make Conv constructors more forgiving about bias type

Author: Michael Abbott

src/layers/conv.jl

@@ -95,8 +95,8 @@ struct Conv{N,M,F,A,V}
 end
 
 """
-    Conv(weight::AbstractArray, bias, [activation; stride, pad, dilation])
-    
+    Conv(weight::AbstractArray, [bias, activation; stride, pad, dilation])
+
 Constructs a convolutional layer with the given weight and bias.
 Accepts the same keywords (and has the same defaults) as the `Conv((4,4), 3=>7, relu)`
 method.
@@ -117,13 +117,13 @@ julia> params(c1) |> length
 2
 ```
 """
-function Conv(w::AbstractArray{T,N}, b::Union{Bool, Zeros, AbstractVector{T}}, σ = identity;
+function Conv(w::AbstractArray{T,N}, bias = true, σ = identity;
               stride = 1, pad = 0, dilation = 1) where {T,N}
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(Conv, pad, size(w)[1:N-2], dilation, stride)
-  bias = create_bias(w, b, size(w, N))
-  return Conv(σ, w, bias, stride, pad, dilation)
+  b = create_bias(w, bias, size(w, N))
+  return Conv(σ, w, b, stride, pad, dilation)
 end
 
 function Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
@@ -206,18 +206,18 @@ struct ConvTranspose{N,M,F,A,V}
 end
 
 """
-    ConvTranspose(weight::AbstractArray, bias, [activation; stride, pad, dilation])
-    
+    ConvTranspose(weight::AbstractArray, [bias, activation; stride, pad, dilation])
+
 Constructs a layer with the given weight and bias arrays.
 Accepts the same keywords as the `ConvTranspose((4,4), 3=>7, relu)` method.
 """
-function ConvTranspose(w::AbstractArray{T,N}, b::Union{Bool, Zeros, AbstractVector{T}}, σ = identity;
+function ConvTranspose(w::AbstractArray{T,N}, bias = true, σ = identity;
                       stride = 1, pad = 0, dilation = 1) where {T,N}
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(ConvTranspose, pad, size(w)[1:N-2], dilation, stride)
-  bias = create_bias(w, b, size(w, N-1))
-  return ConvTranspose(σ, w, bias, stride, pad, dilation)
+  b = create_bias(w, bias, size(w, N-1))
+  return ConvTranspose(σ, w, b, stride, pad, dilation)
 end
 
 function ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
@@ -304,17 +304,17 @@ end
 
 """
     DepthwiseConv(weight::AbstractArray, bias, [activation; stride, pad, dilation])
-    
+
 Constructs a layer with the given weight and bias arrays.
 Accepts the same keywords as the `DepthwiseConv((4,4), 3=>6, relu)` method.
 """
-function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Bool, Zeros, AbstractVector{T}}, σ = identity;
+function DepthwiseConv(w::AbstractArray{T,N}, bias = true, σ = identity;
                       stride = 1, pad = 0, dilation = 1) where {T,N}
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(DepthwiseConv, pad, size(w)[1:N-2], dilation, stride)
-  bias = create_bias(w, b, prod(size(w)[N-1:end]))
-  return DepthwiseConv(σ, w, bias, stride, pad, dilation)
+  b = create_bias(w, bias, prod(size(w)[N-1:end]))
+  return DepthwiseConv(σ, w, b, stride, pad, dilation)
 end
 
 function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
@@ -391,18 +391,18 @@ struct CrossCor{N,M,F,A,V}
 end
 
 """
-    CrossCor(weight::AbstractArray, bias, [activation; stride, pad, dilation])
-    
+    CrossCor(weight::AbstractArray, [bias, activation; stride, pad, dilation])
+
 Constructs a layer with the given weight and bias arrays.
 Accepts the same keywords as the `CrossCor((4,4), 3=>7, relu)` method.
 """
-function CrossCor(w::AbstractArray{T,N}, b::Union{Bool, Zeros, AbstractVector{T}}, σ = identity;
+function CrossCor(w::AbstractArray{T,N}, bias = true, σ = identity;
                   stride = 1, pad = 0, dilation = 1) where {T,N}
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(CrossCor, pad, size(w)[1:N-2], dilation, stride)
-  bias = create_bias(w, b, size(w, N))
-  return CrossCor(σ, w, bias, stride, pad, dilation)
+  b = create_bias(w, bias, size(w, N))
+  return CrossCor(σ, w, b, stride, pad, dilation)
 end
 
 function CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;

test/layers/conv.jl

@@ -188,3 +188,15 @@ end
   # https://github.com/FluxML/Flux.jl/issues/1421
   @test Conv((5, 5), 10 => 20, identity; init = Base.randn).bias isa Vector{Float64}
 end
+
+@testset "constructors: $fun" for fun in [Conv, CrossCor, ConvTranspose, DepthwiseConv]
+  @test fun(rand(2,3,4)).bias isa Vector{Float64}
+  @test fun(rand(2,3,4,5), false).bias isa Flux.Zeros
+  if fun == Conv
+    @test fun(rand(2,3,4,5,6), rand(6)).bias isa Vector{Float64}
+    @test fun(rand(2,3,4,5,6), 1:6).bias isa Vector{Float64}
+  elseif fun == DepthwiseConv
+    @test fun(rand(2,3,4,5,6), rand(30)).bias isa Vector{Float64}
+  end
+  @test_throws DimensionMismatch fun(rand(2,3,4), rand(6))
+end