# are exposed by calls to MLJ.params:
+MLJModelInterface.istransparent(m::Flux.Optimise.AbstractOptimiser) = true
 
 
 ## GENERAL METHOD TO OPTIMIZE A CHAIN

src/metalhead.jl

@@ -0,0 +1,146 @@
+#=
+
+TODO: After https://github.com/FluxML/Metalhead.jl/issues/176:
+
+- Export and externally document `image_builder` method
+
+- Delete definition of `ResNetHack` below
+
+- Change default builder in ImageClassifier (see /src/types.jl) from
+  `image_builder(ResNetHack)` to `image_builder(Metalhead.ResNet)`.
+
+=#
+
+const DISALLOWED_KWARGS = [:imsize, :inchannels, :nclasses]
+const human_disallowed_kwargs = join(map(s->"`$s`", DISALLOWED_KWARGS), ", ", " and ")
+const ERR_METALHEAD_DISALLOWED_KWARGS = ArgumentError(
+    "Keyword arguments $human_disallowed_kwargs are disallowed "*
+    "as their values are inferred from data. "
+)
+
+# # WRAPPING
+
+struct MetalheadBuilder{F} <: MLJFlux.Builder
+    metalhead_constructor::F
+    args
+    kwargs
+end
+
+function Base.show(io::IO, ::MIME"text/plain", w::MetalheadBuilder)
+    println(io, "builder wrapping $(w.metalhead_constructor)")
+    if !isempty(w.args)
+        println(io, "  args:")
+        for (i, arg) in enumerate(w.args)
+            println(io, "    1: $arg")
+        end
+    end
+    if !isempty(w.kwargs)
+        println(io, "  kwargs:")
+        for kwarg in w.kwargs
+            println(io, "    $(first(kwarg)) = $(last(kwarg))")
+        end
+    end
+end
+
+Base.show(io::IO, w::MetalheadBuilder) =
+    print(io, "image_builder($(repr(w.metalhead_constructor)), …)")
+
+
+"""
+    image_builder(metalhead_constructor, args...; kwargs...)
+
+Return an MLJFlux builder object based on the Metalhead.jl constructor/type
+`metalhead_constructor` (eg, `Metalhead.ResNet`). Here `args` and `kwargs` are
+passed to the `MetalheadType` constructor at "build time", along with
+the extra keyword specifiers `imsize=...`, `inchannels=...` and
+`nclasses=...`, with values inferred from the data.
+
+# Example
+
+If in Metalhead.jl you would do
+
+```julia
+using Metalhead
+model = ResNet(50, pretrain=true, inchannels=1, nclasses=10)
+```
+
+then in MLJFlux, it suffices to do
+
+```julia
+using MLJFlux, Metalhead
+builder = image_builder(ResNet, 50, pretrain=true)
+```
+
+which can be used in `ImageClassifier` as in
+
+```julia
+clf = ImageClassifier(
+    builder=builder,
+    epochs=500,
+    optimiser=Flux.Adam(0.001),
+    loss=Flux.crossentropy,
+    batch_size=5,
+)
+```
+
+The keyord arguments `imsize`, `inchannels` and `nclasses` are
+dissallowed in `kwargs` (see above).
+
+"""
+function image_builder(
+    metalhead_constructor,
+    args...;
+    kwargs...
+)
+    kw_names = keys(kwargs)
+    isempty(intersect(kw_names, DISALLOWED_KWARGS)) ||
+        throw(ERR_METALHEAD_DISALLOWED_KWARGS)
+    return MetalheadBuilder(metalhead_constructor, args, kwargs)
+end
+
+MLJFlux.build(
+    b::MetalheadBuilder,
+    rng,
+    n_in,
+    n_out,
+    n_channels
+) =  b.metalhead_constructor(
+    b.args...;
+    b.kwargs...,
+    imsize=n_in,
+    inchannels=n_channels,
+    nclasses=n_out
+)
+
+# See above "TODO" list.
+function VGGHack(
+    depth::Integer=16;
+    imsize=(242,242),
+    inchannels=3,
+    nclasses=1000,
+    batchnorm=false,
+    pretrain=false,
+)
+
+    # Adapted from 
+    # https://github.com/FluxML/Metalhead.jl/blob/9edff63222720ff84671b8087dd71eb370a6c35a/src/convnets/vgg.jl#L165
+    # But we do not ignore `imsize`.
+
+    @assert(
+        depth in keys(Metalhead.vgg_config),
+        "depth must be from one in $(sort(collect(keys(Metalhead.vgg_config))))"
+    )
+    model = Metalhead.VGG(imsize;
+                config = Metalhead.vgg_conv_config[Metalhead.vgg_config[depth]],
+                inchannels,
+                batchnorm,
+                nclasses,
+                fcsize = 4096,
+                dropout = 0.5)
+    if pretrain && !batchnorm
+        Metalhead.loadpretrain!(model, string("VGG", depth))
+    elseif pretrain
+        Metalhead.loadpretrain!(model, "VGG$(depth)-BN)")
+    end
+    return model
+end

src/mlj_model_interface.jl

@@ -40,6 +40,9 @@ end
 
 # # FIT AND  UPDATE
 
+const ERR_BUILDER = 
+    "Builder does not appear to build an architecture compatible with supplied data. "
+
 true_rng(model) = model.rng isa Integer ? MersenneTwister(model.rng) : model.rng
 
 function MLJModelInterface.fit(model::MLJFluxModel,
@@ -51,10 +54,24 @@ function MLJModelInterface.fit(model::MLJFluxModel,
 
     rng = true_rng(model)
     shape = MLJFlux.shape(model, X, y)
-    chain = build(model, rng, shape) |> move
+
+    chain = try
+        build(model, rng, shape) |> move
+    catch ex
+        @error ERR_BUILDER
+    end
+
     penalty = Penalty(model)
     data = move.(collate(model, X, y))
 
+    x = data |> first |> first
+    try
+        chain(x)
+    catch ex
+        @error ERR_BUILDER
+        throw(ex)
+    end 
+
     optimiser = deepcopy(model.optimiser)
 
     chain, history = fit!(model.loss,

src/types.jl

@@ -5,6 +5,9 @@ const MLJFluxModel = Union{MLJFluxProbabilistic,MLJFluxDeterministic}
 
 for Model in [:NeuralNetworkClassifier, :ImageClassifier]
 
+    default_builder_ex =
+        Model == :ImageClassifier ? :(image_builder(VGGHack)) : Short()
+
     ex = quote
         mutable struct $Model{B,F,O,L} <: MLJFluxProbabilistic
             builder::B
@@ -20,7 +23,7 @@ for Model in [:NeuralNetworkClassifier, :ImageClassifier]
             acceleration::AbstractResource  # eg, `CPU1()` or `CUDALibs()`
         end
 
-        function $Model(; builder::B   = Short()
+        function $Model(; builder::B   = $default_builder_ex
                         , finaliser::F = Flux.softmax
                         , optimiser::O = Flux.Optimise.Adam()
                         , loss::L      = Flux.crossentropy
@@ -108,12 +111,9 @@ for Model in [:NeuralNetworkRegressor, :MultitargetNeuralNetworkRegressor]
 
 end
 
-
-
 const Regressor =
     Union{NeuralNetworkRegressor, MultitargetNeuralNetworkRegressor}
 
-
 MMI.metadata_pkg.(
     (
         NeuralNetworkRegressor,

src/utilities.jl

@@ -0,0 +1,43 @@
+# # IMAGE COERCION
+
+# Taken from ScientificTypes.jl to avoid as dependency.
+
+_4Dcollection = AbstractArray{<:Real, 4}
+
+function coerce(y::_4Dcollection, T2::Type{GrayImage})
+    size(y, 3) == 1 || error("Multiple color channels encountered. "*
+                      "Perhaps you want to use `coerce(image_collection, ColorImage)`.")
+    y = dropdims(y, dims=3)
+    return [ColorTypes.Gray.(y[:,:,idx]) for idx=1:size(y,3)]
+end
+
+function coerce(y::_4Dcollection, T2::Type{ColorImage})
+    return [broadcast(ColorTypes.RGB, y[:,:,1, idx], y[:,:,2,idx], y[:,:,3, idx]) for idx=1:size(y,4)]
+end
+
+
+# # SYNTHETIC IMAGES
+
+"""
+    make_images(rng; image_size=(6, 6), n_classes=33, n_images=50, color=false, noise=0.05)
+
+Return synthetic data of the form `(images, labels)` suitable for use
+with MLJ's `ImageClassifier` model. All `images` are distortions of
+`n_classes` fixed images. Two images with the same label correspond to
+the same undistorted image.
+
+"""
+function make_images(rng; image_size=(6, 6), n_classes=33, n_images=50, color=false, noise=0.05)
+    n_channels = color ? 3 : 1
+    image_bag = map(1:n_classes) do _
+        rand(rng, Float32, image_size...,  n_channels)
+    end
+    labels = rand(rng, 1:3, n_images)
+    images = map(labels) do j
+        image_bag[j] + noise*rand(rng, Float32, image_size..., n_channels)
+    end
+    T = color ? ColorImage : GrayImage
+    X = coerce(cat(images...; dims=4), T)
+    y = categorical(labels)
+    return X, y
+end

test/builders.jl

@@ -52,9 +52,11 @@ end
 end
 
 @testset_accelerated "@builder" accel begin
-    builder = MLJFlux.@builder(Flux.Chain(Flux.Dense(n_in, 4,
-                                                     init = (out, in) -> randn(rng, out, in)),
-                                     Flux.Dense(4, n_out)))
+    builder = MLJFlux.@builder(Flux.Chain(Flux.Dense(
+        n_in,
+        4,
+        init = (out, in) -> randn(rng, out, in)
+    ), Flux.Dense(4, n_out)))
     rng = StableRNGs.StableRNG(123)
     chain = MLJFlux.build(builder, rng, 5, 3)
     ps = Flux.params(chain)