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caffe_serializer.cpp
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caffe_serializer.cpp
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.
*/
/*
* Copyright (c) 2017, Open AI Lab
* Author: haitao@openailab.com
* Author: chunyinglv@openailab.com
*/
#include <iostream>
#include <functional>
#include <unordered_map>
#include "compiler.hpp"
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>
#include <google/protobuf/message.h>
#include "type_name.hpp"
#include "caffe_serializer.hpp"
#include "operator_manager.hpp"
#include "operator/conv_param.hpp"
#include "operator/pool_param.hpp"
#include "operator/fc_param.hpp"
#include "operator/concat_param.hpp"
#include "operator/batch_norm_param.hpp"
#include "operator/scale_param.hpp"
#include "operator/lrn_param.hpp"
#include "operator/softmax_param.hpp"
#include "operator/eltwise_param.hpp"
#include "operator/slice_param.hpp"
#include "operator/normalize_param.hpp"
#include "operator/permute_param.hpp"
#include "operator/flatten_param.hpp"
#include "operator/priorbox_param.hpp"
#include "operator/reshape_param.hpp"
#include "operator/detection_output_param.hpp"
#include "operator/rpn_param.hpp"
#include "operator/roi_pooling_param.hpp"
#include "operator/relu_param.hpp"
#include "operator/reorg_param.hpp"
#include "operator/region_param.hpp"
#include "operator/deconv_param.hpp"
#include "operator/resize_param.hpp"
namespace TEngine {
using op_load_t=std::function<bool(StaticGraph *, StaticNode * ,const te_caffe::LayerParameter&)>;
using blob_load_t=std::function<bool(StaticGraph *, StaticNode * , const te_caffe::LayerParameter&)>;
std::unordered_map<std::string,blob_load_t> blob_load_map;
// Check if NetParameter uses old style V0LayerParameter
static bool NetNeedsV0ToV1Upgrade(const te_caffe::NetParameter& caffe_net)
{
for(int i = 0; i < caffe_net.layers_size(); ++i)
{
if(caffe_net.layers(i).has_layer())
return true;
}
return false;
}
// Check if NetParameter uses old style V1LayerParameter
static bool NetNeedsV1ToV2Upgrade(const te_caffe::NetParameter& caffe_net)
{
return (caffe_net.layers_size() > 0);
}
// Check if NetParameter uses old style input fields
static bool NetNeedsInputUpgrade(const te_caffe::NetParameter& caffe_net)
{
return (caffe_net.input_size() > 0);
}
// Check if NetParameter uses old style data transformation fields
static bool NetNeedsDataUpgrade(const te_caffe::NetParameter& caffe_net)
{
for(int i = 0; i < caffe_net.layers_size(); ++i)
{
if(caffe_net.layers(i).type() == te_caffe::V1LayerParameter_LayerType_DATA)
{
te_caffe::DataParameter layer_param = caffe_net.layers(i).data_param();
if(layer_param.has_scale() ||
layer_param.has_mean_file() ||
layer_param.has_crop_size() ||
layer_param.has_mirror())
return true;
}
if(caffe_net.layers(i).type() == te_caffe::V1LayerParameter_LayerType_IMAGE_DATA)
{
te_caffe::ImageDataParameter layer_param = caffe_net.layers(i).image_data_param();
if(layer_param.has_scale() ||
layer_param.has_mean_file() ||
layer_param.has_crop_size() ||
layer_param.has_mirror())
return true;
}
if(caffe_net.layers(i).type() == te_caffe::V1LayerParameter_LayerType_WINDOW_DATA)
{
te_caffe::WindowDataParameter layer_param = caffe_net.layers(i).window_data_param();
if(layer_param.has_scale() ||
layer_param.has_mean_file() ||
layer_param.has_crop_size() ||
layer_param.has_mirror())
return true;
}
}
return false;
}
static bool NetNeedsUpgrade(const char * fname, const te_caffe::NetParameter& caffe_net)
{
if(NetNeedsV0ToV1Upgrade(caffe_net) ||
NetNeedsV1ToV2Upgrade(caffe_net) ||
NetNeedsInputUpgrade(caffe_net) ||
NetNeedsDataUpgrade(caffe_net))
{
LOG_ERROR() << "The input file specified is using deprecated params: "
<< fname << "\n";
LOG_ERROR() << "Please upgrade the input file by using caffe tools(upgrade_net_proto_text/upgrade_net_proto_binary).\n";
return true;
}
return false;
}
bool CaffeSingle::LoadBinaryFile(const char * fname, te_caffe::NetParameter& caffe_net)
{
std::ifstream is(fname, std::ios::in|std::ios::binary);
if(!is.is_open())
{
LOG_ERROR()<<"cannot open file: "<<fname<<"\n";
return false;
}
google::protobuf::io::IstreamInputStream input_stream(&is);
google::protobuf::io::CodedInputStream coded_input(&input_stream);
// SetTotalBytesLimit(max_limit, warning_threshold)
coded_input.SetTotalBytesLimit(1024<<20, 512<<20);
bool ret=caffe_net.ParseFromCodedStream(&coded_input);
is.close();
if(!ret)
LOG_ERROR()<<"parse file: "<<fname<<" failed\n";
if(NetNeedsUpgrade(fname, caffe_net))
return false;
return ret;
}
bool CaffeSingle::LoadTextFile(const char * fname, te_caffe::NetParameter& caffe_net)
{
std::ifstream is(fname, std::ios::in);
if(!is.is_open())
{
LOG_ERROR()<<"cannot open file: "<<fname<<"\n";
return false;
}
google::protobuf::io::IstreamInputStream input_stream(&is);
bool ret= google::protobuf::TextFormat::Parse(&input_stream,&caffe_net);
is.close();
if(!ret)
LOG_ERROR()<<"parse file: "<<fname<<" failed\n";
if(NetNeedsUpgrade(fname, caffe_net))
return false;
return ret;
}
bool CaffeSingle::LoadModel(const std::vector<std::string>& file_list, StaticGraph * graph)
{
te_caffe::NetParameter caffe_net;
if(file_list.size()!=GetFileNum())
return false;
if(!LoadBinaryFile(file_list[0].c_str(), caffe_net))
return false;
SetGraphSource(graph,file_list[0]);
SetGraphSourceFormat(graph,"caffe");
SetGraphConstTensorFile(graph,file_list[0]);
return LoadGraph(caffe_net,graph);
}
bool CaffeSingle::LoadNode(StaticGraph * graph, StaticNode * node,const te_caffe::LayerParameter& layer_param, name_map_t& tensor_name_map)
{
for(int i=0;i<layer_param.bottom_size();i++)
{
const std::string& orig_name=layer_param.bottom(i);
std::string& tensor_name=tensor_name_map[orig_name];
StaticTensor * tensor=FindTensor(graph,tensor_name);
AddNodeInputTensor(node,tensor);
}
for(int i=0;i<layer_param.top_size();i++)
{
const std::string& orig_name=layer_param.top(i);
std::string tensor_name;
if(tensor_name_map.count(orig_name))
tensor_name=GetNodeName(node)+"/"+std::to_string(i);
else
tensor_name=orig_name;
StaticTensor * tensor=CreateStaticTensor(graph,tensor_name);
SetTensorDataLayout(tensor,"NCHW");
SetTensorDataType(tensor,"float32");
AddNodeOutputTensor(node,tensor);
//record the name mapping
tensor_name_map[orig_name]=tensor_name;
}
return true;
}
bool CaffeSingle::LoadGraph(te_caffe::NetParameter& caffe_net, StaticGraph * graph)
{
SetGraphIdentity(graph, "caffe",caffe_net.name(),"0");
name_map_t tensor_name_map;
int layer_num=caffe_net.layer_size();
int i;
for(i=0;i<layer_num;i++)
{
const te_caffe::LayerParameter& layer_param=caffe_net.layer(i);
const std::string& caffe_op_name=layer_param.type();
if(!FindOpLoadMethod(caffe_op_name))
{
LOG_ERROR()<<"cannot find load function for operator: "<<caffe_op_name<<"\n";
break;
}
StaticNode * node=CreateStaticNode(graph,layer_param.name());
if(!LoadNode(graph,node,layer_param,tensor_name_map))
break;
op_load_t op_func=any_cast<op_load_t>(GetOpLoadMethod(caffe_op_name));
if(!op_func(graph,node,layer_param))
break;
}
if(i<layer_num)
return false;
return true;
}
bool CaffeBuddy::LoadModel(const std::vector<std::string>& file_list, StaticGraph * graph)
{
if(file_list.size()!=GetFileNum())
return false;
te_caffe::NetParameter test_net;
if(!LoadTextFile(file_list[0].c_str(),test_net))
{
std::cout<<"FAILED\n";
return false;
}
te_caffe::NetParameter train_net;
if(!LoadBinaryFile(file_list[1].c_str(), train_net))
return false;
SetGraphSource(graph,file_list[1]);
SetGraphSourceFormat(graph,"caffe");
SetGraphConstTensorFile(graph,file_list[1]);
return LoadGraph(test_net,train_net,graph);
}
bool CaffeBuddy::LoadGraph(te_caffe::NetParameter& test_net, te_caffe::NetParameter& train_net,StaticGraph * graph)
{
name_map_t tensor_name_map;
SetGraphIdentity(graph, "caffe",test_net.name(),"0");
/* create the layer name map of the train_net */
std::unordered_map<std::string, const te_caffe::LayerParameter *> train_name_map;
int layer_number;
layer_number=train_net.layer_size();
int i;
for(i=0;i<layer_number;i++)
{
const te_caffe::LayerParameter& layer_param=train_net.layer(i);
train_name_map[layer_param.name()]=&layer_param;
}
layer_number=test_net.layer_size();
int n;
for(n=0;n<layer_number;n++)
{
const te_caffe::LayerParameter& layer_param=test_net.layer(n);
const std::string& caffe_op_name=layer_param.type();
if(!FindOpLoadMethod(caffe_op_name))
{
LOG_ERROR()<<"cannot find load function for operator: "<<caffe_op_name<<"\n";
break;
}
StaticNode * node=CreateStaticNode(graph,layer_param.name());
if(!LoadNode(graph,node,layer_param,tensor_name_map))
break;
op_load_t op_func=any_cast<op_load_t>(GetOpLoadMethod(caffe_op_name));
if(!op_func(graph,node,layer_param))
break;
/*Load pre-trained parameters*/
if(train_name_map.count(layer_param.name()))
{
const te_caffe::LayerParameter * p_train;
p_train=train_name_map[layer_param.name()];
if(p_train->blobs_size())
{
blob_load_t func=blob_load_map[caffe_op_name];
if(!func(graph,node,*p_train))
break;
}
}
}
if(n<layer_number)
return false;
return true;
}
static void LoadCaffeBlob(StaticGraph * graph, StaticNode * node, const std::vector<std::string>& name_list,
const std::vector<std::string>& layout_list,
const te_caffe::LayerParameter& layer_param)
{
unsigned int blob_num=layer_param.blobs_size();
for(unsigned int i=0;i<blob_num && i< name_list.size();i++)
{
std::string new_tensor_name=GetNodeName(node)+"/"+name_list[i];
StaticTensor * tensor=CreateStaticConstTensor(graph,new_tensor_name);
/* load tensor data*/
const te_caffe::BlobProto& blob=layer_param.blobs(i);
std::vector<int> dims;
if(blob.has_shape())
{
for(int i=0;i<blob.shape().dim_size();i++)
{
dims.push_back(blob.shape().dim(i));
}
}
else
{
std::vector<int> temp;
temp.push_back(blob.num());
temp.push_back(blob.channels());
temp.push_back(blob.height());
temp.push_back(blob.width());
int start=0;
while(temp[start]==1) start++;
for(unsigned int i=start;i<temp.size();i++)
dims.push_back(temp[i]);
}
SetTensorDim(tensor,dims);
SetTensorDataType(tensor,"float32");
SetTensorDataLayout(tensor,layout_list[i]);
int mem_size=blob.data_size()*4;
SetTensorSize(tensor,mem_size);
float * ptr=(float *)std::malloc(mem_size+128);
for(int i=0;i<blob.data_size();i++)
ptr[i]=blob.data(i);
SetConstTensorBuffer(tensor,ptr);
SetConstTensorFileLocation(tensor,-1,0);
StaticNode * new_node=CreateStaticNode(graph,new_tensor_name);
StaticOp * const_op=CreateStaticOp(graph,"Const");
SetNodeOp(new_node,const_op);
AddNodeOutputTensor(new_node,tensor);
AddNodeInputTensor(node,tensor);
}
}
static void CreatePresetNode(StaticGraph * graph, StaticNode * node, const char * name, const char * layout,
std::vector<int>& dims, float val)
{
std::string new_tensor_name=GetNodeName(node)+"/"+name;
StaticTensor * tensor=CreateStaticConstTensor(graph,new_tensor_name);
SetTensorDim(tensor,dims);
SetTensorDataType(tensor,"float32");
SetTensorDataLayout(tensor,layout);
int elem_size=1;
for(unsigned int i=0;i < dims.size();i++)
{
elem_size*=dims[i];
}
SetTensorSize(tensor,elem_size*sizeof(float));
float* ptr=(float *)std::malloc(elem_size*sizeof(float));
for(int i=0;i<elem_size;i++)
ptr[i]=val;
SetConstTensorBuffer(tensor,ptr);
SetConstTensorFileLocation(tensor,-1,0);
StaticNode * new_node=CreateStaticNode(graph,new_tensor_name);
StaticOp * const_op=CreateStaticOp(graph,"Const");
SetNodeOp(new_node,const_op);
AddNodeOutputTensor(new_node,tensor);
AddNodeInputTensor(node,tensor);
}
static bool LoadBatchNormBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
const te_caffe::BlobProto& rescale_blob=layer_param.blobs(2);
StaticOp * op=GetNodeOp(node);
BatchNormParam param=any_cast<BatchNormParam>(GetOperatorParam(op));
param.rescale_factor=rescale_blob.data(0);
SetOperatorParam(op,param);
/* for compatible reason, create the two tensors: gamma (1.0) and beta (0.0) */
/* get the dim, i.e., channel size */
const te_caffe::BlobProto& mean_blob=layer_param.blobs(0);
std::vector<int> dims;
dims.push_back(mean_blob.shape().dim(0));
CreatePresetNode(graph,node,"gamma","W",dims,1.0f);
CreatePresetNode(graph,node,"beta","W",dims,0.0f);
std::vector<std::string> name_list={"means","vars"};
std::vector<std::string> layout_list={"W","W"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadFullyConnectedBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
std::vector<std::string> name_list={"weight","bias"};
std::vector<std::string> layout_list={"HW","W"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadScaleBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
std::vector<std::string> name_list={"gamma","beta"};
std::vector<std::string> layout_list={"CHW","W"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadPReLuBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
std::vector<std::string> name_list={"slope"};
std::vector<std::string> layout_list={"W"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadNormalizeBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
std::vector<std::string> name_list={"scale"};
std::vector<std::string> layout_list={"W"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadConvolutionBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
std::vector<std::string> name_list={"weight","bias"};
std::vector<std::string> layout_list={"NCHW","W"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadDeconvolutionBlob(StaticGraph * graph, StaticNode* node, const te_caffe::LayerParameter& layer_param)
{
std::vector<std::string> name_list={"weight","bias"};
std::vector<std::string> layout_list={"NCHW","C"};
LoadCaffeBlob(graph, node,name_list,layout_list,layer_param);
return true;
}
static bool LoadCaffeInputOp(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
StaticOp * op=CreateStaticOp(graph,"InputOp");
SetNodeOp(node,op);
const te_caffe::InputParameter& input_param=layer_param.input_param();
if(input_param.shape_size())
{
std::vector<int> dim;
const te_caffe::BlobShape& blob_shape=input_param.shape(0);
for(int i=0;i<blob_shape.dim_size();i++)
{
dim.push_back(blob_shape.dim(i));
}
StaticTensor * tensor=GetNodeOutputTensor(graph,node,0);
SetTensorDim(tensor,dim);
}
AddGraphInputNode(graph,node);
return true;
}
static bool LoadCaffeSoftmax(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
const te_caffe::SoftmaxParameter& softmax_param=layer_param.softmax_param();
SoftmaxParam param=any_cast<SoftmaxParam>(OpManager::GetOpDefParam("Softmax"));
if(softmax_param.has_axis())
param.axis=softmax_param.axis();
else
param.axis=1;
StaticOp * op=CreateStaticOp(graph,"Softmax");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeReorg(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
const te_caffe::ReorgParameter& caffe_param=layer_param.reorg_param();
ReorgParam param=any_cast<ReorgParam>(OpManager::GetOpDefParam("Reorg"));
param.stride = caffe_param.stride();
StaticOp * op=CreateStaticOp(graph,"Reorg");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeRegion(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
const te_caffe::RegionParameter& caffe_param=layer_param.region_param();
RegionParam param=any_cast<RegionParam>(OpManager::GetOpDefParam("Region"));
param.num_classes = caffe_param.num_classes();
param.num_box = caffe_param.num_box();
param.side = caffe_param.side();
param.coords = caffe_param.coords();
param.confidence_threshold = caffe_param.confidence_threshold();
param.nms_threshold = caffe_param.nms_threshold();
for (int i = 0; i <(int) caffe_param.biases_size(); ++i)
{
param.biases.push_back(caffe_param.biases(i));
}
StaticOp * op=CreateStaticOp(graph,"Region");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeNormalize(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
const te_caffe::NormalizeParameter& normalize_param=layer_param.norm_param();
NormalizeParam param=any_cast<NormalizeParam>(OpManager::GetOpDefParam("Normalize"));
param.across_spatial=normalize_param.across_spatial();
param.channel_shared=normalize_param.channel_shared();
StaticOp * op=CreateStaticOp(graph,"Normalize");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeSlice(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
const te_caffe::SliceParameter& slice_param=layer_param.slice_param();
SliceParam param=any_cast<SliceParam>(OpManager::GetOpDefParam("Slice"));
if(slice_param.has_axis())
param.axis=slice_param.axis();
else
param.axis=1;
StaticOp * op=CreateStaticOp(graph,"Slice");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeReLu(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
ReLuParam param=any_cast<ReLuParam>(OpManager::GetOpDefParam("ReLu"));
const te_caffe::ReLUParameter& caffe_param=layer_param.relu_param();
if(caffe_param.has_negative_slope())
param.negative_slope=static_cast<float>(caffe_param.negative_slope());
else
param.negative_slope=0.f;
StaticOp * op=CreateStaticOp(graph,"ReLu");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeSplit(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
StaticOp * op=CreateStaticOp(graph,"Split");
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeConcat(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
ConcatParam param=any_cast<ConcatParam>(OpManager::GetOpDefParam("Concat"));
const te_caffe::ConcatParameter& concat_param=layer_param.concat_param();
if(concat_param.has_concat_dim())
param.axis=static_cast<int>(concat_param.concat_dim());
else
param.axis=concat_param.axis();
StaticOp * op=CreateStaticOp(graph,"Concat");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffePermute(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
PermuteParam param=any_cast<PermuteParam>(OpManager::GetOpDefParam("Permute"));
const te_caffe::PermuteParameter& permute_param=layer_param.permute_param();
param.order0= permute_param.order(0);
param.order1= permute_param.order(1);
param.order2= permute_param.order(2);
param.order3= permute_param.order(3);
StaticOp * op=CreateStaticOp(graph,"Permute");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeFlatten(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
FlattenParam param=any_cast<FlattenParam>(OpManager::GetOpDefParam("Flatten"));
const te_caffe::FlattenParameter& flatten_param=layer_param.flatten_param();
param.axis= flatten_param.axis();
StaticOp * op=CreateStaticOp(graph,"Flatten");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffePriorBox(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
PriorBoxParam param=any_cast<PriorBoxParam>(OpManager::GetOpDefParam("PriorBox"));
const te_caffe::PriorBoxParameter& caffe_param=layer_param.prior_box_param();
// offset
param.offset = caffe_param.offset();
// img_size
if (caffe_param.has_img_h() && caffe_param.has_img_w())
{
param.img_h = caffe_param.img_h();
param.img_w = caffe_param.img_w();
}
else if (caffe_param.has_img_size())
{
param.img_h = caffe_param.img_size();
param.img_w = caffe_param.img_size();
}
else
{
param.img_h = 0;
param.img_w = 0;
}
// step
if (caffe_param.has_step_h() && caffe_param.has_step_w())
{
param.step_h = caffe_param.step_h();
param.step_w = caffe_param.step_w();
}
else if (caffe_param.has_step())
{
param.step_h = caffe_param.step();
param.step_w = caffe_param.step();
}
else
{
param.step_h = 0;
param.step_w = 0;
}
// min_size, max_size
for (int i = 0; i < caffe_param.min_size_size(); ++i)
{
param.min_size.push_back(caffe_param.min_size(i));
}
for (int i = 0; i < caffe_param.max_size_size(); ++i)
{
param.max_size.push_back(caffe_param.max_size(i));
}
// variance
for (int i = 0; i < caffe_param.variance_size(); ++i)
{
param.variance.push_back(caffe_param.variance(i));
}
// clip
param.clip = caffe_param.clip();
// flip
param.flip = caffe_param.flip();
// aspect_ratio
for (int i = 0; i < caffe_param.aspect_ratio_size(); ++i)
{
param.aspect_ratio.push_back(caffe_param.aspect_ratio(i));
}
StaticOp * op=CreateStaticOp(graph,"PriorBox");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeResize(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
ResizeParam param=any_cast<ResizeParam>(OpManager::GetOpDefParam("Resize"));
const te_caffe::Resize1_Parameter& caffe_param=layer_param.resize1_param();
//
param.scale_h = caffe_param.out_height_scale();
param.scale_w = caffe_param.out_width_scale();
param.type = caffe_param.resize_type();
StaticOp * op=CreateStaticOp(graph,"Resize");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeROIPooling(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
ROIPoolingParam param=any_cast<ROIPoolingParam>(OpManager::GetOpDefParam("ROIPooling"));
const te_caffe::ROIPoolingParameter& caffe_param=layer_param.roi_pooling_param();
//
param.pooled_h = caffe_param.pooled_h();
param.pooled_w = caffe_param.pooled_w();
param.spatial_scale = caffe_param.spatial_scale();
StaticOp * op=CreateStaticOp(graph,"ROIPooling");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeRPN(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
RPNParam param=any_cast<RPNParam>(OpManager::GetOpDefParam("RPN"));
const te_caffe::RPNParameter& caffe_param=layer_param.rpn_param();
//
param.feat_stride = caffe_param.feat_stride();
param.basesize = caffe_param.basesize();
param.min_size = caffe_param.boxminsize();
param.per_nms_topn = caffe_param.per_nms_topn();
param.post_nms_topn = caffe_param.post_nms_topn();
param.nms_thresh = caffe_param.nms_thresh();
for (int i = 0; i < caffe_param.scale_size(); ++i)
{
param.anchor_scales.push_back(caffe_param.scale(i));
}
for (int i = 0; i < caffe_param.ratio_size(); ++i)
{
param.ratios.push_back(caffe_param.ratio(i));
}
StaticOp * op=CreateStaticOp(graph,"RPN");
SetOperatorParam(op,param);
SetOperatorDynamicShape(op);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeDetectionOutput(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
DetectionOutputParam param=any_cast<DetectionOutputParam>(OpManager::GetOpDefParam("DetectionOutput"));
const te_caffe::DetectionOutputParameter& caffe_param=layer_param.detection_output_param();
param.num_classes=caffe_param.num_classes();
param.confidence_threshold=caffe_param.confidence_threshold();
param.keep_top_k=caffe_param.keep_top_k();
param.nms_threshold= caffe_param.nms_param().nms_threshold();
if (caffe_param.nms_param().has_top_k())
{
param.nms_top_k = caffe_param.nms_param().top_k();
}
StaticOp * op=CreateStaticOp(graph,"DetectionOutput");
SetOperatorParam(op,param);
SetOperatorDynamicShape(op);
SetNodeOp(node,op);
return true;
}
static bool LoadCaffeReshape(StaticGraph * graph, StaticNode * node, const te_caffe::LayerParameter& layer_param)
{
ReshapeParam param=any_cast<ReshapeParam>(OpManager::GetOpDefParam("Reshape"));
const te_caffe::ReshapeParameter& caffe_param=layer_param.reshape_param();
//dims
for (int i = 0; i < caffe_param.shape().dim_size(); ++i)
{
param.dims.push_back(caffe_param.shape().dim(i));
}
StaticOp * op=CreateStaticOp(graph,"Reshape");
SetOperatorParam(op,param);
SetNodeOp(node,op);
return true;
}
static EltType ConvertCaffeEltwise(te_caffe::EltwiseParameter_EltwiseOp method)
{
if(method == te_caffe::EltwiseParameter_EltwiseOp_PROD)
return ELT_PROD;
else if (method == te_caffe::EltwiseParameter_EltwiseOp_MAX)
return ELT_MAX;
/* for others, return SUM */
return ELT_SUM;