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Shelfnet works on cuDNN. To test everything else

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-06-22 18:11:19 +02:00
parent 9f1e30eaa9
commit 6fd261f628
11 changed files with 290 additions and 86 deletions
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tests/shelfnet/shelfnet.cpp
+147 -28
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@@ -1,5 +1,9 @@
#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "tkdnn.h"
#include "NetworkViz.h"
const char *output_bin1 = "shelfnet/debug/classification_headers-5.bin";
@@ -29,35 +33,49 @@ const char *backbone[] = {
"shelfnet/layers/backbone-layer4-1-conv2.bin"};
const char *conv_out[] = {
"shelfnet/layers/conv_out-conv-conv.bin",
"shelfnet/layers/conv_out-conv_out.bin",
"shelfnet/layers/conv_out16-conv-conv.bin",
"shelfnet/layers/conv_out16-conv_out.bin",
"shelfnet/layers/conv_out32-conv-conv.bin",
"shelfnet/layers/conv_out32-conv_out.bin",
"shelfnet/layers/conv_out-conv-conv.bin",
"shelfnet/layers/conv_out-conv_out.bin"};
"shelfnet/layers/conv_out32-conv_out.bin"
};
const char *decoder[] = {
"shelfnet/layers/decoder-bottom-conv1.bin",
"shelfnet/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet/layers/decoder-bottom-conv12.bin",
"shelfnet/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet/layers/decoder-up_dense_list-1-conv.bin"};
"shelfnet/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet/layers/decoder-up_dense_list-1-conv.bin"
};
const char *ladder[] = {
"shelfnet/layers/ladder-bottom-conv1.bin",
"shelfnet/layers/ladder-down_conv_list-0.bin",
"shelfnet/layers/ladder-down_conv_list-1.bin",
"shelfnet/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet/layers/ladder-inconv-conv1.bin",
"shelfnet/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet/layers/ladder-inconv-conv12.bin",
"shelfnet/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet/layers/ladder-down_module_list-0-conv12.bin",
"shelfnet/layers/ladder-down_conv_list-0.bin",
"shelfnet/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet/layers/ladder-down_module_list-1-conv12.bin",
"shelfnet/layers/ladder-down_conv_list-1.bin",
"shelfnet/layers/ladder-bottom-conv1.bin",
"shelfnet/layers/ladder-bottom-conv12.bin",
"shelfnet/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet/layers/ladder-up_dense_list-1-conv.bin"};
const char *trans[] = {
@@ -71,11 +89,11 @@ int main()
int classes = 19;
// Network layout
// Network layout
tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
tk::dnn::Network net(dim);
int bi = 0;
int bi = 0, di = 0, li = 0, ci = 0;
new tk::dnn::Conv2d(&net, 64, 7, 7, 2, 2, 3, 3, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
@@ -105,24 +123,122 @@ int main()
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
if(i != 2)
{new tk::dnn::Shortcut(&net, last);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
features.push_back(last);}
features.push_back(last);
}
// for(int i=0; i<features.size(); ++i){
// new tk::dnn::Route(&net, &features[i], 1);
// int out_channel = pow(2,6+i);
// new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
// new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
for(int i=0; i<features.size(); ++i){
new tk::dnn::Route(&net, &features[i], 1);
int out_channel = pow(2,6+i);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
}
//DECODER
last = features[2];
std::vector<tk::dnn::Layer*> up_out;
//bottom
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
up_out.push_back(last);
for(int i=0; i<2; ++i){
int out_channel = pow(2,7-i);
//up-conv
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, decoder[di++], true);
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Shortcut(&net, act, true);
//interpolate
new tk::dnn::Resize(&net, 1,2,2);
new tk::dnn::Shortcut(&net, features[1-i]);
//up-dense
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
up_out.push_back(last);
}
//LADDER
std::vector<tk::dnn::Layer*> down_out;
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
for(int i=0; i<2;++i){
int out_channel = pow(2,6+i);
tk::dnn::Layer* l_last = new tk::dnn::Shortcut(&net, up_out[2-i]);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, l_last);
l_last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
down_out.push_back(l_last);
new tk::dnn::Conv2d (&net, out_channel*2, 3, 3, 2, 2, 1, 1, ladder[li++], false);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
}
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
up_out.clear();
up_out.push_back(last);
for(int i=0; i<2; ++i){
int out_channel = pow(2,7-i);
//up-conv
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, ladder[li++], true);
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Shortcut(&net, act, true);
//interpolate
new tk::dnn::Resize(&net, 1,2,2);
new tk::dnn::Shortcut(&net, down_out[1-i]);
// //up-dense
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
up_out.push_back(last);
}
// for(int i=2;i>=0;--i){
// new tk::dnn::Route(&net, &up_out[i], 1);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, 19, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, 19, net.input_dim.h, net.input_dim.w, true);
// }
new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet/debug/backbone-layer4-1-bn2.bin";
const char *output_bin = "shelfnet/debug/fofmaf.bin";
@@ -210,4 +326,7 @@ int main()
// ret_cudnn_tensorrt |= checkResult(loc->output_dim.tot(), loc->dstData, rt_out4) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
cv::imwrite("test.png", viz);
}