/* 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. */ #pragma once #include "../test_include.h" #include "operators/relu_op.h" namespace paddle_mobile { namespace framework { template class TestReluOp { public: explicit TestReluOp(const Program p) : program_(p) { if (use_optimize_) { to_predict_program_ = program_.optimizeProgram; } else { to_predict_program_ = program_.originProgram; } const std::vector> blocks = to_predict_program_->Blocks(); // DLOG << " **block size " << blocks.size(); for (auto block_desc : blocks) { std::vector> ops = block_desc->Ops(); // DLOG << " ops " << ops.size(); for (auto op : ops) { if (op->Type() == "relu" && op->Input("X")[0] == "batch_norm_34.tmp_2") { DLOG << "in"; std::shared_ptr> test_op = std::make_shared>( op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(), program_.scope); ops_of_block_[*block_desc.get()].push_back(test_op); } } } } std::shared_ptr predict(const Tensor &t1) { // feed auto scope = program_.scope; Variable *x1_feed_value = scope->Var("batch_norm_34.tmp_2"); auto tensor_x1 = x1_feed_value->GetMutable(); tensor_x1->ShareDataWith(t1); Variable *output = scope->Var("batch_norm_34.tmp_3"); auto *output_tensor = output->GetMutable(); output_tensor->mutable_data({1, 2, 3, 4}); // DLOG << typeid(output_tensor).name(); // DLOG << "output_tensor dims: " << output_tensor->dims(); std::shared_ptr out_tensor = std::make_shared(); out_tensor.reset(output_tensor); predict(t1, 0); return out_tensor; // return outvars_tensor; } private: const framework::Program program_; std::shared_ptr to_predict_program_; std::map>>> ops_of_block_; bool use_optimize_ = false; void predict(const Tensor &t1, int block_id) { std::shared_ptr to_predict_block = to_predict_program_->Block(block_id); for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) { auto op = ops_of_block_[*to_predict_block.get()][j]; DLOG << "op -> run()"; op->Run(); } } }; template class TestReluOp; } // namespace framework } // namespace paddle_mobile int main() { DLOG << "----------**********----------"; DLOG << "begin to run Relu Test"; paddle_mobile::Loader loader; auto program = loader.Load(std::string("../../test/models/mobilenet+ssd")); /// input x (1,3,300,300) paddle_mobile::framework::Tensor inputx1; SetupTensor(&inputx1, {1, 2, 3, 4}, static_cast(-1), static_cast(1)); auto *inputx1_ptr = inputx1.data(); paddle_mobile::framework::TestReluOp testReluOp(program); auto output = testReluOp.predict(inputx1); auto *output_ptr = output->data(); for (int i = 0; i < output->numel(); i++) { DLOG << output_ptr[i]; } return 0; }