#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
IC = 3,
IH = 227,
IW = 227,
OH = 350,
OW = 350;
std::vector<float> src_data(product(src_dims));
std::vector<float> dst_data(product(dst_dims));
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
auto src_md = memory::desc(src_dims, dt::f32, tag::nchw);
auto dst_md = memory::desc(dst_dims, dt::f32, tag::nchw);
write_to_dnnl_memory(src_data.data(), src_mem);
auto resampling_pd
auto resampling_prim = resampling_forward(resampling_pd);
std::unordered_map<int, memory> resampling_args;
resampling_prim.execute(engine_stream, resampling_args);
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));
}
@ resampling_linear
Linear (Bilinear, Trilinear) resampling method.
@ forward_training
Forward data propagation (training mode).
#define DNNL_ARG_DST
A special mnemonic for destination argument for primitives that have a single destination.
Definition: dnnl_types.h:2307
#define DNNL_ARG_SRC
A special mnemonic for source argument for primitives that have a single source.
Definition: dnnl_types.h:2283
@ dst_md
destination memory desc
@ src_md
source memory desc
@ resampling_d
resampling descriptor
oneDNN namespace
Definition: dnnl.hpp:74
An execution engine.
Definition: dnnl.hpp:869
kind
Kinds of engines.
Definition: dnnl.hpp:874
dnnl_dim_t dim
Integer type for representing dimension sizes and indices.
Definition: dnnl.hpp:1112
format_tag
Memory format tag specification.
Definition: dnnl.hpp:1205
data_type
Data type specification.
Definition: dnnl.hpp:1130
std::vector< dim > dims
Vector of dimensions.
Definition: dnnl.hpp:1115
An execution stream.
Definition: dnnl.hpp:985
stream & wait()
Waits for all primitives executing in the stream to finish.
Definition: dnnl.hpp:1025