# groups argument in Flux.Conv : a misunderstanding

Hello,

I want to define a convolutional layer in Flux.jl using the groups option. Ideally, I want to use Conv((1,1), 3=>3, groups=3). For reference, the Flux.Conv documentation defines this argument as

• Keyword groups is expected to be an Int . It specifies the number of groups to divide a convolution into.

As I understand, Conv((1,1), 3=>3, groups=3) should define a 1 \times 1 convolution taking as input 3 channels, outputting 3 channels, but the convolution should be layer-wise. In other words, this should be equivalent to three convolutions applied independently to each channel, in particular the number of parameters should be 3 \times (1+1)=6 since a 1=>1 convolution has just 2 parameters.

julia> T = Conv((1,1), 3=>3, groups=3)
Conv((1, 1), 1 => 3)  # 6 parameters


The number of parameters fits, but the in/out channels seems bizarre. And then, when I try to apply T to an array with 3 channels, I get ERROR: AssertionError: DimensionMismatch("Data input channel count (3 vs. 3)") (see below for the full stacktrace).

For the sake of testing, I tried to apply T to an array with 1 channel, and I also get an ERROR: DimensionMismatch("Input channels must match! (1 vs. 1)").

Can someone explain how the groups argument works in Flux.Conv ?

Thanks !

Stacktrace with 3 channels (this should work but does not) :

julia> T(rand(10, 10, 3, 1))
ERROR: AssertionError: DimensionMismatch("Data input channel count (3 vs. 3)")
Stacktrace:
[1] check_dims(x::NTuple{5, Int64}, w::NTuple{5, Int64}, y::NTuple{5, Int64}, cdims::DenseConvDims{3, (1, 1, 1), 3, 3, 3, (1, 1, 1), (0, 0, 0, 0, 0, 0), (1, 1, 1), false})
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/dim_helpers/DenseConvDims.jl:73
[2] conv_direct!(y::Array{Float64, 5}, x::Array{Float64, 5}, w::Array{Float32, 5}, cdims::DenseConvDims{3, (1, 1, 1), 3, 3, 3, (1, 1, 1), (0, 0, 0, 0, 0, 0), (1, 1, 1), false}; alpha::Float64, beta::Bool)
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/impl/conv_direct.jl:51
[3] conv_direct!
@ ~/.julia/packages/NNlib/P9BhZ/src/impl/conv_direct.jl:51 [inlined]
[4] conv!(y::Array{Float64, 5}, in1::Array{Float64, 5}, in2::Array{Float32, 5}, cdims::DenseConvDims{3, (1, 1, 1), 3, 3, 3, (1, 1, 1), (0, 0, 0, 0, 0, 0), (1, 1, 1), false}; kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/conv.jl:293
[5] conv!(y::Array{Float64, 5}, in1::Array{Float64, 5}, in2::Array{Float32, 5}, cdims::DenseConvDims{3, (1, 1, 1), 3, 3, 3, (1, 1, 1), (0, 0, 0, 0, 0, 0), (1, 1, 1), false})
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/conv.jl:291
[6] conv!(y::Array{Float64, 4}, x::Array{Float64, 4}, w::Array{Float32, 4}, cdims::DenseConvDims{2, (1, 1), 3, 3, 3, (1, 1), (0, 0, 0, 0), (1, 1), false}; kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/conv.jl:151
[7] conv!
@ ~/.julia/packages/NNlib/P9BhZ/src/conv.jl:151 [inlined]
[8] conv(x::Array{Float64, 4}, w::Array{Float32, 4}, cdims::DenseConvDims{2, (1, 1), 3, 3, 3, (1, 1), (0, 0, 0, 0), (1, 1), false}; kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/conv.jl:91
[9] conv(x::Array{Float64, 4}, w::Array{Float32, 4}, cdims::DenseConvDims{2, (1, 1), 3, 3, 3, (1, 1), (0, 0, 0, 0), (1, 1), false})
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/conv.jl:89
[10] (::Conv{2, 4, typeof(identity), Array{Float32, 4}, Vector{Float32}})(x::Array{Float64, 4})
@ Flux ~/.julia/packages/Flux/ZnXxS/src/layers/conv.jl:163
[11] top-level scope
@ REPL[48]:1
 errors:


Stacktrace with 1 channel (this should not work and does not work):

julia> T(rand(10,10,1,1))
ERROR: DimensionMismatch("Input channels must match! (1 vs. 1)")
Stacktrace:
[1] DenseConvDims(x_size::NTuple{4, Int64}, w_size::NTuple{4, Int64}; stride::Tuple{Int64, Int64}, padding::NTuple{4, Int64}, dilation::Tuple{Int64, Int64}, flipkernel::Bool, groups::Int64)
@ NNlib ~/.julia/packages/NNlib/P9BhZ/src/dim_helpers/DenseConvDims.jl:30
[2] #DenseConvDims#7
@ ~/.julia/packages/NNlib/P9BhZ/src/dim_helpers/DenseConvDims.jl:60 [inlined]
[3] (::Conv{2, 4, typeof(identity), Array{Float32, 4}, Vector{Float32}})(x::Array{Float64, 4})
@ Flux ~/.julia/packages/Flux/ZnXxS/src/layers/conv.jl:162
[4] top-level scope
@ REPL[49]:1


It seems like you may have missed the preceding warning:

julia> c(rand(10, 10, 3, 1))
┌ Warning: Slow fallback implementation invoked for conv!  You probably don't want this; check your datatypes.
│   yT = Float64
│   T1 = Float64
│   T2 = Float32
└ @ NNlib ~/.julia/packages/NNlib/tvMmZ/src/conv.jl:291


Using Float32, everything works as expected:

julia> c(rand(Float32, 10, 10, 3, 1)) |> size
(10, 10, 3, 1)


That said, the assert being triggered from the fallback definition does look fishy to me. Will report an issue on NNlib for this.

Hi, you are actually right : I didn’t pay attention to the Slow fallback` warning, and things work as intented with Float32. But the

ERROR: AssertionError: DimensionMismatch(“Data input channel count (3 vs. 3)”)

and the stacktrace are super misleading !

Thank you.

1 Like

The stacktrace is super misleading because it shouldn’t be there . The fallback should still work without erroring, so this is a bug. If you wouldn’t mind filing an issue on NNlib, we can track a fix for this.

1 Like

I opened the issue here.
Thank you for your help !