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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>
76 lines
2.7 KiB
C++
76 lines
2.7 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/nn/group_norm.h"
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#include <cmath>
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#include <string>
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#include <vector>
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namespace paddle2onnx {
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REGISTER_MAPPER(group_norm, GroupNormMapper)
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int32_t GroupNormMapper::GetMinOpset(bool verbose) {
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auto input_info = GetInput("X");
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if (input_info[0].Rank() != 4) {
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Error() << "Only support 4D-Tensor as input for GroupNorm" << std::endl;
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return -1;
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}
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return 7;
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}
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void GroupNormMapper::Opset7() {
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auto input_info = GetInput("X");
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auto output_info = GetOutput("Y");
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std::vector<int64_t> shape_val = {0, groups_, -1};
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std::string shape =
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helper_->Constant(GetOnnxDtype(P2ODataType::INT64), shape_val);
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auto reshape_input =
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helper_->MakeNode("Reshape", {input_info[0].name, shape});
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std::string scale_ = helper_->Constant(GetOnnxDtype(input_info[0].dtype),
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std::vector<float>(groups_, 1.0));
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std::string bias_ = helper_->Constant(GetOnnxDtype(input_info[0].dtype),
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std::vector<float>(groups_, 0.0));
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auto reshaped_output = helper_->MakeNode(
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"InstanceNormalization", {reshape_input->output(0), scale_, bias_});
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AddAttribute(reshaped_output, "epsilon", epsilon_);
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auto origin_shape = helper_->MakeNode("Shape", {input_info[0].name});
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if (HasInput("Scale") && HasInput("Bias")) {
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auto scale_info = GetInput("Scale");
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auto bias_info = GetInput("Bias");
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auto output = helper_->MakeNode(
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"Reshape", {reshaped_output->output(0), origin_shape->output(0)});
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std::string unsqueezed_scale =
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helper_->Unsqueeze(scale_info[0].name, {1, 2});
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std::string unsqueezed_bias = helper_->Unsqueeze(bias_info[0].name, {1, 2});
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auto scale_output =
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helper_->MakeNode("Mul", {output->output(0), unsqueezed_scale});
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helper_->MakeNode("Add", {scale_output->output(0), unsqueezed_bias},
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{output_info[0].name});
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} else {
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helper_->MakeNode("Reshape",
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{reshaped_output->output(0), origin_shape->output(0)},
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{output_info[0].name});
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}
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}
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} // namespace paddle2onnx
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