Add attribution.

[ci skip]

Author: maleadt

LICENSE.md

@@ -1,6 +1,6 @@
 The CUDA.jl package is licensed under the MIT "Expat" License:
 
-> Copyright (c) 2019-2020: Julia Computing and other contributors
+> Copyright (c) 2019-present: Julia Computing and other contributors
 > Copyright (c) 2014-2018: Tim Besard
 > Copyright (c) 2013: Dahua Lin
 >

src/nnlib.jl

@@ -35,10 +35,43 @@ Base.unsafe_convert(::Type{CuPtr{T}}, A::NNlib.BatchedAdjOrTrans{T}) where {T} =
 # Upsampling
 #
 
-# An implementation for GPU based bilinear upsampling including its gradient
+# GPU based bilinear upsampling including its gradient
+#
+# Based on the Caffe2 implementation at:
 # The code is a translation from the following files:
-# https://github.com/pytorch/pytorch/blob/master/caffe2/operators/upsample_op.cu
-# https://github.com/pytorch/pytorch/blob/master/caffe2/core/common_gpu.h
+# - https://github.com/pytorch/pytorch/blob/v1.8.0-rc1/caffe2/operators/upsample_op.cu
+# - https://github.com/pytorch/pytorch/blob/v1.8.0-rc1/caffe2/core/common_gpu.h
+#
+# Copyright (c) 2016-2021 Facebook Inc.
+# Copyright (c) 2015 Google Inc.
+# Copyright (c) 2015 Yangqing Jia
+# Copyright 2019-2020 Kakao Brain
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without modification, are
+# permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this list of
+#    conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice, this list of
+#    conditions and the following disclaimer in the documentation and/or other materials
+#    provided with the distribution.
+#
+# 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America and
+#    IDIAP Research Institute nor the names of its contributors may be used to endorse or
+#    promote products derived from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 # Forward and backward pass have been tested to produce the same output
 # as pytorch with align_corners=True - it works modulo bit noise.
@@ -124,7 +157,6 @@ function ∇upsample_bilinear_whcn_kernel!(n_elem, rheight, rwidth, Δ, dx)
     return nothing
 end
 
-
 function NNlib.upsample_bilinear_whcn!(y::CuArray{T,4}, x::CuArray{T,4}) where T
     w,h,c,n = size(x)
     out_w, out_h = (size(y,1), size(y,2))