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79 lines
3.0 KiB
79 lines
3.0 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/plugin/pool_op_plugin.h"
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#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin_factory.h"
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#include "paddle/fluid/operators/math/pooling.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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namespace plugin {
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PoolPlugin* CreatePoolPluginDeserialize(const void* buffer, size_t length) {
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return new PoolPlugin(buffer, length);
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}
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REGISTER_TRT_PLUGIN("pool_plugin", CreatePoolPluginDeserialize);
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nvinfer1::Dims PoolPlugin::getOutputDimensions(int index,
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const nvinfer1::Dims* inputDims,
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int nbInputs) {
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assert(nbInputs == 1);
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assert(index == 0);
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assert(inputDims[0].nbDims == 3);
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nvinfer1::Dims const& input_dims = inputDims[0];
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nvinfer1::Dims output_dims = input_dims;
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output_dims.d[1] = output_shape_[1];
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output_dims.d[2] = output_shape_[2];
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return output_dims;
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}
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int PoolPlugin::enqueue(int batchSize, const void* const* inputs,
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void** outputs, void* workspace, cudaStream_t stream) {
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auto const& input_dims = this->getInputDims(0);
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int input_size = 0;
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float const* idata = reinterpret_cast<float const*>(inputs[0]);
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float** odatas = reinterpret_cast<float**>(outputs);
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std::vector<int> input_shape = input_shape_;
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std::vector<int> output_shape = output_shape_;
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input_shape.insert(input_shape.begin(), batchSize);
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output_shape.insert(output_shape.begin(), batchSize);
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if (pool_type_ == PoolType::max) {
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paddle::operators::math::MaxPool<float> pool_process;
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paddle::operators::math::Pool2dDirectCUDAFunctor<
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paddle::operators::math::MaxPool<float>, float>
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pool2d_forward;
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pool2d_forward(idata, input_shape, output_shape, ksize_, strides_,
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paddings_, pool_process, true, adaptive_, odatas[0], stream);
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} else if (pool_type_ == PoolType::avg) {
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paddle::operators::math::AvgPool<float> pool_process;
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paddle::operators::math::Pool2dDirectCUDAFunctor<
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paddle::operators::math::AvgPool<float>, float>
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pool2d_forward;
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pool2d_forward(idata, input_shape, output_shape, ksize_, strides_,
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paddings_, pool_process, true, adaptive_, odatas[0], stream);
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}
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return cudaGetLastError() != cudaSuccess;
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}
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} // namespace plugin
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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