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97 lines
3.7 KiB
97 lines
3.7 KiB
/* 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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class Conv2dOpConverter : 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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LOG(INFO)
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<< "convert a fluid conv2d op to tensorrt conv layer without bias";
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framework::OpDesc op_desc(op, nullptr);
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PADDLE_ENFORCE_EQ(op_desc.Input("Input").size(), 1);
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PADDLE_ENFORCE_EQ(op_desc.Input("Filter").size(), 1); // Y is a weight
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PADDLE_ENFORCE_EQ(op_desc.Output("Output").size(), 1);
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auto* X = engine_->GetITensor(op_desc.Input("Input").front());
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// Declare weights
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auto* Y_v = scope.FindVar(op_desc.Input("Filter").front());
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PADDLE_ENFORCE_NOT_NULL(Y_v);
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auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
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platform::CPUPlace cpu_place;
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std::unique_ptr<framework::LoDTensor> weight_tensor(
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new framework::LoDTensor());
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weight_tensor->Resize(Y_t->dims());
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TensorCopySync((*Y_t), cpu_place, weight_tensor.get());
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auto* weight_data =
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weight_tensor->mutable_data<float>(platform::CPUPlace());
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PADDLE_ENFORCE_EQ(weight_tensor->dims().size(), 4UL);
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const int n_output = weight_tensor->dims()[0];
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const int filter_h = weight_tensor->dims()[2];
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const int filter_w = weight_tensor->dims()[3];
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const int groups = boost::get<int>(op_desc.GetAttr("groups"));
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const std::vector<int> dilations =
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boost::get<std::vector<int>>(op_desc.GetAttr("dilations"));
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const std::vector<int> strides =
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boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
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const std::vector<int> paddings =
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boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
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nvinfer1::DimsHW nv_ksize(filter_h, filter_w);
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nvinfer1::DimsHW nv_dilations(dilations[0], dilations[1]);
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nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
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nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
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TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(weight_data),
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weight_tensor->memory_size() / sizeof(float)};
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TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
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auto* layer = TRT_ENGINE_ADD_LAYER(
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engine_, Convolution, *const_cast<nvinfer1::ITensor*>(X), n_output,
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nv_ksize, weight.get(), bias.get());
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PADDLE_ENFORCE(layer != nullptr);
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layer->setStride(nv_strides);
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layer->setPadding(nv_paddings);
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layer->setDilation(nv_dilations);
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layer->setNbGroups(groups);
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auto output_name = op_desc.Output("Output").front();
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layer->setName(("conv2d (Output: " + output_name + ")").c_str());
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engine_->weight_map[op_desc.Input("Filter").front()] =
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std::move(weight_tensor);
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layer->getOutput(0)->setName(output_name.c_str());
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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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REGISTER_TRT_OP_CONVERTER(conv2d, Conv2dOpConverter);
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