mmr/tkDNN
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merge with master

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2021-07-20 18:38:46 +02:00
parent 4b3731928c 946b1fe5f5
commit 318dfe4b55
68 changed files with 5577 additions and 276 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/darknet/cfg/yolo4-csp.cfg
+1279
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File diff suppressed because it is too large Load Diff
tests/darknet/cfg/yolo4_416.cfg → tests/darknet/cfg/yolo4_mmr.cfg
+16 -14
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@@ -1,12 +1,11 @@
[net]
# Testing
batch=1
subdivisions=1
# Training
#batch=64
#subdivisions=8
width=416
height=416
width=512
height=512
# width=608
# height=608
channels=3
momentum=0.949
decay=0.0005
@@ -15,11 +14,11 @@ saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.00261
learning_rate=0.0013
burn_in=1000
max_batches = 500500
max_batches = 16000
policy=steps
steps=400000,450000
steps=12800,14400
scales=.1,.1
#cutmix=1
@@ -959,14 +958,14 @@ activation=leaky
size=1
stride=1
pad=1
filters=255
filters=27
activation=linear
[yolo]
mask = 0,1,2
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
classes=80
classes=4
num=9
jitter=.3
ignore_thresh = .7
@@ -978,6 +977,7 @@ iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5
[route]
@@ -1046,14 +1046,14 @@ activation=leaky
size=1
stride=1
pad=1
filters=255
filters=27
activation=linear
[yolo]
mask = 3,4,5
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
classes=80
classes=4
num=9
jitter=.3
ignore_thresh = .7
@@ -1065,6 +1065,7 @@ iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5
[route]
@@ -1133,14 +1134,14 @@ activation=leaky
size=1
stride=1
pad=1
filters=255
filters=27
activation=linear
[yolo]
mask = 6,7,8
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
classes=80
classes=4
num=9
jitter=.3
ignore_thresh = .7
@@ -1153,4 +1154,5 @@ iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5
tests/darknet/cfg/yolo4x.cfg
+1436
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File diff suppressed because it is too large Load Diff
tests/darknet/names/mmr.names
+4
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@@ -0,0 +1,4 @@
blue-cone
yellow-cone
orange-cone
big-orange-cone
tests/darknet/yolo4-csp.cpp
+34
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@@ -0,0 +1,34 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
#include "test.h"
#include "DarknetParser.h"
int main() {
std::string bin_path = "yolo4-csp";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "/debug/layer144_out.bin",
bin_path + "/debug/layer159_out.bin",
bin_path + "/debug/layer174_out.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-csp.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
net->print();
//convert network to tensorRT
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
}
tests/darknet/yolo4_416.cpp → tests/darknet/yolo4_mmr.cpp
+4 -4
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@@ -5,7 +5,7 @@
#include "DarknetParser.h"
int main() {
std::string bin_path = "yolo4_416";
std::string bin_path = "yolo4_mmr";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
@@ -15,9 +15,9 @@ int main() {
bin_path + "/debug/layer161_out.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo4_416.cfg";
std::string name_path = "../tests/darknet/names/coco.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/982LxTQcNQfFQc4/download");
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4_mmr.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/mmr.names";
// downloadWeightsifDoNotExist(input_bins[0], bin_path, "");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
tests/darknet/yolo4x.cpp
+36
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@@ -0,0 +1,36 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
#include "test.h"
#include "DarknetParser.h"
int main() {
std::string bin_path = "yolo4x";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "/debug/layer168_out.bin",
bin_path + "/debug/layer185_out.bin",
bin_path + "/debug/layer202_out.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4x.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
net->print();
//convert network to tensorRT
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
}
tests/shelfnet/shelfnet.cpp
+295
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@@ -0,0 +1,295 @@
#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "tkdnn.h"
#include "NetworkViz.h"
const char *input_bin = "shelfnet/debug/input.bin";
const char *backbone[] = {
"shelfnet/layers/backbone-conv1.bin",
"shelfnet/layers/backbone-layer1-0-conv1.bin",
"shelfnet/layers/backbone-layer1-0-conv2.bin",
"shelfnet/layers/backbone-layer1-1-conv1.bin",
"shelfnet/layers/backbone-layer1-1-conv2.bin",
"shelfnet/layers/backbone-layer2-0-conv1.bin",
"shelfnet/layers/backbone-layer2-0-conv2.bin",
"shelfnet/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet/layers/backbone-layer2-1-conv1.bin",
"shelfnet/layers/backbone-layer2-1-conv2.bin",
"shelfnet/layers/backbone-layer3-0-conv1.bin",
"shelfnet/layers/backbone-layer3-0-conv2.bin",
"shelfnet/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet/layers/backbone-layer3-1-conv1.bin",
"shelfnet/layers/backbone-layer3-1-conv2.bin",
"shelfnet/layers/backbone-layer4-0-conv1.bin",
"shelfnet/layers/backbone-layer4-0-conv2.bin",
"shelfnet/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet/layers/backbone-layer4-1-conv1.bin",
"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"
};
const char *decoder[] = {
"shelfnet/layers/decoder-bottom-conv1.bin",
"shelfnet/layers/decoder-bottom-conv12.bin",
"shelfnet/layers/decoder-up_conv_list-0-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_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-inconv-conv1.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-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[] = {
"shelfnet/layers/trans1-conv.bin",
"shelfnet/layers/trans2-conv.bin",
"shelfnet/layers/trans3-conv.bin"};
int main()
{
downloadWeightsifDoNotExist(input_bin, "shelfnet", "https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download");
int classes = 19;
// Network layout
tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
tk::dnn::Network net(dim);
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, 0.0f, 0.01);
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
for(int i=0; i<2; ++i){
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
}
std::vector<tk::dnn::Layer*> features;
for(int i=0;i<3;++i){
int out_channel = pow(2,7+i);
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
new tk::dnn::Shortcut(&net, bn2);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
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, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
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);
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
}
//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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.0f); //should be 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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, tk::dnn::ResizeMode_t::LINEAR);
// }
new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet/debug/softmax.bin";
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
std::cout<<"Input:"<<std::endl;
//print network model
net.print();
// // convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet"));
tk::dnn::dataDim_t dim1 = dim; //input dim
dnnType *cudnn_out = nullptr;
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TKDNN_TSTART
cudnn_out = net.infer(dim1, data);
TKDNN_TSTOP
dim1.print();
}
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
TKDNN_TSTOP
dim2.print();
}
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
dnnType *out1, *out1_h;
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
std::cout << "CUDNN vs correct" << std::endl;
ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
std::cout << "TRT vs correct" << std::endl;
ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
std::cout << "CUDNN vs TRT " << std::endl;
ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
cv::imwrite("test.png", viz);
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}
tests/shelfnet/shelfnet_berkeley.cpp
+295
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@@ -0,0 +1,295 @@
#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "tkdnn.h"
#include "NetworkViz.h"
const char *input_bin = "shelfnet_berkeley/debug/input.bin";
const char *backbone[] = {
"shelfnet_berkeley/layers/backbone-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer1-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer2-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer2-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer3-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer3-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer4-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer4-1-conv2.bin"};
const char *conv_out[] = {
"shelfnet_berkeley/layers/conv_out-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out-conv_out.bin",
"shelfnet_berkeley/layers/conv_out16-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out16-conv_out.bin",
"shelfnet_berkeley/layers/conv_out32-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out32-conv_out.bin"
};
const char *decoder[] = {
"shelfnet_berkeley/layers/decoder-bottom-conv1.bin",
"shelfnet_berkeley/layers/decoder-bottom-conv12.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet_berkeley/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet_berkeley/layers/decoder-up_dense_list-1-conv.bin"
};
const char *ladder[] = {
"shelfnet_berkeley/layers/ladder-inconv-conv1.bin",
"shelfnet_berkeley/layers/ladder-inconv-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-0-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_conv_list-0.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-1-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_conv_list-1.bin",
"shelfnet_berkeley/layers/ladder-bottom-conv1.bin",
"shelfnet_berkeley/layers/ladder-bottom-conv12.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet_berkeley/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet_berkeley/layers/ladder-up_dense_list-1-conv.bin"};
const char *trans[] = {
"shelfnet_berkeley/layers/trans1-conv.bin",
"shelfnet_berkeley/layers/trans2-conv.bin",
"shelfnet_berkeley/layers/trans3-conv.bin"};
int main()
{
downloadWeightsifDoNotExist(input_bin, "shelfnet_berkeley", "https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download");
int classes = 20;
// Network layout
tk::dnn::dataDim_t dim(1, 3, 736, 1280, 1);
tk::dnn::Network net(dim);
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, 0.0f, 0.01);
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
for(int i=0; i<2; ++i){
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
}
std::vector<tk::dnn::Layer*> features;
for(int i=0;i<3;++i){
int out_channel = pow(2,7+i);
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
new tk::dnn::Shortcut(&net, bn2);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
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, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
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);
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
}
//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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.0f); //should be 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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
// }
new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet_berkeley/debug/softmax.bin";
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
std::cout<<"Input:"<<std::endl;
//print network model
net.print();
// // convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet_berkeley"));
tk::dnn::dataDim_t dim1 = dim; //input dim
dnnType *cudnn_out = nullptr;
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TKDNN_TSTART
cudnn_out = net.infer(dim1, data);
TKDNN_TSTOP
dim1.print();
}
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
TKDNN_TSTOP
dim2.print();
}
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
dnnType *out1, *out1_h;
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
std::cout << "CUDNN vs correct" << std::endl;
ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
std::cout << "TRT vs correct" << std::endl;
ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
std::cout << "CUDNN vs TRT " << std::endl;
ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
cv::imwrite("test.png", viz);
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}
tests/shelfnet/shelfnet_mapillary.cpp
+297
View File
@@ -0,0 +1,297 @@
#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "tkdnn.h"
#include "NetworkViz.h"
const char *input_bin = "shelfnet_mapillary/debug/input.bin";
const char *backbone[] = {
"shelfnet_mapillary/layers/backbone-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer1-0-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer1-0-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer1-1-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer1-1-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer2-0-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer2-0-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet_mapillary/layers/backbone-layer2-1-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer2-1-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer3-0-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer3-0-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet_mapillary/layers/backbone-layer3-1-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer3-1-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer4-0-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer4-0-conv2.bin",
"shelfnet_mapillary/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet_mapillary/layers/backbone-layer4-1-conv1.bin",
"shelfnet_mapillary/layers/backbone-layer4-1-conv2.bin"};
const char *conv_out[] = {
"shelfnet_mapillary/layers/conv_out-conv-conv.bin",
"shelfnet_mapillary/layers/conv_out-conv_out.bin",
"shelfnet_mapillary/layers/conv_out16-conv-conv.bin",
"shelfnet_mapillary/layers/conv_out16-conv_out.bin",
"shelfnet_mapillary/layers/conv_out32-conv-conv.bin",
"shelfnet_mapillary/layers/conv_out32-conv_out.bin"
};
const char *decoder[] = {
"shelfnet_mapillary/layers/decoder-bottom-conv1.bin",
"shelfnet_mapillary/layers/decoder-bottom-conv12.bin",
"shelfnet_mapillary/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet_mapillary/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet_mapillary/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet_mapillary/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet_mapillary/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet_mapillary/layers/decoder-up_dense_list-1-conv.bin"
};
const char *ladder[] = {
"shelfnet_mapillary/layers/ladder-inconv-conv1.bin",
"shelfnet_mapillary/layers/ladder-inconv-conv12.bin",
"shelfnet_mapillary/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet_mapillary/layers/ladder-down_module_list-0-conv12.bin",
"shelfnet_mapillary/layers/ladder-down_conv_list-0.bin",
"shelfnet_mapillary/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet_mapillary/layers/ladder-down_module_list-1-conv12.bin",
"shelfnet_mapillary/layers/ladder-down_conv_list-1.bin",
"shelfnet_mapillary/layers/ladder-bottom-conv1.bin",
"shelfnet_mapillary/layers/ladder-bottom-conv12.bin",
"shelfnet_mapillary/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet_mapillary/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet_mapillary/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet_mapillary/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet_mapillary/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet_mapillary/layers/ladder-up_dense_list-1-conv.bin"};
const char *trans[] = {
"shelfnet_mapillary/layers/trans1-conv.bin",
"shelfnet_mapillary/layers/trans2-conv.bin",
"shelfnet_mapillary/layers/trans3-conv.bin"};
int main()
{
// downloadWeightsifDoNotExist(input_bin, "shelfnet_mapillary", "");
// download the weights from here: https://cloud.hipert.unimore.it/f/652476
// Mapillary Vistas has originally 66 classes, but we reduced them to 15 to improve the results on the categories of our interest.
int classes = 15;
// Network layout
tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
tk::dnn::Network net(dim);
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, 0.0f, 0.01);
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
for(int i=0; i<2; ++i){
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
}
std::vector<tk::dnn::Layer*> features;
for(int i=0;i<3;++i){
int out_channel = pow(2,7+i);
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
new tk::dnn::Shortcut(&net, bn2);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
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, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
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);
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
}
//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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.0f); //should be 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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
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, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
// }
new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet_mapillary/debug/softmax.bin";
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
std::cout<<"Input:"<<std::endl;
//print network model
net.print();
// // convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet_mapillary"));
tk::dnn::dataDim_t dim1 = dim; //input dim
dnnType *cudnn_out = nullptr;
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TKDNN_TSTART
cudnn_out = net.infer(dim1, data);
TKDNN_TSTOP
dim1.print();
}
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
TKDNN_TSTOP
dim2.print();
}
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
dnnType *out1, *out1_h;
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
std::cout << "CUDNN vs correct" << std::endl;
ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
std::cout << "TRT vs correct" << std::endl;
ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
std::cout << "CUDNN vs TRT " << std::endl;
ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
cv::imwrite("test.png", viz);
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}
tests/test_rtinference/rtinference.cpp
+1 -1
View File
@@ -35,7 +35,7 @@ int main(int argc, char *argv[]) {
std::vector<double> stats;
printCenteredTitle(" TENSORRT inference ", '=', 30);
float total_time = 0;
for(int i=0; i<1200; i++) {
for(int i=0; i<64; i++) {
// generate input
for(int j=0; j<netRT.input_dim.tot(); j++) {