You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
81 lines
3.0 KiB
81 lines
3.0 KiB
/* 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"
|
|
#include "paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h"
|
|
|
|
namespace paddle {
|
|
namespace inference {
|
|
namespace tensorrt {
|
|
|
|
/*
|
|
* PRelu converter from fluid to tensorRT.
|
|
*/
|
|
class PReluOpConverter : public OpConverter {
|
|
public:
|
|
void operator()(const framework::proto::OpDesc& op,
|
|
const framework::Scope& scope, bool test_mode) override {
|
|
VLOG(4) << "convert fluid prelu op to tensorrt prelu layer";
|
|
|
|
framework::OpDesc op_desc(op, nullptr);
|
|
// Declare inputs
|
|
int input_num = op_desc.Input("X").size();
|
|
PADDLE_ENFORCE(input_num == 1);
|
|
auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
|
|
// Get output
|
|
size_t output_num = op_desc.Output("Out").size();
|
|
PADDLE_ENFORCE(output_num == 1);
|
|
// Get attrs
|
|
std::string mode = boost::get<std::string>(op_desc.GetAttr("mode"));
|
|
//
|
|
auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
|
|
PADDLE_ENFORCE_NOT_NULL(alpha_var);
|
|
auto* alpha_tensor = alpha_var->GetMutable<framework::LoDTensor>();
|
|
|
|
platform::CUDAPlace place;
|
|
std::unique_ptr<framework::LoDTensor> alpha_tensor_device(
|
|
new framework::LoDTensor());
|
|
alpha_tensor_device->Resize(alpha_tensor->dims());
|
|
TensorCopySync(*alpha_tensor, place, alpha_tensor_device.get());
|
|
float* alpha_data = alpha_tensor_device->mutable_data<float>(place);
|
|
|
|
// Transform alpha to TensorRTEngine::Weight
|
|
TensorRTEngine::Weight alpha_rt(nvinfer1::DataType::kFLOAT,
|
|
static_cast<void*>(alpha_data),
|
|
alpha_tensor_device->numel());
|
|
PReluPlugin* plugin = new PReluPlugin(alpha_rt, mode);
|
|
nvinfer1::IPluginLayer* layer =
|
|
engine_->AddPlugin(&input, input_num, plugin);
|
|
// keep alpha tensor to avoid release it's memory
|
|
engine_->weight_map[op_desc.Input("Alpha")[0]] =
|
|
std::move(alpha_tensor_device);
|
|
|
|
std::string layer_name = "prelu (Output: ";
|
|
auto output_name = op_desc.Output("Out")[0];
|
|
layer->getOutput(0)->setName(output_name.c_str());
|
|
engine_->SetITensor(output_name, layer->getOutput(0));
|
|
layer_name += output_name;
|
|
if (test_mode) {
|
|
engine_->DeclareOutput(output_name);
|
|
}
|
|
layer->setName((layer_name + ")").c_str());
|
|
}
|
|
};
|
|
|
|
} // namespace tensorrt
|
|
} // namespace inference
|
|
} // namespace paddle
|
|
|
|
REGISTER_TRT_OP_CONVERTER(prelu, PReluOpConverter);
|