mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
99b06476c9
to caffe models. Not all layers are supported yet, but it works on the net from the first dlib dnn example program. Will add the rest of the layers soon.
442 lines
15 KiB
C++
442 lines
15 KiB
C++
|
|
#include <dlib/xml_parser.h>
|
|
#include <dlib/matrix.h>
|
|
#include <fstream>
|
|
#include <vector>
|
|
#include <stack>
|
|
#include <set>
|
|
#include <dlib/string.h>
|
|
|
|
using namespace std;
|
|
using namespace dlib;
|
|
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
// Only these computational layers have parameters
|
|
const std::set<string> comp_tags_with_params = {"fc", "fc_no_bias", "con", "bn_con", "bn_fc", "affine", "prelu"};
|
|
|
|
struct layer
|
|
{
|
|
string type; // comp, loss, or input
|
|
int idx;
|
|
|
|
string detail_name; // The name of the tag inside the layer tag. e.g. fc, con, max_pool, input_rgb_image.
|
|
std::map<string,double> attributes;
|
|
matrix<double> params;
|
|
long tag_id = -1; // If this isn't -1 then it means this layer was tagged, e.g. wrapped with tag2<> giving tag_id==2
|
|
long skip_id = -1; // If this isn't -1 then it means this layer draws its inputs from
|
|
// the most recent layer with tag_id==skip_id rather than its immediate predecessor.
|
|
|
|
string caffe_layer_name() const
|
|
{
|
|
if (type == "input")
|
|
return "data";
|
|
else
|
|
return detail_name+to_string(idx);
|
|
}
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
std::vector<layer> parse_dlib_xml(
|
|
const string& xml_filename
|
|
);
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <typename iterator>
|
|
string find_layer_caffe_name (
|
|
iterator i,
|
|
long tag_id
|
|
)
|
|
/*!
|
|
requires
|
|
- i is an iterator pointing to a layer in the list of layers produced by parse_dlib_xml().
|
|
- i is not an input layer.
|
|
ensures
|
|
- if (tag_id == -1) then
|
|
- returns the caffe string name for the previous layer to layer i.
|
|
- else
|
|
- returns the caffe string name for the previous layer to layer i with the given tag_id.
|
|
!*/
|
|
{
|
|
if (tag_id == -1)
|
|
{
|
|
return (i-1)->caffe_layer_name();
|
|
}
|
|
else
|
|
{
|
|
while(true)
|
|
{
|
|
i--;
|
|
// if we hit the end of the network before we found what we were looking for
|
|
if (i->type == "input")
|
|
throw dlib::error("Network definition is bad, a layer wanted to skip back to a non-existing layer.");
|
|
if (i->tag_id == tag_id)
|
|
return i->caffe_layer_name();
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename iterator>
|
|
string find_input_layer_caffe_name (iterator i) { return find_layer_caffe_name(i, i->skip_id); }
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <typename EXP>
|
|
void print_as_np_array(std::ostream& out, const matrix_exp<EXP>& m)
|
|
{
|
|
out << "np.array([";
|
|
for (auto x : m)
|
|
out << x << ",";
|
|
out << "], dtype='float32')";
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
void convert_dlib_xml_to_cafffe_python_code(
|
|
const string& xml_filename
|
|
)
|
|
{
|
|
auto layers = parse_dlib_xml(xml_filename);
|
|
|
|
cout << "import caffe " << endl;
|
|
cout << "from caffe import layers as L, params as P" << endl;
|
|
cout << "import numpy as np" << endl;
|
|
|
|
// dlib nets don't commit to a batch size, so just use 32 as the default
|
|
cout << "batch_size = 32;" << endl;
|
|
if (layers.back().detail_name == "input_rgb_image")
|
|
{
|
|
cout << "input_nr = 150; #WARNING, the source dlib network didn't commit to a specific input size, so we put 150 here as a default." << endl;
|
|
cout << "input_nc = 150; #WARNING, the source dlib network didn't commit to a specific input size, so we put 150 here as a default." << endl;
|
|
cout << "input_k = 3;" << endl;
|
|
}
|
|
else if (layers.back().detail_name == "input_rgb_image_sized")
|
|
{
|
|
cout << "input_nr = " << layers.back().attributes["nr"] << ";" << endl;
|
|
cout << "input_nc = " << layers.back().attributes["nc"] << ";" << endl;
|
|
cout << "input_k = 3;" << endl;
|
|
}
|
|
else if (layers.back().detail_name == "input")
|
|
{
|
|
cout << "input_nr = 150; #WARNING, the source dlib network didn't commit to a specific input size, so we put 150 here as a default." << endl;
|
|
cout << "input_nc = 150; #WARNING, the source dlib network didn't commit to a specific input size, so we put 150 here as a default." << endl;
|
|
cout << "input_k = 1;" << endl;
|
|
}
|
|
else
|
|
{
|
|
throw dlib::error("No known transformation from dlib's " + layers.back().detail_name + " layer to caffe.");
|
|
}
|
|
|
|
cout << "def make_netspec():" << endl;
|
|
cout << " n = caffe.NetSpec(); " << endl;
|
|
cout << " n.data,n.label = L.MemoryData(batch_size=batch_size, channels=input_k, height=input_nr, width=input_nc, ntop=2)" << endl;
|
|
// iterate the layers starting with the input layer
|
|
for (auto i = layers.rbegin(); i != layers.rend(); ++i)
|
|
{
|
|
// skip input and loss layers
|
|
if (i->type == "loss" || i->type == "input")
|
|
continue;
|
|
|
|
|
|
if (i->detail_name == "con")
|
|
{
|
|
cout << " n." << i->caffe_layer_name() << " = L.Convolution(n." << find_input_layer_caffe_name(i);
|
|
cout << ", num_output=" << i->attributes["num_filters"];
|
|
cout << ", kernel_w=" << i->attributes["nc"];
|
|
cout << ", kernel_h=" << i->attributes["nr"];
|
|
cout << ", stride_w=" << i->attributes["stride_x"];
|
|
cout << ", stride_h=" << i->attributes["stride_y"];
|
|
cout << ", pad_w=" << i->attributes["padding_x"];
|
|
cout << ", pad_h=" << i->attributes["padding_y"];
|
|
cout << ");\n";
|
|
}
|
|
else if (i->detail_name == "relu")
|
|
{
|
|
cout << " n." << i->caffe_layer_name() << " = L.ReLU(n." << find_input_layer_caffe_name(i);
|
|
cout << ");\n";
|
|
}
|
|
else if (i->detail_name == "max_pool")
|
|
{
|
|
cout << " n." << i->caffe_layer_name() << " = L.Pooling(n." << find_input_layer_caffe_name(i);
|
|
cout << ", pool=P.Pooling.MAX";
|
|
cout << ", kernel_w=" << i->attributes["nc"];
|
|
cout << ", kernel_h=" << i->attributes["nr"];
|
|
cout << ", stride_w=" << i->attributes["stride_x"];
|
|
cout << ", stride_h=" << i->attributes["stride_y"];
|
|
cout << ", pad_w=" << i->attributes["padding_x"];
|
|
cout << ", pad_h=" << i->attributes["padding_y"];
|
|
cout << ");\n";
|
|
}
|
|
else if (i->detail_name == "avg_pool")
|
|
{
|
|
cout << " n." << i->caffe_layer_name() << " = L.Pooling(n." << find_input_layer_caffe_name(i);
|
|
cout << ", pool=P.Pooling.MAX";
|
|
cout << ", kernel_w=" << i->attributes["nc"];
|
|
cout << ", kernel_h=" << i->attributes["nr"];
|
|
cout << ", stride_w=" << i->attributes["stride_x"];
|
|
cout << ", stride_h=" << i->attributes["stride_y"];
|
|
cout << ", pad_w=" << i->attributes["padding_x"];
|
|
cout << ", pad_h=" << i->attributes["padding_y"];
|
|
cout << ");\n";
|
|
}
|
|
else if (i->detail_name == "fc")
|
|
{
|
|
cout << " n." << i->caffe_layer_name() << " = L.InnerProduct(n." << find_input_layer_caffe_name(i);
|
|
cout << ", num_output=" << i->attributes["num_outputs"];
|
|
cout << ", bias_term=True";
|
|
cout << ");\n";
|
|
}
|
|
else if (i->detail_name == "fc_no_bias")
|
|
{
|
|
cout << " n." << i->caffe_layer_name() << " = L.InnerProduct(n." << find_input_layer_caffe_name(i);
|
|
cout << ", num_output=" << i->attributes["num_outputs"];
|
|
cout << ", bias_term=False";
|
|
cout << ");\n";
|
|
}
|
|
else if (i->detail_name == "bn_con")
|
|
{
|
|
// TODO
|
|
}
|
|
else if (i->detail_name == "bn_fc")
|
|
{
|
|
// TODO
|
|
}
|
|
else if (i->detail_name == "add_prev")
|
|
{
|
|
// TODO
|
|
}
|
|
else
|
|
{
|
|
throw dlib::error("No known transformation from dlib's " + i->detail_name + " layer to caffe.");
|
|
}
|
|
}
|
|
cout << " return n.to_proto();\n\n" << endl;
|
|
|
|
cout << "def save_as_caffe_model(def_file, weights_file):\n";
|
|
cout << " with open(def_file, 'w') as f: f.write(str(make_netspec()));\n";
|
|
cout << " net = caffe.Net(def_file, caffe.TEST);\n";
|
|
cout << " set_network_weights(net);\n";
|
|
cout << " net.save(weights_file);\n\n";
|
|
|
|
|
|
|
|
cout << "def set_network_weights(net):\n";
|
|
cout << " # populate network parameters\n";
|
|
// iterate the layers starting with the input layer
|
|
for (auto i = layers.rbegin(); i != layers.rend(); ++i)
|
|
{
|
|
// skip input and loss layers
|
|
if (i->type == "loss" || i->type == "input")
|
|
continue;
|
|
|
|
|
|
if (i->detail_name == "con")
|
|
{
|
|
const long num_filters = i->attributes["num_filters"];
|
|
matrix<double> weights = trans(rowm(i->params,range(0,i->params.size()-num_filters-1)));
|
|
matrix<double> biases = trans(rowm(i->params,range(i->params.size()-num_filters, i->params.size()-1)));
|
|
|
|
// main filter weights
|
|
cout << " p = "; print_as_np_array(cout,weights); cout << ";\n";
|
|
cout << " p.shape = net.params['"<<i->caffe_layer_name()<<"'][0].data.shape;\n";
|
|
cout << " net.params['"<<i->caffe_layer_name()<<"'][0].data[:] = p;\n";
|
|
|
|
// biases
|
|
cout << " p = "; print_as_np_array(cout,biases); cout << ";\n";
|
|
cout << " p.shape = net.params['"<<i->caffe_layer_name()<<"'][1].data.shape;\n";
|
|
cout << " net.params['"<<i->caffe_layer_name()<<"'][1].data[:] = p;\n";
|
|
}
|
|
else if (i->detail_name == "fc")
|
|
{
|
|
matrix<double> weights = trans(rowm(i->params, range(0,i->params.nr()-2)));
|
|
matrix<double> biases = rowm(i->params, i->params.nr()-1);
|
|
|
|
// main filter weights
|
|
cout << " p = "; print_as_np_array(cout,weights); cout << ";\n";
|
|
cout << " p.shape = net.params['"<<i->caffe_layer_name()<<"'][0].data.shape;\n";
|
|
cout << " net.params['"<<i->caffe_layer_name()<<"'][0].data[:] = p;\n";
|
|
|
|
// biases
|
|
cout << " p = "; print_as_np_array(cout,biases); cout << ";\n";
|
|
cout << " p.shape = net.params['"<<i->caffe_layer_name()<<"'][1].data.shape;\n";
|
|
cout << " net.params['"<<i->caffe_layer_name()<<"'][1].data[:] = p;\n";
|
|
}
|
|
else if (i->detail_name == "fc_no_bias")
|
|
{
|
|
matrix<double> weights = trans(i->params);
|
|
|
|
// main filter weights
|
|
cout << " p = "; print_as_np_array(cout,weights); cout << ";\n";
|
|
cout << " p.shape = net.params['"<<i->caffe_layer_name()<<"'][0].data.shape;\n";
|
|
cout << " net.params['"<<i->caffe_layer_name()<<"'][0].data[:] = p;\n";
|
|
}
|
|
else if (i->detail_name == "bn_con")
|
|
{
|
|
// TODO
|
|
}
|
|
else if (i->detail_name == "bn_fc")
|
|
{
|
|
// TODO
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
int main(int argc, char** argv) try
|
|
{
|
|
// TODO, write out to multiple files or just process one file at a time.
|
|
for (int i = 1; i < argc; ++i)
|
|
convert_dlib_xml_to_cafffe_python_code(argv[i]);
|
|
|
|
return 0;
|
|
}
|
|
catch(std::exception& e)
|
|
{
|
|
cout << "\n\n*************** ERROR CONVERTING TO CAFFE ***************\n" << e.what() << endl;
|
|
return 1;
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
// ----------------------------------------------------------------------------------------
|
|
// ----------------------------------------------------------------------------------------
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
class doc_handler : public document_handler
|
|
{
|
|
public:
|
|
std::vector<layer> layers;
|
|
bool seen_first_tag = false;
|
|
|
|
layer next_layer;
|
|
std::stack<string> current_tag;
|
|
long tag_id = -1;
|
|
|
|
|
|
virtual void start_document (
|
|
)
|
|
{
|
|
layers.clear();
|
|
seen_first_tag = false;
|
|
tag_id = -1;
|
|
}
|
|
|
|
virtual void end_document (
|
|
) { }
|
|
|
|
virtual void start_element (
|
|
const unsigned long line_number,
|
|
const std::string& name,
|
|
const dlib::attribute_list& atts
|
|
)
|
|
{
|
|
if (!seen_first_tag)
|
|
{
|
|
if (name != "net")
|
|
throw dlib::error("The top level XML tag must be a 'net' tag.");
|
|
seen_first_tag = true;
|
|
}
|
|
|
|
if (name == "layer")
|
|
{
|
|
next_layer = layer();
|
|
if (atts["type"] == "skip")
|
|
{
|
|
// Don't make a new layer, just apply the tag id to the previous layer
|
|
if (layers.size() == 0)
|
|
throw dlib::error("A skip layer was found as the first layer, but the first layer should be an input layer.");
|
|
layers.back().skip_id = sa = atts["id"];
|
|
|
|
// We intentionally leave next_layer empty so the end_element() callback
|
|
// don't add it as another layer when called.
|
|
}
|
|
else if (atts["type"] == "tag")
|
|
{
|
|
// Don't make a new layer, just remember the tag id so we can apply it on
|
|
// the next layer.
|
|
tag_id = sa = atts["id"];
|
|
|
|
// We intentionally leave next_layer empty so the end_element() callback
|
|
// don't add it as another layer when called.
|
|
}
|
|
else
|
|
{
|
|
next_layer.idx = sa = atts["idx"];
|
|
next_layer.type = atts["type"];
|
|
if (tag_id != -1)
|
|
{
|
|
next_layer.tag_id = tag_id;
|
|
tag_id = -1;
|
|
}
|
|
}
|
|
}
|
|
else if (current_tag.size() != 0 && current_tag.top() == "layer")
|
|
{
|
|
next_layer.detail_name = name;
|
|
// copy all the XML tag's attributes into the layer struct
|
|
atts.reset();
|
|
while (atts.move_next())
|
|
next_layer.attributes[atts.element().key()] = sa = atts.element().value();
|
|
}
|
|
|
|
current_tag.push(name);
|
|
}
|
|
|
|
virtual void end_element (
|
|
const unsigned long line_number,
|
|
const std::string& name
|
|
)
|
|
{
|
|
current_tag.pop();
|
|
if (name == "layer" && next_layer.type.size() != 0)
|
|
layers.push_back(next_layer);
|
|
}
|
|
|
|
virtual void characters (
|
|
const std::string& data
|
|
)
|
|
{
|
|
if (current_tag.size() == 0)
|
|
return;
|
|
|
|
if (comp_tags_with_params.count(current_tag.top()) != 0)
|
|
{
|
|
istringstream sin(data);
|
|
sin >> next_layer.params;
|
|
}
|
|
|
|
}
|
|
|
|
virtual void processing_instruction (
|
|
const unsigned long line_number,
|
|
const std::string& target,
|
|
const std::string& data
|
|
)
|
|
{
|
|
}
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
std::vector<layer> parse_dlib_xml(
|
|
const string& xml_filename
|
|
)
|
|
{
|
|
doc_handler dh;
|
|
parse_xml(xml_filename, dh);
|
|
if (dh.layers.size() == 0)
|
|
throw dlib::error("No layers found in XML file!");
|
|
|
|
if (dh.layers.back().type != "input")
|
|
throw dlib::error("The network in the XML file is missing an input layer!");
|
|
|
|
return dh.layers;
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|