mirror of
https://github.com/blakeblackshear/frigate.git
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77 lines
2.9 KiB
C++
77 lines
2.9 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*/
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#include <migraphx/config.hpp>
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#include <migraphx/cpu/dnnl.hpp>
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#include <migraphx/op/convolution_backwards.hpp>
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namespace migraphx {
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inline namespace MIGRAPHX_INLINE_NS {
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namespace cpu {
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struct dnnl_deconvolution
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: dnnl_extend_op<dnnl_deconvolution, dnnl::deconvolution_forward, op::convolution_backwards>
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{
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std::vector<int> arg_map(int) const
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{
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return {MIGRAPHX_DNNL_PREFIX(ARG_SRC), MIGRAPHX_DNNL_PREFIX(ARG_WEIGHTS)};
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}
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shape adjust_shape(const shape& x, int i, const shape& output) const
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{
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auto s = base_adjust_shape(x, output);
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if(i == 1)
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{
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// The input and output channels are flipped for dnnl
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auto lens = s.lens();
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std::swap(lens[0], lens[1]);
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auto strides = s.strides();
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std::swap(strides[0], strides[1]);
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return {s.type(), lens, strides};
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}
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return s;
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}
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dnnl::deconvolution_forward::desc
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get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
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{
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// In DNNL dilation is zero-based
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auto dilation = op.dilation;
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std::transform(
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dilation.begin(), dilation.end(), dilation.begin(), [](auto x) { return x - 1; });
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return {dnnl::prop_kind::forward_inference,
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dnnl::algorithm::deconvolution_direct,
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m.at(MIGRAPHX_DNNL_PREFIX(ARG_SRC)),
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m.at(MIGRAPHX_DNNL_PREFIX(ARG_WEIGHTS)),
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m.at(MIGRAPHX_DNNL_PREFIX(ARG_DST)),
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to_dnnl_dims(op.stride),
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to_dnnl_dims(dilation),
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to_dnnl_dims(op.padding),
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to_dnnl_dims(op.padding)};
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}
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};
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} // namespace cpu
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} // namespace MIGRAPHX_INLINE_NS
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} // namespace migraphx
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