bvlc_reference_caffenet的test過程分析1

本文主要總結一下imageNet的test過程的最外層過程，即test() 函數。該函數主要分爲四個部分：
- 創建網絡
- 拷貝權值參數
- 進行forward過程
- 統計最終的Accuracy和Loss
## 創建網絡
c
Net<float> caffe_net(FLAGS_model, caffe::TEST);

## 拷貝權值參數
C
caffe_net.CopyTrainedLayersFrom(FLAGS_weights);

## 進行forward過程
C
for (int i = 0; i < FLAGS_iterations; ++i) {
float iter_loss;
const vector<Blob<float>*>& result =
caffe_net.Forward(&iter_loss); // niuyue: test Forward entrance function, return result contains accuracy and loss
loss += iter_loss;
int idx = 0;
for (int j = 0; j < result.size(); ++j) {
const float* result_vec = result[j]->cpu_data();
for (int k = 0; k < result[j]->count(); ++k, ++idx) {
const float score = result_vec[k];
if (i == 0) {
test_score.push_back(score);
test_score_output_id.push_back(j);
} else {
test_score[idx] += score;
}
const std::string& output_name = caffe_net.blob_names()[
caffe_net.output_blob_indices()[j]];
LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score;
}
}
}

## 統計最後的Accuracy和Lost
C
loss /= FLAGS_iterations;
LOG(INFO) << "Loss: " << loss;
for (int i = 0; i < test_score.size(); ++i) {
const std::string& output_name = caffe_net.blob_names()[
caffe_net.output_blob_indices()[test_score_output_id[i]]];
const float loss_weight = caffe_net.blob_loss_weights()[
caffe_net.output_blob_indices()[test_score_output_id[i]]];
std::ostringstream loss_msg_stream;
const float mean_score = test_score[i] / FLAGS_iterations;
if (loss_weight) {
loss_msg_stream << " (* " << loss_weight
<< " = " << loss_weight * mean_score << " loss)";
}
LOG(INFO) << output_name << " = " << mean_score << loss_msg_stream.str();
}