tensor-tang
7 years ago
commit
5373fe29c2
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{
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global:
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*paddle*;
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local:
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*;
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};
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@ -1 +0,0 @@
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*paddle*
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@ -0,0 +1,80 @@
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/* 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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* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
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*/
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class Pool2dOpConverter : 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, bool test_mode) override {
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VLOG(4)
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<< "convert a fluid pool2d op to tensorrt pool2d layer without bias";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
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PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
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auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
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std::string pool_type =
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boost::get<std::string>(op_desc.GetAttr("pooling_type"));
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std::vector<int> ksize =
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boost::get<std::vector<int>>(op_desc.GetAttr("ksize"));
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std::vector<int> strides =
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boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
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std::vector<int> paddings =
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boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
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const nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
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const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
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const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
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PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
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nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
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if (pool_type == "max") {
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nv_pool_type = nvinfer1::PoolingType::kMAX;
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} else if (pool_type == "avg") {
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nv_pool_type = nvinfer1::PoolingType::kAVERAGE;
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} else {
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PADDLE_THROW("TensorRT unsupported pooling type!");
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}
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
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*const_cast<nvinfer1::ITensor*>(input1),
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nv_pool_type, nv_ksize);
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PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
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layer->setStride(nv_strides);
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layer->setPadding(nv_paddings);
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auto output_name = op_desc.Output("Out")[0];
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engine_->SetITensor(output_name, layer->getOutput(0));
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if (test_mode) {
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engine_->DeclareOutput(output_name);
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}
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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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USE_OP(pool2d);
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REGISTER_TRT_OP_CONVERTER(pool2d, Pool2dOpConverter);
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@ -0,0 +1,60 @@
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/* 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 <gtest/gtest.h>
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#include <fstream>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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TEST(Pool2dOpConverter, main) {
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framework::Scope scope;
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std::unordered_set<std::string> parameters;
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TRTConvertValidation validator(5, parameters, scope, 1 << 15);
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// The ITensor's Dims should not contain the batch size.
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// So, the ITensor's Dims of input and output should be C * H * W.
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validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
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validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("pool2d");
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desc.SetInput("X", {"pool2d-X"});
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desc.SetOutput("Out", {"pool2d-Out"});
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std::vector<int> ksize({2, 2});
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std::vector<int> strides({2, 2});
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std::vector<int> paddings({0, 0});
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std::string pooling_t = "max";
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desc.SetAttr("pooling_type", pooling_t);
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desc.SetAttr("ksize", ksize);
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desc.SetAttr("strides", strides);
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desc.SetAttr("paddings", paddings);
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LOG(INFO) << "set OP";
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validator.SetOp(*desc.Proto());
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LOG(INFO) << "execute";
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validator.Execute(3);
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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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USE_OP(pool2d);
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