1188 lines
45 KiB
C++
1188 lines
45 KiB
C++
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// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#ifdef _MSC_VER
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#define _CRT_SECURE_NO_DEPRECATE
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#endif
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#include "caffe.pb.h"
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#include <algorithm>
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#include <fstream>
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#include <google/protobuf/io/coded_stream.h>
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#include <google/protobuf/io/zero_copy_stream_impl.h>
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#include <google/protobuf/message.h>
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#include <google/protobuf/text_format.h>
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#include <limits.h>
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#include <limits>
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#include <map>
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#include <math.h>
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#include <set>
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#include <stdio.h>
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static bool read_proto_from_text(const char* filepath, google::protobuf::Message* message)
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{
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std::ifstream fs(filepath, std::ifstream::in);
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if (!fs.is_open())
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{
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fprintf(stderr, "open failed %s\n", filepath);
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return false;
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}
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google::protobuf::io::IstreamInputStream input(&fs);
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bool success = google::protobuf::TextFormat::Parse(&input, message);
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fs.close();
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return success;
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}
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static bool read_proto_from_binary(const char* filepath, google::protobuf::Message* message)
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{
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std::ifstream fs(filepath, std::ifstream::in | std::ifstream::binary);
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if (!fs.is_open())
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{
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fprintf(stderr, "open failed %s\n", filepath);
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return false;
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}
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google::protobuf::io::IstreamInputStream input(&fs);
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google::protobuf::io::CodedInputStream codedstr(&input);
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#if GOOGLE_PROTOBUF_VERSION >= 3011000
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codedstr.SetTotalBytesLimit(INT_MAX);
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#else
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codedstr.SetTotalBytesLimit(INT_MAX, INT_MAX / 2);
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#endif
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bool success = message->ParseFromCodedStream(&codedstr);
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fs.close();
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return success;
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}
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int main(int argc, char** argv)
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{
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if (!(argc == 3 || argc == 5))
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{
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fprintf(stderr, "Usage: %s [caffeproto] [caffemodel] [ncnnparam] [ncnnbin]\n", argv[0]);
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return -1;
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}
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const char* caffeproto = argv[1];
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const char* caffemodel = argv[2];
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const char* ncnn_prototxt = argc == 5 ? argv[3] : "ncnn.param";
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const char* ncnn_modelbin = argc == 5 ? argv[4] : "ncnn.bin";
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caffe::NetParameter proto;
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caffe::NetParameter net;
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// load
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bool s0 = read_proto_from_text(caffeproto, &proto);
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if (!s0)
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{
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fprintf(stderr, "read_proto_from_text failed\n");
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return -1;
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}
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bool s1 = read_proto_from_binary(caffemodel, &net);
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if (!s1)
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{
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fprintf(stderr, "read_proto_from_binary failed\n");
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return -1;
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}
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FILE* pp = fopen(ncnn_prototxt, "wb");
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FILE* bp = fopen(ncnn_modelbin, "wb");
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// magic
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fprintf(pp, "7767517\n");
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// rename mapping for identical bottom top style
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std::map<std::string, std::string> blob_name_decorated;
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// bottom blob reference
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std::map<std::string, int> bottom_reference;
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// global definition line
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// [layer count] [blob count]
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int layer_count = proto.layer_size();
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std::set<std::string> blob_names;
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for (int i = 0; i < layer_count; i++)
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{
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const caffe::LayerParameter& layer = proto.layer(i);
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for (int j = 0; j < layer.bottom_size(); j++)
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{
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std::string blob_name = layer.bottom(j);
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if (blob_name_decorated.find(blob_name) != blob_name_decorated.end())
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{
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blob_name = blob_name_decorated[blob_name];
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}
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blob_names.insert(blob_name);
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if (bottom_reference.find(blob_name) == bottom_reference.end())
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{
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bottom_reference[blob_name] = 1;
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}
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else
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{
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bottom_reference[blob_name] = bottom_reference[blob_name] + 1;
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}
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}
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if (layer.bottom_size() == 1 && layer.top_size() == 1 && layer.bottom(0) == layer.top(0))
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{
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std::string blob_name = layer.top(0) + "_" + layer.name();
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blob_name_decorated[layer.top(0)] = blob_name;
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blob_names.insert(blob_name);
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}
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else
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{
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for (int j = 0; j < layer.top_size(); j++)
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{
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std::string blob_name = layer.top(j);
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blob_names.insert(blob_name);
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}
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}
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}
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// remove bottom_reference entry with reference equals to one
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int splitncnn_blob_count = 0;
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std::map<std::string, int>::iterator it = bottom_reference.begin();
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while (it != bottom_reference.end())
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{
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if (it->second == 1)
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{
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bottom_reference.erase(it++);
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}
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else
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{
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splitncnn_blob_count += it->second;
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// fprintf(stderr, "%s %d\n", it->first.c_str(), it->second);
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++it;
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}
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}
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fprintf(pp, "%d %d\n", int(layer_count + bottom_reference.size()), int(blob_names.size() + splitncnn_blob_count));
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// populate
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blob_name_decorated.clear();
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int internal_split = 0;
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for (int i = 0; i < layer_count; i++)
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{
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const caffe::LayerParameter& layer = proto.layer(i);
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// layer definition line, repeated
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// [type] [name] [bottom blob count] [top blob count] [bottom blobs] [top blobs] [layer specific params]
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if (layer.type() == "BN")
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{
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fprintf(pp, "%-16s", "Scale");
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}
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else if (layer.type() == "Convolution")
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{
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const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
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if (convolution_param.group() != 1)
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fprintf(pp, "%-16s", "ConvolutionDepthWise");
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else
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fprintf(pp, "%-16s", "Convolution");
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}
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else if (layer.type() == "ConvolutionDepthwise" || layer.type() == "DepthwiseConvolution")
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{
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fprintf(pp, "%-16s", "ConvolutionDepthWise");
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}
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else if (layer.type() == "Deconvolution")
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{
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const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
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if (convolution_param.group() != 1)
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fprintf(pp, "%-16s", "DeconvolutionDepthWise");
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else
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fprintf(pp, "%-16s", "Deconvolution");
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}
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else if (layer.type() == "MemoryData")
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{
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fprintf(pp, "%-16s", "Input");
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}
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else if (layer.type() == "Python")
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{
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const caffe::PythonParameter& python_param = layer.python_param();
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std::string python_layer_name = python_param.layer();
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if (python_layer_name == "ProposalLayer")
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fprintf(pp, "%-16s", "Proposal");
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else
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fprintf(pp, "%-16s", python_layer_name.c_str());
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}
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else if (layer.type() == "ReLU6")
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{
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fprintf(pp, "%-16s", "Clip");
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}
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else if (layer.type() == "Silence")
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{
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fprintf(pp, "%-16s", "Noop");
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}
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else
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{
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fprintf(pp, "%-16s", layer.type().c_str());
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}
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fprintf(pp, " %-16s %d %d", layer.name().c_str(), layer.bottom_size(), layer.top_size());
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for (int j = 0; j < layer.bottom_size(); j++)
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{
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std::string blob_name = layer.bottom(j);
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if (blob_name_decorated.find(layer.bottom(j)) != blob_name_decorated.end())
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{
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blob_name = blob_name_decorated[layer.bottom(j)];
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}
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if (bottom_reference.find(blob_name) != bottom_reference.end())
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{
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int refidx = bottom_reference[blob_name] - 1;
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bottom_reference[blob_name] = refidx;
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char splitsuffix[256];
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sprintf(splitsuffix, "_splitncnn_%d", refidx);
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blob_name = blob_name + splitsuffix;
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}
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fprintf(pp, " %s", blob_name.c_str());
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}
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// decorated
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if (layer.bottom_size() == 1 && layer.top_size() == 1 && layer.bottom(0) == layer.top(0))
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{
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std::string blob_name = layer.top(0) + "_" + layer.name();
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blob_name_decorated[layer.top(0)] = blob_name;
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fprintf(pp, " %s", blob_name.c_str());
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}
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else
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{
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for (int j = 0; j < layer.top_size(); j++)
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{
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std::string blob_name = layer.top(j);
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fprintf(pp, " %s", blob_name.c_str());
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}
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}
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// find blob binary by layer name
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int netidx;
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for (netidx = 0; netidx < net.layer_size(); netidx++)
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{
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if (net.layer(netidx).name() == layer.name())
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{
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break;
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}
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}
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// layer specific params
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if (layer.type() == "BatchNorm")
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{
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const caffe::LayerParameter& binlayer = net.layer(netidx);
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const caffe::BlobProto& mean_blob = binlayer.blobs(0);
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const caffe::BlobProto& var_blob = binlayer.blobs(1);
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fprintf(pp, " 0=%d", (int)mean_blob.data_size());
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const caffe::BatchNormParameter& batch_norm_param = layer.batch_norm_param();
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float eps = batch_norm_param.eps();
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std::vector<float> ones(mean_blob.data_size(), 1.f);
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fwrite(ones.data(), sizeof(float), ones.size(), bp); // slope
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if (binlayer.blobs_size() < 3)
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{
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fwrite(mean_blob.data().data(), sizeof(float), mean_blob.data_size(), bp);
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float tmp;
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for (int j = 0; j < var_blob.data_size(); j++)
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{
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tmp = var_blob.data().data()[j] + eps;
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fwrite(&tmp, sizeof(float), 1, bp);
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}
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}
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else
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{
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float scale_factor = binlayer.blobs(2).data().data()[0] == 0 ? 0 : 1 / binlayer.blobs(2).data().data()[0];
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// premultiply scale_factor to mean and variance
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float tmp;
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for (int j = 0; j < mean_blob.data_size(); j++)
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{
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tmp = mean_blob.data().data()[j] * scale_factor;
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fwrite(&tmp, sizeof(float), 1, bp);
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}
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for (int j = 0; j < var_blob.data_size(); j++)
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{
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tmp = var_blob.data().data()[j] * scale_factor + eps;
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fwrite(&tmp, sizeof(float), 1, bp);
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}
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}
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std::vector<float> zeros(mean_blob.data_size(), 0.f);
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fwrite(zeros.data(), sizeof(float), zeros.size(), bp); // bias
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}
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else if (layer.type() == "BN")
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{
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const caffe::LayerParameter& binlayer = net.layer(netidx);
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const caffe::BlobProto& scale_blob = binlayer.blobs(0);
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const caffe::BlobProto& shift_blob = binlayer.blobs(1);
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fprintf(pp, " 0=%d", (int)scale_blob.data_size());
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fprintf(pp, " 1=1");
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fwrite(scale_blob.data().data(), sizeof(float), scale_blob.data_size(), bp);
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fwrite(shift_blob.data().data(), sizeof(float), shift_blob.data_size(), bp);
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}
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else if (layer.type() == "Concat")
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{
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const caffe::ConcatParameter& concat_param = layer.concat_param();
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int axis = concat_param.axis() - 1;
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fprintf(pp, " 0=%d", axis);
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}
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else if (layer.type() == "Convolution" || layer.type() == "ConvolutionDepthwise" || layer.type() == "DepthwiseConvolution")
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{
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const caffe::LayerParameter& binlayer = net.layer(netidx);
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const caffe::BlobProto& weight_blob = binlayer.blobs(0);
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const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
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fprintf(pp, " 0=%d", convolution_param.num_output());
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if (convolution_param.has_kernel_w() && convolution_param.has_kernel_h())
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{
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fprintf(pp, " 1=%d", convolution_param.kernel_w());
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fprintf(pp, " 11=%d", convolution_param.kernel_h());
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}
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else
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{
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|
|
fprintf(pp, " 1=%d", convolution_param.kernel_size(0));
|
||
|
|
}
|
||
|
|
fprintf(pp, " 2=%d", convolution_param.dilation_size() != 0 ? convolution_param.dilation(0) : 1);
|
||
|
|
if (convolution_param.has_stride_w() && convolution_param.has_stride_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 3=%d", convolution_param.stride_w());
|
||
|
|
fprintf(pp, " 13=%d", convolution_param.stride_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 3=%d", convolution_param.stride_size() != 0 ? convolution_param.stride(0) : 1);
|
||
|
|
}
|
||
|
|
if (convolution_param.has_pad_w() && convolution_param.has_pad_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 4=%d", convolution_param.pad_w());
|
||
|
|
fprintf(pp, " 14=%d", convolution_param.pad_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 4=%d", convolution_param.pad_size() != 0 ? convolution_param.pad(0) : 0);
|
||
|
|
}
|
||
|
|
fprintf(pp, " 5=%d", convolution_param.bias_term());
|
||
|
|
fprintf(pp, " 6=%d", weight_blob.data_size());
|
||
|
|
|
||
|
|
int num_group = 1;
|
||
|
|
if (layer.type() == "ConvolutionDepthwise" || layer.type() == "DepthwiseConvolution")
|
||
|
|
{
|
||
|
|
num_group = convolution_param.num_output();
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
num_group = convolution_param.group();
|
||
|
|
}
|
||
|
|
|
||
|
|
if (num_group != 1)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 7=%d", num_group);
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int j = 0; j < binlayer.blobs_size(); j++)
|
||
|
|
{
|
||
|
|
int quantize_tag = 0;
|
||
|
|
const caffe::BlobProto& blob = binlayer.blobs(j);
|
||
|
|
|
||
|
|
// we will not quantize the bias values
|
||
|
|
if (j == 0)
|
||
|
|
{
|
||
|
|
// write quantize tag first
|
||
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
||
|
|
|
||
|
|
// write original data
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
// write original data
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Crop")
|
||
|
|
{
|
||
|
|
const caffe::CropParameter& crop_param = layer.crop_param();
|
||
|
|
int num_offset = crop_param.offset_size();
|
||
|
|
if (num_offset == 1)
|
||
|
|
{
|
||
|
|
int offset = crop_param.offset(0);
|
||
|
|
int axis = crop_param.axis() - 1;
|
||
|
|
if (axis == 0)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%d", offset);
|
||
|
|
fprintf(pp, " 1=%d", offset);
|
||
|
|
fprintf(pp, " 2=%d", offset);
|
||
|
|
}
|
||
|
|
else if (axis == 1)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%d", offset);
|
||
|
|
fprintf(pp, " 1=%d", offset);
|
||
|
|
}
|
||
|
|
else if (axis == 2)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%d", offset);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (num_offset == 2)
|
||
|
|
{
|
||
|
|
int woffset = crop_param.offset(1);
|
||
|
|
int hoffset = crop_param.offset(0);
|
||
|
|
fprintf(pp, " 0=%d", woffset);
|
||
|
|
fprintf(pp, " 1=%d", hoffset);
|
||
|
|
}
|
||
|
|
else if (num_offset == 3)
|
||
|
|
{
|
||
|
|
int woffset = crop_param.offset(2);
|
||
|
|
int hoffset = crop_param.offset(1);
|
||
|
|
int coffset = crop_param.offset(0);
|
||
|
|
fprintf(pp, " 0=%d", woffset);
|
||
|
|
fprintf(pp, " 1=%d", hoffset);
|
||
|
|
fprintf(pp, " 2=%d", coffset);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Deconvolution")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
|
||
|
|
const caffe::BlobProto& weight_blob = binlayer.blobs(0);
|
||
|
|
const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
|
||
|
|
fprintf(pp, " 0=%d", convolution_param.num_output());
|
||
|
|
if (convolution_param.has_kernel_w() && convolution_param.has_kernel_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 1=%d", convolution_param.kernel_w());
|
||
|
|
fprintf(pp, " 11=%d", convolution_param.kernel_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 1=%d", convolution_param.kernel_size(0));
|
||
|
|
}
|
||
|
|
fprintf(pp, " 2=%d", convolution_param.dilation_size() != 0 ? convolution_param.dilation(0) : 1);
|
||
|
|
if (convolution_param.has_stride_w() && convolution_param.has_stride_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 3=%d", convolution_param.stride_w());
|
||
|
|
fprintf(pp, " 13=%d", convolution_param.stride_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 3=%d", convolution_param.stride_size() != 0 ? convolution_param.stride(0) : 1);
|
||
|
|
}
|
||
|
|
if (convolution_param.has_pad_w() && convolution_param.has_pad_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 4=%d", convolution_param.pad_w());
|
||
|
|
fprintf(pp, " 14=%d", convolution_param.pad_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 4=%d", convolution_param.pad_size() != 0 ? convolution_param.pad(0) : 0);
|
||
|
|
}
|
||
|
|
fprintf(pp, " 5=%d", convolution_param.bias_term());
|
||
|
|
fprintf(pp, " 6=%d", weight_blob.data_size());
|
||
|
|
|
||
|
|
int group = convolution_param.group();
|
||
|
|
if (group != 1)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 7=%d", group);
|
||
|
|
}
|
||
|
|
|
||
|
|
int quantized_weight = 0;
|
||
|
|
fwrite(&quantized_weight, sizeof(int), 1, bp);
|
||
|
|
|
||
|
|
int maxk = 0;
|
||
|
|
if (convolution_param.has_kernel_w() && convolution_param.has_kernel_h())
|
||
|
|
{
|
||
|
|
maxk = convolution_param.kernel_w() * convolution_param.kernel_h();
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
maxk = convolution_param.kernel_size(0) * convolution_param.kernel_size(0);
|
||
|
|
}
|
||
|
|
for (int g = 0; g < group; g++)
|
||
|
|
{
|
||
|
|
// reorder weight from inch-outch to outch-inch
|
||
|
|
int num_output = convolution_param.num_output() / group;
|
||
|
|
int num_input = weight_blob.data_size() / maxk / num_output / group;
|
||
|
|
const float* weight_data_ptr = weight_blob.data().data() + g * maxk * num_output * num_input;
|
||
|
|
for (int k = 0; k < num_output; k++)
|
||
|
|
{
|
||
|
|
for (int j = 0; j < num_input; j++)
|
||
|
|
{
|
||
|
|
fwrite(weight_data_ptr + (j * num_output + k) * maxk, sizeof(float), maxk, bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int j = 1; j < binlayer.blobs_size(); j++)
|
||
|
|
{
|
||
|
|
const caffe::BlobProto& blob = binlayer.blobs(j);
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "DetectionOutput")
|
||
|
|
{
|
||
|
|
const caffe::DetectionOutputParameter& detection_output_param = layer.detection_output_param();
|
||
|
|
const caffe::NonMaximumSuppressionParameter& nms_param = detection_output_param.nms_param();
|
||
|
|
fprintf(pp, " 0=%d", detection_output_param.num_classes());
|
||
|
|
fprintf(pp, " 1=%e", nms_param.nms_threshold());
|
||
|
|
fprintf(pp, " 2=%d", nms_param.top_k());
|
||
|
|
fprintf(pp, " 3=%d", detection_output_param.keep_top_k());
|
||
|
|
fprintf(pp, " 4=%e", detection_output_param.confidence_threshold());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Dropout")
|
||
|
|
{
|
||
|
|
const caffe::DropoutParameter& dropout_param = layer.dropout_param();
|
||
|
|
if (dropout_param.has_scale_train() && !dropout_param.scale_train())
|
||
|
|
{
|
||
|
|
float scale = 1.f - dropout_param.dropout_ratio();
|
||
|
|
fprintf(pp, " 0=%e", scale);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Eltwise")
|
||
|
|
{
|
||
|
|
const caffe::EltwiseParameter& eltwise_param = layer.eltwise_param();
|
||
|
|
int coeff_size = eltwise_param.coeff_size();
|
||
|
|
fprintf(pp, " 0=%d", (int)eltwise_param.operation());
|
||
|
|
fprintf(pp, " -23301=%d", coeff_size);
|
||
|
|
for (int j = 0; j < coeff_size; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", eltwise_param.coeff(j));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "ELU")
|
||
|
|
{
|
||
|
|
const caffe::ELUParameter& elu_param = layer.elu_param();
|
||
|
|
fprintf(pp, " 0=%e", elu_param.alpha());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Embed")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
|
||
|
|
const caffe::BlobProto& weight_blob = binlayer.blobs(0);
|
||
|
|
const caffe::EmbedParameter& embed_param = layer.embed_param();
|
||
|
|
fprintf(pp, " 0=%d", embed_param.num_output());
|
||
|
|
fprintf(pp, " 1=%d", embed_param.input_dim());
|
||
|
|
fprintf(pp, " 2=%d", embed_param.bias_term());
|
||
|
|
fprintf(pp, " 3=%d", weight_blob.data_size());
|
||
|
|
|
||
|
|
for (int j = 0; j < binlayer.blobs_size(); j++)
|
||
|
|
{
|
||
|
|
int quantize_tag = 0;
|
||
|
|
const caffe::BlobProto& blob = binlayer.blobs(j);
|
||
|
|
|
||
|
|
// write quantize tag first
|
||
|
|
if (j == 0)
|
||
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
||
|
|
|
||
|
|
// write original data
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "InnerProduct")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
|
||
|
|
const caffe::BlobProto& weight_blob = binlayer.blobs(0);
|
||
|
|
const caffe::InnerProductParameter& inner_product_param = layer.inner_product_param();
|
||
|
|
fprintf(pp, " 0=%d", inner_product_param.num_output());
|
||
|
|
fprintf(pp, " 1=%d", inner_product_param.bias_term());
|
||
|
|
fprintf(pp, " 2=%d", weight_blob.data_size());
|
||
|
|
|
||
|
|
for (int j = 0; j < binlayer.blobs_size(); j++)
|
||
|
|
{
|
||
|
|
int quantize_tag = 0;
|
||
|
|
const caffe::BlobProto& blob = binlayer.blobs(j);
|
||
|
|
|
||
|
|
// we will not quantize the bias values
|
||
|
|
if (j == 0)
|
||
|
|
{
|
||
|
|
// write quantize tag first
|
||
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
||
|
|
|
||
|
|
// write original data
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
// write original data
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Input")
|
||
|
|
{
|
||
|
|
const caffe::InputParameter& input_param = layer.input_param();
|
||
|
|
const caffe::BlobShape& bs = input_param.shape(0);
|
||
|
|
if (bs.dim_size() == 4)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd", size_t(bs.dim(3)));
|
||
|
|
fprintf(pp, " 1=%zd", size_t(bs.dim(2)));
|
||
|
|
fprintf(pp, " 2=%zd", size_t(bs.dim(1)));
|
||
|
|
}
|
||
|
|
else if (bs.dim_size() == 3)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd", size_t(bs.dim(2)));
|
||
|
|
fprintf(pp, " 1=%zd", size_t(bs.dim(1)));
|
||
|
|
}
|
||
|
|
else if (bs.dim_size() == 2)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd", size_t(bs.dim(1)));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Interp")
|
||
|
|
{
|
||
|
|
const caffe::InterpParameter& interp_param = layer.interp_param();
|
||
|
|
fprintf(pp, " 0=%d", 2);
|
||
|
|
fprintf(pp, " 1=%e", (float)interp_param.zoom_factor());
|
||
|
|
fprintf(pp, " 2=%e", (float)interp_param.zoom_factor());
|
||
|
|
fprintf(pp, " 3=%d", interp_param.height());
|
||
|
|
fprintf(pp, " 4=%d", interp_param.width());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "LRN")
|
||
|
|
{
|
||
|
|
const caffe::LRNParameter& lrn_param = layer.lrn_param();
|
||
|
|
fprintf(pp, " 0=%d", lrn_param.norm_region());
|
||
|
|
fprintf(pp, " 1=%d", lrn_param.local_size());
|
||
|
|
fprintf(pp, " 2=%e", lrn_param.alpha());
|
||
|
|
fprintf(pp, " 3=%e", lrn_param.beta());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "LSTM")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
|
||
|
|
const caffe::BlobProto& weight_blob = binlayer.blobs(0);
|
||
|
|
const caffe::RecurrentParameter& recurrent_param = layer.recurrent_param();
|
||
|
|
fprintf(pp, " 0=%d", recurrent_param.num_output());
|
||
|
|
fprintf(pp, " 1=%d", weight_blob.data_size());
|
||
|
|
|
||
|
|
for (int j = 0; j < binlayer.blobs_size(); j++)
|
||
|
|
{
|
||
|
|
int quantize_tag = 0;
|
||
|
|
const caffe::BlobProto& blob = binlayer.blobs(j);
|
||
|
|
|
||
|
|
// write quantize tag first
|
||
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
||
|
|
|
||
|
|
// write original data
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "MemoryData")
|
||
|
|
{
|
||
|
|
const caffe::MemoryDataParameter& memory_data_param = layer.memory_data_param();
|
||
|
|
fprintf(pp, " 0=%d", memory_data_param.width());
|
||
|
|
fprintf(pp, " 1=%d", memory_data_param.height());
|
||
|
|
fprintf(pp, " 2=%d", memory_data_param.channels());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "MVN")
|
||
|
|
{
|
||
|
|
const caffe::MVNParameter& mvn_param = layer.mvn_param();
|
||
|
|
fprintf(pp, " 0=%d", mvn_param.normalize_variance());
|
||
|
|
fprintf(pp, " 1=%d", mvn_param.across_channels());
|
||
|
|
fprintf(pp, " 2=%e", mvn_param.eps());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Normalize")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
const caffe::BlobProto& scale_blob = binlayer.blobs(0);
|
||
|
|
const caffe::NormalizeParameter& norm_param = layer.norm_param();
|
||
|
|
fprintf(pp, " 0=%d", norm_param.across_spatial());
|
||
|
|
fprintf(pp, " 1=%d", norm_param.channel_shared());
|
||
|
|
fprintf(pp, " 2=%e", norm_param.eps());
|
||
|
|
fprintf(pp, " 3=%d", scale_blob.data_size());
|
||
|
|
|
||
|
|
fwrite(scale_blob.data().data(), sizeof(float), scale_blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Permute")
|
||
|
|
{
|
||
|
|
const caffe::PermuteParameter& permute_param = layer.permute_param();
|
||
|
|
int order_size = permute_param.order_size();
|
||
|
|
int order_type = 0;
|
||
|
|
if (order_size == 0)
|
||
|
|
order_type = 0;
|
||
|
|
if (order_size == 1)
|
||
|
|
{
|
||
|
|
int order0 = permute_param.order(0);
|
||
|
|
if (order0 == 0)
|
||
|
|
order_type = 0;
|
||
|
|
// permute with N not supported
|
||
|
|
}
|
||
|
|
if (order_size == 2)
|
||
|
|
{
|
||
|
|
int order0 = permute_param.order(0);
|
||
|
|
int order1 = permute_param.order(1);
|
||
|
|
if (order0 == 0)
|
||
|
|
{
|
||
|
|
if (order1 == 1) // 0 1 2 3
|
||
|
|
order_type = 0;
|
||
|
|
else if (order1 == 2) // 0 2 1 3
|
||
|
|
order_type = 2;
|
||
|
|
else if (order1 == 3) // 0 3 1 2
|
||
|
|
order_type = 4;
|
||
|
|
}
|
||
|
|
// permute with N not supported
|
||
|
|
}
|
||
|
|
if (order_size == 3 || order_size == 4)
|
||
|
|
{
|
||
|
|
int order0 = permute_param.order(0);
|
||
|
|
int order1 = permute_param.order(1);
|
||
|
|
int order2 = permute_param.order(2);
|
||
|
|
if (order0 == 0)
|
||
|
|
{
|
||
|
|
if (order1 == 1)
|
||
|
|
{
|
||
|
|
if (order2 == 2) // 0 1 2 3
|
||
|
|
order_type = 0;
|
||
|
|
if (order2 == 3) // 0 1 3 2
|
||
|
|
order_type = 1;
|
||
|
|
}
|
||
|
|
else if (order1 == 2)
|
||
|
|
{
|
||
|
|
if (order2 == 1) // 0 2 1 3
|
||
|
|
order_type = 2;
|
||
|
|
if (order2 == 3) // 0 2 3 1
|
||
|
|
order_type = 3;
|
||
|
|
}
|
||
|
|
else if (order1 == 3)
|
||
|
|
{
|
||
|
|
if (order2 == 1) // 0 3 1 2
|
||
|
|
order_type = 4;
|
||
|
|
if (order2 == 2) // 0 3 2 1
|
||
|
|
order_type = 5;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
// permute with N not supported
|
||
|
|
}
|
||
|
|
fprintf(pp, " 0=%d", order_type);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Pooling")
|
||
|
|
{
|
||
|
|
const caffe::PoolingParameter& pooling_param = layer.pooling_param();
|
||
|
|
fprintf(pp, " 0=%d", pooling_param.pool());
|
||
|
|
if (pooling_param.has_kernel_w() && pooling_param.has_kernel_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 1=%d", pooling_param.kernel_w());
|
||
|
|
fprintf(pp, " 11=%d", pooling_param.kernel_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 1=%d", pooling_param.kernel_size());
|
||
|
|
}
|
||
|
|
if (pooling_param.has_stride_w() && pooling_param.has_stride_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 2=%d", pooling_param.stride_w());
|
||
|
|
fprintf(pp, " 12=%d", pooling_param.stride_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 2=%d", pooling_param.stride());
|
||
|
|
}
|
||
|
|
if (pooling_param.has_pad_w() && pooling_param.has_pad_h())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 3=%d", pooling_param.pad_w());
|
||
|
|
fprintf(pp, " 13=%d", pooling_param.pad_h());
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 3=%d", pooling_param.pad());
|
||
|
|
}
|
||
|
|
fprintf(pp, " 4=%d", pooling_param.has_global_pooling() ? pooling_param.global_pooling() : 0);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Power")
|
||
|
|
{
|
||
|
|
const caffe::PowerParameter& power_param = layer.power_param();
|
||
|
|
fprintf(pp, " 0=%e", power_param.power());
|
||
|
|
fprintf(pp, " 1=%e", power_param.scale());
|
||
|
|
fprintf(pp, " 2=%e", power_param.shift());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "PReLU")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
const caffe::BlobProto& slope_blob = binlayer.blobs(0);
|
||
|
|
fprintf(pp, " 0=%d", slope_blob.data_size());
|
||
|
|
fwrite(slope_blob.data().data(), sizeof(float), slope_blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "PriorBox")
|
||
|
|
{
|
||
|
|
const caffe::PriorBoxParameter& prior_box_param = layer.prior_box_param();
|
||
|
|
|
||
|
|
int num_aspect_ratio = prior_box_param.aspect_ratio_size();
|
||
|
|
for (int j = 0; j < prior_box_param.aspect_ratio_size(); j++)
|
||
|
|
{
|
||
|
|
float ar = prior_box_param.aspect_ratio(j);
|
||
|
|
if (fabs(ar - 1.) < 1e-6)
|
||
|
|
{
|
||
|
|
num_aspect_ratio--;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
float variances[4] = {0.1f, 0.1f, 0.1f, 0.1f};
|
||
|
|
if (prior_box_param.variance_size() == 4)
|
||
|
|
{
|
||
|
|
variances[0] = prior_box_param.variance(0);
|
||
|
|
variances[1] = prior_box_param.variance(1);
|
||
|
|
variances[2] = prior_box_param.variance(2);
|
||
|
|
variances[3] = prior_box_param.variance(3);
|
||
|
|
}
|
||
|
|
else if (prior_box_param.variance_size() == 1)
|
||
|
|
{
|
||
|
|
variances[0] = prior_box_param.variance(0);
|
||
|
|
variances[1] = prior_box_param.variance(0);
|
||
|
|
variances[2] = prior_box_param.variance(0);
|
||
|
|
variances[3] = prior_box_param.variance(0);
|
||
|
|
}
|
||
|
|
|
||
|
|
int flip = prior_box_param.has_flip() ? prior_box_param.flip() : 1;
|
||
|
|
int clip = prior_box_param.has_clip() ? prior_box_param.clip() : 0;
|
||
|
|
int image_width = -233;
|
||
|
|
int image_height = -233;
|
||
|
|
if (prior_box_param.has_img_size())
|
||
|
|
{
|
||
|
|
image_width = prior_box_param.img_size();
|
||
|
|
image_height = prior_box_param.img_size();
|
||
|
|
}
|
||
|
|
else if (prior_box_param.has_img_w() && prior_box_param.has_img_h())
|
||
|
|
{
|
||
|
|
image_width = prior_box_param.img_w();
|
||
|
|
image_height = prior_box_param.img_h();
|
||
|
|
}
|
||
|
|
|
||
|
|
float step_width = -233;
|
||
|
|
float step_height = -233;
|
||
|
|
if (prior_box_param.has_step())
|
||
|
|
{
|
||
|
|
step_width = prior_box_param.step();
|
||
|
|
step_height = prior_box_param.step();
|
||
|
|
}
|
||
|
|
else if (prior_box_param.has_step_w() && prior_box_param.has_step_h())
|
||
|
|
{
|
||
|
|
step_width = prior_box_param.step_w();
|
||
|
|
step_height = prior_box_param.step_h();
|
||
|
|
}
|
||
|
|
|
||
|
|
fprintf(pp, " -23300=%d", prior_box_param.min_size_size());
|
||
|
|
for (int j = 0; j < prior_box_param.min_size_size(); j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", prior_box_param.min_size(j));
|
||
|
|
}
|
||
|
|
fprintf(pp, " -23301=%d", prior_box_param.max_size_size());
|
||
|
|
for (int j = 0; j < prior_box_param.max_size_size(); j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", prior_box_param.max_size(j));
|
||
|
|
}
|
||
|
|
fprintf(pp, " -23302=%d", num_aspect_ratio);
|
||
|
|
for (int j = 0; j < prior_box_param.aspect_ratio_size(); j++)
|
||
|
|
{
|
||
|
|
float ar = prior_box_param.aspect_ratio(j);
|
||
|
|
if (fabs(ar - 1.) < 1e-6)
|
||
|
|
{
|
||
|
|
continue;
|
||
|
|
}
|
||
|
|
fprintf(pp, ",%e", ar);
|
||
|
|
}
|
||
|
|
fprintf(pp, " 3=%e", variances[0]);
|
||
|
|
fprintf(pp, " 4=%e", variances[1]);
|
||
|
|
fprintf(pp, " 5=%e", variances[2]);
|
||
|
|
fprintf(pp, " 6=%e", variances[3]);
|
||
|
|
fprintf(pp, " 7=%d", flip);
|
||
|
|
fprintf(pp, " 8=%d", clip);
|
||
|
|
fprintf(pp, " 9=%d", image_width);
|
||
|
|
fprintf(pp, " 10=%d", image_height);
|
||
|
|
fprintf(pp, " 11=%e", step_width);
|
||
|
|
fprintf(pp, " 12=%e", step_height);
|
||
|
|
fprintf(pp, " 13=%e", prior_box_param.offset());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "PSROIPooling")
|
||
|
|
{
|
||
|
|
const caffe::PSROIPoolingParameter& psroi_pooling_param = layer.psroi_pooling_param();
|
||
|
|
fprintf(pp, " 0=%d", psroi_pooling_param.group_size());
|
||
|
|
fprintf(pp, " 1=%d", psroi_pooling_param.group_size());
|
||
|
|
fprintf(pp, " 2=%e", psroi_pooling_param.spatial_scale());
|
||
|
|
fprintf(pp, " 3=%d", psroi_pooling_param.output_dim());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Python")
|
||
|
|
{
|
||
|
|
const caffe::PythonParameter& python_param = layer.python_param();
|
||
|
|
std::string python_layer_name = python_param.layer();
|
||
|
|
if (python_layer_name == "ProposalLayer")
|
||
|
|
{
|
||
|
|
int feat_stride = 16;
|
||
|
|
sscanf(python_param.param_str().c_str(), "'feat_stride': %d", &feat_stride);
|
||
|
|
|
||
|
|
int base_size = 16;
|
||
|
|
// float ratio;
|
||
|
|
// float scale;
|
||
|
|
int pre_nms_topN = 6000;
|
||
|
|
int after_nms_topN = 300;
|
||
|
|
float nms_thresh = 0.7f;
|
||
|
|
int min_size = 16;
|
||
|
|
fprintf(pp, " 0=%d", feat_stride);
|
||
|
|
fprintf(pp, " 1=%d", base_size);
|
||
|
|
fprintf(pp, " 2=%d", pre_nms_topN);
|
||
|
|
fprintf(pp, " 3=%d", after_nms_topN);
|
||
|
|
fprintf(pp, " 4=%e", nms_thresh);
|
||
|
|
fprintf(pp, " 5=%d", min_size);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "ReLU")
|
||
|
|
{
|
||
|
|
const caffe::ReLUParameter& relu_param = layer.relu_param();
|
||
|
|
if (relu_param.has_negative_slope())
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%e", relu_param.negative_slope());
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "ReLU6")
|
||
|
|
{
|
||
|
|
float min = 0.f;
|
||
|
|
float max = 6.f;
|
||
|
|
fprintf(pp, " 0=%e", min);
|
||
|
|
fprintf(pp, " 1=%e", max);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Reorg")
|
||
|
|
{
|
||
|
|
const caffe::ReorgParameter& reorg_param = layer.reorg_param();
|
||
|
|
fprintf(pp, " 0=%d", reorg_param.stride());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Reshape")
|
||
|
|
{
|
||
|
|
const caffe::ReshapeParameter& reshape_param = layer.reshape_param();
|
||
|
|
const caffe::BlobShape& bs = reshape_param.shape();
|
||
|
|
if (bs.dim_size() == 1)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd 1=-233 2=-233", size_t(bs.dim(0)));
|
||
|
|
}
|
||
|
|
else if (bs.dim_size() == 2)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd 1=-233 2=-233", size_t(bs.dim(1)));
|
||
|
|
}
|
||
|
|
else if (bs.dim_size() == 3)
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd 1=%zd 2=-233", size_t(bs.dim(2)), size_t(bs.dim(1)));
|
||
|
|
}
|
||
|
|
else // bs.dim_size() == 4
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=%zd 1=%zd 2=%zd", size_t(bs.dim(3)), size_t(bs.dim(2)), size_t(bs.dim(1)));
|
||
|
|
}
|
||
|
|
fprintf(pp, " 3=0"); // permute
|
||
|
|
}
|
||
|
|
else if (layer.type() == "ROIAlign")
|
||
|
|
{
|
||
|
|
const caffe::ROIAlignParameter& roi_align_param = layer.roi_align_param();
|
||
|
|
fprintf(pp, " 0=%d", roi_align_param.pooled_w());
|
||
|
|
fprintf(pp, " 1=%d", roi_align_param.pooled_h());
|
||
|
|
fprintf(pp, " 2=%e", roi_align_param.spatial_scale());
|
||
|
|
fprintf(pp, " 3=%d", 0);
|
||
|
|
fprintf(pp, " 4=%d", false);
|
||
|
|
fprintf(pp, " 5=%d", 0);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "ROIPooling")
|
||
|
|
{
|
||
|
|
const caffe::ROIPoolingParameter& roi_pooling_param = layer.roi_pooling_param();
|
||
|
|
fprintf(pp, " 0=%d", roi_pooling_param.pooled_w());
|
||
|
|
fprintf(pp, " 1=%d", roi_pooling_param.pooled_h());
|
||
|
|
fprintf(pp, " 2=%e", roi_pooling_param.spatial_scale());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Scale")
|
||
|
|
{
|
||
|
|
const caffe::LayerParameter& binlayer = net.layer(netidx);
|
||
|
|
|
||
|
|
const caffe::ScaleParameter& scale_param = layer.scale_param();
|
||
|
|
bool scale_weight = scale_param.bias_term() ? (binlayer.blobs_size() == 2) : (binlayer.blobs_size() == 1);
|
||
|
|
if (scale_weight)
|
||
|
|
{
|
||
|
|
const caffe::BlobProto& weight_blob = binlayer.blobs(0);
|
||
|
|
fprintf(pp, " 0=%d", int(weight_blob.data_size()));
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
fprintf(pp, " 0=-233");
|
||
|
|
}
|
||
|
|
|
||
|
|
fprintf(pp, " 1=%d", scale_param.bias_term());
|
||
|
|
|
||
|
|
for (int j = 0; j < binlayer.blobs_size(); j++)
|
||
|
|
{
|
||
|
|
const caffe::BlobProto& blob = binlayer.blobs(j);
|
||
|
|
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "ShuffleChannel")
|
||
|
|
{
|
||
|
|
const caffe::ShuffleChannelParameter& shuffle_channel_param = layer.shuffle_channel_param();
|
||
|
|
fprintf(pp, " 0=%d", shuffle_channel_param.group());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Slice")
|
||
|
|
{
|
||
|
|
const caffe::SliceParameter& slice_param = layer.slice_param();
|
||
|
|
if (slice_param.slice_point_size() == 0)
|
||
|
|
{
|
||
|
|
int num_slice = layer.top_size();
|
||
|
|
fprintf(pp, " -23300=%d", num_slice);
|
||
|
|
for (int j = 0; j < num_slice; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",-233");
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
int num_slice = slice_param.slice_point_size() + 1;
|
||
|
|
fprintf(pp, " -23300=%d", num_slice);
|
||
|
|
int prev_offset = 0;
|
||
|
|
for (int j = 0; j < slice_param.slice_point_size(); j++)
|
||
|
|
{
|
||
|
|
int offset = slice_param.slice_point(j);
|
||
|
|
fprintf(pp, ",%d", offset - prev_offset);
|
||
|
|
prev_offset = offset;
|
||
|
|
}
|
||
|
|
fprintf(pp, ",-233");
|
||
|
|
}
|
||
|
|
int axis = 0;
|
||
|
|
if (slice_param.has_axis())
|
||
|
|
{
|
||
|
|
axis = slice_param.axis() - 1;
|
||
|
|
}
|
||
|
|
else if (slice_param.has_slice_dim())
|
||
|
|
{
|
||
|
|
axis = slice_param.slice_dim() - 1;
|
||
|
|
}
|
||
|
|
fprintf(pp, " 1=%d", axis);
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Softmax")
|
||
|
|
{
|
||
|
|
const caffe::SoftmaxParameter& softmax_param = layer.softmax_param();
|
||
|
|
int dim = softmax_param.axis() - 1;
|
||
|
|
fprintf(pp, " 0=%d", dim);
|
||
|
|
fprintf(pp, " 1=1");
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Threshold")
|
||
|
|
{
|
||
|
|
const caffe::ThresholdParameter& threshold_param = layer.threshold_param();
|
||
|
|
fprintf(pp, " 0=%e", threshold_param.threshold());
|
||
|
|
}
|
||
|
|
else if (layer.type() == "YoloDetectionOutput")
|
||
|
|
{
|
||
|
|
const caffe::YoloDetectionOutputParameter& yolo_detection_output_param = layer.yolo_detection_output_param();
|
||
|
|
|
||
|
|
fprintf(pp, " 0=%d", yolo_detection_output_param.num_classes());
|
||
|
|
fprintf(pp, " 1=%d", yolo_detection_output_param.num_box());
|
||
|
|
fprintf(pp, " 2=%e", yolo_detection_output_param.confidence_threshold());
|
||
|
|
fprintf(pp, " 3=%e", yolo_detection_output_param.nms_threshold());
|
||
|
|
|
||
|
|
int num_bias = yolo_detection_output_param.biases_size();
|
||
|
|
fprintf(pp, " -23304=%d", num_bias);
|
||
|
|
for (int j = 0; j < num_bias; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", yolo_detection_output_param.biases(j));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (layer.type() == "Yolov3DetectionOutput")
|
||
|
|
{
|
||
|
|
const caffe::Yolov3DetectionOutputParameter& yolov3_detection_output_param = layer.yolov3_detection_output_param();
|
||
|
|
|
||
|
|
fprintf(pp, " 0=%d", yolov3_detection_output_param.num_classes());
|
||
|
|
fprintf(pp, " 1=%d", yolov3_detection_output_param.num_box());
|
||
|
|
fprintf(pp, " 2=%e", yolov3_detection_output_param.confidence_threshold());
|
||
|
|
fprintf(pp, " 3=%e", yolov3_detection_output_param.nms_threshold());
|
||
|
|
|
||
|
|
int num_bias = yolov3_detection_output_param.biases_size();
|
||
|
|
fprintf(pp, " -23304=%d", num_bias);
|
||
|
|
for (int j = 0; j < num_bias; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", yolov3_detection_output_param.biases(j));
|
||
|
|
}
|
||
|
|
int num_mask = yolov3_detection_output_param.mask_size();
|
||
|
|
fprintf(pp, " -23305=%d", num_mask);
|
||
|
|
for (int j = 0; j < num_mask; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", (float)yolov3_detection_output_param.mask(j));
|
||
|
|
}
|
||
|
|
int num_anchors = yolov3_detection_output_param.anchors_scale_size();
|
||
|
|
fprintf(pp, " -23306=%d", num_anchors);
|
||
|
|
for (int j = 0; j < num_anchors; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, ",%e", (float)yolov3_detection_output_param.anchors_scale(j));
|
||
|
|
}
|
||
|
|
fprintf(pp, " 7=%d", yolov3_detection_output_param.mask_group_num());
|
||
|
|
}
|
||
|
|
fprintf(pp, "\n");
|
||
|
|
|
||
|
|
// add split layer if top reference larger than one
|
||
|
|
if (layer.bottom_size() == 1 && layer.top_size() == 1 && layer.bottom(0) == layer.top(0))
|
||
|
|
{
|
||
|
|
std::string blob_name = blob_name_decorated[layer.top(0)];
|
||
|
|
if (bottom_reference.find(blob_name) != bottom_reference.end())
|
||
|
|
{
|
||
|
|
int refcount = bottom_reference[blob_name];
|
||
|
|
if (refcount > 1)
|
||
|
|
{
|
||
|
|
char splitname[256];
|
||
|
|
sprintf(splitname, "splitncnn_%d", internal_split);
|
||
|
|
fprintf(pp, "%-16s %-16s %d %d", "Split", splitname, 1, refcount);
|
||
|
|
fprintf(pp, " %s", blob_name.c_str());
|
||
|
|
|
||
|
|
for (int j = 0; j < refcount; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, " %s_splitncnn_%d", blob_name.c_str(), j);
|
||
|
|
}
|
||
|
|
fprintf(pp, "\n");
|
||
|
|
|
||
|
|
internal_split++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
for (int j = 0; j < layer.top_size(); j++)
|
||
|
|
{
|
||
|
|
std::string blob_name = layer.top(j);
|
||
|
|
if (bottom_reference.find(blob_name) != bottom_reference.end())
|
||
|
|
{
|
||
|
|
int refcount = bottom_reference[blob_name];
|
||
|
|
if (refcount > 1)
|
||
|
|
{
|
||
|
|
char splitname[256];
|
||
|
|
sprintf(splitname, "splitncnn_%d", internal_split);
|
||
|
|
fprintf(pp, "%-16s %-16s %d %d", "Split", splitname, 1, refcount);
|
||
|
|
fprintf(pp, " %s", blob_name.c_str());
|
||
|
|
|
||
|
|
for (int j = 0; j < refcount; j++)
|
||
|
|
{
|
||
|
|
fprintf(pp, " %s_splitncnn_%d", blob_name.c_str(), j);
|
||
|
|
}
|
||
|
|
fprintf(pp, "\n");
|
||
|
|
|
||
|
|
internal_split++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
fclose(pp);
|
||
|
|
fclose(bp);
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|