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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>
90 lines
3.0 KiB
C++
90 lines
3.0 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/clip.h"
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namespace paddle2onnx {
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REGISTER_MAPPER(clip, ClipMapper)
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int32_t ClipMapper::GetMinOpset(bool verbose) {
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bool has_max_tensor_input = HasInput("Max");
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bool has_min_tensor_input = HasInput("Min");
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if (has_max_tensor_input || has_min_tensor_input) {
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return 11;
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}
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return 7;
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}
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void ClipMapper::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_max_tensor_input = HasInput("Max");
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bool has_min_tensor_input = HasInput("Min");
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if (has_max_tensor_input || has_min_tensor_input) {
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bool dtype_converted = false;
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std::string input_name = input_info[0].name;
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int32_t dtype = input_info[0].dtype;
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// onnxruntime only supports float input
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if (input_info[0].dtype != P2ODataType::FP32) {
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input_name = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
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P2ODataType::FP32);
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dtype_converted = true;
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dtype = P2ODataType::FP32;
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}
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std::string max_name;
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if (has_max_tensor_input) {
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auto max_info = GetInput("Max");
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max_name = helper_->AutoCast(max_info[0].name, max_info[0].dtype, dtype);
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if (max_info[0].Rank() > 0) {
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max_name = helper_->Squeeze(max_name, {});
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}
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} else {
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float max_val;
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GetAttr("max", &max_val);
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max_name = helper_->Constant({}, GetOnnxDtype(dtype), max_val);
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}
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std::string min_name;
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if (has_min_tensor_input) {
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auto min_info = GetInput("Min");
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min_name = helper_->AutoCast(min_info[0].name, min_info[0].dtype, dtype);
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if (min_info[0].Rank() > 0) {
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min_name = helper_->Squeeze(min_name, {});
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}
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} else {
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float min_val;
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GetAttr("min", &min_val);
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min_name = helper_->Constant({}, GetOnnxDtype(dtype), min_val);
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}
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if (dtype_converted) {
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auto node = helper_->MakeNode("Clip", {input_name, min_name, max_name});
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helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
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output_info[0].dtype);
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} else {
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helper_->MakeNode("Clip", {input_name, min_name, max_name},
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{output_info[0].name});
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}
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} else {
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float max_val;
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GetAttr("max", &max_val);
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float min_val;
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GetAttr("min", &min_val);
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helper_->Clip(input_info[0].name, output_info[0].name, min_val, max_val,
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input_info[0].dtype);
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}
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}
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} // namespace paddle2onnx
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