Developer Guide and Reference

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

Resampling Primitive Example

This C++ API example demonstrates how to create and execute a Resampling 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 resampling_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, // input tensor height IW = 227, // input tensor width OH = 350, // output tensor height OW = 350; // output tensor width // Source (src) and destination (dst) dimensions. memory::dims src_dims = {N, IC, IH, IW}; memory::dims dst_dims = {N, IC, OH, OW}; // Allocate buffers. std::vector<float> src_data(product(src_dims)); std::vector<float> dst_data(product(dst_dims)); // Initialize src tensor. 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 src_md = memory::desc(src_dims, dt::f32, tag::nchw); auto dst_md = memory::desc(dst_dims, dt::f32, tag::nchw); auto src_mem = memory(src_md, engine); auto dst_mem = memory(dst_md, engine); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); // Create operation descriptor. auto resampling_d = resampling_forward::desc(prop_kind::forward_training, algorithm::resampling_linear, src_md, dst_md); // Create primitive descriptor. auto resampling_pd = resampling_forward::primitive_desc(resampling_d, engine); // Create the primitive. auto resampling_prim = resampling_forward(resampling_pd); // Primitive arguments. std::unordered_map<int, memory> resampling_args; resampling_args.insert({DNNL_ARG_SRC, src_mem}); resampling_args.insert({DNNL_ARG_DST, dst_mem}); // Primitive execution: resampling. resampling_prim.execute(engine_stream, resampling_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( resampling_example, parse_engine_kind(argc, argv)); }

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