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image.jl
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image.jl
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## BASIC IMAGE TESTS GREY
Random.seed!(123)
mutable struct mynn <: MLJFlux.Builder
kernel1
kernel2
end
MLJFlux.build(model::mynn, ip, op, n_channels) =
Flux.Chain(Flux.Conv(model.kernel1, n_channels=>2),
Flux.Conv(model.kernel2, 2=>1),
x->reshape(x, :, size(x)[end]),
Flux.Dense(16, op))
builder = mynn((2,2), (2,2))
# collection of gray images as a 4D array in WHCN format:
raw_images = rand(Float32, 6, 6, 1, 50);
# as a vector of Matrix{<:AbstractRGB}
images = coerce(raw_images, GrayImage);
@test scitype(images) == AbstractVector{GrayImage{6,6}}
labels = categorical(rand(1:5, 50));
losses = []
@testset_accelerated "ImageClassifier basic tests" accel begin
Random.seed!(123)
model = MLJFlux.ImageClassifier(builder=builder,
epochs=10,
acceleration=accel)
fitresult, cache, _report = MLJBase.fit(model, 0, images, labels)
pred = MLJBase.predict(model, fitresult, images[1:6])
model.epochs = 15
MLJBase.update(model, 0, fitresult, cache, images, labels)
pred = MLJBase.predict(model, fitresult, images[1:6])
# try with batch_size > 1:
model = MLJFlux.ImageClassifier(builder=builder, epochs=10, batch_size=2,
acceleration=accel)
@time fitresult, cache, _report = MLJBase.fit(model, 0, images, labels);
first_last_training_loss = _report[1][[1, end]]
push!(losses, first_last_training_loss[2])
# @show first_last_training_loss
# tests update logic, etc (see test_utililites.jl):
@test basictest(MLJFlux.ImageClassifier, images, labels,
model.builder, model.optimiser, 0.95, accel)
end
# check different resources (CPU1, CUDALibs, etc)) give about the same loss:
reference = losses[1]
@test all(x->abs(x - reference)/reference < 1e-5, losses[2:end])
## MNIST IMAGES TEST
mutable struct MyConvBuilder <: MLJFlux.Builder end
using Flux.Data:MNIST
images, labels = MNIST.images(), MNIST.labels();
labels = categorical(labels);
function flatten(x::AbstractArray)
return reshape(x, :, size(x)[end])
end
function MLJFlux.build(builder::MyConvBuilder, n_in, n_out, n_channels)
cnn_output_size = [3,3,32]
return Chain(
Conv((3, 3), n_channels=>16, pad=(1,1), relu),
MaxPool((2,2)),
Conv((3, 3), 16=>32, pad=(1,1), relu),
MaxPool((2,2)),
Conv((3, 3), 32=>32, pad=(1,1), relu),
MaxPool((2,2)),
flatten,
Dense(prod(cnn_output_size), n_out))
end
losses = []
@testset_accelerated "Image MNIST" accel begin
Random.seed!(123)
model = MLJFlux.ImageClassifier(builder=MyConvBuilder(),
acceleration=accel,
batch_size=50)
@time fitresult, cache, _report =
MLJBase.fit(model, 0, images[1:500], labels[1:500]);
first_last_training_loss = _report[1][[1, end]]
push!(losses, first_last_training_loss[2])
# @show first_last_training_loss
pred = mode.(MLJBase.predict(model, fitresult, images[501:600]));
error = misclassification_rate(pred, labels[501:600])
@test error < 0.2
end
# check different resources (CPU1, CUDALibs, etc)) give about the same loss:
reference = losses[1]
@test all(x->abs(x - reference)/reference < 1e-4, losses[2:end])
## BASIC IMAGE TESTS COLOR
builder = mynn((2,2), (2,2))
# collection of color images as a 4D array in WHCN format:
raw_images = rand(Float32, 6, 6, 3, 50);
images = coerce(raw_images, ColorImage);
@test scitype(images) == AbstractVector{ColorImage{6,6}}
labels = categorical(rand(1:5, 50));
losses = []
@testset_accelerated "ColorImages" accel begin
Random.seed!(123)
model = MLJFlux.ImageClassifier(builder=builder,
epochs=10,
acceleration=accel)
# tests update logic, etc (see test_utililites.jl):
@test basictest(MLJFlux.ImageClassifier, images, labels,
model.builder, model.optimiser, 0.95, accel)
@time fitresult, cache, _report = MLJBase.fit(model, 0, images, labels)
pred = MLJBase.predict(model, fitresult, images[1:6])
first_last_training_loss = _report[1][[1, end]]
push!(losses, first_last_training_loss[2])
# @show first_last_training_loss
# try with batch_size > 1:
model = MLJFlux.ImageClassifier(builder=builder,
epochs=10,
batch_size=2,
acceleration=accel)
fitresult, cache, _report = MLJBase.fit(model, 0, images, labels);
end
# check different resources (CPU1, CUDALibs, etc)) give about the same loss:
reference = losses[1]
@test all(x->abs(x - reference)/reference < 1e-5, losses[2:end])
true