wehub-resource-sync
84 lines
2.9 KiB
C++
84 lines
2.9 KiB
C++
/* Copyright (c) 2018 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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#include "paddle/fluid/inference/tensorrt/plugin/stack_op_plugin.h"
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namespace paddle::inference::tensorrt {
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/*
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* Stack converter from fluid to tensorRT.
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*/
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class StackOpConverter : 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(4) << "convert stack op to tensorrt stack layer";
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framework::OpDesc op_desc(op, nullptr);
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auto input = op_desc.Input("X");
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int input_num = input.size();
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std::vector<nvinfer1::ITensor*> inputs;
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auto output_name = op_desc.Output("Y").front();
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for (int i = 0; i < input_num; ++i) {
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inputs.push_back(engine_->GetITensor(input[i]));
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if (op_desc.HasAttr("out_threshold")) {
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float out_scale =
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PADDLE_GET_CONST(float, op_desc.GetAttr("out_threshold"));
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engine_->SetTensorDynamicRange(inputs[i], out_scale);
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}
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}
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int axis = PADDLE_GET_CONST(int, op_desc.GetAttr("axis"));
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int output_rank = inputs[0]->getDimensions().nbDims + 1;
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if (axis < 0) {
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axis = axis + output_rank;
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}
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// Now, axis is relative to output_rank.
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auto* shape_tensor = Shape(inputs[0]);
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std::vector<nvinfer1::ITensor*> shape_tensor_vec;
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for (int i = 0; i < output_rank; i++) {
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if (i < axis) {
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shape_tensor_vec.push_back(GetEleTensorOfShape(shape_tensor, i));
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} else if (i > axis) {
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shape_tensor_vec.push_back(GetEleTensorOfShape(shape_tensor, i - 1));
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} else {
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shape_tensor_vec.push_back(Add1DConstantLayer(1));
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}
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}
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auto* after_shape_tensor = Concat(shape_tensor_vec);
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for (int i = 0; i < input_num; ++i) {
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inputs[i] = Reshape(inputs[i],
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after_shape_tensor,
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("stack: reshape: (Output( " + std::to_string(i) +
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" )" + output_name + ")")
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.c_str());
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}
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auto* layer = TRT_ENGINE_ADD_LAYER(
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engine_, Concatenation, inputs.data(), inputs.size());
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layer->setAxis(axis);
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ReplenishLayerAndOutput(layer, "stack", {output_name}, test_mode);
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}
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(stack, StackOpConverter);
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