Remove short-form

Author: Avik Pal

src/nnpack/nnlib.jl

@@ -1,6 +1,3 @@
-const AA{N} = AbstractArray{Float32,N}
-const AA1 = Union{AA{2}, AA{3}, AA{4}, AA{5}}
-
 #NOTE: Commenting out the activation functions until sure what to do
 
 # relu(x::AA1) = nnp_relu_output(x, inplace ? x : similar(x), threadpool = shared_threadpool)
@@ -26,7 +23,7 @@ softmax(x::A) where A<:AbstractVecOrMat{Float32} =
 maxpool(x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float64, 4} =
     maxpool(Float32.(x), k, pad = pad, stride = stride)
 
-function maxpool(x::A, k; pad = map(_->0,k), stride = k) where A<:AA{4}
+function maxpool(x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float32, 4}
     pad_, stride_ = expand(Val{length(k)}, pad), expand(Val{length(k)}, stride)
     ((size(x, 1) - k[1] + 2 * pad_[1]) % stride_[1] == 0 && (size(x, 2) - k[2] + 2 * pad_[2]) % stride_[2] == 0) || error("Choose the stride, pad and kernel size properly")
     maxpool!(similar(x, pdims(size(x), k, pad_, stride_)), x, k, pad = pad_, stride = stride_)
@@ -35,13 +32,13 @@ end
 maxpool!(y::A, x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float64, 4} =
     maxpool!(Float32.(y), Float32.(x), k, pad = pad, stride = stride)
 
-maxpool!(y::A, x::A, k; pad = map(_->0,k), stride = k) where A<:AA{4} =
+maxpool!(y::A, x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float32, 4} =
     nnp_max_pooling_output(x, y, k, padding = expand(Val{length(k)}, pad), stride = expand(Val{length(k)}, stride), threadpool = shared_threadpool)
 
 conv(x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float64, 4} =
     conv(Float32.(x), Float32.(w), pad = pad, stride = stride, dilation = dilation, algo = algo)
 
-function conv(x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AA{4}
+function conv(x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float32, 4}
     dilation == 1 || dilation == (1, 1) || error("NNPACK does not support dilation > 1")
     pad_, stride_ = padtuple(x, pad), padtuple(x, stride)
     ((size(x, 1) - size(w, 1) + 2 * pad_[1]) % stride_[1] == 0 && (size(x, 2) - size(w, 2) + 2 * pad_[2]) % stride_[2] == 0) || error("Choose the stride, pad and kernel size properly")
@@ -53,7 +50,7 @@ end
 conv(x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float64, 4}, A2<:AbstractArray{Float64, 1}} =
     conv(Float32.(x), Float32.(w), Float32.(b), pad = pad, stride = stride, dilation = dilation, algo = algo)
 
-function conv(x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AA{4}, A2<:AA{1}}
+function conv(x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float32, 4}, A2<:AbstractArray{Float32, 1}}
     dilation == 1 || dilation == (1, 1) || error("NNPACK does not support dilation > 1")
     pad_, stride_ = padtuple(x, pad), padtuple(x, stride)
     ((size(x, 1) - size(w, 1) + 2 * pad_[1]) % stride_[1] == 0 && (size(x, 2) - size(w, 2) + 2 * pad_[2]) % stride_[2] == 0) || error("Choose the stride, pad and kernel size properly")
@@ -63,7 +60,7 @@ end
 crosscor(x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float64, 4}, A2<:AbstractArray{Float64, 1}} =
     crosscor(Float32.(x), Float32.(w), Float32.(b), pad = pad, stride = stride, dilation = dilation, algo = algo)
 
-function crosscor(x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AA{4}, A2<:AA{1}}
+function crosscor(x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float32, 4}, A2<:AbstractArray{Float32, 1}}
     dilation == 1 || dilation == (1, 1) || error("NNPACK does not support dilation > 1")
     pad_, stride_ = padtuple(x, pad), padtuple(x, stride)
     ((size(x, 1) - size(w, 1) + 2 * pad_[1]) % stride_[1] == 0 && (size(x, 2) - size(w, 2) + 2 * pad_[2]) % stride_[2] == 0) || error("Choose the stride, pad and kernel size properly")
@@ -73,21 +70,21 @@ end
 conv!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where {A1<:AbstractArray{Float64, 4}, A2<:AbstractArray{Float64, 1}} =
     conv(Float32.(y), Float32.(x), Float32.(w), Float32.(b), pad = pad, stride = stride, dilation = dilation, algo = algo, flipkernel = flipkernel)
 
-function conv!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where {A1<:AA{4}, A2<:AA{1}}
+function conv!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where {A1<:AbstractArray{Float32, 4}, A2<:AbstractArray{Float32, 1}}
     flipkernel == 0 && (w = reverse(reverse(w, dims=1), dims=2))
     nnp_convolution_output(y, x, w, b, algo = algo, padding = pad, stride = stride, threadpool = shared_threadpool)
 end
 
 crosscor!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float64, 4}, A2<:AbstractArray{Float64, 1}} =
     conv!(Float32.(y), Float32.(x), Float32.(w), Float32.(b), pad = pad, stride = stride, dilation = dilation, algo = algo, flipkernel = 1)
 
-crosscor!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AA{4}, A2<:AA{1}} =
+crosscor!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float32, 4}, A2<:AbstractArray{Float32, 1}} =
     conv!(y, x, w, b, pad = pad, stride = stride, dilation = dilation, algo = algo, flipkernel = 1)
 
 ∇conv_data(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float64, 4} =
     ∇conv_data(Float32.(dy), Float32.(x), Float32.(w), pad = pad, stride = stride, dilation = dilation, algo = algo)
 
-function ∇conv_data(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AA{4}
+function ∇conv_data(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float32, 4}
     dilation == 1 || dilation == (1, 1) || error("NNPACK does not support dilation > 1")
     pad_, stride_ = padtuple(x, pad), padtuple(x, stride)
     ((size(x, 1) - size(w, 1) + 2 * pad_[1]) % stride_[1] == 0 && (size(x, 2) - size(w, 2) + 2 * pad_[2]) % stride_[2] == 0) || error("Choose the stride, pad and kernel size properly")
@@ -97,15 +94,15 @@ end
 ∇conv_data!(dx::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AbstractArray{Float64, 4} =
     ∇conv_data!(Float32.(dx), Float32.(dy), Float32.(x), Float32.(w), pad = pad, stride = stride, dilation = dilation, algo = algo, flipkernel = flipkernel)
 
-function ∇conv_data!(dx::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AA{4}
+function ∇conv_data!(dx::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AbstractArray{Float32, 4}
     flipkernel == 0 && (w = reverse(reverse(w, dims=1), dims=2))
     nnp_convolution_input_gradient(dx, x, dy, w, padding = pad, stride = stride, algo = algo, threadpool = shared_threadpool)
 end
 
 ∇conv_filter(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float64, 4} =
     ∇conv_filter(Float32.(dy), Float32.(x), Float32.(w), pad = pad, stride = stride, dilation = dilation, algo = algo)
 
-function ∇conv_filter(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AA{4}
+function ∇conv_filter(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float32, 4}
     dilation == 1 || dilation == (1, 1) || error("NNPACK does not support dilation > 1")
     pad_, stride_ = padtuple(x, pad), padtuple(x, stride)
     ((size(x, 1) - size(w, 1) + 2 * pad_[1]) % stride_[1] == 0 && (size(x, 2) - size(w, 2) + 2 * pad_[2]) % stride_[2] == 0) || error("Choose the stride, pad and kernel size properly")
@@ -115,7 +112,7 @@ end
 ∇conv_filter!(dw::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AbstractArray{Float64, 4} =
     ∇conv_filter!(Float32.(dw), Float32.(dy), Float32.(x), Float32.(w), pad = pad, stride = stride, dilation = dilation, algo = algo, flipkernel = flipkernel)
 
-function ∇conv_filter!(dw::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AA{4}
+function ∇conv_filter!(dw::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AbstractArray{Float32, 4}
     flipkernel == 0 && (w = reverse(reverse(w, dims=1), dims=2))
     dw .= nnp_convolution_kernel_gradient(dw, x, dy, w, padding = pad, stride = stride, algo = algo, threadpool = shared_threadpool)
     flipkernel == 0 ? reverse(reverse(dw, dims=1), dims=2) : dw