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#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; |
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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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#ifdef USE_OPENCV |
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::google::InitGoogleLogging(argv[0]); |
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FLAGS_alsologtostderr = 1; |
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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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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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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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#else |
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LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV."; |
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#endif |
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return 0; |
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} |
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