Francesco Gatti
414 lines
18 KiB
C++
414 lines
18 KiB
C++
#include <iostream>
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#include "kernels.h"
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#include "Yolo3Detection.h"
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#include "tkdnn.h"
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#include <vector>
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#include <numeric> // std::iota
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#include <algorithm> // std::sort
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// #include "utils.h"
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const char *input_bin = "resnet101_cnet/debug/input.bin";
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const char *conv1_bin = "resnet101_cnet/layers/conv1.bin";
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//layer1
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const char *layer1_bin[]={
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"resnet101_cnet/layers/layer1-0-conv1.bin",
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"resnet101_cnet/layers/layer1-0-conv2.bin",
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"resnet101_cnet/layers/layer1-0-conv3.bin",
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"resnet101_cnet/layers/layer1-0-downsample-0.bin",
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"resnet101_cnet/layers/layer1-1-conv1.bin",
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"resnet101_cnet/layers/layer1-1-conv2.bin",
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"resnet101_cnet/layers/layer1-1-conv3.bin",
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"resnet101_cnet/layers/layer1-2-conv1.bin",
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"resnet101_cnet/layers/layer1-2-conv2.bin",
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"resnet101_cnet/layers/layer1-2-conv3.bin"};
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//layer2
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const char *layer2_bin[]={
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"resnet101_cnet/layers/layer2-0-conv1.bin",
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"resnet101_cnet/layers/layer2-0-conv2.bin",
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"resnet101_cnet/layers/layer2-0-conv3.bin",
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"resnet101_cnet/layers/layer2-0-downsample-0.bin",
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"resnet101_cnet/layers/layer2-1-conv1.bin",
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"resnet101_cnet/layers/layer2-1-conv2.bin",
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"resnet101_cnet/layers/layer2-1-conv3.bin",
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"resnet101_cnet/layers/layer2-2-conv1.bin",
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"resnet101_cnet/layers/layer2-2-conv2.bin",
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"resnet101_cnet/layers/layer2-2-conv3.bin",
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"resnet101_cnet/layers/layer2-3-conv1.bin",
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"resnet101_cnet/layers/layer2-3-conv2.bin",
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"resnet101_cnet/layers/layer2-3-conv3.bin"
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};
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//layer3
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const char *layer3_bin[]={
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"resnet101_cnet/layers/layer3-0-conv1.bin",
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"resnet101_cnet/layers/layer3-0-conv2.bin",
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"resnet101_cnet/layers/layer3-0-conv3.bin",
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"resnet101_cnet/layers/layer3-0-downsample-0.bin",
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"resnet101_cnet/layers/layer3-1-conv1.bin",
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"resnet101_cnet/layers/layer3-1-conv2.bin",
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"resnet101_cnet/layers/layer3-1-conv3.bin",
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"resnet101_cnet/layers/layer3-2-conv1.bin",
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"resnet101_cnet/layers/layer3-2-conv2.bin",
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"resnet101_cnet/layers/layer3-2-conv3.bin",
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"resnet101_cnet/layers/layer3-3-conv1.bin",
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"resnet101_cnet/layers/layer3-3-conv2.bin",
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"resnet101_cnet/layers/layer3-3-conv3.bin",
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"resnet101_cnet/layers/layer3-4-conv1.bin",
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"resnet101_cnet/layers/layer3-4-conv2.bin",
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"resnet101_cnet/layers/layer3-4-conv3.bin",
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"resnet101_cnet/layers/layer3-5-conv1.bin",
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"resnet101_cnet/layers/layer3-5-conv2.bin",
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"resnet101_cnet/layers/layer3-5-conv3.bin",
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"resnet101_cnet/layers/layer3-6-conv1.bin",
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"resnet101_cnet/layers/layer3-6-conv2.bin",
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"resnet101_cnet/layers/layer3-6-conv3.bin",
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"resnet101_cnet/layers/layer3-7-conv1.bin",
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"resnet101_cnet/layers/layer3-7-conv2.bin",
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"resnet101_cnet/layers/layer3-7-conv3.bin",
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"resnet101_cnet/layers/layer3-8-conv1.bin",
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"resnet101_cnet/layers/layer3-8-conv2.bin",
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"resnet101_cnet/layers/layer3-8-conv3.bin",
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"resnet101_cnet/layers/layer3-9-conv1.bin",
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"resnet101_cnet/layers/layer3-9-conv2.bin",
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"resnet101_cnet/layers/layer3-9-conv3.bin",
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"resnet101_cnet/layers/layer3-10-conv1.bin",
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"resnet101_cnet/layers/layer3-10-conv2.bin",
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"resnet101_cnet/layers/layer3-10-conv3.bin",
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"resnet101_cnet/layers/layer3-11-conv1.bin",
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"resnet101_cnet/layers/layer3-11-conv2.bin",
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"resnet101_cnet/layers/layer3-11-conv3.bin",
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"resnet101_cnet/layers/layer3-12-conv1.bin",
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"resnet101_cnet/layers/layer3-12-conv2.bin",
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"resnet101_cnet/layers/layer3-12-conv3.bin",
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"resnet101_cnet/layers/layer3-13-conv1.bin",
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"resnet101_cnet/layers/layer3-13-conv2.bin",
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"resnet101_cnet/layers/layer3-13-conv3.bin",
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"resnet101_cnet/layers/layer3-14-conv1.bin",
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"resnet101_cnet/layers/layer3-14-conv2.bin",
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"resnet101_cnet/layers/layer3-14-conv3.bin",
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"resnet101_cnet/layers/layer3-15-conv1.bin",
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"resnet101_cnet/layers/layer3-15-conv2.bin",
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"resnet101_cnet/layers/layer3-15-conv3.bin",
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"resnet101_cnet/layers/layer3-16-conv1.bin",
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"resnet101_cnet/layers/layer3-16-conv2.bin",
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"resnet101_cnet/layers/layer3-16-conv3.bin",
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"resnet101_cnet/layers/layer3-17-conv1.bin",
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"resnet101_cnet/layers/layer3-17-conv2.bin",
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"resnet101_cnet/layers/layer3-17-conv3.bin",
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"resnet101_cnet/layers/layer3-18-conv1.bin",
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"resnet101_cnet/layers/layer3-18-conv2.bin",
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"resnet101_cnet/layers/layer3-18-conv3.bin",
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"resnet101_cnet/layers/layer3-19-conv1.bin",
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"resnet101_cnet/layers/layer3-19-conv2.bin",
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"resnet101_cnet/layers/layer3-19-conv3.bin",
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"resnet101_cnet/layers/layer3-20-conv1.bin",
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"resnet101_cnet/layers/layer3-20-conv2.bin",
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"resnet101_cnet/layers/layer3-20-conv3.bin",
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"resnet101_cnet/layers/layer3-21-conv1.bin",
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"resnet101_cnet/layers/layer3-21-conv2.bin",
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"resnet101_cnet/layers/layer3-21-conv3.bin",
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"resnet101_cnet/layers/layer3-22-conv1.bin",
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"resnet101_cnet/layers/layer3-22-conv2.bin",
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"resnet101_cnet/layers/layer3-22-conv3.bin"};
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//layer4
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const char *layer4_bin[]={
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"resnet101_cnet/layers/layer4-0-conv1.bin",
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"resnet101_cnet/layers/layer4-0-conv2.bin",
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"resnet101_cnet/layers/layer4-0-conv3.bin",
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"resnet101_cnet/layers/layer4-0-downsample-0.bin",
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"resnet101_cnet/layers/layer4-1-conv1.bin",
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"resnet101_cnet/layers/layer4-1-conv2.bin",
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"resnet101_cnet/layers/layer4-1-conv3.bin",
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"resnet101_cnet/layers/layer4-2-conv1.bin",
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"resnet101_cnet/layers/layer4-2-conv2.bin",
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"resnet101_cnet/layers/layer4-2-conv3.bin"};
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const char *d_conv1_bin = "resnet101_cnet/layers/deconv_layers-0-conv_offset_mask.bin";
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const char *deform1_bin = "resnet101_cnet/layers/deconv_layers-0.bin";
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const char *deconv1_bin = "resnet101_cnet/layers/deconv_layers-3.bin";
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const char *d_conv2_bin = "resnet101_cnet/layers/deconv_layers-6-conv_offset_mask.bin";
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const char *deform2_bin = "resnet101_cnet/layers/deconv_layers-6.bin";
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const char *deconv2_bin = "resnet101_cnet/layers/deconv_layers-9.bin";
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const char *d_conv3_bin = "resnet101_cnet/layers/deconv_layers-12-conv_offset_mask.bin";
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const char *deform3_bin = "resnet101_cnet/layers/deconv_layers-12.bin";
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const char *deconv3_bin = "resnet101_cnet/layers/deconv_layers-15.bin";
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const char *hm_conv1_bin = "resnet101_cnet/layers/hm-0.bin";
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const char *hm_conv2_bin = "resnet101_cnet/layers/hm-2.bin";
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const char *wh_conv1_bin = "resnet101_cnet/layers/wh-0.bin";
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const char *wh_conv2_bin = "resnet101_cnet/layers/wh-2.bin";
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const char *reg_conv1_bin = "resnet101_cnet/layers/reg-0.bin";
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const char *reg_conv2_bin = "resnet101_cnet/layers/reg-2.bin";
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//final
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const char *fc_bin = "resnet101_cnet/layers/fc.bin";
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const char *output_bin[]={
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"resnet101_cnet/debug/hm.bin",
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"resnet101_cnet/debug/wh.bin",
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"resnet101_cnet/debug/reg.bin"};
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int main()
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{
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downloadWeightsifDoNotExist(input_bin, "resnet101_cnet", "https://cloud.hipert.unimore.it/s/5BTjHMWBcJk8g3i/download");
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 512, 512, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d conv1(&net, 64, 7, 7, 2, 2, 3, 3, conv1_bin, true);
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tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling maxpool4(&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
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//layer 1
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int id_layer1_bin = 0;
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tk::dnn::Layer *last = &maxpool4;
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for(int i=0; i<3;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 64, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, layer1_bin[id_layer1_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
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if(i==0) {
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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} else {
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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// layer 2
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int id_layer2_bin = 0;
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for(int i=0; i<4;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 128, 1, 1, 1, 1, 0, 0, layer2_bin[id_layer2_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2;
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if(i==0)
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 128, 3, 3, 2, 2, 1, 1, layer2_bin[id_layer2_bin++], true);
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else
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 128, 3, 3, 1, 1, 1, 1, layer2_bin[id_layer2_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 512, 1, 1, 1, 1, 0, 0, layer2_bin[id_layer2_bin++], true);
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if(i==0)
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{
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 512, 1, 1, 2, 2, 0, 0, layer2_bin[id_layer2_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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}
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else
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{
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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// layer 3
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int id_layer3_bin = 0;
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for(int i=0; i<23;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer3_bin[id_layer3_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2;
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if(i==0)
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 256, 3, 3, 2, 2, 1, 1, layer3_bin[id_layer3_bin++], true);
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else
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, layer3_bin[id_layer3_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 1024, 1, 1, 1, 1, 0, 0, layer3_bin[id_layer3_bin++], true);
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if(i==0)
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{
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 1024, 1, 1, 2, 2, 0, 0, layer3_bin[id_layer3_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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}
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else
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{
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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// layer 4
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int id_layer4_bin = 0;
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for(int i=0; i<3;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 512, 1, 1, 1, 1, 0, 0, layer4_bin[id_layer4_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2;
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if(i==0)
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 512, 3, 3, 2, 2, 1, 1, layer4_bin[id_layer4_bin++], true);
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else
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 512, 3, 3, 1, 1, 1, 1, layer4_bin[id_layer4_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 2048, 1, 1, 1, 1, 0, 0, layer4_bin[id_layer4_bin++], true);
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if(i==0)
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{
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 2048, 1, 1, 2, 2, 0, 0, layer4_bin[id_layer4_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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}
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else
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{
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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tk::dnn::DeformConv2d *layer0_deform1 = new tk::dnn::DeformConv2d(&net, 256, 1, 3, 3, 1, 1, 1, 1, deform1_bin, d_conv1_bin, true);
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tk::dnn::Activation *layer0_deform1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::DeConv2d *layer0_deconv1 = new tk::dnn::DeConv2d(&net, 256, 4, 4, 2, 2, 1, 1, deconv1_bin, true);
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tk::dnn::Activation *layer0_deconv1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::DeformConv2d *layer1_deform1 = new tk::dnn::DeformConv2d(&net, 128, 1, 3, 3, 1, 1, 1, 1, deform2_bin, d_conv2_bin, true);
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tk::dnn::Activation *layer1_deform1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::DeConv2d *layer1_deconv1 = new tk::dnn::DeConv2d(&net, 128, 4, 4, 2, 2, 1, 1, deconv2_bin, true);
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tk::dnn::Activation *layer1_deconv1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::DeformConv2d *layer2_deform1 = new tk::dnn::DeformConv2d(&net, 64, 1, 3, 3, 1, 1, 1, 1, deform3_bin, d_conv3_bin, true);
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tk::dnn::Activation *layer2_deform1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::DeConv2d *layer2_deconv1 = new tk::dnn::DeConv2d(&net, 64, 4, 4, 2, 2, 1, 1, deconv3_bin, true);
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tk::dnn::Activation *layer2_deconv1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer *route_1_0_layers[1] = { layer2_deconv1_relu };
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tk::dnn::Conv2d *hm_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
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tk::dnn::Activation *hm_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false);
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hm->setFinal();
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int kernel = 3;
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int pad = (kernel - 1)/2;
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tk::dnn::Activation *hm_sig = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_SIGMOID);
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tk::dnn::Pooling *hmax = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX);
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hmax->setFinal();
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *wh_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
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tk::dnn::Activation *wh_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false);
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wh->setFinal();
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tk::dnn::Route *route_2_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *reg_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
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tk::dnn::Activation *reg_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false);
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reg->setFinal();
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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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|
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//print network model
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net.print();
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|
|
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//convert network to tensorRT
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tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("resnet101_cnet"));
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|
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|
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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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|
TKDNN_TSTART
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net.infer(dim1, data);
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|
TKDNN_TSTOP
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dim1.print();
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}
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// printDeviceVector(64, cudnn_out, true);
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|
|
|
tk::dnn::dataDim_t dim2 = dim;
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
|
{
|
|
dim2.print();
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|
TKDNN_TSTART
|
|
netRT.infer(dim2, data);
|
|
TKDNN_TSTOP
|
|
dim2.print();
|
|
}
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|
|
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tk::dnn::Layer *outs[3] = { hm, wh, reg };
|
|
int out_count = 1;
|
|
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
|
|
for(int i=0; i<3; i++) {
|
|
printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
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|
|
|
outs[i]->output_dim.print();
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|
|
|
dnnType *out, *out_h;
|
|
int odim = outs[i]->output_dim.tot();
|
|
readBinaryFile(output_bin[i], odim, &out_h, &out);
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|
// std::cout<<"OUTPUT BIN:\n";
|
|
// printDeviceVector(odim, cudnn_out, true);
|
|
// std::cout<<"FILE BIN:\n";
|
|
// printDeviceVector(odim, out, true);
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|
|
|
dnnType *cudnn_out, *rt_out;
|
|
cudnn_out = outs[i]->dstData;
|
|
rt_out = (dnnType *)netRT.buffersRT[i+out_count];
|
|
// there is the maxpool. It isn't an output but it is necessary for the process section
|
|
if(i==0)
|
|
out_count ++;
|
|
|
|
std::cout<<"CUDNN vs correct";
|
|
ret_cudnn |= checkResult(odim, cudnn_out, out) == 0 ? 0: ERROR_CUDNN;
|
|
std::cout<<"TRT vs correct";
|
|
ret_tensorrt |= checkResult(odim, rt_out, out) == 0 ? 0 : ERROR_TENSORRT;
|
|
std::cout<<"CUDNN vs TRT ";
|
|
ret_cudnn_tensorrt |= checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
|
|
}
|
|
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
|
|
}
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