зеркало из https://github.com/microsoft/caffe.git
Enhance help, log message & format of the feature extraction example
This commit is contained in:
Kai Li
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dfe63805e9
Коммит
01bb481702
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@ -15,7 +15,6 @@
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using namespace caffe;
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template<typename Dtype>
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int feature_extraction_pipeline(int argc, char** argv);
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@ -29,11 +28,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
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const int num_required_args = 6;
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if (argc < num_required_args) {
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LOG(ERROR)<<
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"This program takes in a trained network and an input data layer, and then"
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" extract features of the input data produced by the net.\n"
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"Usage: demo_extract_features pretrained_net_param"
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" feature_extraction_proto_file extract_feature_blob_name"
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" save_feature_leveldb_name num_mini_batches [CPU/GPU] [DEVICE_ID=0]";
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"This program takes in a trained network and an input data layer, and then"
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" extract features of the input data produced by the net.\n"
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"Usage: demo_extract_features pretrained_net_param"
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" feature_extraction_proto_file extract_feature_blob_name"
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" save_feature_leveldb_name num_mini_batches [CPU/GPU] [DEVICE_ID=0]";
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return 1;
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}
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int arg_pos = num_required_args;
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@ -63,33 +62,34 @@ int feature_extraction_pipeline(int argc, char** argv) {
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&pretrained_net_param);
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// Expected prototxt contains at least one data layer such as
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// the layer data_layer_name and one feature blob such as the
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// fc7 top blob to extract features.
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/*
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layers {
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layer {
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name: "data_layer_name"
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type: "data"
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source: "/path/to/your/images/to/extract/feature/images_leveldb"
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meanfile: "/path/to/your/image_mean.binaryproto"
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batchsize: 128
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cropsize: 227
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mirror: false
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}
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top: "data_blob_name"
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top: "label_blob_name"
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}
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layers {
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layer {
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name: "drop7"
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type: "dropout"
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dropout_ratio: 0.5
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}
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bottom: "fc7"
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top: "fc7"
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}
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*/
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NetParameter feature_extraction_net_param;;
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// the layer data_layer_name and one feature blob such as the
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// fc7 top blob to extract features.
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/*
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layers {
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layer {
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name: "data_layer_name"
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type: "data"
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source: "/path/to/your/images/to/extract/feature/images_leveldb"
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meanfile: "/path/to/your/image_mean.binaryproto"
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batchsize: 128
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cropsize: 227
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mirror: false
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}
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top: "data_blob_name"
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top: "label_blob_name"
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}
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layers {
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layer {
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name: "drop7"
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type: "dropout"
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dropout_ratio: 0.5
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}
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bottom: "fc7"
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top: "fc7"
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}
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*/
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NetParameter feature_extraction_net_param;
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;
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string feature_extraction_proto(argv[++arg_pos]);
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ReadProtoFromTextFile(feature_extraction_proto,
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&feature_extraction_net_param);
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@ -98,11 +98,9 @@ int feature_extraction_pipeline(int argc, char** argv) {
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feature_extraction_net->CopyTrainedLayersFrom(pretrained_net_param);
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string extract_feature_blob_name(argv[++arg_pos]);
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if (!feature_extraction_net->HasBlob(extract_feature_blob_name)) {
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LOG(ERROR)<< "Unknown feature blob name " << extract_feature_blob_name <<
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" in the network " << feature_extraction_proto;
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return 1;
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}
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CHECK(feature_extraction_net->HasBlob(extract_feature_blob_name))
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<< "Unknown feature blob name " << extract_feature_blob_name
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<< " in the network " << feature_extraction_proto;
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string save_feature_leveldb_name(argv[++arg_pos]);
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leveldb::DB* db;
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@ -110,9 +108,10 @@ int feature_extraction_pipeline(int argc, char** argv) {
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options.error_if_exists = true;
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options.create_if_missing = true;
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options.write_buffer_size = 268435456;
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LOG(INFO) << "Opening leveldb " << save_feature_leveldb_name;
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leveldb::Status status = leveldb::DB::Open(
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options, save_feature_leveldb_name.c_str(), &db);
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LOG(INFO)<< "Opening leveldb " << save_feature_leveldb_name;
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leveldb::Status status = leveldb::DB::Open(options,
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save_feature_leveldb_name.c_str(),
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&db);
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CHECK(status.ok()) << "Failed to open leveldb " << save_feature_leveldb_name;
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int num_mini_batches = atoi(argv[++arg_pos]);
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@ -124,51 +123,52 @@ int feature_extraction_pipeline(int argc, char** argv) {
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const int max_key_str_length = 100;
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char key_str[max_key_str_length];
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int num_bytes_of_binary_code = sizeof(Dtype);
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vector<Blob<float>* > input_vec;
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vector<Blob<float>*> input_vec;
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int image_index = 0;
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for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
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feature_extraction_net->Forward(input_vec);
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const shared_ptr<Blob<Dtype> > feature_blob =
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feature_extraction_net->GetBlob(extract_feature_blob_name);
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const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
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->GetBlob(extract_feature_blob_name);
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int num_features = feature_blob->num();
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int dim_features = feature_blob->count() / num_features;
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for (int n = 0; n < num_features; ++n) {
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datum.set_height(dim_features);
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datum.set_width(1);
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datum.set_channels(1);
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datum.clear_data();
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datum.clear_float_data();
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string* datum_string = datum.mutable_data();
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const Dtype* feature_blob_data = feature_blob->cpu_data();
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for (int d = 0; d < dim_features; ++d) {
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const char* data_byte = reinterpret_cast<const char*>(feature_blob_data + d);
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for(int i = 0; i < num_bytes_of_binary_code; ++i) {
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datum_string->push_back(data_byte[i]);
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}
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}
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string value;
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datum.SerializeToString(&value);
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snprintf(key_str, max_key_str_length, "%d", image_index);
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batch->Put(string(key_str), value);
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if (++image_index % 1000 == 0) {
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db->Write(leveldb::WriteOptions(), batch);
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LOG(ERROR) << "Extracted features of " << image_index << " query images.";
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delete batch;
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batch = new leveldb::WriteBatch();
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}
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datum.set_height(dim_features);
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datum.set_width(1);
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datum.set_channels(1);
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datum.clear_data();
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datum.clear_float_data();
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string* datum_string = datum.mutable_data();
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const Dtype* feature_blob_data = feature_blob->cpu_data();
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for (int d = 0; d < dim_features; ++d) {
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const char* data_byte = reinterpret_cast<const char*>(feature_blob_data
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+ d);
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for (int i = 0; i < num_bytes_of_binary_code; ++i) {
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datum_string->push_back(data_byte[i]);
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}
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}
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string value;
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datum.SerializeToString(&value);
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snprintf(key_str, max_key_str_length, "%d", image_index);
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batch->Put(string(key_str), value);
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if (++image_index % 1000 == 0) {
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db->Write(leveldb::WriteOptions(), batch);
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LOG(ERROR)<< "Extracted features of " << image_index << " query images.";
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delete batch;
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batch = new leveldb::WriteBatch();
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}
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}
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} // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
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} // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
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// write the last batch
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if (image_index % 1000 != 0) {
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db->Write(leveldb::WriteOptions(), batch);
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LOG(ERROR) << "Extracted features of " << image_index << " query images.";
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LOG(ERROR)<< "Extracted features of " << image_index << " query images.";
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delete batch;
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batch = new leveldb::WriteBatch();
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}
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delete batch;
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delete db;
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LOG(ERROR)<< "Successfully ended!";
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LOG(ERROR)<< "Successfully extracted the features!";
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return 0;
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}
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