add Temporarily add code with bug

fea/anakin-support-x86
nhzlx 7 years ago
parent 4a0761781c
commit d384d39a68

@ -1,6 +1,6 @@
# Add TRT tests
nv_library(tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc
DEPS tensorrt_engine mul_op)
nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@ -13,3 +13,6 @@ nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine activation_op SERIAL)
nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine pool_op SERIAL)

@ -0,0 +1,75 @@
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
*/
class Pool2dOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4)
<< "convert a fluid pool2d op to tensorrt pool2d layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
std::string pool_type =
boost::get<std::string>(op_desc.GetAttr("pooling_type"));
std::vector<int> ksize =
boost::get<std::vector<int>>(op_desc.GetAttr("ksize"));
std::vector<int> strides =
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
const nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
nvinfer1::PoolingType pool_t = nvinfer1::PoolingType::kMAX;
if (pool_type == "max") {
pool_t = nvinfer1::PoolingType::kMAX;
} else if (pool_type == "avg") {
pool_t = nvinfer1::PoolingType::kAVERAGE;
} else {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
pool_t, nv_ksize);
PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
layer->setStride(nv_strides);
layer->setPadding(nv_paddings);
auto output_name = op_desc.Output("Out")[0];
engine_->SetITensor(output_name, layer->getOutput(0));
if (test_mode) {
engine_->DeclareOutput(output_name);
}
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(pool2d);
REGISTER_TRT_OP_CONVERTER(pool2d, Pool2dOpConverter);

@ -0,0 +1,63 @@
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <fstream>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace paddle {
namespace inference {
namespace tensorrt {
TEST(Pool2dOpConverter, main) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("pool2d-X", nvinfer1::Dims4(10, 3, 2, 2));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims4(10, 3, 1, 1));
// Prepare Op description
framework::OpDesc desc;
desc.SetType("pool2d");
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> strides({1, 1});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
desc.SetAttr("pooling_type", pooling_t);
desc.SetAttr("ksize", ksize);
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
// std::string temp = "";
// (*desc.Proto()).SerializeToString(&temp);
// std::cout << temp << std::endl;
// std::ofstream f("__temp__", std::ios::out);
// f << temp;
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(10);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(pool2d);
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