#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
IC = 3,
IH = 227,
IW = 227;
std::vector<float> src_data(product(src_dims));
std::vector<float> scale_shift_data(product(scale_shift_dims));
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
auto mid = scale_shift_data.begin() + IC;
std::generate(scale_shift_data.begin(), mid, []() {
static int i = 0;
return std::sin(i++ * 2.f);
});
std::generate(mid, scale_shift_data.end(), []() {
static int i = 0;
return std::tan(i++);
});
auto src_md = memory::desc(src_dims, dt::f32, tag::nchw);
auto scale_shift_md = memory::desc(scale_shift_dims, dt::f32, tag::nc);
auto src_mem = memory(src_md, engine);
auto scale_shift_mem = memory(scale_shift_md, engine);
write_to_dnnl_memory(src_data.data(), src_mem);
write_to_dnnl_memory(scale_shift_data.data(), scale_shift_mem);
auto bnorm_d = batch_normalization_forward::desc(
auto bnorm_pd
= batch_normalization_forward::primitive_desc(bnorm_d, engine);
auto mean_mem = memory(bnorm_pd.mean_desc(), engine);
auto variance_mem = memory(bnorm_pd.variance_desc(), engine);
auto workspace_mem = memory(bnorm_pd.workspace_desc(), engine);
auto bnorm_prim = batch_normalization_forward(bnorm_pd);
std::unordered_map<int, memory> bnorm_args;
bnorm_prim.execute(engine_stream, bnorm_args);
engine_stream.wait();
read_from_dnnl_memory(src_data.data(), src_mem);
}
int main(int argc, char **argv) {
return handle_example_errors(
batch_normalization_example, parse_engine_kind(argc, argv));
}
@ 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_WORKSPACE
Workspace tensor argument.
Definition: dnnl_types.h:2366
#define DNNL_ARG_SCALE_SHIFT
A special mnemonic for scale and shift argument of normalization primitives.
Definition: dnnl_types.h:2333
#define DNNL_ARG_MEAN
Mean values tensor argument.
Definition: dnnl_types.h:2360
#define DNNL_ARG_VARIANCE
Variance values tensor argument.
Definition: dnnl_types.h:2362
#define DNNL_ARG_SRC
A special mnemonic for source argument for primitives that have a single source.
Definition: dnnl_types.h:2283
@ src_md
source memory desc
@ use_scale_shift
Use scale and shift parameters.
@ fuse_norm_relu
Fuse normalization with ReLU.
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