// Copyright (c) 2019 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 #include #include #include #include "lite/api/cxx_api.h" #include "lite/api/paddle_use_kernels.h" #include "lite/api/paddle_use_ops.h" #include "lite/api/paddle_use_passes.h" #include "lite/api/test_helper.h" #include "lite/core/op_registry.h" namespace paddle { namespace lite { #ifdef LITE_WITH_ARM void TestModel(const std::vector& valid_places, const Place& preferred_place) { DeviceInfo::Init(); DeviceInfo::Global().SetRunMode(lite_api::LITE_POWER_HIGH, FLAGS_threads); lite::Predictor predictor; predictor.Build(FLAGS_model_dir, "", "", preferred_place, valid_places); auto* input_image = predictor.GetInput(0); input_image->Resize({1, 3, 1333, 800}); auto* input_image_data = input_image->mutable_data(); std::ifstream read_file("/data/local/tmp/pjc/faster_rcnn_img.txt"); for (int i = 0; i < input_image->numel(); i++) { read_file >> input_image_data[i]; } read_file.close(); LOG(INFO) << "image data:" << input_image_data[0] << " " << input_image_data[input_image->numel() - 1]; auto* im_info = predictor.GetInput(1); im_info->Resize({1, 3}); auto* im_info_data = im_info->mutable_data(); im_info_data[0] = 1333; im_info_data[1] = 800; im_info_data[2] = 1; auto* im_shape = predictor.GetInput(2); im_shape->Resize({1, 3}); auto* im_shape_data = im_shape->mutable_data(); im_shape_data[0] = 1333; im_shape_data[1] = 800; im_shape_data[2] = 1; for (int i = 0; i < FLAGS_warmup; ++i) { predictor.Run(); } auto start = GetCurrentUS(); for (int i = 0; i < FLAGS_repeats; ++i) { predictor.Run(); } LOG(INFO) << "================== Speed Report ==================="; LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0 << " ms in average."; auto* out = predictor.GetOutput(0); auto* out_data = out->data(); LOG(INFO) << "==========output data==============="; LOG(INFO) << out->dims(); for (int i = 0; i < out->numel(); i++) { LOG(INFO) << out_data[i]; } } TEST(Faster_RCNN, test_arm) { std::vector valid_places({ Place{TARGET(kHost), PRECISION(kFloat)}, Place{TARGET(kARM), PRECISION(kFloat)}, }); TestModel(valid_places, Place({TARGET(kARM), PRECISION(kFloat)})); } #endif // LITE_WITH_ARM } // namespace lite } // namespace paddle