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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>
60 lines
1.8 KiB
C++
60 lines
1.8 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/instance_norm.h"
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#include <string>
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#include <vector>
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namespace paddle2onnx {
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REGISTER_MAPPER(instance_norm, InstanceNormMapper)
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int32_t InstanceNormMapper::GetMinOpset(bool verbose) {
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auto input_info = GetInput("X");
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int num_groups = input_info[0].shape[1];
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if (num_groups < 0) {
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Error() << "The dimension in axis=1 of input tensor must be known, but now it's unknown." << 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 InstanceNormMapper::Opset7() {
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auto input_info = GetInput("X");
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auto output_info = GetOutput("Y");
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int num_groups = input_info[0].shape[1];
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std::string scale = "";
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if (HasInput("Scale")) {
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scale = GetInput("Scale")[0].name;
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} else {
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scale = helper_->Constant(GetOnnxDtype(input_info[0].dtype), std::vector<float>(num_groups, 1.0));
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}
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std::string bias = "";
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if (HasInput("Bias")) {
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bias = GetInput("Bias")[0].name;
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} else {
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bias = helper_->Constant(GetOnnxDtype(input_info[0].dtype), std::vector<float>(num_groups, 0.0));
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}
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auto node = helper_->MakeNode(
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"InstanceNormalization",
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{input_info[0].name, scale, bias},
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{output_info[0].name});
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AddAttribute(node, "epsilon", epsilon_);
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}
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} // namespace paddle2onnx
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