Francesco Gatti
351 lines
16 KiB
C++
351 lines
16 KiB
C++
#include <iostream>
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#include "tkdnn.h"
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const char *input_bin = "dla34/debug/input.bin";
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const char *conv1_bin = "dla34/layers/features-init_block-conv1-conv.bin";
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const char *conv2_bin = "dla34/layers/features-init_block-conv2-conv.bin";
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const char *conv3_bin = "dla34/layers/features-init_block-conv3-conv.bin";
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// s - stage, t - tree
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const char *s1_t1_conv1_bin = "dla34/layers/features-stage1-tree1-body-conv1-conv.bin";
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const char *s1_t1_conv2_bin = "dla34/layers/features-stage1-tree1-body-conv2-conv.bin";
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const char *s1_t1_project = "dla34/layers/features-stage1-tree1-project_conv-conv.bin";
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const char *s1_t2_conv1_bin = "dla34/layers/features-stage1-tree2-body-conv1-conv.bin";
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const char *s1_t2_conv2_bin = "dla34/layers/features-stage1-tree2-body-conv2-conv.bin";
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const char *s1_root_conv1_bin = "dla34/layers/features-stage1-root-conv-conv.bin";
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const char *s2_t1_t1_conv1_bin = "dla34/layers/features-stage2-tree1-tree1-body-conv1-conv.bin";
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const char *s2_t1_t1_conv2_bin = "dla34/layers/features-stage2-tree1-tree1-body-conv2-conv.bin";
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const char *s2_t1_t1_project = "dla34/layers/features-stage2-tree1-tree1-project_conv-conv.bin";
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const char *s2_t1_t2_conv1_bin = "dla34/layers/features-stage2-tree1-tree2-body-conv1-conv.bin";
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const char *s2_t1_t2_conv2_bin = "dla34/layers/features-stage2-tree1-tree2-body-conv2-conv.bin";
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const char *s2_t1_root_conv1_bin = "dla34/layers/features-stage2-tree1-root-conv-conv.bin";
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const char *s2_t2_t1_conv1_bin = "dla34/layers/features-stage2-tree2-tree1-body-conv1-conv.bin";
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const char *s2_t2_t1_conv2_bin = "dla34/layers/features-stage2-tree2-tree1-body-conv2-conv.bin";
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const char *s2_t2_t2_conv1_bin = "dla34/layers/features-stage2-tree2-tree2-body-conv1-conv.bin";
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const char *s2_t2_t2_conv2_bin = "dla34/layers/features-stage2-tree2-tree2-body-conv2-conv.bin";
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const char *s2_t2_root_conv1_bin = "dla34/layers/features-stage2-tree2-root-conv-conv.bin";
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const char *s3_t1_t1_conv1_bin = "dla34/layers/features-stage3-tree1-tree1-body-conv1-conv.bin";
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const char *s3_t1_t1_conv2_bin = "dla34/layers/features-stage3-tree1-tree1-body-conv2-conv.bin";
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const char *s3_t1_t1_project = "dla34/layers/features-stage3-tree1-tree1-project_conv-conv.bin";
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const char *s3_t1_t2_conv1_bin = "dla34/layers/features-stage3-tree1-tree2-body-conv1-conv.bin";
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const char *s3_t1_t2_conv2_bin = "dla34/layers/features-stage3-tree1-tree2-body-conv2-conv.bin";
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const char *s3_t1_root_conv1_bin = "dla34/layers/features-stage3-tree1-root-conv-conv.bin";
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const char *s3_t2_t1_conv1_bin = "dla34/layers/features-stage3-tree2-tree1-body-conv1-conv.bin";
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const char *s3_t2_t1_conv2_bin = "dla34/layers/features-stage3-tree2-tree1-body-conv2-conv.bin";
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const char *s3_t2_t2_conv1_bin = "dla34/layers/features-stage3-tree2-tree2-body-conv1-conv.bin";
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const char *s3_t2_t2_conv2_bin = "dla34/layers/features-stage3-tree2-tree2-body-conv2-conv.bin";
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const char *s3_t2_root_conv1_bin = "dla34/layers/features-stage3-tree2-root-conv-conv.bin";
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const char *s4_t1_conv1_bin = "dla34/layers/features-stage4-tree1-body-conv1-conv.bin";
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const char *s4_t1_conv2_bin = "dla34/layers/features-stage4-tree1-body-conv2-conv.bin";
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const char *s4_t1_project = "dla34/layers/features-stage4-tree1-project_conv-conv.bin";
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const char *s4_t2_conv1_bin = "dla34/layers/features-stage4-tree2-body-conv1-conv.bin";
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const char *s4_t2_conv2_bin = "dla34/layers/features-stage4-tree2-body-conv2-conv.bin";
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const char *s4_root_conv1_bin = "dla34/layers/features-stage4-root-conv-conv.bin";
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//final
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const char *fc_bin = "dla34/layers/output.bin";
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const char *output_bin = "dla34/debug/output.bin";
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int main()
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{
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 224, 224, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Layer *last1, *last2, *last3, *last4;
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tk::dnn::Conv2d conv1(&net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
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tk::dnn::Activation relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d conv2(&net, 16, 3, 3, 1, 1, 1, 1, conv2_bin, true);
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tk::dnn::Activation relu2(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d conv3(&net, 32, 3, 3, 2, 2, 1, 1, conv3_bin, true);
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tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
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last1 = &relu3;
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// level 2
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// tree 1
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tk::dnn::Conv2d s1_t1_conv1(&net, 64, 3, 3, 2, 2, 1, 1, s1_t1_conv1_bin, true);
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tk::dnn::Activation s1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s1_t1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t1_conv2_bin, true);
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last2 = &s1_t1_conv2;
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// get the basicblock input and apply maxpool conv2d and relu
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tk::dnn::Layer *route_s1_t1_layers[1] = { last1 };
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tk::dnn::Route route_s1_t1(&net, route_s1_t1_layers, 1);
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// downsample
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tk::dnn::Pooling s1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
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// project
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tk::dnn::Conv2d s1_t1_residual1_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_t1_project, true);
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tk::dnn::Shortcut s1_t1_s1(&net, last2);
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tk::dnn::Activation s1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s1_t1_relu;
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// tree 2
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tk::dnn::Conv2d s1_t2_conv1(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv1_bin, true);
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tk::dnn::Activation s1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s1_t2_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv2_bin, true);
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tk::dnn::Shortcut s1_t2_s1(&net, last1);
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tk::dnn::Activation s1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
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last2 = &s1_t2_relu;
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// root
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// join last1 and net in single input 128, 56, 56
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tk::dnn::Layer *route_s1_root_layers[2] = { last2, last1 };
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tk::dnn::Route route_s1_root(&net, route_s1_root_layers, 2);
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tk::dnn::Conv2d s1_root_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_root_conv1_bin, true);
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tk::dnn::Activation s1_root_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s1_root_relu;
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// level 3
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// tree 1
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// tree 1
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tk::dnn::Conv2d s2_t1_t1_conv1(&net, 128, 3, 3, 2, 2, 1, 1, s2_t1_t1_conv1_bin, true);
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tk::dnn::Activation s2_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s2_t1_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t1_conv2_bin, true);
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last2 = &s2_t1_t1_conv2;
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// get the basicblock input and apply maxpool conv2d and relu
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tk::dnn::Layer *route_s2_t1_t1_layers[1] = { last1 };
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tk::dnn::Route route_s2_t1_t1(&net, route_s2_t1_t1_layers, 1);
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// downsample
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tk::dnn::Pooling s2_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
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last4 = &s2_t1_t1_maxpool1;
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// project
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tk::dnn::Conv2d s2_t1_t1_residual1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_t1_project, true);
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tk::dnn::Shortcut s2_t1_t1_s1(&net, last2);
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tk::dnn::Activation s2_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s2_t1_t1_relu;
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// tree 2
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tk::dnn::Conv2d s2_t1_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv1_bin, true);
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tk::dnn::Activation s2_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s2_t1_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv2_bin, true);
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tk::dnn::Shortcut s2_t1_t2_s1(&net, last1);
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tk::dnn::Activation s2_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
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last2 = &s2_t1_t2_relu;
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// root
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// join last1 and net in single input 128, 56, 56
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tk::dnn::Layer *route_s2_t1_root_layers[2] = { last2, last1 };
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tk::dnn::Route route_s2_t1_root(&net, route_s2_t1_root_layers, 2);
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tk::dnn::Conv2d s2_t1_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_root_conv1_bin, true);
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tk::dnn::Activation s2_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s2_t1_root_relu;
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last3 = &s2_t1_root_relu;
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// tree 2
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// tree 1
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tk::dnn::Conv2d s2_t2_t1_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv1_bin, true);
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tk::dnn::Activation s2_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s2_t2_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv2_bin, true);
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tk::dnn::Shortcut s2_t2_t1_s1(&net, last1);
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tk::dnn::Activation s2_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s2_t2_t1_relu;
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// tree 2
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tk::dnn::Conv2d s2_t2_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv1_bin, true);
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tk::dnn::Activation s2_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s2_t2_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv2_bin, true);
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tk::dnn::Shortcut s2_t2_t2_s1(&net, last1);
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tk::dnn::Activation s2_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
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last2 = &s2_t2_t2_relu;
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// root
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// join last1 and net in single input 128, 56, 56
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tk::dnn::Layer *route_s2_t2_root_layers[4] = { last2, last1, last4, last3};
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tk::dnn::Route route_s2_t2_root(&net, route_s2_t2_root_layers, 4);
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tk::dnn::Conv2d s2_t2_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t2_root_conv1_bin, true);
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tk::dnn::Activation s2_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s2_t2_root_relu;
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// level 4
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// tree 1
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// tree 1
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tk::dnn::Conv2d s3_t1_t1_conv1(&net, 256, 3, 3, 2, 2, 1, 1, s3_t1_t1_conv1_bin, true);
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tk::dnn::Activation s3_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s3_t1_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t1_conv2_bin, true);
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last2 = &s3_t1_t1_conv2;
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// get the basicblock input and apply maxpool conv2d and relu
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tk::dnn::Layer *route_s3_t1_t1_layers[1] = { last1 };
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tk::dnn::Route route_s3_t1_t1(&net, route_s3_t1_t1_layers, 1);
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// downsample
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tk::dnn::Pooling s3_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
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last4 = &s3_t1_t1_maxpool1;
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// project
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tk::dnn::Conv2d s3_t1_t1_residual1_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_t1_project, true);
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tk::dnn::Shortcut s3_t1_t1_s1(&net, last2);
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tk::dnn::Activation s3_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s3_t1_t1_relu;
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// tree 2
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tk::dnn::Conv2d s3_t1_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv1_bin, true);
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tk::dnn::Activation s3_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s3_t1_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv2_bin, true);
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tk::dnn::Shortcut s3_t1_t2_s1(&net, last1);
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tk::dnn::Activation s3_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
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last2 = &s3_t1_t2_relu;
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// root
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// join last1 and net in single input 256, 56, 56
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tk::dnn::Layer *route_s3_t1_root_layers[2] = { last2, last1 };
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tk::dnn::Route route_s3_t1_root(&net, route_s3_t1_root_layers, 2);
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tk::dnn::Conv2d s3_t1_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_root_conv1_bin, true);
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tk::dnn::Activation s3_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s3_t1_root_relu;
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last3 = &s3_t1_root_relu;
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// tree 2
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// tree 1
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tk::dnn::Conv2d s3_t2_t1_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv1_bin, true);
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tk::dnn::Activation s3_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s3_t2_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv2_bin, true);
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tk::dnn::Shortcut s3_t2_t1_s1(&net, last1);
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tk::dnn::Activation s3_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s3_t2_t1_relu;
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// tree 2
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tk::dnn::Conv2d s3_t2_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv1_bin, true);
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tk::dnn::Activation s3_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s3_t2_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv2_bin, true);
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tk::dnn::Shortcut s3_t2_t2_s1(&net, last1);
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tk::dnn::Activation s3_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
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last2 = &s3_t2_t2_relu;
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// root
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// join last1 and net in single input 256, 56, 56
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tk::dnn::Layer *route_s3_t2_root_layers[4] = { last2, last1, last4, last3};
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tk::dnn::Route route_s3_t2_root(&net, route_s3_t2_root_layers, 4);
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tk::dnn::Conv2d s3_t2_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t2_root_conv1_bin, true);
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tk::dnn::Activation s3_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s3_t2_root_relu;
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// level 4
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// tree 1
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tk::dnn::Conv2d s4_t1_conv1(&net, 512, 3, 3, 2, 2, 1, 1, s4_t1_conv1_bin, true);
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tk::dnn::Activation s4_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s4_t1_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t1_conv2_bin, true);
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last2 = &s4_t1_conv2;
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// get the basicblock input and apply maxpool conv2d and relu
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tk::dnn::Layer *route_s4_t1_layers[1] = { last1 };
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tk::dnn::Route route_s4_t1(&net, route_s4_t1_layers, 1);
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// downsample
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tk::dnn::Pooling s4_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
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last4 = &s4_t1_maxpool1;
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// project
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tk::dnn::Conv2d s4_t1_residual1_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_t1_project, true);
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tk::dnn::Shortcut s4_t1_s1(&net, last2);
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tk::dnn::Activation s4_t1_relu(&net, CUDNN_ACTIVATION_RELU);
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last1 = &s4_t1_relu;
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// tree 2
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tk::dnn::Conv2d s4_t2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv1_bin, true);
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tk::dnn::Activation s4_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d s4_t2_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv2_bin, true);
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tk::dnn::Shortcut s4_t2_s1(&net, last1);
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tk::dnn::Activation s4_t2_relu(&net, CUDNN_ACTIVATION_RELU);
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last2 = &s4_t2_relu;
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// root
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// join last1 and net in single input 128, 56, 56
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tk::dnn::Layer *route_s4_root_layers[3] = { last2, last1, last4 };
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tk::dnn::Route route_s4_root(&net, route_s4_root_layers, 3);
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tk::dnn::Conv2d s4_root_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_root_conv1_bin, true);
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tk::dnn::Activation s4_root_relu(&net, CUDNN_ACTIVATION_RELU);
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//final
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tk::dnn::Pooling avgpool(&net, 7, 7, 7, 7, 0, 0, tk::dnn::POOLING_AVERAGE);
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tk::dnn::Dense fc(&net, 1000, fc_bin);
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// Load input
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dnnType *data;
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dnnType *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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//printDeviceVector(64, data, true);
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//print network model
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net.print();
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//convert network to tensorRT
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tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34"));
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tk::dnn::dataDim_t out_dim;
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out_dim = net.layers[net.num_layers-1]->output_dim;
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dnnType *cudnn_out, *rt_out;
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tk::dnn::dataDim_t dim1 = dim; //input dim
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printCenteredTitle(" CUDNN inference ", '=', 30);
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{
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dim1.print();
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TIMER_START
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net.infer(dim1, data);
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TIMER_STOP
|
|
dim1.print();
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}
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cudnn_out = net.layers[net.num_layers-1]->dstData;
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// printDeviceVector(64, cudnn_out, true);
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|
|
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tk::dnn::dataDim_t dim2 = dim;
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
|
{
|
|
dim2.print();
|
|
TIMER_START
|
|
netRT.infer(dim2, data);
|
|
TIMER_STOP
|
|
dim2.print();
|
|
}
|
|
rt_out = (dnnType *)netRT.buffersRT[1];
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|
|
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|
|
printCenteredTitle(std::string(" RESNET CHECK RESULTS ").c_str(), '=', 30);
|
|
dnnType *out, *out_h;
|
|
int odim = out_dim.tot();
|
|
readBinaryFile(output_bin, odim, &out_h, &out);
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|
|
|
std::cout<<"CUDNN vs correct";
|
|
int ret_cudnn = checkResult(odim, cudnn_out, out) == 0 ? 0: ERROR_CUDNN;
|
|
std::cout<<"TRT vs correct";
|
|
int ret_tensorrt = checkResult(odim, rt_out, out) == 0 ? 0 : ERROR_TENSORRT;
|
|
std::cout<<"CUDNN vs TRT ";
|
|
int ret_cudnn_tensorrt = checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
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|
|
|
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
|
|
}
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