`Chain(::Vector)` instability

Manifest.toml

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Project.toml

[deps]
CoordinateTransformations = "150eb455-5306-5404-9cee-2592286d6298"
DataAugmentation = "88a5189c-e7ff-4f85-ac6b-e6158070f02e"
DataLoaders = "2e981812-ef13-4a9c-bfa0-ab13047b12a9"
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ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
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ParameterSchedulers = "d7d3b36b-41b8-4d0d-a2bf-768c6151755e"

test.jl

using Flux
using FluxTraining
using ParameterSchedulers
using MLDatasets
using MLDatasets: FileDataset
using DataLoaders: DataLoader
using DataAugmentation
using CoordinateTransformations
using Metalhead
using MLUtils
using Statistics
using ImageCore

import MLDataPattern

# bugfix for mapobs
Base.getindex(data::MLUtils.MappedData, idx::Integer) = data.f(getobs(data.data, idx))
Base.getindex(data::MLUtils.MappedData, idxs::AbstractVector) =
    batch(map(Base.Fix1(getindex, data), idxs))

# bugfix for one hot arrays
Flux._indices(x::Base.ReshapedArray{<:Any, <:Any, <:Flux.OneHotVector}) =
  reshape([parent(x).indices], x.dims[2:end])

# hack for DataLoaders.jl + MLUtils.jl
MLDataPattern.LearnBase.getobs(x, i) = MLUtils.getobs(x, i)
MLDataPattern.LearnBase.nobs(x) = MLUtils.numobs(x)

# bug fix for ADAMW + FluxTraining
FluxTraining.setlearningrate!(os::Flux.Optimiser, lr) = foreach(os) do o
    if hasproperty(o, :eta)
        o.eta = lr
    end
end

struct Pad{N} <: DataAugmentation.Transform
    size::NTuple{N, Int}
end
Pad(size::Int, N::Int) = Pad(ntuple(i -> size, N))

function DataAugmentation.apply(pad::Pad{M}, item::Image{N}; randstate) where {M, N}
    @assert M == 2*N "For an Image{N}, supply a Pad{2*N}."

    output = NNlib.pad_zeros(item.data, pad.size)

    return Image(output, item.bounds)
end

const CIFAR10_MEAN = (0.4914, 0.4822, 0.4465)
const CIFAR10_STD = (0.2471, 0.2435, 0.2616)

apply_augmenation(pipeline, x) = itemdata(DataAugmentation.apply(pipeline, Image(x)))
apply_augmenation(pipeline, x::AbstractVector) =
    map(Base.Fix1(apply_augmenation, pipeline), x)
apply_augmenation(pipeline, x::NamedTuple{(:image, :label)}) =
    (image = apply_augmenation(pipeline, x.image), label = x.label)
map_augmentation(pipeline, data) =
    MLUtils.mapobs(Base.Fix1(apply_augmenation, pipeline), data)

## defining the data

traindata = (image = mapobs(DataAugmentation.tensortoimage, CIFAR10(:train).features),
             label = Flux.onehotbatch(CIFAR10(:train).targets, 0:9))
# valdata = (image = mapobs(DataAugmentation.tensortoimage, CIFAR10(:test).features),
#            label = Flux.onehotbatch(CIFAR10(:test).targets, 0:9))
traindata = traindata |> shuffleobs
traindata, valdata = splitobs(traindata; at = 0.8)

## data augmentation

augmentations = Rotate(10) |>
                Pad(4, 4) |>
                RandomCrop((32, 32)) |>
                Maybe(FlipX()) |>
                ImageToTensor() |>
                Normalize(CIFAR10_MEAN, CIFAR10_STD)
trainaug = map_augmentation(augmentations, traindata)
valaug = map_augmentation(ImageToTensor() |> Normalize(CIFAR10_MEAN, CIFAR10_STD), valdata)
;

## model definition

m = ResNet(18; nclasses = 10)

## data loaders

bs = 64
trainloader = DataLoader(BatchView(trainaug; batchsize = bs), nothing; buffered = true)
valloader = DataLoader(BatchView(valaug; batchsize = bs), nothing; buffered = true)
;

## training setup

lossfn = Flux.Losses.logitcrossentropy

# define schedule and optimizer
initial_lr = 0.1
schedule = Interpolator(Step(initial_lr, 0.5, [50, 25, 25]),
                        length(trainloader))
optim = Flux.Optimiser(Momentum(initial_lr), WeightDecay(1e-4))
optim = ParameterSchedulers.Scheduler(schedule, optim, (o, s) -> o[1].eta = s)

# callbacks
logger = TensorBoardBackend("tblogs")
# schcb = Scheduler(LearningRate => schedule)
hlogcb = LogHyperParams(logger)
mlogcb = LogMetrics(logger)
valcb = Metrics(Metric(accuracy; phase = TrainingPhase, name = "train_acc"),
                Metric(accuracy; phase = ValidationPhase, name = "val_acc"))

# setup learner object
learner = Learner(m, lossfn;
                  data = (trainloader, valloader),
                  optimizer = optim,
                  callbacks = [ToGPU(), mlogcb, valcb])

## train model

FluxTraining.fit!(learner, 100)
# make sure to close logger due to network fs
close(logger.logger)