#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
using tag = memory::format_tag;
using dt = memory::data_type;
const memory::dim N = 3,
IC = 32,
IH = 13,
IW = 13,
OC = 64,
KH = 3,
KW = 3,
PH_L = 1,
PH_R = 1,
PW_L = 1,
PW_R = 1,
SH = 4,
SW = 4,
OH = (IH - KH + PH_L + PH_R) / SH + 1,
OW = (IW - KW + PW_L + PW_R) / SW + 1;
memory::dims src_dims = {N, IC, IH, IW};
memory::dims weights_dims = {OC, IC, KH, KW};
memory::dims bias_dims = {OC};
memory::dims dst_dims = {N, OC, OH, OW};
memory::dims strides_dims = {SH, SW};
memory::dims padding_dims_l = {PH_L, PW_L};
memory::dims padding_dims_r = {PH_R, PW_R};
std::vector<float> src_data(product(src_dims));
std::vector<float> weights_data(product(weights_dims));
std::vector<float> bias_data(OC);
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);
});
std::generate(weights_data.begin(), weights_data.end(), []() {
static int i = 0;
return std::sin(i++ * 2.f);
});
std::generate(bias_data.begin(), bias_data.end(), []() {
static int i = 0;
return std::tanh(i++);
});
auto user_src_mem = memory({src_dims, dt::f32, tag::nchw},
engine);
auto user_weights_mem = memory({weights_dims, dt::f32, tag::oihw},
engine);
auto user_dst_mem = memory({dst_dims, dt::f32, tag::nchw},
engine);
auto conv_src_md = memory::desc(src_dims, dt::f32, tag::any);
auto conv_weights_md = memory::desc(weights_dims, dt::f32, tag::any);
auto conv_dst_md = memory::desc(dst_dims, dt::f32, tag::any);
auto user_bias_md = memory::desc(bias_dims, dt::f32, tag::a);
auto user_bias_mem = memory(user_bias_md, engine);
write_to_dnnl_memory(src_data.data(), user_src_mem);
write_to_dnnl_memory(weights_data.data(), user_weights_mem);
write_to_dnnl_memory(bias_data.data(), user_bias_mem);
auto conv_desc = convolution_forward::desc(prop_kind::forward_training,
algorithm::convolution_direct, conv_src_md, conv_weights_md,
user_bias_md, conv_dst_md, strides_dims, padding_dims_l,
padding_dims_r);
const float scale = 1.f;
const float alpha = 0.f;
const float beta = 0.f;
post_ops conv_ops;
conv_ops.append_eltwise(scale, algorithm::eltwise_relu, alpha, beta);
primitive_attr conv_attr;
conv_attr.set_post_ops(conv_ops);
auto conv_pd
= convolution_forward::primitive_desc(conv_desc, conv_attr, engine);
auto conv_src_mem = user_src_mem;
auto conv_weights_mem = user_weights_mem;
auto conv_dst_mem = user_dst_mem;
if (conv_pd.src_desc() != user_src_mem.get_desc()) {
conv_src_mem = memory(conv_pd.src_desc(), engine);
reorder(user_src_mem, conv_src_mem)
.execute(engine_stream, user_src_mem, conv_src_mem);
}
if (conv_pd.weights_desc() != user_weights_mem.get_desc()) {
conv_weights_mem = memory(conv_pd.weights_desc(), engine);
reorder(user_weights_mem, conv_weights_mem)
.execute(engine_stream, user_weights_mem, conv_weights_mem);
}
if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
conv_dst_mem = memory(conv_pd.dst_desc(), engine);
}
auto conv_prim = convolution_forward(conv_pd);
std::unordered_map<int, memory> conv_args;
conv_prim.execute(engine_stream, conv_args);
if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
reorder(conv_dst_mem, user_dst_mem)
.execute(engine_stream, conv_dst_mem, user_dst_mem);
} else
user_dst_mem = conv_dst_mem;
engine_stream.wait();
read_from_dnnl_memory(dst_data.data(), user_dst_mem);
}
int main(int argc, char **argv) {
return handle_example_errors(
convolution_example, parse_engine_kind(argc, argv));
}
#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
#define DNNL_ARG_BIAS
Bias tensor argument.
Definition: dnnl_types.h:2357
#define DNNL_ARG_WEIGHTS
A special mnemonic for primitives that have a single weights argument.
Definition: dnnl_types.h:2330
oneDNN namespace
Definition: dnnl.hpp:74
An execution engine.
Definition: dnnl.hpp:869
kind
Kinds of engines.
Definition: dnnl.hpp:874
An execution stream.
Definition: dnnl.hpp:985