batch_norm_op.cc

// Copyright (c) 2019 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 "lite/kernels/bm/bridges/registry.h"
#include "lite/backends/bm/builder.h"
#include "bmcompiler_if.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace bm {
namespace bridges {

node_map_type BatchNormConverter(const std::shared_ptr<lite::OpLite> bn_op,
                            graph_ctx_type* graph_ctx,
                            const node_map_type& input_nodes) {
    // output converted nodes
    node_map_type output_nodes;
    
    auto scope = bn_op->scope();
    auto op_info = bn_op->op_info();
    auto op_type = op_info->Type();
    auto unique_op_name = lite::bm::UniqueName(op_type);
    
    // input
    auto x_var_name = op_info->Input("X").front();
    auto x = scope->FindVar(x_var_name)->GetMutable<lite::Tensor>();
    auto x_dims = x->dims();
    const long int* x_shape_data = const_cast<const long int*>(&x_dims.data()[0]);
    int i_x_shape_data[x_dims.size()];
    for (size_t i = 0; i < x_dims.size(); i++) {
        i_x_shape_data[i] = static_cast<int>(x_shape_data[i]);
    }
    
    int channel_size = x_dims[1];

    auto scale_var_name = op_info->Input("Scale").front();
    auto scale = scope->FindVar(scale_var_name)->GetMutable<lite::Tensor>();
    
    auto bias_var_name = op_info->Input("Bias").front();
    auto bias = scope->FindVar(bias_var_name)->GetMutable<lite::Tensor>();
    
    auto mean_var_name = op_info->Input("Mean").front();
    auto mean = scope->FindVar(mean_var_name)->GetMutable<lite::Tensor>();
 
    auto variance_var_name = op_info->Input("Variance").front();
    auto variance = scope->FindVar(variance_var_name)->GetMutable<lite::Tensor>();

    // output
    auto output_var_name = op_info->Output("Y").front();
    auto output = scope->FindVar(output_var_name)->GetMutable<lite::Tensor>();
    auto output_dims = output->dims();
    const long int* output_shape_data = const_cast<const long int*>(&output_dims.data()[0]);
    int i_output_shape_data[output_dims.size()];
    for (size_t i = 0; i < output_dims.size(); i++) {
        i_output_shape_data[i] = static_cast<int>(output_shape_data[i]);
    }
    
    auto epsilon = op_info->GetAttr<float>("epsilon");
    auto unique_bn_out_name = lite::bm::UniqueName("batch_norm_out");

    auto* scale_data = scale->mutable_data<float>();
    auto* bias_data = bias->mutable_data<float>();
    auto* mean_data = mean->mutable_data<float>();
    auto* variance_data = variance->mutable_data<float>();
 
    for (int c = 0; c < channel_size; c++) {
        float inv_scale = 1.f / (std::sqrt(variance_data[c] + epsilon));
        bias_data[c] = bias_data[c] - inv_scale * scale_data[c] * mean_data[c];
        scale_data[c] = inv_scale * scale_data[c];
    }

    const int input_num = 1;
    int **shape = new int *[input_num];
    int *dim = new int[input_num];
    const char **name = new const char *[input_num];
    
    name[0] = static_cast<const char*>(x_var_name.c_str());
    dim[0] = x_dims.size();
    shape[0] = i_x_shape_data;
                        
    add_scale_layer(graph_ctx->bm_compiler_handle,
        input_num,
        shape,
        dim,
        name,
        const_cast<const int*>(i_output_shape_data),
        output_dims.size(),
        static_cast<const char*>(output_var_name.c_str()),
        static_cast<const char*>(unique_op_name.c_str()),
        static_cast<const float*>(scale->mutable_data<float>()),
        static_cast<const float*>(bias->mutable_data<float>()),
        1,
        1,
        1);
   
    delete [] shape;
    delete [] name;
    delete [] dim;

    output_nodes[output_var_name] = output_var_name;
    return output_nodes;
}

}  // namespace bridges
}  // namespace bm
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_BM_BRIDGE(batch_norm, paddle::lite::kernels::bm::bridges::BatchNormConverter);