Deep Neural Network Library (DNNL)  1.1.3
Performance library for Deep Learning
cnn_inference_f32.cpp

This C++ API example demonstrates how to build an AlexNet neural network topology for forward-pass inference.

Annotated version: CNN f32 inference example

/*******************************************************************************
* Copyright 2016-2019 Intel Corporation
*
* 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
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* 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.
*******************************************************************************/
#include <assert.h>
#include <chrono>
#include <numeric>
#include <vector>
#include <unordered_map>
#include "example_utils.hpp"
using namespace dnnl;
using namespace std;
memory::dim product(const memory::dims &dims) {
return std::accumulate(dims.begin(), dims.end(), (memory::dim)1,
std::multiplies<memory::dim>());
}
void simple_net(engine::kind engine_kind, int times = 100) {
using tag = memory::format_tag;
using dt = memory::data_type;
//[Initialize engine and stream]
engine eng(engine_kind, 0);
stream s(eng);
//[Initialize engine and stream]
//[Create network]
std::vector<primitive> net;
std::vector<std::unordered_map<int, memory>> net_args;
//[Create network]
const memory::dim batch = 1;
// AlexNet: conv1
// {batch, 3, 227, 227} (x) {96, 3, 11, 11} -> {batch, 96, 55, 55}
// strides: {4, 4}
memory::dims conv1_src_tz = {batch, 3, 227, 227};
memory::dims conv1_weights_tz = {96, 3, 11, 11};
memory::dims conv1_bias_tz = {96};
memory::dims conv1_dst_tz = {batch, 96, 55, 55};
memory::dims conv1_strides = {4, 4};
memory::dims conv1_padding = {0, 0};
//[Allocate buffers]
std::vector<float> user_src(batch * 3 * 227 * 227);
std::vector<float> user_dst(batch * 1000);
std::vector<float> conv1_weights(product(conv1_weights_tz));
std::vector<float> conv1_bias(product(conv1_bias_tz));
//[Allocate buffers]
//[Create user memory]
auto user_src_memory = memory({{conv1_src_tz}, dt::f32, tag::nchw}, eng);
write_to_dnnl_memory(user_src.data(), user_src_memory);
auto user_weights_memory
= memory({{conv1_weights_tz}, dt::f32, tag::oihw}, eng);
write_to_dnnl_memory(conv1_weights.data(), user_weights_memory);
auto conv1_user_bias_memory
= memory({{conv1_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(conv1_bias.data(), conv1_user_bias_memory);
//[Create user memory]
//[Create convolution memory descriptors]
auto conv1_src_md = memory::desc({conv1_src_tz}, dt::f32, tag::any);
auto conv1_bias_md = memory::desc({conv1_bias_tz}, dt::f32, tag::any);
auto conv1_weights_md = memory::desc({conv1_weights_tz}, dt::f32, tag::any);
auto conv1_dst_md = memory::desc({conv1_dst_tz}, dt::f32, tag::any);
//[Create convolution memory descriptors]
//[Create convolution descriptor]
algorithm::convolution_direct, conv1_src_md, conv1_weights_md,
conv1_bias_md, conv1_dst_md, conv1_strides, conv1_padding,
conv1_padding);
//[Create convolution descriptor]
//[Create convolution primitive descriptor]
auto conv1_prim_desc = convolution_forward::primitive_desc(conv1_desc, eng);
//[Create convolution primitive descriptor]
//[Reorder data and weights]
auto conv1_src_memory = user_src_memory;
if (conv1_prim_desc.src_desc() != user_src_memory.get_desc()) {
conv1_src_memory = memory(conv1_prim_desc.src_desc(), eng);
net.push_back(reorder(user_src_memory, conv1_src_memory));
net_args.push_back({{DNNL_ARG_FROM, user_src_memory},
{DNNL_ARG_TO, conv1_src_memory}});
}
auto conv1_weights_memory = user_weights_memory;
if (conv1_prim_desc.weights_desc() != user_weights_memory.get_desc()) {
conv1_weights_memory = memory(conv1_prim_desc.weights_desc(), eng);
reorder(user_weights_memory, conv1_weights_memory)
.execute(s, user_weights_memory, conv1_weights_memory);
}
//[Reorder data and weights]
//[Create memory for output]
auto conv1_dst_memory = memory(conv1_prim_desc.dst_desc(), eng);
//[Create memory for output]
//[Create convolution primitive]
net.push_back(convolution_forward(conv1_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv1_src_memory},
{DNNL_ARG_WEIGHTS, conv1_weights_memory},
{DNNL_ARG_BIAS, conv1_user_bias_memory},
{DNNL_ARG_DST, conv1_dst_memory}});
//[Create convolution primitive]
// AlexNet: relu1
// {batch, 96, 55, 55} -> {batch, 96, 55, 55}
const float negative1_slope = 1.0f;
//[Create relu primitive]
algorithm::eltwise_relu, conv1_dst_memory.get_desc(),
negative1_slope);
auto relu1_prim_desc = eltwise_forward::primitive_desc(relu1_desc, eng);
net.push_back(eltwise_forward(relu1_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv1_dst_memory},
{DNNL_ARG_DST, conv1_dst_memory}});
//[Create relu primitive]
// AlexNet: lrn1
// {batch, 96, 55, 55} -> {batch, 96, 55, 55}
// local size: 5
// alpha1: 0.0001
// beta1: 0.75
const memory::dim local1_size = 5;
const float alpha1 = 0.0001f;
const float beta1 = 0.75f;
const float k1 = 1.0f;
// create lrn primitive and add it to net
algorithm::lrn_across_channels, conv1_dst_memory.get_desc(),
local1_size, alpha1, beta1, k1);
auto lrn1_prim_desc = lrn_forward::primitive_desc(lrn1_desc, eng);
auto lrn1_dst_memory = memory(lrn1_prim_desc.dst_desc(), eng);
net.push_back(lrn_forward(lrn1_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv1_dst_memory},
{DNNL_ARG_DST, lrn1_dst_memory}});
// AlexNet: pool1
// {batch, 96, 55, 55} -> {batch, 96, 27, 27}
// kernel: {3, 3}
// strides: {2, 2}
memory::dims pool1_dst_tz = {batch, 96, 27, 27};
memory::dims pool1_kernel = {3, 3};
memory::dims pool1_strides = {2, 2};
memory::dims pool_padding = {0, 0};
auto pool1_dst_md = memory::desc({pool1_dst_tz}, dt::f32, tag::any);
//[Create pooling primitive]
algorithm::pooling_max, lrn1_dst_memory.get_desc(), pool1_dst_md,
pool1_strides, pool1_kernel, pool_padding, pool_padding);
auto pool1_pd = pooling_forward::primitive_desc(pool1_desc, eng);
auto pool1_dst_memory = memory(pool1_pd.dst_desc(), eng);
net.push_back(pooling_forward(pool1_pd));
net_args.push_back({{DNNL_ARG_SRC, lrn1_dst_memory},
{DNNL_ARG_DST, pool1_dst_memory}});
//[Create pooling primitive]
// AlexNet: conv2
// {batch, 96, 27, 27} (x) {2, 128, 48, 5, 5} -> {batch, 256, 27, 27}
// strides: {1, 1}
memory::dims conv2_src_tz = {batch, 96, 27, 27};
memory::dims conv2_weights_tz = {2, 128, 48, 5, 5};
memory::dims conv2_bias_tz = {256};
memory::dims conv2_dst_tz = {batch, 256, 27, 27};
memory::dims conv2_strides = {1, 1};
memory::dims conv2_padding = {2, 2};
std::vector<float> conv2_weights(product(conv2_weights_tz));
std::vector<float> conv2_bias(product(conv2_bias_tz));
// create memory for user data
auto conv2_user_weights_memory
= memory({{conv2_weights_tz}, dt::f32, tag::goihw}, eng);
write_to_dnnl_memory(conv2_weights.data(), conv2_user_weights_memory);
auto conv2_user_bias_memory
= memory({{conv2_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(conv2_bias.data(), conv2_user_bias_memory);
// create memory descriptors for convolution data w/ no specified format
auto conv2_src_md = memory::desc({conv2_src_tz}, dt::f32, tag::any);
auto conv2_bias_md = memory::desc({conv2_bias_tz}, dt::f32, tag::any);
auto conv2_weights_md = memory::desc({conv2_weights_tz}, dt::f32, tag::any);
auto conv2_dst_md = memory::desc({conv2_dst_tz}, dt::f32, tag::any);
// create a convolution
algorithm::convolution_direct, conv2_src_md, conv2_weights_md,
conv2_bias_md, conv2_dst_md, conv2_strides, conv2_padding,
conv2_padding);
auto conv2_prim_desc = convolution_forward::primitive_desc(conv2_desc, eng);
auto conv2_src_memory = pool1_dst_memory;
if (conv2_prim_desc.src_desc() != conv2_src_memory.get_desc()) {
conv2_src_memory = memory(conv2_prim_desc.src_desc(), eng);
net.push_back(reorder(pool1_dst_memory, conv2_src_memory));
net_args.push_back({{DNNL_ARG_FROM, pool1_dst_memory},
{DNNL_ARG_TO, conv2_src_memory}});
}
auto conv2_weights_memory = conv2_user_weights_memory;
if (conv2_prim_desc.weights_desc()
!= conv2_user_weights_memory.get_desc()) {
conv2_weights_memory = memory(conv2_prim_desc.weights_desc(), eng);
reorder(conv2_user_weights_memory, conv2_weights_memory)
.execute(s, conv2_user_weights_memory, conv2_weights_memory);
}
auto conv2_dst_memory = memory(conv2_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(convolution_forward(conv2_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv2_src_memory},
{DNNL_ARG_WEIGHTS, conv2_weights_memory},
{DNNL_ARG_BIAS, conv2_user_bias_memory},
{DNNL_ARG_DST, conv2_dst_memory}});
// AlexNet: relu2
// {batch, 256, 27, 27} -> {batch, 256, 27, 27}
const float negative2_slope = 1.0f;
// create relu primitive and add it to net
algorithm::eltwise_relu, conv2_dst_memory.get_desc(),
negative2_slope);
auto relu2_prim_desc = eltwise_forward::primitive_desc(relu2_desc, eng);
net.push_back(eltwise_forward(relu2_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv2_dst_memory},
{DNNL_ARG_DST, conv2_dst_memory}});
// AlexNet: lrn2
// {batch, 256, 27, 27} -> {batch, 256, 27, 27}
// local size: 5
// alpha2: 0.0001
// beta2: 0.75
const memory::dim local2_size = 5;
const float alpha2 = 0.0001f;
const float beta2 = 0.75f;
const float k2 = 1.0f;
// create lrn primitive and add it to net
algorithm::lrn_across_channels, conv2_prim_desc.dst_desc(),
local2_size, alpha2, beta2, k2);
auto lrn2_prim_desc = lrn_forward::primitive_desc(lrn2_desc, eng);
auto lrn2_dst_memory = memory(lrn2_prim_desc.dst_desc(), eng);
net.push_back(lrn_forward(lrn2_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv2_dst_memory},
{DNNL_ARG_DST, lrn2_dst_memory}});
// AlexNet: pool2
// {batch, 256, 27, 27} -> {batch, 256, 13, 13}
// kernel: {3, 3}
// strides: {2, 2}
memory::dims pool2_dst_tz = {batch, 256, 13, 13};
memory::dims pool2_kernel = {3, 3};
memory::dims pool2_strides = {2, 2};
memory::dims pool2_padding = {0, 0};
auto pool2_dst_md = memory::desc({pool2_dst_tz}, dt::f32, tag::any);
// create a pooling
algorithm::pooling_max, lrn2_dst_memory.get_desc(), pool2_dst_md,
pool2_strides, pool2_kernel, pool2_padding, pool2_padding);
auto pool2_pd = pooling_forward::primitive_desc(pool2_desc, eng);
auto pool2_dst_memory = memory(pool2_pd.dst_desc(), eng);
// create pooling primitive an add it to net
net.push_back(pooling_forward(pool2_pd));
net_args.push_back({{DNNL_ARG_SRC, lrn2_dst_memory},
{DNNL_ARG_DST, pool2_dst_memory}});
// AlexNet: conv3
// {batch, 256, 13, 13} (x) {384, 256, 3, 3}; -> {batch, 384, 13, 13};
// strides: {1, 1}
memory::dims conv3_src_tz = {batch, 256, 13, 13};
memory::dims conv3_weights_tz = {384, 256, 3, 3};
memory::dims conv3_bias_tz = {384};
memory::dims conv3_dst_tz = {batch, 384, 13, 13};
memory::dims conv3_strides = {1, 1};
memory::dims conv3_padding = {1, 1};
std::vector<float> conv3_weights(product(conv3_weights_tz));
std::vector<float> conv3_bias(product(conv3_bias_tz));
// create memory for user data
auto conv3_user_weights_memory
= memory({{conv3_weights_tz}, dt::f32, tag::oihw}, eng);
write_to_dnnl_memory(conv3_weights.data(), conv3_user_weights_memory);
auto conv3_user_bias_memory
= memory({{conv3_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(conv3_bias.data(), conv3_user_bias_memory);
// create memory descriptors for convolution data w/ no specified format
auto conv3_src_md = memory::desc({conv3_src_tz}, dt::f32, tag::any);
auto conv3_bias_md = memory::desc({conv3_bias_tz}, dt::f32, tag::any);
auto conv3_weights_md = memory::desc({conv3_weights_tz}, dt::f32, tag::any);
auto conv3_dst_md = memory::desc({conv3_dst_tz}, dt::f32, tag::any);
// create a convolution
algorithm::convolution_direct, conv3_src_md, conv3_weights_md,
conv3_bias_md, conv3_dst_md, conv3_strides, conv3_padding,
conv3_padding);
auto conv3_prim_desc = convolution_forward::primitive_desc(conv3_desc, eng);
auto conv3_src_memory = pool2_dst_memory;
if (conv3_prim_desc.src_desc() != conv3_src_memory.get_desc()) {
conv3_src_memory = memory(conv3_prim_desc.src_desc(), eng);
net.push_back(reorder(pool2_dst_memory, conv3_src_memory));
net_args.push_back({{DNNL_ARG_FROM, pool2_dst_memory},
{DNNL_ARG_TO, conv3_src_memory}});
}
auto conv3_weights_memory = conv3_user_weights_memory;
if (conv3_prim_desc.weights_desc()
!= conv3_user_weights_memory.get_desc()) {
conv3_weights_memory = memory(conv3_prim_desc.weights_desc(), eng);
reorder(conv3_user_weights_memory, conv3_weights_memory)
.execute(s, conv3_user_weights_memory, conv3_weights_memory);
}
auto conv3_dst_memory = memory(conv3_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(convolution_forward(conv3_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv3_src_memory},
{DNNL_ARG_WEIGHTS, conv3_weights_memory},
{DNNL_ARG_BIAS, conv3_user_bias_memory},
{DNNL_ARG_DST, conv3_dst_memory}});
// AlexNet: relu3
// {batch, 384, 13, 13} -> {batch, 384, 13, 13}
const float negative3_slope = 1.0f;
// create relu primitive and add it to net
algorithm::eltwise_relu, conv3_dst_memory.get_desc(),
negative3_slope);
auto relu3_prim_desc = eltwise_forward::primitive_desc(relu3_desc, eng);
net.push_back(eltwise_forward(relu3_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv3_dst_memory},
{DNNL_ARG_DST, conv3_dst_memory}});
// AlexNet: conv4
// {batch, 384, 13, 13} (x) {2, 192, 192, 3, 3}; ->
// {batch, 384, 13, 13};
// strides: {1, 1}
memory::dims conv4_src_tz = {batch, 384, 13, 13};
memory::dims conv4_weights_tz = {2, 192, 192, 3, 3};
memory::dims conv4_bias_tz = {384};
memory::dims conv4_dst_tz = {batch, 384, 13, 13};
memory::dims conv4_strides = {1, 1};
memory::dims conv4_padding = {1, 1};
std::vector<float> conv4_weights(product(conv4_weights_tz));
std::vector<float> conv4_bias(product(conv4_bias_tz));
// create memory for user data
auto conv4_user_weights_memory
= memory({{conv4_weights_tz}, dt::f32, tag::goihw}, eng);
write_to_dnnl_memory(conv4_weights.data(), conv4_user_weights_memory);
auto conv4_user_bias_memory
= memory({{conv4_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(conv4_bias.data(), conv4_user_bias_memory);
// create memory descriptors for convolution data w/ no specified format
auto conv4_src_md = memory::desc({conv4_src_tz}, dt::f32, tag::any);
auto conv4_bias_md = memory::desc({conv4_bias_tz}, dt::f32, tag::any);
auto conv4_weights_md = memory::desc({conv4_weights_tz}, dt::f32, tag::any);
auto conv4_dst_md = memory::desc({conv4_dst_tz}, dt::f32, tag::any);
// create a convolution
algorithm::convolution_direct, conv4_src_md, conv4_weights_md,
conv4_bias_md, conv4_dst_md, conv4_strides, conv4_padding,
conv4_padding);
auto conv4_prim_desc = convolution_forward::primitive_desc(conv4_desc, eng);
auto conv4_src_memory = conv3_dst_memory;
if (conv4_prim_desc.src_desc() != conv4_src_memory.get_desc()) {
conv4_src_memory = memory(conv4_prim_desc.src_desc(), eng);
net.push_back(reorder(conv3_dst_memory, conv4_src_memory));
net_args.push_back({{DNNL_ARG_FROM, conv3_dst_memory},
{DNNL_ARG_TO, conv4_src_memory}});
}
auto conv4_weights_memory = conv4_user_weights_memory;
if (conv4_prim_desc.weights_desc()
!= conv4_user_weights_memory.get_desc()) {
conv4_weights_memory = memory(conv4_prim_desc.weights_desc(), eng);
reorder(conv4_user_weights_memory, conv4_weights_memory)
.execute(s, conv4_user_weights_memory, conv4_weights_memory);
}
auto conv4_dst_memory = memory(conv4_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(convolution_forward(conv4_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv4_src_memory},
{DNNL_ARG_WEIGHTS, conv4_weights_memory},
{DNNL_ARG_BIAS, conv4_user_bias_memory},
{DNNL_ARG_DST, conv4_dst_memory}});
// AlexNet: relu4
// {batch, 384, 13, 13} -> {batch, 384, 13, 13}
const float negative4_slope = 1.0f;
// create relu primitive and add it to net
algorithm::eltwise_relu, conv4_dst_memory.get_desc(),
negative4_slope);
auto relu4_prim_desc = eltwise_forward::primitive_desc(relu4_desc, eng);
net.push_back(eltwise_forward(relu4_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv4_dst_memory},
{DNNL_ARG_DST, conv4_dst_memory}});
// AlexNet: conv5
// {batch, 384, 13, 13} (x) {2, 128, 192, 3, 3}; -> {batch, 256, 13, 13};
// strides: {1, 1}
memory::dims conv5_src_tz = {batch, 384, 13, 13};
memory::dims conv5_weights_tz = {2, 128, 192, 3, 3};
memory::dims conv5_bias_tz = {256};
memory::dims conv5_dst_tz = {batch, 256, 13, 13};
memory::dims conv5_strides = {1, 1};
memory::dims conv5_padding = {1, 1};
std::vector<float> conv5_weights(product(conv5_weights_tz));
std::vector<float> conv5_bias(product(conv5_bias_tz));
// create memory for user data
auto conv5_user_weights_memory
= memory({{conv5_weights_tz}, dt::f32, tag::goihw}, eng);
write_to_dnnl_memory(conv5_weights.data(), conv5_user_weights_memory);
auto conv5_user_bias_memory
= memory({{conv5_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(conv5_bias.data(), conv5_user_bias_memory);
// create memory descriptors for convolution data w/ no specified format
auto conv5_src_md = memory::desc({conv5_src_tz}, dt::f32, tag::any);
auto conv5_weights_md = memory::desc({conv5_weights_tz}, dt::f32, tag::any);
auto conv5_bias_md = memory::desc({conv5_bias_tz}, dt::f32, tag::any);
auto conv5_dst_md = memory::desc({conv5_dst_tz}, dt::f32, tag::any);
// create a convolution
algorithm::convolution_direct, conv5_src_md, conv5_weights_md,
conv5_bias_md, conv5_dst_md, conv5_strides, conv5_padding,
conv5_padding);
auto conv5_prim_desc = convolution_forward::primitive_desc(conv5_desc, eng);
auto conv5_src_memory = conv4_dst_memory;
if (conv5_prim_desc.src_desc() != conv5_src_memory.get_desc()) {
conv5_src_memory = memory(conv5_prim_desc.src_desc(), eng);
net.push_back(reorder(conv4_dst_memory, conv5_src_memory));
net_args.push_back({{DNNL_ARG_FROM, conv4_dst_memory},
{DNNL_ARG_TO, conv5_src_memory}});
}
auto conv5_weights_memory = conv5_user_weights_memory;
if (conv5_prim_desc.weights_desc()
!= conv5_user_weights_memory.get_desc()) {
conv5_weights_memory = memory(conv5_prim_desc.weights_desc(), eng);
reorder(conv5_user_weights_memory, conv5_weights_memory)
.execute(s, conv5_user_weights_memory, conv5_weights_memory);
}
auto conv5_dst_memory = memory(conv5_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(convolution_forward(conv5_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv5_src_memory},
{DNNL_ARG_WEIGHTS, conv5_weights_memory},
{DNNL_ARG_BIAS, conv5_user_bias_memory},
{DNNL_ARG_DST, conv5_dst_memory}});
// AlexNet: relu5
// {batch, 256, 13, 13} -> {batch, 256, 13, 13}
const float negative5_slope = 1.0f;
// create relu primitive and add it to net
algorithm::eltwise_relu, conv5_dst_memory.get_desc(),
negative5_slope);
auto relu5_prim_desc = eltwise_forward::primitive_desc(relu5_desc, eng);
net.push_back(eltwise_forward(relu5_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, conv5_dst_memory},
{DNNL_ARG_DST, conv5_dst_memory}});
// AlexNet: pool5
// {batch, 256, 13, 13} -> {batch, 256, 6, 6}
// kernel: {3, 3}
// strides: {2, 2}
memory::dims pool5_dst_tz = {batch, 256, 6, 6};
memory::dims pool5_kernel = {3, 3};
memory::dims pool5_strides = {2, 2};
memory::dims pool5_padding = {0, 0};
std::vector<float> pool5_dst(product(pool5_dst_tz));
auto pool5_dst_md = memory::desc({pool5_dst_tz}, dt::f32, tag::any);
// create a pooling
algorithm::pooling_max, conv5_dst_memory.get_desc(), pool5_dst_md,
pool5_strides, pool5_kernel, pool5_padding, pool5_padding);
auto pool5_pd = pooling_forward::primitive_desc(pool5_desc, eng);
auto pool5_dst_memory = memory(pool5_pd.dst_desc(), eng);
// create pooling primitive an add it to net
net.push_back(pooling_forward(pool5_pd));
net_args.push_back({{DNNL_ARG_SRC, conv5_dst_memory},
{DNNL_ARG_DST, pool5_dst_memory}});
// fc6 inner product {batch, 256, 6, 6} (x) {4096, 256, 6, 6}-> {batch,
// 4096}
memory::dims fc6_src_tz = {batch, 256, 6, 6};
memory::dims fc6_weights_tz = {4096, 256, 6, 6};
memory::dims fc6_bias_tz = {4096};
memory::dims fc6_dst_tz = {batch, 4096};
std::vector<float> fc6_weights(product(fc6_weights_tz));
std::vector<float> fc6_bias(product(fc6_bias_tz));
// create memory for user data
auto fc6_user_weights_memory
= memory({{fc6_weights_tz}, dt::f32, tag::oihw}, eng);
write_to_dnnl_memory(fc6_weights.data(), fc6_user_weights_memory);
auto fc6_user_bias_memory = memory({{fc6_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(fc6_bias.data(), fc6_user_bias_memory);
// create memory descriptors for convolution data w/ no specified format
auto fc6_src_md = memory::desc({fc6_src_tz}, dt::f32, tag::any);
auto fc6_bias_md = memory::desc({fc6_bias_tz}, dt::f32, tag::any);
auto fc6_weights_md = memory::desc({fc6_weights_tz}, dt::f32, tag::any);
auto fc6_dst_md = memory::desc({fc6_dst_tz}, dt::f32, tag::any);
// create a inner_product
fc6_src_md, fc6_weights_md, fc6_bias_md, fc6_dst_md);
auto fc6_prim_desc = inner_product_forward::primitive_desc(fc6_desc, eng);
auto fc6_src_memory = pool5_dst_memory;
if (fc6_prim_desc.src_desc() != fc6_src_memory.get_desc()) {
fc6_src_memory = memory(fc6_prim_desc.src_desc(), eng);
net.push_back(reorder(pool5_dst_memory, fc6_src_memory));
net_args.push_back({{DNNL_ARG_FROM, pool5_dst_memory},
{DNNL_ARG_TO, fc6_src_memory}});
}
auto fc6_weights_memory = fc6_user_weights_memory;
if (fc6_prim_desc.weights_desc() != fc6_user_weights_memory.get_desc()) {
fc6_weights_memory = memory(fc6_prim_desc.weights_desc(), eng);
reorder(fc6_user_weights_memory, fc6_weights_memory)
.execute(s, fc6_user_weights_memory, fc6_weights_memory);
}
auto fc6_dst_memory = memory(fc6_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(inner_product_forward(fc6_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, fc6_src_memory},
{DNNL_ARG_WEIGHTS, fc6_weights_memory},
{DNNL_ARG_BIAS, fc6_user_bias_memory},
{DNNL_ARG_DST, fc6_dst_memory}});
// fc7 inner product {batch, 4096} (x) {4096, 4096}-> {batch, 4096}
memory::dims fc7_weights_tz = {4096, 4096};
memory::dims fc7_bias_tz = {4096};
memory::dims fc7_dst_tz = {batch, 4096};
std::vector<float> fc7_weights(product(fc7_weights_tz));
std::vector<float> fc7_bias(product(fc7_bias_tz));
// create memory for user data
auto fc7_user_weights_memory
= memory({{fc7_weights_tz}, dt::f32, tag::nc}, eng);
write_to_dnnl_memory(fc7_weights.data(), fc7_user_weights_memory);
auto fc7_user_bias_memory = memory({{fc7_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(fc7_bias.data(), fc7_user_bias_memory);
// create memory descriptors for convolution data w/ no specified format
auto fc7_bias_md = memory::desc({fc7_bias_tz}, dt::f32, tag::any);
auto fc7_weights_md = memory::desc({fc7_weights_tz}, dt::f32, tag::any);
auto fc7_dst_md = memory::desc({fc7_dst_tz}, dt::f32, tag::any);
// create a inner_product
fc6_dst_memory.get_desc(), fc7_weights_md, fc7_bias_md, fc7_dst_md);
auto fc7_prim_desc = inner_product_forward::primitive_desc(fc7_desc, eng);
auto fc7_weights_memory = fc7_user_weights_memory;
if (fc7_prim_desc.weights_desc() != fc7_user_weights_memory.get_desc()) {
fc7_weights_memory = memory(fc7_prim_desc.weights_desc(), eng);
reorder(fc7_user_weights_memory, fc7_weights_memory)
.execute(s, fc7_user_weights_memory, fc7_weights_memory);
}
auto fc7_dst_memory = memory(fc7_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(inner_product_forward(fc7_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, fc6_dst_memory},
{DNNL_ARG_WEIGHTS, fc7_weights_memory},
{DNNL_ARG_BIAS, fc7_user_bias_memory},
{DNNL_ARG_DST, fc7_dst_memory}});
// fc8 inner product {batch, 4096} (x) {1000, 4096}-> {batch, 1000}
memory::dims fc8_weights_tz = {1000, 4096};
memory::dims fc8_bias_tz = {1000};
memory::dims fc8_dst_tz = {batch, 1000};
std::vector<float> fc8_weights(product(fc8_weights_tz));
std::vector<float> fc8_bias(product(fc8_bias_tz));
// create memory for user data
auto fc8_user_weights_memory
= memory({{fc8_weights_tz}, dt::f32, tag::nc}, eng);
write_to_dnnl_memory(fc8_weights.data(), fc8_user_weights_memory);
auto fc8_user_bias_memory = memory({{fc8_bias_tz}, dt::f32, tag::x}, eng);
write_to_dnnl_memory(fc8_bias.data(), fc8_user_bias_memory);
auto user_dst_memory = memory({{fc8_dst_tz}, dt::f32, tag::nc}, eng);
write_to_dnnl_memory(user_dst.data(), user_dst_memory);
// create memory descriptors for convolution data w/ no specified format
auto fc8_bias_md = memory::desc({fc8_bias_tz}, dt::f32, tag::any);
auto fc8_weights_md = memory::desc({fc8_weights_tz}, dt::f32, tag::any);
auto fc8_dst_md = memory::desc({fc8_dst_tz}, dt::f32, tag::any);
// create a inner_product
fc7_dst_memory.get_desc(), fc8_weights_md, fc8_bias_md, fc8_dst_md);
auto fc8_prim_desc = inner_product_forward::primitive_desc(fc8_desc, eng);
auto fc8_weights_memory = fc8_user_weights_memory;
if (fc8_prim_desc.weights_desc() != fc8_user_weights_memory.get_desc()) {
fc8_weights_memory = memory(fc8_prim_desc.weights_desc(), eng);
reorder(fc8_user_weights_memory, fc8_weights_memory)
.execute(s, fc8_user_weights_memory, fc8_weights_memory);
}
auto fc8_dst_memory = memory(fc8_prim_desc.dst_desc(), eng);
// create convolution primitive and add it to net
net.push_back(inner_product_forward(fc8_prim_desc));
net_args.push_back({{DNNL_ARG_SRC, fc7_dst_memory},
{DNNL_ARG_WEIGHTS, fc8_weights_memory},
{DNNL_ARG_BIAS, fc8_user_bias_memory},
{DNNL_ARG_DST, fc8_dst_memory}});
// create reorder between internal and user data if it is needed and
// add it to net after pooling
if (fc8_dst_memory != user_dst_memory) {
net.push_back(reorder(fc8_dst_memory, user_dst_memory));
net_args.push_back({{DNNL_ARG_FROM, fc8_dst_memory},
{DNNL_ARG_TO, user_dst_memory}});
}
//[Execute model]
for (int j = 0; j < times; ++j) {
assert(net.size() == net_args.size() && "something is missing");
for (size_t i = 0; i < net.size(); ++i)
net.at(i).execute(s, net_args.at(i));
}
//[Execute model]
s.wait();
}
int main(int argc, char **argv) {
try {
auto begin = chrono::duration_cast<chrono::milliseconds>(
chrono::steady_clock::now().time_since_epoch())
.count();
int times = 100;
simple_net(parse_engine_kind(argc, argv), times);
auto end = chrono::duration_cast<chrono::milliseconds>(
chrono::steady_clock::now().time_since_epoch())
.count();
cout << "Use time " << (end - begin) / (times + 0.0) << "\n";
} catch (error &e) {
std::cerr << "status: " << e.status << std::endl;
std::cerr << "message: " << e.message << std::endl;
}
return 0;
}