wehub-resource-sync
80 lines
3.1 KiB
C++
80 lines
3.1 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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class FlipOpConverter : 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 a flip op to tensorrt 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::vector<int> axis =
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PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("axis"));
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for (size_t i = 0; i < axis.size(); ++i) {
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axis[i] += (axis[i] < 0) ? input_dims.nbDims : 0;
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}
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nvinfer1::ITensor* shape_tensor = Shape(input);
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// getAxisLength default is a scalar
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auto getAxisLength = [&](int axis, bool scalar = true) {
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int d = input_dims.d[axis];
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if (d >= 0) {
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return Add1DConstantLayer(d, "", scalar);
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} else {
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return GetEleTensorOfShape(shape_tensor, axis, scalar);
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}
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};
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for (size_t i = 0; i < axis.size(); ++i) {
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auto loop = TRT_ENGINE_ADD_LAYER(engine_, Loop);
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nvinfer1::ITensor* tripLimit = getAxisLength(axis[i]);
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loop->addTripLimit(*tripLimit, nvinfer1::TripLimit::kCOUNT);
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auto iterator = loop->addIterator(*input, axis[i], true);
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std::vector<int32_t> zero_vec{0};
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std::vector<int32_t> one_vec{1};
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auto zero = Add1DConstantLayer(zero_vec);
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auto one = Add1DConstantLayer(one_vec);
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nvinfer1::IRecurrenceLayer* iRec = loop->addRecurrence(*zero);
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nvinfer1::ITensor* iCur = iRec->getOutput(0);
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auto iNext = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*iCur,
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*one,
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nvinfer1::ElementWiseOperation::kSUM);
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iRec->setInput(1, *iNext->getOutput(0));
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nvinfer1::ILoopOutputLayer* loopOut = loop->addLoopOutput(
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*iterator->getOutput(0), nvinfer1::LoopOutput::kCONCATENATE, axis[i]);
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loopOut->setInput(1, *tripLimit);
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input = loopOut->getOutput(0);
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}
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *input);
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "flip", {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(flip, FlipOpConverter);
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