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.
126 lines
4.9 KiB
126 lines
4.9 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/anakin/convert/batch_norm.h"
|
|
#include <math.h>
|
|
#include <map>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
using anakin::graph::GraphGlobalMem;
|
|
using anakin::AK_FLOAT;
|
|
using anakin::saber::NV;
|
|
using anakin::saber::Shape;
|
|
|
|
namespace paddle {
|
|
namespace inference {
|
|
namespace anakin {
|
|
|
|
void BatchNormOpConverter::operator()(const framework::proto::OpDesc &op,
|
|
const framework::Scope &scope,
|
|
bool test_mode) {
|
|
framework::OpDesc op_desc(op, nullptr);
|
|
PADDLE_ENFORCE_EQ(op_desc.Output("Y").size(), 1);
|
|
std::map<std::string, std::string> inputs;
|
|
for (auto k : {"X", "Scale", "Bias", "Mean", "Variance"}) {
|
|
PADDLE_ENFORCE_EQ(op_desc.Input(k).size(), 1UL);
|
|
auto v = op_desc.Input(k).front();
|
|
inputs.insert({k, v});
|
|
}
|
|
|
|
auto output = op_desc.Output("Y").front();
|
|
auto op_name = op_desc.Type() + ":" + op_desc.Output("Y").front();
|
|
bool is_test = boost::get<bool>(op_desc.GetAttr("is_test"));
|
|
auto epsilon = boost::get<float>(op_desc.GetAttr("epsilon"));
|
|
|
|
auto bn_op_name = op_name + ":bn";
|
|
auto bn_output = bn_op_name + "_output";
|
|
engine_->AddOp(bn_op_name, "BatchNorm", {inputs["X"]}, {bn_output});
|
|
engine_->AddOpAttr(bn_op_name, "epsilon", epsilon);
|
|
|
|
auto scale_op_name = op_name + ":scale";
|
|
auto get_lod_tensor = [this, &scope, &op_name](const std::string &var_name,
|
|
framework::LoDTensor *tensor) {
|
|
auto *v = scope.FindVar(var_name);
|
|
PADDLE_ENFORCE_NOT_NULL(v);
|
|
auto *t = v->GetMutable<framework::LoDTensor>();
|
|
tensor->Resize(t->dims());
|
|
TensorCopySync(*t, platform::CPUPlace(), tensor);
|
|
};
|
|
|
|
framework::LoDTensor bias_t;
|
|
framework::LoDTensor mean_t;
|
|
framework::LoDTensor scale_t;
|
|
framework::LoDTensor variance_t;
|
|
get_lod_tensor(inputs["Bias"], &bias_t);
|
|
get_lod_tensor(inputs["Mean"], &mean_t);
|
|
get_lod_tensor(inputs["Scale"], &scale_t);
|
|
get_lod_tensor(inputs["Variance"], &variance_t);
|
|
|
|
auto fill_shape = [](size_t n, std::vector<int> shape) {
|
|
shape.insert(shape.begin(), 1);
|
|
if (shape.size() < n) {
|
|
shape.insert(shape.end(), n - shape.size(), 1);
|
|
}
|
|
return shape;
|
|
};
|
|
Shape shape1(fill_shape(4, framework::vectorize2int(mean_t.dims())));
|
|
Shape shape2(fill_shape(4, framework::vectorize2int(variance_t.dims())));
|
|
auto *weight1 =
|
|
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape1);
|
|
auto *mean_data = static_cast<float *>(weight1->h_tensor().mutable_data());
|
|
std::copy_n(mean_t.data<float>(), mean_t.numel(), mean_data);
|
|
engine_->AddOpAttr(bn_op_name, "weight_1", *weight1);
|
|
|
|
auto *weight2 =
|
|
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape2);
|
|
auto *variance_data =
|
|
static_cast<float *>(weight2->h_tensor().mutable_data());
|
|
std::copy_n(variance_t.data<float>(), variance_t.numel(), variance_data);
|
|
engine_->AddOpAttr(bn_op_name, "weight_2", *weight2);
|
|
|
|
Shape shape3(std::vector<int>({1, 1, 1, 1}));
|
|
auto *weight3 =
|
|
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape3);
|
|
auto *alpha_data = static_cast<float *>(weight3->h_tensor().mutable_data());
|
|
float weight3_data[] = {1};
|
|
std::copy(std::begin(weight3_data), std::end(weight3_data), alpha_data);
|
|
engine_->AddOpAttr(bn_op_name, "weight_3", *weight3);
|
|
|
|
Shape scale_shape(fill_shape(4, framework::vectorize2int(scale_t.dims())));
|
|
auto *scale =
|
|
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(scale_shape);
|
|
auto *scale_data = static_cast<float *>(scale->h_tensor().mutable_data());
|
|
std::copy_n(scale_t.data<float>(), scale_t.numel(), scale_data);
|
|
|
|
Shape bias_shape(fill_shape(4, framework::vectorize2int(bias_t.dims())));
|
|
auto *bias =
|
|
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(bias_shape);
|
|
auto *bias_data = static_cast<float *>(bias->h_tensor().mutable_data());
|
|
std::copy_n(bias_t.data<float>(), bias_t.numel(), bias_data);
|
|
|
|
engine_->AddOp(scale_op_name, "Scale", {bn_output}, {output});
|
|
engine_->AddOpAttr(scale_op_name, "axis", 1);
|
|
engine_->AddOpAttr(scale_op_name, "num_axes", 1);
|
|
engine_->AddOpAttr(scale_op_name, "bias_term", true);
|
|
engine_->AddOpAttr(scale_op_name, "weight_1", *scale);
|
|
engine_->AddOpAttr(scale_op_name, "weight_2", *bias);
|
|
}
|
|
|
|
} // namespace anakin
|
|
} // namespace inference
|
|
} // namespace paddle
|
|
|
|
REGISTER_ANAKIN_OP_CONVERTER(batch_norm, BatchNormOpConverter);
|