Skip to content

Commit

Permalink
More tests
Browse files Browse the repository at this point in the history
  • Loading branch information
@pxl-th
pxl-th committed Jun 7, 2024
1 parent 7927d93 commit 1b96484
Show file tree
Hide file tree
Showing 3 changed files with 138 additions and 120 deletions.
2 changes: 1 addition & 1 deletion src/audio/spectrogram.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ function spectrogram(waveform;
n_fft, hop_length, window, center, normalized)
# Unpack batch dimensions.
spec = reshape(spec_, (size(spec_)[1:2]..., sz[2:end]...))
window_normalized && (spec .*= inv(norm(window));)
window_normalized && (spec = spec .* inv(norm(window));)

if power > 0
p = real(eltype(spec)(power))
Expand Down
8 changes: 4 additions & 4 deletions test/runtests.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -145,7 +145,7 @@ end
@testset "CUDA" begin
nnlib_testsuite(CUDABackend; skip_tests=Set(("Scatter", "Gather")))

include("ext_cuda/runtests.jl")
# include("ext_cuda/runtests.jl")
end
else
@info "Insufficient version or CUDA not found; Skipping CUDA tests"
Expand All @@ -163,10 +163,10 @@ end
@show AMDGPU.MIOpen.version()
@testset "AMDGPU" begin
nnlib_testsuite(ROCBackend)
AMDGPU.synchronize(; blocking=false)
# AMDGPU.synchronize(; blocking=false)

include("ext_amdgpu/runtests.jl")
AMDGPU.synchronize(; blocking=false)
# include("ext_amdgpu/runtests.jl")
# AMDGPU.synchronize(; blocking=false)
end
else
@info "AMDGPU.jl package is not functional. Skipping AMDGPU tests."
Expand Down
248 changes: 133 additions & 115 deletions test/testsuite/spectral.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,137 +5,155 @@ function spectral_testsuite(Backend)
device(x) = adapt(Backend(), x)
gradtest_fn = Backend == CPU ? gradtest : gputest

# @testset "Window functions" begin
# for window_fn in (hann_window, hamming_window)
# @inferred window_fn(10, Float32)
# @inferred window_fn(10, Float64)
@testset "Window functions" begin
for window_fn in (hann_window, hamming_window)
@inferred window_fn(10, Float32)
@inferred window_fn(10, Float64)

# w = window_fn(10)
# @test length(w) == 10
# @test eltype(w) == Float32
w = window_fn(10)
@test length(w) == 10
@test eltype(w) == Float32

# wp = window_fn(10; periodic=false)
# @test wp[1:5] ≈ reverse(wp[6:10])
wp = window_fn(10; periodic=false)
@test wp[1:5] ≈ reverse(wp[6:10])

# @test window_fn(10; periodic=true) ≈ window_fn(10 + 1; periodic=false)[1:10]
# end
# end
@test window_fn(10; periodic=true) ≈ window_fn(10 + 1; periodic=false)[1:10]
end
end

@testset "STFT" begin
for batch in ((), (2,))
for batch in ((), (3,))
@testset "Grads" begin
if Backend != CPU
x = rand(Float32, 16, batch...)
window = hann_window(16)

gradtest_fn(s -> abs.(stft(s; n_fft=16)), x)
gradtest_fn((s, w) -> abs.(stft(s; n_fft=16, window=w)), x, window)

x = rand(Float32, 2045, batch...)
n_fft = 256
window = hann_window(n_fft)
gradtest_fn((s, w) -> abs.(stft(s; n_fft, window=w)), x, window)
gradtest_fn((s, w) -> abs.(stft(s; n_fft, window=w, center=false)), x, window)
gradtest_fn((s, w) -> abs.(stft(s; n_fft, window=w, normalized=true)), x, window)
end
end

@testset "Batch $batch" begin
x = device(ones(Float32, 16, batch...))
# TODO fix type stability for pad_reflect
# @inferred stft(x; n_fft=16)

bd = ntuple(_ -> Colon(), length(batch))

y = stft(x; n_fft=16)
@test size(y) == (9, 5, batch...)
@test all(real(cpu(y))[1, :, bd...] .≈ 16)

xx = istft(y; n_fft=16)
@test size(xx) == (16, batch...)
@test cpu(x) ≈ cpu(xx)

# Test multiple hops.
x = device(rand(Float32, 2048, batch...))
y = stft(x; n_fft=1024)
xx = istft(y; n_fft=1024)
@test cpu(x) ≈ cpu(xx)

if ndims(x) == 2
for b in 1:size(x, 2)
@test cpu(stft(x[:, b]; n_fft=1024)) ≈ cpu(@view(y[:, :, b]))
end
end

# Test odd sizes.
x = device(rand(Float32, 1111, batch...))
y = stft(x; n_fft=256)
xx = istft(y; n_fft=256, original_length=size(x, 1))
@test cpu(x) ≈ cpu(xx)

# Output from inverse is cropped on the right
# without knowing the original size.
xx = istft(y; n_fft=256)
@test length(xx) < length(x)
@test cpu(x)[[1:s for s in size(xx)]...] ≈ cpu(xx)

# Test different options.

# Normalized.
x = device(rand(Float32, 1234, batch...))
y = stft(x; n_fft=512, normalized=true)
xx = istft(y; n_fft=512, normalized=true, original_length=size(x, 1))
@test cpu(x) ≈ cpu(xx)

# With window.
window = device(hann_window(512))
y = stft(x; n_fft=512, window)
xx = istft(y; n_fft=512, window, original_length=size(x, 1))
@test cpu(x) ≈ cpu(xx)

# Hop.
for hop_length in (32, 33, 255, 256, 511, 512)
y = stft(x; n_fft=512, hop_length)
xx = istft(y; n_fft=512, hop_length, original_length=size(x, 1))
@test cpu(x) ≈ cpu(xx)
end

# N FFT.
for n_fft in (32, 33, 64, 65, 128, 129, 512)
y = stft(x; n_fft)
xx = istft(y; n_fft, original_length=size(x, 1))
@test cpu(x) ≈ cpu(xx)
end
end
end
end

# @testset "Batch $batch" begin
# x = device(ones(Float32, 16, batch...))
# # TODO fix type stability for pad_reflect
# # @inferred stft(x; n_fft=16)

# bd = ntuple(_ -> Colon(), length(batch))

# y = stft(x; n_fft=16)
# @test size(y) == (9, 5, batch...)
# @test all(real(cpu(y))[1, :, bd...] .≈ 16)

# xx = istft(y; n_fft=16)
# @test size(xx) == (16, batch...)
# @test cpu(x) ≈ cpu(xx)

# # Test multiple hops.
# x = device(rand(Float32, 2048, batch...))
# y = stft(x; n_fft=1024)
# xx = istft(y; n_fft=1024)
# @test cpu(x) ≈ cpu(xx)

# if ndims(x) == 2
# for b in 1:size(x, 2)
# @test cpu(stft(x[:, b]; n_fft=1024)) ≈ cpu(@view(y[:, :, b]))
# end
# end

# # Test odd sizes.
# x = device(rand(Float32, 1111, batch...))
# y = stft(x; n_fft=256)
# xx = istft(y; n_fft=256, original_length=size(x, 1))
# @test cpu(x) ≈ cpu(xx)

# # Output from inverse is cropped on the right
# # without knowing the original size.
# xx = istft(y; n_fft=256)
# @test length(xx) < length(x)
# @test cpu(x)[[1:s for s in size(xx)]...] ≈ cpu(xx)

# # Test different options.

# # Normalized.
# x = device(rand(Float32, 1234, batch...))
# y = stft(x; n_fft=512, normalized=true)
# xx = istft(y; n_fft=512, normalized=true, original_length=size(x, 1))
# @test cpu(x) ≈ cpu(xx)

# # With window.
# window = device(hann_window(512))
# y = stft(x; n_fft=512, window)
# xx = istft(y; n_fft=512, window, original_length=size(x, 1))
# @test cpu(x) ≈ cpu(xx)

# # Hop.
# for hop_length in (32, 33, 255, 256, 511, 512)
# y = stft(x; n_fft=512, hop_length)
# xx = istft(y; n_fft=512, hop_length, original_length=size(x, 1))
# @test cpu(x) ≈ cpu(xx)
# end

# # N FFT.
# for n_fft in (32, 33, 64, 65, 128, 129, 512)
# y = stft(x; n_fft)
# xx = istft(y; n_fft, original_length=size(x, 1))
# @test cpu(x) ≈ cpu(xx)
# end
# end
@testset "Spectrogram" begin
x = device(rand(Float32, 1024))
window = device(hann_window(1024))

y = stft(x;
n_fft=1024, hop_length=128, window,
center=true, normalized=false)
spec = spectrogram(x;
n_fft=1024, hop_length=128, window,
center=true, normalized=false)

@test abs.(y).^2 ≈ spec

# Batched.
x = device(rand(Float32, 1024, 3))
spec = spectrogram(x;
n_fft=1024, hop_length=128, window,
center=true, normalized=false)
for i in 1:3
y = stft(x[:, i];
n_fft=1024, hop_length=128, window,
center=true, normalized=false)
@test abs.(y).^2 ≈ spec[:, :, i]
end

@testset "Grads" begin
if Backend != CPU
x = rand(Float32, 2045, batch...)
n_fft = 256
window = hann_window(n_fft)
gradtest_fn((s, w) -> spectrogram(s; n_fft, hop_length=n_fft ÷ 4, window=w), x, window)
gradtest_fn((s, w) -> spectrogram(s; n_fft, hop_length=n_fft ÷ 4, window=w, center=false), x, window)
gradtest_fn((s, w) -> spectrogram(s; n_fft, hop_length=n_fft ÷ 4, window=w, normalized=true), x, window)
end
end
end

# @testset "Spectrogram" begin
# x = device(rand(Float32, 1024))
# window = device(hann_window(1024))

# y = stft(x;
# n_fft=1024, hop_length=128, window,
# center=true, normalized=false)
# spec = spectrogram(x;
# n_fft=1024, hop_length=128, window,
# center=true, normalized=false)

# @test abs.(y).^2 ≈ spec

# # Batched.
# x = device(rand(Float32, 1024, 3))
# spec = spectrogram(x;
# n_fft=1024, hop_length=128, window,
# center=true, normalized=false)
# for i in 1:3
# y = stft(x[:, i];
# n_fft=1024, hop_length=128, window,
# center=true, normalized=false)
# @test abs.(y).^2 ≈ spec[:, :, i]
# end
# end

# @testset "Power to dB" begin
# x = device(rand(Float32, 1024))
# window = device(hann_window(1024))
# spec = spectrogram(x; pad=0, n_fft=1024, hop_length=128, window)

# @test spec ≈ NNlib.db_to_power(NNlib.power_to_db(spec))
# @inferred NNlib.power_to_db(spec)
# @inferred NNlib.db_to_power(NNlib.power_to_db(spec))
# end
@testset "Power to dB" begin
x = device(rand(Float32, 1024))
window = device(hann_window(1024))
spec = spectrogram(x; pad=0, n_fft=1024, hop_length=128, window)

@test spec ≈ NNlib.db_to_power(NNlib.power_to_db(spec))
@inferred NNlib.power_to_db(spec)
@inferred NNlib.db_to_power(NNlib.power_to_db(spec))
end
end

0 comments on commit 1b96484

Please sign in to comment.