This repository has been archived by the owner on Sep 28, 2024. It is now read-only.
-
-
Notifications
You must be signed in to change notification settings - Fork 41
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
5 changed files
with
207 additions
and
178 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,130 +1,19 @@ | ||
| export | ||
| SparseKernel, | ||
| SparseKernel1D, | ||
| SparseKernel2D, | ||
| SparseKernel3D | ||
| export WaveletTransform | ||
|
|
||
|
|
||
| struct SparseKernel{N,T,S} | ||
| conv_blk::T | ||
| out_weight::S | ||
| end | ||
|
|
||
| function SparseKernel(filter::NTuple{N,T}, ch::Pair{S, S}; init=Flux.glorot_uniform) where {N,T,S} | ||
| input_dim, emb_dim = ch | ||
| conv = Conv(filter, input_dim=>emb_dim, relu; stride=1, pad=1, init=init) | ||
| W_out = Dense(emb_dim, input_dim; init=init) | ||
| return SparseKernel{N,typeof(conv),typeof(W_out)}(conv, W_out) | ||
| end | ||
|
|
||
| function SparseKernel1D(k::Int, α, c::Int=1; init=Flux.glorot_uniform) | ||
| input_dim = c*k | ||
| emb_dim = 128 | ||
| return SparseKernel((3, ), input_dim=>emb_dim; init=init) | ||
| end | ||
|
|
||
| function SparseKernel2D(k::Int, α, c::Int=1; init=Flux.glorot_uniform) | ||
| input_dim = c*k^2 | ||
| emb_dim = α*k^2 | ||
| return SparseKernel((3, 3), input_dim=>emb_dim; init=init) | ||
| end | ||
|
|
||
| function SparseKernel3D(k::Int, α, c::Int=1; init=Flux.glorot_uniform) | ||
| input_dim = c*k^2 | ||
| emb_dim = α*k^2 | ||
| conv = Conv((3, 3, 3), emb_dim=>emb_dim, relu; stride=1, pad=1, init=init) | ||
| W_out = Dense(emb_dim, input_dim; init=init) | ||
| return SparseKernel{3,typeof(conv),typeof(W_out)}(conv, W_out) | ||
| end | ||
|
|
||
| Flux.@functor SparseKernel | ||
|
|
||
| function (l::SparseKernel)(X::AbstractArray) | ||
| bch_sz, _, dims_r... = reverse(size(X)) | ||
| dims = reverse(dims_r) | ||
|
|
||
| X_ = l.conv_blk(X) # (dims..., emb_dims, B) | ||
| X_ = reshape(X_, prod(dims), :, bch_sz) # (prod(dims), emb_dims, B) | ||
| Y = l.out_weight(batched_transpose(X_)) # (in_dims, prod(dims), B) | ||
| Y = reshape(batched_transpose(Y), dims..., :, bch_sz) # (dims..., in_dims, B) | ||
| return collect(Y) | ||
| end | ||
|
|
||
|
|
||
| struct MWT_CZ1d{T,S,R,Q,P} | ||
| k::Int | ||
| L::Int | ||
| A::T | ||
| B::S | ||
| C::R | ||
| T0::Q | ||
| ec_s::P | ||
| ec_d::P | ||
| rc_e::P | ||
| rc_o::P | ||
| end | ||
|
|
||
| function MWT_CZ1d(k::Int=3, α::Int=5, L::Int=0, c::Int=1; base::Symbol=:legendre, init=Flux.glorot_uniform) | ||
| H0, H1, G0, G1, Φ0, Φ1 = get_filter(base, k) | ||
| H0r = zero_out!(H0 * Φ0) | ||
| G0r = zero_out!(G0 * Φ0) | ||
| H1r = zero_out!(H1 * Φ1) | ||
| G1r = zero_out!(G1 * Φ1) | ||
|
|
||
| dim = c*k | ||
| A = SpectralConv(dim=>dim, (α,); init=init) | ||
| B = SpectralConv(dim=>dim, (α,); init=init) | ||
| C = SpectralConv(dim=>dim, (α,); init=init) | ||
| T0 = Dense(k, k) | ||
|
|
||
| ec_s = vcat(H0', H1') | ||
| ec_d = vcat(G0', G1') | ||
| rc_e = vcat(H0r, G0r) | ||
| rc_o = vcat(H1r, G1r) | ||
| return MWT_CZ1d(k, L, A, B, C, T0, ec_s, ec_d, rc_e, rc_o) | ||
| end | ||
|
|
||
| function wavelet_transform(l::MWT_CZ1d, X::AbstractArray{T,4}) where {T} | ||
| N = size(X, 3) | ||
| Xa = vcat(view(X, :, :, 1:2:N, :), view(X, :, :, 2:2:N, :)) | ||
| d = NNlib.batched_mul(Xa, l.ec_d) | ||
| s = NNlib.batched_mul(Xa, l.ec_s) | ||
| return d, s | ||
| struct WaveletTransform{N, S}<:AbstractTransform | ||
| modes::NTuple{N, S} # N == ndims(x) | ||
| end | ||
|
|
||
| function even_odd(l::MWT_CZ1d, X::AbstractArray{T,4}) where {T} | ||
| bch_sz, N, dims_r... = reverse(size(X)) | ||
| dims = reverse(dims_r) | ||
| @assert dims[1] == 2*l.k | ||
| Xₑ = NNlib.batched_mul(X, l.rc_e) | ||
| Xₒ = NNlib.batched_mul(X, l.rc_o) | ||
| # x = torch.zeros(B, N*2, c, self.k, | ||
| # device = x.device) | ||
| # x[..., ::2, :, :] = x_e | ||
| # x[..., 1::2, :, :] = x_o | ||
| return X | ||
| end | ||
| Base.ndims(::WaveletTransform{N}) where {N} = N | ||
|
|
||
| function (l::MWT_CZ1d)(X::T) where {T<:AbstractArray} | ||
| bch_sz, N, dims_r... = reverse(size(X)) | ||
| ns = floor(log2(N)) | ||
| stop = ns - l.L | ||
| # function transform(wt::WaveletTransform, 𝐱::AbstractArray) | ||
| # return fft(Zygote.hook(real, 𝐱), 1:ndims(wt)) # [size(x)..., in_chs, batch] | ||
| # end | ||
|
|
||
| # decompose | ||
| Ud = T[] | ||
| Us = T[] | ||
| for i in 1:stop | ||
| d, X = wavelet_transform(l, X) | ||
| push!(Ud, l.A(d)+l.B(d)) | ||
| push!(Us, l.C(d)) | ||
| end | ||
| X = l.T0(X) | ||
| # function truncate_modes(wt::WaveletTransform, 𝐱_fft::AbstractArray) | ||
| # return view(𝐱_fft, map(d->1:d, wt.modes)..., :, :) # [ft.modes..., in_chs, batch] | ||
| # end | ||
|
|
||
| # reconstruct | ||
| for i in stop:-1:1 | ||
| X += Us[i] | ||
| X = vcat(X, Ud[i]) # x = torch.cat((x, Ud[i]), -1) | ||
| X = even_odd(l, X) | ||
| end | ||
| return X | ||
| end | ||
| # function inverse(wt::WaveletTransform, 𝐱_fft::AbstractArray) | ||
| # return real(ifft(𝐱_fft, 1:ndims(wt))) # [size(x_fft)..., out_chs, batch] | ||
| # end |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
File renamed without changes.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,53 +0,0 @@ | ||
| @testset "SparseKernel" begin | ||
| T = Float32 | ||
| k = 3 | ||
| batch_size = 32 | ||
|
|
||
| @testset "1D SparseKernel" begin | ||
| α = 4 | ||
| c = 1 | ||
| in_chs = 20 | ||
| X = rand(T, in_chs, c*k, batch_size) | ||
|
|
||
| l1 = SparseKernel1D(k, α, c) | ||
| Y = l1(X) | ||
| @test l1 isa SparseKernel{1} | ||
| @test size(Y) == size(X) | ||
|
|
||
| gs = gradient(()->sum(l1(X)), Flux.params(l1)) | ||
| @test length(gs.grads) == 4 | ||
| end | ||
|
|
||
| @testset "2D SparseKernel" begin | ||
| α = 4 | ||
| c = 3 | ||
| Nx = 5 | ||
| Ny = 7 | ||
| X = rand(T, Nx, Ny, c*k^2, batch_size) | ||
|
|
||
| l2 = SparseKernel2D(k, α, c) | ||
| Y = l2(X) | ||
| @test l2 isa SparseKernel{2} | ||
| @test size(Y) == size(X) | ||
|
|
||
| gs = gradient(()->sum(l2(X)), Flux.params(l2)) | ||
| @test length(gs.grads) == 4 | ||
| end | ||
|
|
||
| @testset "3D SparseKernel" begin | ||
| α = 4 | ||
| c = 3 | ||
| Nx = 5 | ||
| Ny = 7 | ||
| Nz = 13 | ||
| X = rand(T, Nx, Ny, Nz, α*k^2, batch_size) | ||
|
|
||
| l3 = SparseKernel3D(k, α, c) | ||
| Y = l3(X) | ||
| @test l3 isa SparseKernel{3} | ||
| @test size(Y) == (Nx, Ny, Nz, c*k^2, batch_size) | ||
|
|
||
| gs = gradient(()->sum(l3(X)), Flux.params(l3)) | ||
| @test length(gs.grads) == 4 | ||
| end | ||
| end | ||
Oops, something went wrong.