wehub-resource-sync
83 lines
2.9 KiB
C++
83 lines
2.9 KiB
C++
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle::inference::tensorrt {
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/*
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* Unbind Op
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*/
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class UnbindOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope,
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bool test_mode) override {
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VLOG(3) << "convert a unbind op to tensorrt layer";
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framework::OpDesc op_desc(op, nullptr);
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std::string input_x_name = op_desc.Input("X").front();
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auto* input_x_tensor = engine_->GetITensor(input_x_name);
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auto in_dims = input_x_tensor->getDimensions();
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auto in_shape_tensor = Shape(input_x_tensor);
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auto rank = in_dims.nbDims;
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int axis = 0;
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if (op_desc.HasAttr("axis")) {
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axis = PADDLE_GET_CONST(int, op_desc.GetAttr("axis"));
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if (axis < 0) {
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axis += rank;
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}
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}
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std::vector<nvinfer1::ITensor*> in_shape_tensors;
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std::vector<nvinfer1::ITensor*> newDims_tensors;
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for (int32_t i = 0; i < rank; ++i) {
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in_shape_tensors.push_back(GetEleTensorOfShape(in_shape_tensor, i));
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if (i != axis) {
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newDims_tensors.push_back(GetEleTensorOfShape(in_shape_tensor, i));
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}
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}
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auto newDims_tensor = Concat(newDims_tensors);
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std::vector<nvinfer1::ITensor*> start_tensors;
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std::vector<nvinfer1::ITensor*> size_tensors = in_shape_tensors;
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nvinfer1::Dims stride;
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stride.nbDims = rank;
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for (int i = 0; i < rank; ++i) {
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if (axis == i) {
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size_tensors[i] = Add1DConstantLayer(1);
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}
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start_tensors.push_back(Add1DConstantLayer(0));
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stride.d[i] = 1;
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}
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int ii = 0;
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for (auto& output_name : op_desc.Output("Out")) {
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start_tensors[axis] = Add1DConstantLayer(ii++);
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// 1 slice
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auto inputSliced = TRT_ENGINE_ADD_LAYER(
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engine_, Slice, *input_x_tensor, stride, stride, stride);
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inputSliced->setInput(1, *Concat(start_tensors));
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inputSliced->setInput(2, *Concat(size_tensors));
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auto inputSliced_out = inputSliced->getOutput(0);
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// 2 reshape
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auto inputReshaped =
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TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *inputSliced_out);
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inputReshaped->setInput(1, *newDims_tensor);
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ReplenishLayerAndOutput(
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inputReshaped, "unbind", {output_name}, test_mode);
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}
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}
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(unbind, UnbindOpConverter);
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