perseusdg
266 lines
16 KiB
C++
266 lines
16 KiB
C++
#include <iostream>
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#include <vector>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <tkdnn.h>
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#include "tkDNN/NetworkViz.h"
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const char* encoder_conv1_bin = "monodepth2_640/layers/encoder/encoder-conv1.bin";
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const char* encoder_layer1_bin[] = {
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"monodepth2_640/layers/encoder/encoder-layer1-0-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer1-0-conv2.bin",
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"monodepth2_640/layers/encoder/encoder-layer1-1-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer1-1-conv2.bin",
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};
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const char* encoder_layer2_bin[] = {
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"monodepth2_640/layers/encoder/encoder-layer2-0-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer2-0-conv2.bin",
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"monodepth2_640/layers/encoder/encoder-layer2-0-downsample-0.bin",
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"monodepth2_640/layers/encoder/encoder-layer2-1-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer2-1-conv2.bin"
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};
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const char* encoder_layer3_bin[]={
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"monodepth2_640/layers/encoder/encoder-layer3-0-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer3-0-conv2.bin",
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"monodepth2_640/layers/encoder/encoder-layer3-0-downsample-0.bin",
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"monodepth2_640/layers/encoder/encoder-layer3-1-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer3-1-conv2.bin"
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};
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const char* encoder_layer4_bin[] = {
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"monodepth2_640/layers/encoder/encoder-layer4-0-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer4-0-conv2.bin",
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"monodepth2_640/layers/encoder/encoder-layer4-0-downsample-0.bin",
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"monodepth2_640/layers/encoder/encoder-layer4-1-conv1.bin",
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"monodepth2_640/layers/encoder/encoder-layer4-1-conv2.bin"
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};
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const char *encoder_fc_bin = "monodepth2_640/layers/encoder/encoder-fc.bin";
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const char* decoder_layer_bin[] = {
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"monodepth2_640/layers/depth_decoder/decoder-0-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-1-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-2-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-3-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-4-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-5-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-6-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-7-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-8-conv-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-9-conv-conv.bin"
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};
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const char* decoder_dispconv_layer_bin[] = {
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"monodepth2_640/layers/depth_decoder/decoder-10-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-11-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-12-conv.bin",
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"monodepth2_640/layers/depth_decoder/decoder-13-conv.bin"
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};
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const char* output_bin[] = {
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"monodepth2_640/debug/outputs/output-disp-0.bin",
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"monodepth2_640/debug/outputs/output-disp-1.bin",
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"monodepth2_640/debug/outputs/output-disp-2.bin",
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"monodepth2_640/debug/outputs/output-disp-3.bin"
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};
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const char* input_bin = "monodepth2_640/debug/input.bin";
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int main(){
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downloadWeightsifDoNotExist(input_bin, "monodepth2_640", "https://cloud.hipert.unimore.it/s/iYw9QwgP6CsqxLR/download");
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tk::dnn::dataDim_t dim(1,3,192,640,1);
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tk::dnn::Network net(dim);
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tk::dnn::Layer* muladd_sub = new tk::dnn::MulAdd(&net, 1.0f, -0.45f);
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tk::dnn::Layer* muladd_mul = new tk::dnn::MulAdd(&net, 1.0f / 0.225f, 0.0f);
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tk::dnn::Layer* encoder_conv = new tk::dnn::Conv2d(&net,64,7,7,2,2,3,3,encoder_conv1_bin,true);
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tk::dnn::Layer* encoder_relu = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_maxpool = new tk::dnn::Pooling(&net,3,3,2,2,1,1,tk::dnn::POOLING_MAX);
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//layer-1
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tk::dnn::Layer* encoder_layer_1_0_convbn_1 = new tk::dnn::Conv2d(&net,64,3,3,1,1,1,1,encoder_layer1_bin[0],true);
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tk::dnn::Layer* encoder_relu_1 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_1_0_convbn_2 = new tk::dnn::Conv2d(&net,64,3,3,1,1,1,1,encoder_layer1_bin[1],true);
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tk::dnn::Layer* encoder_layer_1_0_shortcut_1 = new tk::dnn::Shortcut(&net,encoder_maxpool);
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tk::dnn::Layer* encoder_relu_2 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_1_1_convbn_1 = new tk::dnn::Conv2d(&net,64,3,3,1,1,1,1,encoder_layer1_bin[2],true);
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tk::dnn::Layer* encoder_relu_3 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_1_1_convbn_2 = new tk::dnn::Conv2d(&net,64,3,3,1,1,1,1,encoder_layer1_bin[3],true);
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tk::dnn::Layer* encoder_layer_1_1_shortcut_1 = new tk::dnn::Shortcut(&net,encoder_relu_2);
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tk::dnn::Layer* encoder_relu_4 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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//layer-2
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tk::dnn::Layer* encoder_layer_2_0_convbn_1 = new tk::dnn::Conv2d(&net,128,3,3,2,2,1,1,encoder_layer2_bin[0],true);
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tk::dnn::Layer* encoder_relu_5 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_2_0_convbn_2 = new tk::dnn::Conv2d(&net,128,3,3,1,1,1,1,encoder_layer2_bin[1],true);
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tk::dnn::Layer* encoder_layer_2_0_route = new tk::dnn::Route(&net,&encoder_relu_4,1);
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tk::dnn::Layer* encoder_layer_2_0_downsample_convbn = new tk::dnn::Conv2d(&net,128,1,1,2,2,0,0,encoder_layer2_bin[2],true);
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tk::dnn::Layer* encoder_layer_2_0_shortcut = new tk::dnn::Shortcut(&net,encoder_layer_2_0_convbn_2);
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tk::dnn::Layer* encoder_relu_6 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_2_1_convbn_1 = new tk::dnn::Conv2d(&net,128,3,3,1,1,1,1,encoder_layer2_bin[3],true);
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tk::dnn::Layer* encoder_relu_7 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_2_1_convbn_2 = new tk::dnn::Conv2d(&net,128,3,3,1,1,1,1,encoder_layer2_bin[4],true);
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tk::dnn::Layer* encoder_layer_2_1shortcut = new tk::dnn::Shortcut(&net,encoder_relu_6);
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tk::dnn::Layer* encoder_relu_8 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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//layer-3
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tk::dnn::Layer* encoder_layer_3_0_convbn_1 = new tk::dnn::Conv2d(&net,256,3,3,2,2,1,1,encoder_layer3_bin[0],true);
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tk::dnn::Layer* encoder_relu_9 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_3_0_convbn_2 = new tk::dnn::Conv2d(&net,256,3,3,1,1,1,1,encoder_layer3_bin[1],true);
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tk::dnn::Layer* encoder_layer_3_0_route = new tk::dnn::Route(&net,&encoder_relu_8,1);
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tk::dnn::Layer* encoder_layer_3_0_downsample_convbn = new tk::dnn::Conv2d(&net,256,1,1,2,2,0,0,encoder_layer3_bin[2],true);
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tk::dnn::Layer* encoder_layer_3_0_shortcut = new tk::dnn::Shortcut(&net,encoder_layer_3_0_convbn_2);
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tk::dnn::Layer* encoder_relu_10 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_3_1_convbn_1 = new tk::dnn::Conv2d(&net,256,3,3,1,1,1,1,encoder_layer3_bin[3],true);
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tk::dnn::Layer* encoder_relu_11 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_3_1_convbn_2 = new tk::dnn::Conv2d(&net,256,3,3,1,1,1,1,encoder_layer3_bin[4],true);
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tk::dnn::Layer* encoder_layer_3_1shortcut = new tk::dnn::Shortcut(&net,encoder_relu_10);
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tk::dnn::Layer* encoder_relu_12 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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//layer-4
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tk::dnn::Layer* encoder_layer_4_0_convbn_1 = new tk::dnn::Conv2d(&net,512,3,3,2,2,1,1,encoder_layer4_bin[0],true);
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tk::dnn::Layer* encoder_relu_13 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_4_0_convbn_2 = new tk::dnn::Conv2d(&net,512,3,3,1,1,1,1,encoder_layer4_bin[1],true);
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tk::dnn::Layer* encoder_layer_4_0_route = new tk::dnn::Route(&net,&encoder_relu_12,1);
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tk::dnn::Layer* encoder_layer_4_0_downsample_convbn = new tk::dnn::Conv2d(&net,512,1,1,2,2,0,0,encoder_layer4_bin[2],true);
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tk::dnn::Layer* encoder_layer_4_0_shortcut = new tk::dnn::Shortcut(&net,encoder_layer_4_0_convbn_2);
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tk::dnn::Layer* encoder_relu_14 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_4_1_convbn_1 = new tk::dnn::Conv2d(&net,512,3,3,1,1,1,1,encoder_layer4_bin[3],true);
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tk::dnn::Layer* encoder_relu_15 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer* encoder_layer_4_1_convbn_2 = new tk::dnn::Conv2d(&net,512,3,3,1,1,1,1,encoder_layer4_bin[4],true);
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tk::dnn::Layer* encoder_layer_4_1shortcut = new tk::dnn::Shortcut(&net,encoder_relu_14);
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tk::dnn::Layer* encoder_relu_16 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
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//decoder
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tk::dnn::Layer* decoder_reflection_padding_2d = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_4_0 = new tk::dnn::Conv2d(&net,256,3,3,1,1,0,0,decoder_layer_bin[0]);
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tk::dnn::Layer* decoder_elu = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_upsampling_2d = new tk::dnn::Upsample(&net,2);
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tk::dnn::Layer* concatenate_layer[2] = {decoder_upsampling_2d,encoder_relu_12};
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tk::dnn::Layer* decoder_concatenate = new tk::dnn::Route(&net,concatenate_layer,2);
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tk::dnn::Layer* decoder_reflection_padding_2d_1 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_4_1 = new tk::dnn::Conv2d(&net,256,3,3,1,1,0,0,decoder_layer_bin[1]);
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tk::dnn::Layer* decoder_elu_1 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_reflection_padding_2d_2 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_3_0 = new tk::dnn::Conv2d(&net,128,3,3,1,1,0,0,decoder_layer_bin[2]);
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tk::dnn::Layer* decoder_elu_2 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_upsampling_2d_1 = new tk::dnn::Upsample(&net,2);
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tk::dnn::Layer* concatenate_layer_1[2] = {decoder_upsampling_2d_1,encoder_relu_8};
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tk::dnn::Layer* decoder_concatenate_layer_1 = new tk::dnn::Route{&net,concatenate_layer_1,2};
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tk::dnn::Layer* decoder_reflection_padding_2d_3 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_3_1 = new tk::dnn::Conv2d(&net,128,3,3,1,1,0,0,decoder_layer_bin[3]);
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tk::dnn::Layer* decoder_elu_3 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_reflection_padding_2d_5 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_2_0 = new tk::dnn::Conv2d(&net,64,3,3,1,1,0,0,decoder_layer_bin[4]);
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tk::dnn::Layer* decoder_elu_4 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_upsampling_2d_2 = new tk::dnn::Upsample(&net,2);
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tk::dnn::Layer* concatenate_layer_2[2] = {decoder_upsampling_2d_2,encoder_relu_4};
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tk::dnn::Layer* decoder_concatenate_layer_2 = new tk::dnn::Route(&net,concatenate_layer_2,2);
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tk::dnn::Layer* decoder_reflection_padding_2d_6 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_2_1 = new tk::dnn::Conv2d(&net,64,3,3,1,1,0,0,decoder_layer_bin[5]);
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tk::dnn::Layer* decoder_elu_5 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_reflection_padding_2d_8 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_1_0 = new tk::dnn::Conv2d(&net,32,3,3,1,1,0,0,decoder_layer_bin[6]);
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tk::dnn::Layer* decoder_elu_6 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_upsampling_2d_3 = new tk::dnn::Upsample(&net,2);
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tk::dnn::Layer* concatenate_layer_3[2] = {decoder_upsampling_2d_3,encoder_relu};
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tk::dnn::Layer* decoder_concatenate_layer_3 = new tk::dnn::Route(&net,concatenate_layer_3,2);
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tk::dnn::Layer* decoder_reflection_padding_2d_9 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_1_1 = new tk::dnn::Conv2d(&net,32,3,3,1,1,0,0,decoder_layer_bin[7]);
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tk::dnn::Layer* decoder_elu_7 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_reflection_padding_2d_11 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_0_0 = new tk::dnn::Conv2d(&net,16,3,3,1,1,0,0,decoder_layer_bin[8]);
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tk::dnn::Layer* decoder_elu_8 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_upsampling_2d_4 = new tk::dnn::Upsample(&net,2);
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tk::dnn::Layer* decoder_reflection_padding_2d_12 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_upconv_0_1 = new tk::dnn::Conv2d(&net,16,3,3,1,1,0,0,decoder_layer_bin[9]);
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tk::dnn::Layer* decoder_elu_9 = new tk::dnn::Activation(&net,tk::dnn::ACTIVATION_ELU);
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tk::dnn::Layer* decoder_reflection_padding_2d_13 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_dispconv_0 = new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[0]);
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tk::dnn::Layer* disp0 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
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disp0->setFinal();
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tk::dnn::Layer* route_elu_7 = new tk::dnn::Route(&net,&decoder_elu_7,1);
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tk::dnn::Layer* decoder_reflection_padding_2d_10 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_dispconv_1 = new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[1]);
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tk::dnn::Layer* disp1 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
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disp1->setFinal();
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tk::dnn::Layer* route_elu_5 = new tk::dnn::Route(&net,&decoder_elu_5,1);
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tk::dnn::Layer* decoder_reflection_padding_2d_7 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_dispconv_2 = new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[2]);
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tk::dnn::Layer* disp2 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
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disp2->setFinal();
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tk::dnn::Layer* route_elu_3 = new tk::dnn::Route(&net,&decoder_elu_3,1);
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tk::dnn::Layer* decoder_reflection_padding_2d_4 = new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
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tk::dnn::Layer* decoder_dispconv_3 = new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[3]);
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tk::dnn::Layer* disp3 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
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disp3->setFinal();
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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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std::cout<<"INPUT DIMENSIONS : "<<dim.tot()<<std::endl;
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net.print();
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tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("monodepth2_640"));
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tk::dnn::dataDim_t dim1 = dim;
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dnnType *cudnn_out = nullptr;
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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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tk::dnn::dataDim_t dim2 = dim;
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printCenteredTitle(" TENSORRT inference ", '=', 30);
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{
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dim2.print();
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TKDNN_TSTART
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netRT.infer(dim2, data);
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TKDNN_TSTOP
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dim2.print();
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}
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tk::dnn::Layer *outs[4] = {disp0,disp1,disp2,disp3};
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std::cout<<std::endl<<std::endl;
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disp3->output_dim.print();
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int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
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for(int i=0;i<4;i++){
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printCenteredTitle((std::string("MONODEPTH2 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;
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int odim = outs[i]->output_dim.tot();
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readBinaryFile(output_bin[i], odim, &out_h, &out);
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dnnType *cudnn_out, *rt_out;
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cudnn_out = outs[i]->dstData;
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rt_out = (dnnType *)netRT.buffersRT[1+i];
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std::cout<<"CUDNN vs correct";
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ret_cudnn |= checkResult(odim, cudnn_out, out) == 0 ? 0: ERROR_CUDNN;
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std::cout<<"TRT vs correct";
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ret_tensorrt |= checkResult(odim, rt_out, out) == 0 ? 0 : ERROR_TENSORRT;
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std::cout<<"CUDNN vs TRT ";
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ret_cudnn_tensorrt |= checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
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|
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cv::Mat depth_mat = vizData2Mat(outs[i]->dstData, outs[i]->output_dim, outs[i]->output_dim.h, outs[i]->output_dim.w);
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cv::imshow("depth", depth_mat);
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cv::waitKey(0);
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}
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|
|
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return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
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|
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} |