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
This C++ API example demonstrates how to build an AlexNet neural network topology for forward-pass inference. Annotated version: CNN f32 inference example
/******************************************************************************* * Copyright 2016-2022 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 <vector> #include <unordered_map> #include "oneapi/dnnl/dnnl.hpp" #include "example_utils.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 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 primitive descriptor] auto conv1_prim_desc = convolution_forward::primitive_desc(eng, 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 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 = 0.0f; //[Create relu primitive] auto relu1_prim_desc = eltwise_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::eltwise_relu, conv1_dst_memory.get_desc(), conv1_dst_memory.get_desc(), negative1_slope); 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_prim_desc = lrn_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::lrn_across_channels, conv1_dst_memory.get_desc(), conv1_dst_memory.get_desc(), local1_size, alpha1, beta1, k1); 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_dilation = {0, 0}; memory::dims pool_padding = {0, 0}; auto pool1_dst_md = memory::desc({pool1_dst_tz}, dt::f32, tag::any); //[Create pooling primitive] auto pool1_pd = pooling_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::pooling_max, lrn1_dst_memory.get_desc(), pool1_dst_md, pool1_strides, pool1_kernel, pool_dilation, pool_padding, pool_padding); 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_prim_desc = convolution_forward::primitive_desc(eng, 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_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_prim_desc = eltwise_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::eltwise_relu, conv2_dst_memory.get_desc(), conv2_dst_memory.get_desc(), negative2_slope); 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_prim_desc = lrn_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::lrn_across_channels, conv2_prim_desc.dst_desc(), conv2_prim_desc.dst_desc(), local2_size, alpha2, beta2, k2); 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_dilation = {0, 0}; memory::dims pool2_padding = {0, 0}; auto pool2_dst_md = memory::desc({pool2_dst_tz}, dt::f32, tag::any); // create a pooling auto pool2_pd = pooling_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::pooling_max, lrn2_dst_memory.get_desc(), pool2_dst_md, pool2_strides, pool2_kernel, pool2_dilation, pool2_padding, pool2_padding); 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_prim_desc = convolution_forward::primitive_desc(eng, 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_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_prim_desc = eltwise_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::eltwise_relu, conv3_dst_memory.get_desc(), conv3_dst_memory.get_desc(), negative3_slope); 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_prim_desc = convolution_forward::primitive_desc(eng, 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_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_prim_desc = eltwise_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::eltwise_relu, conv4_dst_memory.get_desc(), conv4_dst_memory.get_desc(), negative4_slope); 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_prim_desc = convolution_forward::primitive_desc(eng, 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_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_prim_desc = eltwise_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::eltwise_relu, conv5_dst_memory.get_desc(), conv5_dst_memory.get_desc(), negative5_slope); 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_dilation = {0, 0}; 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_pd = pooling_forward::primitive_desc(eng, prop_kind::forward_inference, algorithm::pooling_max, conv5_dst_memory.get_desc(), pool5_dst_md, pool5_strides, pool5_kernel, pool5_dilation, pool5_padding, pool5_padding); 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_prim_desc = inner_product_forward::primitive_desc(eng, prop_kind::forward_inference, fc6_src_md, fc6_weights_md, fc6_bias_md, fc6_dst_md); 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_prim_desc = inner_product_forward::primitive_desc(eng, prop_kind::forward_inference, fc6_dst_memory.get_desc(), fc7_weights_md, fc7_bias_md, fc7_dst_md); 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_prim_desc = inner_product_forward::primitive_desc(eng, prop_kind::forward_inference, fc7_dst_memory.get_desc(), fc8_weights_md, fc8_bias_md, fc8_dst_md); 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(); } 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)); }