mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
77 lines
3.1 KiB
C++
77 lines
3.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle2onnx/mapper/tensor/scale.h"
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#include <vector>
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namespace paddle2onnx {
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REGISTER_MAPPER(scale, ScaleMapper)
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void ScaleMapper::Opset7() {
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auto input_info = GetInput("X");
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auto output_info = GetOutput("Out");
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bool has_scale_tensor = HasInput("ScaleTensor");
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bool is_scale_1 = ((scale_ - 1.0) < 1e-06 && (scale_ - 1.0) > -1e-06);
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bool is_bias_0 = (bias_ < 1e-06 && bias_ > -1e-06);
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if (!has_scale_tensor && is_scale_1 && is_bias_0) {
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helper_->MakeNode("Identity", {input_info[0].name}, {output_info[0].name});
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} else {
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auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
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P2ODataType::FP32);
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std::string out = input;
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if (bias_after_scale_) {
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if (!is_scale_1 || HasInput("ScaleTensor")) {
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if (HasInput("ScaleTensor")) {
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auto scale_info = GetInput("ScaleTensor");
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auto scale = helper_->AutoCast(
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scale_info[0].name, scale_info[0].dtype, P2ODataType::FP32);
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out = helper_->MakeNode("Mul", {out, scale})->output(0);
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} else {
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auto scale = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
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std::vector<float>(1, scale_));
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out = helper_->MakeNode("Mul", {out, scale})->output(0);
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}
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}
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if (!is_bias_0) {
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auto bias = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
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std::vector<float>(1, bias_));
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out = helper_->MakeNode("Add", {out, bias})->output(0);
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}
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} else {
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if (!is_bias_0) {
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auto bias = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
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std::vector<float>(1, bias_));
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out = helper_->MakeNode("Add", {out, bias})->output(0);
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}
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if (!is_scale_1 || HasInput("ScaleTensor")) {
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if (HasInput("ScaleTensor")) {
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auto scale_info = GetInput("ScaleTensor");
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auto scale = helper_->AutoCast(
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scale_info[0].name, scale_info[0].dtype, P2ODataType::FP32);
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out = helper_->MakeNode("Mul", {out, scale})->output(0);
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} else {
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auto scale = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
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std::vector<float>(1, scale_));
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out = helper_->MakeNode("Mul", {out, scale})->output(0);
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}
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}
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}
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helper_->AutoCast(out, output_info[0].name, P2ODataType::FP32,
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output_info[0].dtype);
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}
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}
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} // namespace paddle2onnx
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