Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_python36and37_to_paddle_build
test=developdzhwinter-patch-1
minqiyang
6 years ago
commit
d68b9ede44
@ -1,3 +1,4 @@
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nv_library(tensorrt_plugin
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SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu
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avg_pool_op_plugin.cu
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DEPS enforce tensorrt_engine)
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@ -0,0 +1,64 @@
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// 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/avg_pool_op_plugin.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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nvinfer1::Dims AvgPoolPlugin::getOutputDimensions(
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int index, const nvinfer1::Dims* inputDims, 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 AvgPoolPlugin::enqueue(int batchSize, const void* const* inputs,
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void** outputs, void* workspace,
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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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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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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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pool2d_forward(idata, input_shape, output_shape, ksize_, strides_, paddings_,
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pool_process, true, odatas[0], stream);
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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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@ -0,0 +1,111 @@
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// 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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#pragma once
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#include <cassert>
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#include <vector>
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#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.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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class AvgPoolPlugin : public PluginTensorRT {
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private:
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bool ceil_mode_;
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std::vector<int> ksize_;
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std::vector<int> strides_;
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std::vector<int> paddings_;
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std::vector<int> input_shape_;
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std::vector<int> output_shape_;
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protected:
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size_t getSerializationSize() override {
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return SerializedSize(ceil_mode_) + SerializedSize(ksize_) +
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SerializedSize(strides_) + SerializedSize(paddings_) +
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SerializedSize(input_shape_) + getBaseSerializationSize();
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}
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// TRT will call this func when we need to serialize the configuration of
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// tensorrt.
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// It should not be called by users.
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void serialize(void *buffer) override {
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serializeBase(buffer);
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SerializeValue(&buffer, ceil_mode_);
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SerializeValue(&buffer, ksize_);
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SerializeValue(&buffer, strides_);
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SerializeValue(&buffer, paddings_);
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SerializeValue(&buffer, input_shape_);
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}
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public:
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AvgPoolPlugin(bool ceil_mode, std::vector<int> ksize,
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std::vector<int> strides, std::vector<int> paddings,
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std::vector<int> input_shape)
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: ceil_mode_(ceil_mode),
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ksize_(ksize),
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strides_(strides),
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paddings_(paddings),
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input_shape_(input_shape) {
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int output_h, output_w;
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output_shape_ = input_shape_;
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if (!ceil_mode_) {
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output_h =
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(input_shape[1] - ksize_[0] + 2 * paddings_[0]) / strides_[0] + 1;
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output_w =
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(input_shape[2] - ksize_[1] + 2 * paddings_[1]) / strides_[1] + 1;
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} else {
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output_h =
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(input_shape[1] - ksize_[0] + 2 * paddings_[0] + strides_[0] - 1) /
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strides_[0] +
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1;
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output_w =
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(input_shape[2] - ksize_[1] + 2 * paddings_[1] + strides_[1] - 1) /
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strides_[1] +
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1;
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}
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output_shape_[1] = output_h;
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output_shape_[2] = output_w;
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}
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// It was used for tensorrt deserialization.
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// It should not be called by users.
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AvgPoolPlugin(void const *serialData, size_t serialLength) {
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deserializeBase(serialData, serialLength);
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DeserializeValue(&serialData, &serialLength, &ceil_mode_);
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DeserializeValue(&serialData, &serialLength, &ksize_);
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DeserializeValue(&serialData, &serialLength, &strides_);
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DeserializeValue(&serialData, &serialLength, &paddings_);
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DeserializeValue(&serialData, &serialLength, &input_shape_);
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}
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AvgPoolPlugin *clone() const override {
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return new AvgPoolPlugin(ceil_mode_, ksize_, strides_, paddings_,
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input_shape_);
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}
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const char *getPluginType() const override { return "avg_pool"; }
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int getNbOutputs() const override { return 1; }
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nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims *inputs,
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int nbInputDims) override;
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int initialize() override { return 0; }
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int enqueue(int batchSize, const void *const *inputs, void **outputs,
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void *workspace, cudaStream_t stream) override;
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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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