Add a new option to add a labels file and a means file.
This commit is contained in:
@@ -10,14 +10,14 @@ using namespace std;
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static void show_help(const char *progname, int exitcode) {
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cerr << "Usage: " << progname << " -m MODEL -w WEIGHTS INPUTS..." << endl
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<< endl
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<< R"END(
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Simulate the specified network on the specified inputs.
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<< R"END(Simulate the specified network on the specified inputs.
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Options:
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-h show this message
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-m (required) the model file to simulate
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-n (required) the model file to simulate
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-w (required) the trained weights
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)END" << endl;
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-m means file. Will be substracted from input if available.
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-l labels file. Will be used to print prediction labels if available.)END" << endl;
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exit(exitcode);
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}
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@@ -32,10 +32,12 @@ static void check_file(const char *filename) {
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Options Options::parse(const int argc, char *const argv[]) {
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string model;
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string weights;
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string means;
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string labels;
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char c;
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while ((c = getopt(argc, argv, "hm:w:")) != -1) {
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while ((c = getopt(argc, argv, "hm:w:n:l:")) != -1) {
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switch (c) {
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case 'h':
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show_help(argv[0], 0);
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@@ -46,16 +48,27 @@ Options Options::parse(const int argc, char *const argv[]) {
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weights = optarg;
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break;
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case 'm':
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case 'n':
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check_file(optarg);
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model = optarg;
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break;
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case 'm':
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check_file(optarg);
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means = optarg;
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break;
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case 'l':
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check_file(optarg);
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labels = optarg;
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break;
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case '?':
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show_help(argv[0], 1);
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break;
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default:
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cerr << "Unhandled option: " << c << endl;
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abort();
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}
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}
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@@ -78,23 +91,36 @@ Options Options::parse(const int argc, char *const argv[]) {
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show_help(argv[0], 1);
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}
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return Options(move(model), move(weights), move(inputs));
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return Options(move(model), move(weights), move(means), move(labels), move(inputs));
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}
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Options::Options(string &&model, string &&weights, vector<string> &&inputs) noexcept:
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Options::Options(string &&model, string &&weights, string&& means, string&& labels, vector<string> &&inputs) noexcept:
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modelPath(move(model)),
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weightsPath(move(weights)),
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inputPaths(move(inputs)) {
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meansPath(means),
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labelsPath(labels),
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inputPaths(move(inputs))
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{
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}
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const string &Options::model() const {
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const string& Options::model() const {
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return modelPath;
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}
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const string &Options::weights() const {
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const string& Options::weights() const {
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return weightsPath;
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}
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const vector<string> &Options::inputs() const {
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const vector<string>& Options::inputs() const {
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return inputPaths;
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}
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const string& Options::means() const
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{
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return meansPath;
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}
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const string& Options::labels() const
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{
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return labelsPath;
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}
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@@ -12,17 +12,20 @@ namespace fmri {
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public:
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static Options parse(const int argc, char *const argv[]);
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const string &model() const;
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const string& model() const;
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const string& weights() const;
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const string& means() const;
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const string& labels() const;
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const string &weights() const;
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const vector<string> &inputs() const;
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const vector<string>& inputs() const;
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private:
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const string modelPath;
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const string weightsPath;
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const string meansPath;
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const string labelsPath;
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const vector<string> inputPaths;
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Options(string &&, string &&, vector<string> &&) noexcept;
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Options(string &&, string &&, string&&, string&&, vector<string> &&) noexcept;
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};
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}
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@@ -1,5 +1,5 @@
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#include <cassert>
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#include <iostream>
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#include <iterator>
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#include <vector>
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#include "Simulator.hpp"
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@@ -8,7 +8,7 @@ using namespace caffe;
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using namespace std;
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using namespace fmri;
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Simulator::Simulator(const string& model_file, const string& weights_file) :
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Simulator::Simulator(const string& model_file, const string& weights_file, const string& means_file) :
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net(model_file, TEST)
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{
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net.CopyTrainedLayersFrom(weights_file);
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@@ -21,16 +21,18 @@ Simulator::Simulator(const string& model_file, const string& weights_file) :
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input_geometry.height, input_geometry.width);
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/* Forward dimension change to all layers. */
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net.Reshape();
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if (means_file != "") {
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means = processMeans(means_file);
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}
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}
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void Simulator::simulate(const string& image_file)
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vector<Simulator::DType> Simulator::simulate(const string& image_file)
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{
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cv::Mat im = cv::imread(image_file, -1);
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if (im.empty()) {
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cerr << "Unable to read " << image_file << endl;
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return;
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}
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assert(!im.empty());
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auto input = preprocess(im);
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auto channels = getWrappedInputLayer();
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@@ -44,10 +46,7 @@ void Simulator::simulate(const string& image_file)
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const DType *end = begin + output_layer->channels();
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vector<DType> result(begin, end);
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// TODO: visualize, rather than just print.
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for (auto v : result) {
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cout << v << endl;
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}
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return result;
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}
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vector<cv::Mat> Simulator::getWrappedInputLayer()
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@@ -111,8 +110,36 @@ cv::Mat Simulator::preprocess(cv::Mat original) const
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cv::Mat sample_float;
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resized.convertTo(sample_float, num_channels == 3 ? CV_32FC3 : CV_32FC1);
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// TODO: substract means.
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// Don't know if necessary yet.
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if (means.empty()) {
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return sample_float;
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}
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cv::Mat normalized;
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cv::subtract(sample_float, means, normalized);
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return normalized;
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return sample_float;
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}
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cv::Mat Simulator::processMeans(const string &means_file) const
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{
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BlobProto proto;
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ReadProtoFromBinaryFileOrDie(means_file, &proto);
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Blob<DType> mean_blob;
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mean_blob.FromProto(proto);
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assert(mean_blob.channels() == num_channels);
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vector<cv::Mat> channels;
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float* data = mean_blob.mutable_cpu_data();
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for (unsigned int i = 0; i < num_channels; ++i) {
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channels.emplace_back(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
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data += mean_blob.height() * mean_blob.width();
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}
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cv::Mat mean;
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cv::merge(channels, mean);
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return cv::Mat(input_geometry, mean.type(), cv::mean(mean));
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}
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@@ -17,16 +17,18 @@ namespace fmri {
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public:
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typedef float DType;
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Simulator(const string &model_file, const string &weights_file);
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Simulator(const string &model_file, const string &weights_file, const string &means_file = "");
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void simulate(const string &input_file);
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vector<DType> simulate(const string &input_file);
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private:
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caffe::Net<DType> net;
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cv::Size input_geometry;
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cv::Mat means;
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unsigned int num_channels;
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vector<cv::Mat> getWrappedInputLayer();
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cv::Mat preprocess(cv::Mat original) const;
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cv::Mat processMeans(const string &means_file) const;
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};
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}
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@@ -10,7 +10,7 @@ int main(int argc, char *const argv[]) {
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Options options = Options::parse(argc, argv);
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Simulator simulator(options.model(), options.weights());
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Simulator simulator(options.model(), options.weights(), options.means());
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for (const auto &image : options.inputs()) {
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simulator.simulate(image);
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