You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
100 lines
3.1 KiB
100 lines
3.1 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/fluid/inference/tests/api/tester_helper.h"
|
|
|
|
namespace paddle {
|
|
namespace inference {
|
|
|
|
struct DataReader {
|
|
explicit DataReader(const std::string &path)
|
|
: file(new std::ifstream(path)) {}
|
|
|
|
bool NextBatch(std::vector<PaddleTensor> *input, int batch_size) {
|
|
PADDLE_ENFORCE_EQ(batch_size, 1);
|
|
std::string line;
|
|
PaddleTensor tensor;
|
|
tensor.dtype = PaddleDType::INT64;
|
|
tensor.lod.emplace_back(std::vector<size_t>({0}));
|
|
std::vector<int64_t> data;
|
|
|
|
for (int i = 0; i < batch_size; i++) {
|
|
if (!std::getline(*file, line)) return false;
|
|
inference::split_to_int64(line, ' ', &data);
|
|
}
|
|
tensor.lod.front().push_back(data.size());
|
|
|
|
tensor.data.Resize(data.size() * sizeof(int64_t));
|
|
memcpy(tensor.data.data(), data.data(), data.size() * sizeof(int64_t));
|
|
tensor.shape.push_back(data.size());
|
|
tensor.shape.push_back(1);
|
|
input->assign({tensor});
|
|
return true;
|
|
}
|
|
|
|
std::unique_ptr<std::ifstream> file;
|
|
};
|
|
|
|
void Main(int batch_size) {
|
|
// shape --
|
|
// Create Predictor --
|
|
AnalysisConfig config;
|
|
config.model_dir = FLAGS_infer_model;
|
|
config.use_gpu = false;
|
|
config.enable_ir_optim = true;
|
|
|
|
std::vector<PaddleTensor> input_slots, output_slots;
|
|
DataReader reader(FLAGS_infer_data);
|
|
std::vector<std::vector<PaddleTensor>> input_slots_all;
|
|
|
|
if (FLAGS_test_all_data) {
|
|
LOG(INFO) << "test all data";
|
|
int num_batches = 0;
|
|
while (reader.NextBatch(&input_slots, FLAGS_batch_size)) {
|
|
input_slots_all.emplace_back(input_slots);
|
|
++num_batches;
|
|
}
|
|
LOG(INFO) << "total number of samples: " << num_batches * FLAGS_batch_size;
|
|
TestPrediction(config, input_slots_all, &output_slots, FLAGS_num_threads);
|
|
return;
|
|
}
|
|
|
|
// one batch starts
|
|
// data --
|
|
reader.NextBatch(&input_slots, FLAGS_batch_size);
|
|
input_slots_all.emplace_back(input_slots);
|
|
TestPrediction(config, input_slots_all, &output_slots, FLAGS_num_threads);
|
|
|
|
// Get output
|
|
LOG(INFO) << "get outputs " << output_slots.size();
|
|
|
|
for (auto &output : output_slots) {
|
|
LOG(INFO) << "output.shape: " << to_string(output.shape);
|
|
// no lod ?
|
|
CHECK_EQ(output.lod.size(), 0UL);
|
|
LOG(INFO) << "output.dtype: " << output.dtype;
|
|
std::stringstream ss;
|
|
for (int i = 0; i < 5; i++) {
|
|
ss << static_cast<float *>(output.data.data())[i] << " ";
|
|
}
|
|
LOG(INFO) << "output.data summary: " << ss.str();
|
|
// one batch ends
|
|
}
|
|
}
|
|
|
|
TEST(text_classification, basic) { Main(FLAGS_batch_size); }
|
|
|
|
} // namespace inference
|
|
} // namespace paddle
|