// 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 "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_FPGA TEST(ResNet50, test) { lite::Predictor predictor; std::vector valid_places( {Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)}}); predictor.Build(FLAGS_model_dir, "", "", valid_places); auto* input_tensor = predictor.GetInput(0); input_tensor->Resize(DDim(std::vector({1, 3, 224, 224}))); auto* data = input_tensor->mutable_data(); auto item_size = input_tensor->dims().production(); for (int i = 0; i < item_size; i++) { data[i] = 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 ==================="; } #endif } // namespace lite } // namespace paddle