inference.cpp

#include "inference.hpp"
#include <assert.h>
#include <chrono>
#include <vector>
#include <unordered_map>
#include "dnnl.hpp"

using namespace dnnl;
void simple_net(engine::kind engine_kind, int times = 100) {
using tag = memory::format_tag;
using dt = memory::data_type;
/// Initialize an engine and stream. The last parameter in the call represents
/// the index of the engine.
/// @snippet cnn_inference_f32.cpp Initialize engine and stream
//[Initialize engine and stream]
engine eng(engine_kind, 0);
stream s(eng);
//[Initialize engine and stream]
/// Create a vector for the primitives and a vector to hold memory
/// that will be used as arguments.
/// @snippet cnn_inference_f32.cpp Create network
//[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 for input and output data, weights, and bias.
/// @snippet cnn_inference_f32.cpp Allocate buffers
//[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 memory that describes data layout in the buffers. This example uses
/// tag::nchw (batch-channels-height-width) for input data and tag::oihw
/// for weights.
/// @snippet cnn_inference_f32.cpp Create user memory
//[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 memory descriptors with layout tag::any. The `any` format enables
/// the convolution primitive to choose the data format that will result in
/// best performance based on its input parameters (convolution kernel
/// sizes, strides, padding, and so on). If the resulting format is different
/// from `nchw`, the user data must be transformed to the format required for
/// the convolution (as explained below).
/// @snippet cnn_inference_f32.cpp Create convolution memory descriptors
//[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 a convolution descriptor by specifying propagation kind,
/// [convolution algorithm](@ref dev_guide_convolution), shapes of input,
/// weights, bias, output, convolution strides, padding, and kind of padding.
/// Propagation kind is set to prop_kind::forward_inference to optimize for
/// inference execution and omit computations that are necessary only for
/// backward propagation.
/// @snippet cnn_inference_f32.cpp Create convolution descriptor
//[Create convolution descriptor]
auto conv1_desc = convolution_forward::desc(prop_kind::forward_inference,
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 a convolution primitive descriptor. Once created, this
/// descriptor has specific formats instead of the `any` format specified
/// in the convolution descriptor.
/// @snippet cnn_inference_f32.cpp Create convolution primitive descriptor
//[Create convolution primitive descriptor]
auto conv1_prim_desc = convolution_forward::primitive_desc(conv1_desc, eng);
//[Create convolution primitive descriptor]
/// Check whether data and weights formats required by convolution is different
/// from the user format. In case it is different change the layout using
/// reorder primitive.
/// @snippet cnn_inference_f32.cpp Reorder data and weights
//[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 a memory primitive for output.
/// @snippet cnn_inference_f32.cpp Create memory for output
//[Create memory for output]
auto conv1_dst_memory = memory(conv1_prim_desc.dst_desc(), eng);
//[Create memory for output]
/// Create a convolution primitive and add it to the net.
/// @snippet cnn_inference_f32.cpp 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 = 0.0f;
/// Create the relu primitive. For better performance, keep the input data
/// format for ReLU (as well as for other operation primitives until another
/// convolution or inner product is encountered) the same as the one chosen
/// for convolution. Also note that ReLU is done in-place by using conv1 memory.
/// @snippet cnn_inference_f32.cpp Create relu primitive
//[Create relu primitive]
auto relu1_desc = eltwise_forward::desc(prop_kind::forward_inference,
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
auto lrn1_desc = lrn_forward::desc(prop_kind::forward_inference,
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);
/// For training execution, pooling requires a private workspace memory
/// to perform the backward pass. However, pooling should not use 'workspace'
/// for inference, because this is detrimental to performance.
/// @snippet cnn_inference_f32.cpp Create pooling primitive
///
/// The example continues to create more layers according
/// to the AlexNet topology.
//[Create pooling primitive]
auto pool1_desc = pooling_forward::desc(prop_kind::forward_inference,
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
auto conv2_desc = convolution_forward::desc(prop_kind::forward_inference,
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 = 0.0f;
// create relu primitive and add it to net
auto relu2_desc = eltwise_forward::desc(prop_kind::forward_inference,
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
auto lrn2_desc = lrn_forward::desc(prop_kind::forward_inference,
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
auto pool2_desc = pooling_forward::desc(prop_kind::forward_inference,
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
auto conv3_desc = convolution_forward::desc(prop_kind::forward_inference,
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 = 0.0f;
// create relu primitive and add it to net
auto relu3_desc = eltwise_forward::desc(prop_kind::forward_inference,
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
auto conv4_desc = convolution_forward::desc(prop_kind::forward_inference,
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 = 0.0f;
// create relu primitive and add it to net
auto relu4_desc = eltwise_forward::desc(prop_kind::forward_inference,
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
auto conv5_desc = convolution_forward::desc(prop_kind::forward_inference,
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 = 0.0f;
// create relu primitive and add it to net
auto relu5_desc = eltwise_forward::desc(prop_kind::forward_inference,
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
auto pool5_desc = pooling_forward::desc(prop_kind::forward_inference,
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
auto fc6_desc = inner_product_forward::desc(prop_kind::forward_inference,
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
auto fc7_desc = inner_product_forward::desc(prop_kind::forward_inference,
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
auto fc8_desc = inner_product_forward::desc(prop_kind::forward_inference,
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}});
}
/// @page cnn_inference_f32_cpp
/// Finally, execute the primitives. For this example, the net is executed
/// multiple times and each execution is timed individually.
/// @snippet cnn_inference_f32.cpp Execute model
//[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();
}
void cnn_inference_f32(engine::kind engine_kind) {
auto begin = std::chrono::duration_cast<std::chrono::milliseconds>(
std::chrono::steady_clock::now().time_since_epoch())
.count();
int times = 100;
simple_net(engine_kind, times);
auto end = std::chrono::duration_cast<std::chrono::milliseconds>(
std::chrono::steady_clock::now().time_since_epoch())
.count();
std::cout << "Use time: " << (end - begin) / (times + 0.0)
<< " ms per iteration." << std::endl;
}
int main(int argc, char **argv) {
return handle_example_errors(
cnn_inference_f32, parse_engine_kind(argc, argv));
}