mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
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>
65 lines
2.5 KiB
C++
65 lines
2.5 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle2onnx/mapper/tensor/gaussian_random.h"
|
|
|
|
namespace paddle2onnx {
|
|
REGISTER_MAPPER(gaussian_random, GaussianRandomMapper)
|
|
|
|
int32_t GaussianRandomMapper::GetMinOpset(bool verbose) {
|
|
if (HasInput("ShapeTensor") && !IsConstantInput("ShapeTensor")) {
|
|
Logger(verbose, 9) << "While ShapeTensor as input and it's not a constant tensor, " << RequireOpset(9) << std::endl;
|
|
return 9;
|
|
}
|
|
if (HasInput("ShapeTensorList")) {
|
|
Logger(verbose, 9) << "While ShapeTensorList as input, " << RequireOpset(9) << std::endl;
|
|
return 9;
|
|
}
|
|
return 7;
|
|
}
|
|
|
|
void GaussianRandomMapper::Opset7() {
|
|
auto out_info = GetOutput("Out");
|
|
std::string shape_tensor_name = "";
|
|
std::vector<int64_t> shape;
|
|
if (HasInput("ShapeTensor")) {
|
|
if (!TryGetInputValue("ShapeTensor", &shape)) {
|
|
auto shape_info = GetInput("ShapeTensor");
|
|
shape_tensor_name = helper_->AutoCast(shape_info[0].name, shape_info[0].dtype, P2ODataType::INT64);
|
|
}
|
|
} else if (HasInput("ShapeTensorList")) {
|
|
auto shape_info = GetInput("ShapeTensorList");
|
|
shape_tensor_name = helper_->ConcatIndices(shape_info);
|
|
} else {
|
|
shape.assign(shape_.begin(), shape_.end());
|
|
}
|
|
if (shape.size() > 0) {
|
|
auto node = helper_->MakeNode("RandomNormal", {}, {out_info[0].name});
|
|
AddAttribute(node, "dtype", GetOnnxDtype(out_info[0].dtype));
|
|
AddAttribute(node, "mean", mean_);
|
|
AddAttribute(node, "scale", std_);
|
|
AddAttribute(node, "shape", shape_);
|
|
AddAttribute(node, "seed", static_cast<float>(seed_));
|
|
} else {
|
|
auto tensor = helper_->ConstOfShape(shape_tensor_name, GetOnnxDtype(out_info[0].dtype), float(0));
|
|
auto node = helper_->MakeNode("RandomNormalLike", {tensor}, {out_info[0].name});
|
|
AddAttribute(node, "dtype", GetOnnxDtype(out_info[0].dtype));
|
|
AddAttribute(node, "mean", mean_);
|
|
AddAttribute(node, "scale", std_);
|
|
AddAttribute(node, "seed", static_cast<float>(seed_));
|
|
}
|
|
}
|
|
|
|
} // namespace paddle2onnx
|