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98 lines
4.0 KiB
98 lines
4.0 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/batch_norm.h"
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#include <math.h>
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#include <algorithm>
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#include <map>
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#include <string>
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#include <vector>
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#include "paddle/fluid/inference/anakin/convert/helper.h"
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namespace paddle {
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namespace inference {
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namespace anakin {
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template <typename TargetT, ::anakin::Precision PrecisionT>
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void BatchNormOpConverter<TargetT, PrecisionT>::operator()(
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const framework::proto::OpDesc &op, const framework::BlockDesc &block_desc,
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const framework::Scope &scope, bool test_mode) {
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framework::OpDesc op_desc(op, nullptr);
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PADDLE_ENFORCE_EQ(op_desc.Output("Y").size(), 1);
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std::map<std::string, std::string> inputs;
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for (auto k : {"X", "Scale", "Bias", "Mean", "Variance"}) {
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PADDLE_ENFORCE_EQ(op_desc.Input(k).size(), 1UL);
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}
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auto input = op_desc.Input("X").front();
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auto output = op_desc.Output("Y").front();
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auto op_name = op_desc.Type() + ":" + op_desc.Output("Y").front();
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auto epsilon = boost::get<float>(op_desc.GetAttr("epsilon"));
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auto bn_op_name = op_name + ":bn";
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auto bn_output = bn_op_name + "_output";
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this->engine_->AddOp(bn_op_name, "BatchNorm", {input}, {bn_output});
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this->engine_->AddOpAttr(bn_op_name, "epsilon", epsilon);
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this->engine_->AddOpAttr(bn_op_name, "momentum", static_cast<float>(1.0));
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auto scale_op_name = op_name + ":scale";
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this->engine_->AddOp(scale_op_name, "Scale", {bn_output}, {output});
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this->engine_->AddOpAttr(scale_op_name, "axis", 1);
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this->engine_->AddOpAttr(scale_op_name, "num_axes", 1);
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this->engine_->AddOpAttr(scale_op_name, "bias_term", true);
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auto *mean_v = scope.FindVar(op_desc.Input("Mean").front());
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PADDLE_ENFORCE_NOT_NULL(mean_v);
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auto weight1 = pblock_from_var<TargetT>(*mean_v);
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this->engine_->AddOpAttr(bn_op_name, "weight_1", *weight1);
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auto *variance_v = scope.FindVar(op_desc.Input("Variance").front());
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PADDLE_ENFORCE_NOT_NULL(variance_v);
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auto weight2 = pblock_from_var<TargetT>(*variance_v);
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this->engine_->AddOpAttr(bn_op_name, "weight_2", *weight2);
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auto *weight3 = pblock_from_vector<TargetT>(std::vector<float>({1}));
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this->engine_->AddOpAttr(bn_op_name, "weight_3", *weight3);
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auto *scale_v = scope.FindVar(op_desc.Input("Scale").front());
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PADDLE_ENFORCE_NOT_NULL(scale_v);
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auto scale = pblock_from_var<TargetT>(*scale_v);
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this->engine_->AddOpAttr(scale_op_name, "weight_1", *scale);
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auto *bias_v = scope.FindVar(op_desc.Input("Bias").front());
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PADDLE_ENFORCE_NOT_NULL(bias_v);
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auto bias = pblock_from_var<TargetT>(*bias_v);
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this->engine_->AddOpAttr(scale_op_name, "weight_2", *bias);
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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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#ifdef PADDLE_WITH_CUDA
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using bn_nv_fp32 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::NV, ::anakin::Precision::FP32>;
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using bn_nv_int8 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::NV, ::anakin::Precision::INT8>;
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REGISTER_CUDA_ANAKIN_OP_CONVERTER(batch_norm, bn_nv_fp32);
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REGISTER_CUDA_INT8_ANAKIN_OP_CONVERTER(batch_norm, bn_nv_int8);
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#endif
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using bn_cpu_fp32 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::X86, ::anakin::Precision::FP32>;
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using bn_cpu_int8 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::X86, ::anakin::Precision::INT8>;
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REGISTER_CPU_ANAKIN_OP_CONVERTER(batch_norm, bn_cpu_fp32);
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REGISTER_CPU_INT8_ANAKIN_OP_CONVERTER(batch_norm, bn_cpu_int8);
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