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87 lines
3.6 KiB
87 lines
3.6 KiB
// 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/anakin/convert/conv2d.h"
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#include <algorithm>
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#include <memory>
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#include <vector>
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using anakin::graph::GraphGlobalMem;
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using anakin::AK_FLOAT;
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using anakin::saber::NV;
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using anakin::saber::Shape;
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using anakin::PTuple;
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namespace paddle {
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namespace inference {
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namespace anakin {
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void Conv2dOpConverter::operator()(const framework::proto::OpDesc &op,
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const framework::Scope &scope,
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bool test_mode) {
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framework::OpDesc op_desc(op, nullptr);
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PADDLE_ENFORCE_EQ(op_desc.Input("Input").size(), 1UL);
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PADDLE_ENFORCE_EQ(op_desc.Input("Filter").size(), 1UL);
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PADDLE_ENFORCE_EQ(op_desc.Output("Output").size(), 1UL);
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auto input_name = op_desc.Input("Input").front();
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auto output_name = op_desc.Output("Output").front();
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auto op_name = op_desc.Type() + ":" + op_desc.Output("Output").front();
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engine_->AddOp(op_name, "Convolution", {input_name}, {output_name});
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auto *filter_v = scope.FindVar(op_desc.Input("Filter").front());
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PADDLE_ENFORCE_NOT_NULL(filter_v);
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auto *filter_t = filter_v->GetMutable<framework::LoDTensor>();
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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(filter_t->dims());
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TensorCopySync((*filter_t), platform::CPUPlace(), weight_tensor.get());
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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 n_input = weight_tensor->dims()[1];
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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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auto filter_num = n_input * filter_h * filter_w;
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engine_->AddOpAttr<int>(op_name, "filter_num", filter_num);
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engine_->AddOpAttr<PTuple<int>>(op_name, "kernel_size", {filter_h, filter_w});
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auto strides = boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
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engine_->AddOpAttr<PTuple<int>>(op_name, "strides", strides);
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auto paddings = boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
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engine_->AddOpAttr<PTuple<int>>(op_name, "padding", paddings);
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auto dilations = boost::get<std::vector<int>>(op_desc.GetAttr("dilations"));
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engine_->AddOpAttr<PTuple<int>>(op_name, "dilation_rate", dilations);
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const int groups = boost::get<int>(op_desc.GetAttr("groups"));
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engine_->AddOpAttr(op_name, "group", groups);
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engine_->AddOpAttr(op_name, "axis", 1);
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engine_->AddOpAttr(op_name, "bias_term", false);
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auto weight_shape = framework::vectorize2int(filter_t->dims());
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Shape anakin_shape(weight_shape);
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auto *weight1 =
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GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(anakin_shape);
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float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
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std::copy_n(weight_tensor->data<float>(), weight_tensor->numel(), cpu_data);
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weight1->d_tensor().set_shape(anakin_shape);
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weight1->d_tensor().copy_from(weight1->h_tensor());
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engine_->AddOpAttr(op_name, "weight_1", *weight1);
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}
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} // namespace anakin
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} // namespace inference
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} // namespace paddle
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REGISTER_ANAKIN_OP_CONVERTER(conv2d, Conv2dOpConverter);
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