// 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 "lite/kernels/x86/concat_compute.h" #include #include #include #include "lite/core/op_registry.h" namespace paddle { namespace lite { namespace kernels { namespace x86 { TEST(concat_x86, retrive_op) { auto concat = KernelRegistry::Global().Create( "concat"); ASSERT_FALSE(concat.empty()); ASSERT_TRUE(concat.front()); } TEST(concat_x86, init) { ConcatCompute concat; ASSERT_EQ(concat.precision(), PRECISION(kFloat)); ASSERT_EQ(concat.target(), TARGET(kX86)); } TEST(concat_x86, run_test) { lite::Tensor x1, x2, out; constexpr int batch_size = 1; std::vector x1_shape{batch_size, 1, 3, 3}; x1.Resize(lite::DDim(x1_shape)); std::vector x2_shape{batch_size, 1, 3, 3}; x2.Resize(lite::DDim(x2_shape)); std::vector x = {&x1, &x2}; std::vector out_shape{batch_size, 2, 3, 3}; out.Resize(lite::DDim(out_shape)); auto x1_data = x1.mutable_data(); auto x2_data = x2.mutable_data(); auto out_data = out.mutable_data(); for (int64_t i = 0; i < x1.dims().production(); i++) { x1_data[i] = 1; x2_data[i] = 2; } ConcatCompute concat; operators::ConcatParam param; param.x = x; param.output = &out; param.axis = 1; concat.SetParam(param); concat.Run(); std::cout << "output: "; for (int i = 0; i < out.dims().production(); i++) { std::cout << out_data[i] << " "; } std::cout << std::endl; } } // namespace x86 } // namespace kernels } // namespace lite } // namespace paddle USE_LITE_KERNEL(concat, kX86, kFloat, kNCHW, def);