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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>
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/range.h"
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namespace paddle2onnx {
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REGISTER_MAPPER(range, RangeMapper)
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void RangeMapper::Opset11() {
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auto start_info = GetInput("Start");
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auto end_info = GetInput("End");
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auto step_info = GetInput("Step");
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auto out_info = GetOutput("Out");
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int32_t out_dtype = -1;
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// TODO(jiangjiajun) cast for constant is an eleminable operation
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std::vector<std::string> aligned_inputs = helper_->DtypeAlignment(
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{start_info[0], end_info[0], step_info[0]}, &out_dtype);
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std::vector<int64_t> empty_axes;
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// // Trick for tensorrt
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// if (out_dtype == P2ODataType::INT32 || out_dtype == P2ODataType::INT64 ||
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// true) {
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// if (start_info[0].Rank() != 1) {
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// aligned_inputs[0] = helper_->Reshape(aligned_inputs[0], {-1});
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// }
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// if (end_info[0].Rank() != 1) {
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// aligned_inputs[1] = helper_->Reshape(aligned_inputs[1], {-1});
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// }
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// if (step_info[0].Rank() != 1) {
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// aligned_inputs[2] = helper_->Reshape(aligned_inputs[2], {-1});
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// }
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// auto length = helper_->MakeNode("Sub", {aligned_inputs[1],
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// aligned_inputs[0]})->output(0);
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// length = helper_->AutoCast(length, out_dtype, P2ODataType::INT64);
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// auto one = helper_->Constant({1}, GetOnnxDtype(out_dtype), int64_t(1));
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// auto expaned_one = helper_->MakeNode("Expand", {one,
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// length})->output(0); auto axis = helper_->Constant({},
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// ONNX_NAMESPACE::TensorProto::INT64, int64_t(0)); auto cumsumed_data =
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// helper_->MakeNode("CumSum", {expaned_one, axis})->output(0);
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// cumsumed_data = helper_->MakeNode("Sub", {cumsumed_data,
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// one})->output(0);
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//
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// auto zero = helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64,
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// int64_t(0));
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// auto new_step = helper_->AutoCast(aligned_inputs[2], step_info[0].dtype,
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// P2ODataType::INT64);
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// helper_->MakeNode("Slice", {cumsumed_data, zero, length, zero,
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// new_step}, {out_info[0].name}); return;
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// }
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// TODO(jiangjiajun) squeeze for constant is an eleminable operation
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if (start_info[0].shape.size() > 0) {
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aligned_inputs[0] = helper_->Squeeze(aligned_inputs[0], empty_axes);
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}
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if (end_info[0].shape.size() > 0) {
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aligned_inputs[1] = helper_->Squeeze(aligned_inputs[1], empty_axes);
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}
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if (step_info[0].shape.size() > 0) {
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aligned_inputs[2] = helper_->Squeeze(aligned_inputs[2], empty_axes);
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}
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auto out = helper_->MakeNode("Range", aligned_inputs)->output(0);
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helper_->AutoCast(out, out_info[0].name, out_dtype, out_info[0].dtype);
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}
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} // namespace paddle2onnx
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