зеркало из https://github.com/microsoft/caffe.git
120 строки
3.3 KiB
C++
120 строки
3.3 KiB
C++
#include <stdint.h>
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#include <algorithm>
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#include <string>
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#include <utility>
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#include <vector>
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#include "boost/scoped_ptr.hpp"
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#include "gflags/gflags.h"
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#include "glog/logging.h"
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#include "caffe/proto/caffe.pb.h"
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#include "caffe/util/db.hpp"
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#include "caffe/util/io.hpp"
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using namespace caffe; // NOLINT(build/namespaces)
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using std::max;
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using std::pair;
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using boost::scoped_ptr;
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DEFINE_string(backend, "lmdb",
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"The backend {leveldb, lmdb} containing the images");
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int main(int argc, char** argv) {
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::google::InitGoogleLogging(argv[0]);
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#ifndef GFLAGS_GFLAGS_H_
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namespace gflags = google;
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#endif
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gflags::SetUsageMessage("Compute the mean_image of a set of images given by"
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" a leveldb/lmdb\n"
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"Usage:\n"
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" compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n");
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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if (argc < 2 || argc > 3) {
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gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/compute_image_mean");
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return 1;
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}
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scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
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db->Open(argv[1], db::READ);
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scoped_ptr<db::Cursor> cursor(db->NewCursor());
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BlobProto sum_blob;
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int count = 0;
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// load first datum
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Datum datum;
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datum.ParseFromString(cursor->value());
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if (DecodeDatumNative(&datum)) {
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LOG(INFO) << "Decoding Datum";
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}
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sum_blob.set_num(1);
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sum_blob.set_channels(datum.channels());
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sum_blob.set_height(datum.height());
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sum_blob.set_width(datum.width());
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const int data_size = datum.channels() * datum.height() * datum.width();
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int size_in_datum = std::max<int>(datum.data().size(),
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datum.float_data_size());
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for (int i = 0; i < size_in_datum; ++i) {
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sum_blob.add_data(0.);
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}
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LOG(INFO) << "Starting Iteration";
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while (cursor->valid()) {
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Datum datum;
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datum.ParseFromString(cursor->value());
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DecodeDatumNative(&datum);
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const std::string& data = datum.data();
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size_in_datum = std::max<int>(datum.data().size(),
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datum.float_data_size());
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CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
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size_in_datum;
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if (data.size() != 0) {
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CHECK_EQ(data.size(), size_in_datum);
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for (int i = 0; i < size_in_datum; ++i) {
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sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
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}
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} else {
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CHECK_EQ(datum.float_data_size(), size_in_datum);
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for (int i = 0; i < size_in_datum; ++i) {
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sum_blob.set_data(i, sum_blob.data(i) +
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static_cast<float>(datum.float_data(i)));
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}
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}
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++count;
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if (count % 10000 == 0) {
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LOG(INFO) << "Processed " << count << " files.";
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}
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cursor->Next();
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}
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if (count % 10000 != 0) {
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LOG(INFO) << "Processed " << count << " files.";
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}
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for (int i = 0; i < sum_blob.data_size(); ++i) {
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sum_blob.set_data(i, sum_blob.data(i) / count);
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}
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// Write to disk
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if (argc == 3) {
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LOG(INFO) << "Write to " << argv[2];
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WriteProtoToBinaryFile(sum_blob, argv[2]);
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}
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const int channels = sum_blob.channels();
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const int dim = sum_blob.height() * sum_blob.width();
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std::vector<float> mean_values(channels, 0.0);
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LOG(INFO) << "Number of channels: " << channels;
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for (int c = 0; c < channels; ++c) {
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for (int i = 0; i < dim; ++i) {
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mean_values[c] += sum_blob.data(dim * c + i);
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}
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LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
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}
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return 0;
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}
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