diff --git a/paddle/fluid/inference/api/helper.h b/paddle/fluid/inference/api/helper.h index 9a393a61c4b4586009a022884e88f3f5c6392ed3..7830e859567747e6c05686335919e8346f76320d 100644 --- a/paddle/fluid/inference/api/helper.h +++ b/paddle/fluid/inference/api/helper.h @@ -113,6 +113,16 @@ static void TensorAssignData(PaddleTensor *tensor, } } +template +static void TensorAssignData(PaddleTensor *tensor, + const std::vector> &data, + const std::vector &lod) { + int size = lod[lod.size() - 1]; + tensor->shape.assign({size, 1}); + tensor->lod.assign({lod}); + TensorAssignData(tensor, data); +} + template static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor, const std::vector> &data) { diff --git a/paddle/fluid/inference/tests/api/analyzer_lac_tester.cc b/paddle/fluid/inference/tests/api/analyzer_lac_tester.cc index 142801382b4fdeaa63f51390b63cf6db6cb8f60d..2213971c1764b1a0bddfce5830bbdf2ffedd61ee 100644 --- a/paddle/fluid/inference/tests/api/analyzer_lac_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_lac_tester.cc @@ -98,10 +98,8 @@ void GetOneBatch(std::vector *input_slots, DataRecord *data, auto one_batch = data->NextBatch(); PaddleTensor input_tensor; input_tensor.name = "word"; - input_tensor.shape.assign({static_cast(one_batch.data.size()), 1}); - input_tensor.lod.assign({one_batch.lod}); input_tensor.dtype = PaddleDType::INT64; - TensorAssignData(&input_tensor, {one_batch.data}); + TensorAssignData(&input_tensor, {one_batch.data}, one_batch.lod); PADDLE_ENFORCE_EQ(batch_size, static_cast(one_batch.lod.size() - 1)); input_slots->assign({input_tensor}); } diff --git a/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc b/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc index 8aaab6d6649e1d4b6db7695df0e9dd219c89422c..9d3c7519430522878ace697ea5ed38aebb6b0855 100644 --- a/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc @@ -19,11 +19,9 @@ namespace inference { using contrib::AnalysisConfig; struct DataRecord { - std::vector> query_data_all, title_data_all; + std::vector> query, title; std::vector lod1, lod2; - size_t batch_iter{0}; - size_t batch_size{1}; - size_t num_samples; // total number of samples + size_t batch_iter{0}, batch_size{1}, num_samples; // total number of samples DataRecord() = default; explicit DataRecord(const std::string &path, int batch_size = 1) : batch_size(batch_size) { @@ -33,22 +31,9 @@ struct DataRecord { DataRecord data; size_t batch_end = batch_iter + batch_size; // NOTE skip the final batch, if no enough data is provided. - if (batch_end <= query_data_all.size()) { - data.query_data_all.assign(query_data_all.begin() + batch_iter, - query_data_all.begin() + batch_end); - data.title_data_all.assign(title_data_all.begin() + batch_iter, - title_data_all.begin() + batch_end); - // Prepare LoDs - data.lod1.push_back(0); - data.lod2.push_back(0); - CHECK(!data.query_data_all.empty()); - CHECK(!data.title_data_all.empty()); - CHECK_EQ(data.query_data_all.size(), data.title_data_all.size()); - for (size_t j = 0; j < data.query_data_all.size(); j++) { - // calculate lod - data.lod1.push_back(data.lod1.back() + data.query_data_all[j].size()); - data.lod2.push_back(data.lod2.back() + data.title_data_all[j].size()); - } + if (batch_end <= query.size()) { + GetInputPerBatch(query, &data.query, &data.lod1, batch_iter, batch_end); + GetInputPerBatch(title, &data.title, &data.lod2, batch_iter, batch_end); } batch_iter += batch_size; return data; @@ -67,8 +52,8 @@ struct DataRecord { // load title data std::vector title_data; split_to_int64(data[1], ' ', &title_data); - query_data_all.push_back(std::move(query_data)); - title_data_all.push_back(std::move(title_data)); + query.push_back(std::move(query_data)); + title.push_back(std::move(title_data)); } num_samples = num_lines; } @@ -80,15 +65,9 @@ void PrepareInputs(std::vector *input_slots, DataRecord *data, lod_query_tensor.name = "left"; lod_title_tensor.name = "right"; auto one_batch = data->NextBatch(); - int size1 = one_batch.lod1[one_batch.lod1.size() - 1]; // token batch size - int size2 = one_batch.lod2[one_batch.lod2.size() - 1]; // token batch size - lod_query_tensor.shape.assign({size1, 1}); - lod_query_tensor.lod.assign({one_batch.lod1}); - lod_title_tensor.shape.assign({size2, 1}); - lod_title_tensor.lod.assign({one_batch.lod2}); // assign data - TensorAssignData(&lod_query_tensor, one_batch.query_data_all); - TensorAssignData(&lod_title_tensor, one_batch.title_data_all); + TensorAssignData(&lod_query_tensor, one_batch.query, one_batch.lod1); + TensorAssignData(&lod_title_tensor, one_batch.title, one_batch.lod2); // Set inputs. input_slots->assign({lod_query_tensor, lod_title_tensor}); for (auto &tensor : *input_slots) { diff --git a/paddle/fluid/inference/tests/api/analyzer_ner_tester.cc b/paddle/fluid/inference/tests/api/analyzer_ner_tester.cc index f19a2ed59ef2f666393124323ffee2f1e79ccf06..f8635968cebc490cf156162efaef4bbbab9dcc3e 100644 --- a/paddle/fluid/inference/tests/api/analyzer_ner_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_ner_tester.cc @@ -19,11 +19,9 @@ namespace inference { using contrib::AnalysisConfig; struct DataRecord { - std::vector> word_data_all, mention_data_all; + std::vector> word, mention; std::vector 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 + size_t batch_iter{0}, batch_size{1}, num_samples; // total number of samples DataRecord() = default; explicit DataRecord(const std::string &path, int batch_size = 1) : batch_size(batch_size) { @@ -33,20 +31,10 @@ struct DataRecord { DataRecord data; size_t batch_end = batch_iter + batch_size; // NOTE skip the final batch, if no enough data is provided. - if (batch_end <= word_data_all.size()) { - data.word_data_all.assign(word_data_all.begin() + batch_iter, - word_data_all.begin() + batch_end); - data.mention_data_all.assign(mention_data_all.begin() + batch_iter, - mention_data_all.begin() + batch_end); - // Prepare LoDs - data.lod.push_back(0); - CHECK(!data.word_data_all.empty()); - CHECK(!data.mention_data_all.empty()); - CHECK_EQ(data.word_data_all.size(), data.mention_data_all.size()); - for (size_t j = 0; j < data.word_data_all.size(); j++) { - // calculate lod - data.lod.push_back(data.lod.back() + data.word_data_all[j].size()); - } + if (batch_end <= word.size()) { + GetInputPerBatch(word, &data.word, &data.lod, batch_iter, batch_end); + GetInputPerBatch(mention, &data.mention, &data.lod, batch_iter, + batch_end); } batch_iter += batch_size; return data; @@ -65,8 +53,8 @@ struct DataRecord { // load mention data std::vector mention_data; split_to_int64(data[3], ' ', &mention_data); - word_data_all.push_back(std::move(word_data)); - mention_data_all.push_back(std::move(mention_data)); + word.push_back(std::move(word_data)); + mention.push_back(std::move(mention_data)); } num_samples = num_lines; } @@ -78,14 +66,10 @@ void PrepareInputs(std::vector *input_slots, DataRecord *data, lod_word_tensor.name = "word"; lod_mention_tensor.name = "mention"; auto one_batch = data->NextBatch(); - int size = one_batch.lod[one_batch.lod.size() - 1]; // token batch size - lod_word_tensor.shape.assign({size, 1}); - lod_word_tensor.lod.assign({one_batch.lod}); - lod_mention_tensor.shape.assign({size, 1}); - lod_mention_tensor.lod.assign({one_batch.lod}); // assign data - TensorAssignData(&lod_word_tensor, one_batch.word_data_all); - TensorAssignData(&lod_mention_tensor, one_batch.mention_data_all); + TensorAssignData(&lod_word_tensor, one_batch.word, one_batch.lod); + TensorAssignData(&lod_mention_tensor, one_batch.mention, + one_batch.lod); // Set inputs. input_slots->assign({lod_word_tensor, lod_mention_tensor}); for (auto &tensor : *input_slots) { diff --git a/paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc b/paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc index f5082cd60f1ae4e4eaf9dbe59a446ace900ee456..e6d6cd2960b394e8cd20b473bed90ce511f806be 100644 --- a/paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc @@ -18,12 +18,9 @@ namespace paddle { namespace inference { struct DataRecord { - std::vector> title1_all, title2_all, title3_all, l1_all; std::vector> title1, title2, title3, l1; - std::vector title1_lod, title2_lod, title3_lod, l1_lod; - size_t batch_iter{0}; - size_t batch_size{1}; - size_t num_samples; // total number of samples + std::vector lod1, lod2, lod3, l1_lod; + size_t batch_iter{0}, batch_size{1}, num_samples; // total number of samples DataRecord() = default; explicit DataRecord(const std::string &path, int batch_size = 1) : batch_size(batch_size) { @@ -33,41 +30,11 @@ struct DataRecord { DataRecord data; size_t batch_end = batch_iter + batch_size; // NOTE skip the final batch, if no enough data is provided. - if (batch_end <= title1_all.size()) { - data.title1_all.assign(title1_all.begin() + batch_iter, - title1_all.begin() + batch_end); - data.title2_all.assign(title2_all.begin() + batch_iter, - title2_all.begin() + batch_end); - data.title3_all.assign(title3_all.begin() + batch_iter, - title3_all.begin() + batch_end); - data.l1_all.assign(l1_all.begin() + batch_iter, - l1_all.begin() + batch_end); - // Prepare LoDs - data.title1_lod.push_back(0); - data.title2_lod.push_back(0); - data.title3_lod.push_back(0); - data.l1_lod.push_back(0); - CHECK(!data.title1_all.empty()); - CHECK(!data.title2_all.empty()); - CHECK(!data.title3_all.empty()); - CHECK(!data.l1_all.empty()); - CHECK_EQ(data.title1_all.size(), data.title2_all.size()); - CHECK_EQ(data.title1_all.size(), data.title3_all.size()); - CHECK_EQ(data.title1_all.size(), data.l1_all.size()); - for (size_t j = 0; j < data.title1_all.size(); j++) { - data.title1.push_back(data.title1_all[j]); - data.title2.push_back(data.title2_all[j]); - data.title3.push_back(data.title3_all[j]); - data.l1.push_back(data.l1_all[j]); - // calculate lod - data.title1_lod.push_back(data.title1_lod.back() + - data.title1_all[j].size()); - data.title2_lod.push_back(data.title2_lod.back() + - data.title2_all[j].size()); - data.title3_lod.push_back(data.title3_lod.back() + - data.title3_all[j].size()); - data.l1_lod.push_back(data.l1_lod.back() + data.l1_all[j].size()); - } + if (batch_end <= title1.size()) { + GetInputPerBatch(title1, &data.title1, &data.lod1, batch_iter, batch_end); + GetInputPerBatch(title2, &data.title2, &data.lod2, batch_iter, batch_end); + GetInputPerBatch(title3, &data.title3, &data.lod3, batch_iter, batch_end); + GetInputPerBatch(l1, &data.l1, &data.l1_lod, batch_iter, batch_end); } batch_iter += batch_size; return data; @@ -92,10 +59,10 @@ struct DataRecord { // load l1 data std::vector l1_data; split_to_int64(data[3], ' ', &l1_data); - title1_all.push_back(std::move(title1_data)); - title2_all.push_back(std::move(title2_data)); - title3_all.push_back(std::move(title3_data)); - l1_all.push_back(std::move(l1_data)); + title1.push_back(std::move(title1_data)); + title2.push_back(std::move(title2_data)); + title3.push_back(std::move(title3_data)); + l1.push_back(std::move(l1_data)); } num_samples = num_lines; } @@ -109,24 +76,11 @@ void PrepareInputs(std::vector *input_slots, DataRecord *data, title3_tensor.name = "title3"; l1_tensor.name = "l1"; auto one_batch = data->NextBatch(); - int title1_size = one_batch.title1_lod[one_batch.title1_lod.size() - 1]; - title1_tensor.shape.assign({title1_size, 1}); - title1_tensor.lod.assign({one_batch.title1_lod}); - int title2_size = one_batch.title2_lod[one_batch.title2_lod.size() - 1]; - title2_tensor.shape.assign({title2_size, 1}); - title2_tensor.lod.assign({one_batch.title2_lod}); - int title3_size = one_batch.title3_lod[one_batch.title3_lod.size() - 1]; - title3_tensor.shape.assign({title3_size, 1}); - title3_tensor.lod.assign({one_batch.title3_lod}); - int l1_size = one_batch.l1_lod[one_batch.l1_lod.size() - 1]; - l1_tensor.shape.assign({l1_size, 1}); - l1_tensor.lod.assign({one_batch.l1_lod}); - // assign data - TensorAssignData(&title1_tensor, one_batch.title1); - TensorAssignData(&title2_tensor, one_batch.title2); - TensorAssignData(&title3_tensor, one_batch.title3); - TensorAssignData(&l1_tensor, one_batch.l1); + TensorAssignData(&title1_tensor, one_batch.title1, one_batch.lod1); + TensorAssignData(&title2_tensor, one_batch.title2, one_batch.lod2); + TensorAssignData(&title3_tensor, one_batch.title3, one_batch.lod3); + TensorAssignData(&l1_tensor, one_batch.l1, one_batch.l1_lod); // Set inputs. input_slots->assign({title1_tensor, title2_tensor, title3_tensor, l1_tensor}); for (auto &tensor : *input_slots) { diff --git a/paddle/fluid/inference/tests/api/tester_helper.h b/paddle/fluid/inference/tests/api/tester_helper.h index ef7e2198c5db4e723406fe1a31d2c3dde5121931..7eb44d9f4ea6e27a504984eac4f960bddc9032e1 100644 --- a/paddle/fluid/inference/tests/api/tester_helper.h +++ b/paddle/fluid/inference/tests/api/tester_helper.h @@ -176,6 +176,18 @@ void SetFakeImageInput(std::vector> *inputs, (*inputs).emplace_back(input_slots); } +void GetInputPerBatch(const std::vector> &in, + std::vector> *out, + std::vector *lod, size_t batch_iter, + size_t batch_end) { + lod->clear(); + lod->push_back(0); + for (auto it = in.begin() + batch_iter; it < in.begin() + batch_end; it++) { + out->push_back(*it); + lod->push_back(lod->back() + (*it).size()); // calculate lod + } +} + void TestOneThreadPrediction( const PaddlePredictor::Config *config, const std::vector> &inputs,