.. index:: pair: page; Pooling Primitive Example .. _doxid-pooling_example_cpp: Pooling Primitive Example ========================= This C++ API example demonstrates how to create and execute a :ref:`Pooling ` primitive in forward training propagation mode. .. 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 pooling_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, // input channels IH = 27, // input tensor height IW = 27, // input tensor width KH = 11, // kernel height KW = 11, // kernel width PH_L = 0, // height padding: left PH_R = 0, // height padding: right PW_L = 0, // width padding: left PW_R = 0, // width padding: right SH = 4, // height-wise stride SW = 4, // width-wise stride DH = 1, // height-wise dilation DW = 1; // width-wise dilation const :ref:`memory::dim ` OH = (IH - ((KH - 1) * DH + KH) + PH_L + PH_R) / SH + 1; const :ref:`memory::dim ` OW = (IW - ((KW - 1) * DW + KW) + PW_L + PW_R) / SW + 1; // Source (src) and destination (dst) tensors dimensions. :ref:`memory::dims ` src_dims = {N, IC, IH, IW}; :ref:`memory::dims ` dst_dims = {N, IC, OH, OW}; // Kernel dimensions. :ref:`memory::dims ` kernel_dims = {KH, KW}; // Strides, padding dimensions. :ref:`memory::dims ` strides_dims = {SH, SW}; :ref:`memory::dims ` padding_dims_l = {PH_L, PW_L}; :ref:`memory::dims ` padding_dims_r = {PH_R, PW_R}; :ref:`memory::dims ` dilation = {DH, DW}; // Allocate buffers. std::vector src_data(product(src_dims)); std::vector dst_data(product(dst_dims)); std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Create memory descriptors and memory objects for src and dst. auto :ref:`src_md ` = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, tag::nchw); auto src_mem = :ref:`memory `(src_md, :ref:`engine `); auto :ref:`dst_md ` = :ref:`memory::desc `(dst_dims, :ref:`dt::f32 `, tag::nchw); auto dst_mem = :ref:`memory `(dst_md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); // Create primitive descriptor. auto pooling_pd = :ref:`pooling_forward::primitive_desc `(:ref:`engine `, :ref:`prop_kind::forward_training `, :ref:`algorithm::pooling_max `, src_md, dst_md, strides_dims, kernel_dims, dilation, padding_dims_l, padding_dims_r); // Create workspace memory objects using memory descriptor created by the // primitive descriptor. // NOTE: Here, the workspace is required to save the indices where maximum // was found, and is used in backward pooling to perform upsampling. auto workspace_mem = :ref:`memory `(pooling_pd.workspace_desc(), :ref:`engine `); // Create the primitive. auto pooling_prim = :ref:`pooling_forward `(pooling_pd); // Primitive arguments. Set up in-place execution by assigning src as DST. std::unordered_map pooling_args; pooling_args.insert({:ref:`DNNL_ARG_SRC `, src_mem}); pooling_args.insert({:ref:`DNNL_ARG_DST `, dst_mem}); pooling_args.insert({:ref:`DNNL_ARG_WORKSPACE `, workspace_mem}); // Primitive execution: pooling. pooling_prim.execute(engine_stream, pooling_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( pooling_example, parse_engine_kind(argc, argv)); }