Bump compat for Metalhead

Project.toml

@@ -21,12 +21,13 @@ ColorTypes = "0.10.3, 0.11"
 ComputationalResources = "0.3.2"
 Flux = "0.13, 0.14"
 MLJModelInterface = "1.1.1"
-Metalhead = "0.7"
+Metalhead = "0.8"
 ProgressMeter = "1.7.1"
 Tables = "1.0"
 julia = "1.6"
 
 [extras]
+cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -36,4 +37,4 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["LinearAlgebra", "MLJBase", "Random", "StableRNGs", "Statistics", "StatsBase", "Test"]
+test = ["cuDNN", "LinearAlgebra", "MLJBase", "Random", "StableRNGs", "Statistics", "StatsBase", "Test"]

src/metalhead.jl

@@ -4,10 +4,10 @@ TODO: After https://github.com/FluxML/Metalhead.jl/issues/176:
 
 - Export and externally document `image_builder` method
 
-- Delete definition of `ResNetHack` below
+- Delete definition of `VGGHack` below
 
 - Change default builder in ImageClassifier (see /src/types.jl) from
-  `image_builder(ResNetHack)` to `image_builder(Metalhead.ResNet)`.
+  `image_builder(VGGHack)` to `image_builder(Metalhead.VGG)`.
 
 =#
 
@@ -51,7 +51,7 @@ Base.show(io::IO, w::MetalheadBuilder) =
 
 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
+passed as arguments to `metalhead_constructor` at "build time", along with
 the extra keyword specifiers `imsize=...`, `inchannels=...` and
 `nclasses=...`, with values inferred from the data.
 
@@ -61,14 +61,14 @@ If in Metalhead.jl you would do
 
 ```julia
 using Metalhead
-model = ResNet(50, pretrain=true, inchannels=1, nclasses=10)
+model = ResNet(50, pretrain=false, inchannels=1, nclasses=10)
 ```
 
 then in MLJFlux, it suffices to do
 
 ```julia
 using MLJFlux, Metalhead
-builder = image_builder(ResNet, 50, pretrain=true)
+builder = image_builder(ResNet, 50, pretrain=false)
 ```
 
 which can be used in `ImageClassifier` as in
@@ -122,25 +122,31 @@ function VGGHack(
     pretrain=false,
 )
 
-    # Adapted from 
-    # https://github.com/FluxML/Metalhead.jl/blob/9edff63222720ff84671b8087dd71eb370a6c35a/src/convnets/vgg.jl#L165
+    # Adapted from
+    # https://github.com/FluxML/Metalhead.jl/blob/4e5b8f16964468518eeb6eb8d7e5f85af4ecf959/src/convnets/vgg.jl#L161
     # 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))))"
+        depth in keys(Metalhead.VGG_CONFIGS),
+        "depth must be from one in $(sort(collect(keys(Metalhead.VGG_CONFIGS))))"
     )
     model = Metalhead.VGG(imsize;
-                config = Metalhead.vgg_conv_config[Metalhead.vgg_config[depth]],
+                config = Metalhead.VGG_CONV_CONFIGS[Metalhead.VGG_CONFIGS[depth]],
                 inchannels,
                 batchnorm,
                 nclasses,
-                fcsize = 4096,
-                dropout = 0.5)
-    if pretrain && !batchnorm
-        Metalhead.loadpretrain!(model, string("VGG", depth))
-    elseif pretrain
-        Metalhead.loadpretrain!(model, "VGG$(depth)-BN)")
+                dropout_prob = 0.5)
+    if pretrain
+        imsize == (224, 224) || @warn "Using `pretrain=true` may not work unless "*
+            "image size is `(224, 224)`, which it is not. "
+        artifact_name = string("vgg", depth)
+        if batchnorm
+            artifact_name *= "_bn"
+        else
+            artifact_name *= "-IMAGENET1K_V1"
+        end
+        loadpretrain!(model, artifact_name)
     end
+
     return model
 end

src/mlj_model_interface.jl

@@ -40,7 +40,7 @@ end
 
 # # FIT AND  UPDATE
 
-const ERR_BUILDER = 
+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
@@ -60,17 +60,18 @@ function MLJModelInterface.fit(model::MLJFluxModel,
     catch ex
         @error ERR_BUILDER
     end
-    
+
     penalty = Penalty(model)
     data = move.(collate(model, X, y))
 
-    x = data |> first |> first
+    x = data[1][1]
+
     try
         chain(x)
     catch ex
         @error ERR_BUILDER
         throw(ex)
-    end 
+    end
 
     optimiser = deepcopy(model.optimiser)
 

test/builders.jl

@@ -47,7 +47,7 @@ end
 
     # reproducibility (without dropout):
     chain2 = MLJFlux.build(builder, StableRNGs.StableRNG(123), 5, 3)
-    x = rand(5)
+    x = rand(Float32, 5)
     @test chain(x) ≈ chain2(x)
 end
 

test/core.jl

@@ -7,7 +7,7 @@ rowvec(y::Vector) = reshape(y, 1, length(y))
 @test MLJFlux.MLJModelInterface.istransparent(Flux.Adam(0.1))
 
 @testset "nrows" begin
-    Xmatrix = rand(stable_rng, 10, 3)
+    Xmatrix = rand(stable_rng, Float32, 10, 3)
     X = MLJBase.table(Xmatrix)
     @test MLJFlux.nrows(X) == 10
     @test MLJFlux.nrows(Tables.columntable(X)) == 10
@@ -19,7 +19,7 @@ end
     # convert to a column table:
     X = MLJBase.table(Xmatrix)
 
-    y = rand(stable_rng, 10)
+    y = rand(stable_rng, Float32, 10)
     model = MLJFlux.NeuralNetworkRegressor()
     model.batch_size= 3
     @test MLJFlux.collate(model, X, y) ==
@@ -38,7 +38,7 @@ end
                             reshape([1; 0], (2,1))]))
 
     # MultitargetNeuralNetworRegressor:
-    ymatrix = rand(stable_rng, 10, 2)
+    ymatrix = rand(stable_rng, Float32, 10, 2)
     y = MLJBase.table(ymatrix) # a rowaccess table
     model = MLJFlux.NeuralNetworkRegressor()
     model.batch_size= 3
@@ -54,7 +54,7 @@ end
                   ymatrix'[:,7:9], ymatrix'[:,10:10]]))
 
     # ImageClassifier
-    Xmatrix = coerce(rand(stable_rng, 6, 6, 1, 10), GrayImage)
+    Xmatrix = coerce(rand(stable_rng, Float32, 6, 6, 1, 10), GrayImage)
     y = categorical(['a', 'b', 'a', 'a', 'b', 'a', 'a', 'a', 'b', 'a'])
     model = MLJFlux.ImageClassifier(batch_size=2)
 
@@ -69,7 +69,7 @@ end
 
 end
 
-Xmatrix = rand(stable_rng, 100, 5)
+Xmatrix = rand(stable_rng, Float32, 100, 5)
 X = MLJBase.table(Xmatrix)
 y = Xmatrix[:, 1] + Xmatrix[:, 2] + Xmatrix[:, 3] +
     Xmatrix[:, 4] + Xmatrix[:, 5]

test/image.jl

@@ -8,17 +8,29 @@ mutable struct MyNeuralNetwork <: MLJFlux.Builder
     kernel2
 end
 
-function MLJFlux.build(model::MyNeuralNetwork, rng, ip, op, n_channels)
+# to get a matrix whose last dimension mathces that of the array input (the batch size):
+function make2d(x)
+    l = length(x)
+    b = size(x)[end]
+    reshape(x, div(l, b), b)
+end
+
+function MLJFlux.build(builder::MyNeuralNetwork, rng, ip, op, n_channels)
     init = Flux.glorot_uniform(rng)
-    Flux.Chain(
-        Flux.Conv(model.kernel1, n_channels=>2, init=init),
-        Flux.Conv(model.kernel2, 2=>1, init=init),
-        x->reshape(x, :, size(x)[end]),
-        Flux.Dense(16, op, init=init))
+    front = Flux.Chain(
+        Flux.Conv(builder.kernel1, n_channels=>2, init=init),
+        Flux.Conv(builder.kernel2, 2=>1, init=init),
+        make2d,
+    )
+    d = Flux.outputsize(front, (ip..., n_channels, 1))[1]
+    return Flux.Chain(
+        front,
+        Flux.Dense(d, op, init=init)
+    )
 end
 
 builder = MyNeuralNetwork((2,2), (2,2))
-images, labels = MLJFlux.make_images(stable_rng)
+images, labels = MLJFlux.make_images(stable_rng);
 losses = []
 
 @testset_accelerated "ImageClassifier basic tests" accel begin
@@ -69,8 +81,6 @@ reference = losses[1]
 
 # # BASIC IMAGE TESTS COLOR
 
-# In this case we use the default ResNet builder
-
 builder = MyNeuralNetwork((2,2), (2,2))
 images, labels = MLJFlux.make_images(stable_rng, color=true)
 losses = []
@@ -112,12 +122,13 @@ reference = losses[1]
 @test all(x->abs(x - reference)/reference < 1e-5, losses[2:end])
 
 
-# # SMOKE TEST FOR DEFAULT BUILDER 
+# # SMOKE TEST FOR DEFAULT BUILDER
 
-images, labels = MLJFlux.make_images(stable_rng, image_size=(32, 32), n_images=12, noise=0.2, color=true);
+images, labels = MLJFlux.make_images(stable_rng, image_size=(32, 32), n_images=12,
+noise=0.2, color=true);
 
 @testset_accelerated "ImageClassifier basic tests" accel begin
-    model = MLJFlux.ImageClassifier(epochs=10,
+    model = MLJFlux.ImageClassifier(epochs=5,
                                     batch_size=4,
                                     acceleration=accel,
                                     rng=stable_rng)

test/metalhead.jl

@@ -42,16 +42,15 @@ end
     @test builder.metalhead_constructor == Metalhead.VGG
     @test builder.args == (depth, )
     @test (; builder.kwargs...) == (; batchnorm=true)
-    ref_chain = Metalhead.VGG(
-        imsize;
-        config = Metalhead.vgg_conv_config[Metalhead.vgg_config[depth]],
-        inchannels,
-        batchnorm=true,
-        nclasses,
-        fcsize = 4096,
-        dropout = 0.5
-    )
-    # needs https://github.com/FluxML/Metalhead.jl/issues/176
+
+    ## needs https://github.com/FluxML/Metalhead.jl/issues/176:
+    # ref_chain = Metalhead.VGG(
+    #     imsize;
+    #     config = Metalhead.VGG_CONV_CONFIGS[Metalhead.VGG_CONFIGS[depth]],
+    #     inchannels,
+    #     batchnorm=true,
+    #     nclasses,
+    # )
     # chain =
     #    MLJFlux.build(builder, StableRNGs.StableRNG(123), imsize, nclasses, inchannels)
     # @test length.(MLJFlux.Flux.params(ref_chain)) ==

test/mlj_model_interface.jl

@@ -44,6 +44,7 @@ end
 
     # integration test:
     X, y = MLJBase.make_regression(10)
+    X = Float32.(MLJBase.Tables.matrix(X)) |> MLJBase.Tables.table
     mach = MLJBase.machine(model, X, y)
     MLJBase.fit!(mach, verbosity=0)
     losses = MLJBase.training_losses(mach)

test/runtests.jl

@@ -10,6 +10,7 @@ import Random.seed!
 using Statistics
 import StatsBase
 using StableRNGs
+using cuDNN
 
 using ComputationalResources
 using ComputationalResources: CPU1, CUDALibs