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darknet_io.cpp
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darknet_io.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
// (3-clause BSD License)
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * Neither the names of the copyright holders nor the names of the contributors
// may be used to endorse or promote products derived from this software
// without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall copyright holders or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*M///////////////////////////////////////////////////////////////////////////////////////
//MIT License
//
//Copyright (c) 2017 Joseph Redmon
//
//Permission is hereby granted, free of charge, to any person obtaining a copy
//of this software and associated documentation files (the "Software"), to deal
//in the Software without restriction, including without limitation the rights
//to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
//copies of the Software, and to permit persons to whom the Software is
//furnished to do so, subject to the following conditions:
//
//The above copyright notice and this permission notice shall be included in all
//copies or substantial portions of the Software.
//
//THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
//IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
//FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
//AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
//LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
//OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
//SOFTWARE.
//
//M*/
#include "../precomp.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
#include "darknet_io.hpp"
namespace cv {
namespace dnn {
namespace darknet {
template<typename T>
T getParam(const std::map<std::string, std::string> ¶ms, const std::string param_name, T init_val)
{
std::map<std::string, std::string>::const_iterator it = params.find(param_name);
if (it != params.end()) {
std::stringstream ss(it->second);
ss >> init_val;
}
return init_val;
}
static const std::string kFirstLayerName = "data";
class setLayersParams {
NetParameter *net;
int layer_id;
std::string last_layer;
std::vector<std::string> fused_layer_names;
public:
setLayersParams(NetParameter *_net) :
net(_net), layer_id(0), last_layer(kFirstLayerName)
{}
void setLayerBlobs(int i, std::vector<cv::Mat> blobs)
{
cv::dnn::LayerParams ¶ms = net->layers[i].layerParams;
params.blobs = blobs;
}
cv::dnn::LayerParams getParamConvolution(int kernel, int pad,
int stride, int filters_num)
{
cv::dnn::LayerParams params;
params.name = "Convolution-name";
params.type = "Convolution";
params.set<int>("kernel_size", kernel);
params.set<int>("pad", pad);
params.set<int>("stride", stride);
params.set<bool>("bias_term", false); // true only if(BatchNorm == false)
params.set<int>("num_output", filters_num);
return params;
}
void setConvolution(int kernel, int pad, int stride,
int filters_num, int channels_num, int use_batch_normalize, int use_relu)
{
cv::dnn::LayerParams conv_param =
getParamConvolution(kernel, pad, stride, filters_num);
darknet::LayerParameter lp;
std::string layer_name = cv::format("conv_%d", layer_id);
// use BIAS in any case
if (!use_batch_normalize) {
conv_param.set<bool>("bias_term", true);
}
lp.layer_name = layer_name;
lp.layer_type = conv_param.type;
lp.layerParams = conv_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
if (use_batch_normalize)
{
cv::dnn::LayerParams bn_param;
bn_param.name = "BatchNorm-name";
bn_param.type = "BatchNorm";
bn_param.set<bool>("has_weight", true);
bn_param.set<bool>("has_bias", true);
bn_param.set<float>("eps", 1E-6); // .000001f in Darknet Yolo
darknet::LayerParameter lp;
std::string layer_name = cv::format("bn_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = bn_param.type;
lp.layerParams = bn_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
}
if (use_relu)
{
cv::dnn::LayerParams activation_param;
activation_param.set<float>("negative_slope", 0.1f);
activation_param.name = "ReLU-name";
activation_param.type = "ReLU";
darknet::LayerParameter lp;
std::string layer_name = cv::format("relu_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = activation_param.type;
lp.layerParams = activation_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
}
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setConvolutionNew(int kernel, int pad, int stride,
int filters_num, int channels_num, int use_batch_normalize, int use_relu,int use_leaky)
{
cv::dnn::LayerParams conv_param =
getParamConvolution(kernel, pad, stride, filters_num);
darknet::LayerParameter lp;
std::string layer_name = cv::format("conv_%d", layer_id);
// use BIAS in any case
if (!use_batch_normalize) {
conv_param.set<bool>("bias_term", true);
}
lp.layer_name = layer_name;
lp.layer_type = conv_param.type;
lp.layerParams = conv_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
if (use_batch_normalize)
{
cv::dnn::LayerParams bn_param;
bn_param.name = "BatchNorm-name";
bn_param.type = "BatchNorm";
bn_param.set<bool>("has_weight", true);
bn_param.set<bool>("has_bias", true);
bn_param.set<float>("eps", 1E-6); // .000001f in Darknet Yolo
darknet::LayerParameter lp;
std::string layer_name = cv::format("bn_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = bn_param.type;
lp.layerParams = bn_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
}
if (use_relu)
{
cv::dnn::LayerParams activation_param;
if(use_leaky){
activation_param.set<float>("negative_slope", 0.1f);}
else{
activation_param.set<float>("negative_slope", 0.f);
}
activation_param.name = "ReLU-name";
activation_param.type = "ReLU";
darknet::LayerParameter lp;
std::string layer_name = cv::format("relu_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = activation_param.type;
lp.layerParams = activation_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
}
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setMaxpool(size_t kernel, size_t pad, size_t stride)
{
cv::dnn::LayerParams maxpool_param;
maxpool_param.set<cv::String>("pool", "max");
maxpool_param.set<int>("kernel_size", kernel);
maxpool_param.set<int>("pad", pad);
maxpool_param.set<int>("stride", stride);
maxpool_param.set<cv::String>("pad_mode", "SAME");
maxpool_param.name = "Pooling-name";
maxpool_param.type = "Pooling";
darknet::LayerParameter lp;
std::string layer_name = cv::format("pool_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = maxpool_param.type;
lp.layerParams = maxpool_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setMaxpoolNew(size_t kernel, size_t pad,size_t stride, size_t strideW,size_t strideH)
{
// replace setMaxpool to setMaxpoolNew for ocr ctc
cv::dnn::LayerParams maxpool_param;
maxpool_param.set<cv::String>("pool", "max");
maxpool_param.set<int>("kernel_size", kernel);
maxpool_param.set<int>("pad", pad);
maxpool_param.set<int>("stride", stride);
maxpool_param.set<int>("strideW", strideW);
maxpool_param.set<int>("strideH", strideH);
//printf("getConvPoolPaddings %d %d %d \n" , strideW,strideH,pad);
if(strideW!=strideH){
maxpool_param.set<cv::String>("pad_mode", "VALID");//stride 2,1 for ocr
}
else{
maxpool_param.set<cv::String>("pad_mode", "SAME");
}
//maxpool_param.set<cv::String>("pad_mode", "SAME");
maxpool_param.name = "Pooling-name";
maxpool_param.type = "Pooling";
darknet::LayerParameter lp;
std::string layer_name = cv::format("pool_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = maxpool_param.type;
lp.layerParams = maxpool_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setAvgpool()
{
cv::dnn::LayerParams avgpool_param;
avgpool_param.set<cv::String>("pool", "ave");
avgpool_param.set<bool>("global_pooling", true);
avgpool_param.name = "Pooling-name";
avgpool_param.type = "Pooling";
darknet::LayerParameter lp;
std::string layer_name = cv::format("avgpool_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = avgpool_param.type;
lp.layerParams = avgpool_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setSoftmax()
{
cv::dnn::LayerParams softmax_param;
softmax_param.name = "Softmax-name";
softmax_param.type = "Softmax";
darknet::LayerParameter lp;
std::string layer_name = cv::format("softmax_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = softmax_param.type;
lp.layerParams = softmax_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setConcat(int number_of_inputs, int *input_indexes)
{
cv::dnn::LayerParams concat_param;
concat_param.name = "Concat-name";
concat_param.type = "Concat";
concat_param.set<int>("axis", 1); // channels are in axis = 1
darknet::LayerParameter lp;
std::string layer_name = cv::format("concat_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = concat_param.type;
lp.layerParams = concat_param;
for (int i = 0; i < number_of_inputs; ++i)
lp.bottom_indexes.push_back(fused_layer_names.at(input_indexes[i]));
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setIdentity(int bottom_index)
{
cv::dnn::LayerParams identity_param;
identity_param.name = "Identity-name";
identity_param.type = "Identity";
darknet::LayerParameter lp;
std::string layer_name = cv::format("identity_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = identity_param.type;
lp.layerParams = identity_param;
lp.bottom_indexes.push_back(fused_layer_names.at(bottom_index));
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setReorg(int stride)
{
cv::dnn::LayerParams reorg_params;
reorg_params.name = "Reorg-name";
reorg_params.type = "Reorg";
reorg_params.set<int>("reorg_stride", stride);
darknet::LayerParameter lp;
std::string layer_name = cv::format("reorg_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = reorg_params.type;
lp.layerParams = reorg_params;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setPermute(bool isDarknetLayer = true)
{
cv::dnn::LayerParams permute_params;
permute_params.name = "Permute-name";
permute_params.type = "Permute";
int permute[] = { 0, 2, 3, 1 };
cv::dnn::DictValue paramOrder = cv::dnn::DictValue::arrayInt(permute, 4);
permute_params.set("order", paramOrder);
darknet::LayerParameter lp;
std::string layer_name = cv::format("permute_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = permute_params.type;
lp.layerParams = permute_params;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
if (isDarknetLayer)
{
layer_id++;
fused_layer_names.push_back(last_layer);
}
}
void setRegion(float thresh, int coords, int classes, int anchors, int classfix, int softmax, int softmax_tree, float *biasData)
{
cv::dnn::LayerParams region_param;
region_param.name = "Region-name";
region_param.type = "Region";
region_param.set<float>("thresh", thresh);
region_param.set<int>("coords", coords);
region_param.set<int>("classes", classes);
region_param.set<int>("anchors", anchors);
region_param.set<int>("classfix", classfix);
region_param.set<bool>("softmax_tree", softmax_tree);
region_param.set<bool>("softmax", softmax);
cv::Mat biasData_mat = cv::Mat(1, anchors * 2, CV_32F, biasData).clone();
region_param.blobs.push_back(biasData_mat);
darknet::LayerParameter lp;
std::string layer_name = "detection_out";
lp.layer_name = layer_name;
lp.layer_type = region_param.type;
lp.layerParams = region_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setYolo(int classes, const std::vector<int>& mask, const std::vector<float>& anchors)
{
cv::dnn::LayerParams region_param;
region_param.name = "Region-name";
region_param.type = "Region";
const int numAnchors = mask.size();
region_param.set<int>("classes", classes);
region_param.set<int>("anchors", numAnchors);
region_param.set<bool>("logistic", true);
std::vector<float> usedAnchors(numAnchors * 2);
for (int i = 0; i < numAnchors; ++i)
{
usedAnchors[i * 2] = anchors[mask[i] * 2];
usedAnchors[i * 2 + 1] = anchors[mask[i] * 2 + 1];
}
cv::Mat biasData_mat = cv::Mat(1, numAnchors * 2, CV_32F, &usedAnchors[0]).clone();
region_param.blobs.push_back(biasData_mat);
darknet::LayerParameter lp;
std::string layer_name = cv::format("yolo_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = region_param.type;
lp.layerParams = region_param;
lp.bottom_indexes.push_back(last_layer);
lp.bottom_indexes.push_back(kFirstLayerName);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setShortcut(int from)
{
cv::dnn::LayerParams shortcut_param;
shortcut_param.name = "Shortcut-name";
shortcut_param.type = "Eltwise";
shortcut_param.set<std::string>("op", "sum");
darknet::LayerParameter lp;
std::string layer_name = cv::format("shortcut_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = shortcut_param.type;
lp.layerParams = shortcut_param;
lp.bottom_indexes.push_back(fused_layer_names.at(from));
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setUpsample(int scaleFactor)
{
cv::dnn::LayerParams param;
param.name = "Upsample-name";
param.type = "Resize";
param.set<int>("zoom_factor", scaleFactor);
param.set<String>("interpolation", "nearest");
darknet::LayerParameter lp;
std::string layer_name = cv::format("upsample_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = param.type;
lp.layerParams = param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
};
std::string escapeString(const std::string &src)
{
std::string dst;
for (size_t i = 0; i < src.size(); ++i)
if (src[i] > ' ' && src[i] <= 'z')
dst += src[i];
return dst;
}
template<typename T>
std::vector<T> getNumbers(const std::string &src)
{
std::vector<T> dst;
std::stringstream ss(src);
for (std::string str; std::getline(ss, str, ',');) {
std::stringstream line(str);
T val;
line >> val;
dst.push_back(val);
}
return dst;
}
bool ReadDarknetFromCfgStream(std::istream &ifile, NetParameter *net)
{
bool read_net = false;
int layers_counter = -1;
for (std::string line; std::getline(ifile, line);) {
line = escapeString(line);
if (line.empty()) continue;
switch (line[0]) {
case '\0': break;
case '#': break;
case ';': break;
case '[':
if (line == "[net]") {
read_net = true;
}
else {
// read section
read_net = false;
++layers_counter;
const size_t layer_type_size = line.find("]") - 1;
CV_Assert(layer_type_size < line.size());
std::string layer_type = line.substr(1, layer_type_size);
net->layers_cfg[layers_counter]["type"] = layer_type;
}
break;
default:
// read entry
const size_t separator_index = line.find('=');
CV_Assert(separator_index < line.size());
if (separator_index != std::string::npos) {
std::string name = line.substr(0, separator_index);
std::string value = line.substr(separator_index + 1, line.size() - (separator_index + 1));
name = escapeString(name);
value = escapeString(value);
if (name.empty() || value.empty()) continue;
if (read_net)
net->net_cfg[name] = value;
else
net->layers_cfg[layers_counter][name] = value;
}
}
}
std::string anchors = net->layers_cfg[net->layers_cfg.size() - 1]["anchors"];
std::vector<float> vec = getNumbers<float>(anchors);
std::map<std::string, std::string> &net_params = net->net_cfg;
net->width = getParam(net_params, "width", 416);
net->height = getParam(net_params, "height", 416);
net->channels = getParam(net_params, "channels", 3);
CV_Assert(net->width > 0 && net->height > 0 && net->channels > 0);
int current_channels = net->channels;
net->out_channels_vec.resize(net->layers_cfg.size());
layers_counter = -1;
setLayersParams setParams(net);
typedef std::map<int, std::map<std::string, std::string> >::iterator it_type;
for (it_type i = net->layers_cfg.begin(); i != net->layers_cfg.end(); ++i) {
++layers_counter;
std::map<std::string, std::string> &layer_params = i->second;
std::string layer_type = layer_params["type"];
if (layer_type == "convolutional")
{
int kernel_size = getParam<int>(layer_params, "size", -1);
int pad = getParam<int>(layer_params, "pad", 0);
int stride = getParam<int>(layer_params, "stride", 1);
int filters = getParam<int>(layer_params, "filters", -1);
std::string activation = getParam<std::string>(layer_params, "activation", "linear");
bool batch_normalize = getParam<int>(layer_params, "batch_normalize", 0) == 1;
if(activation != "linear" && activation != "leaky" && activation != "relu")
CV_Error(cv::Error::StsParseError, "Unsupported activation: " + activation);
int flipped = getParam<int>(layer_params, "flipped", 0);
if (flipped == 1)
CV_Error(cv::Error::StsNotImplemented, "Transpose the convolutional weights is not implemented");
// correct the strange value of pad=1 for kernel_size=1 in the Darknet cfg-file
if (kernel_size < 3) pad = 0;
CV_Assert(kernel_size > 0 && filters > 0);
CV_Assert(current_channels > 0);
/*
setParams.setConvolution(kernel_size, pad, stride, filters, current_channels,
batch_normalize, activation == "leaky");
*/
setParams.setConvolutionNew(kernel_size, pad, stride, filters, current_channels,
batch_normalize, activation != "linear",activation == "leaky");
current_channels = filters;
}
else if (layer_type == "maxpool")
{
/*
strideW,strideH
*/
int kernel_size = getParam<int>(layer_params, "size", 2);
int strideW = getParam<int>(layer_params, "strideW", 0);
int strideH = getParam<int>(layer_params, "strideH", 0);
int stride = getParam<int>(layer_params, "stride", 2);
if(strideW==0){
strideW =stride;
strideH =stride;
}
int pad = getParam<int>(layer_params, "pad", 0);
//setParams.setMaxpool(kernel_size, pad, stride);
setParams.setMaxpoolNew(kernel_size, pad, stride,strideW,strideH);
}
else if (layer_type == "avgpool")
{
setParams.setAvgpool();
}
else if (layer_type == "softmax")
{
int groups = getParam<int>(layer_params, "groups", 1);
if (groups != 1)
CV_Error(Error::StsNotImplemented, "Softmax from Darknet with groups != 1");
setParams.setSoftmax();
}
else if (layer_type == "route")
{
std::string bottom_layers = getParam<std::string>(layer_params, "layers", "");
CV_Assert(!bottom_layers.empty());
std::vector<int> layers_vec = getNumbers<int>(bottom_layers);
current_channels = 0;
for (size_t k = 0; k < layers_vec.size(); ++k) {
layers_vec[k] = layers_vec[k] > 0 ? layers_vec[k] : (layers_vec[k] + layers_counter);
current_channels += net->out_channels_vec[layers_vec[k]];
}
if (layers_vec.size() == 1)
setParams.setIdentity(layers_vec.at(0));
else
setParams.setConcat(layers_vec.size(), layers_vec.data());
}
else if (layer_type == "reorg")
{
int stride = getParam<int>(layer_params, "stride", 2);
current_channels = current_channels * (stride*stride);
setParams.setReorg(stride);
}
else if (layer_type == "region")
{
float thresh = getParam<float>(layer_params, "thresh", 0.001);
int coords = getParam<int>(layer_params, "coords", 4);
int classes = getParam<int>(layer_params, "classes", -1);
int num_of_anchors = getParam<int>(layer_params, "num", -1);
int classfix = getParam<int>(layer_params, "classfix", 0);
bool softmax = (getParam<int>(layer_params, "softmax", 0) == 1);
bool softmax_tree = (getParam<std::string>(layer_params, "tree", "").size() > 0);
std::string anchors_values = getParam<std::string>(layer_params, "anchors", std::string());
CV_Assert(!anchors_values.empty());
std::vector<float> anchors_vec = getNumbers<float>(anchors_values);
CV_Assert(classes > 0 && num_of_anchors > 0 && (num_of_anchors * 2) == anchors_vec.size());
setParams.setPermute(false);
setParams.setRegion(thresh, coords, classes, num_of_anchors, classfix, softmax, softmax_tree, anchors_vec.data());
}
else if (layer_type == "shortcut")
{
std::string bottom_layer = getParam<std::string>(layer_params, "from", "");
CV_Assert(!bottom_layer.empty());
int from = std::atoi(bottom_layer.c_str());
from += layers_counter;
current_channels = net->out_channels_vec[from];
setParams.setShortcut(from);
}
else if (layer_type == "upsample")
{
int scaleFactor = getParam<int>(layer_params, "stride", 1);
setParams.setUpsample(scaleFactor);
}
else if (layer_type == "yolo")
{
int classes = getParam<int>(layer_params, "classes", -1);
int num_of_anchors = getParam<int>(layer_params, "num", -1);
std::string anchors_values = getParam<std::string>(layer_params, "anchors", std::string());
CV_Assert(!anchors_values.empty());
std::vector<float> anchors_vec = getNumbers<float>(anchors_values);
std::string mask_values = getParam<std::string>(layer_params, "mask", std::string());
CV_Assert(!mask_values.empty());
std::vector<int> mask_vec = getNumbers<int>(mask_values);
CV_Assert(classes > 0 && num_of_anchors > 0 && (num_of_anchors * 2) == anchors_vec.size());
setParams.setPermute(false);
setParams.setYolo(classes, mask_vec, anchors_vec);
}
else {
CV_Error(cv::Error::StsParseError, "Unknown layer type: " + layer_type);
}
net->out_channels_vec[layers_counter] = current_channels;
}
return true;
}
bool ReadDarknetFromWeightsStream(std::istream &ifile, NetParameter *net)
{
int32_t major_ver, minor_ver, revision;
ifile.read(reinterpret_cast<char *>(&major_ver), sizeof(int32_t));
ifile.read(reinterpret_cast<char *>(&minor_ver), sizeof(int32_t));
ifile.read(reinterpret_cast<char *>(&revision), sizeof(int32_t));
uint64_t seen;
if ((major_ver * 10 + minor_ver) >= 2) {
ifile.read(reinterpret_cast<char *>(&seen), sizeof(uint64_t));
}
else {
int32_t iseen = 0;
ifile.read(reinterpret_cast<char *>(&iseen), sizeof(int32_t));
seen = iseen;
}
bool transpose = (major_ver > 1000) || (minor_ver > 1000);
if(transpose)
CV_Error(cv::Error::StsNotImplemented, "Transpose the weights (except for convolutional) is not implemented");
int current_channels = net->channels;
int cv_layers_counter = -1;
int darknet_layers_counter = -1;
setLayersParams setParams(net);
typedef std::map<int, std::map<std::string, std::string> >::iterator it_type;
for (it_type i = net->layers_cfg.begin(); i != net->layers_cfg.end(); ++i) {
++darknet_layers_counter;
++cv_layers_counter;
std::map<std::string, std::string> &layer_params = i->second;
std::string layer_type = layer_params["type"];
if (layer_type == "convolutional")
{
int kernel_size = getParam<int>(layer_params, "size", -1);
int filters = getParam<int>(layer_params, "filters", -1);
std::string activation = getParam<std::string>(layer_params, "activation", "linear");
bool use_batch_normalize = getParam<int>(layer_params, "batch_normalize", 0) == 1;
CV_Assert(kernel_size > 0 && filters > 0);
CV_Assert(current_channels > 0);
size_t const weights_size = filters * current_channels * kernel_size * kernel_size;
int sizes_weights[] = { filters, current_channels, kernel_size, kernel_size };
cv::Mat weightsBlob;
weightsBlob.create(4, sizes_weights, CV_32F);
CV_Assert(weightsBlob.isContinuous());
cv::Mat meanData_mat(1, filters, CV_32F); // mean
cv::Mat stdData_mat(1, filters, CV_32F); // variance
cv::Mat weightsData_mat(1, filters, CV_32F);// scale
cv::Mat biasData_mat(1, filters, CV_32F); // bias
ifile.read(reinterpret_cast<char *>(biasData_mat.ptr<float>()), sizeof(float)*filters);
if (use_batch_normalize) {
ifile.read(reinterpret_cast<char *>(weightsData_mat.ptr<float>()), sizeof(float)*filters);
ifile.read(reinterpret_cast<char *>(meanData_mat.ptr<float>()), sizeof(float)*filters);
ifile.read(reinterpret_cast<char *>(stdData_mat.ptr<float>()), sizeof(float)*filters);
}
ifile.read(reinterpret_cast<char *>(weightsBlob.ptr<float>()), sizeof(float)*weights_size);
// set convolutional weights
std::vector<cv::Mat> conv_blobs;
conv_blobs.push_back(weightsBlob);
if (!use_batch_normalize) {
// use BIAS in any case
conv_blobs.push_back(biasData_mat);
}
setParams.setLayerBlobs(cv_layers_counter, conv_blobs);
// set batch normalize (mean, variance, scale, bias)
if (use_batch_normalize) {
++cv_layers_counter;
std::vector<cv::Mat> bn_blobs;
bn_blobs.push_back(meanData_mat);
bn_blobs.push_back(stdData_mat);
bn_blobs.push_back(weightsData_mat);
bn_blobs.push_back(biasData_mat);
setParams.setLayerBlobs(cv_layers_counter, bn_blobs);
}
//if(activation == "leaky" )
if(activation == "leaky" || activation == "relu")
++cv_layers_counter;
}
if (layer_type == "region" || layer_type == "yolo")
{
++cv_layers_counter; // For permute.
}
current_channels = net->out_channels_vec[darknet_layers_counter];
}
return true;
}
}
void ReadNetParamsFromCfgStreamOrDie(std::istream &ifile, darknet::NetParameter *net)
{
if (!darknet::ReadDarknetFromCfgStream(ifile, net)) {
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter stream");
}
}
void ReadNetParamsFromBinaryStreamOrDie(std::istream &ifile, darknet::NetParameter *net)
{
if (!darknet::ReadDarknetFromWeightsStream(ifile, net)) {
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter stream");
}
}
}
}