wehub-resource-sync
126 lines
4.8 KiB
C++
126 lines
4.8 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 {
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namespace inference {
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namespace tensorrt {
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/*
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* Scale Op
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*/
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class ScaleOpConverter : 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 scale op to tensorrt mul layer without bias";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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std::vector<nvinfer1::ITensor*> itensors;
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std::string input_name = op_desc.Input("X").front();
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std::string out_name = op_desc.Output("Out").front();
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auto input = engine_->GetITensor(input_name);
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bool bias_after_scale =
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PADDLE_GET_CONST(bool, op_desc.GetAttr("bias_after_scale"));
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float bias = PADDLE_GET_CONST(float, op_desc.GetAttr("bias"));
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float scale = PADDLE_GET_CONST(float, op_desc.GetAttr("scale"));
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bool is_int = input->getType() == nvinfer1::DataType::kINT32;
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nvinfer1::ILayer* layer = nullptr;
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nvinfer1::ITensor* bias_tensor =
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is_int ? Add1DConstantLayer(
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static_cast<int>(bias > 0 ? bias + 0.5 : bias - 0.5))
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: Add1DConstantLayer(bias);
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bool is_bias_0 = bias == 0;
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std::vector<int32_t> bias_shapes(input->getDimensions().nbDims, 1);
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auto* bias_shapes_tensor = Add1DConstantLayer(bias_shapes);
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auto* reshape_layer_bias =
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TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *bias_tensor);
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reshape_layer_bias->setInput(1, *bias_shapes_tensor);
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bool has_scale_tensor;
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nvinfer1::ITensor* scale_tensor;
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bool is_scale_1;
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auto scale_inputs = op_desc.Inputs();
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if (scale_inputs.find("ScaleTensor") != scale_inputs.end() &&
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!op_desc.Input("ScaleTensor").empty()) { // has EndsTensor input
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has_scale_tensor = true;
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scale_tensor = engine_->GetITensor(op_desc.Input("ScaleTensor")[0]);
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is_scale_1 = false;
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} else {
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has_scale_tensor = false;
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scale_tensor = is_int ? Add1DConstantLayer(static_cast<int>(
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scale > 0 ? scale + 0.5 : scale - 0.5))
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: Add1DConstantLayer(scale);
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is_scale_1 = scale == 1;
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}
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std::vector<int32_t> scale_shapes(input->getDimensions().nbDims, 1);
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auto* scale_shapes_tensor = Add1DConstantLayer(scale_shapes);
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auto* reshape_layer_scale =
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TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *scale_tensor);
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reshape_layer_scale->setInput(1, *scale_shapes_tensor);
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if (!has_scale_tensor && is_scale_1 && is_bias_0) {
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layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *input);
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} else {
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if (bias_after_scale) {
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if (!is_scale_1) {
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*input,
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*reshape_layer_scale->getOutput(0),
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nvinfer1::ElementWiseOperation::kPROD);
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input = layer->getOutput(0);
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}
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if (!is_bias_0) {
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*input,
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*reshape_layer_bias->getOutput(0),
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nvinfer1::ElementWiseOperation::kSUM);
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}
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} else {
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if (!is_bias_0) {
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*input,
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*reshape_layer_bias->getOutput(0),
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nvinfer1::ElementWiseOperation::kSUM);
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input = layer->getOutput(0);
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}
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if (!is_scale_1) {
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*input,
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*reshape_layer_scale->getOutput(0),
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nvinfer1::ElementWiseOperation::kPROD);
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}
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}
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}
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ReplenishLayerAndOutput(layer, "scale", {out_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(scale, ScaleOpConverter);
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