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research-project/src/fmri/Simulator.cpp

234 lines
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C++

#include <cassert>
#include <iostream>
#include <optional>
#include <vector>
#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "Simulator.hpp"
#include "Range.hpp"
using namespace caffe;
using namespace std;
using namespace fmri;
struct Simulator::Impl
{
caffe::Net<DType> net;
cv::Size input_geometry;
optional<cv::Mat> means;
unsigned int num_channels;
map<string, LayerInfo> layerInfo_;
Impl(const string& model_file, const string& weights_file, const string& means_file);
vector<cv::Mat> getWrappedInputLayer();
cv::Mat preprocess(cv::Mat original) const;
vector<LayerData> simulate(const string &input_file);
const map<string, LayerInfo>& layerInfo() const;
void computeLayerInfo();
void loadMeans(const string &means_file);
void ensureNoInPlaceLayers();
};
// Create simple forwarding functions.
Simulator::Simulator(const string& model_file, const string& weights_file, const string& means_file) :
pImpl(new Impl(model_file, weights_file, means_file))
{
}
vector<LayerData> Simulator::simulate(const string& image_file)
{
return pImpl->simulate(image_file);
}
Simulator::Impl::Impl(const string& model_file, const string& weights_file, const string& means_file) :
net(model_file, TEST)
{
net.CopyTrainedLayersFrom(weights_file);
ensureNoInPlaceLayers();
auto input_layer = net.input_blobs()[0];
input_geometry = cv::Size(input_layer->width(), input_layer->height());
num_channels = input_layer->channels();
input_layer->Reshape(1, num_channels,
input_geometry.height, input_geometry.width);
/* Forward dimension change to all layers. */
net.Reshape();
if (!means_file.empty()) {
loadMeans(means_file);
}
computeLayerInfo();
}
void Simulator::Impl::loadMeans(const string &means_file)
{// Read in the means file
BlobProto proto;
ReadProtoFromBinaryFileOrDie(means_file, &proto);
Blob<DType> mean_blob;
mean_blob.FromProto(proto);
CHECK_EQ(mean_blob.channels(), num_channels) << "Number of channels should match!" << endl;
vector<cv::Mat> channels;
float *data = mean_blob.mutable_cpu_data();
for (auto i : Range(num_channels)) {
(void)i;// Suppress unused warning
channels.emplace_back(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
data += mean_blob.height() * mean_blob.width();
}
cv::Mat mean;
merge(channels, mean);
this->means = cv::Mat(input_geometry, mean.type(), cv::mean(mean));
}
vector<LayerData> Simulator::Impl::simulate(const string& image_file)
{
cv::Mat im = cv::imread(image_file, -1);
assert(!im.empty());
auto input = preprocess(im);
auto channels = getWrappedInputLayer();
cv::split(input, channels);
net.Forward();
vector<LayerData> result;
const auto& names = net.layer_names();
const auto& results = net.top_vecs();
for (auto i : Range(names.size())) {
CHECK_EQ(results[i].size(), 1) << "Multiple outputs per layer are not supported!" << endl;
const auto blob = results[i][0];
result.emplace_back(names[i], blob->shape(), blob->cpu_data());
}
return result;
}
vector<cv::Mat> Simulator::Impl::getWrappedInputLayer()
{
vector<cv::Mat> channels;
auto input_layer = net.input_blobs()[0];
const int width = input_geometry.width;
const int height = input_geometry.height;
DType* input_data = input_layer->mutable_cpu_data();
for (auto i : Range(num_channels)) {
(void)i;// Suppress unused warning
channels.emplace_back(height, width, CV_32FC1, input_data);
input_data += width * height;
}
return channels;
}
static cv::Mat fix_channels(const int num_channels, cv::Mat original) {
cv::Mat converted;
if (num_channels == 1 && original.channels() == 3) {
cv::cvtColor(original, converted, cv::COLOR_BGR2GRAY);
} else if (num_channels == 1 && original.channels() == 4) {
cv::cvtColor(original, converted, cv::COLOR_BGRA2GRAY);
} else if (num_channels == 3 && original.channels() == 1) {
cv::cvtColor(original, converted, cv::COLOR_GRAY2BGR);
} else if (num_channels == 3 && original.channels() == 4) {
cv::cvtColor(original, converted, cv::COLOR_BGRA2BGR);
} else {
CHECK(num_channels == original.channels()) << "Cannot convert between channel types. ";
return original;
}
return converted;
}
static cv::Mat resize(const cv::Size& targetSize, cv::Mat original)
{
if (targetSize != original.size()) {
cv::Mat resized;
cv::resize(original, resized, targetSize);
return resized;
}
return original;
}
cv::Mat Simulator::Impl::preprocess(cv::Mat original) const
{
auto converted = fix_channels(num_channels, std::move(original));
auto resized = resize(input_geometry, converted);
cv::Mat sample_float;
resized.convertTo(sample_float, num_channels == 3 ? CV_32FC3 : CV_32FC1);
if (!means) {
return sample_float;
}
cv::Mat normalized;
cv::subtract(sample_float, *means, normalized);
return normalized;
}
const map<string, LayerInfo> &Simulator::Impl::layerInfo() const
{
return layerInfo_;
}
void Simulator::Impl::computeLayerInfo()
{
const auto& names = net.layer_names();
const auto& layers = net.layers();
CHECK_EQ(names.size(), layers.size()) << "Size mismatch";
for (auto i : Range(names.size())) {
auto& layer = layers[i];
LayerInfo layerInfo(names[i], layer->type(), layer->blobs());
CHECK_NE(layerInfo.type(), LayerInfo::Type::Split) << "Split layers are not supported!";
layerInfo_.emplace(names[i], std::move(layerInfo));
}
}
void Simulator::Impl::ensureNoInPlaceLayers()
{
auto blobList = net.top_vecs();
typeof(blobList) uniqueVecs;
unique_copy(blobList.begin(), blobList.end(), back_inserter(uniqueVecs));
LOG_IF(ERROR, blobList.size() != uniqueVecs.size())
<< "Network file contains in-place layers, layer-state will not be accurate\n"
<< "If accurate results are desired, see the deinplace script in tools." << endl;
}
Simulator::~Simulator()
{
// Empty but defined constructor.
}
const map<string, LayerInfo> & Simulator::layerInfo() const
{
return pImpl->layerInfo();
}