no method matching getobs(::NamedTuple{(:x,), Tuple{Matrix{Float32}}}

[zsz00]

I test the code of
https://carlolucibello.github.io/GraphNeuralNetworks.jl/stable/

I got an error:

GraphNeuralNetworks v0.3.6

julia> versioninfo()
Julia Version 1.7.1
Commit ac5cc99908 (2021-12-22 19:35 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-12.0.1 (ORCJIT, cascadelake)

[CarloLucibello]

It's not clear what code you are running. Can you post the full example (not as an image but in a julia code block)?

[zsz00]

example code from:
https://carlolucibello.github.io/GraphNeuralNetworks.jl/stable/#Training

julia> using GraphNeuralNetworks, Graphs, Flux, CUDA, Statistics

julia> all_graphs = GNNGraph[];

julia> for _ in 1:1000
           g = GNNGraph(random_regular_graph(10, 4),  
                       ndata=(; x = randn(Float32, 16,10)),  # input node features
                       gdata=(; y = randn(Float32)))         # regression target   
           push!(all_graphs, g)
       end

julia> gbatch = Flux.batch(all_graphs)
GNNGraph:
    num_nodes = 10000
    num_edges = 40000
    num_graphs = 1000
    ndata:
        x => (16, 10000)
    gdata:
        y => (1000,)

julia> device = CUDA.functional() ? Flux.gpu : Flux.cpu;

julia> model = GNNChain(GCNConv(16 => 64),
                        BatchNorm(64),     # Apply batch normalization on node features (nodes dimension is batch dimension)
                        x -> relu.(x),     
                        GCNConv(64 => 64, relu),
                        GlobalPool(mean),  # aggregate node-wise features into graph-wise features
                        Dense(64, 1)) |> device;

julia> ps = Flux.params(model);

julia> opt = ADAM(1f-4);

gtrain = getgraph(gbatch, 1:800)
gtest = getgraph(gbatch, 801:gbatch.num_graphs)
train_loader = Flux.Data.DataLoader(gtrain, batchsize=32, shuffle=true)
test_loader = Flux.Data.DataLoader(gtest, batchsize=32, shuffle=false)

loss(g::GNNGraph) = mean((vec(model(g, g.ndata.x)) - g.gdata.y).^2)

loss(loader) = mean(loss(g |> device) for g in loader)

for epoch in 1:100
    for g in train_loader
        g = g |> device
        grad = gradient(() -> loss(g), ps)
        Flux.Optimise.update!(opt, ps, grad)
    end

    @info (; epoch, train_loss=loss(train_loader), test_loss=loss(test_loader))
end

[CarloLucibello]

This code works fine for me

[zsz00]

julia> gbatch
GNNGraph:
    num_nodes = 10000
    num_edges = 40000
    num_graphs = 1000
    ndata:
        x => (16, 10000)
    gdata:
        y => (1000,)

julia> getgraph(gbatch, 1:10)
ERROR: MethodError: no method matching getobs(::NamedTuple{(:x,), Tuple{Matrix{Float32}}}, ::BitVector)
Closest candidates are:
  getobs(::GNNGraph, ::Any) at ~/.julia/packages/GraphNeuralNetworks/8LJLl/src/GNNGraphs/gnngraph.jl:227
Stacktrace:
 [1] getgraph(g::GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}, i::UnitRange{Int64}; nmap::Bool)
   @ GraphNeuralNetworks.GNNGraphs ~/.julia/packages/GraphNeuralNetworks/8LJLl/src/GNNGraphs/transform.jl:323
 [2] getgraph(g::GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}, i::UnitRange{Int64})
   @ GraphNeuralNetworks.GNNGraphs ~/.julia/packages/GraphNeuralNetworks/8LJLl/src/GNNGraphs/transform.jl:293
 [3] top-level scope
   @ REPL[15]:1
 [4] top-level scope
   @ ~/.julia/packages/CUDA/zwPff/src/initialization.jl:52

what might have caused this??

julia 1.7.1, GraphNeuralNetworks v0.3.6

[CarloLucibello]

I see. I think there is some version conflict with Flux, LearnBase, and MLDataUtils. Can you post the versions in your Manifest for those 3 packages?

[zsz00]

find Flux, LearnBase in my Manifest.

(@v1.7) pkg> st --manifest Flux
      Status `~/.julia/environments/v1.7/Manifest.toml`
  [587475ba] Flux v0.12.8

(@v1.7) pkg> st --manifest MLDataUtils
  No Matches in `~/.julia/environments/v1.7/Manifest.toml`

(@v1.7) pkg> st --manifest LearnBase
      Status `~/.julia/environments/v1.7/Manifest.toml`
  [7f8f8fb0] LearnBase v0.4.1

(@v1.7) pkg>

[zsz00]

I removed MLJ.jl, and up LearnBase.jl , then run the code is OK .
LearnBase.jl is v0.5.3