From c85bad4427ca96631700c7c224317fa7fac7d439 Mon Sep 17 00:00:00 2001
From: Dhairya Gandhi <dhairya@juliacopmuting.com>
Date: Tue, 8 Oct 2019 20:26:09 +0530
Subject: [PATCH] replace weight with filter

---
 src/layers/conv.jl | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/layers/conv.jl b/src/layers/conv.jl
index ad9164c4..a60749e3 100644
--- a/src/layers/conv.jl
+++ b/src/layers/conv.jl
@@ -59,7 +59,7 @@ function Conv(w::AbstractArray{T,N}, b::Union{Nothing, Zeros, AbstractVector{T}}
 end
 
 """
-    convweight(filter::Tuple, in=>out)
+    convfilter(filter::Tuple, in=>out)
 
 Constructs a standard convolutional weight matrix with given `filter` and
 channels from `in` to `out`.
@@ -67,14 +67,14 @@ channels from `in` to `out`.
 Accepts the keyword `init` (default: `glorot_uniform`) to control the sampling
 distribution.
 
-See also: [`depthwiseconvweight`](@ref)
+See also: [`depthwiseconvfilter`](@ref)
 """
-convweight(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
+convfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
   init = glorot_uniform) where N = init(filter..., ch...)
 
 function Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
      init = glorot_uniform,  stride = 1, pad = 0, dilation = 1,
-     weight = convweight(k, ch, init = init), bias = zeros(ch[2])) where N
+     weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N
 
   Conv(weight, bias, σ,
        stride = stride, pad = pad, dilation = dilation)
@@ -152,7 +152,7 @@ end
 
 function ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
               init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
-              weight = convweight(k, reverse(ch), init = init), bias = zeros(ch[2])) where N
+              weight = convfilter(k, reverse(ch), init = init), bias = zeros(ch[2])) where N
   
   ConvTranspose(weight, bias, σ,
               stride = stride, pad = pad, dilation = dilation)
@@ -243,7 +243,7 @@ function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Nothing, Zeros, AbstractV
 end
 
 """
-    depthwiseconvweight(filter::Tuple, in=>out)
+    depthwiseconvfilter(filter::Tuple, in=>out)
 
 Constructs a depthwise convolutional weight array defined by `filter` and channels
 from `in` to `out`.
@@ -251,14 +251,14 @@ from `in` to `out`.
 Accepts the keyword `init` (default: `glorot_uniform`) to control the sampling
 distribution.
 
-See also: [`convweight`](@ref)
+See also: [`convfilter`](@ref)
 """
-depthwiseconvweight(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
+depthwiseconvfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
   init = glorot_uniform) where N = init(filter..., div(ch[2], ch[1]), ch[1])
 
 function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
      init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
-     weight = depthwiseconvweight(k, ch, init = init), bias = zeros(ch[2])) where N
+     weight = depthwiseconvfilter(k, ch, init = init), bias = zeros(ch[2])) where N
   @assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels"
 
   return DepthwiseConv(
@@ -350,7 +350,7 @@ end
 
 function CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
      init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
-     weight = convweight(k, ch, init = init), bias = zeros(ch[2])) where N
+     weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N
 
   CrossCor(weight, bias, σ,
        stride = stride, pad = pad, dilation = dilation)