.. index:: pair: example; concat.cpp .. _doxid-concat_8cpp-example: concat.cpp ========== Annotated version: :ref:`Concat Primitive Example ` Annotated version: :ref:`Concat Primitive Example ` .. ref-code-block:: cpp /******************************************************************************* * Copyright 2020-2022 Intel Corporation * * 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 #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace :ref:`dnnl `; using :ref:`tag ` = :ref:`memory::format_tag `; using :ref:`dt ` = :ref:`memory::data_type `; void concat_example(:ref:`dnnl::engine::kind ` engine_kind) { // Create execution dnnl::engine. :ref:`dnnl::engine ` :ref:`engine `(engine_kind, 0); // Create dnnl::stream. :ref:`dnnl::stream ` engine_stream(:ref:`engine `); // Tensor dimensions. const :ref:`memory::dim ` N = 3, // batch size IC = 3, // channels IH = 120, // tensor height IW = 120; // tensor width // Number of source (src) tensors. const int num_src = 10; // Concatenation axis. const int axis = 1; // src tensors dimensions :ref:`memory::dims ` src_dims = {N, IC, IH, IW}; // Allocate buffers. std::vector src_data(product(src_dims)); // Initialize src. // NOTE: In this example, the same src memory buffer is used to demonstrate // concatenation for simplicity std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Create a memory descriptor and memory object for each src tensor. std::vector src_mds; std::vector src_mems; for (int n = 0; n < num_src; ++n) { auto md = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, tag::nchw); auto mem = :ref:`memory `(md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), mem); src_mds.push_back(md); src_mems.push_back(mem); } // Create primitive descriptor. auto concat_pd = :ref:`concat::primitive_desc `(:ref:`engine `, axis, src_mds); // Create destination (dst) memory object using the memory descriptor // created by the primitive. auto dst_mem = :ref:`memory `(concat_pd.dst_desc(), :ref:`engine `); // Create the primitive. auto concat_prim = :ref:`concat `(concat_pd); // Primitive arguments. std::unordered_map concat_args; for (int n = 0; n < num_src; ++n) concat_args.insert({DNNL_ARG_MULTIPLE_SRC + n, src_mems[n]}); concat_args.insert({:ref:`DNNL_ARG_DST `, dst_mem}); // Primitive execution: concatenation. concat_prim.execute(engine_stream, concat_args); // Wait for the computation to finalize. engine_stream.wait(); } int main(int argc, char **argv) { return handle_example_errors(concat_example, parse_engine_kind(argc, argv)); }