wehub-resource-sync
82 lines
3.2 KiB
C++
82 lines
3.2 KiB
C++
/* Copyright (c) 2021 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 {
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namespace inference {
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namespace tensorrt {
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/*
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* FlattenOp trt converter
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*/
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class FlattenOpConverter : 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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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
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int dims = input->getDimensions().nbDims;
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nvinfer1::IShuffleLayer* layer = nullptr;
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auto* shape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shape, *input);
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nvinfer1::Dims start_dim, size_dim, stride_dim;
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start_dim.nbDims = 1;
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size_dim.nbDims = 1;
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stride_dim.nbDims = 1;
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start_dim.d[0] = 1;
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size_dim.d[0] = dims - 1;
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stride_dim.d[0] = 1;
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auto* slice_layer = TRT_ENGINE_ADD_LAYER(engine_,
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Slice,
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*(shape_layer->getOutput(0)),
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start_dim,
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size_dim,
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stride_dim);
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uint32_t reduce_dim = 1;
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auto* reduce_prod_layer =
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TRT_ENGINE_ADD_LAYER(engine_,
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Reduce,
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*(slice_layer->getOutput(0)),
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nvinfer1::ReduceOperation::kPROD,
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reduce_dim,
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true);
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int32_t* constant_weight_data = new int32_t[1];
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constant_weight_data[0] = -1;
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TensorRTEngine::Weight constant_weight{
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nvinfer1::DataType::kINT32,
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static_cast<void*>(constant_weight_data),
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1};
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nvinfer1::Dims constant_dims;
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constant_dims.nbDims = 1;
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constant_dims.d[0] = 1;
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auto* constant_layer = TRT_ENGINE_ADD_LAYER(
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engine_, Constant, constant_dims, constant_weight.get());
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std::vector<nvinfer1::ITensor*> itensors;
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itensors.push_back(constant_layer->getOutput(0));
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itensors.push_back(reduce_prod_layer->getOutput(0));
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auto* concat_layer =
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TRT_ENGINE_ADD_LAYER(engine_, Concatenation, itensors.data(), 2);
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concat_layer->setAxis(0);
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layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
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layer->setInput(1, *(concat_layer->getOutput(0)));
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "flatten", {output_name}, test_mode);
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}
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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REGISTER_TRT_OP_CONVERTER(flatten, FlattenOpConverter);
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