migrate

Author: yuehhua

src/Transform/wavelet_transform.jl

@@ -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

src/operator_kernel.jl

@@ -2,7 +2,11 @@ export
     OperatorConv,
     SpectralConv,
     OperatorKernel,
-    GraphKernel
+    GraphKernel,
+    SparseKernel,
+    SparseKernel1D,
+    SparseKernel2D,
+    SparseKernel3D
 
 struct OperatorConv{P, T, S, TT}
     weight::T
@@ -216,6 +220,141 @@ function Base.show(io::IO, l::GraphKernel)
     print(io, ")")
 end
 
+"""
+    SparseKernel(κ, ch, σ=identity)
+
+Sparse kernel layer.
+
+## Arguments
+
+* `κ`: A neural network layer for approximation, e.g. a `Dense` layer or a MLP.
+* `ch`: Channel size for linear transform, e.g. `32`.
+* `σ`: Activation function.
+"""
+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
+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
+
+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
+
+    # 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)
+
+    # 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
+
 
 #########
 # utils #

test/polynomials.jl renamed to test/Transform/polynomials.jl

@@ -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