未验证 提交 3c3ad1e4 编写于 作者: T tensor-tang 提交者: GitHub

Merge branch 'develop' into refine/ut/lac

......@@ -118,13 +118,6 @@ void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
input_slots->assign({input_tensor});
}
static void PrintTime(const double latency, const int bs, const int repeat) {
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
<< ", avg latency: " << latency / repeat << "ms";
LOG(INFO) << "=====================================";
}
void BenchAllData(const std::string &model_path, const std::string &data_file,
const int batch_size, const int repeat) {
NativeConfig config;
......@@ -150,7 +143,7 @@ void BenchAllData(const std::string &model_path, const std::string &data_file,
sum += timer.toc();
}
}
PrintTime(sum, batch_size, repeat);
PrintTime(batch_size, repeat, 1, 0, sum / repeat);
}
const int64_t lac_ref_data[] = {24, 25, 25, 25, 38, 30, 31, 14, 15, 44, 24, 25,
......@@ -206,7 +199,7 @@ void TestLACPrediction(const std::string &model_path,
for (int i = 0; i < repeat; i++) {
predictor->Run(input_slots, &outputs_slots);
}
PrintTime(timer.toc(), batch_size, repeat);
PrintTime(batch_size, repeat, 1, 0, timer.toc() / repeat);
// check result
EXPECT_EQ(outputs_slots.size(), 1UL);
......
......@@ -25,6 +25,7 @@ DEFINE_string(infer_model, "", "model path");
DEFINE_string(infer_data, "", "data path");
DEFINE_int32(batch_size, 10, "batch size.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
DEFINE_bool(test_all_data, false, "Test the all dataset in data file.");
namespace paddle {
namespace inference {
......@@ -35,6 +36,7 @@ struct DataRecord {
std::vector<size_t> lod; // two inputs have the same lod info.
size_t batch_iter{0};
size_t batch_size{1};
size_t num_samples; // total number of samples
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
......@@ -81,6 +83,7 @@ struct DataRecord {
word_data_all.push_back(std::move(word_data));
mention_data_all.push_back(std::move(mention_data));
}
num_samples = num_lines;
}
};
......@@ -120,21 +123,38 @@ void TestChineseNERPrediction() {
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
std::vector<PaddleTensor> input_slots;
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> outputs;
Timer timer;
if (FLAGS_test_all_data) {
LOG(INFO) << "test all data";
double sum = 0;
size_t num_samples;
for (int i = 0; i < FLAGS_repeat; i++) {
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
num_samples = data.num_samples;
for (size_t bid = 0; bid < num_samples; ++bid) {
PrepareInputs(&input_slots, &data, FLAGS_batch_size);
timer.tic();
predictor->Run(input_slots, &outputs);
sum += timer.toc();
}
}
LOG(INFO) << "total number of samples: " << num_samples;
PrintTime(FLAGS_batch_size, FLAGS_repeat, 1, 0, sum / FLAGS_repeat);
LOG(INFO) << "average latency of each sample: "
<< sum / FLAGS_repeat / num_samples;
return;
}
// Prepare inputs.
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
PrepareInputs(&input_slots, &data, FLAGS_batch_size);
std::vector<PaddleTensor> outputs;
Timer timer;
timer.tic();
for (int i = 0; i < FLAGS_repeat; i++) {
predictor->Run(input_slots, &outputs);
}
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << FLAGS_batch_size
<< ", repeat: " << FLAGS_repeat
<< ", latency: " << timer.toc() / FLAGS_repeat << "ms";
LOG(INFO) << "=====================================";
PrintTime(FLAGS_batch_size, FLAGS_repeat, 1, 0, timer.toc() / FLAGS_repeat);
PADDLE_ENFORCE(outputs.size(), 1UL);
auto &out = outputs[0];
......
......@@ -31,25 +31,11 @@ DEFINE_int32(repeat, 1, "How many times to repeat run.");
DEFINE_int32(topn, -1, "Run top n batches of data to save time");
namespace paddle {
template <typename T>
std::string to_string(const std::vector<T> &vec) {
std::stringstream ss;
for (const auto &c : vec) {
ss << c << " ";
}
return ss.str();
}
void PrintTime(const double latency, const int bs, const int repeat) {
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
<< ", avg latency: " << latency / repeat << "ms";
LOG(INFO) << "=====================================";
}
namespace inference {
struct DataReader {
DataReader(const std::string &path) : file(new std::ifstream(path)) {}
explicit DataReader(const std::string &path)
: file(new std::ifstream(path)) {}
bool NextBatch(PaddleTensor *tensor, int batch_size) {
PADDLE_ENFORCE_EQ(batch_size, 1);
......@@ -107,8 +93,7 @@ void Main(int batch_size) {
++num_batches;
}
}
PrintTime(sum, batch_size, num_batches);
PrintTime(batch_size, FLAGS_repeat, 1, 0, sum / FLAGS_repeat);
// Get output
LOG(INFO) << "get outputs " << output_slots.size();
......@@ -129,4 +114,5 @@ void Main(int batch_size) {
TEST(text_classification, basic) { Main(FLAGS_batch_size); }
} // namespace inference
} // namespace paddle
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