Developer Guide and Reference

  • 2022.1
  • 04/11/2022
  • Public Content
Contents

Local Response Normalization Primitive Example

This C++ API demonstrates how to create and execute a Local response normalization primitive in forward training propagation mode.
/******************************************************************************* * Copyright 2020 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 <algorithm> #include <cmath> #include <iostream> #include <string> #include <vector> #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace dnnl; using tag = memory::format_tag; using dt = memory::data_type; void lrn_example(dnnl::engine::kind engine_kind) { // Create execution dnnl::engine. dnnl::engine engine(engine_kind, 0); // Create dnnl::stream. dnnl::stream engine_stream(engine); // Tensor dimensions. const memory::dim N = 3, // batch size IC = 3, // channels IH = 227, // tensor height IW = 227; // tensor width // Source (src) and destination (dst) tensors dimensions. memory::dims src_dims = {N, IC, IH, IW}; // Allocate buffers. std::vector<float> src_data(product(src_dims)); std::vector<float> 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 src_md = memory::desc(src_dims, dt::f32, tag::nchw); auto src_mem = memory(src_md, engine); auto dst_mem = memory(src_md, engine); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); // Create operation descriptor. const memory::dim local_size = 5; const float alpha = 1.e-4f; const float beta = 0.75f; const float k = 1.f; auto lrn_d = lrn_forward::desc(prop_kind::forward_training, algorithm::lrn_across_channels, src_md, local_size, alpha, beta, k); // Create primitive descriptor. auto lrn_pd = lrn_forward::primitive_desc(lrn_d, engine); // 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 = memory(lrn_pd.workspace_desc(), engine); // Create the primitive. auto lrn_prim = lrn_forward(lrn_pd); // Primitive arguments. std::unordered_map<int, memory> lrn_args; lrn_args.insert({DNNL_ARG_SRC, src_mem}); lrn_args.insert({DNNL_ARG_WORKSPACE, workspace_mem}); lrn_args.insert({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)); }

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