.. index:: pair: example; lrn.cpp .. _doxid-lrn_8cpp-example: lrn.cpp ======= Annotated version: :ref:`Local Response Normalization Primitive Example ` Annotated version: :ref:`Local Response Normalization 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 lrn_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 = 227, // tensor height IW = 227; // tensor width // Source (src) and destination (dst) tensors dimensions. :ref:`memory::dims ` src_dims = {N, IC, IH, IW}; // Allocate buffers. std::vector src_data(product(src_dims)); std::vector dst_data(product(src_dims)); std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Create src and dst memory descriptors and memory objects. auto :ref:`src_md ` = :ref:`memory::desc `(src_dims, dt::f32, tag::nchw); auto :ref:`dst_md ` = :ref:`memory::desc `(src_dims, dt::f32, tag::nchw); auto src_mem = :ref:`memory `(src_md, :ref:`engine `); auto dst_mem = :ref:`memory `(src_md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); // Create operation descriptor. const :ref:`memory::dim ` local_size = 5; const float alpha = 1.e-4f; const float beta = 0.75f; const float k = 1.f; // Create primitive descriptor. auto lrn_pd = :ref:`lrn_forward::primitive_desc `(:ref:`engine `, :ref:`prop_kind::forward_training `, :ref:`algorithm::lrn_across_channels `, src_md, dst_md, local_size, alpha, beta, k); // Create workspace memory object using memory descriptors created by the // primitive descriptor. // NOTE: Here, workspace may or may not be required in forward training // mode, and is used to speed-up the backward propagation. auto workspace_mem = :ref:`memory `(lrn_pd.workspace_desc(), :ref:`engine `); // Create the primitive. auto lrn_prim = :ref:`lrn_forward `(lrn_pd); // Primitive arguments. std::unordered_map lrn_args; lrn_args.insert({:ref:`DNNL_ARG_SRC `, src_mem}); lrn_args.insert({:ref:`DNNL_ARG_WORKSPACE `, workspace_mem}); lrn_args.insert({:ref:`DNNL_ARG_DST `, dst_mem}); // Primitive execution. lrn_prim.execute(engine_stream, lrn_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(dst_data.data(), dst_mem); } int main(int argc, char **argv) { return handle_example_errors(lrn_example, parse_engine_kind(argc, argv)); }