wehub-resource-sync
106 lines
3.7 KiB
C++
106 lines
3.7 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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namespace paddle::inference::tensorrt {
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/*
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* PRelu converter from paddle to tensorRT.
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*/
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class PReluOpConverter : 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 prelu op to tensorrt prelu layer";
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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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auto input_dims = input->getDimensions();
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// Get attrs
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std::string mode = PADDLE_GET_CONST(std::string, op_desc.GetAttr("mode"));
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std::string data_format = "NCHW";
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if (op_desc.HasAttr("data_format")) {
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data_format =
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PADDLE_GET_CONST(std::string, op_desc.GetAttr("data_format"));
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}
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auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
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auto* alpha_weight = alpha_var->GetMutable<phi::DenseTensor>();
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auto w_dims = alpha_weight->dims();
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auto alpha_data =
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engine_->GetFp32TrtWeight(op_desc.Input("Alpha")[0], *alpha_weight);
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nvinfer1::Dims trt_w_dims;
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trt_w_dims.nbDims = w_dims.size();
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for (int i = 0; i < trt_w_dims.nbDims; i++) {
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trt_w_dims.d[i] = w_dims[i];
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}
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nvinfer1::ITensor* alpha_tensor =
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TRT_ENGINE_ADD_LAYER(engine_, Constant, trt_w_dims, alpha_data.get())
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->getOutput(0);
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auto alpha_dims = alpha_tensor->getDimensions();
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nvinfer1::ITensor* real_alpha_tensor = alpha_tensor;
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if (alpha_dims.nbDims != input_dims.nbDims) {
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auto* reshape_layer =
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TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *alpha_tensor);
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int c = alpha_dims.d[0];
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std::vector<nvinfer1::ITensor*> itensors;
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auto* n_tensor = Add1DConstantLayer(1);
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auto* c_tensor = Add1DConstantLayer(c);
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nvinfer1::ITensor* hw_tensor = nullptr;
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nvinfer1::ITensor* shape_tensor = nullptr;
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if (input_dims.nbDims - 2 > 0) {
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hw_tensor =
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Add1DConstantLayer(std::vector<int32_t>(input_dims.nbDims - 2, 1));
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}
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if (data_format == "NCHW") {
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if (hw_tensor != nullptr) {
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shape_tensor = Concat(
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std::vector<nvinfer1::ITensor*>{n_tensor, c_tensor, hw_tensor});
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} else {
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shape_tensor =
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Concat(std::vector<nvinfer1::ITensor*>{n_tensor, c_tensor});
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}
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} else {
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if (hw_tensor != nullptr) {
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shape_tensor = Concat(
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std::vector<nvinfer1::ITensor*>{n_tensor, hw_tensor, c_tensor});
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} else {
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shape_tensor =
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Concat(std::vector<nvinfer1::ITensor*>{n_tensor, c_tensor});
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}
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}
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reshape_layer->setInput(1, *shape_tensor);
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real_alpha_tensor = reshape_layer->getOutput(0);
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}
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nvinfer1::ILayer* layer = nullptr;
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, ParametricReLU, *input, *real_alpha_tensor);
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "prelu", {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(prelu, PReluOpConverter);
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