Remove AbstractArray

Author: Avik Pal

src/nnpack/interface.jl

@@ -1,4 +1,4 @@
-flipweight(w::AbstractArray{<:Any,4}) = w[end:-1:1,end:-1:1,:,:]
+flipweight(w::Array{<:Any,4}) = w[end:-1:1,end:-1:1,:,:]
 
 function check_support(x, k, pad, stride, dilation = 1)
     fallback = false
@@ -11,84 +11,43 @@ end
 softmax!(y::A, x::A) where A<:AbstractVecOrMat{Float32} =
     nnp_softmax_output(x, y)
 
-function maxpool(x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float32, 4}
+function maxpool!(y::A, x::A, k; pad = map(_->0,k), stride = k) where A<:Array{Float32, 4}
     pad_, stride_, fallback = check_support(x, k, pad, stride)
     if fallback
-        maxpool_cpu!(similar(x, pdims(size(x), k, pad_, stride_)), x, k, pad = pad_, stride = stride_)
+        maxpool_cpu!(y, x, k, pad = pad_, stride = stride_)
     else
-        maxpool!(similar(x, pdims(size(x), k, pad_, stride_)), x, k, pad = pad_, stride = stride_)
+        nnp_max_pooling_output(x, y, k, padding = expand(Val{length(k)}, pad), stride = expand(Val{length(k)}, stride))
     end
 end
 
-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))
-
-function conv(x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float32, 4}
+function conv!(y::A1, x::A1, w::A1; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A1<:Array{Float32, 4}
     pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
-    y = similar(x, cdims(size(x), dilation_dims(w, dilation), pad_, stride_))
-    if fallback
-        conv2d!(y, x, w, padding = pad, stride = stride, dilation = dilation)
-    else
-        conv!(y, x, w, zeros(Float32, size(y, 3)), pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo))
-    end
-end
-
-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}}
-    pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
-    y = similar(x, cdims(size(x), dilation_dims(w, dilation), pad_, stride_))
-    if fallback
-        conv2d!(y, x, w, padding = pad, stride = stride, dilation = dilation)
-    else
-        conv!(y, x, w, b, pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo))
-    end
-end
-
-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}}
-    pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
-    y = similar(x, cdims(size(x), dilation_dims(w, dilation), pad_, stride_))
+    flipkernel == 0 && (w .= flipweight(w))
     if fallback
         conv2d!(y, x, w, padding = pad, stride = stride, dilation = dilation, mode = 1)
     else
-        conv!(y, x, w, b, pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo), flipkernel = 1)
-    end
-end
-
-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}}
-    if flipkernel == 0
-        w = flipweight(w)
+        nnp_convolution_output(y, x, w, zeros(Float32, size(y, 3)), algo = algo, padding = pad, stride = stride)
     end
-    nnp_convolution_output(y, x, w, b, algo = algo, padding = pad, stride = stride)
 end
 
-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)
-
-function ∇conv_data(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float32, 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<:Array{Float32, 4}
     pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
     if fallback
-        conv2d_grad_x!(zeros(Float32, size(x)), x, w, dy, padding = pad_, stride = stride_, dilation = dilation)
+        conv2d_grad_x!(dx, x, w, dy, padding = pad_, stride = stride_, dilation = dilation)
     else
-        ∇conv_data!(zeros(Float32, size(x)), dy, x, w; pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo))
+        flipkernel == 0 && (w .= flipweight(w))
+        nnp_convolution_input_gradient(dx, x, dy, w, padding = pad, stride = stride, algo = algo)
     end
 end
 
-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 = flipweight(w))
-    nnp_convolution_input_gradient(dx, x, dy, w, padding = pad, stride = stride, algo = algo)
-end
-
-function ∇conv_filter(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float32, 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<:Array{Float32, 4}
     pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
     if fallback
-        conv2d_grad_w!(zeros(Float32, size(w)), x, w, dy, padding = pad_, stride = stride_, dilation = dilation)
+        conv2d_grad_w!(dw, x, w, dy, padding = pad_, stride = stride_, dilation = dilation)
     else
-        ∇conv_filter!(zeros(Float32, size(w)), dy, x, w; pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo))
+        flipkernel == 0 && (w .= flipweight(w))
+        nnp_convolution_kernel_gradient(dw, x, dy, w, padding = pad, stride = stride, algo = algo)
+        flipkernel && (dw .= flipkernel(dw))
+        dw
     end
 end
-
-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 = flipweight(w))
-    nnp_convolution_kernel_gradient(dw, x, dy, w, padding = pad, stride = stride, algo = algo)
-    flipkernel && (dw .= flipkernel(dw))
-    return dw
-end