mirror of
https://github.com/blakeblackshear/frigate.git
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116 lines
4.4 KiB
C++
116 lines
4.4 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/onnx/op_parser.hpp>
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#include <migraphx/onnx/pooling.hpp>
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#include <migraphx/ranges.hpp>
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#include <migraphx/op/pooling.hpp>
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#include <migraphx/make_op.hpp>
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#include <migraphx/onnx/checks.hpp>
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#include <migraphx/onnx/broadcast_qdq.hpp>
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#include <migraphx/instruction.hpp>
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namespace migraphx {
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inline namespace MIGRAPHX_INLINE_NS {
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namespace onnx {
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/*
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*********************************************************************************
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* Reference: see QLinearAveragePool and QLinearGlobalAveragePool in *
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* github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md *
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*********************************************************************************
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*/
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struct parse_qlinearpooling : op_parser<parse_qlinearpooling>
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{
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std::vector<op_desc> operators() const
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{
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return {{"QLinearGlobalAveragePool", "average"}, {"QLinearAveragePool", "average"}};
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}
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void check_inputs(const op_desc& opd, const std::vector<instruction_ref>& args) const
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{
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const auto& in_x = args[0];
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const auto onnx_name = opd.onnx_name;
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if(in_x->get_shape().ndim() <= 2)
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MIGRAPHX_THROW(onnx_name + ": input dimensions too small");
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auto type_x = in_x->get_shape().type();
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if(type_x != migraphx::shape::int8_type and type_x != migraphx::shape::uint8_type)
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MIGRAPHX_THROW(onnx_name + ": unsupported input type");
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const auto& zero_pt_x = args[2];
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if(type_x != zero_pt_x->get_shape().type())
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MIGRAPHX_THROW(onnx_name + ": mismatched type: input zero point");
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if(args.size() == 5)
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{
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const auto& zero_pt_y = args[4];
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if(type_x != zero_pt_y->get_shape().type())
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MIGRAPHX_THROW(onnx_name + ": mismatched type: output zero point");
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}
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}
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instruction_ref parse(const op_desc& opd,
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const onnx_parser& parser,
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const onnx_parser::node_info& info,
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const std::vector<instruction_ref>& args) const
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{
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if(contains(info.attributes, "channel_last"))
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{
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int channels_last =
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parser.parse_value(info.attributes.at("channels_last")).template at<int>();
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if(channels_last != 0)
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MIGRAPHX_THROW(opd.onnx_name + ": channels_last (N x D1..Dn x C) is not supported");
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}
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check_inputs(opd, args);
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// Input: X
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const auto& in_x = args[0];
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const auto& scale_x = args[1];
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const auto& zero_pt_x = args[2];
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auto dquant_x = bcast_qdq_instr("dequantizelinear", in_x, scale_x, zero_pt_x, info);
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// Output Y = pooling_op(X)
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auto out_y = add_pooling_op(opd, info, dquant_x);
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const auto& in_scale_y = args[3];
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// zero_pt for Y is supplied as the last optional argument..
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if(args.size() == 5)
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return (bcast_qdq_instr("quantizelinear", out_y, in_scale_y, args[4], info));
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// if no zero_pt: just broadcast the scale..
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auto bcast_scale_y = bcast_scalar_instr(out_y->get_shape(), in_scale_y, info);
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return (info.add_instruction(migraphx::make_op("quantizelinear"), out_y, bcast_scale_y));
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}
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};
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} // namespace onnx
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} // namespace MIGRAPHX_INLINE_NS
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} // namespace migraphx
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