// 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/core/op_registry.h" #include "lite/kernels/x86/activation_compute.h" namespace paddle { namespace lite { namespace kernels { namespace x86 { TEST(relu_x86, retrive_op) { auto relu = KernelRegistry::Global().Create("relu"); ASSERT_FALSE(relu.empty()); ASSERT_TRUE(relu.front()); } TEST(relu_x86, init) { ReluCompute relu; ASSERT_EQ(relu.precision(), PRECISION(kFloat)); ASSERT_EQ(relu.target(), TARGET(kX86)); } TEST(relu_x86, run_test) { lite::Tensor x, out; constexpr int batch_size = 1; std::vector x_shape{batch_size, 3, 2, 2}; x.Resize(lite::DDim(x_shape)); std::vector out_shape{batch_size, 3, 2, 2}; out.Resize(lite::DDim(out_shape)); auto x_data = x.mutable_data(); auto out_data = out.mutable_data(); for (int64_t i = 0; i < x.dims().production(); i++) { int sign = i % 2 == 0 ? 1 : -1; x_data[i] = static_cast(i * sign); } // ReluCompute relu; ReluCompute relu; operators::ActivationParam param; param.X = &x; param.Out = &out; relu.SetParam(param); relu.Run(); LOG(INFO) << "output: "; for (int i = 0; i < out.dims().production(); i++) { LOG(INFO) << out_data[i]; int sign = i % 2 == 0 ? 1 : 0; ASSERT_EQ(out_data[i], i * sign); } } } // namespace x86 } // namespace kernels } // namespace lite } // namespace paddle USE_LITE_KERNEL(relu, kX86, kFloat, kNCHW, def);