oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning
1.96.0
batch_normalization.cpp

Annotated version: Batch Normalization Primitive Example

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* Copyright 2020 Intel Corporation
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* 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
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* http://www.apache.org/licenses/LICENSE-2.0
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* 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.
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#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
using namespace dnnl;
void batch_normalization_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, // tensor height
IW = 227; // tensor width
// Source (src) and destination (dst) tensors dimensions.
memory::dims src_dims = {N, IC, IH, IW};
// Scale/shift tensor dimensions.
memory::dims scale_shift_dims = {2, IC};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> scale_shift_data(product(scale_shift_dims));
// Initialize src.
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;
// Initialize scale.
std::generate(scale_shift_data.begin(), mid, []() {
static int i = 0;
return std::sin(i++ * 2.f);
});
// Initialize shift.
std::generate(mid, scale_shift_data.end(), []() {
static int i = 0;
return std::tan(i++);
});
// Create src and scale/shift memory descriptors and memory objects.
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 data to memory object's handle.
write_to_dnnl_memory(src_data.data(), src_mem);
write_to_dnnl_memory(scale_shift_data.data(), scale_shift_mem);
// Create operation descriptor.
// Create primitive descriptor.
auto bnorm_pd
// Create memory objects using memory descriptors created by the primitive
// descriptor: mean, variance, workspace.
// NOTE: Here, the ReLU post-ops require a workspace for later usage in
// backward propagation mode.
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);
// Create the primitive.
auto bnorm_prim = batch_normalization_forward(bnorm_pd);
// Primitive arguments. Set up in-place execution by assigning src as DST.
std::unordered_map<int, memory> bnorm_args;
bnorm_args.insert({DNNL_ARG_SRC, src_mem});
bnorm_args.insert({DNNL_ARG_MEAN, mean_mem});
bnorm_args.insert({DNNL_ARG_VARIANCE, variance_mem});
bnorm_args.insert({DNNL_ARG_SCALE_SHIFT, scale_shift_mem});
bnorm_args.insert({DNNL_ARG_WORKSPACE, workspace_mem});
bnorm_args.insert({DNNL_ARG_DST, src_mem});
// Primitive execution: batch normalization with ReLU.
bnorm_prim.execute(engine_stream, bnorm_args);
// Wait for the computation to finalize.
engine_stream.wait();
// Read data from memory object's handle.
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));
}
DNNL_ARG_WORKSPACE
#define DNNL_ARG_WORKSPACE
Workspace tensor argument.
Definition: dnnl_types.h:2078
dnnl.hpp
dnnl::stream
An execution stream.
Definition: dnnl.hpp:975
dnnl::engine
An execution engine.
Definition: dnnl.hpp:859
dnnl::stream::wait
stream & wait()
Waits for all primitives executing in the stream to finish.
Definition: dnnl.hpp:1015
dnnl::prop_kind::forward_training
@ forward_training
Forward data propagation (training mode).
dnnl::engine::kind
kind
Kinds of engines.
Definition: dnnl.hpp:864
DNNL_ARG_DST
#define DNNL_ARG_DST
A special mnemonic for destination argument for primitives that have a single destination.
Definition: dnnl_types.h:2019
DNNL_ARG_SCALE_SHIFT
#define DNNL_ARG_SCALE_SHIFT
A special mnemonic for scale and shift argument of normalization primitives.
Definition: dnnl_types.h:2045
dnnl::normalization_flags::fuse_norm_relu
@ fuse_norm_relu
Fuse normalization with ReLU.
dnnl::batch_normalization_forward
Batch normalization forward propagation primitive.
Definition: dnnl.hpp:6084
dnnl::memory::data_type
data_type
Data type specification.
Definition: dnnl.hpp:1120
dnnl::memory::format_tag
format_tag
Memory format tag specification.
Definition: dnnl.hpp:1195
dnnl::query::src_md
@ src_md
source memory desc
DNNL_ARG_SRC
#define DNNL_ARG_SRC
A special mnemonic for source argument for primitives that have a single source.
Definition: dnnl_types.h:1995
dnnl::batch_normalization_forward::primitive_desc
Primitive descriptor for a batch normalization forward propagation primitive.
Definition: dnnl.hpp:6116
dnnl::normalization_flags::use_scale_shift
@ use_scale_shift
Use scale and shift parameters.
dnnl::memory::dim
dnnl_dim_t dim
Integer type for representing dimension sizes and indices.
Definition: dnnl.hpp:1102
dnnl::memory
Memory object.
Definition: dnnl.hpp:1098
dnnl::batch_normalization_forward::desc
Descriptor for a batch normalization forward propagation primitive.
Definition: dnnl.hpp:6086
dnnl::memory::dims
std::vector< dim > dims
Vector of dimensions.
Definition: dnnl.hpp:1105
dnnl::memory::desc
A memory descriptor.
Definition: dnnl.hpp:1718
dnnl
oneDNN namespace
Definition: dnnl.hpp:74
DNNL_ARG_MEAN
#define DNNL_ARG_MEAN
Mean values tensor argument.
Definition: dnnl_types.h:2072
DNNL_ARG_VARIANCE
#define DNNL_ARG_VARIANCE
Variance values tensor argument.
Definition: dnnl_types.h:2074