.. index:: pair: example; batch_normalization.cpp .. _doxid-batch_normalization_8cpp-example: batch_normalization.cpp ======================= Annotated version: :ref:`Batch Normalization Primitive Example ` Annotated version: :ref:`Batch 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 batch_normalization_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}; // Scale/shift tensor dimensions. :ref:`memory::dims ` scaleshift_dims = {IC}; // Allocate buffers. std::vector src_data(product(src_dims)); std::vector scale_data(product(scaleshift_dims)); std::vector shift_data(product(scaleshift_dims)); // Initialize src. std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Initialize scale. std::generate(scale_data.begin(), scale_data.end(), []() { static int i = 0; return std::sin(i++ * 2.f); }); // Initialize shift. std::generate(shift_data.begin(), shift_data.end(), []() { static int i = 0; return std::tan(float(i++)); }); // Create src and scale/shift memory descriptors and memory objects. auto :ref:`src_md ` = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, tag::nchw); auto :ref:`dst_md ` = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, tag::nchw); auto scaleshift_md = :ref:`memory::desc `(scaleshift_dims, :ref:`dt::f32 `, tag::x); auto src_mem = :ref:`memory `(src_md, :ref:`engine `); auto scale_mem = :ref:`memory `(scaleshift_md, :ref:`engine `); auto shift_mem = :ref:`memory `(scaleshift_md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); write_to_dnnl_memory(scale_data.data(), scale_mem); write_to_dnnl_memory(shift_data.data(), shift_mem); // Create primitive descriptor. auto bnorm_pd = :ref:`batch_normalization_forward::primitive_desc `(:ref:`engine `, :ref:`prop_kind::forward_training `, src_md, dst_md, 1.e-10f, :ref:`normalization_flags::use_scale ` | :ref:`normalization_flags::use_shift ` | :ref:`normalization_flags::fuse_norm_relu `); // Create memory objects using memory descriptors created by the primitive // descriptor: mean, variance, workspace. // NOTE: Here, the ReLU post-ops require a workspace for later usage in // backward propagation mode. auto mean_mem = :ref:`memory `(bnorm_pd.mean_desc(), :ref:`engine `); auto variance_mem = :ref:`memory `(bnorm_pd.variance_desc(), :ref:`engine `); auto workspace_mem = :ref:`memory `(bnorm_pd.workspace_desc(), :ref:`engine `); // Create the primitive. auto bnorm_prim = :ref:`batch_normalization_forward `(bnorm_pd); // Primitive arguments. Set up in-place execution by assigning src as DST. std::unordered_map bnorm_args; bnorm_args.insert({:ref:`DNNL_ARG_SRC `, src_mem}); bnorm_args.insert({:ref:`DNNL_ARG_MEAN `, mean_mem}); bnorm_args.insert({:ref:`DNNL_ARG_VARIANCE `, variance_mem}); bnorm_args.insert({:ref:`DNNL_ARG_SCALE `, scale_mem}); bnorm_args.insert({:ref:`DNNL_ARG_SHIFT `, shift_mem}); bnorm_args.insert({:ref:`DNNL_ARG_WORKSPACE `, workspace_mem}); bnorm_args.insert({:ref:`DNNL_ARG_DST `, src_mem}); // Primitive execution: batch normalization with ReLU. bnorm_prim.execute(engine_stream, bnorm_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(src_data.data(), src_mem); } int main(int argc, char **argv) { return handle_example_errors( batch_normalization_example, parse_engine_kind(argc, argv)); }