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115 lines
3.6 KiB
C++
115 lines
3.6 KiB
C++
/*******************************************************************************
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* Copyright 2020 Intel Corporation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*******************************************************************************/
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/// @example resampling.cpp
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/// > Annotated version: @ref resampling_example_cpp
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///
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/// @page resampling_example_cpp_short
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///
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/// This C++ API example demonstrates how to create and execute a
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/// [Resampling](@ref dev_guide_resampling) primitive in forward training
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/// propagation mode.
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///
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/// @page resampling_example_cpp Resampling Primitive Example
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/// @copydetails resampling_example_cpp_short
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///
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/// @include resampling.cpp
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <string>
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#include <vector>
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#include "example_utils.hpp"
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#include "oneapi/dnnl/dnnl.hpp"
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using namespace dnnl;
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using tag = memory::format_tag;
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using dt = memory::data_type;
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void resampling_example(dnnl::engine::kind engine_kind) {
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// Create execution dnnl::engine.
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dnnl::engine engine(engine_kind, 0);
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// Create dnnl::stream.
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dnnl::stream engine_stream(engine);
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// Tensor dimensions.
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const memory::dim N = 3, // batch size
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IC = 3, // channels
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IH = 227, // input tensor height
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IW = 227, // input tensor width
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OH = 350, // output tensor height
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OW = 350; // output tensor width
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// Source (src) and destination (dst) dimensions.
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memory::dims src_dims = {N, IC, IH, IW};
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memory::dims dst_dims = {N, IC, OH, OW};
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// Allocate buffers.
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std::vector<float> src_data(product(src_dims));
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std::vector<float> dst_data(product(dst_dims));
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// Initialize src tensor.
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std::generate(src_data.begin(), src_data.end(), []() {
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static int i = 0;
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return std::cos(i++ / 10.f);
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});
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// Create memory descriptors and memory objects for src and dst.
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auto src_md = memory::desc(src_dims, dt::f32, tag::nchw);
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auto dst_md = memory::desc(dst_dims, dt::f32, tag::nchw);
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auto src_mem = memory(src_md, engine);
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auto dst_mem = memory(dst_md, engine);
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// Write data to memory object's handle.
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write_to_dnnl_memory(src_data.data(), src_mem);
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// Create operation descriptor.
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auto resampling_d = resampling_forward::desc(prop_kind::forward_training,
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algorithm::resampling_linear, src_md, dst_md);
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// Create primitive descriptor.
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auto resampling_pd
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= resampling_forward::primitive_desc(resampling_d, engine);
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// Create the primitive.
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auto resampling_prim = resampling_forward(resampling_pd);
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// Primitive arguments.
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std::unordered_map<int, memory> resampling_args;
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resampling_args.insert({DNNL_ARG_SRC, src_mem});
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resampling_args.insert({DNNL_ARG_DST, dst_mem});
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// Primitive execution: resampling.
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resampling_prim.execute(engine_stream, resampling_args);
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// Wait for the computation to finalize.
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engine_stream.wait();
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// Read data from memory object's handle.
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read_from_dnnl_memory(dst_data.data(), dst_mem);
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}
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int main(int argc, char **argv) {
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return handle_example_errors(
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resampling_example, parse_engine_kind(argc, argv));
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}
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