test_all_test script save results in separate files

Fix nms thresh
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
2022-03-30 15:47:41 +02:00 · 2022-02-15 19:57:57 +01:00 · 2021-11-29 17:33:57 +01:00 · 2021-11-25 11:32:38 +01:00 · 2021-11-24 18:20:26 +01:00 · 2021-11-23 16:29:40 +01:00
123 changed files with 17891 additions and 1331 deletions
@@ -12,5 +12,11 @@ build/
 *.hdf5
 *.pk
 *.table
+cmake-build-release/
 demo/COCO_val2017
-demo/BDD100K_val
+demo/BDD100K_val
+/.vs
+cmake-build-minsizerel/*
+scripts/COCO_val2017/*
+scripts/COCO_val2017.zip
+scripts/all_labels.txt
@@ -1,8 +1,15 @@
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.15)

 project (tkDNN)
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC -Wno-deprecated-declarations -Wno-unused-variable")
+if(UNIX)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -fPIC -Wno-deprecated-declarations")
+endif()
+if(WIN32)
+set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_FLAGS "/O2 /FS /EHsc")
+set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
+endif(WIN32)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/tkDNN)

 # project specific flags
@@ -10,7 +17,13 @@ if(DEBUG)
    add_definitions(-DDEBUG)
 endif()

-add_definitions(-DTKDNN_PATH="${CMAKE_CURRENT_SOURCE_DIR}")
+if(TKDNN_PATH)
+    message("SET TKDNN_PATH:"${TKDNN_PATH})
+    add_definitions(-DTKDNN_PATH="${TKDNN_PATH}")
+else()
+    add_definitions(-DTKDNN_PATH="${CMAKE_CURRENT_SOURCE_DIR}")
+endif()
+

 #-------------------------------------------------------------------------------
 # CUDA
@@ -28,19 +41,24 @@ include_directories(${CUDNN_INCLUDE_DIR})
 file(GLOB tkdnn_CUSRC "src/kernels/*.cu" "src/sorting.cu")
 cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${CUDNN_INCLUDE_DIRS})
 cuda_add_library(kernels SHARED ${tkdnn_CUSRC})
+target_link_libraries(kernels ${CUDA_CUBLAS_LIBRARIES})


 #-------------------------------------------------------------------------------
 # External Libraries
 #-------------------------------------------------------------------------------
 find_package(Eigen3 REQUIRED)
+message("Eigen DIR: " ${EIGEN3_INCLUDE_DIR})
 include_directories(${EIGEN3_INCLUDE_DIR})

 find_package(OpenCV REQUIRED)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
+# if(OpenCV_CUDA_VERSION)
+#     add_compile_definitions(OPENCV_CUDACONTRIB)
+# endif()

 # gives problems in cross-compiling, probably malformed cmake config
-#find_package(yaml-cpp REQUIRED)
+find_package(yaml-cpp REQUIRED)

 #-------------------------------------------------------------------------------
 # Build Libraries
@@ -48,7 +66,7 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
 file(GLOB tkdnn_SRC "src/*.cpp")
 set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS} yaml-cpp)

-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES})
 add_library(tkDNN SHARED ${tkdnn_SRC})
 target_link_libraries(tkDNN ${tkdnn_LIBS})
@@ -77,6 +95,7 @@ foreach(test_SRC ${darknet_SRC})
    set(test_NAME test_${test_NAME})
    add_executable(${test_NAME} ${test_SRC})
    target_link_libraries(${test_NAME} tkDNN)
+    install(TARGETS ${test_NAME} DESTINATION bin)
 endforeach()

 # MOBILENET
@@ -103,6 +122,27 @@ target_link_libraries(test_resnet101_cnet tkDNN)
 add_executable(test_dla34_cnet tests/centernet/dla34_cnet/dla34_cnet.cpp)
 target_link_libraries(test_dla34_cnet tkDNN)

+add_executable(test_dla34_cnet3d tests/centernet/dla34_cnet3d/dla34_cnet3d.cpp)
+target_link_libraries(test_dla34_cnet3d tkDNN)
+
+# CENTERTRACK
+
+add_executable(test_dla34_ctrack tests/centertrack/dla34_ctrack/dla34_ctrack.cpp)
+target_link_libraries(test_dla34_ctrack tkDNN)
+
+# SHELFNET
+add_executable(test_shelfnet tests/shelfnet/shelfnet.cpp)
+target_link_libraries(test_shelfnet tkDNN)
+
+add_executable(test_shelfnet_berkeley tests/shelfnet/shelfnet_berkeley.cpp)
+target_link_libraries(test_shelfnet_berkeley tkDNN)
+
+add_executable(test_shelfnet_mapillary tests/shelfnet/shelfnet_mapillary.cpp)
+target_link_libraries(test_shelfnet_mapillary tkDNN)
+
+add_executable(test_shelfnet_coco tests/shelfnet/shelfnet_coco.cpp)
+target_link_libraries(test_shelfnet_coco tkDNN)
+
 # DEMOS
 add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
 target_link_libraries(test_rtinference tkDNN)
@@ -113,6 +153,15 @@ target_link_libraries(map_demo tkDNN)
 add_executable(demo demo/demo/demo.cpp)
 target_link_libraries(demo tkDNN)

+add_executable(demo3D demo/demo/demo3D.cpp)
+target_link_libraries(demo3D tkDNN)
+
+add_executable(demoTracker demo/demo/demoTracker.cpp)
+target_link_libraries(demoTracker tkDNN)
+
+add_executable(seg_demo demo/demo/seg_demo.cpp)
+target_link_libraries(seg_demo tkDNN)
+
 #-------------------------------------------------------------------------------
 # Install
 #-------------------------------------------------------------------------------
@@ -123,7 +172,10 @@ target_link_libraries(demo tkDNN)
 message("install dir:" ${CMAKE_INSTALL_PREFIX})
 install(DIRECTORY include/ DESTINATION include/)
 install(TARGETS tkDNN kernels DESTINATION lib)
+install(TARGETS test_simple test_mnist test_mnistRT test_rtinference demo map_demo DESTINATION bin)
 install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/cmake/" # source directory
        DESTINATION "share/tkDNN/cmake/" # target directory
 )
-
+install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/tests/" # source directory
+        DESTINATION "share/tkDNN/tests" # target directory
+)
@@ -3,74 +3,105 @@ tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives,
 The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training. 


-If you use tkDNN in your research, please cite one of the following papers. For use in commercial solutions, write at gattifrancesco@hotmail.it and micaela.verucchi@unimore.it or refer to https://hipert.unimore.it/ .
+If you use tkDNN in your research, please cite the [following paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9212130&casa_token=sQTJXi7tJNoAAAAA:BguH9xCIY48MxbtDS3LXzIXzO-9sWArm7Hd7y7BwaLmqRuM_Gx8bOYizFPNMNtpo5K0kB-P-). For use in commercial solutions, write at gattifrancesco@hotmail.it and micaela.verucchi@unimore.it or refer to https://hipert.unimore.it/ .

 ```
-Accepted paper @ IRC 2020, will soon be published.
-M. Verucchi, L. Bartoli, F. Bagni, F. Gatti, P. Burgio and M. Bertogna, "Real-Time clustering and LiDAR-camera fusion on embedded platforms for self-driving cars",  in proceedings in IEEE Robotic Computing (2020)
-
-Accepted paper @ ETFA 2020, will soon be published.
-M. Verucchi, G. Brilli, D. Sapienza, M. Verasani, M. Arena, F. Gatti, A. Capotondi, R. Cavicchioli, M. Bertogna, M. Solieri
-"A Systematic Assessment of Embedded Neural Networks for Object Detection", in IEEE International Conference on Emerging Technologies and Factory Automation (2020)
+@inproceedings{verucchi2020systematic,
+  title={A Systematic Assessment of Embedded Neural Networks for Object Detection},
+  author={Verucchi, Micaela and Brilli, Gianluca and Sapienza, Davide and Verasani, Mattia and Arena, Marco and Gatti, Francesco and Capotondi, Alessandro and Cavicchioli, Roberto and Bertogna, Marko and Solieri, Marco},
+  booktitle={2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
+  volume={1},
+  pages={937--944},
+  year={2020},
+  organization={IEEE}
+}
 ```

-## Results
-Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimesion as the input size, on 
+### What's new
+#### 20 July 2021
+- [x] Support to sematic segmentation [README](docs/README_seg.md)
+- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
+#### 24 November 2021
+- [x] Support to sematic segmentation on cuda 11
+- [x] Support to TensorRT8 (thanks to [Harshvardhan Chandirasekar](https://github.com/perseusdg)). 
+
+TensorRT8 (and therefore Jetpack 4.6) is currently supported only on the branch tensorrt8 due to [performance issue with TensorRT8](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-8.html)). We will merge it to the master as soon as those issues are fixed (probably in future minor releases).
+
+## FPS Results
+Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on 
  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
+  * Xavier NX, Jetpack 4.4  (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ). 
  * Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 );
  * Jetson Nano, Jetpack 4.4  (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ). 

 | Platform   | Network    | FP32, B=1 | FP32, B=4	| FP16, B=1 |	FP16, B=4 |	INT8, B=1 |	INT8, B=4 | 
 | :------:   | :-----:    | :-----:   | :-----:   | :-----:   |	:-----:   |	:-----:   |	:-----:   | 
-| RTX 2080Ti | yolo4  320 | 118,59	  |237,31	    | 207,81	  | 443,32	  | 262,37	  | 530,93    | 
-| RTX 2080Ti | yolo4  416 | 104,81	  |162,86	    | 169,06	  | 293,78	  | 206,93	  | 353,26    | 
-| RTX 2080Ti | yolo4  512 | 92,98	    |132,43	    | 140,36	  | 215,17	  | 165,35	  | 254,96    | 
-| RTX 2080Ti | yolo4  608 | 63,77	    |81,53	    | 111,39	  | 152,89	  | 127,79	  | 184,72    | 
-| AGX Xavier | yolo4 320  |	26,78	    |32,05	    | 57,14	    | 79,05	    | 73,15	    | 97,56     |
-| AGX Xavier | yolo4 416  |	19,96	    |21,52	    | 41,01	    | 49,00	    | 50,81	    | 60,61     |
-| AGX Xavier | yolo4 512  |	16,58	    |16,98	    | 31,12	    | 33,84	    | 37,82	    | 41,28     |
-| AGX Xavier | yolo4 608  |	9,45 	    |10,13	    | 21,92	    | 23,36	    | 27,05	    | 28,93     |
-| Tx2        | yolo4 320	| 11,18	    | 12,07	    | 15,32	    | 16,31     | -         | -         |
-| Tx2        | yolo4 416	| 7,30	    | 7,58	    | 9,45	    | 9,90      | -         | -         |
-| Tx2        | yolo4 512	| 5,96	    | 5,95	    | 7,22	    | 7,23      | -         | -         |
-| Tx2        | yolo4 608	| 3,63	    | 3,65	    | 4,67	    | 4,70      | -         | -         |
-| Nano       | yolo4 320	| 4,23	    | 4,55	    | 6,14	    | 6,53      | -         | -         |
-| Nano       | yolo4 416	| 2,88	    | 3,00	    | 3,90	    | 4,04      | -         | -         |
-| Nano       | yolo4 512	| 2,32	    | 2,34	    | 3,02	    | 3,04      | -         | -         |
-| Nano       | yolo4 608	| 1,40	    | 1,41	    | 1,92	    | 1,93      | -         | -         |
+| RTX 2080Ti | yolo4 320  | 118.59	  | 237.31	  | 207.81	  | 443.32	  | 262.37	  | 530.93    | 
+| RTX 2080Ti | yolo4 416  | 104.81	  | 162.86	  | 169.06	  | 293.78	  | 206.93	  | 353.26    | 
+| RTX 2080Ti | yolo4 512  | 92.98	    | 132.43	  | 140.36	  | 215.17	  | 165.35	  | 254.96    | 
+| RTX 2080Ti | yolo4 608  | 63.77	    | 81.53	    | 111.39	  | 152.89	  | 127.79	  | 184.72    | 
+| AGX Xavier | yolo4 320  |	26.78	    | 32.05	    | 57.14	    | 79.05	    | 73.15	    | 97.56     |
+| AGX Xavier | yolo4 416  |	19.96	    | 21.52	    | 41.01	    | 49.00	    | 50.81	    | 60.61     |
+| AGX Xavier | yolo4 512  |	16.58	    | 16.98	    | 31.12	    | 33.84	    | 37.82	    | 41.28     |
+| AGX Xavier | yolo4 608  |	9.45 	    | 10.13	    | 21.92	    | 23.36	    | 27.05	    | 28.93     |
+| Xavier NX  | yolo4 320  |	14.56	    | 16.25	    | 30.14	    | 41.15	    | 42.13	    | 53.42     |
+| Xavier NX  | yolo4 416  |	10.02	    | 10.60	    | 22.43	    | 25.59	    | 29.08	    | 32.94     |
+| Xavier NX  | yolo4 512  |	8.10	    | 8.32	    | 15.78	    | 17.13	    | 20.51	    | 22.46     |
+| Xavier NX  | yolo4 608  |	5.26	    | 5.18	    | 11.54	    | 12.06	    | 15.09	    | 15.82     |
+| Tx2        | yolo4 320	| 11.18	    | 12.07	    | 15.32	    | 16.31     | -         | -         |
+| Tx2        | yolo4 416	| 7.30	    | 7.58	    | 9.45	    | 9.90      | -         | -         |
+| Tx2        | yolo4 512	| 5.96	    | 5.95	    | 7.22	    | 7.23      | -         | -         |
+| Tx2        | yolo4 608	| 3.63	    | 3.65	    | 4.67	    | 4.70      | -         | -         |
+| Nano       | yolo4 320	| 4.23	    | 4.55	    | 6.14	    | 6.53      | -         | -         |
+| Nano       | yolo4 416	| 2.88	    | 3.00	    | 3.90	    | 4.04      | -         | -         |
+| Nano       | yolo4 512	| 2.32	    | 2.34	    | 3.02	    | 3.04      | -         | -         |
+| Nano       | yolo4 608	| 1.40	    | 1.41	    | 1.92	    | 1.93      | -         | -         |
+
+## MAP Results
+Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
+
+|                      | CodaLab       | CodaLab   | CodaLab       | CodaLab     | tkDNN map     | tkDNN map |
+| -------------------- | :-----------: | :-------: | :-----------: | :---------: | :-----------: | :-------: |
+|                      | **tkDNN**     | **tkDNN** | **darknet**   | **darknet** | **tkDNN**     | **tkDNN** |
+|                      | MAP(0.5:0.95) | AP50      | MAP(0.5:0.95) | AP50        | MAP(0.5:0.95) | AP50      |
+| Yolov3 (416x416)     | 0.381         | 0.675     | 0.380         | 0.675       | 0.372         | 0.663     |
+| yolov4 (416x416)     | 0.468         | 0.705     | 0.471         | 0.710       | 0.459         | 0.695     |
+| yolov3tiny (416x416) | 0.096         | 0.202     | 0.096         | 0.201       | 0.093         | 0.198     |
+| yolov4tiny (416x416) | 0.202         | 0.400     | 0.201         | 0.400       | 0.197         | 0.395     |
+| Cnet-dla34 (512x512) | 0.366         | 0.543     | \-            | \-          | 0.361         | 0.535     |
+| mv2SSD (512x512)     | 0.226         | 0.381     | \-            | \-          | 0.223         | 0.378     |

 ## Index
 - [tkDNN](#tkdnn)
  - [Index](#index)
  - [Dependencies](#dependencies)
-  - [About OpenCV](#about-opencv)
  - [How to compile this repo](#how-to-compile-this-repo)
  - [Workflow](#workflow)
-  - [How to export weights](#how-to-export-weights)
-    - [1)Export weights from darknet](#1export-weights-from-darknet)
-    - [2)Export weights for DLA34 and ResNet101](#2export-weights-for-dla34-and-resnet101)
-    - [3)Export weights for CenterNet](#3export-weights-for-centernet)
-    - [4)Export weights for MobileNetSSD](#4export-weights-for-mobilenetssd)
-  - [Run the demo](#run-the-demo)
-    - [FP16 inference](#fp16-inference)
-    - [INT8 inference](#int8-inference)
-  - [mAP demo](#map-demo)
+  - [Exporting weights](#exporting-weights)
+  - [Run the demos](#run-the-demos)
+  - [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
  - [Existing tests and supported networks](#existing-tests-and-supported-networks)
  - [References](#references)
-
-
-
+  

 ## Dependencies
-This branch works on every NVIDIA GPU that supports the dependencies:
-* CUDA 10.0
-* CUDNN 7.603
-* TENSORRT 6.01
-* OPENCV 3.4
-* yaml-cpp 0.5.2 (sudo apt install libyaml-cpp-dev)
+This branch works on every NVIDIA GPU that supports the following (latest tested) dependencies:
+* CUDA 11.0 (or >= 10) [the segmentation only works with CUDA 10 for now]
+* cuDNN 8.0.4 (or >= 7.3)
+* TensorRT 7.2.0 (or >=5)
+* OpenCV 4.5.2 (or >=4)
+* cmake 3.21 (or >= 3.15)
+* yaml-cpp 0.5.2
+* eigen3 3.3.4
+* curl 7.58

-## About OpenCV
+```
+sudo apt install libyaml-cpp-dev curl libeigen3-dev
+
+```
+
+#### About OpenCV
 To compile and install OpenCV4 with contrib us the script ```install_OpenCV4.sh```. It will download and compile OpenCV in Download folder.
 ```
 bash scripts/install_OpenCV4.sh
@@ -96,212 +127,23 @@ Steps needed to do inference on tkDNN with a custom neural network.
 * Create a new test and define the network, layer by layer using the weights extracted and the output to check the results. 
 * Do inference.

-## How to export weights
+## Exporting weights

-Weights are essential for any network to run inference. For each test a folder organized as follow is needed (in the build folder):
-```
-    test_nn
-        |---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
-        |---- debug/  (folder containing a binary file for each layer with the corresponding outputs)
-```
-Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
+For specific details on how to export weights see [HERE](./docs/exporting_weights.md).

-### 1)Export weights from darknet
-To export weights for NNs that are defined in darknet framework, use [this](https://git.hipert.unimore.it/fgatti/darknet.git) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
-
-```
-git clone https://git.hipert.unimore.it/fgatti/darknet.git
-cd darknet
-make
-mkdir layers debug
-./darknet export <path-to-cfg-file> <path-to-weights> layers
-```
-N.b. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug. 
-
-### 2)Export weights for DLA34 and ResNet101 
-To get weights and outputs needed to run the tests dla34 and resnet101 use the Python script and the Anaconda environment included in the repository.   
-
-Create Anaconda environment and activate it:
-```
-conda env create -f file_name.yml
-source activate env_name
-python <script name>
-```
-### 3)Export weights for CenterNet
-To get the weights needed to run Centernet tests use [this](https://github.com/sapienzadavide/CenterNet.git) fork of the original Centernet. 
-```
-git clone https://github.com/sapienzadavide/CenterNet.git
-```
-* follow the instruction in the README.md and INSTALL.md
-
-```
-python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_resdcn101.pth --exp_wo --exp_wo_dim 512
-python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512
-```
-### 4)Export weights for MobileNetSSD
-To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network. 
-
-```
-git clone https://github.com/mive93/pytorch-ssd
-cd pytorch-ssd
-conda env create -f env_mobv2ssd.yml
-python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
-```
-
-## Darknet Parser
-tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*:
-```
-// example of parsing yolo4
-tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
-net->print();
-```
-All models from darknet are now parsed directly from cfg, you still need to export the weights with the descripted tools in the previus section.
-<details>
-  <summary>Supported layers</summary>
-  convolutional
-  maxpool
-  avgpool
-  shortcut
-  upsample
-  route
-  reorg
-  region
-  yolo
-</details>
-<details>
-  <summary>Supported activations</summary>
-  relu
-  leaky
-  mish
-</details>
-
-## Run the demo 
-This is an example using yolov4.
-
-To run the an object detection first create the .rt file by running:
-```
-rm yolo4_fp32.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo4            # run the yolo test (is slow)
-```
-If you get problems in the creation, try to check the error activating the debug of TensorRT in this way:
-```
-cmake .. -DDEBUG=True
-make
-```
-
-Once you have succesfully created your rt file, run the demo: 
-```
-./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
-```
-In general the demo program takes 6 parameters:
-```
-./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag>
-```
-where
-*  ```<network-rt-file>``` is the rt file generated by a test
-*  ```<<path-to-video>``` is the path to a video file or a camera input  
-*  ```<kind-of-network>``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
-*  ```<number-of-classes>```is the number of classes the network is trained on
-*  ```<n-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
-*  ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
-
-N.b. By default it is used FP32 inference
+## Run the demos 

+For specific details on how to run:
+- 2D object detection demos, details on FP16, INT8 and batching see [HERE](./docs/demo.md).
+- segmentation demos see [HERE](./docs/README_seg.md).
+- 2D/3D object detection and tracking demos see [HERE](./docs/README_2d3dtracking.md).
+- mAP demo to evaluate 2D object detectors see [HERE](./docs/mAP_demo.md).

 ![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)

-### FP16 inference
+## tkDNN on Windows 10 (experimental)

-To run the an object detection demo with FP16 inference follow these steps (example with yolov3):
-```
-export TKDNN_MODE=FP16  # set the half floating point optimization
-rm yolo3_fp16.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
-```
-N.b. Using FP16 inference will lead to some errors in the results (first or second decimal). 
-
-### INT8 inference
-
-To run the an object detection demo with INT8 inference three environment variables need to be set:
-  * ```export TKDNN_MODE=INT8```: set the 8-bit integer optimization
-  * ```export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt``` : image_list.txt has in each line the absolute path to a calibration image
-  * ```export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt```: label_list.txt has in each line the absolute path to a calibration label
-  
-You should provide image_list.txt and label_list.txt, using training images. However, if you want to quickly test the INT8 inference you can run (from this repo root folder)
-```
-bash scripts/download_validation.sh COCO
-```
-to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD insted of COCO to download BDD validation. 
-
-Then a complete example using yolo3 and COCO dataset would be:
-```
-export TKDNN_MODE=INT8
-export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
-export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
-rm yolo3_int8.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
-```
-N.B. 
- * Using INT8 inference will lead to some errors in the results. 
- * The test will be slower: this is due to the INT8 calibration, which may take some time to complete. 
- * INT8 calibration requires TensorRT version greater than or equal to 6.0
- * Only 100 images are used to create the calibration table by default (set in the code).
-
-### BatchSize bigger than 1
-```
-export TKDNN_BATCHSIZE=2
-# build tensorRT files
-```
-This will create a TensorRT file with the desidered **max** batch size.
-The test will still run with a batch of 1, but the created tensorRT can manage the desidered batch size.
-
-### Test batch Inference
-This will test the network with random input and check if the output of each batch is the same.
-```
-./test_rtinference <network-rt-file> <number-of-batches>
-# <number-of-batches> should be less or equal to the max batch size of the <network-rt-file>
-
-# example
-export TKDNN_BATCHSIZE=4           # set max batch size
-rm yolo3_fp32.rt                   # be sure to delete(or move) old tensorRT files
-./test_yolo3                       # build RT file
-./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4
-```
-
-## mAP demo
-
-To compute mAP, precision, recall and f1score, run the map_demo.
-
-A validation set is needed. 
-To download COCO_val2017 (80 classes) run (form the root folder): 
-```
-bash scripts/download_validation.sh COCO
-```
-To download Berkeley_val (10 classes) run (form the root folder): 
-```
-bash scripts/download_validation.sh BDD
-```
-
-To compute the map, the following parameters are needed:
-```
-./map_demo <network rt> <network type [y|c|m]> <labels file path> <config file path>
-```
-where 
-* ```<network rt>```: rt file of a chosen network on which compute the mAP.
-* ```<network type [y|c|m]>```: type of network. Right now only y(yolo), c(centernet) and m(mobilenet) are allowed
-* ```<labels file path>```: path to a text file containing all the paths of the ground-truth labels. It is important that all the labels of the ground-truth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the ground-truth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg. 
-* ```<config file path>```: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml
-
-Example:
-
-```
-cd build
-./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
-```
-
-This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to subit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
+For specific details on how to run tkDNN on Windows 10 see [HERE](./docs/windows.md).

 ## Existing tests and supported networks

@@ -328,8 +170,18 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
 | resnet101_cnet    | Centernet (Resnet101 backend)<sup>4</sup>     | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/5BTjHMWBcJk8g3i/download)     |
 | csresnext50-panet-spp    | Cross Stage Partial Network <sup>7</sup>     | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/Kcs4xBozwY4wFx8/download)     |
 | yolo4             | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
-| yolo4_berkeley             | Yolov4 <sup>8</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)                          | 10        | 540x320       | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download)     |
-| yolo4tiny             | Yolov4 tiny                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download)     |
+| yolo4_320         | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 320x320       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
+| yolo4_512         | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
+| yolo4_608         | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 608x608       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
+| yolo4_berkeley             | Yolov4 <sup>8</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)                          | 10        | 544x320       | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download)     |
+| yolo4tiny             | Yolov4 tiny <sup>9</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download)     |
+| yolo4x             | Yolov4x-mish  <sup>9</sup>                          | [COCO 2017](http://cocodataset.org/)                          | 80        | 640x640       | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download)     |
+| yolo4tiny_512           | Yolov4 tiny <sup>9</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download)     |
+| yolo4x-cps            | Scaled Yolov4 <sup>10</sup>                          | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download)     |
+| shelfnet              | ShelfNet18_realtime<sup>11</sup>                      | [Cityscapes](https://www.cityscapes-dataset.com/)                          | 19        | 1024x1024       | [weights](https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download)                                                                   |
+| shelfnet_berkeley              | ShelfNet18_realtime<sup>11</sup>                           | [DeepDrive](https://bdd-data.berkeley.edu/)                          | 20        | 1024x1024       | [weights](https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download)                                                                   |
+| dla34_cnet3d        | Centernet3D (DLA34 backend)<sup>4</sup>         | [KITTI 2017](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)                          | 1        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/2MDyWGzQsTKMjmR/download)     |
+| dla34_ctrack        | CenterTrack (DLA34 backend)<sup>12</sup>         | [NuScenes 3D](https://www.nuscenes.org/)                          | 7        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/rjNfgGL9FtAXLHp/download)     |


 ## References
@@ -342,3 +194,7 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
 6. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
 7. Wang, Chien-Yao, et al. "CSPNet: A New Backbone that can Enhance Learning Capability of CNN." arXiv preprint arXiv:1911.11929 (2019).
 8. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).
+9. Bochkovskiy, Alexey, "Yolo v4, v3 and v2 for Windows and Linux" (https://github.com/AlexeyAB/darknet)
+10. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "Scaled-YOLOv4: Scaling Cross Stage Partial Network." arXiv preprint arXiv:2011.08036 (2020).
+11. Zhuang, Juntang, et al. "ShelfNet for fast semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
+12. Zhou, Xingyi, Vladlen Koltun, and Philipp Krähenbühl. "Tracking objects as points." European Conference on Computer Vision. Springer, Cham, 2020.
@@ -3,5 +3,5 @@ map_points  : 101   #number of recall points (0 for all, 101 for COCO, 11 Pascal
 map_levels  : 10    #number of IoU step for the AP
 map_step    : 0.05  #step of IoU
 IoU_thresh  : 0.5   #starting IoU threshold
-conf_thresh : 0.0   #threshold on the condifence of the bbox
+conf_thresh : 0.001   #threshold on the condifence of the bbox
 verbose     : false #print on screen information
@@ -1,7 +1,7 @@
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>     /* srand, rand */
-#include <unistd.h>
+//#include <unistd.h>
 #include <mutex>

 #include "CenternetDetection.h"
@@ -9,7 +9,6 @@
 #include "Yolo3Detection.h"

 bool gRun;
-bool SAVE_RESULT = false;

 void sig_handler(int signo) {
    std::cout<<"request gateway stop\n";
@@ -18,35 +17,53 @@ void sig_handler(int signo) {

 int main(int argc, char *argv[]) {

-    std::cout<<"detection\n";
    signal(SIGINT, sig_handler);

-
-    std::string net = "yolo3_berkeley.rt";
+    // get config file path and read it
+    #ifdef __linux__ 
+        std::string config_file = "../demo/demoConfig.yaml";
+    #elif _WIN32
+        std::string config_file = "..\\..\\..\\demo\\demoConfig.yaml";
+    #endif
    if(argc > 1)
-        net = argv[1]; 
-    std::string input = "../demo/yolo_test.mp4";
-    if(argc > 2)
-        input = argv[2]; 
-    char ntype = 'y';
-    if(argc > 3)
-        ntype = argv[3][0]; 
-    int n_classes = 80;
-    if(argc > 4)
-        n_classes = atoi(argv[4]); 
-    int n_batch = 1;
-    if(argc > 5)
-        n_batch = atoi(argv[5]); 
-    bool show = true;
-    if(argc > 6)
-        show = atoi(argv[6]); 
+        config_file = argv[1]; 
+    
+    YAML::Node conf =  YAMLloadConf(config_file);
+    if(!conf)
+        FatalError("Problem with config file");

+    // read settings from config file
+    std::string net = YAMLgetConf<std::string>(conf, "net", "yolo4tiny_fp32.rt");
+    if(!fileExist(net.c_str()))
+        FatalError("The given network does not exist. Create the rt first.");
+
+    #ifdef __linux__ 
+        std::string input = YAMLgetConf<std::string>(conf, "input", "../demo/yolo_test.mp4");
+    #elif _WIN32
+        std::string input = YAMLgetConf(conf, "win_input", "..\\..\\..\\demo\\yolo_test.mp4");
+    #endif
+    if(!fileExist(input.c_str()))
+        FatalError("The given input video does not exist.");
+    
+    char ntype          = YAMLgetConf<char>(conf, "ntype", 'y');
+    int n_classes       = YAMLgetConf<int>(conf, "n_classes", 80);
+    int n_batch         = YAMLgetConf<int>(conf, "n_batch", 1);
    if(n_batch < 1 || n_batch > 64)
        FatalError("Batch dim not supported");
+    float conf_thresh   = YAMLgetConf<float>(conf, "conf_thresh", 0.3);
+    bool show           = YAMLgetConf<bool>(conf, "show", true);
+    bool save           = YAMLgetConf<bool>(conf, "save", false);

-    if(!show)
-        SAVE_RESULT = true;
-
+    std::cout   <<"Net settings - net: "<< net
+                <<", ntype: "<< ntype
+                <<", n_classes: "<< n_classes
+                <<", n_batch: "<< n_batch
+                <<", conf_thresh: "<< conf_thresh<<"\n"; 
+    std::cout   <<"Demo settings - input: "<< input
+                <<", show: "<< show
+                <<", save: "<< save<<"\n\n"; 
+    
+    // create detection network
    tk::dnn::Yolo3Detection yolo;
    tk::dnn::CenternetDetection cnet;
    tk::dnn::MobilenetDetection mbnet;  
@@ -69,10 +86,9 @@ int main(int argc, char *argv[]) {
        FatalError("Network type not allowed (3rd parameter)\n");
    }

-    detNN->init(net, n_classes, n_batch);
-
-    gRun = true;
+    detNN->init(net, n_classes, n_batch, conf_thresh);

+    // open video stream
    cv::VideoCapture cap(input);
    if(!cap.isOpened())
        gRun = false; 
@@ -80,19 +96,21 @@ int main(int argc, char *argv[]) {
        std::cout<<"camera started\n";

    cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
+    if(save) {
        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
    }

-    cv::Mat frame;
    if(show)
        cv::namedWindow("detection", cv::WINDOW_NORMAL);

+    cv::Mat frame;
    std::vector<cv::Mat> batch_frame;
    std::vector<cv::Mat> batch_dnn_input;

+    // start detection loop
+    gRun = true;
    while(gRun) {
        batch_dnn_input.clear();
        batch_frame.clear();
@@ -120,19 +138,18 @@ int main(int argc, char *argv[]) {
                cv::waitKey(1);
            }
        }
-        if(n_batch == 1 && SAVE_RESULT)
+        if(n_batch == 1 && save)
            resultVideo << frame;
    }

    std::cout<<"detection end\n";   
-    double mean = 0; 
    
+    double mean = 0; 
    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
    std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";    
    std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";    
    for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
    std::cout<<"Avg: "<<mean/n_batch<<" ms\t"<<1000/(mean/n_batch)<<" FPS\n"<<COL_END;   
-    

    return 0;
 }
@@ -0,0 +1,159 @@
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>     /* srand, rand */
+//#include <unistd.h>
+#include <mutex>
+
+#include "demo_utils.h"
+#include "CenternetDetection3D.h"
+
+bool gRun;
+bool SAVE_RESULT = false;
+
+void sig_handler(int signo) {
+    std::cout<<"request gateway stop\n";
+    gRun = false;
+}
+
+int main(int argc, char *argv[]) {
+
+    std::cout<<"detection\n";
+    signal(SIGINT, sig_handler);
+
+
+    std::string net = "dla34_cnet3d_fp32.rt";
+    if(argc > 1)
+        net = argv[1]; 
+    #ifdef __linux__ 
+        std::string input = "../demo/yolo_test.mp4";
+    #elif _WIN32
+        std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
+    #endif
+
+    if(argc > 2)
+        input = argv[2]; 
+    std::string calib_params = "";
+    if(argc > 3)
+        calib_params = argv[3]; 
+    char ntype = 'c';
+    if(argc > 4)
+        ntype = argv[4][0]; 
+    int n_classes = 3;
+    if(argc > 5)
+        n_classes = atoi(argv[5]); 
+    int n_batch = 1;
+    if(argc > 6)
+        n_batch = atoi(argv[6]); 
+        
+    bool show = true;
+    if(argc > 7)
+        show = atoi(argv[7]); 
+    float conf_thresh=0.3;
+    if(argc > 8)
+        conf_thresh = atof(argv[8]);     
+
+    if(n_batch < 1 || n_batch > 64)
+        FatalError("Batch dim not supported");
+
+    if(!show)
+	    SAVE_RESULT = true;
+
+    tk::dnn::CenternetDetection3D cnet;
+
+    tk::dnn::DetectionNN3D *detNN;  
+
+    switch(ntype)
+    {
+        case 'c':
+            detNN = &cnet;
+            break;
+        default:
+        FatalError("Network type not allowed (3rd parameter)\n");
+    }
+    std::vector<cv::Mat> calibs;
+    if(!calib_params.empty() && calib_params!="NULL") {
+        std::cout<<"calib_params: "<<calib_params<<std::endl;
+        cv::Mat calib;
+        // the calibration matrix must be a 3x3 matrix
+        readCalibrationMatrix(calib_params, calib);
+        for(int bi=0; bi< n_batch; ++bi)
+            calibs.push_back(calib);
+    }
+    detNN->init(net, n_classes, n_batch, conf_thresh, calibs);
+
+    gRun = true;
+
+    cv::VideoCapture cap(input);
+    if(!cap.isOpened())
+        gRun = false; 
+    else
+        std::cout<<"camera started\n";
+
+    cv::VideoWriter resultVideo;
+    if(SAVE_RESULT) {
+        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
+        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
+        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+    }
+    cv::Mat frame;
+    if(show)
+	    cv::namedWindow("detection", cv::WINDOW_NORMAL);
+    
+    std::vector<cv::Mat> batch_frame;
+    std::vector<cv::Mat> batch_dnn_input;
+
+    while(gRun) {
+        batch_dnn_input.clear();
+        batch_frame.clear();
+        
+        for(int bi=0; bi< n_batch; ++bi){
+            cap >> frame; 
+            if(!frame.data) 
+                break;
+            batch_frame.push_back(frame);
+
+            // this will be resized to the net format
+            batch_dnn_input.push_back(frame.clone());
+        } 
+        if(!frame.data) 
+            break;  
+ 
+        //inference
+        detNN->update(batch_dnn_input, n_batch, false, nullptr, false);
+        detNN->draw(batch_frame);
+
+        if(show){
+            for(int bi=0; bi< n_batch; ++bi){
+                cv::imshow("detection", batch_frame[bi]);
+                cv::waitKey(1);
+            }
+        }
+        if(n_batch == 1 && SAVE_RESULT)
+            resultVideo << frame;
+    }
+
+    std::cout<<"detection end\n";   
+    double mean = 0; 
+    
+    std::cout<<COL_GREENB<<"\n\nTime preprocessing stats:\n";
+    std::cout<<"Min: "<<*std::min_element(detNN->pre_stats.begin(), detNN->pre_stats.end())<<" ms\n";    
+    std::cout<<"Max: "<<*std::max_element(detNN->pre_stats.begin(), detNN->pre_stats.end())<<" ms\n";    
+    for(int i=0; i<detNN->pre_stats.size(); i++) mean += detNN->pre_stats[i]; mean /= detNN->pre_stats.size();
+    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+    mean=0;
+    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
+    std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())<<" ms\n";    
+    std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())<<" ms\n";    
+    for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
+    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+    mean=0;
+    std::cout<<COL_GREENB<<"\n\nTime postprocessing stats:\n";
+    std::cout<<"Min: "<<*std::min_element(detNN->post_stats.begin(), detNN->post_stats.end())<<" ms\n";    
+    std::cout<<"Max: "<<*std::max_element(detNN->post_stats.begin(), detNN->post_stats.end())<<" ms\n";    
+    for(int i=0; i<detNN->post_stats.size(); i++) mean += detNN->post_stats[i]; mean /= detNN->post_stats.size();
+    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+    
+    
+    return 0;
+}
+
@@ -0,0 +1,160 @@
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>     /* srand, rand */
+//#include <unistd.h>
+#include <mutex>
+
+#include "demo_utils.h"
+#include "CenterTrack.h"
+
+bool gRun;
+bool SAVE_RESULT = false;
+
+void sig_handler(int signo) {
+    std::cout<<"request gateway stop\n";
+    gRun = false;
+}
+
+int main(int argc, char *argv[]) {
+
+    std::cout<<"detection\n";
+    signal(SIGINT, sig_handler);
+
+
+    std::string net = "dla34_cnet3d_track_fp32.rt";
+    if(argc > 1)
+        net = argv[1]; 
+    #ifdef __linux__ 
+        std::string input = "../demo/yolo_test.mp4";
+    #elif _WIN32
+        std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
+    #endif
+
+    if(argc > 2)
+        input = argv[2]; 
+    std::string calib_params = "";
+    if(argc > 3)
+        calib_params = argv[3];
+    char ntype = 'c';
+    if(argc > 4)
+        ntype = argv[4][0]; 
+    int n_classes = 3;
+    if(argc > 5)
+        n_classes = atoi(argv[5]); 
+    int n_batch = 1;
+    if(argc > 6)
+        n_batch = atoi(argv[6]); 
+    bool show = true;
+    if(argc > 7)
+        show = atoi(argv[7]); 
+    float conf_thresh=0.3;
+    if(argc > 8)
+        conf_thresh = atof(argv[8]);   
+    bool t3d = true;
+    if(argc > 9)
+        t3d = atoi(argv[9]);
+    if(n_batch < 1 || n_batch > 64)
+        FatalError("Batch dim not supported");
+
+    if(!show)
+	    SAVE_RESULT = true;
+
+    tk::dnn::CenterTrack ctrack;
+
+    tk::dnn::TrackingNN *trackNN;  
+
+    switch(ntype)
+    {
+        case 'c':
+            trackNN = &ctrack;
+            break;
+        default:
+        FatalError("Network type not allowed (3rd parameter)\n");
+    }
+    std::vector<cv::Mat> calibs;
+    if(!calib_params.empty() && calib_params!="NULL") {
+        std::cout<<"calib_params: "<<calib_params<<std::endl;
+        cv::Mat calib;
+        // the calibration matrix must be a 3x3 matrix
+        readCalibrationMatrix(calib_params, calib);
+        for(int bi=0; bi< n_batch; ++bi)
+            calibs.push_back(calib);
+    }
+    trackNN->init(net, n_classes, n_batch, conf_thresh, t3d, calibs);
+
+    gRun = true;
+
+    cv::VideoCapture cap(input);
+    if(!cap.isOpened())
+        gRun = false; 
+    else
+        std::cout<<"camera started\n";
+
+    cv::VideoWriter resultVideo;
+    if(SAVE_RESULT) {
+        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
+        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
+        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+    }
+    cv::Mat frame;
+    if(show)
+	    cv::namedWindow("detection", cv::WINDOW_NORMAL);
+    
+    std::vector<cv::Mat> batch_frame;
+    std::vector<cv::Mat> batch_dnn_input;
+
+    while(gRun) {
+        batch_dnn_input.clear();
+        batch_frame.clear();
+        
+        for(int bi=0; bi< n_batch; ++bi){
+            cap >> frame; 
+            if(!frame.data) 
+                break;
+            batch_frame.push_back(frame);
+
+            // this will be resized to the net format
+            batch_dnn_input.push_back(frame.clone());
+        } 
+        if(!frame.data) 
+            break;  
+ 
+        //inference
+        trackNN->update(batch_dnn_input, n_batch, false, nullptr, false);
+        trackNN->draw(batch_frame);
+
+        if(show){
+            for(int bi=0; bi< n_batch; ++bi){
+                cv::imshow("detection", batch_frame[bi]);
+                cv::waitKey(1);
+            }
+        }
+        if(n_batch == 1 && SAVE_RESULT)
+            resultVideo << frame;
+    }
+
+    std::cout<<"detection end\n";   
+    double mean = 0; 
+    
+    std::cout<<COL_GREENB<<"\n\nTime preprocessing stats:\n";
+    std::cout<<"Min: "<<*std::min_element(trackNN->pre_stats.begin(), trackNN->pre_stats.end())<<" ms\n";    
+    std::cout<<"Max: "<<*std::max_element(trackNN->pre_stats.begin(), trackNN->pre_stats.end())<<" ms\n";    
+    for(int i=0; i<trackNN->pre_stats.size(); i++) mean += trackNN->pre_stats[i]; mean /= trackNN->pre_stats.size();
+    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+    mean=0;
+    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
+    std::cout<<"Min: "<<*std::min_element(trackNN->stats.begin(), trackNN->stats.end())<<" ms\n";    
+    std::cout<<"Max: "<<*std::max_element(trackNN->stats.begin(), trackNN->stats.end())<<" ms\n";    
+    for(int i=0; i<trackNN->stats.size(); i++) mean += trackNN->stats[i]; mean /= trackNN->stats.size();
+    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+    mean=0;
+    std::cout<<COL_GREENB<<"\n\nTime postprocessing stats:\n";
+    std::cout<<"Min: "<<*std::min_element(trackNN->post_stats.begin(), trackNN->post_stats.end())<<" ms\n";    
+    std::cout<<"Max: "<<*std::max_element(trackNN->post_stats.begin(), trackNN->post_stats.end())<<" ms\n";    
+    for(int i=0; i<trackNN->post_stats.size(); i++) mean += trackNN->post_stats[i]; mean /= trackNN->post_stats.size();
+    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+    
+    
+    return 0;
+}
+
@@ -2,7 +2,10 @@
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>     /* srand, rand */
+#ifdef __linux__
 #include <unistd.h>
+#endif
+
 #include <mutex>
 #include "utils.h"

@@ -31,10 +34,12 @@ int main(int argc, char *argv[])
    const char *config_filename = "../demo/config.yaml";
    const char * net = "yolo3.rt";
    const char * labels_path = "../demo/COCO_val2017/all_labels.txt";
+    int n_batches = 1;
+    float confidence_thresh = 0.3;
    bool show = false;
    bool write_dets = false;
    bool write_res_on_file = true;
-    bool write_coco_json = true;
+    bool write_coco_json = false;
    int n_images = 5000;

    bool verbose;
@@ -53,6 +58,12 @@ int main(int argc, char *argv[])
        labels_path = argv[3]; 
    if(argc > 4)
        config_filename = argv[4]; 
+    if(argc > 5)
+        n_batches = atoi(argv[5]); 
+    if(argc > 6)
+        confidence_thresh = atof(argv[6]); 
+
+    std::cout<<"conf t: "<<confidence_thresh<<std::endl;

    //check if files needed exist
    if(!fileExist(config_filename))
@@ -80,9 +91,9 @@ int main(int argc, char *argv[])
    }

    if(write_res_on_file){
-        times.open("times_"+net_name+".csv");
+        times.open("times_"+net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh)+".csv");
        memory.open("memory.csv", std::ios_base::app);
-        memory<<net<<";";
+        memory<<net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh)<<";";
    }

    // instantiate detector
@@ -105,7 +116,7 @@ int main(int argc, char *argv[])
        default:
            FatalError("Network type not allowed (3rd parameter)\n");
    }
-    detNN->init(net, n_classes);
+    detNN->init(net, n_classes, 1, conf_thresh);

    //read images 
    std::ifstream all_labels(labels_path);
@@ -118,90 +129,109 @@ int main(int argc, char *argv[])
    if(show)
        cv::namedWindow("detection", cv::WINDOW_NORMAL);

+    bool file_ok = false;
+
    int images_done;
-    for (images_done=0 ; std::getline(all_labels, l_filename) && images_done < n_images ; ++images_done) {
-        std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
+    for (images_done=0 ; images_done < n_images ;) {
+        

-        tk::dnn::Frame f;
-        f.lFilename = l_filename;
-        f.iFilename = l_filename;
-        convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
-
-        // read frame
-        if(!fileExist(f.iFilename.c_str()))
-            FatalError("Wrong image file path.");
-
-        cv::Mat frame = cv::imread(f.iFilename.c_str(), cv::IMREAD_COLOR);
+        int cur_batches = 0;
        std::vector<cv::Mat> batch_frames;
-        batch_frames.push_back(frame);
-        int height = frame.rows;
-        int width = frame.cols;
-
-        if(!frame.data) 
-            break;
        std::vector<cv::Mat> batch_dnn_input;
-        batch_dnn_input.push_back(frame.clone());
+            
+        std::vector<tk::dnn::Frame> cur_frames;
+        for(;cur_batches<n_batches && images_done < n_images;cur_batches++, ++images_done){
+
+            std::getline(all_labels, l_filename);
+            file_ok = all_labels ? true : false ;
+            if (!file_ok)
+                break;
+
+            tk::dnn::Frame f;
+            f.lFilename = l_filename;
+            f.iFilename = l_filename;
+            convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
+
+            // read frame
+            if(!fileExist(f.iFilename.c_str()))
+                FatalError("Wrong image file path.");
+
+            cv::Mat frame = cv::imread(f.iFilename.c_str(), cv::IMREAD_COLOR);
+            batch_frames.push_back(frame);
+            f.height = frame.rows;
+            f.width = frame.cols;
+
+            if(!frame.data) 
+                break;
+            batch_dnn_input.push_back(frame.clone());
+
+            // read and save groundtruth labels
+            if(fileExist(f.lFilename.c_str()))
+            {
+                std::ifstream labels(f.lFilename);
+                for(std::string line; std::getline(labels, line); ){
+                    std::istringstream in(line); 
+                    tk::dnn::BoundingBox b;
+                    in >> b.cl >> b.x >> b.y >> b.w >> b.h;  
+                    b.prob = 1;
+                    b.truthFlag = 1;
+                    f.gt.push_back(b);
+
+                    if(show)// draw rectangle for groundtruth
+                        cv::rectangle(batch_frames[cur_batches], cv::Point((b.x-b.w/2)*f.width, (b.y-b.h/2)*f.height), cv::Point((b.x+b.w/2)*f.width,(b.y+b.h/2)*f.height), cv::Scalar(0, 255, 0), 2);             
+                }
+            } 
+
+            cur_frames.push_back(f);
+        }
+        if (!file_ok)
+            break;

        //inference 
-        detected_bbox.clear();
-        detNN->update(batch_dnn_input,1,write_res_on_file, &times, write_coco_json);
+        detNN->update(batch_dnn_input,cur_batches,write_res_on_file, &times, write_coco_json);
        detNN->draw(batch_frames);
-        detected_bbox = detNN->detected;

-        if(write_coco_json)
-            printJsonCOCOFormat(&coco_json, f.iFilename.c_str(), detected_bbox, classes,  width, height);        
+        for(int j=0;j<cur_frames.size(); ++j){
+            if(write_coco_json)
+            printJsonCOCOFormat(&coco_json, cur_frames[j].iFilename.c_str(), detNN->batchDetected[j], classes,  cur_frames[j].width, cur_frames[j].height);        

-        std::ofstream myfile;
-        if(write_dets)
-            myfile.open ("det/"+f.lFilename.substr(f.lFilename.find("labels/") + 7));
+            std::ofstream myfile;
+            if(write_dets)
+                myfile.open ("det/"+cur_frames[j].lFilename.substr(cur_frames[j].lFilename.find("labels/") + 7));

-        // save detections labels
-        for(auto d:detected_bbox){
-            //convert detected bb in the same format as label
-            //<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
-            tk::dnn::BoundingBox b;
-            b.x = (d.x + d.w/2) / width;
-            b.y = (d.y + d.h/2) / height;
-            b.w = d.w / width;
-            b.h = d.h / height;
-            b.prob = d.prob;
-            b.cl = d.cl;
-            f.det.push_back(b);
+            // save detections labels
+            for(auto d:detNN->batchDetected[j]){
+                //convert detected bb in the same format as label
+                //<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
+                tk::dnn::BoundingBox b;
+                b.x = (d.x + d.w/2) / cur_frames[j].width;
+                b.y = (d.y + d.h/2) / cur_frames[j].height;
+                b.w = d.w / cur_frames[j].width;
+                b.h = d.h / cur_frames[j].height;
+                b.prob = d.prob;
+                b.cl = d.cl;
+                cur_frames[j].det.push_back(b);
+
+                if(write_dets)
+                    myfile << d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
+
+                if(show)// draw rectangle for detection
+                    cv::rectangle(batch_frames[j], cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(0, 0, 255), 2);             
+                }

            if(write_dets)
-                myfile << d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
-
-			if(show)// draw rectangle for detection
-                cv::rectangle(batch_frames[0], cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(0, 0, 255), 2);             
-        }
-
-        if(write_dets)
-            myfile.close();
-
-        // read and save groundtruth labels
-        if(fileExist(f.lFilename.c_str()))
-        {
-            std::ifstream labels(l_filename);
-            for(std::string line; std::getline(labels, line); ){
-                std::istringstream in(line); 
-                tk::dnn::BoundingBox b;
-                in >> b.cl >> b.x >> b.y >> b.w >> b.h;  
-                b.prob = 1;
-                b.truthFlag = 1;
-                f.gt.push_back(b);
-
-                if(show)// draw rectangle for groundtruth
-                    cv::rectangle(batch_frames[0], cv::Point((b.x-b.w/2)*width, (b.y-b.h/2)*height), cv::Point((b.x+b.w/2)*width,(b.y+b.h/2)*height), cv::Scalar(0, 255, 0), 2);             
-            }
-        }    
+                myfile.close();
      
-        images.push_back(f);
+            images.push_back(cur_frames[j]);
        
-        if(show){
-            cv::imshow("detection", batch_frames[0]);
-            cv::waitKey(0);
+            if(show){
+                cv::imshow("detection", batch_frames[j]);
+                cv::waitKey(0);
+            }
+            
        }
-
+        std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\tcur batch:\t"<<cur_batches<< "\n"<<COL_END;
+        
        getMemUsage(vm, rss);
        vm_total += vm;
        rss_total += rss;
@@ -218,11 +248,11 @@ int main(int argc, char *argv[])
    std::cout << "Avg VM[MB]: " << vm_total/images_done/1024.0 << ";Avg RSS[MB]: " << rss_total/images_done/1024.0 << std::endl;

    //compute mAP
-    double AP = tk::dnn::computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net_name);
+    double AP = tk::dnn::computeMapNIoULevels(images,classes,IoU_thresh,confidence_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh));
    std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;

    //compute average precision, recall and f1score
-    tk::dnn::computeTPFPFN(images,classes,IoU_thresh,conf_thresh, verbose, write_res_on_file, net_name);
+    tk::dnn::computeTPFPFN(images,classes,IoU_thresh,confidence_thresh, verbose, write_res_on_file, net_name +"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh));

    if(write_res_on_file){
        memory<<vm_total/images_done/1024.0<<";"<<rss_total/images_done/1024.0<<"\n";
@@ -0,0 +1,148 @@
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>     /* srand, rand */
+#include <unistd.h>
+#include <mutex>
+
+#include "SegmentationNN.h"
+
+bool gRun;
+bool SAVE_RESULT = true;
+
+void sig_handler(int signo) {
+    std::cout<<"request gateway stop\n";
+    gRun = false;
+}
+
+void writePred(const std::string& images_names, const std::string& gt_folder, const std::string& out_folder, tk::dnn::SegmentationNN& segNN, int& width, int& height, bool show=false){
+    std::ifstream all_gt(images_names);
+    std::string filename;
+    cv::Mat frame;
+    for (; std::getline(all_gt, filename); ) {
+        std::cout<<filename<<std::endl;
+        frame = cv::imread(gt_folder + filename);
+        height = frame.rows;
+        width = frame.cols;
+        segNN.updateOriginal(frame, false);
+        if(show)
+            segNN.draw();
+        cv::imwrite(out_folder + filename, segNN.segmented[0]);
+    }
+}
+
+int main(int argc, char *argv[]) {
+
+    std::cout<<"detection\n";
+    signal(SIGINT, sig_handler);
+
+
+    std::string net = "shelfnet_fp32.rt";
+    if(argc > 1)
+        net = argv[1]; 
+    std::string input = "../demo/yolo_test.mp4";
+    if(argc > 2)
+        input = argv[2]; 
+    int n_batch = 1;
+    if(argc > 3)
+        n_batch = atoi(argv[3]); 
+    int n_classes = 19;
+    if(argc > 4)
+        n_classes = atoi(argv[4]); 
+    bool resize = false;
+    if(argc > 5)
+        resize = atoi(argv[5]); 
+    int baseline_resize = 1024;
+    if(argc > 6)
+        baseline_resize = atoi(argv[6]); 
+    bool show = true;
+    if(argc > 7)
+        show = atoi(argv[7]); 
+    bool write_pred = false;
+    if(argc > 8)
+        write_pred = atoi(argv[8]); 
+
+    if(resize && (baseline_resize < 0 || baseline_resize > 5000)) 
+        FatalError("Problem with baseline resize")
+    if(n_batch < 1 || n_batch > 64)
+        FatalError("Batch dim not supported");
+
+    //net initialization 
+    tk::dnn::SegmentationNN segNN;
+    segNN.init(net, n_classes, n_batch);
+
+    int height = 0, width = 0;
+    int basewidth=baseline_resize,  hsize; 
+    
+    if(write_pred){
+        std::string gt_folder = "../demo/CityScapes_val/images/";
+        std::string images_names = "../demo/CityScapes_val/all_images.txt";
+        std::string out_folder = "seg/";
+
+        writePred(images_names, gt_folder, out_folder, segNN, width, height, show);
+    }
+    else{
+        if(!show)
+            SAVE_RESULT = true;
+
+        gRun = true;
+
+        cv::VideoCapture cap(input);
+        if(!cap.isOpened())
+            gRun = false; 
+        else
+            std::cout<<"camera started\n";
+
+        cv::VideoWriter resultVideo;
+        if(SAVE_RESULT) {
+            int w,h;
+            if(resize){
+                w = basewidth;
+                h = int((float(cap.get(cv::CAP_PROP_FRAME_HEIGHT))*float(basewidth/float(cap.get(cv::CAP_PROP_FRAME_WIDTH)))));
+            }
+            else{
+                w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
+                h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
+            }
+            resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+        }
+
+        cv::Mat frame;
+        while(gRun) {
+            cap >> frame; 
+            if(!frame.data) 
+                break;
+
+            if(resize){
+                hsize  = int((float(frame.rows)*float(basewidth/float(frame.cols))));
+                cv::resize(frame, frame, cv::Size(basewidth, hsize));
+            }
+
+            height = frame.rows;
+            width = frame.cols;
+
+            //inference
+            segNN.updateOriginal(frame, true);
+            if(show)
+                segNN.draw();
+
+            if(SAVE_RESULT)
+                resultVideo << segNN.segmented[0];
+        }
+    }
+
+    std::cout<<"segmentation end\n";   
+    double mean = 0, mean_pre = 0, mean_post = 0;
+    
+    std::cout<<COL_GREENB<<"\n\nTime stats for size ["<<width<<","<<height<<"] :\n";
+    
+    for(int i=0; i<segNN.stats.size(); i++) mean += segNN.stats[i]; mean /= segNN.stats.size();
+    for(int i=0; i<segNN.stats_pre.size(); i++) mean_pre += segNN.stats_pre[i]; mean_pre /= segNN.stats_pre.size();
+    for(int i=0; i<segNN.stats_post.size(); i++) mean_post += segNN.stats_post[i]; mean_post /= segNN.stats_post.size();
+    std::cout<<"Avg pre:\t"<<mean_pre<<" ms\t"<<1000/(mean_pre)<<" FPS\n";   
+    std::cout<<"Avg inf:\t"<<mean<<" ms\t"<<1000/(mean)<<" FPS\n";   
+    std::cout<<"Avg post:\t"<<mean_post<<" ms\t"<<1000/(mean_post)<<" FPS\n\n";   
+    std::cout<<"Avg tot:\t"<<(mean_pre + mean_post + mean) <<" ms\t"<<1000/((mean_pre + mean_post + mean))<<" FPS\n"<<COL_END;   
+    
+    return 0;
+}
+
@@ -0,0 +1,14 @@
+# video input
+input     : "../demo/yolo_test.mp4"
+win_input : "..\\..\\..\\demo\\yolo_test.mp4"
+
+# network config
+net         : "yolo4tiny_fp32.rt"
+ntype       : 'y'
+n_classes   : 80
+n_batch     : 1
+conf_thresh : 0.3
+
+# demo config
+show  : true
+save  : true
@@ -0,0 +1,92 @@
+# 2D/3D Object Detection and Tracking
+
+Currently tkDNN supports only CenterTrack as 3DOD & 2D/3D Tracker network.
+
+## 3D Object Detection
+
+To run the 3D object detection demo follow these steps (example with CenterNet based on DLA34):
+```
+rm dla34_cnet3d_fp32.rt        # be sure to delete(or move) old tensorRT files
+./test_dla34_cnet3d            # run the yolo test (is slow)
+./demo3D dla34_cnet3d_fp32.rt ../demo/yolo_test.mp4 NULL c
+```
+The demo3D program takes the same parameters of the demo program:
+```
+./demo3D <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
+```
+where
+
+* ```<calibration-file>``` is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
+
+![demo](https://user-images.githubusercontent.com/11939259/126784875-c4285497-d369-424f-abda-58274cd747ac.gif)
+
+## Object Detection and Tracking
+
+To run the 3D object detection & tracking demo follow these steps (example with CenterTrack based on DLA34):
+```
+rm dla34_ctrack_fp32.rt  # be sure to delete(or move) old tensorRT files
+./test_dla34_ctrack      # run the yolo test (is slow)
+./demoTracker dla34_ctrack_fp32.rt ../demo/yolo_test.mp4 NULL c
+```
+
+The demoTracker program takes the same parameters of the demo program:
+```
+./demoTracker <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh> <2D/3D-flag>
+```
+
+where
+
+* ```<calibration-file>``` is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
+*  ```<2D/3D-flag>``` if set to 0 the demo will be in the 2D mode, while if set to 1 the demo will be in the 3D mode (Default is 1 - 3D mode).
+
+![demo](https://user-images.githubusercontent.com/11939259/126784878-513fa9e8-864a-4c24-b4bd-199737184708.gif)
+
+## FPS Results
+
+Inference FPS of shelfnet with tkDNN, average of 1200 images on:
+  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
+  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
+
+### 3D OD and Tracking
+
+| Platform   | Test                        | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS | INT8, ms  | INT8, FPS | 
+| :------:   | :-----:                     | :-----: | :-----:   | :-----:   | :-----:   |	:-----:   | :-----:   | :-----:   | 
+| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | pre     | 4.43883   |  225.285  |  4.42951  |  225.759   |  4.44278  |  225.084  |
+| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | inf     | 9.03454   |  110.686  |  6.02013  |  166.109   |  5.31611  |  188.108  |
+| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | post    | 0.96631   |  1034.87  |  0.96824  |  1032.80   |  0.95066  |  1051.90  |
+| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | tot     | 14.4397   |  69.2535  |  11.4179  |  87.5818   |  10.7095  |  93.3750  |
+| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | pre     | 4.60075   |  217.356  |  4.28658  |  233.286   |  4.29473  |  232.844  | 
+| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | inf     | 8.48365   |  117.874  |  5.25150  |  190.422   |  4.58463  |  218.120  |  
+| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | post    | 0.99484   |  1005.19  |  0.91776  |  1089.61   |  0.89853  |  1112.93  |  
+| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | tot     | 14.0792   |  71.0266  |  10.4558  |  95.6405   |  9.77788  |  102.272  |  
+| AGX Xavier | CenterTrack3D 512x512 (B=1) | pre     | 34.9915   |  28.5784  |  33.5976  |  29.7440   |  34.4425  |  29.0339  |
+| AGX Xavier | CenterTrack3D 512x512 (B=1) | inf     | 76.3579   |  13.0962  |  52.4759  |  19.0564   |  51.4610  |  19.4322  |
+| AGX Xavier | CenterTrack3D 512x512 (B=1) | post    | 3.38576   |  295.355  |  3.26010  |  306.739   |  3.19770  |  312.725  |
+| AGX Xavier | CenterTrack3D 512x512 (B=1) | tot     | 114.735   |  8.71574  |  89.3336  |  11.1940   |  89.1012  |  11.2232  |
+| AGX Xavier | CenterTrack3D 512x512 (B=4) | pre     | 32.8933   |  30.4014  |  32.7950  |  30.4925   |  32.9603  |  30.3396  | 
+| AGX Xavier | CenterTrack3D 512x512 (B=4) | inf     | 74.2840   |  13.4618  |  50.3858  |  19.8469   |  49.2030  |  20.3240  |  
+| AGX Xavier | CenterTrack3D 512x512 (B=4) | post    | 3.14888   |  317.574  |  3.13615  |  318.862   |  3.02550  |  330.524  |  
+| AGX Xavier | CenterTrack3D 512x512 (B=4) | tot     | 110.326   |  9.06404  |  86.3169  |  11.5852   |  85.1888  |  11.7386  |  
+
+
+### 2D OD and Tracking
+
+| Platform   | Test                        | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS | INT8, ms  | INT8, FPS | 
+| :------:   | :-----:                     | :-----: | :-----:   | :-----:   | :-----:   |	:-----:   | :-----:   | :-----:   | 
+| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | pre     | 4.44386   |  225.030  |  4.43828  |  225.313   |  4.47747  |  223.340  |
+| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | inf     | 9.08365   |  110.088  |  6.04842  |  165.332   |  5.34787  |  186.990  |
+| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | post    | 0.98593   |  1014.27  |  0.97745  |  1023.07   |  0.96595  |  1035.25  |
+| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | tot     | 14.5134   |  68.9018  |  11.4642  |  87.2281   |  10.7913  |  92.6672  |
+| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | pre     | 4.41188   |  226.661  |  4.50800  |  221.828   |  4.29238  |  232.971  | 
+| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | inf     | 8.29015   |  120.625  |  5.38630  |  185.656   |  4.58500  |  218.103  |  
+| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | post    | 0.96847   |  1032.55  |  0.97997  |  1020.44   |  0.91791  |  1089.43  |  
+| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | tot     | 13.6705   |  73.1502  |  10.8743  |  91.9602   |  9.79528  |  102.090  |  
+| AGX Xavier | CenterTrack2D 512x512 (B=1) | pre     | 33.4745   |  29.8735  |  33.4847  |  29.8643   |  33.5022  |  29.8488  |
+| AGX Xavier | CenterTrack2D 512x512 (B=1) | inf     | 76.2077   |  13.1220  |  52.5111  |  19.0436   |  51.6057  |  19.3777  |
+| AGX Xavier | CenterTrack2D 512x512 (B=1) | post    | 3.26055   |  306.697  |  3.26806  |  305.992   |  3.21988  |  310.571  |
+| AGX Xavier | CenterTrack2D 512x512 (B=1) | tot     | 111.943   |  8.93312  |  89.2639  |  11.2027   |  88.3278  |  11.3215  |
+| AGX Xavier | CenterTrack2D 512x512 (B=4) | pre     | 32.8323   |  30.4579  |  32.8595  |  30.4326   |  32.8195  |  30.4697  | 
+| AGX Xavier | CenterTrack2D 512x512 (B=4) | inf     | 74.3075   |  13.4576  |  50.3555  |  19.8588   |  49.1805  |  20.3333  |  
+| AGX Xavier | CenterTrack2D 512x512 (B=4) | post    | 3.12360   |  320.143  |  3.13570  |  318.908   |  3.04943  |  327.931  |  
+| AGX Xavier | CenterTrack2D 512x512 (B=4) | tot     | 110.263   |  9.06920  |  86.3507  |  11.5807   |  85.0494  |  11.7579  |  
+
@@ -0,0 +1,68 @@
+# Semantic Segmentation with tkDNN
+
+Currently tkDNN supports only ShelfNet as semantic segmentation network.
+
+
+## Run the demo
+
+To run the semantic segmentation demo follow these steps (example with shelfnet):
+```
+rm shelfnet_fp32.rt        # be sure to delete(or move) old tensorRT files
+export TKDNN_BATCHSIZE=4   # be sure you have batch size > than 1 if you want to run inference on images bigger than 1024
+./test_shelfnet            # run the yolo test (is slow)
+./demo shelfnet_fp32.rt ../demo/yolo_test.mp4 1 19
+```
+In general the demo program takes the following parameters:
+```
+./seg_demo <network-rt-file> <path-to-video> <n-batches> <number-of-classes> <resize-flag> <baseline-resize> <show-flag> <write-pred>
+```
+where
+*  ```<network-rt-file>``` is the rt file generated by a test
+*  ```<<path-to-video>``` is the path to a video file or a camera input  
+*  ```<n-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
+*  ```<number-of-classes>```is the number of classes the network is trained on
+*  ```<resize-flag>``` if set to 0 the demo will not resize the input frames, but use it as it is, otherwise it will resize it.
+*  ```<baseline-resize>``` is ```<resize-flag>``` is set to 1, then the input frames will be proportionally resized using ```<baseline-resize>``` as width baseline.
+*  ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
+*  ```<write-pred>``` if set to 0 (default) the demo will run, otherwise the evaluation of a dataset will run and the output of the segmentation will be saved. Attention: this is under development and paths are embedded, so change them in the code in advance.
+
+NB) By default it is used FP32 inference
+NB) The batching is not used to work on more streams, rather to work on more tiles of the same image. Shelfnet never resized the input image, therefore for images greater than 1024x1024 tiles of 1024x1024 are given in input to the network in batch. 
+
+![demo](https://user-images.githubusercontent.com/11939259/126784236-38d24fc3-02df-4514-81c4-497e87e40b65.gif "Results on yolo_test.mp4")  
+
+For other demo videos refer to [this playlist](https://www.youtube.com/playlist?list=PLv0nEQYDD45y5EdSiywwCGPBmJVUzIWwe).
+
+NB) The gif and the videos are obtained with Mapillary Vistas weights, that we cannot publicly share due to its license restrictions. However, you can train Shelfnet using Mapillary and [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of the original repo.
+
+
+## FPS Results
+
+Inference FPS of shelfnet with tkDNN, average of 1200 images on:
+  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
+  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
+
+| Platform   | Test                     | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS  | INT8, ms |	INT8, FPS | 
+| :------:   | :-----:                  | :-----: | :-----:   | :-----:   | :-----:   |	:-----:    | :-----:  |	:-----:   | 
+| RTX 2080Ti | shelfnet 1024x1024 (B=1) | pre     | 6.11863   |  163.435  |   5.81465 |  171.979   |  5.88699 |   169.866 |
+| RTX 2080Ti | shelfnet 1024x1024 (B=1) | inf     | 11.5464   |  86.6074  |   7.35396 |  135.981   |  6.37623 |   156.832 |
+| RTX 2080Ti | shelfnet 1024x1024 (B=1) | post    | 4.09058   |  244.464  |   3.91961 |  255.128   |  4.07343 |   245.493 |
+| RTX 2080Ti | shelfnet 1024x1024 (B=1) | tot     | 21.7556   |  45.9652  |   17.0882 |  58.5199   |  16.3366 |   61.2121 |
+| RTX 2080Ti | shelfnet 2048x2048 (B=4) | pre     | 25.435    |  39.3158  |   25.2953 |  39.5331   |  25.9303 |   38.565  | 
+| RTX 2080Ti | shelfnet 2048x2048 (B=4) | inf     | 36.5015   |  27.3961  |   17.0534 |  58.6395   |  15.6061 |   64.0773 |  
+| RTX 2080Ti | shelfnet 2048x2048 (B=4) | post    | 17.3917   |  57.4985  |   17.1649 |  58.2583   |  17.5539 |   56.9675 |  
+| RTX 2080Ti | shelfnet 2048x2048 (B=4) | tot     | 79.3283   |  12.6058  |   59.5136 |  16.8029   |  59.0903 |   16.9233 |  
+| AGX Xavier | shelfnet 1024x1024 (B=1) | pre     | 8.0174    |  124.729  |   7.5117  |  133.126   |  7.47333 |   133.809 |
+| AGX Xavier | shelfnet 1024x1024 (B=1) | inf     | 72.4173   |  13.8089  |   37.505  |  26.6631   |  31.3286 |   31.9197 |
+| AGX Xavier | shelfnet 1024x1024 (B=1) | post    | 8.89958   |  112.365  |   8.83576 |  113.176   |  9.42655 |   106.083 |
+| AGX Xavier | shelfnet 1024x1024 (B=1) | tot     | 89.3342   |  11.1939  |   53.8525 |  18.5692   |  48.2285 |   20.7346 |
+| AGX Xavier | shelfnet 2048x2048 (B=4) | pre     | 47.1454   |  21.211   |   21.6475 |  46.1947   |  21.4201 |   46.6851 | 
+| AGX Xavier | shelfnet 2048x2048 (B=4) | inf     | 266.537   |  3.75183  |   128.321 |  7.79293   |  107.621 |   9.29185 |  
+| AGX Xavier | shelfnet 2048x2048 (B=4) | post    | 44.0711   |  22.6906  |   40.1732 |  24.8922   |  39.873  |   25.0796 |  
+| AGX Xavier | shelfnet 2048x2048 (B=4) | tot     | 357.753   |  2.79522  |   190.142 |  5.25922   |  168.914 |   5.92016 |  
+
+
+## Known issues
+
+When creating the rt file all the checks returns errors. It is due to a different resize function and handling of the original ShelfNet outputs.
+However, the network is supposed to work. 
@@ -0,0 +1,120 @@
+# 2D Object Detection with tkDNN
+
+## Supported Networks
+
+* Yolo4, Yolo4-csp, Yolo4x, Yolo4_berkeley, Yolo4tiny
+* Yolo3, Yolo3_berkeley, Yolo3_coco4, Yolo3_flir, Yolo3_512, Yolo3tiny, Yolo3tiny_512
+* Yolo2, Yolo2_voc, Yolo2tiny
+* Csresnext50-panet-spp, Csresnext50-panet-spp_berkeley
+* Resnet101_cnet, Dla34_cnet
+* Mobilenetv2ssd, Mobilenetv2ssd512, Bdd-mobilenetv2ssd
+
+## Index
+
+ - [2D Object Detection](#2d-object-detection)
+ - [FP16 inference](#fp16-inference)
+ - [INT8 inference](#int8-inference)
+ - [Batching](#batching)
+
+### 2D Object Detection
+This is an example using yolov4.
+
+To run the an object detection first create the .rt file by running:
+```
+rm yolo4_fp32.rt        # be sure to delete(or move) old tensorRT files
+./test_yolo4            # run the yolo test (is slow)
+```
+If you get problems in the creation, try to check the error activating the debug of TensorRT in this way:
+```
+cmake .. -DDEBUG=True
+make
+```
+
+Once you have successfully created your rt file, run the demo: 
+```
+./demo <path-to-config>
+```
+In general the demo program takes 1 parameter, the ```<path-to-config>``` that is the path to che configuration file. The parameter is optional and its default value is ```"../demo/demoConfig.yaml"```. 
+
+The config file is a yaml file with the following attributes:
+*  ```net``` is the rt file generated by a test
+*  ```input``` is the path to a video file or a camera input  (on Linux)
+*  ```win_input``` is the path to a video file or a camera input  (on Windows)
+*  ```ntype``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
+*  ```n_classes``` is the number of classes the network is trained on
+*  ```n_batch``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
+*  ```conf_thresh``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
+*  ```show``` if set to 0 the demo will not show the visualization (if n-batches ==1)
+*  ```save``` if set to 1 the demo will save the video of the demo into result.mp4 (if n-batches ==1)
+
+N.B. By default it is used FP32 inference
+
+
+![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
+
+
+### FP16 inference
+
+To run the demo with FP16 inference follow these steps (example with yolov3):
+```
+export TKDNN_MODE=FP16  # set the half floating point optimization
+rm yolo3_fp16.rt        # be sure to delete(or move) old tensorRT files
+./test_yolo3            # run the yolo test (is slow)
+# set net: yolo3_fp16.rt in the config-file
+./demo 
+```
+N.B. Using FP16 inference will lead to some errors in the results (first or second decimal). 
+
+### INT8 inference
+
+To run the demo with INT8 inference three environment variables need to be set:
+
+  * ```export TKDNN_MODE=INT8```: set the 8-bit integer optimization
+  * ```export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt``` : image_list.txt has in each line the absolute path to a calibration image
+  * ```export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt```: label_list.txt has in each line the absolute path to a calibration label
+  
+You should provide image_list.txt and label_list.txt, using training images. However, if you want to quickly test the INT8 inference you can run (from this repo root folder)
+```
+bash scripts/download_validation.sh COCO
+```
+to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD instead of COCO to download BDD validation. 
+
+Then a complete example using yolo3 and COCO dataset would be:
+```
+export TKDNN_MODE=INT8
+export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
+export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
+rm yolo3_int8.rt        # be sure to delete(or move) old tensorRT files
+./test_yolo3            # run the yolo test (is slow)
+# set net: yolo3_int8.rt in the config-file
+./demo 
+```
+N.B. 
+
+ * Using INT8 inference will lead to some errors in the results. 
+ * The test will be slower: this is due to the INT8 calibration, which may take some time to complete. 
+ * INT8 calibration requires TensorRT version greater than or equal to 6.0
+ * Only 100 images are used to create the calibration table by default (set in the code).
+
+### Batching
+
+#### BatchSize bigger than 1
+```
+export TKDNN_BATCHSIZE=2
+# build tensorRT files
+```
+This will create a TensorRT file with the desired **max** batch size.
+The test will still run with a batch of 1, but the created tensorRT can manage the desired batch size.
+
+#### Test batch Inference
+This will test the network with random input and check if the output of each batch is the same.
+```
+./test_rtinference <network-rt-file> <number-of-batches>
+# <number-of-batches> should be less or equal to the max batch size of the <network-rt-file>
+
+# example
+export TKDNN_BATCHSIZE=4           # set max batch size
+rm yolo3_fp32.rt                   # be sure to delete(or move) old tensorRT files
+./test_yolo3                       # build RT file
+./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4
+```
@@ -0,0 +1,115 @@
+# tkDNN export weights
+
+## Index 
+
+ - [How to export weights](#how-to-export-weights)
+    - [1)Export weights from darknet](#1export-weights-from-darknet)
+    - [2)Export weights for DLA34 and ResNet101](#2export-weights-for-dla34-and-resnet101)
+    - [3)Export weights for CenterNet](#3export-weights-for-centernet)
+    - [4)Export weights for MobileNetSSD](#4export-weights-for-mobilenetssd)
+    - [5)Export weights for CenterTrack](#5export-weights-for-centertrack)
+    - [6)Export weights for ShelfNet](#6export-weights-for-shelfnet)
+ - [Darknet Parser](#darknet-parser)
+
+## How to export weights
+
+Weights are essential for any network to run inference. For each test a folder organized as follow is needed (in the build folder):
+```
+    test_nn
+        |---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
+        |---- debug/  (folder containing a binary file for each layer with the corresponding outputs)
+```
+Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
+
+### 1)Export weights from darknet
+To export weights for NNs that are defined in darknet framework, use [this](https://git.hipert.unimore.it/fgatti/darknet.git) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
+
+```
+git clone https://git.hipert.unimore.it/fgatti/darknet.git
+cd darknet
+make
+mkdir layers debug
+./darknet export <path-to-cfg-file> <path-to-weights> layers
+```
+N.B. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug. 
+
+### 2)Export weights for DLA34 and ResNet101 
+To get weights and outputs needed to run the tests dla34 and resnet101 use the Python script and the Anaconda environment included in the repository.   
+
+Create Anaconda environment and activate it:
+```
+conda env create -f file_name.yml
+source activate env_name
+python <script name>
+```
+### 3)Export weights for CenterNet
+To get the weights needed to run Centernet tests use [this](https://github.com/sapienzadavide/CenterNet.git) fork of the original Centernet. 
+```
+git clone https://github.com/sapienzadavide/CenterNet.git
+```
+* follow the instruction in the README.md and INSTALL.md
+
+```
+python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_resdcn101.pth --exp_wo --exp_wo_dim 512
+python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512
+```
+### 4)Export weights for MobileNetSSD
+To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network. 
+
+```
+git clone https://github.com/mive93/pytorch-ssd
+cd pytorch-ssd
+conda env create -f env_mobv2ssd.yml
+python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
+```
+### 5)Export weights for CenterTrack
+To get the weights needed to run CenterTrack tests use [this](https://github.com/sapienzadavide/CenterTrack.git) fork of the original CenterTrack. 
+```
+git clone https://github.com/sapienzadavide/CenterTrack.git
+```
+* follow the instruction in the README.md and INSTALL.md
+
+```
+python demo.py tracking,ddd --load_model ../models/nuScenes_3Dtracking.pth --dataset nuscenes --pre_hm --track_thresh 0.1 --demo /path/to/image/or/folder/or/video/or/webcam --test_focal_length 633 --exp_wo --exp_wo_dim 512 --input_h 512 --input_w 512
+```
+
+### 6)Export weights for ShelfNet
+To get the weights needed to run Shelfnet tests use [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of a Pytorch implementation of Shelfnet network. 
+
+```
+git clone https://git.hipert.unimore.it/mverucchi/shelfnet
+cd shelfnet 
+cd ShelfNet18_realtime
+conda env create --file shelfnet_env.yml
+conda activate shelfnet
+mkdir layer debug
+python export.py
+```
+
+## Darknet Parser
+tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*:
+```
+// example of parsing yolo4
+tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
+net->print();
+```
+All models from darknet are now parsed directly from cfg, you still need to export the weights with the described tools in the previous section.
+<details>
+  <summary>Supported layers</summary>
+  convolutional
+  maxpool
+  avgpool
+  shortcut
+  upsample
+  route
+  reorg
+  region
+  yolo
+</details>
+<details>
+  <summary>Supported activations</summary>
+  relu
+  leaky
+  mish
+  logistic
+</details>
@@ -0,0 +1,32 @@
+# Run the mAP demo
+
+To compute mAP, precision, recall and f1score to evaluate 2D object detectors, run the map_demo.
+
+A validation set is needed. 
+To download COCO_val2017 (80 classes) run (form the root folder): 
+```
+bash scripts/download_validation.sh COCO
+```
+To download Berkeley_val (10 classes) run (form the root folder): 
+```
+bash scripts/download_validation.sh BDD
+```
+
+To compute the map, the following parameters are needed:
+```
+./map_demo <network rt> <network type [y|c|m]> <labels file path> <config file path>
+```
+where 
+* ```<network rt>```: rt file of a chosen network on which compute the mAP.
+* ```<network type [y|c|m]>```: type of network. Right now only y(yolo), c(centernet) and m(mobilenet) are allowed
+* ```<labels file path>```: path to a text file containing all the paths of the ground-truth labels. It is important that all the labels of the ground-truth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the ground-truth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg. 
+* ```<config file path>```: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml
+
+Example:
+
+```
+cd build
+./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
+```
+
+This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to submit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
@@ -0,0 +1,95 @@
+# tkDNN on Windows 
+
+## Index
+
+ - [Dependencies-Windows](#dependencies-windows)
+ - [Compiling tkDNN on Windows](#compiling-tkdnn-on-windows)
+ - [Run the demo on Windows](#run-the-demo-on-windows)
+    - [FP16 inference windows](#fp16-inference-windows)
+    - [INT8 inference windows](#int8-inference-windows)
+ - [Known issues with tkDNN on Windows](#known-issues-with-tkdnn-on-windows)
+
+### Dependencies-Windows 
+This branch should work on every NVIDIA GPU supported in windows with the following dependencies:
+
+* WINDOWS 10 1803 or HIGHER 
+* CUDA 10.0 (Recommended CUDA 11.2 )
+* CUDNN 7.6 (Recommended CUDNN 8.1.1 )
+* TENSORRT 6.0.1 (Recommended TENSORRT 7.2.3.4 )
+* OPENCV 3.4 (Recommended OPENCV 4.2.0 )
+* MSVC 16.7 
+* YAML-CPP 
+* EIGEN3
+* 7ZIP (ADD TO PATH)
+* NINJA 1.10
+
+
+All the above mentioned dependencies except 7ZIP can be installed using Microsoft's [VCPKG](https://github.com/microsoft/vcpkg.git) .
+After bootstrapping VCPKG the dependencies can be built and installed using the following command :
+
+```
+opencv4(normal) - vcpkg.exe install opencv4[tbb,jpeg,tiff,opengl,openmp,png,ffmpeg,eigen]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
+
+opencv4(cuda) - vcpkg.exe install opencv4[cuda,nonfree,contrib,eigen,tbb,jpeg,tiff,opengl,openmp,png,ffmpeg]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
+```
+To build opencv4 with cuda and cudnn version corresponding to your cuda version,vcpkg's cudnn portfile needs to be modified by adding ```$ENV{CUDA_PATH}```  at lines 16 and 17 in the portfile.cmake 
+
+After VCPKG finishes building and installing all the packages delete C:\temp_vcpkg_build and add C:\opt\x64-windows\bin and C:\opt\x64-windows\debug\bin to path 
+
+### Compiling tkDNN on Windows 
+
+tkDNN is built with cmake(3.15+) on windows along with ninja.Msbuild and NMake Makefiles are drastically slower when compiling the library compared to windows
+```
+git clone https://github.com/ceccocats/tkDNN.git
+cd tkdnn-windows
+mkdir build
+cd build
+cmake -DCMAKE_BUILD_TYPE=Release -G"Ninja" ..
+ninja -j4
+```
+
+### Run the demo on Windows 
+
+This example uses yolo4_tiny.\
+To run the object detection file create .rt file bu running:
+```
+.\test_yolo4tiny.exe
+```
+
+Once the rt file has been successfully create,run the demo using the following command:
+```
+.\demo.exe yolo4tiny_fp32.rt ..\demo\yolo_test.mp4 y 
+```
+ For general info on more demo paramters,check Run the demo section on top 
+ To run the test_all_tests.sh on windows,use git bash or msys2 
+
+### FP16 inference windows 
+
+This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):
+```
+set TKDNN_MODE=FP16
+del /f yolo4tiny_fp16.rt
+.\test_yolo4tiny.exe
+.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4
+```
+
+### INT8 inference windows 
+To run object detection demo with INT8 (example with yolo4tiny):
+```
+set TKDNN_MODE=INT8
+set TKDNN_CALIB_LABEL_PATH=..\demo\COCO_val2017\all_labels.txt
+set TKDNN_CALIB_IMG_PATH=..\demo\COCO_val2017\all_images.txt
+del /f  yolo4tiny_int8.rt        # be sure to delete(or move) old tensorRT files
+.\test_yolo4tiny.exe           # run the yolo test (is slow)
+.\demo.exe yolo4tiny_int8.rt ..\demo\yolo_test.mp4 y
+
+```
+
+### Known issues with tkDNN on Windows
+
+Mobilenet and Centernet demos work properly only when built with msvc 16.7 in Release Mode,when built in debug mode for the mentioned networks one might encounter opencv assert errors
+
+All Darknet models work properly with demo using MSVC version(16.7-16.9)
+
+It is recommended to use Nvidia Driver(465+),Cuda unknown errors have been observed when using older drivers on pascal(SM 61) devices.
+
@@ -0,0 +1,181 @@
+#ifndef CENTERTRACK_H
+#define CENTERTRACK_H
+
+#include <opencv2/videoio.hpp>
+#include "opencv2/opencv.hpp"
+#include "kernels.h"
+#include "utils.h"
+#include "tkdnn.h"
+#include <time.h>
+#include <vector>
+#include <numeric>      // std::iota
+#include <algorithm>    // std::sort
+
+#include "TrackingNN.h"
+
+#include "kernelsThrust.h"
+
+
+namespace tk { namespace dnn { 
+
+struct detectionRes
+{
+    float score;
+    int cl;
+    cv::Mat ct, tr, bb0, bb1;
+    float dep;
+    float dim[3];
+    float alpha;
+    float x,y,z;
+    float rot_y;
+    detectionRes() : ct(cv::Mat(cv::Size(1,2), CV_32F)),
+                          tr(cv::Mat(cv::Size(1,2), CV_32F)),
+                          bb0(cv::Mat(cv::Size(1,2), CV_32F)),
+                          bb1(cv::Mat(cv::Size(1,2), CV_32F)) { }
+    ~detectionRes() {
+        ct.release();
+        tr.release();
+        bb0.release();
+        bb1.release();
+    }
+};
+
+struct trackingRes
+{
+    struct detectionRes det_res;
+    int tracking_id;
+    int age;
+    int active;
+    int color;
+};
+
+class CenterTrack : public TrackingNN
+{
+public:
+    tk::dnn::dataDim_t dim;
+    tk::dnn::dataDim_t dim2;
+    tk::dnn::dataDim_t dim_hm; 
+    tk::dnn::dataDim_t dim_wh; 
+    tk::dnn::dataDim_t dim_reg;
+    tk::dnn::dataDim_t dim_track;
+    tk::dnn::dataDim_t dim_dep;
+    tk::dnn::dataDim_t dim_rot;
+    tk::dnn::dataDim_t dim_dim;
+    tk::dnn::dataDim_t dim_amodel_offset;
+
+    /* preprocessing */
+    #ifdef OPENCV_CUDACONTRIB
+        float *mean_d;
+        float *stddev_d;
+    #else
+        cv::Vec<float, 3> mean;
+        cv::Vec<float, 3> stddev;
+        dnnType *input;
+    #endif
+    float *d_ptrs;
+
+    std::vector<cv::Mat> inputCalibs;
+
+    std::vector<cv::Size> szOld;
+
+    cv::Mat src;
+    cv::Mat dst;
+    cv::Mat dst2;  
+    cv::Mat trans, trans2, transOut;
+
+    /* pre inf */
+    bool iter0;
+    dnnType *input_pre_inf_d;
+    bool test_pre_inf = true;
+    dnnType *img_d, *hm_d;
+    tk::dnn::dataDim_t dim_in0;
+    tk::dnn::dataDim_t dim_in1;
+    dnnType *out_d;
+
+
+    /* postprocessing */
+    int K = 100;
+    int width = 128;//56;        // TODO
+
+    // pointer used in the kernels
+    float *src_out;
+    int *ids_out;
+
+    float *topk_scores;
+    int *topk_inds_;
+    float *topk_ys_;
+    float *topk_xs_;
+    int *ids_d, *ids_;
+
+    float *ones;
+
+    float *scores, *scores_d;
+    int *clses, *clses_d; 
+    int *topk_inds_d;
+    float *topk_ys_d; 
+    float *topk_xs_d;
+    int *inttopk_xs_d, *inttopk_ys_d;
+    
+    float *bbx0, *bby0, *bbx1, *bby1;
+    float *bbx0_d, *bby0_d, *bbx1_d, *bby1_d;
+
+    int *intxs, *intys;
+
+    float *track, *dep, *rot, *dim_, *wh, *amodel_offset;
+    float *track_d, *dep_d, *rot_d, *dim_d, *wh_d, *amodel_offset_d;
+    
+    float *target_coords;
+
+    /* visualization */
+    cv::Mat r;
+    std::vector<cv::Mat> calibs;
+    cv::Mat corners, pts3DHomo;  
+
+    std::vector<std::vector<int>> faceId;
+    cv::Scalar trColors[256];
+    bool mode3D;
+
+    //processing
+    struct threshold op;
+    float outThresh = 0.1;
+    float newThresh = 0.3;
+    // float peakThreshold = 0.2;
+    // float centerThreshold = 0.3; //default 0.5
+    
+
+    //detections
+    std::vector<struct detectionRes> detRes;
+    int countDet;
+    //tracks
+    std::vector<std::vector<struct trackingRes>> trRes;
+    std::vector<int> countTr;
+    std::vector<int> trackId;
+  
+    
+    bool init_preprocessing();
+    bool init_pre_inf();
+    bool init_postprocessing();
+    bool init_visualization(const int n_classes);
+    void pre_inf(const int bi);
+    void _get_additional_inputs();
+    cv::Mat transform_preds_with_trans(float x1, float x2);
+    void tracking(const int bi);
+
+public:
+    tk::dnn::Network *pre_phase_net = nullptr;
+    CenterTrack() {};
+    ~CenterTrack() {};
+    bool init(const std::string& tensor_path, const int n_classes=3, const int n_batches=1, 
+              const float conf_thresh=0.3, const bool mode_3d=true, 
+              const std::vector<cv::Mat>& k_calibs=std::vector<cv::Mat>());
+    void preprocess(cv::Mat &frame, const int bi=0);
+    void postprocess(const int bi=0,const bool mAP=false);
+    void draw(std::vector<cv::Mat>& frames);
+};
+
+
+} // namespace dnn
+} // namespace tk
+
+
+#endif /*CENTERTRACK_H*/
@@ -73,7 +73,7 @@ public:
    CenternetDetection() {};
    ~CenternetDetection() {}; 

-    bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1);
+    bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1, const float conf_thresh=0.3);
    void preprocess(cv::Mat &frame, const int bi=0);
    void postprocess(const int bi=0,const bool mAP=false);
 };
@@ -0,0 +1,101 @@
+#ifndef CENTERNETDETECTION3D_H
+#define CENTERNETDETECTION3D_H
+
+#include "kernels.h"
+#include <opencv2/videoio.hpp>
+#include "opencv2/opencv.hpp"
+#include <time.h>
+#include <vector>
+#include <numeric>      // std::iota
+#include <algorithm>    // std::sort
+
+#include "DetectionNN3D.h"
+
+#include "kernelsThrust.h"
+
+
+namespace tk { namespace dnn { 
+
+class CenternetDetection3D : public DetectionNN3D
+{
+private:
+    tk::dnn::dataDim_t dim;
+    tk::dnn::dataDim_t dim2;
+    tk::dnn::dataDim_t dim_hm; 
+    tk::dnn::dataDim_t dim_wh; 
+    tk::dnn::dataDim_t dim_reg;
+    tk::dnn::dataDim_t dim_dep;
+    tk::dnn::dataDim_t dim_rot;
+    tk::dnn::dataDim_t dim_dim;
+
+    std::vector<cv::Mat> inputCalibs;
+    float *topk_scores;
+    int *topk_inds_;
+    float *topk_ys_;
+    float *topk_xs_;
+    int *ids_d, *ids_;
+
+    float *ones;
+
+    float *scores, *scores_d;
+    int *clses, *clses_d; 
+    int *topk_inds_d;
+    float *topk_ys_d; 
+    float *topk_xs_d;
+    int *inttopk_xs_d, *inttopk_ys_d;
+
+    float *xs, *ys;
+
+    float *dep, *rot, *dim_, *wh;
+    float *dep_d, *rot_d, *dim_d, *wh_d;
+    
+    float *target_coords;
+
+    #ifdef OPENCV_CUDACONTRIB
+        float *mean_d;
+        float *stddev_d;
+    #else
+        cv::Vec<float, 3> mean;
+        cv::Vec<float, 3> stddev;
+        dnnType *input;
+    #endif
+    cv::Mat r;
+    float *d_ptrs;
+    
+    cv::Size sz_old;
+
+    cv::Mat src;
+    cv::Mat dst;
+    cv::Mat dst2;  
+    cv::Mat trans, trans2;
+    std::vector<cv::Mat> calibs;
+
+    //processing
+    int K = 100;
+    int width = 128;//56;        // TODO
+
+    // pointer used in the kernels
+    float *srcOut;
+    int *idsOut;
+
+    struct threshold op;
+    cv::Mat corners, pts3DHomo;  
+
+    std::vector<std::vector<int>> faceId;
+
+public:
+    CenternetDetection3D() {};
+    ~CenternetDetection3D() {}; 
+
+    bool init(const std::string& tensor_path, const int n_classes=3, const int n_batches=1, const float conf_thresh=0.3, const std::vector<cv::Mat>& k_calibs=std::vector<cv::Mat>());
+    void preprocess(cv::Mat &frame, const int bi=0);
+    void postprocess(const int bi=0,const bool mAP=false);
+    void draw(std::vector<cv::Mat>& frames);
+};
+
+
+} // namespace dnn
+} // namespace tk
+
+
+#endif /*CENTERNETDETECTION_H*/
@@ -24,7 +24,10 @@ namespace tk { namespace dnn {
        int num = 1;
        int pad = 0;
        int coords = 4;
+        int nms_kind = 0;
+        int new_coords= 0;
        float scale_xy = 1;
+        float nms_thresh = 0.45;
        std::vector<int> layers;
        std::string activation = "linear";

@@ -4,7 +4,10 @@
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>    
+#ifdef __linux__
 #include <unistd.h>
+#endif 
+
 #include <mutex>
 #include "utils.h"

@@ -14,7 +17,7 @@

 #include "tkdnn.h"

-// #define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
+//#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.

 #ifdef OPENCV_CUDACONTRIB
 #include <opencv2/cudawarping.hpp>
@@ -76,15 +79,15 @@ class DetectionNN {
        ~DetectionNN(){};

        /**
-         * Method used to inialize the class, allocate memory and compute 
+         * Method used to initialize the class, allocate memory and compute 
         * needed data.
         * 
-         * @param tensor_path path to the rt file og the NN.
+         * @param tensor_path path to the rt file of the NN.
         * @param n_classes number of classes for the given dataset.
         * @param n_batches maximum number of batches to use in inference
         * @return true if everything is correct, false otherwise.
         */
-        virtual bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1) = 0;
+        virtual bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1, const float conf_thresh=0.3) = 0;
        
        /**
         * This method performs the whole detection of the NN.
@@ -141,16 +144,15 @@ class DetectionNN {
        }      

        /**
-         * Method to draw boundixg boxes and labels on a frame.
+         * Method to draw bounding boxes and labels on a frame.
         * 
-         * @param frames orginal frame to draw bounding box on.
+         * @param frames original frame to draw bounding box on.
         */
        void draw(std::vector<cv::Mat>& frames) {
            tk::dnn::box b;
            int x0, w, x1, y0, h, y1;
            int objClass;
            std::string det_class;
-
            int baseline = 0;
            float font_scale = 0.5;
            int thickness = 2;   
@@ -0,0 +1,161 @@
+#ifndef DETECTIONNN3D_H
+#define DETECTIONNN3D_H
+
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>
+#ifdef __linux__    
+#include <unistd.h>
+#endif
+
+#include <mutex>
+#include "utils.h"
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "tkdnn.h"
+
+// #define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
+
+#ifdef OPENCV_CUDACONTRIB
+#include <opencv2/cudawarping.hpp>
+#include <opencv2/cudaarithm.hpp>
+#endif
+
+
+namespace tk { namespace dnn {
+
+class DetectionNN3D {
+
+    protected:
+        tk::dnn::NetworkRT *netRT = nullptr;
+        dnnType *input_d;
+
+        std::vector<cv::Size> originalSize;
+
+        cv::Scalar colors[256];
+
+        int nBatches = 1;
+
+#ifdef OPENCV_CUDACONTRIB
+        cv::cuda::GpuMat bgr[3];
+        cv::cuda::GpuMat imagePreproc;
+#else
+        cv::Mat bgr[3];
+        cv::Mat imagePreproc;
+        dnnType *input;
+#endif
+
+        /**
+         * This method preprocess the image, before feeding it to the NN.
+         *
+         * @param frame original frame to adapt for inference.
+         * @param bi batch index
+         */
+        virtual void preprocess(cv::Mat &frame, const int bi=0) = 0;
+
+        /**
+         * This method postprocess the output of the NN to obtain the correct 
+         * boundig boxes. 
+         * 
+         * @param bi batch index
+         * @param mAP set to true only if all the probabilities for a bounding 
+         *            box are needed, as in some cases for the mAP calculation
+         */
+        virtual void postprocess(const int bi=0,const bool mAP=false) = 0;
+
+    public:
+        int classes = 0;
+        float confThreshold = 0.3; /*threshold on the confidence of the boxes*/
+        
+        std::vector<tk::dnn::box3D> detected3D; /*bounding boxes in output*/
+        std::vector<std::vector<tk::dnn::box3D>> batchDetected; /*bounding boxes in output*/
+        std::vector<double> pre_stats, stats, post_stats, visual_stats; /*keeps track of inference times (ms)*/
+        std::vector<std::string> classesNames;
+
+        DetectionNN3D() {};
+        ~DetectionNN3D(){};
+
+        /**
+         * Method used to initialize the class, allocate memory and compute 
+         * needed data.
+         * 
+         * @param tensor_path path to the rt file of the NN.
+         * @param n_classes number of classes for the given dataset.
+         * @param n_batches maximum number of batches to use in inference.
+         * @return true if everything is correct, false otherwise.
+         */
+        virtual bool init(const std::string& tensor_path, const int n_classes=3, const int n_batches=1, 
+                            const float conf_thresh=0.3, const std::vector<cv::Mat>& k_calibs=std::vector<cv::Mat>()) = 0;
+
+        /**
+         * This method performs the whole detection of the NN.
+         * 
+         * @param frames frames to run detection on.
+         * @param cur_batches number of batches to use in inference.
+         * @param save_times if set to true, preprocess, inference and postprocess times 
+         *        are saved on a csv file, otherwise not.
+         * @param times pointer to the output stream where to write times.
+         * @param mAP set to true only if all the probabilities for a bounding 
+         *            box are needed, as in some cases for the mAP calculation.
+         */
+        void update(std::vector<cv::Mat>& frames, const int cur_batches=1, bool save_times=false, 
+                    std::ofstream *times=nullptr, const bool mAP=false){
+            if(save_times && times==nullptr)
+                FatalError("save_times set to true, but no valid ofstream given");
+            if(cur_batches > nBatches)
+                FatalError("A batch size greater than nBatches cannot be used");
+
+            originalSize.clear();
+            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30); 
+            {
+                TKDNN_TSTART
+                for(int bi=0; bi<cur_batches;++bi){
+                    if(!frames[bi].data)
+                        FatalError("No image data feed to detection");
+                    originalSize.push_back(frames[bi].size());
+                    preprocess(frames[bi], bi);   
+                }
+                TKDNN_TSTOP
+                pre_stats.push_back(t_ns); 
+                if(save_times) *times<<t_ns<<";";
+            }
+
+            //do inference
+            tk::dnn::dataDim_t dim = netRT->input_dim;
+            dim.n = cur_batches;
+            {
+                if(TKDNN_VERBOSE) dim.print();
+                TKDNN_TSTART
+                netRT->infer(dim, input_d);
+                TKDNN_TSTOP
+                if(TKDNN_VERBOSE) dim.print();
+                stats.push_back(t_ns);
+                if(save_times) *times<<t_ns<<";";
+            }
+
+            batchDetected.clear();
+            {
+                TKDNN_TSTART
+                for(int bi=0; bi<cur_batches;++bi)
+                    postprocess(bi, mAP);
+                TKDNN_TSTOP
+                post_stats.push_back(t_ns);
+                if(save_times) *times<<t_ns<<"\n";
+            }
+        }
+
+        /**
+         * Method to draw bounding boxes and labels on a frame.
+         * 
+         * @param frames original frame to draw bounding box on.
+         */    
+        virtual void draw(std::vector<cv::Mat>& frames){};
+          
+};
+
+}}
+
+#endif /* DETECTIONNN3D_H*/
@@ -1,7 +1,14 @@
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>     /* srand, rand */
+
+#ifdef __linux__
 #include <unistd.h>
+#elif _WIN32
+#define _USE_MATH_DEFINES
+#include <math.h>
+#endif 
+
 #include <mutex>
 #include <Eigen/Dense>
 #include "utils.h"
@@ -44,7 +51,7 @@ class ImuOdom {
        virtual ~ImuOdom() {}

        /**
-         * Method used for inizialize the class
+         * Method used for initialize the class
         * 
         * @return Success of the initialization
         */
@@ -141,7 +148,7 @@ class ImuOdom {
            //odomPOS = odomPOS + deltaP.cast<double>(); // V2
            odomROT = odomROT * q.normalized().toRotationMatrix();
            
-            // compute euler
+            // compute Euler
            auto newEULER = odomROT.eulerAngles(0, 1, 2);
            for(int i=0; i<3; i++) {
                while( fabs(newEULER(i) - odomEULER(i)) > M_PI_2 )  {
@@ -11,8 +11,11 @@
 #include <fstream>
 #include <iomanip>
 #include <signal.h>
-#include <stdlib.h>    
+#include <stdlib.h>
+#ifdef __linux__    
 #include <unistd.h>
+#endif
+
 #include <mutex>

 #include "NvInfer.h"
@@ -19,8 +19,10 @@ enum layerType_t {
    LAYER_ACTIVATION_CRELU,
    LAYER_ACTIVATION_LEAKY,
    LAYER_ACTIVATION_MISH,
+    LAYER_ACTIVATION_LOGISTIC,
    LAYER_FLATTEN,
    LAYER_RESHAPE,
+    LAYER_RESIZE,
    LAYER_MULADD,
    LAYER_POOLING,
    LAYER_SOFTMAX,
@@ -54,6 +56,10 @@ public:

    int id = 0;
    bool final;        //if the layer is the final one
+    uint n_params = 0;
+    uint feature_map_size = 0;
+    long unsigned MACC = 0;
+

    std::string getLayerName() {
        layerType_t type = getLayerType();
@@ -68,8 +74,10 @@ public:
            case LAYER_ACTIVATION_CRELU:    return "ActivationCReLU";
            case LAYER_ACTIVATION_LEAKY:    return "ActivationLeaky";
            case LAYER_ACTIVATION_MISH:     return "ActivationMish";
+            case LAYER_ACTIVATION_LOGISTIC: return "ActivationLogistic";
            case LAYER_FLATTEN:             return "Flatten";
            case LAYER_RESHAPE:             return "Reshape";
+            case LAYER_RESIZE:              return "Resize";
            case LAYER_MULADD:              return "MulAdd";
            case LAYER_POOLING:             return "Pooling";
            case LAYER_SOFTMAX:             return "Softmax";
@@ -171,7 +179,7 @@ public:


 /**
-    Input layer (it doesnt need weigths)
+    Input layer (it doesn't need weights)
 */
 class Input : public Layer {

@@ -207,24 +215,26 @@ public:


 /**
-    Avaible activation functions
+    Available activation functions
 */
 typedef enum {
    ACTIVATION_ELU     = 100,
    ACTIVATION_LEAKY   = 101,
-    ACTIVATION_MISH   = 102
+    ACTIVATION_MISH   = 102,
+    ACTIVATION_LOGISTIC   = 103
 } tkdnnActivationMode_t;

 /**
-    Activation layer (it doesnt need weigths)
+    Activation layer (it doesn't need weights)
 */
 class Activation : public Layer {

 public:
    int act_mode;
    float ceiling;
+    float slope;

-    Activation(Network *net, int act_mode, const float ceiling=0.0); 
+    Activation(Network *net, int act_mode, const float ceiling=0.0, const float slope=0.1); 
    virtual ~Activation();
    virtual layerType_t getLayerType() { 
        if(act_mode == CUDNN_ACTIVATION_CLIPPED_RELU)
@@ -233,6 +243,8 @@ public:
            return LAYER_ACTIVATION_LEAKY;
        else if (act_mode == ACTIVATION_MISH)
            return LAYER_ACTIVATION_MISH;
+        else if (act_mode == ACTIVATION_LOGISTIC)
+            return LAYER_ACTIVATION_LOGISTIC;
        else
            return LAYER_ACTIVATION;
         };
@@ -318,9 +330,9 @@ public:
    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);

    const bool bidirectional = true; /**> is the net bidir */
-    bool returnSeq = false;       /**> if false return only the result of last timestep */
+    bool returnSeq = false;       /**> if false return only the result of last timestamp */
    int stateSize = 0; /**> number of hidden states */
-    int seqLen = 0;    /**> number of timesteps */
+    int seqLen = 0;    /**> number of timestamp */
    int numLayers = 1; /**> number of internal layers */

 protected:
@@ -367,7 +379,7 @@ public:


 /**
-    Deformable Convolutionl 2d layer
+    Deformable Convolutional 2d layer
 */  
 class DeformConv2d : public LayerWgs {

@@ -427,6 +439,23 @@ public:

 };

+enum ResizeMode_t { NEAREST= 0, 
+                    LINEAR= 1};
+
+/**
+    Resize layer
+*/
+class Resize : public Layer {
+
+public:
+    Resize(Network *net, int scale_c, int scale_h, int scale_w, bool fixed=false, ResizeMode_t mode=NEAREST);
+    virtual ~Resize();
+    virtual layerType_t getLayerType() { return LAYER_RESIZE; };
+
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
+
+    ResizeMode_t mode;
+};

 /**
    MulAdd layer
@@ -449,7 +478,7 @@ protected:


 /**
-    Avaible pooling functions (padding on tkDNN is not supported)
+    Available pooling functions (padding on tkDNN is not supported)
 */
 typedef enum {
    POOLING_MAX     = 0,
@@ -460,7 +489,7 @@ typedef enum {

 /**
    Pooling layer
-    currenty supported only 2d pooing (also on 3d input)
+    currently supported only 2d pooing (also on 3d input)
 */
 class Pooling : public Layer {

@@ -526,7 +555,7 @@ public:

 /**
    Reorg layer
-    Mantain same dimension but change C*H*W distribution
+    Maintains same dimension but change C*H*W distribution
 */
 class Reorg : public Layer {

@@ -547,7 +576,7 @@ public:
 class Shortcut : public Layer {

 public:
-    Shortcut(Network *net, Layer *backLayer); 
+    Shortcut(Network *net, Layer *backLayer, bool mul=false); 
    virtual ~Shortcut();
    virtual layerType_t getLayerType() { return LAYER_SHORTCUT; };

@@ -555,11 +584,12 @@ public:

 public:
    Layer *backLayer;
+    bool mul = false;
 };

 /**
    Upsample layer
-    Mantain same dimension but change C*H*W distribution
+    Maintains same dimension but change C*H*W distribution
 */
 class Upsample : public Layer {

@@ -590,6 +620,16 @@ struct sortable_bbox {
    int cl;
    float **probs;
 };
+struct box3D {
+    int cl;
+    std::vector<float> corners;
+    float prob;
+
+    void print() 
+    {
+        std::cout<<"\tcl: "<<cl<<"\tprob: "<<prob<<"\tshape corners: "<<corners.size()<<std::endl;
+    }
+};

 /**
    Yolo3 layer
@@ -610,24 +650,28 @@ public:
        int sort_class;
    };

-    Yolo(Network *net, int classes, int num, std::string fname_weights,int n_masks=3, float scale_xy=1);
+    enum nmsKind_t {GREEDY_NMS=0, DIOU_NMS=1};
+
+    Yolo(Network *net, int classes, int num, std::string fname_weights,int n_masks=3, float scale_xy=1, double nms_thresh=0.45, nmsKind_t nsm_kind=GREEDY_NMS, int new_coords=0);
    virtual ~Yolo();
    virtual layerType_t getLayerType() { return LAYER_YOLO; };

-    int classes, num, n_masks;
+    int classes, num, n_masks, new_coords;
    dnnType *mask_h, *mask_d; //anchors
    dnnType *bias_h, *bias_d; //anchors
    float scaleXY;
+    double nms_thresh;
+    nmsKind_t nsm_kind; 
    std::vector<std::string> classesNames;

    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
-    int computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh);
+    int computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh, int new_coords=0);

    dnnType *predictions;

-    static const int MAX_DETECTIONS = 8192;
+    static const int MAX_DETECTIONS = 8192*2;
    static Yolo::detection *allocateDetections(int nboxes, int classes);
-    static void             mergeDetections(Yolo::detection *dets, int ndets, int classes);
+    static void             mergeDetections(Yolo::detection *dets, int ndets, int classes, double nms_thresh=0.45, nmsKind_t nsm_kind=GREEDY_NMS);
 };

 /**
@@ -65,7 +65,7 @@ public:
    MobilenetDetection() {};
    ~MobilenetDetection() {}; 

-    bool init(const std::string& tensor_path, const int n_classes, const int n_batches=1);
+    bool init(const std::string& tensor_path, const int n_classes, const int n_batches=1, const float conf_thresh=0.3);
    void preprocess(cv::Mat &frame, const int bi=0);
    void postprocess(const int bi=0,const bool mAP=false);
 };
@@ -7,12 +7,12 @@
 namespace tk { namespace dnn {

 /**
-    Data rapresentation beetween layers
+    Data representation between layers
    n = batch size
    c = channels
-    h = heigth (lines)
+    h = height (lines)
    w = width  (rows)
-    l = lenght (3rd dimension)
+    l = length (3rd dimension)
 */
 struct dataDim_t {

@@ -43,13 +43,14 @@ public:
    void releaseLayers();

    /**
-        Do inferece for every added layer
+        Do inference for every added layer
    */
    dnnType* infer(dataDim_t &dim, dnnType* data);

    bool addLayer(Layer *l);
    void print();
    const char *getNetworkRTName(const char *network_name);
+    void adjustFeatureMapSizeWithShortcuts();

    cudnnDataType_t dataType;
    cudnnTensorFormat_t tensorFormat;
@@ -6,6 +6,7 @@
 #include "Network.h"
 #include "Layer.h"
 #include "NvInfer.h"
+#include <memory>

 namespace tk { namespace dnn {

@@ -24,6 +25,7 @@ template<typename T> T readBUF(const char*& buffer)

 using namespace nvinfer1;
 #include "pluginsRT/ActivationLeakyRT.h"
+#include "pluginsRT/ActivationLogisticRT.h"
 #include "pluginsRT/ActivationReLUCeilingRT.h"
 #include "pluginsRT/ActivationMishRT.h"
 #include "pluginsRT/ReorgRT.h"
@@ -59,6 +61,7 @@ public:
 #if NV_TENSORRT_MAJOR >= 6  
    nvinfer1::IBuilderConfig *configRT;
 #endif
+    
    nvinfer1::ICudaEngine *engineRT;
    nvinfer1::IExecutionContext *contextRT;

@@ -91,7 +94,7 @@ public:
    }

    /**
-        Do inferece
+        Do inference
    */
    dnnType* infer(dataDim_t &dim, dnnType* data);
    void enqueue(int batchSize = 1);    
@@ -105,6 +108,7 @@ public:
    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Route *l);
    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Flatten *l);
    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reshape *l);
+    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Resize *l);
    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reorg *l);
    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Region *l);
    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Shortcut *l);
@@ -114,6 +118,9 @@ public:

    bool serialize(const char *filename);
    bool deserialize(const char *filename);
+
+
+
 };

 }}
@@ -5,8 +5,8 @@

 namespace tk { namespace dnn {

-cv::Mat vizFloat2colorMap(cv::Mat map);
-cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim);
+cv::Mat vizFloat2colorMap(cv::Mat map, double min=0, double max=0, int classes=19);
+cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int img_h, int img_w, double min=0, double max=0, int classes=19);
 cv::Mat vizLayer2Mat(tk::dnn::Network *net, int layer, int imgdim = 1000);
    
 }}
@@ -0,0 +1,404 @@
+#ifndef SEGMENTATIONNN_H
+#define SEGMENTATIONNN_H
+
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>    
+#include <unistd.h>
+#include <mutex>
+#include "utils.h"
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/core/hal/interface.h>
+
+#include "tkdnn.h"
+#include "NetworkViz.h"
+#include "kernelsThrust.h"
+
+namespace tk { namespace dnn {
+
+class SegmentationNN {
+
+    protected:
+        tk::dnn::NetworkRT *netRT = nullptr;
+        int nBatches = 1;
+
+        std::vector<cv::Size> originalSize;
+        cv::Mat bgr[3];
+        dnnType *input;
+        dnnType *input_d;
+        float* confidences_h;
+
+        float * tmpInputData_d;
+        float *tmpOutData_d;
+        float *tmpOutData_h;
+
+        float *mean_d, *stddev_d;
+
+        cublasHandle_t cublasHandle;
+
+        void computeBorders(const int or_width, const int or_height, int& top, int& bottom, int& left, int&right){
+            top = 0;
+            bottom = 0;
+            left = 0;
+            right = 0;
+
+            if(or_height != or_width){
+                if(or_height < or_width){
+                    top = (or_width - or_height)/2;
+                    bottom = or_width - top - or_height;
+                }
+                else{
+                    left = (or_height - or_width)/2;
+                    right = or_height - left - or_width;
+                }
+            }
+        }
+
+        /**
+         * This method preprocess the image, before feeding it to the NN.
+         *
+         * @param frame original frame to adapt for inference.
+         * @param bi batch index
+         */
+        void preprocess(cv::Mat &frame, const int bi=0) {
+            originalSize[bi] = frame.size();
+
+            frame.convertTo(frame, CV_32FC3, 1 / 255.0, 0);
+            int H = frame.rows;
+            int W = frame.cols;
+            cv::Mat frame_cropped;
+
+            int top, bottom, left, right;
+            computeBorders(W, H, top, bottom, left, right);
+            cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0) );
+
+            tk::dnn::dataDim_t idim = netRT->input_dim;
+
+            resize(frame_cropped, frame_cropped, cv::Size(idim.w, idim.h));
+            
+            cv::split(frame_cropped, bgr);
+            for (int i = 0; i < idim.c; i++){
+                int idx = i * frame_cropped.rows * frame_cropped.cols;
+                int ch = idim.c-1 -i;
+                memcpy((void *)&input[idx + idim.tot()*bi], (void *)bgr[ch].data, frame_cropped.rows * frame_cropped.cols * sizeof(dnnType));
+            }
+
+            checkCuda(cudaMemcpyAsync(input_d+ idim.tot()*bi, input + idim.tot()*bi, idim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
+
+            normalize(input_d + idim.tot()*bi, idim.c, idim.h, idim.w, mean_d, stddev_d);
+        }        
+
+        /**
+         * This method postprocess the output of the NN to obtain the correct 
+         * boundig boxes. 
+         * 
+         * @param bi batch index
+         */
+        void postprocess(const int bi=0, bool appy_colormap = true) {
+            dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
+
+            dataDim_t odim = netRT->output_dim;
+            
+            matrixTranspose(cublasHandle, rt_out, tmpInputData_d, odim.c, odim.w*odim.h);
+            maxElem(tmpInputData_d, tmpOutData_d, odim.c, odim.h, odim.w);
+            checkCuda(cudaMemcpy(tmpOutData_h, tmpOutData_d, odim.w*odim.h * sizeof(float), cudaMemcpyDeviceToHost));
+
+            dataDim_t vdim = odim;
+            vdim.c = 1;
+
+            cv::Mat colored;
+
+            if(appy_colormap)
+                colored = vizData2Mat(tmpOutData_h, vdim, netRT->input_dim.h, netRT->input_dim.w, 0, classes, classes);
+            else{
+                cv::Mat colored_fp32 (cv::Size(odim.w, odim.h),CV_32FC1, tmpOutData_h);
+                colored_fp32.convertTo(colored, CV_8UC1);
+            }
+            
+            int max_dim = (originalSize[bi].width > originalSize[bi].height) ? originalSize[bi].width : originalSize[bi].height;
+            resize(colored, colored, cv::Size(max_dim, max_dim));
+            int top, bottom, left, right;
+            computeBorders(originalSize[bi].width, originalSize[bi].height, top, bottom, left, right);
+            cv::Rect roi(left,top,originalSize[bi].width, originalSize[bi].height);
+            cv::Mat or_size (colored, roi);
+            segmented[bi] = or_size;
+        };
+
+    public:
+        int classes = 0;
+        std::vector<double> stats; /*keeps track of inference times (ms)*/
+        std::vector<double> stats_pre; 
+        std::vector<double> stats_post; 
+        std::vector<std::string> classesNames;
+        std::vector<cv::Mat> segmented;
+
+        SegmentationNN() {
+            checkERROR( cublasCreate(&cublasHandle) );
+        };
+        ~SegmentationNN(){
+            checkERROR( cublasDestroy(cublasHandle) );
+        };
+
+        /**
+         * Method used to inialize the class, allocate memory and compute 
+         * needed data.
+         * 
+         * @param tensor_path path to the rt file og the NN.
+         * @param n_classes number of classes for the given dataset.
+         * @param n_batches maximum number of batches to use in inference
+         * @return true if everything is correct, false otherwise.
+         */
+        bool init(const std::string& tensor_path, const int n_classes=19, const int n_batches=1){
+            std::cout<<(tensor_path).c_str()<<"\n";
+            if(!fileExist(tensor_path.c_str()))
+                FatalError("This file do not exists" + tensor_path );
+
+            netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
+            classes = n_classes;
+            nBatches = n_batches;
+
+            checkCuda(cudaMallocHost(&input, sizeof(dnnType) * netRT->input_dim.tot() * nBatches));
+            checkCuda(cudaMalloc(&input_d, sizeof(dnnType) * netRT->input_dim.tot() * nBatches));
+
+            dataDim_t odim = netRT->output_dim;
+
+            checkCuda(cudaMallocHost(&confidences_h, sizeof(float) * odim.tot()));
+            checkCuda(cudaMalloc(&tmpInputData_d, sizeof(float) * odim.tot()));
+            checkCuda(cudaMalloc(&tmpOutData_d, sizeof(float) * odim.w*odim.h));
+            checkCuda(cudaMallocHost(&tmpOutData_h, sizeof(float) * odim.w*odim.h));
+
+            segmented.resize(nBatches);
+            originalSize.resize(nBatches);
+
+            std::vector<float> mean = {0.485, 0.456, 0.406};
+            std::vector<float> stddev = {0.229, 0.224, 0.225};
+
+            checkCuda(cudaMalloc(&mean_d, sizeof(float) * mean.size()));
+            checkCuda(cudaMalloc(&stddev_d, sizeof(float) * stddev.size()));
+
+            checkCuda(cudaMemcpyAsync(mean_d, mean.data(), mean.size() * sizeof(float), cudaMemcpyHostToDevice, netRT->stream));
+            checkCuda(cudaMemcpyAsync(stddev_d, stddev.data(), stddev.size() * sizeof(float), cudaMemcpyHostToDevice, netRT->stream));
+
+            return true;
+        }
+        
+        /**
+         * This method performs the whole detection of the NN.
+         * 
+         * @param frames frames to run detection on.
+         * @param cur_batches number of batches to use in inference
+         * @param save_times if set to true, preprocess, inference and postprocess times 
+         *        are saved on a csv file, otherwise not.
+         * @param times pointer to the output stream where to write times
+         * @param mAP set to true only if all the probabilities for a bounding 
+         *            box are needed, as in some cases for the mAP calculation
+         */
+        void update(std::vector<cv::Mat>& frames, const int cur_batches=1, bool apply_colormap=true){
+            if(cur_batches > nBatches)
+                FatalError("A batch size greater than nBatches cannot be used");
+
+            originalSize.clear();
+            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30); 
+            {
+                TKDNN_TSTART
+                for(int bi=0; bi<cur_batches;++bi){
+                    if(!frames[bi].data)
+                        FatalError("No image data feed to detection");
+                    originalSize.push_back(frames[bi].size());
+                    preprocess(frames[bi], bi);    
+                }
+                TKDNN_TSTOP
+                stats_pre.push_back(t_ns);
+            }
+
+            //do inference
+            tk::dnn::dataDim_t dim = netRT->input_dim;
+            dim.n = cur_batches;
+            {
+                if(TKDNN_VERBOSE) dim.print();
+                TKDNN_TSTART
+                netRT->infer(dim, input_d);
+                TKDNN_TSTOP
+                if(TKDNN_VERBOSE) dim.print();
+                stats.push_back(t_ns);
+            }
+
+            {
+                TKDNN_TSTART
+                for(int bi=0; bi<cur_batches;++bi)
+                    postprocess(bi, apply_colormap);
+                TKDNN_TSTOP
+                stats_post.push_back(t_ns);
+            }
+        }     
+
+        void updateOriginal(cv::Mat frame, bool apply_colormap=true){
+
+            std::vector<cv::Mat> splitted_frames;
+            int H, W, net_H, net_W;
+            int top = 0, bottom = 0, left = 0, right = 0;
+            std::vector<std::pair<int,int>> pos;
+
+            {
+                TKDNN_TSTART
+                cv::Size original_size =  frame.size();
+
+                frame.convertTo(frame, CV_32FC3, 1 / 255.0, 0);
+                H = frame.rows;
+                W = frame.cols;
+                net_H = netRT->input_dim.h; 
+                net_W = netRT->input_dim.w;
+
+                cv::Mat frame_cropped;
+                
+                if( H <= net_H && W <= net_W ){ // smaller size wrt network
+                    top = (net_H - H)/2;
+                    bottom = net_H - H - top ;
+                    left = (net_W - W)/2;
+                    right = net_W - W - left ;
+                    cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0) );
+                    splitted_frames.push_back(frame_cropped);
+                }
+                else{ //bigger size wrt network
+
+                    
+                    if(H < net_H  ||  W < net_W){
+                        if(H < net_H){
+                            top = (net_H - H)/2;
+                            bottom = net_H - H - top ;
+                        }
+                        else{
+                            left = (net_W - W)/2;
+                            right = net_W - W - left ;
+                        }
+                        cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0));
+                    }
+
+                    for(int x=0; x+net_W<=W ;){
+                        for(int y=0; y+net_H <=H ; ){
+                            cv::Rect roi(x, y, net_W, net_H);
+                            cv::Mat image_roi = frame(roi);
+                            splitted_frames.push_back(image_roi);
+                            pos.push_back(std::make_pair(x,y));
+                            
+                            y += net_H;
+                            if(y == H)
+                                break;
+                            if(y + net_H > H) y = H - net_H;
+                        }
+                        x += net_W;
+                        if(x == W)
+                            break;
+                        if(x + net_W > W) x = W - net_W;
+                    }
+                }
+
+                tk::dnn::dataDim_t idim = netRT->input_dim;
+
+                if(splitted_frames.size()> nBatches)
+                    FatalError(std::to_string(splitted_frames.size()) + " min batches required");
+
+                for(int bi=0; bi<splitted_frames.size();++bi){
+                    cv::split(splitted_frames[bi], bgr);
+                    for (int i = 0; i < idim.c; i++){
+                        int idx = i * splitted_frames[bi].rows * splitted_frames[bi].cols;
+                        int ch = idim.c-1 -i;
+                        memcpy((void *)&input[idx + idim.tot()*bi], (void *)bgr[ch].data, splitted_frames[bi].rows * splitted_frames[bi].cols * sizeof(dnnType));
+                    }
+
+                    checkCuda(cudaMemcpyAsync(input_d+ idim.tot()*bi, input + idim.tot()*bi, idim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
+                    normalize(input_d + idim.tot()*bi, idim.c, idim.h, idim.w, mean_d, stddev_d);
+                }
+                TKDNN_TSTOP
+                stats_pre.push_back(t_ns);
+            }
+
+            tk::dnn::dataDim_t dim = netRT->input_dim;
+            dim.n = splitted_frames.size();
+            {
+                if(TKDNN_VERBOSE) dim.print();
+                TKDNN_TSTART
+                netRT->infer(dim, input_d);
+                TKDNN_TSTOP
+                if(TKDNN_VERBOSE) dim.print();
+                stats.push_back(t_ns);
+            }
+
+            dataDim_t odim = netRT->output_dim;
+
+            std::vector<cv::Mat> out_img;
+
+            {
+                TKDNN_TSTART
+
+                for(int bi=0; bi<splitted_frames.size();++bi){
+
+                    dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;               
+                    
+                    matrixTranspose(cublasHandle, rt_out, tmpInputData_d, odim.c, odim.w*odim.h);
+                    maxElem(tmpInputData_d, tmpOutData_d, odim.c, odim.h, odim.w);
+                    checkCuda(cudaMemcpy(tmpOutData_h, tmpOutData_d, odim.w*odim.h * sizeof(float), cudaMemcpyDeviceToHost));
+
+                    dataDim_t vdim = odim;
+                    vdim.c = 1;
+
+                    cv::Mat colored;
+
+                    if(apply_colormap)
+                        colored = vizData2Mat(tmpOutData_h, vdim, netRT->input_dim.h, netRT->input_dim.w, 0, classes, classes);
+                    else{
+                        cv::Mat colored_fp32 (cv::Size(odim.w, odim.h),CV_32FC1, tmpOutData_h);
+                        colored_fp32.convertTo(colored, CV_8UC1);
+                    }
+                    out_img.push_back(colored);
+                }
+
+
+                cv::Mat seg(frame.size(), out_img[0].type());
+                if(out_img.size() == 1)
+                {
+                    cv::Rect roi(left, top, W, H);
+                    seg = out_img[0](roi);
+                }
+                else{
+                    int bi=0;
+
+                    if(top == 0 && left == 0){
+
+                        for(int i=0; i<out_img.size(); ++i){
+                            cv::Mat roi_collage = seg(cv::Rect( pos[i].first ,pos[i].second,out_img[i].cols,out_img[i].rows));
+                            out_img[i].copyTo(roi_collage);
+                        }
+                    }
+                    else{
+                        FatalError("Not handled case")
+                    }
+                }
+                segmented[0] = seg;
+                
+                TKDNN_TSTOP
+                stats_post.push_back(t_ns);
+            }
+        } 
+
+        /**
+         * Method to draw boundixg boxes and labels on a frame.
+         */
+        cv::Mat draw(const int cur_batches=1) {
+            for(int i=0; i<cur_batches; ++i){
+
+                cv::imshow("segmented", segmented[i]);
+                cv::resizeWindow("segmented", cv::Size(512,288));
+                cv::waitKey(1);
+            }
+            return segmented[0];
+        }
+
+};
+
+}}
+
+#endif /* SEGMENTATIONNN_H*/
@@ -0,0 +1,158 @@
+#ifndef TRACKINGNN_H
+#define TRACKINGNN_H
+
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>
+#ifdef __linux__    
+#include <unistd.h>
+#endif
+
+#include <mutex>
+#include "utils.h"
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "tkdnn.h"
+
+// #define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
+
+#ifdef OPENCV_CUDACONTRIB
+#include <opencv2/cudawarping.hpp>
+#include <opencv2/cudaarithm.hpp>
+#endif
+
+
+namespace tk { namespace dnn {
+
+class TrackingNN {
+
+    protected:
+        tk::dnn::NetworkRT *netRT = nullptr;
+        dnnType *input_d;
+
+        std::vector<cv::Size> originalSize;
+
+        cv::Scalar colors[256];
+
+        int nBatches = 1;
+
+#ifdef OPENCV_CUDACONTRIB
+        cv::cuda::GpuMat bgr[3];
+        cv::cuda::GpuMat imagePreproc;
+#else
+        cv::Mat bgr[3];
+        cv::Mat imagePreproc;
+        dnnType *input;
+#endif
+
+        /**
+         * This method preprocess the image, before feeding it to the NN.
+         *
+         * @param frame original frame to adapt for inference.
+         * @param bi batch index
+         */
+        virtual void preprocess(cv::Mat &frame, const int bi=0) = 0;
+
+        /**
+         * This method postprocess the output of the NN to obtain the correct 
+         * boundig boxes. 
+         * 
+         * @param bi batch index
+         * @param mAP set to true only if all the probabilities for a bounding 
+         *            box are needed, as in some cases for the mAP calculation
+         */
+        virtual void postprocess(const int bi=0,const bool mAP=false) = 0;
+
+    public:
+        int classes = 0;
+        float confThreshold = 0.3; /*threshold on the confidence of the boxes*/
+        
+        std::vector<double> pre_stats, stats, post_stats, visual_stats; /*keeps track of inference times (ms)*/
+        std::vector<std::string> classesNames;
+
+        TrackingNN() {};
+        ~TrackingNN(){};
+
+        /**
+         * Method used to initialize the class, allocate memory and compute 
+         * needed data.
+         * 
+         * @param tensor_path path to the rt file of the NN.
+         * @param n_classes number of classes for the given dataset.
+         * @param n_batches maximum number of batches to use in inference.
+         * @return true if everything is correct, false otherwise.
+         */
+        virtual bool init(const std::string& tensor_path, const int n_classes=3, const int n_batches=1, 
+                            const float conf_thresh=0.3, const bool mode_3d=true, const std::vector<cv::Mat>& k_calibs=std::vector<cv::Mat>()) = 0;
+
+        /**
+         * This method performs the whole detection and tracking of the NN.
+         * 
+         * @param frames frames to run detection and trcking on.
+         * @param cur_batches number of batches to use in inference.
+         * @param save_times if set to true, preprocess, inference and postprocess times 
+         *        are saved on a csv file, otherwise not.
+         * @param times pointer to the output stream where to write times.
+         * @param mAP set to true only if all the probabilities for a bounding 
+         *            box are needed, as in some cases for the mAP calculation.
+         */
+        void update(std::vector<cv::Mat>& frames, const int cur_batches=1, bool save_times=false, 
+                    std::ofstream *times=nullptr, const bool mAP=false){
+            if(save_times && times==nullptr)
+                FatalError("save_times set to true, but no valid ofstream given");
+            if(cur_batches > nBatches)
+                FatalError("A batch size greater than nBatches cannot be used");
+
+            originalSize.clear();
+            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30); 
+            {
+                TKDNN_TSTART
+                for(int bi=0; bi<cur_batches;++bi){
+                    if(!frames[bi].data)
+                        FatalError("No image data feed to detection");
+                    originalSize.push_back(frames[bi].size());
+                    preprocess(frames[bi], bi);   
+                }
+                TKDNN_TSTOP
+                pre_stats.push_back(t_ns); 
+                if(save_times) *times<<t_ns<<";";
+            }
+
+            //do inference
+            tk::dnn::dataDim_t dim = netRT->input_dim;
+            dim.n = cur_batches;
+            {
+                if(TKDNN_VERBOSE) dim.print();
+                TKDNN_TSTART
+                netRT->infer(dim, input_d);
+                TKDNN_TSTOP
+                if(TKDNN_VERBOSE) dim.print();
+                stats.push_back(t_ns);
+                if(save_times) *times<<t_ns<<";";
+            }
+
+            {
+                TKDNN_TSTART
+                for(int bi=0; bi<cur_batches;++bi)
+                    postprocess(bi, mAP);
+                TKDNN_TSTOP
+                post_stats.push_back(t_ns);
+                if(save_times) *times<<t_ns<<"\n";
+            }
+        }
+
+        /**
+         * Method to draw bounding boxes and labels on a frame.
+         * 
+         * @param frames original frame to draw bounding box on.
+         */    
+        virtual void draw(std::vector<cv::Mat>& frames){};
+          
+};
+
+}}
+
+#endif /* TRACKINGNN_H*/
@@ -24,7 +24,7 @@ public:
    Yolo3Detection() {};
    ~Yolo3Detection() {}; 

-    bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1);
+    bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1, const float conf_thresh=0.3);
    void preprocess(cv::Mat &frame, const int bi=0);
    void postprocess(const int bi=0,const bool mAP=false);
 };
@@ -0,0 +1,22 @@
+#ifndef DEMO_UTILS_H
+#define DEMO_UTILS_H
+
+#include <iostream>
+#include <sstream>
+#include <fstream>
+#include <iomanip>
+#include <stdlib.h>
+
+#ifdef __linux__
+#include <unistd.h>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include <yaml-cpp/yaml.h>
+#endif
+
+
+void readCalibrationMatrix(const std::string& path, cv::Mat& calib_mat);
+
+#endif //DEMO_UTILS_H
@@ -18,6 +18,8 @@ struct Frame
    std::string iFilename;
    std::vector<BoundingBox> gt;
    std::vector<BoundingBox> det;
+    int width;
+    int height;

    void print() const;
 };
@@ -73,12 +75,12 @@ double computeMap(  std::vector<Frame> &images,const int classes,
 *                  all the recall levels are evaluated, otherwise only 
 *                  map_point recall levels are used. For COCO evaluation 
 *                  101 points are used.
- * @param map_step step used to increment IoU theshold
+ * @param map_step step used to increment IoU threshold
 * @param map_levels number of IoU step to perform
 * @param verbose is set to true, prints on screen additional info
 * @param write_on_file if set to true, the results produced by this function 
 *                      are written on file
- * @param net name of the considerd neural network 
+ * @param net name of the considered neural network 
 *
 * @return  mAP IoU_tresh:IoU_tresh+map_step*map_levels (e.g. mAP 0.5:0.95 when 
 *          map_step=0.05 and map_levels=10)
@@ -89,7 +91,7 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
                const int map_levels=10, const bool verbose=false, 
                const bool write_on_file = false, std::string net = "");
 /**
- * This method computes the numper of True Positive (TP), False Positive (FP),
+ * This method computes the number of True Positive (TP), False Positive (FP),
 * False Negative (FN), precision, recall and f1-score.
 * Those values are computer over all the detections, over all the classes.
 *
@@ -101,7 +103,7 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
 * @param verbose is set to true, prints on screen additional info
 * @param write_on_file if set to true, the results produced by this function 
 *                      are written on file
- * @param net name of the considerd neural network 
+ * @param net name of the considered neural network 
 */
 void computeTPFPFN( std::vector<Frame> &images,const int classes,
                    const float IoU_thresh=0.5, const float conf_thresh=0.3, 
@@ -4,7 +4,7 @@
 #include "utils.h"

 void activationELUForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
-void activationLEAKYForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
+void activationLEAKYForward(dnnType *srcData, dnnType *dstData, int size, float slope, cudaStream_t stream = cudaStream_t(0));
 void activationReLUCeilingForward(dnnType *srcData, dnnType *dstData, int size, const float ceiling, cudaStream_t stream = cudaStream_t(0));
 void activationLOGISTICForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
 void activationSIGMOIDForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
@@ -24,7 +24,7 @@ void softmaxForward(float *input, int n, int batch, int batch_offset,
                    int groups, int group_offset, int stride, float temp, float *output, cudaStream_t stream = cudaStream_t(0));

 void shortcutForward(dnnType *srcData, dnnType *dstData, int n1, int c1, int h1, int w1, int s1,
-                     int n2, int c2, int h2, int w2, int s2,
+                     int n2, int c2, int h2, int w2, int s2, bool mul,
                     cudaStream_t stream = cudaStream_t(0));

 void upsampleForward(dnnType *srcData, dnnType *dstData,
@@ -2,6 +2,7 @@
 #define KERNELSTHRUST_H


+#include <thrust/extrema.h>
 #include <thrust/sort.h>
 #include <thrust/execution_policy.h>
 #include <thrust/functional.h>
@@ -9,6 +10,8 @@
 #include <thrust/iterator/constant_iterator.h>
 #include <thrust/gather.h>
 #include <thrust/copy.h>
+#include <thrust/device_ptr.h>
+

 #include "tkdnn.h"

@@ -29,11 +32,15 @@ void topk(dnnType *src_begin, int *idsrc, int K, float *topk_scores,
            int *topk_inds, float *topk_ys, float *topk_xs);
 // void sortAndTopKonDevice(dnnType *src_begin, int *idsrc, float *topk_scores, int *topk_inds, float *topk_ys, float *topk_xs, const int size, const int K, const int n_classes);            
 void normalize(float *bgr, const int ch, const int h, const int w, const float *mean, const float *stddev);
+void transformDep(float *src_begin, float *src_end, float *dst_begin, float *dst_end);
 void subtractWithThreshold(dnnType *src_begin, dnnType *src_end, dnnType *src2_begin, dnnType *src_out, struct threshold op);
 void topKxyclasses(int *ids_begin, int *ids_end, const int K, const int size, const int wh, int *clses, int *xs, int *ys);
 void topKxyAddOffset(int * ids_begin, const int K, const int size, int *intxs_begin, int *intys_begin, 
                     float *xs_begin, float *ys_begin, dnnType *src_begin, float *src_out, int *ids_out);
 void bboxes(int * ids_begin, const int K, const int size, float *xs_begin, float *ys_begin, 
            dnnType *src_begin, float *bbx0, float *bbx1, float *bby0, float *bby1, float *src_out, int *ids_out);
+void getRecordsFromTopKId(int * ids_begin, const int K, const int ch, const int size, dnnType *src_begin, float *src_out, int *ids_out);
+
+void maxElem(dnnType *src_begin, dnnType *dst_begin, const int c, const int h, const int w);

 #endif //KERNELSTHRUST_H
@@ -4,9 +4,8 @@
 class ActivationLeakyRT : public IPlugin {

 public:
-	ActivationLeakyRT() {
-
-
+	ActivationLeakyRT(float s) {
+		slope = s;
 	}

 	~ActivationLeakyRT(){
@@ -42,19 +41,21 @@ public:
 	virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {

 		activationLEAKYForward((dnnType*)reinterpret_cast<const dnnType*>(inputs[0]), 
-											reinterpret_cast<dnnType*>(outputs[0]), batchSize*size, stream);
+											reinterpret_cast<dnnType*>(outputs[0]), batchSize*size, slope, stream);
 		return 0;
 	}


 	virtual size_t getSerializationSize() override {
-		return 1*sizeof(int);
+		return 1*sizeof(int) + 1*sizeof(float);
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, size);
+		assert(buf == a + getSerializationSize());
 	}

 	int size;
+	float slope;
 };
@@ -0,0 +1,60 @@
+#include<cassert>
+#include "../kernels.h"
+
+class ActivationLogisticRT : public IPlugin {
+
+public:
+    ActivationLogisticRT() {
+
+
+    }
+
+    ~ActivationLogisticRT(){
+
+    }
+
+    int getNbOutputs() const override {
+        return 1;
+    }
+
+    Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
+        return inputs[0];
+    }
+
+    void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
+        size = 1;
+        for(int i=0; i<outputDims[0].nbDims; i++)
+            size *= outputDims[0].d[i];
+    }
+
+    int initialize() override {
+
+        return 0;
+    }
+
+    virtual void terminate() override {
+    }
+
+    virtual size_t getWorkspaceSize(int maxBatchSize) const override {
+        return 0;
+    }
+
+    virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {
+
+        activationLOGISTICForward((dnnType*)reinterpret_cast<const dnnType*>(inputs[0]),
+                                  reinterpret_cast<dnnType*>(outputs[0]), batchSize*size, stream);
+        return 0;
+    }
+
+
+    virtual size_t getSerializationSize() override {
+        return 1*sizeof(int);
+    }
+
+    virtual void serialize(void* buffer) override {
+        char *buf = reinterpret_cast<char*>(buffer);
+        tk::dnn::writeBUF(buf, size);
+    }
+
+    int size;
+};
@@ -52,8 +52,9 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, size);
+		assert(buf == a + getSerializationSize());
 	}

 	int size;
@@ -51,9 +51,10 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, ceiling);
 		tk::dnn::writeBUF(buf, size);
+		assert(buf = a + getSerializationSize());
 		
 	}

@@ -52,8 +52,9 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, size);
+		assert(buf == a + getSerializationSize());
 	}

 	int size;
@@ -30,7 +30,7 @@ public:
 		height_ones = (i_h + 2 * ph - (1 * (kh - 1) + 1)) / sh + 1;
 		width_ones = (i_w + 2 * pw - (1 * (kw - 1) + 1)) / sw + 1;
 		dim_ones = i_c * kh * kw * 1 * height_ones * width_ones;
-		std::cout<<i_c * o_c * kh * kw * 1<<"\n";
+		
 		checkCuda( cudaMalloc(&data_d, i_c * o_c * kh * kw * 1 * sizeof(dnnType)));
 		checkCuda( cudaMalloc(&bias2_d, o_c*sizeof(dnnType)));
 		checkCuda( cudaMalloc(&ones_d1, height_ones * width_ones * sizeof(dnnType)));
@@ -89,7 +89,7 @@ public:
 		for(int b=0; b<batchSize; b++) {
 			checkCuda(cudaMemcpy(offset, output_conv + b * 3 * chunk_dim, 2*chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
 			checkCuda(cudaMemcpy(mask, output_conv + b * 3 * chunk_dim + 2*chunk_dim, chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
-			// kernel sigmoide
+			// kernel sigmoid
 			activationSIGMOIDForward(mask, mask, chunk_dim);
 			// deformable convolution
 			dcnV2CudaForward(stat, handle, 
@@ -116,7 +116,7 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, chunk_dim);
 		tk::dnn::writeBUF(buf, kh);
 		tk::dnn::writeBUF(buf, kw);
@@ -163,6 +163,7 @@ public:
        for(int i=0; i<dim_ones; i++)
    		tk::dnn::writeBUF(buf, aus[i]);
 		free(aus);
+		assert(buf == a + getSerializationSize());
 	}

 	cublasStatus_t stat; 
@@ -65,12 +65,13 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a = buf;
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
 		tk::dnn::writeBUF(buf, rows);
 		tk::dnn::writeBUF(buf, cols);
+		assert(buf == a + getSerializationSize());
 	}

 	int c, h, w;
@@ -55,7 +55,7 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;

 		tk::dnn::writeBUF(buf, this->c);
 		tk::dnn::writeBUF(buf, this->h);
@@ -65,6 +65,7 @@ public:
 		tk::dnn::writeBUF(buf, this->stride_W);
 		tk::dnn::writeBUF(buf, this->winSize);
 		tk::dnn::writeBUF(buf, this->padding);
+		assert(buf == a + getSerializationSize());
 	}

 	int n, c, h, w;
@@ -73,13 +73,14 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, classes);
 		tk::dnn::writeBUF(buf, coords);
 		tk::dnn::writeBUF(buf, num);
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
+		assert(buf == a + getSerializationSize());
 	}

 	int c, h, w;
@@ -52,11 +52,12 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, stride);
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
+		assert(buf == a + getSerializationSize());
 	}

 	int c, h, w, stride;
@@ -50,11 +50,12 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a = buf;
 		tk::dnn::writeBUF(buf, n);
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
+		assert(buf == a + getSerializationSize());
 	}

 	int n, c, h, w;
@@ -52,7 +52,7 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;

 		tk::dnn::writeBUF(buf, o_c);
 		tk::dnn::writeBUF(buf, o_h);
@@ -61,6 +61,7 @@ public:
 		tk::dnn::writeBUF(buf, i_c);
 		tk::dnn::writeBUF(buf, i_h);
 		tk::dnn::writeBUF(buf, i_w);
+		assert(buf == a + getSerializationSize());
 	}

 	int i_c, i_h, i_w, o_c, o_h, o_w;
@@ -75,7 +75,7 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, groups);
 		tk::dnn::writeBUF(buf, group_id);
 		tk::dnn::writeBUF(buf, in);
@@ -85,6 +85,7 @@ public:
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
+		assert(buf == a + getSerializationSize());
 	}

 	static const int MAX_INPUTS = 4;
@@ -4,10 +4,11 @@
 class ShortcutRT : public IPlugin {

 public:
-	ShortcutRT(tk::dnn::dataDim_t bdim) {
+	ShortcutRT(tk::dnn::dataDim_t bdim, bool mul) {
 		this->bc = bdim.c;
 		this->bh = bdim.h;
 		this->bw = bdim.w;
+		this->mul = mul;
 	}

 	~ShortcutRT(){
@@ -47,28 +48,30 @@ public:
 		dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);

 		checkCuda( cudaMemcpyAsync(dstData, srcData, batchSize*c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));
-		for(int b=0; b < batchSize; ++b)
-			shortcutForward(srcDataBack + b*bc*bh*bw, dstData + b*c*h*w, 1, c, h, w, 1, 1, bc, bh, bw, 1, stream);
+		shortcutForward(srcDataBack, dstData, batchSize, c, h, w, 1, batchSize, bc, bh, bw, 1, mul, stream);

 		return 0;
 	}


 	virtual size_t getSerializationSize() override {
-		return 6*sizeof(int);
+		return 6*sizeof(int) + sizeof(bool);
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, bc);
 		tk::dnn::writeBUF(buf, bh);
 		tk::dnn::writeBUF(buf, bw);
+		tk::dnn::writeBUF(buf, mul);
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
+		assert(buf == a + getSerializationSize());
 		
 	}

 	int c, h, w;
 	int bc, bh, bw;
+	bool mul;
 };
@@ -54,11 +54,12 @@ public:
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
 		tk::dnn::writeBUF(buf, stride);
 		tk::dnn::writeBUF(buf, c);
 		tk::dnn::writeBUF(buf, h);
 		tk::dnn::writeBUF(buf, w);
+		assert(buf == a + getSerializationSize());
 	}

 	int c, h, w, stride;
@@ -8,12 +8,15 @@ class YoloRT : public IPlugin {


 public:
-	YoloRT(int classes, int num, tk::dnn::Yolo *yolo = nullptr, int n_masks=3, float scale_xy=1) {
+	YoloRT(int classes, int num, tk::dnn::Yolo *yolo = nullptr, int n_masks=3, float scale_xy=1, float nms_thresh=0.45, int nms_kind=0, int new_coords=0) {

 		this->classes = classes;
 		this->num = num;
 		this->n_masks = n_masks;
 		this->scaleXY = scale_xy;
+		this->nms_thresh = nms_thresh;
+		this->nms_kind = nms_kind;
+		this->new_coords = new_coords;

        mask = new dnnType[n_masks];
        bias = new dnnType[num*n_masks*2];
@@ -61,17 +64,23 @@ public:

 		checkCuda( cudaMemcpyAsync(dstData, srcData, batchSize*c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));

-		for (int b = 0; b < batchSize; ++b){
-			for(int n = 0; n < n_masks; ++n){
-				int index = entry_index(b, n*w*h, 0);
-				activationLOGISTICForward(srcData + index, dstData + index, 2*w*h, stream);

-				if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
-				
-				index = entry_index(b, n*w*h, 4);
-				activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*w*h, stream);
-			}
-		}
+        for (int b = 0; b < batchSize; ++b){
+            for(int n = 0; n < n_masks; ++n){
+                int index = entry_index(b, n*w*h, 0);
+                if (new_coords == 1){
+                    if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
+                }
+                else{
+                    activationLOGISTICForward(srcData + index, dstData + index, 2*w*h, stream); //x,y
+
+                    if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
+
+                    index = entry_index(b, n*w*h, 4);
+                    activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*w*h, stream);
+                }
+            }
+        }

 		//std::cout<<"YOLO END\n";
 		return 0;
@@ -79,22 +88,29 @@ public:


 	virtual size_t getSerializationSize() override {
-		return 6*sizeof(int) + sizeof(float)+ n_masks*sizeof(dnnType) + num*n_masks*2*sizeof(dnnType) + YOLORT_CLASSNAME_W*classes*sizeof(char);
+		return 8*sizeof(int) + 2*sizeof(float)+ n_masks*sizeof(dnnType) + num*n_masks*2*sizeof(dnnType) + YOLORT_CLASSNAME_W*classes*sizeof(char);
 	}

 	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
-		tk::dnn::writeBUF(buf, classes);
-		tk::dnn::writeBUF(buf, num);
-		tk::dnn::writeBUF(buf, n_masks);
-		tk::dnn::writeBUF(buf, c);
-		tk::dnn::writeBUF(buf, h);
-		tk::dnn::writeBUF(buf, w);
-		tk::dnn::writeBUF(buf, scaleXY);
-        for(int i=0; i<n_masks; i++)
-    		tk::dnn::writeBUF(buf, mask[i]);
-        for(int i=0; i<n_masks*2*num; i++)
-    		tk::dnn::writeBUF(buf, bias[i]);
+		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
+		tk::dnn::writeBUF(buf, classes); 	//std::cout << "Classes :" << classes << std::endl;
+		tk::dnn::writeBUF(buf, num); 		//std::cout << "Num : " << num << std::endl;
+		tk::dnn::writeBUF(buf, n_masks); 	//std::cout << "N_Masks" << n_masks << std::endl;
+		tk::dnn::writeBUF(buf, scaleXY); 	//std::cout << "ScaleXY :" << scaleXY << std::endl;
+		tk::dnn::writeBUF(buf, nms_thresh); //std::cout << "nms_thresh :" << nms_thresh << std::endl;
+		tk::dnn::writeBUF(buf, nms_kind); 	//std::cout << "nms_kind : " << nms_kind << std::endl;
+		tk::dnn::writeBUF(buf, new_coords); //std::cout << "new_coords : " << new_coords << std::endl;
+		tk::dnn::writeBUF(buf, c); 			//std::cout << "C : " << c << std::endl;
+		tk::dnn::writeBUF(buf, h); 			//std::cout << "H : " << h << std::endl;
+		tk::dnn::writeBUF(buf, w); 			//std::cout << "C : " << c << std::endl;
+		for (int i = 0; i < n_masks; i++)
+		{
+			tk::dnn::writeBUF(buf, mask[i]); //std::cout << "mask[i] : " << mask[i] << std::endl;
+		}
+		for (int i = 0; i < n_masks * 2 * num; i++)
+		{
+			tk::dnn::writeBUF(buf, bias[i]); //std::cout << "bias[i] : " << bias[i] << std::endl;
+		}

 		// save classes names
 		for(int i=0; i<classes; i++) {
@@ -104,11 +120,15 @@ public:
 				tk::dnn::writeBUF(buf, tmp[j]);
 			}
 		}
+		assert(buf == a + getSerializationSize());
 	}

 	int c, h, w;
    int classes, num, n_masks;
 	float scaleXY;
+	float nms_thresh;
+	int nms_kind;
+	int new_coords;
 	std::vector<std::string> classesNames;

    dnnType *mask;
@@ -20,7 +20,7 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
    }
    if(output_bins.size() != outputs.size()) {
        std::cout<<output_bins.size()<<" "<<outputs.size()<<"\n";
-        FatalError("outputs size missmatch");
+        FatalError("outputs size mismatch");
    }

    // Load input
@@ -29,7 +29,8 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
    readBinaryFile(input_bins[0], net->input_dim.tot(), &input_h, &data);

    // outputs
-    dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()]; 
+    //dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()];
+    std::vector<dnnType *> cudnn_out,rt_out;

    tk::dnn::dataDim_t dim1 =  net->input_dim; //input dim
    printCenteredTitle(" CUDNN inference ", '=', 30); {
@@ -39,7 +40,7 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
        TKDNN_TSTOP
        dim1.print();   
    }
-    for(int i=0; i<outputs.size(); i++) cudnn_out[i] = outputs[i]->dstData;
+    for(int i=0; i<outputs.size(); i++) cudnn_out.push_back(outputs[i]->dstData);

    if(netRT != nullptr) {
        tk::dnn::dataDim_t dim2 = net->input_dim;
@@ -50,7 +51,7 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
            TKDNN_TSTOP
            dim2.print();
        }
-        for(int i=0; i<outputs.size(); i++) rt_out[i] = (dnnType*)netRT->buffersRT[i+1];
+        for(int i=0; i<outputs.size(); i++) rt_out.push_back((dnnType*)netRT->buffersRT[i+1]);
    }

    int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
@@ -12,9 +12,16 @@
 #include <cublas_v2.h>
 #include <cudnn.h>

-#include <unistd.h>
-#include <ios>

+#ifdef __linux__
+#include <unistd.h>
+
+#endif
+
+#include <ios>
+#include <chrono>
+
+#include <yaml-cpp/yaml.h>

 #define dnnType float

@@ -39,6 +46,7 @@
 #define TKDNN_VERBOSE 0

 // Simple Timer 
+#ifdef __linux__
 #define TKDNN_TSTART timespec start, end;                               \
                    clock_gettime(CLOCK_MONOTONIC, &start);            

@@ -48,6 +56,14 @@
    if(show) std::cout<<col<<"Time:"<<std::setw(16)<<t_ns<<" ms\n"<<COL_END; 

 #define TKDNN_TSTOP TKDNN_TSTOP_C(COL_CYANB, TKDNN_VERBOSE)
+#elif _WIN32
+#define TKDNN_TSTART auto start = std::chrono::high_resolution_clock::now();
+#define TKDNN_TSTOP auto stop = std::chrono::high_resolution_clock::now(); \
+std::chrono::duration<double> duration = stop -start;                      \
+auto time_ms = std::chrono::duration_cast<std::chrono::milliseconds>(duration);\
+double t_ns = time_ms.count();
+#endif
+

 /********************************************************
 * Prints the error message, and exits
@@ -105,7 +121,7 @@ void printCenteredTitle(const char *title, char fill, int dim = 30);
 bool fileExist(const char *fname);
 void downloadWeightsifDoNotExist(const std::string& input_bin, const std::string& test_folder, const std::string& weights_url);
 void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** data_d, int seek = 0);
-int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device = true, int limit = 10);
+int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device = true, int limit = 10, bool verbose=true);
 void printDeviceVector(int size, dnnType* vec_d, bool device = true);
 float getColor(const int c, const int x, const int max);
 void resize(int size, dnnType **data);
@@ -122,4 +138,19 @@ static inline bool isCudaPointer(void *data) {
  cudaPointerAttributes attr;
  return cudaPointerGetAttributes(&attr, data) == 0;
 }
+
+inline YAML::Node YAMLloadConf(const std::string& conf_file) {
+    std::cerr<<"Loading YAML: "<<conf_file<<"\n";
+    return YAML::LoadFile(conf_file);
+}
+
+template<typename T>
+inline T YAMLgetConf(YAML::Node conf, std::string key, T defaultVal) {
+    T val = defaultVal;
+    if(conf && conf[key]) {
+        val = conf[key].as<T>();
+    }
+    return val;
+}
+
 #endif //UTILS_H
@@ -0,0 +1,37 @@
+import sys
+import pandas as pd 
+
+if len(sys.argv) < 3:
+    print("Error: two csv files are needed, old first new second")
+    exit(1)
+
+old_perf_file = str(sys.argv[1])
+new_perf_file = str(sys.argv[2])
+
+verbose = False
+if len(sys.argv) == 4:
+    verbose = bool(sys.argv[3])
+
+print("Comparing {} vs {}".format(old_perf_file, new_perf_file))
+
+df_old = pd.read_csv (old_perf_file, sep=';', header=None, index_col=0)
+df_new = pd.read_csv (new_perf_file, sep=';', header=None, index_col=0)
+
+for index, row in df_new.iterrows():
+    if index in df_old.index:
+        if verbose:
+            print("New: ",row[1], row[2], row[3])
+            print("Old: ",df_old.loc[index][1], df_old.loc[index][2], df_old.loc[index][3])
+
+        print(index, end=': ')
+        if abs(row[1] - df_old.loc[index][1]) < df_old.loc[index][1]*0.1: 
+            print("similar performance")
+        elif (row[1] < df_old.loc[index][1]): 
+            print('\x1b[3;30;42m' + 'faster' + '\x1b[0m')
+        elif (row[1] > df_old.loc[index][1]): 
+            if row[1] > df_old.loc[index][1] + df_old.loc[index][1] * 0.5 :
+                print('\x1b[3;30;41m' + 'WAY SLOWER' + '\x1b[0m')
+            else:
+                print('\x1b[3;30;41m' + 'slower' + '\x1b[0m')
+
+
@@ -0,0 +1,39 @@
+import os
+import urllib.request as dowReq
+import zipfile
+
+val = input("Enter BDD or COCO :")
+if(val == "COCO"):
+    url = "https://cloud.hipert.unimore.it/s/LNxBDk4wzqXPL8c/download"
+    lib = "..\demo\COCO_val2017"
+    lib_zip = "COCO_val2017.zip"
+elif(val == "BDD"):
+    url = "https://cloud.hipert.unimore.it/s/bikqk3FzCq2tg4D/download"
+    lib = "..\demo\BDD100k_val"
+    lib_zip = "BDD100k_val.zip"
+
+dowReq.urlretrieve(url,lib_zip)
+
+with zipfile.ZipFile(lib_zip,'r') as zip_ref:
+    zip_ref.extractall(lib)
+
+labelFolder = lib + "\labels"
+imageFolder = lib + "\images"
+
+file1 = open(".\\..\\demo\\all_labels.txt","a")
+path1 = os.path.realpath(labelFolder)
+for file in os.listdir(labelFolder):
+    valTemp = path1 + "\\" + file
+    valTemp = valTemp + '\n'
+    file1.write(valTemp)
+file1.close()
+
+file2 = open(".\\..\\demo\\all_images.txt","a")
+path2 = os.path.realpath(imageFolder)
+for file in os.listdir(imageFolder):
+    pathtemp = path2 + "\\" + file
+    pathtemp = pathtemp + '\n'
+    file2.write(pathtemp)
+file2.close()
+
+print("Completed")
@@ -1,6 +1,6 @@
 #!/bin/bash

-cd build
+#cd build

 RED='\033[1;31m'
 GREEN='\033[1;32m'
@@ -29,24 +29,28 @@ function print_output {

 } 

+out_dir=results
 out_file=results.log
-rm $out_file
+rm -rf $out_dir/
+mkdir -p $out_dir

 function test_net {
-    ./test_$1 &>> $out_file
+    ./test_$1 &> $out_dir/$1_${TKDNN_MODE}_build_$out_file
    print_output $? $1
-    ./test_rtinference $1*.rt $TKDNN_BATCHSIZE &>> $out_file
+    ./test_rtinference $1*.rt 1 &> $out_dir/$1_${TKDNN_MODE}_inference_batch1_$out_file
+    print_output $? "infer $1"
+    ./test_rtinference $1*.rt $TKDNN_BATCHSIZE &> $out_dir/$1_${TKDNN_MODE}_inference_batch${TKDNN_BATCHSIZE}_$out_file
    print_output $? "batched $1"
 }


-modes=( 1 ) # only FP32
-# modes=( 1 2 ) # FP32 and FP16
+# modes=( 1 ) # only FP32
+modes=( 1 2 ) # FP32 and FP16
 # modes=( 1 2 3 ) # FP32, FP16 and INT8

 for i in "${modes[@]}"
 do
-    rm *rt
+    rm -f *rt
    if [ $i -eq 1 ]
    then
        export TKDNN_MODE=FP32
@@ -69,29 +73,41 @@ do
    echo -e "${ORANGE}Batch $TKDNN_BATCHSIZE ${NC}"
    
    test_net mnist    
-    ./test_imuodom &>> $out_file
-    print_output $? imuodom
+    # ./test_imuodom &>> $out_file
+    # print_output $? imuodom

    test_net yolo4
-    test_net yolo4_berkeley
-    test_net yolo4tiny
-    test_net yolo3
-    test_net yolo3_berkeley
-    test_net yolo3_coco4
-    test_net yolo3_flir
-    test_net yolo3_512
-    test_net yolo3tiny
-    test_net yolo3tiny_512
-    test_net yolo2
-    test_net yolo2_voc
-    #test_net yolo2tiny
-    test_net csresnext50-panet-spp
-    #test_net csresnext50-panet-spp_berkeley
-    test_net resnet101_cnet
-    test_net dla34_cnet
-    test_net mobilenetv2ssd
-    test_net mobilenetv2ssd512
-    test_net bdd-mobilenetv2ssd
+    # test_net yolo4_320
+    # test_net yolo4_320_coco2
+    # test_net yolo4_512
+    # test_net yolo4_608
+    # test_net yolo4-csp
+    # test_net yolo4x
+    # test_net yolo4_berkeley
+    # test_net yolo4_berkeley_f1
+    # test_net yolo4tiny
+    # test_net yolo4tiny_512
+    # test_net yolo3
+    # test_net yolo3_berkeley
+    # test_net yolo3_coco4
+    # test_net yolo3_flir
+    # test_net yolo3_512
+    # test_net yolo3tiny
+    # test_net yolo3tiny_512
+    # test_net yolo2
+    # test_net yolo2_voc
+    # test_net yolo2tiny
+    # test_net csresnext50-panet-spp
+    # test_net csresnext50-panet-spp_berkeley
+    # test_net resnet101_cnet
+    # test_net dla34_cnet
+    # test_net dla34_cnet3d
+    # test_net mobilenetv2ssd
+    # test_net mobilenetv2ssd512
+    # test_net bdd-mobilenetv2ssd
+    # test_net dla34_ctrack
+    # test_net shelfnet
+    # test_net shelfnet_berkeley
 done

-echo "If errors occured, check logfile $out_file" 
+echo "If errors occured, check logfiles in directory: $out_dir" 
@@ -0,0 +1,52 @@
+#!/bin/bash
+
+function test_inference {
+    ./test_$1
+    ./test_rtinference $1_$2.rt 1 
+    ./test_rtinference $1_$2.rt 4
+}
+
+sudo jeston_clock
+
+# modes=( 1 ) # only FP32
+# modes=( 1 2 ) # FP32 and FP16
+modes=( 1 2 3 ) # FP32, FP16 and INT8
+
+rm times_rtinference.csv
+for i in "${modes[@]}"
+do
+    rm *rt
+    if [ $i -eq 1 ]
+    then
+        export TKDNN_MODE=FP32
+        mode=fp32
+        echo -e "${ORANGE}Test FP32${NC}"
+    fi
+    if [ $i -eq 2 ]
+    then
+        export TKDNN_MODE=FP16
+        mode=fp16
+        echo -e "${ORANGE}Test FP16${NC}"
+    fi
+    if [ $i -eq 3 ]
+    then
+        export TKDNN_MODE=INT8
+        export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
+        export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
+        mode=int8
+        echo -e "${ORANGE}Test INT8${NC}"
+        
+	fi
+
+    export TKDNN_BATCHSIZE=4
+    echo -e "${ORANGE}Batch $TKDNN_BATCHSIZE ${NC}"
+    
+    test_inference yolo4_320 $mode
+    test_inference yolo4 $mode
+    test_inference yolo4_512 $mode
+    test_inference yolo4_608 $mode
+    test_inference yolo4tiny $mode
+done
+
+
+
@@ -5,11 +5,12 @@

 namespace tk { namespace dnn {

-Activation::Activation(Network *net, int act_mode, const float ceiling) : 
+Activation::Activation(Network *net, int act_mode, const float ceiling, const float slope) : 
    Layer(net) {

-    this->act_mode = act_mode;
-    this->ceiling = ceiling;
+    this->act_mode  = act_mode;
+    this->ceiling   = ceiling;
+    this->slope     = slope;
    checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );

    if(int(act_mode) < 100) {
@@ -46,12 +47,15 @@ Activation::~Activation() {

 dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
    if(act_mode == ACTIVATION_LEAKY) {
-        activationLEAKYForward(srcData, dstData, dim.tot());
-
+        activationLEAKYForward(srcData, dstData, dim.tot(), this->slope);
    }
    else if(act_mode == ACTIVATION_MISH) {
        activationMishForward(srcData, dstData, dim.tot());

+    }
+    else if(act_mode == ACTIVATION_LOGISTIC) {
+        activationLOGISTICForward(srcData, dstData, dim.tot());
+
    } else {
        dnnType alpha = dnnType(1);
        dnnType beta  = dnnType(0);
@@ -0,0 +1,900 @@
+#include "CenterTrack.h"
+
+
+namespace tk { namespace dnn {
+
+
+bool CenterTrack::init(const std::string& tensor_path, const int n_classes, const int n_batches, 
+                                    const float conf_thresh, const bool mode_3d, const std::vector<cv::Mat>& k_calibs) {
+    netRT         = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
+    dim           = netRT->input_dim;
+    dim.c         = 3;
+    nBatches      = n_batches;
+    confThreshold = conf_thresh;
+    mode3D = mode_3d;
+    inputCalibs   = k_calibs;
+    init_preprocessing();
+    init_pre_inf();
+    init_postprocessing();
+    init_visualization(n_classes);
+
+    return true;
+}
+
+bool CenterTrack::init_preprocessing(){
+    //image transformation
+    src      = cv::Mat(cv::Size(2,3), CV_32F);
+    dst      = cv::Mat(cv::Size(2,3), CV_32F);
+    dst2     = cv::Mat(cv::Size(2,3), CV_32F);
+    trans    = cv::Mat(cv::Size(3,2), CV_32F);
+    trans2   = cv::Mat(cv::Size(3,2), CV_32F);
+    transOut = cv::Mat(cv::Size(3,2), CV_32F);
+
+    dst2.at<float>(0,0) = width * 0.5;
+    dst2.at<float>(0,1) = width * 0.5;
+    dst2.at<float>(1,0) = width * 0.5;
+    dst2.at<float>(1,1) = width * 0.5 +  width * -0.5; 
+    dst2.at<float>(2,0) = dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
+    dst2.at<float>(2,1) = dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
+
+    for(int bi=0; bi<nBatches; bi++) {
+        szOld.push_back(cv::Size(0,0));
+    }
+    
+#ifdef OPENCV_CUDACONTRIB
+    std::cout<<"OPENCV CPMTROB\n";
+    checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
+    checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
+    float mean[3]   = {0.40789655, 0.44719303, 0.47026116};
+    float stddev[3] = {0.2886383, 0.27408165, 0.27809834};
+    
+    checkCuda( cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
+    checkCuda( cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
+#else
+    std::cout<<"NO OPENCV CPMTROB\n";
+    checkCuda( cudaMallocHost(&input, sizeof(dnnType)*dim.tot() * nBatches));
+    mean    << 0.40789655, 0.44719303, 0.47026116;
+    stddev  << 0.2886383, 0.27408165, 0.27809834;
+    
+#endif
+
+    checkCuda( cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot() * nBatches));
+    checkCuda( cudaMalloc(&input_pre_inf_d, sizeof(dnnType)*dim.tot()));
+    checkCuda( cudaMalloc(&d_ptrs, dim.tot() * sizeof(float)) );
+
+    return true;
+}
+
+bool CenterTrack::init_pre_inf(){
+    // initial steps: the first part of the network
+    const char *pre_img_conv1_bin = "dla34_ctrack/layers/base-pre_img_layer-0.bin";
+    const char *pre_hm_conv1_bin  = "dla34_ctrack/layers/base-pre_hm_layer-0.bin";
+    const char *conv1_bin         = "dla34_ctrack/layers/base-base_layer-0.bin";
+    const char *conv2_bin         = "dla34_ctrack/layers/base-level0-0.bin";
+    dim_in0 = tk::dnn::dataDim_t(1, 3, 512, 512, 1);
+    dim_in1 = tk::dnn::dataDim_t(1, 1, 512, 512, 1);
+    
+    checkCuda( cudaMalloc(&out_d, netRT->input_dim.tot()*sizeof(dnnType)) );
+    checkCuda( cudaMalloc(&img_d, dim_in0.tot()*sizeof(dnnType)) );
+    checkCuda( cudaMalloc(&hm_d, dim_in1.tot()*sizeof(dnnType)) );
+    // init to zeros hm
+    dnnType *hm_h;
+    checkCuda( cudaMallocHost(&hm_h, 1 * dim.h * dim.w*sizeof(dnnType)) ); 
+    for(int i=0; i<1 * dim.h * dim.w; i++)
+        hm_h[i] = 0.0f;
+    checkCuda( cudaMemcpy(hm_d, hm_h, 1 * dim.h * dim.w * sizeof(dnnType), cudaMemcpyHostToDevice) );
+    checkCuda( cudaFreeHost(hm_h) );
+    dnnType *i0_h, *i1_h, *i2_h;
+    // dnnType *i0_d, *i1_d, *i2_d;
+
+    // const char *input_bin = "dla34_ctrack/debug/input.bin";
+    // const char *pre_img_bin = "dla34_ctrack/debug/pre_imgages.bin";
+    // const char *pre_hm_bin = "dla34_ctrack/debug/pre_hms.bin";
+    // readBinaryFile(pre_img_bin, dim_in0.tot(), &i0_h, &img_d);
+    // readBinaryFile(pre_hm_bin, dim_in1.tot(), &i1_h, &hm_d);
+    // readBinaryFile(input_bin, dim_in0.tot(), &i2_h, &input_pre_inf_d);
+
+    pre_phase_net = new tk::dnn::Network(dim_in0);
+    //pre-img
+    tk::dnn::Input      *in_pre_img    = new tk::dnn::Input(pre_phase_net, dim_in0, img_d);
+    tk::dnn::Conv2d     *pre_img_conv1 = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, pre_img_conv1_bin, true);
+    tk::dnn::Activation *pre_img_relu  = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
+    //pre-hm
+    tk::dnn::Input      *in_pre_hm     = new tk::dnn::Input(pre_phase_net, dim_in1, hm_d);
+    tk::dnn::Conv2d     *pre_hm_conv1  = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, pre_hm_conv1_bin, true);
+    tk::dnn::Activation *pre_hm_relu   = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
+    // image input
+    tk::dnn::Input      *input_image   = new tk::dnn::Input(pre_phase_net, dim_in0, input_pre_inf_d);
+    tk::dnn::Conv2d     *conv1         = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
+    tk::dnn::Activation *relu1         = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
+   
+    tk::dnn::Shortcut   *s0_input      = new tk::dnn::Shortcut(pre_phase_net, pre_img_relu);
+    tk::dnn::Shortcut   *s1_input      = new tk::dnn::Shortcut(pre_phase_net, pre_hm_relu);
+    // output data
+    out_d = s1_input->dstData;
+    //print network model
+    pre_phase_net->print();
+
+    iter0=true; // in the first iteration the last input is equal to the current input.
+    return true;
+}
+
+bool CenterTrack::init_postprocessing(){
+    srand(0); //seed = 0 for random colors
+
+    dim_hm              = tk::dnn::dataDim_t(1, 10, 128, 128, 1);
+    dim_wh              = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
+    dim_reg             = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
+    dim_track           = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
+    dim_dep             = tk::dnn::dataDim_t(1, 1, 128, 128, 1);
+    dim_rot             = tk::dnn::dataDim_t(1, 8, 128, 128, 1);
+    dim_dim             = tk::dnn::dataDim_t(1, 3, 128, 128, 1);
+    dim_amodel_offset   = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
+
+    checkCuda( cudaMalloc(&topk_scores, dim_hm.c * K *sizeof(float)) );
+    checkCuda( cudaMalloc(&topk_inds_, dim_hm.c * K *sizeof(int)) );      
+    checkCuda( cudaMalloc(&topk_ys_, dim_hm.c * K *sizeof(float)) );      
+    checkCuda( cudaMalloc(&topk_xs_, dim_hm.c * K *sizeof(float)) );    
+    checkCuda( cudaMalloc(&ids_d, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
+    checkCuda( cudaMallocHost(&ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
+    for(int i=0; i<dim_hm.c * dim_hm.h * dim_hm.w; i++){
+        ids_[i] = i;
+    }
+    
+    checkCuda( cudaMalloc(&ones, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
+    float *ones_h;
+    checkCuda( cudaMallocHost(&ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
+    for(int i=0; i<dim_dep.c * dim_dep.h * dim_dep.w; i++)
+        ones_h[i] = 1.0f;
+    checkCuda( cudaMemcpy(ones, ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float), cudaMemcpyHostToDevice) );
+    checkCuda( cudaFreeHost(ones_h) );
+
+    checkCuda( cudaMallocHost(&scores, K *sizeof(float)) );
+    checkCuda( cudaMalloc(&scores_d, K *sizeof(float)) );
+
+    checkCuda( cudaMallocHost(&clses, K *sizeof(int)) );
+    checkCuda( cudaMalloc(&clses_d, K *sizeof(int)) );
+
+    checkCuda( cudaMalloc(&topk_inds_d, K *sizeof(int)) );
+    checkCuda( cudaMalloc(&topk_ys_d, K *sizeof(float)) );     
+    checkCuda( cudaMalloc(&topk_xs_d, K *sizeof(float)) ); 
+    checkCuda( cudaMalloc(&inttopk_ys_d, K *sizeof(int)) );
+    checkCuda( cudaMalloc(&inttopk_xs_d, K *sizeof(int)) );
+
+    checkCuda( cudaMallocHost(&bbx0, K * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&bby0, K * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&bbx1, K * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&bby1, K * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&bbx0_d, K * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&bby0_d, K * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&bbx1_d, K * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&bby1_d, K * sizeof(float)) );
+
+    checkCuda( cudaMallocHost(&intxs, K  * sizeof(int)) ); 
+    checkCuda( cudaMallocHost(&intys, K  * sizeof(int)) ); 
+
+    checkCuda( cudaMallocHost(&track, K * dim_track.c * sizeof(float)) );
+    checkCuda( cudaMallocHost(&dep, K * dim_dep.c * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&rot, K * dim_rot.c * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&dim_, K * dim_dim.c * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&wh, K * dim_wh.c * sizeof(float)) );
+    checkCuda( cudaMallocHost(&amodel_offset, K * dim_amodel_offset.c * sizeof(float)) );
+    checkCuda( cudaMalloc(&track_d, K * dim_track.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&dep_d, K * dim_dep.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&rot_d, K * dim_rot.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&dim_d, K * dim_dim.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&wh_d, K * dim_wh.c * sizeof(float)) );  
+    checkCuda( cudaMalloc(&amodel_offset_d, K * dim_amodel_offset.c * sizeof(float)) ); 
+
+    checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
+
+    for(int bi=0; bi<nBatches; bi++) {
+        cv::Mat calibs_ = cv::Mat::zeros(cv::Size(4,3), CV_32F);        
+        if(inputCalibs.size() == 0 || inputCalibs[bi].empty()) {
+            calibs_.at<float>(0,0) = 633.0;
+            calibs_.at<float>(1,1) = 633.0;
+            calibs_.at<float>(2,2) = 1.0;
+        }
+        calibs_.at<float>(2,2) = 1.0;
+        calibs.push_back(calibs_);
+    }
+
+    // Alloc array used in the kernel 
+    checkCuda( cudaMalloc(&src_out, K *sizeof(float)) );
+    checkCuda( cudaMalloc(&ids_out, K *sizeof(int)) );
+
+    trRes.resize(nBatches);
+    countTr.resize(nBatches, 0);
+    trackId.resize(nBatches, 0);
+
+    return true;
+}
+
+bool CenterTrack::init_visualization(const int n_classes){
+    classes = n_classes;
+    // const char *kitti_class_name[] = {
+    //         "person", "car", "bicycle"};
+    // classesNames = std::vector<std::string>(kitti_class_name, std::end( kitti_class_name));
+    
+    const char *class_name[] = {"car", "truck", "bus", "trailer", "construction_vehicle", "pedestrian", 
+                                        "motorcycle", "bicycle", "traffic_cone", "barrier"};
+    classesNames = std::vector<std::string>(class_name, std::end( class_name));
+    
+    // const char *coco_class_name[] = {
+    //         "person", "bicycle", "car", "motorcycle", "airplane", 
+    //         "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
+    //         "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
+    //         "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
+    //         "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
+    //         "snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
+    //         "skateboard", "surfboard", "tennis racket", "bottle", "wine glass",
+    //         "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
+    //         "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
+    //         "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv",
+    //         "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
+    //         "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+    //         "scissors", "teddy bear", "hair drier", "toothbrush"
+    //         };
+    // classesNames = std::vector<std::string>(coco_class_name, std::end( coco_class_name));
+
+    for(int c=0; c<classes; c++) {
+        int offset  = c*123457 % classes;
+        float r     = getColor(2, offset, classes);
+        float g     = getColor(1, offset, classes);
+        float b     = getColor(0, offset, classes);
+        colors[c]   = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
+    }
+    for(int c=0; c<256; c++) {
+        int offset  = c * 123457 % 256;
+        float r     = getColor(2, offset, 256);
+        float g     = getColor(1, offset, 256);
+        float b     = getColor(0, offset, 256);
+        trColors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
+    }
+
+    r = cv::Mat(cv::Size(3,3), CV_32F);
+    r.at<float>(0,1) = 0.0;
+    r.at<float>(1,0) = 0.0;
+    r.at<float>(1,1) = 1.0;
+    r.at<float>(1,2) = 0.0;
+    r.at<float>(2,1) = 0.0;
+                        
+    corners = cv::Mat(cv::Size(8,3), CV_32F);
+    corners.at<float>(1,0) = 0.0;
+    corners.at<float>(1,1) = 0.0;
+    corners.at<float>(1,2) = 0.0;
+    corners.at<float>(1,3) = 0.0;
+
+    pts3DHomo = cv::Mat(cv::Size(8,4), CV_32F);
+    pts3DHomo.at<float>(3,0) = 1.0;
+    pts3DHomo.at<float>(3,1) = 1.0;
+    pts3DHomo.at<float>(3,2) = 1.0;
+    pts3DHomo.at<float>(3,3) = 1.0;
+    pts3DHomo.at<float>(3,4) = 1.0;
+    pts3DHomo.at<float>(3,5) = 1.0;
+    pts3DHomo.at<float>(3,6) = 1.0;
+    pts3DHomo.at<float>(3,7) = 1.0;
+
+    faceId.push_back({0,1,5,4});
+    faceId.push_back({1,2,6, 5});
+    faceId.push_back({3,0,4,7});
+    faceId.push_back({2,3,7,6});
+    // ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
+
+    return true;
+}
+
+void CenterTrack::_get_additional_inputs(){
+    //None no additional input
+}
+
+void CenterTrack::pre_inf(const int bi){
+    TKDNN_TSTART
+    tk::dnn::dataDim_t dim_aus;
+    pre_phase_net->infer(dim_aus, nullptr);
+    TKDNN_TSTOP
+    checkCuda( cudaDeviceSynchronize() );
+    checkCuda( cudaMemcpy(input_d+ netRT->input_dim.tot()*bi, pre_phase_net->layers[pre_phase_net->num_layers-1]->dstData, netRT->input_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
+    checkCuda( cudaDeviceSynchronize() );
+}
+
+void CenterTrack::preprocess(cv::Mat &frame, const int bi){
+    cv::Size sz = originalSize[bi];
+    // float scale = 1.0;
+    float new_height = dim.h;//sz.height * scale;
+    float new_width = dim.w;//sz.width * scale;
+    if(sz.height != szOld[bi].height && sz.width != szOld[bi].width){
+        if(inputCalibs.size() == 0 || inputCalibs[bi].empty()) {
+            calibs[bi].at<float>(0,2) = new_width / 2.0f;
+            calibs[bi].at<float>(1,2) = new_height /2.0f;
+        }
+        else {
+            calibs[bi].at<float>(0,0) = inputCalibs[bi].at<float>(0,0) * dim.w / sz.width;
+            calibs[bi].at<float>(0,2) = inputCalibs[bi].at<float>(0,2) * dim.w / sz.width;
+            calibs[bi].at<float>(1,1) = inputCalibs[bi].at<float>(1,1) * dim.h / sz.height;
+            calibs[bi].at<float>(1,2) = inputCalibs[bi].at<float>(1,2) * dim.h / sz.height;
+        }
+        
+        float c[] = {new_width / 2.0f, new_height /2.0f};
+        float s[] = {float(dim.w), float(dim.h)};
+        // float s = new_width >= new_height ? new_width : new_height;
+        // ----------- get_affine_transform
+        // rot_rad = pi * 0 / 100 --> 0
+        //dim.print();
+        src.at<float>(0,0) = c[0];
+        src.at<float>(0,1) = c[1];
+        src.at<float>(1,0) = c[0];
+        src.at<float>(1,1) = c[1] + s[0] * -0.5;
+        dst.at<float>(0,0) = dim.w * 0.5;
+        dst.at<float>(0,1) = dim.h * 0.5;
+        dst.at<float>(1,0) = dim.w * 0.5;
+        dst.at<float>(1,1) = dim.h * 0.5 +  dim.w * -0.5; 
+        
+        src.at<float>(2,0) = src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
+        src.at<float>(2,1) = src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
+        dst.at<float>(2,0) = dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
+        dst.at<float>(2,1) = dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
+
+
+        trans = cv::getAffineTransform( src, dst );
+        trans2 = cv::getAffineTransform( dst2, src );
+        trans2.convertTo(transOut, CV_32F);
+    }
+    szOld[bi] = sz;
+#ifdef OPENCV_CUDACONTRIB
+    cv::cuda::GpuMat im_Orig; 
+    cv::cuda::GpuMat imageF1_d, imageF2_d;
+        
+    im_Orig = cv::cuda::GpuMat(frame);
+    cv::cuda::resize (im_Orig, imageF1_d, cv::Size(dim.w, dim.h)); 
+    // imageF1_d = im_Orig;
+    checkCuda( cudaDeviceSynchronize() );
+    
+    sz = imageF1_d.size();
+
+    cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(dim.w, dim.h), cv::INTER_LINEAR );
+    checkCuda( cudaDeviceSynchronize() );
+    
+    imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0); 
+    checkCuda( cudaDeviceSynchronize() );
+    
+    dim2 = dim;
+    cv::cuda::GpuMat bgr[3]; 
+    cv::cuda::split(imageF1_d,bgr);//split source
+
+    for(int i=0; i<dim.c; i++)
+        checkCuda( cudaMemcpy(d_ptrs + i*dim.h * dim.w, (float*)bgr[i].data, dim.h * dim.w * sizeof(float), cudaMemcpyDeviceToDevice) );
+        
+    normalize(d_ptrs, dim.c, dim.h, dim.w, mean_d, stddev_d);
+
+    checkCuda( cudaMemcpy(input_pre_inf_d, d_ptrs, dim2.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
+    checkCuda( cudaDeviceSynchronize() );
+
+#else
+    cv::Mat imageF;
+    resize(frame, imageF, cv::Size(dim.w, dim.h));
+    // imageF = frame;
+    sz = imageF.size();
+    cv::warpAffine(imageF, imageF, trans, cv::Size(dim.w, dim.h), cv::INTER_LINEAR );
+
+    // cv::imshow("warp", imageF);
+
+    sz = imageF.size();
+    imageF.convertTo(imageF, CV_32FC3, 1/255.0); 
+ 
+    dim2 = dim;
+    //split channels
+    cv::Mat bgr[3]; 
+    cv::split(imageF,bgr);//split source
+    
+    for(int i=0; i<3; i++){
+        bgr[i] = bgr[i] - mean[i];
+        bgr[i] = bgr[i] / stddev[i];
+    }
+    for(int i=0; i<dim2.c; i++) {
+        int idx = i * imageF.rows * imageF.cols;
+        int ch = i; 
+        memcpy((void*)&input[idx], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
+    }
+    checkCuda( cudaMemcpyAsync(input_pre_inf_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
+    checkCuda( cudaDeviceSynchronize() );
+
+#endif
+
+    if(iter0) {
+        checkCuda( cudaMemcpy(img_d, input_pre_inf_d, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
+        checkCuda( cudaDeviceSynchronize() );
+        iter0=false;
+    }
+    pre_inf(bi);
+
+    checkCuda( cudaMemcpy(img_d, input_pre_inf_d, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
+    checkCuda( cudaDeviceSynchronize() );
+}
+
+cv::Mat CenterTrack::transform_preds_with_trans(float x1, float x2){
+    cv::Mat target_coords(cv::Size(1,3), CV_32F);
+    target_coords.at<float>(0,0) = x1;
+    target_coords.at<float>(0,1) = x2;
+    target_coords.at<float>(0,2) = 1.0;
+    return transOut * target_coords;
+}
+
+void CenterTrack::tracking(const int bi) {
+    float item_size[countDet];
+    int item_cl[countDet];
+    float dets[2*countDet];
+    for(int i=0; i<countDet; i++){
+        item_size[i] = (detRes[i].bb1.at<float>(0,0) - detRes[i].bb0.at<float>(0,0)) * 
+                       (detRes[i].bb1.at<float>(0,1) - detRes[i].bb0.at<float>(0,1));
+        item_cl[i]   =  detRes[i].cl;
+        dets[i*2]    =  detRes[i].ct.at<float>(0,0);
+        dets[i*2+1]  =  detRes[i].ct.at<float>(0,1);
+    }
+    
+    float track_size[countTr[bi]];
+    int track_cl[countTr[bi]];
+    float tracks[2*countTr[bi]];
+    for(int i=0; i<countTr[bi]; i++){
+        track_size[i] = (trRes[bi][i].det_res.bb1.at<float>(0,0) - trRes[bi][i].det_res.bb0.at<float>(0,0)) * 
+                        (trRes[bi][i].det_res.bb1.at<float>(0,1) - trRes[bi][i].det_res.bb0.at<float>(0,1));
+        track_cl[i]   =  trRes[bi][i].det_res.cl;
+        tracks[i*2]   =  trRes[bi][i].det_res.ct.at<float>(0,0);
+        tracks[i*2+1] =  trRes[bi][i].det_res.ct.at<float>(0,1);
+    } 
+    float dist[countTr[bi]*countDet];
+    bool invalid;
+    for(int i=0; i<countTr[bi]; i++){
+        for(int j=0; j<countDet; j++){
+            dist[j*countTr[bi]+i] =  pow((tracks[i*2] - dets[j*2]), 2) + 
+                                     pow((tracks[i*2+1] - dets[j*2+1]), 2);
+            invalid               =  dist[j*countTr[bi]+i] > track_size[i] || 
+                                     dist[j*countTr[bi]+i] > item_size[j] || 
+                                     item_cl[j] != track_cl[i];
+            dist[j*countTr[bi]+i] = dist[j*countTr[bi]+i] + invalid * (1 << 18);
+        }
+    }
+    int matched_indices[2*countTr[bi]];
+    float min_tr;
+    int min_idtr = -1;
+    for(int i=0; i<countTr[bi]; i++) {
+        matched_indices[i*2]    = -1;
+        matched_indices[i*2+1]  = -1;
+    }
+    for(int i=0; i<countDet; i++){
+        min_tr=(1 << 18);
+        for(int j=0; j<countTr[bi]; j++){
+            if(dist[i*countTr[bi]+j]<min_tr) {
+                min_tr   = dist[i*countTr[bi]+j];
+                min_idtr = j;
+            }
+        }
+        if(min_tr < (1<<16)) {
+            for(int j=0; j<countDet; j++)
+                dist[j*countTr[bi]+min_idtr] = (1 << 18);
+            matched_indices[2*min_idtr]   = min_idtr;
+            matched_indices[2*min_idtr+1] = i;
+        }
+    }
+    
+    bool unmatched_dets[countDet];
+    for(int i=0; i<countDet; i++)
+        unmatched_dets[i] = false;
+    bool unmatched_tracks[countTr[bi]];
+    for(int i=0; i<countTr[bi]; i++) 
+        unmatched_tracks[i] = false;
+    for(int i=0; i<countTr[bi]; i++) {
+        if(matched_indices[2*i] != -1)
+            unmatched_tracks[matched_indices[2*i]]=true;
+        if(matched_indices[2*i+1] != -1)
+            unmatched_dets[matched_indices[2*i+1]]=true;        
+    }
+
+    //match
+    for(int i=0; i<countTr[bi]; i++) {
+        if(matched_indices[2*i+1] != -1 && matched_indices[2*i] != -1) { //second condition is optional
+            int tr_id = matched_indices[2*i];
+            int d_id  = matched_indices[2*i+1];
+            
+            // trRes[tr_id].det_res = detRes[d_id];
+            trRes[bi][tr_id].det_res.score  = detRes[d_id].score;
+            trRes[bi][tr_id].det_res.cl     = detRes[d_id].cl;
+            trRes[bi][tr_id].det_res.ct     = detRes[d_id].ct;
+            trRes[bi][tr_id].det_res.tr     = detRes[d_id].tr;
+            trRes[bi][tr_id].det_res.bb0    = detRes[d_id].bb0;
+            trRes[bi][tr_id].det_res.bb1    = detRes[d_id].bb1;
+            trRes[bi][tr_id].det_res.dep    = detRes[d_id].dep;
+            trRes[bi][tr_id].det_res.dim[0] = detRes[d_id].dim[0];
+            trRes[bi][tr_id].det_res.dim[1] = detRes[d_id].dim[1];
+            trRes[bi][tr_id].det_res.dim[2] = detRes[d_id].dim[2];
+            trRes[bi][tr_id].det_res.alpha  = detRes[d_id].alpha;
+            trRes[bi][tr_id].det_res.x      = detRes[d_id].x;
+            trRes[bi][tr_id].det_res.y      = detRes[d_id].y;
+            trRes[bi][tr_id].det_res.z      = detRes[d_id].z;
+            trRes[bi][tr_id].det_res.rot_y  = detRes[d_id].rot_y;
+            // trRes[bi][matched_indices[2*i]].tracking_id = ; is the same 
+            // trRes[bi][matched_indices[2*i]].color = ; is the same
+            trRes[bi][tr_id].age            = 1; 
+            trRes[bi][tr_id].active         = trRes[bi][tr_id].active+1;
+        }
+    }
+    //delete target umatched track
+    int new_count_tr = 0;
+    for(int i=0; i<countTr[bi]; i++) {
+        if(unmatched_tracks[i]) 
+            new_count_tr++;
+    }
+    if(new_count_tr == 0 && countTr[bi] != 0) {        //reset
+        trRes[bi].clear();
+        countTr[bi] = 0;
+    }
+    int old_count_tr = countTr[bi];
+    if(countTr[bi] != 0 && new_count_tr != countTr[bi]) {
+        std::vector<struct trackingRes> new_tr_res;
+        int id_new_tr=0;
+        for(int i=0; i<countTr[bi]; i++) {
+            if(unmatched_tracks[i]) {
+                struct trackingRes new_tr_res_;
+                // new_tr_res_new_det_res.det_res = trRes[i].det_res;
+                new_tr_res_.det_res.score   = trRes[bi][i].det_res.score;
+                new_tr_res_.det_res.cl      = trRes[bi][i].det_res.cl;
+                new_tr_res_.det_res.ct      = trRes[bi][i].det_res.ct;
+                new_tr_res_.det_res.tr      = trRes[bi][i].det_res.tr;
+                new_tr_res_.det_res.bb0     = trRes[bi][i].det_res.bb0;
+                new_tr_res_.det_res.bb1     = trRes[bi][i].det_res.bb1;
+                new_tr_res_.det_res.dep     = trRes[bi][i].det_res.dep;
+                new_tr_res_.det_res.dim[0]  = trRes[bi][i].det_res.dim[0];
+                new_tr_res_.det_res.dim[1]  = trRes[bi][i].det_res.dim[1];
+                new_tr_res_.det_res.dim[2]  = trRes[bi][i].det_res.dim[2];
+                new_tr_res_.det_res.alpha   = trRes[bi][i].det_res.alpha;
+                new_tr_res_.det_res.x       = trRes[bi][i].det_res.x;
+                new_tr_res_.det_res.y       = trRes[bi][i].det_res.y;
+                new_tr_res_.det_res.z       = trRes[bi][i].det_res.z;
+                new_tr_res_.det_res.rot_y   = trRes[bi][i].det_res.rot_y;
+                new_tr_res_.tracking_id     = trRes[bi][i].tracking_id;
+                new_tr_res_.age             = trRes[bi][i].age;
+                new_tr_res_.active          = trRes[bi][i].active;
+                new_tr_res_.color           = trRes[bi][i].color;
+                id_new_tr ++;
+                new_tr_res.push_back(new_tr_res_);
+            }
+        }
+        
+        if(countTr[bi]) {
+            trRes[bi].clear();
+        }
+        countTr[bi] = new_count_tr;
+        trRes[bi] = new_tr_res;
+    }
+    
+    int count_tr_ = countTr[bi];
+    for(int i=0; i<countDet; i++) {
+        if((!unmatched_dets[i]) && detRes[i].score > newThresh) {
+            count_tr_ ++;
+            struct trackingRes new_tr_res_;
+            new_tr_res_.det_res.score   = detRes[i].score;
+            new_tr_res_.det_res.cl      = detRes[i].cl;
+            new_tr_res_.det_res.ct      = detRes[i].ct;
+            new_tr_res_.det_res.tr      = detRes[i].tr;
+            new_tr_res_.det_res.bb0     = detRes[i].bb0;
+            new_tr_res_.det_res.bb1     = detRes[i].bb1;
+            new_tr_res_.det_res.dep     = detRes[i].dep;
+            new_tr_res_.det_res.dim[0]  = detRes[i].dim[0];
+            new_tr_res_.det_res.dim[1]  = detRes[i].dim[1];
+            new_tr_res_.det_res.dim[2]  = detRes[i].dim[2];
+            new_tr_res_.det_res.alpha   = detRes[i].alpha;
+            new_tr_res_.det_res.x       = detRes[i].x;
+            new_tr_res_.det_res.y       = detRes[i].y;
+            new_tr_res_.det_res.z       = detRes[i].z;
+            new_tr_res_.det_res.rot_y   = detRes[i].rot_y;
+            new_tr_res_.tracking_id     = trackId[bi]++; 
+            new_tr_res_.age             = 1; 
+            new_tr_res_.active          = 1;
+            new_tr_res_.color           = rand() % 256;
+            if(trRes.size() <= bi) {
+                std::vector<struct trackingRes> v_new_tr_res_;
+                v_new_tr_res_.push_back(new_tr_res_);    
+                trRes.push_back(v_new_tr_res_);
+            }
+            else
+                trRes[bi].push_back(new_tr_res_);
+        }
+    }
+
+    countTr[bi] = count_tr_;
+    //reset the tracker id
+    if(trackId[bi] == 1000)
+        trackId[bi] = 0;
+    detRes.clear();
+    
+}
+
+void CenterTrack::postprocess(const int bi, const bool mAP) {
+    dnnType *rt_out[9];
+    rt_out[0] = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
+    rt_out[1] = (dnnType *)netRT->buffersRT[2]+ netRT->buffersDIM[2].tot()*bi;
+    rt_out[2] = (dnnType *)netRT->buffersRT[3]+ netRT->buffersDIM[3].tot()*bi; 
+    rt_out[3] = (dnnType *)netRT->buffersRT[4]+ netRT->buffersDIM[4].tot()*bi; 
+    rt_out[4] = (dnnType *)netRT->buffersRT[5]+ netRT->buffersDIM[5].tot()*bi; 
+    rt_out[5] = (dnnType *)netRT->buffersRT[6]+ netRT->buffersDIM[6].tot()*bi; 
+    rt_out[6] = (dnnType *)netRT->buffersRT[7]+ netRT->buffersDIM[7].tot()*bi; 
+    rt_out[7] = (dnnType *)netRT->buffersRT[8]+ netRT->buffersDIM[8].tot()*bi; 
+    rt_out[8] = (dnnType *)netRT->buffersRT[9]+ netRT->buffersDIM[9].tot()*bi; 
+    
+    // ------------------------------------ process --------------------------------------------
+    
+    activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
+    checkCuda( cudaDeviceSynchronize() );    
+
+    // output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
+    activationSIGMOIDForward(rt_out[5], rt_out[5], dim_dep.tot());
+    checkCuda( cudaDeviceSynchronize() );
+    transformDep(ones, ones + dim_dep.tot(), rt_out[5], rt_out[5] + dim_dep.tot());
+    checkCuda( cudaDeviceSynchronize() );
+
+    // nms 
+    subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0], op);
+    
+    // ----------- nms end
+    // ----------- topk  
+      
+    if(K > dim_hm.h * dim_hm.w){
+        printf ("Error topk (K is too large)\n");
+        return;
+    }
+    
+    checkCuda( cudaMemcpy(ids_d, ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int), cudaMemcpyHostToDevice) );    
+
+    sort(rt_out[0],rt_out[0]+dim_hm.tot(),ids_d);
+    checkCuda( cudaDeviceSynchronize() );   
+
+    topk(rt_out[0], ids_d, K, scores_d, topk_inds_d, topk_ys_d, topk_xs_d);
+    checkCuda( cudaDeviceSynchronize() );    
+    
+    checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );
+
+    topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
+    checkCuda( cudaDeviceSynchronize() ); 
+    checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
+    checkCuda( cudaMemcpy(topk_ys_d, (float *)inttopk_ys_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
+    
+    checkCuda( cudaMemcpy(intxs, inttopk_xs_d, K * sizeof(int), cudaMemcpyDeviceToHost) ); 
+    checkCuda( cudaMemcpy(intys, inttopk_ys_d, K * sizeof(int), cudaMemcpyDeviceToHost) ); 
+    
+    checkCuda( cudaMemcpy(clses, clses_d, K*sizeof(int), cudaMemcpyDeviceToHost) );
+    
+    // ----------- topk end 
+    
+    topKxyAddOffset(topk_inds_d, K, dim_reg.h*dim_reg.w, inttopk_xs_d, inttopk_ys_d, topk_xs_d, topk_ys_d, rt_out[3], src_out, ids_out);
+    checkCuda( cudaDeviceSynchronize() );
+
+    bboxes(topk_inds_d, K, dim_wh.h*dim_wh.w, topk_xs_d, topk_ys_d, rt_out[2], bbx0_d, bbx1_d, bby0_d, bby1_d, src_out, ids_out);
+    checkCuda( cudaDeviceSynchronize() );
+    checkCuda( cudaMemcpy(bbx0, bbx0_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
+    checkCuda( cudaMemcpy(bby0, bby0_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
+    checkCuda( cudaMemcpy(bbx1, bbx1_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
+    checkCuda( cudaMemcpy(bby1, bby1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
+
+    //regression heads
+    // ['tracking', 'dep', 'rot', 'dim', 'amodel_offset',
+    // 'nuscenes_att', 'velocity']
+    getRecordsFromTopKId(topk_inds_d, K, dim_track.c, dim_track.h * dim_track.w, rt_out[4], track_d, ids_out);
+    checkCuda( cudaMemcpy(track, track_d, K * dim_track.c * sizeof(float), cudaMemcpyDeviceToHost) );
+
+    getRecordsFromTopKId(topk_inds_d, K, dim_dep.c, dim_dep.h * dim_dep.w, rt_out[5], dep_d, ids_out);
+    checkCuda( cudaMemcpy(dep, dep_d, K * dim_dep.c * sizeof(float), cudaMemcpyDeviceToHost) );
+    
+    getRecordsFromTopKId(topk_inds_d, K, dim_rot.c, dim_rot.h * dim_rot.w, rt_out[6], rot_d, ids_out);
+    checkCuda( cudaMemcpy(rot, rot_d, K * dim_rot.c * sizeof(float), cudaMemcpyDeviceToHost) );
+    
+    getRecordsFromTopKId(topk_inds_d, K, dim_dim.c, dim_dim.h * dim_dim.w, rt_out[7], dim_d, ids_out);
+    checkCuda( cudaMemcpy(dim_, dim_d, K * dim_dim.c * sizeof(float), cudaMemcpyDeviceToHost) );
+
+    getRecordsFromTopKId(topk_inds_d, K, dim_amodel_offset.c, dim_amodel_offset.h * dim_amodel_offset.w, rt_out[8], amodel_offset_d, ids_out);
+    checkCuda( cudaMemcpy(amodel_offset, amodel_offset_d, K * dim_amodel_offset.c * sizeof(float), cudaMemcpyDeviceToHost) );    
+    
+    // ---------------------------------- post-process -----------------------------------------
+    
+    countDet = 0;
+    detRes.clear();
+    for(int i=0; i<K; i++){
+        if(scores[i] < outThresh) 
+            break;
+        
+        countDet ++;
+        struct detectionRes new_det_res;
+        new_det_res.score = scores[i]; 
+        new_det_res.cl = clses[i]+1; 
+        // ret_s=scores[i];
+        // ret_c=clses[i]+1;
+        new_det_res.ct     = transform_preds_with_trans(intxs[i], intys[i]);
+        new_det_res.tr     = transform_preds_with_trans(intxs[i] + track[i], intys[i] + track[i+K]);
+        new_det_res.tr     = new_det_res.tr -new_det_res.ct;
+        new_det_res.bb0    = transform_preds_with_trans(bbx0[i], bby0[i]);
+        new_det_res.bb1    = transform_preds_with_trans(bbx1[i], bby1[i]);
+        new_det_res.ct     = transform_preds_with_trans(((bbx0[i]+bbx1[i])/2 + amodel_offset[i]), 
+                                        ((bby0[i]+bby1[i])/2 + amodel_offset[i+K]));
+        new_det_res.dep    = dep[i]; 
+        new_det_res.dim[0] = dim_[i];
+        new_det_res.dim[1] = dim_[i+K];
+        new_det_res.dim[2] = dim_[i+2*K];
+        
+        // unproject_2d_to_3d
+        new_det_res.z = dep[i] - calibs[bi].at<float>(2,3);
+        new_det_res.x = ((float)new_det_res.ct.at<float>(0,0) * dep[i] - calibs[bi].at<float>(0,3) - 
+                        calibs[bi].at<float>(0,2) * new_det_res.z) / calibs[bi].at<float>(0,0);
+        new_det_res.y = ((float)new_det_res.ct.at<float>(0,1) * dep[i] - calibs[bi].at<float>(1,3) - 
+                        calibs[bi].at<float>(1,2) * new_det_res.z) / calibs[bi].at<float>(1,1) + (dim_[i] / 2);
+        
+        // alpha2rot_y
+        // idx = rot[:, 1] > rot[:, 5]
+        // alpha1 = np.arctan2(rot[:, 2], rot[:, 3]) + (-0.5 * np.pi)
+        // alpha2 = np.arctan2(rot[:, 6], rot[:, 7]) + ( 0.5 * np.pi)
+        // return alpha1 * idx + alpha2 * (1 - idx)
+        if(rot[1*K + i] > rot[5*K + i])
+            new_det_res.alpha = std::atan2(rot[2*K + i], rot[3*K + i]) -0.5 * M_PI;
+        else
+            new_det_res.alpha = std::atan2(rot[6*K + i], rot[7*K + i]) +0.5 * M_PI;
+        new_det_res.rot_y = (new_det_res.alpha + std::atan2((float)new_det_res.ct.at<float>(0,0) - calibs[bi].at<float>(0,2), calibs[bi].at<float>(0,0)));
+        new_det_res.ct    = new_det_res.ct + new_det_res.tr;   //dest  
+        detRes.push_back(new_det_res);    
+    }    
+    // track step
+    tracking(bi);
+}
+
+void CenterTrack::draw(std::vector<cv::Mat>& frames) {
+    struct trackingRes t;
+    float sc;
+    int id;
+    std::string txt;
+    int baseline = 0;
+    float font_scale = 0.8;
+    int thickness = 2;
+    
+    for(int bi=0; bi<frames.size(); ++bi) {
+        float scale_x = float(originalSize[bi].width)/dim.w;
+        float scale_y = float(originalSize[bi].height)/dim.h;
+        resize(frames[bi], frames[bi], originalSize[bi]);
+        // draw dets
+        for(int i=0; trRes.size() != 0 && i<trRes[bi].size(); i++) {
+            t = trRes[bi][i];
+            id = t.tracking_id;
+            txt = classesNames[t.det_res.cl-1]+'-'+std::to_string(id); //forse ha bisogno di cl-1
+            cv::Size text_size = getTextSize(txt, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
+            
+            if(t.det_res.score > confThreshold){// && t.active!=0) {
+                if(!mode3D) {
+                    cv::rectangle(frames[bi],  
+                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y), 
+                                    cv::Point(t.det_res.bb1.at<float>(0,0) * scale_x, t.det_res.bb1.at<float>(0,1) * scale_y), 
+                                    trColors[t.color], thickness);                      
+                    cv::rectangle(frames[bi],  
+                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y - text_size.height - thickness), 
+                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x + text_size.width, t.det_res.bb0.at<float>(0,1) * scale_y), 
+                                    trColors[t.color], -1);                      
+                                        
+                    cv::putText(frames[bi], txt, 
+                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y - thickness -1), 
+                                    cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), 1);
+
+                    cv::arrowedLine(frames[bi], 
+                                    cv::Point((int)t.det_res.ct.at<float>(0,0) * scale_x, (int)t.det_res.ct.at<float>(0,1) * scale_y), 
+                                    cv::Point((int)(t.det_res.ct.at<float>(0,0) * scale_x + t.det_res.tr.at<float>(0,0) * scale_x),
+                                              (int)(t.det_res.ct.at<float>(0,1) * scale_y + t.det_res.tr.at<float>(0,1) * scale_y)),
+                                    cv::Scalar(255, 0, 255), 2);
+                }
+                //3d
+                if(mode3D && t.det_res.z > 1){
+                    r.at<float>(0,0) = std::cos(t.det_res.rot_y);
+                    r.at<float>(0,2) = std::sin(t.det_res.rot_y);
+                    r.at<float>(2,0) = -std::sin(t.det_res.rot_y);
+                    r.at<float>(2,2) = std::cos(t.det_res.rot_y);
+
+                    corners.at<float>(0,0) = t.det_res.dim[2]/2;
+                    corners.at<float>(0,1) = t.det_res.dim[2]/2;
+                    corners.at<float>(0,2) = -t.det_res.dim[2]/2;
+                    corners.at<float>(0,3) = -t.det_res.dim[2]/2;
+                    corners.at<float>(0,4) = t.det_res.dim[2]/2;
+                    corners.at<float>(0,5) = t.det_res.dim[2]/2;
+                    corners.at<float>(0,6) = -t.det_res.dim[2]/2;
+                    corners.at<float>(0,7) = -t.det_res.dim[2]/2;
+
+                    corners.at<float>(1,4) = -t.det_res.dim[0];
+                    corners.at<float>(1,5) = -t.det_res.dim[0];
+                    corners.at<float>(1,6) = -t.det_res.dim[0];
+                    corners.at<float>(1,7) = -t.det_res.dim[0];
+                    
+                    corners.at<float>(2,0) = t.det_res.dim[1]/2;
+                    corners.at<float>(2,1) = -t.det_res.dim[1]/2;
+                    corners.at<float>(2,2) = -t.det_res.dim[1]/2;
+                    corners.at<float>(2,3) = t.det_res.dim[1]/2;
+                    corners.at<float>(2,4) = t.det_res.dim[1]/2;
+                    corners.at<float>(2,5) = -t.det_res.dim[1]/2;
+                    corners.at<float>(2,6) = -t.det_res.dim[1]/2;
+                    corners.at<float>(2,7) = t.det_res.dim[1]/2;
+                    
+                    cv::Mat aus = r * corners;
+
+                    for(int k=0; k<8; k++) {
+                        aus.at<float>(0,k) += t.det_res.x;
+                        aus.at<float>(1,k) += t.det_res.y;
+                        aus.at<float>(2,k) += t.det_res.z;
+                    }
+                    
+                    // corners.copyTo(pts3DHomo(cv::Rect(0, 0, 8, 3)));
+                    for(int k1=0; k1<3; k1++) {
+                        for(int k2=0; k2<8; k2++)
+                            pts3DHomo.at<float>(k1,k2) = aus.at<float>(k1,k2); 
+                    }
+                    
+                    aus.release();
+                    aus = calibs[bi] * pts3DHomo;
+                    std::vector<float> res_corners;
+                    for(int k=0; k<8; k++) {
+                        res_corners.push_back(aus.at<float>(0,k) / aus.at<float>(2,k));
+                        res_corners.push_back(aus.at<float>(1,k) / aus.at<float>(2,k));
+                    }
+                    aus.release();
+                    for(int ind_f=3; ind_f>=0; ind_f--) {
+                        for(int j=0; j<4; j++) {
+                            cv::line(frames[bi], 
+                                    cv::Point((int)res_corners.at(faceId.at(ind_f).at(j) * 2) * scale_x, 
+                                              (int)res_corners.at(faceId.at(ind_f).at(j) * 2 + 1) * scale_y),
+                                    cv::Point((int)res_corners.at(faceId.at(ind_f).at((j+1)%4) * 2) * scale_x, 
+                                              (int)res_corners.at(faceId.at(ind_f).at((j+1)%4) * 2 + 1) * scale_y), 
+                                     trColors[t.color], 2);
+                            if(ind_f == 0 && j==3) {
+                                cv::line(frames[bi], 
+                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(0) * 2) * scale_x, 
+                                                  (int)res_corners.at(faceId.at(ind_f).at(0) * 2 + 1) * scale_y),
+                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(2) * 2) * scale_x, 
+                                                  (int)res_corners.at(faceId.at(ind_f).at(2) * 2 + 1) * scale_y), trColors[t.color], 2);
+                                cv::line(frames[bi], 
+                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(1) * 2) * scale_x, 
+                                                  (int)res_corners.at(faceId.at(ind_f).at(1) * 2 + 1) * scale_y),
+                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(3) * 2) * scale_x, 
+                                                  (int)res_corners.at(faceId.at(ind_f).at(3) * 2 + 1) * scale_y), trColors[t.color], 2);
+                            }
+                        }
+                    }
+                    float bb0=(1 << 10), bb1=0, bb2=(1 << 10), bb3=0;
+                    for(int k=0; k<8; k++) {
+                        if(res_corners[2*k] < bb0)
+                            bb0 = res_corners[2*k];
+                        if(res_corners[2*k] > bb1)
+                            bb1 = res_corners[2*k];
+                        if(res_corners[2*k+1] < bb2)
+                            bb2 = res_corners[2*k+1];
+                        if(res_corners[2*k+1] > bb3)
+                            bb3 = res_corners[2*k+1];
+                                
+                    }
+                    // if(not no_bbox):
+                    // cv::rectangle(frame,  
+                    //             cv::Point(bb0, bb2), 
+                    //             cv::Point(bb1, bb3), 
+                    //             trColors[t.color], thickness);                      
+                    cv::rectangle(frames[bi],  
+                                cv::Point(bb0 * scale_x, bb2 * scale_y - text_size.height - thickness), 
+                                cv::Point(bb0 * scale_x + text_size.width, bb2 * scale_y), 
+                                trColors[t.color], -1);                      
+                                        
+                    cv::putText(frames[bi], txt, 
+                                cv::Point(bb0 * scale_x, bb2 * scale_y - thickness -1), 
+                                cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), 1);
+
+                    cv::arrowedLine(frames[bi], 
+                                cv::Point((int)((bb0 + bb1)/2) * scale_x, (int)((bb2 + bb3)/2) * scale_y), 
+                                cv::Point((int)((bb0 + bb1)/2 + t.det_res.tr.at<float>(0,0)) * scale_x,
+                                          (int)((bb2 + bb3)/2 + t.det_res.tr.at<float>(0,1)) * scale_y),
+                                cv::Scalar(255, 0, 255), 2);
+                }
+            }
+        }
+    }
+}
+
+}}
+
+
@@ -3,11 +3,12 @@

 namespace tk { namespace dnn {

-bool CenternetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches){
+bool CenternetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches, const float conf_thresh){
    std::cout<<(tensor_path).c_str()<<"\n";
    netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
    classes = n_classes;
    nBatches = n_batches;
+    confThreshold = conf_thresh;

    dim = netRT->input_dim;

@@ -118,6 +119,7 @@ bool CenternetDetection::init(const std::string& tensor_path, const int n_classe
    dst2.at<float>(2,0)=dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
    dst2.at<float>(2,1)=dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
    
+    return true;
 }


@@ -371,10 +373,10 @@ void CenternetDetection::postprocess(const int bi, const bool mAP){
                    // std::cout<<"th: "<<scores[j]<<" - cl: "<<clses[j]<<" i: "<<i<<std::endl;
                    //add coco bbox
                    //det[0:4], i, det[4]
-                    int x0   = target_coords[j*4];
-                    int y0   = target_coords[j*4+1];
-                    int x1   = target_coords[j*4+2];
-                    int y1   = target_coords[j*4+3];
+                    float x0   = target_coords[j*4];
+                    float y0   = target_coords[j*4+1];
+                    float x1   = target_coords[j*4+2];
+                    float y1   = target_coords[j*4+3];
                    int obj_class = clses[j];
                    float prob = scores[j];
                    // std::cout<<"("<<x0<<", "<<y0<<"),("<<x1<<", "<<y1<<")"<<std::endl;
@@ -0,0 +1,541 @@
+#include "CenternetDetection3D.h"
+
+
+namespace tk { namespace dnn {
+
+bool CenternetDetection3D::init(const std::string& tensor_path, const int n_classes, const int n_batches, 
+                                const float conf_thresh, const std::vector<cv::Mat>& k_calibs) {
+    std::cout<<(tensor_path).c_str()<<"\n";
+    netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
+    classes = n_classes;
+    nBatches = n_batches;
+    confThreshold = conf_thresh;
+    inputCalibs = k_calibs;
+    dim = netRT->input_dim;
+
+    const char *kitti_class_name[] = {
+            "person", "car", "bicycle"};
+    classesNames = std::vector<std::string>(kitti_class_name, std::end( kitti_class_name));
+    
+    for(int c=0; c<classes; c++) {
+        int offset = c*123457 % classes;
+        float r = getColor(2, offset, classes);
+        float g = getColor(1, offset, classes);
+        float b = getColor(0, offset, classes);
+        colors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
+    }
+    
+    src = cv::Mat(cv::Size(2,3), CV_32F);
+    dst = cv::Mat(cv::Size(2,3), CV_32F);
+    dst2 = cv::Mat(cv::Size(2,3), CV_32F);
+    trans = cv::Mat(cv::Size(3,2), CV_32F);
+    trans2 = cv::Mat(cv::Size(3,2), CV_32F);
+
+    checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot() * nBatches));
+
+    dim_hm = tk::dnn::dataDim_t(1, 3, 128, 128, 1);
+    dim_wh = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
+    dim_reg = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
+    dim_dep = tk::dnn::dataDim_t(1, 1, 128, 128, 1);
+    dim_rot = tk::dnn::dataDim_t(1, 8, 128, 128, 1);
+    dim_dim = tk::dnn::dataDim_t(1, 3, 128, 128, 1);
+
+    checkCuda( cudaMalloc(&topk_scores, dim_hm.c * K *sizeof(float)) );
+    checkCuda( cudaMalloc(&topk_inds_, dim_hm.c * K *sizeof(int)) );      
+    checkCuda( cudaMalloc(&topk_ys_, dim_hm.c * K *sizeof(float)) );      
+    checkCuda( cudaMalloc(&topk_xs_, dim_hm.c * K *sizeof(float)) );    
+    checkCuda( cudaMalloc(&ids_d, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
+    checkCuda( cudaMallocHost(&ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
+    for(int i =0; i<dim_hm.c * dim_hm.h * dim_hm.w; i++){
+        ids_[i] = i;
+    }
+    
+    checkCuda( cudaMalloc(&ones, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
+    float *ones_h;
+    checkCuda( cudaMallocHost(&ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
+    for(int i=0; i<dim_dep.c * dim_dep.h * dim_dep.w; i++)
+        ones_h[i]=1.0f;
+    checkCuda( cudaMemcpy(ones, ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float), cudaMemcpyHostToDevice) );
+    checkCuda( cudaFreeHost(ones_h) );
+
+    checkCuda( cudaMallocHost(&scores, K *sizeof(float)) );
+    checkCuda( cudaMalloc(&scores_d, K *sizeof(float)) );
+
+    checkCuda( cudaMallocHost(&clses, K *sizeof(int)) );
+    checkCuda( cudaMalloc(&clses_d, K *sizeof(int)) );
+
+    checkCuda( cudaMalloc(&topk_inds_d, K *sizeof(int)) );
+    checkCuda( cudaMalloc(&topk_ys_d, K *sizeof(float)) );     
+    checkCuda( cudaMalloc(&topk_xs_d, K *sizeof(float)) ); 
+    checkCuda( cudaMalloc(&inttopk_ys_d, K *sizeof(int)) );
+    checkCuda( cudaMalloc(&inttopk_xs_d, K *sizeof(int)) );
+
+    checkCuda( cudaMallocHost(&xs, K  * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&ys, K  * sizeof(float)) ); 
+
+    checkCuda( cudaMallocHost(&dep, K * dim_dep.c * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&rot, K * dim_rot.c * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&dim_, K * dim_dim.c * sizeof(float)) ); 
+    checkCuda( cudaMallocHost(&wh, K * dim_wh.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&dep_d, K * dim_dep.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&rot_d, K * dim_rot.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&dim_d, K * dim_dim.c * sizeof(float)) ); 
+    checkCuda( cudaMalloc(&wh_d, K * dim_wh.c * sizeof(float)) ); 
+
+    checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
+
+#ifdef OPENCV_CUDACONTRIB
+
+    checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
+    checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
+    float mean[3] = {0.485, 0.456, 0.406};
+    float stddev[3] = {0.229, 0.224, 0.225};
+    
+    checkCuda(cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
+    checkCuda(cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
+#else
+    checkCuda(cudaMallocHost(&input, sizeof(dnnType)*netRT->input_dim.tot() * nBatches));
+    mean << 0.485, 0.456, 0.406;
+    stddev << 0.229, 0.224, 0.225;
+#endif
+
+    for(int bi=0; bi<nBatches; bi++) {
+        cv::Mat calibs_ = cv::Mat::zeros(cv::Size(4,3), CV_32F);        
+        if(inputCalibs.size() == 0 || inputCalibs[bi].empty()) {
+            calibs_.at<float>(0,0) = 707.0493;
+            calibs_.at<float>(0,2) = 604.0814;
+            calibs_.at<float>(1,1) = 707.0493;
+            calibs_.at<float>(1,2) = 180.5066;
+            calibs_.at<float>(0,3) = 45.75831;
+            calibs_.at<float>(1,3) = -0.3454157;
+            calibs_.at<float>(2,2) = 1.0;
+            calibs_.at<float>(2,3) = 0.004981016;
+        }
+        else {
+            calibs_.at<float>(0,0) = inputCalibs[bi].at<float>(0,0);// * (1440.0/dim.w);// / 1440;
+            calibs_.at<float>(0,2) = inputCalibs[bi].at<float>(0,2);// * (1440.0/dim.w);// / 1440;
+            calibs_.at<float>(1,1) = inputCalibs[bi].at<float>(1,1);// * (1080.0/dim.h);//dim.h / 1080;
+            calibs_.at<float>(1,2) = inputCalibs[bi].at<float>(1,2);// * (1080.0/dim.h);//dim.h / 1080;
+            calibs_.at<float>(2,2) = 1.0;
+        }
+        // calibs_.at<float>(0,3) = 45.75831;
+        // calibs_.at<float>(1,3) = -0.3454157;
+        // calibs_.at<float>(2,2) = 1.0;
+        // calibs_.at<float>(2,3) = 0.004981016;
+        calibs.push_back(calibs_);
+    }
+
+    r = cv::Mat(cv::Size(3,3), CV_32F);
+    r.at<float>(0,1) = 0.0;
+    r.at<float>(1,0) = 0.0;
+    r.at<float>(1,1) = 1.0;
+    r.at<float>(1,2) = 0.0;
+    r.at<float>(2,1) = 0.0;
+                        
+    corners = cv::Mat(cv::Size(8,3), CV_32F);
+    corners.at<float>(1,0) = 0.0;
+    corners.at<float>(1,1) = 0.0;
+    corners.at<float>(1,2) = 0.0;
+    corners.at<float>(1,3) = 0.0;
+
+    pts3DHomo = cv::Mat(cv::Size(8,4), CV_32F);
+    pts3DHomo.at<float>(3,0) = 1.0;
+    pts3DHomo.at<float>(3,1) = 1.0;
+    pts3DHomo.at<float>(3,2) = 1.0;
+    pts3DHomo.at<float>(3,3) = 1.0;
+    pts3DHomo.at<float>(3,4) = 1.0;
+    pts3DHomo.at<float>(3,5) = 1.0;
+    pts3DHomo.at<float>(3,6) = 1.0;
+    pts3DHomo.at<float>(3,7) = 1.0;
+    
+    checkCuda( cudaMalloc(&d_ptrs, dim.c * dim.h*dim.w * sizeof(float)) );
+
+    // Alloc array used in the kernel 
+    checkCuda( cudaMalloc(&srcOut, K *sizeof(float)) );
+    checkCuda( cudaMalloc(&idsOut, K *sizeof(int)) );
+
+    dst2.at<float>(0,0)=width * 0.5;
+    dst2.at<float>(0,1)=width * 0.5;
+    dst2.at<float>(1,0)=width * 0.5;
+    dst2.at<float>(1,1)=width * 0.5 +  width * -0.5; 
+    
+    dst2.at<float>(2,0)=dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
+    dst2.at<float>(2,1)=dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
+
+    faceId.push_back({0,1,5,4});
+    faceId.push_back({1,2,6, 5});
+    faceId.push_back({2,3,7,6});
+    faceId.push_back({3,0,4,7});
+    // ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
+
+    return true;
+}
+
+void CenternetDetection3D::preprocess(cv::Mat &frame, const int bi){    
+    cv::Size sz = originalSize[bi];
+    float new_height = dim.h;//sz.height * scale;
+    float new_width = dim.w;//sz.width * scale;
+    if(sz.height != sz_old.height && sz.width != sz_old.width){
+
+        if(inputCalibs.size() == 0 || inputCalibs[bi].empty()) {
+            calibs[bi].at<float>(0,2) = new_width / 2.0f;
+            calibs[bi].at<float>(1,2) = new_height /2.0f;
+        }
+        else {
+            calibs[bi].at<float>(0,0) = inputCalibs[bi].at<float>(0,0) * 2.0 * dim.w / sz.width;
+            calibs[bi].at<float>(0,2) = inputCalibs[bi].at<float>(0,2) * dim.w / sz.width ;
+            calibs[bi].at<float>(1,1) = inputCalibs[bi].at<float>(1,1) * 2.0 * dim.h / sz.height;
+            calibs[bi].at<float>(1,2) = inputCalibs[bi].at<float>(1,2) * dim.h / sz.height;
+        }
+        float c[] = {new_width / 2.0f, new_height /2.0f};
+        float s[] = {new_width, new_height};
+        // ----------- get_affine_transform
+        // rot_rad = pi * 0 / 100 --> 0
+        
+        src.at<float>(0,0)=c[0];
+        src.at<float>(0,1)=c[1];
+        src.at<float>(1,0)=c[0];
+        src.at<float>(1,1)=c[1] + s[0] * -0.5;
+        dst.at<float>(0,0)=netRT->input_dim.w * 0.5;
+        dst.at<float>(0,1)=netRT->input_dim.h * 0.5;
+        dst.at<float>(1,0)=netRT->input_dim.w * 0.5;
+        dst.at<float>(1,1)=netRT->input_dim.h * 0.5 +  netRT->input_dim.w * -0.5; 
+        
+        src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
+        src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
+        dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
+        dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
+
+        trans = cv::getAffineTransform( src, dst );
+        // end_t = std::chrono::steady_clock::now();
+        // std::cout << " TIME gett affine trans: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+        // step_t = end_t;
+        
+        trans2 = cv::getAffineTransform( dst2, src );
+        // end_t = std::chrono::steady_clock::now();
+        // std::cout << " TIME getAffineTrans 2: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+        // step_t = end_t;
+    }
+    sz_old = sz;
+#ifdef OPENCV_CUDACONTRIB
+    // std::cout<<"OPENCV CPMTROB\n";
+    cv::cuda::GpuMat im_Orig; 
+    cv::cuda::GpuMat imageF1_d, imageF2_d;
+        
+    im_Orig = cv::cuda::GpuMat(frame);
+    cv::cuda::resize (im_Orig, imageF1_d, cv::Size(dim.w, dim.h));//cv::Size(new_width, new_height)); 
+    // imageF1_d = im_Orig;
+    checkCuda( cudaDeviceSynchronize() );
+    
+    sz = imageF1_d.size();
+    // std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+    
+    cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(netRT->input_dim.w, netRT->input_dim.h), cv::INTER_LINEAR );
+    checkCuda( cudaDeviceSynchronize() );
+    
+    imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0); 
+    checkCuda( cudaDeviceSynchronize() );
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME convert: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+    
+    dim2 = dim;
+    cv::cuda::GpuMat bgr[3]; 
+    cv::cuda::split(imageF1_d,bgr);//split source
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME split: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+
+    for(int i=0; i<dim.c; i++)
+        checkCuda( cudaMemcpy(d_ptrs + i*dim.h * dim.w, (float*)bgr[i].data, dim.h * dim.w * sizeof(float), cudaMemcpyDeviceToDevice) );
+        
+    normalize(d_ptrs, dim.c, dim.h, dim.w, mean_d, stddev_d);
+    
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME normalize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+
+    checkCuda(cudaMemcpy(input_d+ netRT->input_dim.tot()*bi, d_ptrs, dim2.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
+    
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME Memcpy to input_d: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+#else
+    // std::cout<<"NO OPENCV CPMTROB\n";
+    cv::Mat imageF;
+    resize(frame, imageF, cv::Size(dim.w, dim.h));//cv::Size(new_width, new_height));
+    // imageF = frame;
+    sz = imageF.size();
+    // std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+
+    cv::Mat trans = cv::getAffineTransform( src, dst );
+    cv::warpAffine(imageF, imageF, trans, cv::Size(netRT->input_dim.w, netRT->input_dim.h), cv::INTER_LINEAR );
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME warpAffine: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+
+    sz = imageF.size();
+    // std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
+    imageF.convertTo(imageF, CV_32FC3, 1/255.0); 
+    // end_t = std::chrono::steady_clock::now();
+    // std::cout << " TIME convertto: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
+    // step_t = end_t;
+    dim2 = dim;
+    //split channels
+    cv::Mat bgr[3]; 
+    cv::split(imageF,bgr);//split source
+    for(int i=0; i<3; i++){
+        bgr[i] = bgr[i] - mean[i];
+        bgr[i] = bgr[i] / stddev[i];
+    }
+
+    //write channels
+    for(int i=0; i<dim2.c; i++) {
+        int idx = i*imageF.rows*imageF.cols;
+        int ch = dim2.c-3 +i;
+        // std::cout<<"i: "<<i<<", idx: "<<idx<<", ch: "<<ch<<std::endl;
+        memcpy((void*)&input[idx+ netRT->input_dim.tot()*bi], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
+    }
+    checkCuda(cudaMemcpyAsync(input_d+ netRT->input_dim.tot()*bi, input+ netRT->input_dim.tot()*bi, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
+#endif
+}
+
+void CenternetDetection3D::postprocess(const int bi, const bool mAP) {
+    dnnType *rt_out[7];
+    rt_out[0] = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
+    rt_out[1] = (dnnType *)netRT->buffersRT[2]+ netRT->buffersDIM[2].tot()*bi;
+    rt_out[2] = (dnnType *)netRT->buffersRT[3]+ netRT->buffersDIM[3].tot()*bi; 
+    rt_out[3] = (dnnType *)netRT->buffersRT[4]+ netRT->buffersDIM[4].tot()*bi; 
+    rt_out[4] = (dnnType *)netRT->buffersRT[5]+ netRT->buffersDIM[5].tot()*bi; 
+    rt_out[5] = (dnnType *)netRT->buffersRT[6]+ netRT->buffersDIM[6].tot()*bi; 
+    rt_out[6] = (dnnType *)netRT->buffersRT[7]+ netRT->buffersDIM[7].tot()*bi; 
+    
+    // ------------------------------------ process --------------------------------------------
+    activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
+    checkCuda( cudaDeviceSynchronize() );
+
+    // output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
+    activationSIGMOIDForward(rt_out[4], rt_out[4], dim_dep.tot());
+    checkCuda( cudaDeviceSynchronize() );
+    transformDep(ones, ones + dim_dep.tot(), rt_out[4], rt_out[4] + dim_dep.tot());
+    checkCuda( cudaDeviceSynchronize() );
+
+    subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0], op);
+
+    // ----------- nms end
+    // ----------- topk  
+      
+    if(K > dim_hm.h * dim_hm.w){
+        printf ("Error topk (K is too large)\n");
+        return;
+    }
+    
+    checkCuda( cudaMemcpy(ids_d, ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int), cudaMemcpyHostToDevice) );    
+
+    sort(rt_out[0],rt_out[0]+dim_hm.tot(),ids_d);
+    checkCuda( cudaDeviceSynchronize() );   
+
+    topk(rt_out[0], ids_d, K, scores_d, topk_inds_d, topk_ys_d, topk_xs_d);
+    checkCuda( cudaDeviceSynchronize() );    
+
+    checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );
+
+    topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
+
+    checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
+    checkCuda( cudaMemcpy(topk_ys_d, (float *)inttopk_ys_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
+    
+    checkCuda( cudaMemcpy(clses, clses_d, K*sizeof(int), cudaMemcpyDeviceToHost) );
+
+    // ----------- topk end 
+    
+    topKxyAddOffset(topk_inds_d, K, dim_reg.h*dim_reg.w, inttopk_xs_d, inttopk_ys_d, topk_xs_d, topk_ys_d, rt_out[3], srcOut, idsOut);
+    // checkCuda( cudaDeviceSynchronize() );
+    
+   
+    getRecordsFromTopKId(topk_inds_d, K, dim_dep.c, dim_dep.h * dim_dep.w, rt_out[4], dep_d, idsOut);
+    checkCuda( cudaMemcpy(dep, dep_d, K * dim_dep.c * sizeof(float), cudaMemcpyDeviceToHost) );
+    
+    getRecordsFromTopKId(topk_inds_d, K, dim_rot.c, dim_rot.h * dim_rot.w, rt_out[5], rot_d, idsOut);
+    checkCuda( cudaMemcpy(rot, rot_d, K * dim_rot.c * sizeof(float), cudaMemcpyDeviceToHost) );
+    
+    getRecordsFromTopKId(topk_inds_d, K, dim_dim.c, dim_dim.h * dim_dim.w, rt_out[6], dim_d, idsOut);
+    checkCuda( cudaMemcpy(dim_, dim_d, K * dim_dim.c * sizeof(float), cudaMemcpyDeviceToHost) );
+
+    getRecordsFromTopKId(topk_inds_d, K, dim_wh.c, dim_wh.h * dim_wh.w, rt_out[2], wh_d, idsOut);
+    checkCuda( cudaMemcpy(wh, wh_d, K * dim_wh.c * sizeof(float), cudaMemcpyDeviceToHost) );
+   
+    checkCuda( cudaMemcpy(xs, topk_xs_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
+    checkCuda( cudaMemcpy(ys, topk_ys_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
+  
+    // ---------------------------------- post-process -----------------------------------------
+    
+    // ddd_post_process_2d
+    cv::Mat new_pt1(cv::Size(1,2), CV_32F);
+    cv::Mat new_pt2(cv::Size(1,2), CV_32F); 
+
+    for(int i = 0; i<K; i++){
+        new_pt1.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*xs[i] +
+                                static_cast<float>(trans2.at<double>(0,1))*ys[i] +
+                                static_cast<float>(trans2.at<double>(0,2))*1.0;
+        new_pt1.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*xs[i] +
+                                static_cast<float>(trans2.at<double>(1,1))*ys[i] +
+                                static_cast<float>(trans2.at<double>(1,2))*1.0;
+
+        new_pt2.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*wh[i] +
+                                static_cast<float>(trans2.at<double>(0,1))*wh[K+i] +
+                                static_cast<float>(trans2.at<double>(0,2))*1.0;
+        new_pt2.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*wh[i] +
+                                static_cast<float>(trans2.at<double>(1,1))*wh[K+i] +
+                                static_cast<float>(trans2.at<double>(1,2))*1.0;
+
+        target_coords[i*4] = new_pt1.at<float>(0,0);
+        target_coords[i*4+1] = new_pt1.at<float>(0,1);
+        target_coords[i*4+2] = new_pt2.at<float>(0,0);
+        target_coords[i*4+3] = new_pt2.at<float>(0,1);
+    }
+    
+    float alpha;
+    float x, y, z, rot_y;
+    detected3D.clear();
+    for(int i = 0; i<classes; i++){      
+        for(int j=0; j<K; j++){
+            if(clses[j] == i){
+                //get alpha
+                if(rot[1*K + j] > rot[5*K + j])
+                    alpha = std::atan2(rot[2*K + j], rot[3*K + j]) -0.5 * M_PI;
+                else
+                    alpha = std::atan2(rot[6*K + j], rot[7*K + j]) +0.5 * M_PI;  
+                
+                // unproject_2d_to_3d
+                z = dep[j] - calibs[bi].at<float>(2,3);// z = depth - P[2, 3]
+                x = (target_coords[j*4] * dep[j] - calibs[bi].at<float>(0,3) - calibs[bi].at<float>(0,2) * z) / calibs[bi].at<float>(0,0);
+                y = (target_coords[j*4+1] * dep[j] - calibs[bi].at<float>(1,3) - calibs[bi].at<float>(1,2) * z) / calibs[bi].at<float>(1,1) + (dim_[j] / 2);
+                // alpha2rot_y
+                rot_y = (alpha + std::atan2(target_coords[j*4] - calibs[bi].at<float>(0,2), calibs[bi].at<float>(0,0)));
+                if(rot_y>M_PI)
+                    rot_y -= 2*M_PI;
+                if(rot_y<M_PI)
+                    rot_y += 2*M_PI;   
+
+                if(scores[j] > confThreshold) {
+                    if(z>0) {
+                        // compute_box_3d
+                        r.at<float>(0,0) = std::cos(rot_y);
+                        r.at<float>(0,2) = std::sin(rot_y);
+                        r.at<float>(2,0) = -std::sin(rot_y);
+                        r.at<float>(2,2) = std::cos(rot_y);
+
+                        corners.at<float>(0,0) = dim_[2*K+j]/2;
+                        corners.at<float>(0,1) = dim_[2*K+j]/2;
+                        corners.at<float>(0,2) = -dim_[2*K+j]/2;
+                        corners.at<float>(0,3) = -dim_[2*K+j]/2;
+                        corners.at<float>(0,4) = dim_[2*K+j]/2;
+                        corners.at<float>(0,5) = dim_[2*K+j]/2;
+                        corners.at<float>(0,6) = -dim_[2*K+j]/2;
+                        corners.at<float>(0,7) = -dim_[2*K+j]/2;
+
+                        corners.at<float>(1,4) = -dim_[j];
+                        corners.at<float>(1,5) = -dim_[j];
+                        corners.at<float>(1,6) = -dim_[j];
+                        corners.at<float>(1,7) = -dim_[j];
+                        
+                        corners.at<float>(2,0) = dim_[K+j]/2;
+                        corners.at<float>(2,1) = -dim_[K+j]/2;
+                        corners.at<float>(2,2) = -dim_[K+j]/2;
+                        corners.at<float>(2,3) = dim_[K+j]/2;
+                        corners.at<float>(2,4) = dim_[K+j]/2;
+                        corners.at<float>(2,5) = -dim_[K+j]/2;
+                        corners.at<float>(2,6) = -dim_[K+j]/2;
+                        corners.at<float>(2,7) = dim_[K+j]/2;
+                        cv::Mat aus = r * corners;
+
+                        for(int k=0; k<8; k++) {
+                            aus.at<float>(0,k) += x;
+                            aus.at<float>(1,k) += y;
+                            aus.at<float>(2,k) += z;
+                        }
+                        // corners.copyTo(pts3DHomo(cv::Rect(0, 0, 8, 3)));
+                        for(int k1=0; k1<3; k1++) {
+                            for(int k2=0; k2<8; k2++)
+                                pts3DHomo.at<float>(k1,k2) = aus.at<float>(k1,k2); 
+                        }
+                        aus.release();
+                        aus = calibs[bi] * pts3DHomo;
+                        
+                        tk::dnn::box3D res;
+                        for(int k=0; k<8; k++) {
+                            res.corners.push_back(aus.at<float>(0,k) / aus.at<float>(2,k));
+                            res.corners.push_back(aus.at<float>(1,k) / aus.at<float>(2,k));
+                        }
+                        res.cl = i;
+                        res.prob = scores[j];
+                        //res.print();
+                        detected3D.push_back(res);  
+                    }
+                }
+            }
+        }
+    }
+    batchDetected.push_back(detected3D);
+}
+
+void CenternetDetection3D::draw(std::vector<cv::Mat>& frames) {
+    tk::dnn::box3D b;
+    int x0, w, x1, y0, h, y1;
+    int objClass;
+    std::string det_class;
+
+    int baseline = 0;
+    float font_scale = 0.5;
+    int thickness = 2;   
+    
+    for(int bi=0; bi<frames.size(); ++bi){
+        float scale_x = float(originalSize[bi].width)/dim.w;
+        float scale_y = float(originalSize[bi].height)/dim.h;
+        resize(frames[bi], frames[bi], originalSize[bi]);
+        // draw dets
+        for(int i=0; i<batchDetected[bi].size(); i++) {
+            b = batchDetected[bi][i];
+
+            for(int ind_f = 3; ind_f>=0; ind_f--) {
+                for(int j=0; j<4; j++) {
+                    cv::line(frames[bi], cv::Point(b.corners.at(faceId.at(ind_f).at(j) * 2) * scale_x, 
+                                            b.corners.at(faceId.at(ind_f).at(j) * 2 + 1) * scale_y),
+                                    cv::Point(b.corners.at(faceId.at(ind_f).at((j+1)%4) * 2) * scale_x, 
+                                            b.corners.at(faceId.at(ind_f).at((j+1)%4) * 2 + 1) * scale_y), 
+                                    colors[b.cl], 2);
+                    if(ind_f == 0) {
+                        cv::line(frames[bi], cv::Point(b.corners.at(faceId.at(ind_f).at(0) * 2)  * scale_x, 
+                                                b.corners.at(faceId.at(ind_f).at(0) * 2 + 1)* scale_y),
+                                        cv::Point(b.corners.at(faceId.at(ind_f).at(2) * 2)  * scale_x, 
+                                                b.corners.at(faceId.at(ind_f).at(2) * 2 + 1) * scale_y), colors[b.cl], 2);
+                        cv::line(frames[bi], cv::Point(b.corners.at(faceId.at(ind_f).at(1) * 2)* scale_x, 
+                                                b.corners.at(faceId.at(ind_f).at(1) * 2 + 1)* scale_y),
+                                        cv::Point(b.corners.at(faceId.at(ind_f).at(3) * 2)* scale_x, 
+                                                b.corners.at(faceId.at(ind_f).at(3) * 2 + 1)* scale_y), colors[b.cl], 2);
+                    }
+                }
+            }
+            // draw label
+            cv::Size text_size = getTextSize(classesNames[b.cl], cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
+            cv::rectangle(frames[bi],  cv::Point(b.corners.at(faceId.at(0).at(0) * 2)* scale_x, 
+                                            b.corners.at(faceId.at(0).at(0) * 2 + 1)* scale_y), 
+                                cv::Point((b.corners.at(faceId.at(0).at(0) * 2)* scale_x + text_size.width - 2), 
+                                            (b.corners.at(faceId.at(0).at(0) * 2 + 1)* scale_y - text_size.height - 2)), colors[b.cl], -1);                      
+            cv::putText(frames[bi], classesNames[b.cl], cv::Point(b.corners.at(faceId.at(0).at(0) * 2)* scale_x,  
+                                                            (b.corners.at(faceId.at(0).at(0) * 2 + 1)* scale_y - (baseline / 2))), 
+                                                cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
+        }
+    }
+}
+
+}}
+
+
@@ -166,6 +166,11 @@ Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
    }
    initCUDNN(deConv);

+    if(this->groups != 1)
+        MACC = kernelH*kernelW*output_dim.c*output_dim.w*output_dim.h;
+    else
+        MACC = input_dim.c*kernelH*kernelW*output_dim.c*output_dim.w*output_dim.h;
+
    // allocate warkspace
    if (ws_sizeInBytes!=0) {
        checkCuda( cudaMalloc(&workSpace, ws_sizeInBytes) );
@@ -37,7 +37,10 @@ namespace tk { namespace dnn {
        std::string name,value;
        if(!divideNameAndValue(line, name, value))
            return false;
-        if(name.find("width") !=  std::string::npos)
+
+        if(name.find("new_coords") !=  std::string::npos)
+            fields.new_coords = std::stoi(value);
+        else if(name.find("width") !=  std::string::npos)
            fields.width = std::stoi(value);
        else if(name.find("height") !=  std::string::npos)
            fields.height = std::stoi(value);
@@ -79,6 +82,13 @@ namespace tk { namespace dnn {
            fields.group_id = std::stoi(value);
        else if(name.find("scale_x_y") !=  std::string::npos)
            fields.scale_xy = std::stof(value);
+        else if(name.find("beta_nms") !=  std::string::npos)
+            fields.nms_thresh = std::stof(value);
+        else if(name.find("nms_kind") !=  std::string::npos){
+            if(value == "greedynms") fields.nms_kind = 0;
+            else if(value == "diounms") fields.nms_kind = 1;
+            else std::cout<<"Not supported nms_kind "<<value<<", setting to greedynms"<<std::endl;
+        }
        else if(name.find("from") !=  std::string::npos)
            fields.layers.push_back(std::stof(value));
        else if(name.find("mask") !=  std::string::npos){
@@ -161,7 +171,7 @@ namespace tk { namespace dnn {
        } else if(f.type == "yolo") {
            std::string wgs = wgs_path + "/g" + std::to_string(netLayers.size()) + ".bin";
            //printf("%d %d %s %d %f\n", f.classes, f.num/f.n_mask, wgs.c_str(), f.n_mask, f.scale_xy);
-            tk::dnn::Yolo *l = new tk::dnn::Yolo(net, f.classes, f.num/f.n_mask, wgs, f.n_mask, f.scale_xy);
+            tk::dnn::Yolo *l = new tk::dnn::Yolo(net, f.classes, f.num/f.n_mask, wgs, f.n_mask, f.scale_xy, f.nms_thresh, (tk::dnn::Yolo::nmsKind_t) f.nms_kind, f.new_coords);
            if(names.size() != f.classes)
                FatalError("Mismatch between number of classes and names");
            l->classesNames = names;
@@ -177,6 +187,7 @@ namespace tk { namespace dnn {
            if(f.activation == "relu") act = tkdnnActivationMode_t(CUDNN_ACTIVATION_RELU);
            else if(f.activation == "leaky") act = tk::dnn::ACTIVATION_LEAKY;
            else if(f.activation == "mish") act = tk::dnn::ACTIVATION_MISH;
+            else if(f.activation == "logistic") act = tk::dnn::ACTIVATION_LOGISTIC;
            else { FatalError("activation not supported: " + f.activation); }
            netLayers[netLayers.size()-1] = new tk::dnn::Activation(net, act);
        };
@@ -201,7 +212,7 @@ namespace tk { namespace dnn {

        tk::dnn::Network *net = nullptr;
        
-        // layers without activations to retrive correct id number
+        // layers without activations to retrieve correct id number
        std::vector<tk::dnn::Layer*> netLayers;

        std::ifstream if_cfg(cfg_file);
@@ -73,6 +73,12 @@ DeformConv2d::DeformConv2d( Network *net, int out_ch, int deformable_group, int
    
    output_dim.c = out_ch;
    initCUDNN();
+
+    if(this->deformableGroup != 1)
+        MACC = kernelH*kernelW*output_dim.c*output_dim.w*output_dim.h;
+    else
+        MACC = input_dim.c*kernelH*kernelW*output_dim.c*output_dim.w*output_dim.h;
+        
    //allocate data for infer result
    checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
 }
@@ -95,7 +101,7 @@ dnnType* DeformConv2d::infer(dataDim_t &dim, dnnType* srcData) {
    // split conv2d outputs into offset and mask
    checkCuda(cudaMemcpy(offset, output_conv, 2*chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
    checkCuda(cudaMemcpy(mask, output_conv + 2*chunk_dim, chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
-    // kernel sigmoide
+    // kernel sigmoid
    activationSIGMOIDForward(mask, mask, chunk_dim);

    // deformable convolution
@@ -37,7 +37,7 @@ dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
    // place bias into dstData
    checkCuda( cudaMemcpy(dstData, bias_d, dim_y*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
    
-    //do matrix moltiplication
+    //do matrix multiplication
    checkERROR( cublasSgemv(net->cublasHandle, CUBLAS_OP_T,
                            dim_x, dim_y,
                            &alpha,
@@ -87,17 +87,22 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
    checkCUDNN(cudnnCreateRNNDescriptor(&rnnDesc));

 #if CUDNN_MAJOR > 7
-    checkCUDNN(cudnnSetRNNDescriptor_v6(net->cudnnHandle,
+    checkCUDNN(cudnnSetRNNDescriptor_v6(net->cudnnHandle,rnnDesc, stateSize, numLayers, dropoutDesc,
+                                        cudnnRNNInputMode_t::CUDNN_LINEAR_INPUT,
+            //(bidirectional ? cudnnDirectionMode_t::CUDNN_BIDIRECTIONAL : cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL),
+                                        cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL,
+                                        cudnnRNNMode_t::CUDNN_LSTM,
+                                        cudnnRNNAlgo_t::CUDNN_RNN_ALGO_STANDARD,
+                                        net->dataType));
 #else
-    checkCUDNN(cudnnSetRNNDescriptor(net->cudnnHandle,
-#endif
-        rnnDesc, stateSize, numLayers, dropoutDesc,
+    checkCUDNN(cudnnSetRNNDescriptor(net->cudnnHandle,rnnDesc, stateSize, numLayers, dropoutDesc,
        cudnnRNNInputMode_t::CUDNN_LINEAR_INPUT,
        //(bidirectional ? cudnnDirectionMode_t::CUDNN_BIDIRECTIONAL : cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL),
        cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL,
        cudnnRNNMode_t::CUDNN_LSTM,
        cudnnRNNAlgo_t::CUDNN_RNN_ALGO_STANDARD,
        net->dataType));
+#endif


    // Get temp space sizes
@@ -133,7 +138,7 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
    output_dim = input_dim;
    output_dim.c = stateSize*(bidirectional ? 2 : 1);

-    // if retunseq is disabled only the last timestep is returned
+    // if retunseq is disabled only the last timestamp is returned
    if(!returnSeq) {
        output_dim.h = 1;
        output_dim.w = 1;
@@ -254,7 +259,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
            rnnDesc,
            seqLen,                     // number of time steps (nT)
            x_desc_vec_.data(),         // input array of desc (nT*nC_in)
-            srcF,                    // input pointer
+            srcF,                       // input pointer
            hx_desc_,                   // initial hidden state desc     
            hx_ptr,                     // initial hidden state pointer 
            cx_desc_,                   // initial cell state desc      
@@ -281,7 +286,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
            rnnDesc,
            seqLen,                     // number of time steps (nT)
            x_desc_vec_.data(),         // input array of desc (nT*nC_in)
-            srcB,                    // input pointer
+            srcB,                       // input pointer
            hx_desc_,                   // initial hidden state desc     
            hx_ptr,                     // initial hidden state pointer 
            cx_desc_,                   // initial cell state desc      
@@ -289,7 +294,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
            w_desc_,                    // weights desc
            wb_ptr,                     // weights pointer
            y_desc_vec_.data(),         // output desc     (nT*nC_out)
-            dstB_NR,                       // output pointer
+            dstB_NR,                    // output pointer
            hy_desc_,                   // final hidden state desc        
            hy_ptr,                     // final hidden state pointer 
            cy_desc_,                   // final cell state desc          
@@ -307,7 +312,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
            one_output_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
    }    

-    // if retunseq is disabled only the last timestep is returned
+    // if retunseq is disabled only the last timestamp is returned
    if(returnSeq) {
        // forward transpose
        matrixTranspose(net->cublasHandle, dstF, dstData, 
@@ -18,6 +18,8 @@ Layer::Layer(Network *net) {
        if(!net->addLayer(this))
            FatalError("Net reached max number of layers");    
    }
+
+    feature_map_size = input_dim.tot() + output_dim.tot();
 }

 Layer::~Layer() {
@@ -19,6 +19,8 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
    int seek = 0;
    readBinaryFile(weights_path.c_str(), inputs*outputs*kh*kw*kl, &data_h, &data_d, seek);
    seek += inputs*outputs*kh*kw*kl;
+    n_params = seek;
+    
    this->additional_bias = additional_bias;
    if(additional_bias) {
        readBinaryFile(weights_path.c_str(), outputs, &bias2_h, &bias2_d, seek);
@@ -26,15 +28,17 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
    }
    
    readBinaryFile(weights_path.c_str(), outputs, &bias_h, &bias_d, seek);
+    seek += outputs;

    this->batchnorm = batchnorm;
    if(batchnorm) {
-        seek += outputs;
+        
        readBinaryFile(weights_path.c_str(), outputs, &scales_h, &scales_d, seek);
        seek += outputs;
        readBinaryFile(weights_path.c_str(), outputs, &mean_h, &mean_d, seek);
        seek += outputs;
        readBinaryFile(weights_path.c_str(), outputs, &variance_h, &variance_d, seek);
+        seek += outputs;

        float eps = TKDNN_BN_MIN_EPSILON;

@@ -105,7 +109,7 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
        float2half(tmp_d, variance16_d, b_size);
        cudaMemcpy(variance16_h, variance16_d, b_size*sizeof(__half), cudaMemcpyDeviceToHost);

-        //conver scales
+        //convert scales
        float2half(scales_d, scales16_d, b_size);
        cudaMemcpy(scales16_h, scales16_d, b_size*sizeof(__half), cudaMemcpyDeviceToHost);
    
@@ -126,12 +126,13 @@ float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b){
    return iou;
 }

-bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches){
+bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches, const float conf_thresh){
    std::cout<<(tensor_path).c_str()<<"\n";
    netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
    imageSize = netRT->input_dim.h;
    classes = n_classes;
    nBatches = n_batches;
+    confThreshold = conf_thresh;

    SSDSpec specs[N_SSDSPEC];

@@ -197,7 +198,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
        "bottle" , "wine glass" , "cup" , "fork" , "knife" , "spoon" , "bowl" , "banana" , 
        "apple" , "sandwich" , "orange" , "broccoli" , "carrot" , "hot dog" , "pizza" , 
        "donut" , "cake" , "chair" , "sofa" , "pottedplant" , "bed" , "diningtable" , 
-        "toilet" , "tvmonitor" , "laptop" , "mouse" , "remote" , "keyboard" , 
+    "toilet" , "tvmonitor" , "laptop" , "mouse" , "remote" , "keyboard" , 
        "cell phone" , "microwave" , "oven" , "toaster" , "sink" , "refrigerator" , 
        "book" , "clock" , "vase" , "scissors" , "teddy bear" , "hair drier" , "toothbrush"};
        classesNames = std::vector<std::string>(classes_names_, std::end(classes_names_));
@@ -206,7 +207,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
    else{
        FatalError("Number of classes not supported for mobilenet");
    }
-    return 1;
+    return true;
 }

 void MobilenetDetection::preprocess(cv::Mat &frame, const int bi){
@@ -12,7 +12,7 @@ MulAdd::MulAdd(Network *net, dnnType mul, dnnType add) : Layer(net) {

    int size = input_dim.tot();

-    // create a vector with all value setted to add 
+    // create a vector with all value set to add 
    dnnType *add_vector_h = new dnnType[size];
    for(int i=0; i<size; i++)
        add_vector_h[i] = add;
@@ -96,6 +96,28 @@ dataDim_t Network::getOutputDim() {
            return layers[num_layers-1]->output_dim;
 }

+void Network::adjustFeatureMapSizeWithShortcuts(){
+    layerType_t layer_type;
+    int shortcutted_idx;
+
+    for(int i=0; i<num_layers; i++) {
+        layer_type = layers[i]->getLayerType();
+        if(layer_type == LAYER_SHORTCUT){
+            shortcutted_idx = -1;
+            for(int j=0; j<num_layers; j++) {
+                if(static_cast<tk::dnn::Shortcut*>(layers[i])->backLayer == layers[j]){
+                    shortcutted_idx = j;
+                    break;
+                }
+            }
+            if(shortcutted_idx == -1)
+                FatalError("Problem when computing featuer_map_size with shortcuts");
+            for(int j=shortcutted_idx+1; j<i; ++j)
+                layers[j]->feature_map_size += layers[shortcutted_idx]->output_dim.tot();
+        }
+    }
+}
+
 void Network::print() {

    printCenteredTitle(" NETWORK MODEL ", '=', 60);
@@ -106,10 +128,21 @@ void Network::print() {
    std::cout.width(16); std::cout<<std::left<<"output (H*W,CH)";
    std::cout<<"\n";

+    adjustFeatureMapSizeWithShortcuts();
+    
+    long long unsigned int tot_params = 0;
+    long long unsigned int max_feature_map_size = 0;
+    long long unsigned int tot_MACC = 0;
+    
    for(int i=0; i<num_layers; i++) {
        dataDim_t in = layers[i]->input_dim;
        dataDim_t out = layers[i]->output_dim;

+        tot_params += layers[i]->n_params;
+        tot_MACC += layers[i]->MACC;
+        if(layers[i]->feature_map_size> max_feature_map_size)
+            max_feature_map_size = layers[i]->feature_map_size;
+
        std::cout.width(3); std::cout<<std::right<<i;
        std::cout<<" ";
        std::cout.width(16); std::cout<<std::left<<layers[i]->getLayerName();
@@ -128,6 +161,9 @@ void Network::print() {
    }
    printCenteredTitle("", '=', 60);
    std::cout<<"\n";
+    std::cout<<"N params: "<<tot_params<<std::endl;
+    std::cout<<"Max feature map size: "<<max_feature_map_size<<std::endl;
+    std::cout<<"N MACC: "<<tot_MACC<<std::endl<<std::endl;
    printCudaMemUsage();
 }
 const char *Network::getNetworkRTName(const char *network_name){
@@ -140,6 +140,7 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
        engineRT = builderRT->buildEngineWithConfig(*networkRT, *configRT);
 #else 
        engineRT = builderRT->buildCudaEngine(*networkRT);
+        //engineRT = std::shared_ptr<nvinfer1::ICudaEngine>(builderRT->buildCudaEngine(*networkRT));
 #endif
        if(engineRT == nullptr)
            FatalError("cloud not build cuda engine")
@@ -163,7 +164,7 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
 	// note that indices are guaranteed to be less than IEngine::getNbBindings()
 	buf_input_idx = engineRT->getBindingIndex("data"); 
    buf_output_idx = engineRT->getBindingIndex("out");
-    std::cout<<"input idex = "<<buf_input_idx<<" -> output index = "<<buf_output_idx<<"\n";
+    std::cout<<"input index = "<<buf_input_idx<<" -> output index = "<<buf_output_idx<<"\n";


    Dims iDim = engineRT->getBindingDimensions(buf_input_idx);
@@ -226,7 +227,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
        return convert_layer(input, (Conv2d*) l);
    if(type == LAYER_POOLING)
        return convert_layer(input, (Pooling*) l);
-    if(type == LAYER_ACTIVATION || type == LAYER_ACTIVATION_CRELU || type == LAYER_ACTIVATION_LEAKY || type == LAYER_ACTIVATION_MISH)
+    if(type == LAYER_ACTIVATION || type == LAYER_ACTIVATION_CRELU || type == LAYER_ACTIVATION_LEAKY || type == LAYER_ACTIVATION_MISH || type == LAYER_ACTIVATION_LOGISTIC)
        return convert_layer(input, (Activation*) l);
    if(type == LAYER_SOFTMAX)
        return convert_layer(input, (Softmax*) l);
@@ -236,6 +237,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
        return convert_layer(input, (Flatten*) l);
    if(type == LAYER_RESHAPE)
        return convert_layer(input, (Reshape*) l);
+    if(type == LAYER_RESIZE)
+        return convert_layer(input, (Resize*) l);
    if(type == LAYER_REORG)
        return convert_layer(input, (Reorg*) l);
    if(type == LAYER_REGION)
@@ -389,13 +392,13 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Activation *l) {
        
 #if NV_TENSORRT_MAJOR < 6                
        // plugin version
-        IPlugin *plugin = new ActivationLeakyRT();
+        IPlugin *plugin = new ActivationLeakyRT(l->slope);
        IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
        checkNULL(lRT);
        return lRT;
 #else 
        IActivationLayer *lRT = networkRT->addActivation(*input, ActivationType::kLEAKY_RELU);
-        lRT->setAlpha(0.1);
+        lRT->setAlpha(l->slope);
        checkNULL(lRT);
        return lRT;
 #endif
@@ -421,6 +424,12 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Activation *l) {
        checkNULL(lRT);
        return lRT;
    }
+    else if(l->act_mode == ACTIVATION_LOGISTIC) {
+        IPlugin *plugin = new ActivationLogisticRT();
+        IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
+        checkNULL(lRT);
+        return lRT;
+    }
    else {
        FatalError("this Activation mode is not yet implemented");
        return NULL;
@@ -472,13 +481,23 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Flatten *l) {
 ILayer* NetworkRT::convert_layer(ITensor *input, Reshape *l) {
    // std::cout<<"convert Reshape\n";

-    l->output_dim.print();
    IPlugin *plugin = new ReshapeRT(l->output_dim);
    IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
    checkNULL(lRT);
    return lRT;
 }

+ILayer* NetworkRT::convert_layer(ITensor *input, Resize *l) {
+    // std::cout<<"convert Resize\n";
+
+    IResizeLayer *lRT = networkRT->addResize(*input); //default is kNEAREST
+    checkNULL(lRT);
+    Dims d{};
+    lRT->setResizeMode(ResizeMode(l->mode));
+    lRT->setOutputDimensions(DimsCHW{l->output_dim.c, l->output_dim.h, l->output_dim.w});
+    return lRT;
+}
+
 ILayer* NetworkRT::convert_layer(ITensor *input, Reorg *l) {
    //std::cout<<"convert Reorg\n";

@@ -506,7 +525,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Shortcut *l) {
    
    ITensor *back_tens = tensors[l->backLayer];

-    if(l->backLayer->output_dim.c == l->output_dim.c)
+    if(l->backLayer->output_dim.c == l->output_dim.c && !l->mul) 
    {
        IElementWiseLayer *lRT = networkRT->addElementWise(*input, *back_tens, ElementWiseOperation::kSUM);
        checkNULL(lRT);
@@ -515,7 +534,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Shortcut *l) {
    else
    {
        // plugin version
-        IPlugin *plugin = new ShortcutRT(l->backLayer->output_dim);
+        IPlugin *plugin = new ShortcutRT(l->backLayer->output_dim, l->mul);
        ITensor **inputs = new ITensor*[2];
        inputs[0] = input;
        inputs[1] = back_tens; 
@@ -529,7 +548,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Yolo *l) {
    //std::cout<<"convert Yolo\n";

    //std::cout<<"New plugin YOLO\n";
-    IPlugin *plugin = new YoloRT(l->classes, l->num, l, l->n_masks, l->scaleXY);
+    IPlugin *plugin = new YoloRT(l->classes, l->num, l, l->n_masks, l->scaleXY, l->nms_thresh, l->nsm_kind, l->new_coords);
    IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
    checkNULL(lRT);
    return lRT;
@@ -561,7 +580,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, DeformConv2d *l) {
    IPluginLayer *lRT = networkRT->addPlugin(inputs, 2, *plugin);
    checkNULL(lRT);
    lRT->setName( ("Deformable" + std::to_string(l->id)).c_str() );
-    delete(inputs);
+    delete[](inputs);
    // batchnorm
    void *bias_b, *power_b, *mean_b, *variance_b, *scales_b;
    if(dtRT == DataType::kHALF) {
@@ -638,43 +657,61 @@ bool NetworkRT::deserialize(const char *filename) {


 IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialData, size_t serialLength) {
-    const char * buf = reinterpret_cast<const char*>(serialData);
+    const char * buf = reinterpret_cast<const char*>(serialData),*bufCheck = buf;

    std::string name(layerName);
    //std::cout<<name<<std::endl;

    if(name.find("ActivationLeaky") == 0) {
-        ActivationLeakyRT *a = new ActivationLeakyRT();
+        ActivationLeakyRT *a = new ActivationLeakyRT(readBUF<float>(buf));
        a->size = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return a;
    }
    if(name.find("ActivationMish") == 0) {
        ActivationMishRT *a = new ActivationMishRT();
        a->size = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
+        return a;
+    }
+    if(name.find("ActivationLogistic") == 0) {
+        ActivationLogisticRT *a = new ActivationLogisticRT();
+        a->size = readBUF<int>(buf);
+        return a;
+    }
+    if(name.find("ActivationLogistic") == 0) {
+        ActivationLogisticRT *a = new ActivationLogisticRT();
+        a->size = readBUF<int>(buf);
        return a;
    }
    if(name.find("ActivationCReLU") == 0) {
-        ActivationReLUCeiling *a = new ActivationReLUCeiling(readBUF<float>(buf));
+        float activationReluTemp = readBUF<float>(buf);
+        ActivationReLUCeiling* a = new ActivationReLUCeiling(activationReluTemp);
        a->size = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return a;
    }

    if(name.find("Region") == 0) {
-        RegionRT *r = new RegionRT(readBUF<int>(buf),    //classes
-                                    readBUF<int>(buf),    //coords
-                                    readBUF<int>(buf));   //num
+        int classesTemp = readBUF<int>(buf);
+        int coordsTemp = readBUF<int>(buf);
+        int numTemp = readBUF<int>(buf);
+        RegionRT* r = new RegionRT(classesTemp, coordsTemp, numTemp);

        r->c = readBUF<int>(buf);
        r->h = readBUF<int>(buf);
        r->w = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

    if(name.find("Reorg") == 0) {
-        ReorgRT *r = new ReorgRT(readBUF<int>(buf)); //stride
+        int strideTemp = readBUF<int>(buf);
+        ReorgRT *r = new ReorgRT(strideTemp);
        r->c = readBUF<int>(buf);
        r->h = readBUF<int>(buf);
        r->w = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

@@ -685,32 +722,39 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
        bdim.w = readBUF<int>(buf);
        bdim.l = 1;

-        ShortcutRT *r = new ShortcutRT(bdim);
+        ShortcutRT *r = new ShortcutRT(bdim, readBUF<bool>(buf));
        r->c = readBUF<int>(buf);
        r->h = readBUF<int>(buf);
        r->w = readBUF<int>(buf);
        return r;
+        assert(buf == bufCheck + serialLength);
    } 

    if(name.find("Pooling") == 0) {
-        MaxPoolFixedSizeRT *r = new MaxPoolFixedSizeRT( readBUF<int>(buf), //c
-                                                        readBUF<int>(buf), //h
-                                                        readBUF<int>(buf), //w
-                                                        readBUF<int>(buf), //n
-                                                        readBUF<int>(buf), //strideH
-                                                        readBUF<int>(buf), //strideW
-                                                        readBUF<int>(buf), //winSize
-                                                        readBUF<int>(buf)); //padding
+        int cTemp = readBUF<int>(buf);
+        int hTemp = readBUF<int>(buf);
+        int wTemp = readBUF<int>(buf);
+        int nTemp = readBUF<int>(buf);
+        int strideHTemp = readBUF<int>(buf);
+        int strideWTemp = readBUF<int>(buf);
+        int winSizeTemp = readBUF<int>(buf);
+        int paddingTemp = readBUF<int>(buf);
+
+        MaxPoolFixedSizeRT* r = new MaxPoolFixedSizeRT(cTemp, hTemp, wTemp, nTemp, strideHTemp, strideWTemp, winSizeTemp, paddingTemp);
+        assert(buf == bufCheck + serialLength);
        return r;
    }

    if(name.find("Resize") == 0) {
-        ResizeLayerRT *r = new ResizeLayerRT(readBUF<int>(buf), //o_c
-                                            readBUF<int>(buf), //o_h
-                                            readBUF<int>(buf)); //o_w
+        int o_cTemp = readBUF<int>(buf);
+        int o_hTemp = readBUF<int>(buf);
+        int o_wTemp = readBUF<int>(buf);
+        ResizeLayerRT* r = new ResizeLayerRT(o_cTemp, o_hTemp, o_wTemp);
+
        r->i_c = readBUF<int>(buf);
        r->i_h = readBUF<int>(buf);
        r->i_w = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

@@ -721,6 +765,7 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
        r->w = readBUF<int>(buf);
        r->rows = readBUF<int>(buf);
        r->cols = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

@@ -732,19 +777,28 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
        new_dim.h = readBUF<int>(buf);
        new_dim.w = readBUF<int>(buf);
        ReshapeRT *r = new ReshapeRT(new_dim); 
+        assert(buf == bufCheck + serialLength);
        
        return r;
    } 

    if(name.find("Yolo") == 0) {
-        YoloRT *r = new YoloRT(readBUF<int>(buf),    //classes
-                                readBUF<int>(buf),   //num
-                                nullptr,
-                                readBUF<int>(buf));   //n_masks
+
+        int classes_temp = readBUF<int>(buf);
+        int num_temp = readBUF<int>(buf);
+        int n_masks_temp = readBUF<int>(buf);
+        float scale_xy_temp = readBUF<float>(buf);
+        float nms_thresh_temp = readBUF<float>(buf);
+        int nms_kind_temp = readBUF<int>(buf);
+        int new_coords_temp = readBUF<int>(buf);
+
+       YoloRT *r = new YoloRT(classes_temp,num_temp,nullptr,n_masks_temp,scale_xy_temp,nms_thresh_temp,nms_kind_temp,new_coords_temp);  
+
+
+
        r->c = readBUF<int>(buf);
        r->h = readBUF<int>(buf);
        r->w = readBUF<int>(buf);
-        r->scaleXY = readBUF<float>(buf);
        for(int i=0; i<r->n_masks; i++)
            r->mask[i] = readBUF<dnnType>(buf);
        for(int i=0; i<r->n_masks*2*r->num; i++)
@@ -758,36 +812,54 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
 				tmp[j] = readBUF<char>(buf);
            r->classesNames[i] = std::string(tmp);
 		}
+        assert(buf == bufCheck + serialLength);

        yolos[n_yolos++] = r;
        return r;
    } 
    if(name.find("Upsample") == 0) {
-        UpsampleRT *r = new UpsampleRT(readBUF<int>(buf)); //stride
+        int strideTemp = readBUF<int>(buf);
+        UpsampleRT* r = new UpsampleRT(strideTemp);
        r->c = readBUF<int>(buf);
        r->h = readBUF<int>(buf);
        r->w = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

    if(name.find("Route") == 0) {
-        RouteRT *r = new RouteRT(readBUF<int>(buf),readBUF<int>(buf));
+        int groupsTemp = readBUF<int>(buf);
+        int group_idTemp = readBUF<int>(buf);
+        RouteRT* r = new RouteRT(groupsTemp, group_idTemp);
        r->in = readBUF<int>(buf);
        for(int i=0; i<RouteRT::MAX_INPUTS; i++)
            r->c_in[i] = readBUF<int>(buf);
        r->c = readBUF<int>(buf);
        r->h = readBUF<int>(buf);
        r->w = readBUF<int>(buf);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

    if(name.find("Deformable") == 0) {
-        DeformableConvRT *r = new DeformableConvRT(readBUF<int>(buf), readBUF<int>(buf), readBUF<int>(buf),
-                                                    readBUF<int>(buf), readBUF<int>(buf), readBUF<int>(buf),
-                                                    readBUF<int>(buf), readBUF<int>(buf),
-                                                    readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),
-                                                    readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),
-                                                    nullptr); 
+        int chuck_dimTemp = readBUF<int>(buf);
+        int khTemp = readBUF<int>(buf);
+        int kwTemp = readBUF<int>(buf);
+        int shTemp = readBUF<int>(buf);
+        int swTemp = readBUF<int>(buf);
+        int phTemp = readBUF<int>(buf);
+        int pwTemp = readBUF<int>(buf);
+        int deformableGroupTemp = readBUF<int>(buf);
+        int i_nTemp = readBUF<int>(buf);
+        int i_cTemp = readBUF<int>(buf);
+        int i_hTemp = readBUF<int>(buf);
+        int i_wTemp = readBUF<int>(buf);
+        int o_nTemp = readBUF<int>(buf);
+        int o_cTemp = readBUF<int>(buf);
+        int o_hTemp = readBUF<int>(buf);
+        int o_wTemp = readBUF<int>(buf);
+
+        DeformableConvRT* r = new DeformableConvRT(chuck_dimTemp, khTemp, kwTemp, shTemp, swTemp, phTemp, pwTemp, deformableGroupTemp, i_nTemp, i_cTemp, i_hTemp, i_wTemp, o_nTemp, o_cTemp, o_hTemp, o_wTemp, nullptr);
        dnnType *aus = new dnnType[r->chunk_dim*2];
        for(int i=0; i<r->chunk_dim*2; i++)
    		aus[i] = readBUF<dnnType>(buf);
@@ -818,6 +890,7 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
    		aus[i] = readBUF<dnnType>(buf);
 		checkCuda( cudaMemcpy(r->ones_d2, aus, sizeof(dnnType)*r->dim_ones, cudaMemcpyHostToDevice) );
        free(aus);
+        assert(buf == bufCheck + serialLength);
        return r;
    } 

@@ -6,23 +6,389 @@

 namespace tk { namespace dnn {

-cv::Mat vizFloat2colorMap(cv::Mat map) {
+cv::Mat mapillary_15_map(cv::Mat adjMap){
+
+    // cv::imshow("test", adjMap);
+    // cv::waitKey(0);
+    cv::Mat M1(1, 256, CV_8UC1), M2(1, 256, CV_8UC1), M3(1, 256, CV_8UC1);
+
+    //animal
+    M3.at<uchar>(0)=165;
+    M2.at<uchar>(0)=42;
+    M1.at<uchar>(0)=45;
+
+    //curb
+    M3.at<uchar>(1)=196;
+    M2.at<uchar>(1)=196;
+    M1.at<uchar>(1)=196;
+
+    //barrier
+    M3.at<uchar>(2)=90;
+    M2.at<uchar>(2)=120;
+    M1.at<uchar>(2)=150;
+
+    //road
+    M3.at<uchar>(3)=128;
+    M2.at<uchar>(3)=64;
+    M1.at<uchar>(3)=128;
+
+    //building
+    M3.at<uchar>(4)=70;
+    M2.at<uchar>(4)=70;
+    M1.at<uchar>(4)=70;
+
+    //person
+    M3.at<uchar>(5)=220;
+    M2.at<uchar>(5)=20;
+    M1.at<uchar>(5)=60;
+
+    //roadmark
+    M3.at<uchar>(6)=255;
+    M2.at<uchar>(6)=255;
+    M1.at<uchar>(6)=255;
+
+    //nature
+    M3.at<uchar>(7)=107;
+    M2.at<uchar>(7)=142;
+    M1.at<uchar>(7)=35;
+
+    //sky
+    M3.at<uchar>(8)=70;
+    M2.at<uchar>(8)=130;
+    M1.at<uchar>(8)=180;
+
+    //billboard
+    M3.at<uchar>(9)=220;
+    M2.at<uchar>(9)=220;
+    M1.at<uchar>(9)=220;
+
+    //pole
+    M3.at<uchar>(10)=153;
+    M2.at<uchar>(10)=153;
+    M1.at<uchar>(10)=153;
+
+    //traffic sign
+    M3.at<uchar>(11)=128;
+    M2.at<uchar>(11)=128;
+    M1.at<uchar>(11)=128;
+
+    //bike
+    M3.at<uchar>(12)=119;
+    M2.at<uchar>(12)=11;
+    M1.at<uchar>(12)=32;
+
+    //vehicle
+    M3.at<uchar>(13)=0;
+    M2.at<uchar>(13)=0;
+    M1.at<uchar>(13)=142;
+
+    //void
+    for(int i=14;i<256;i++)
+    {
+        M1.at<uchar>(i)=0;
+        M2.at<uchar>(i)=0;
+        M3.at<uchar>(i)=0;
+    }
+
+    cv::Mat r1,r2,r3;
+
+    cv::LUT(adjMap,M1,r1);
+    cv::LUT(adjMap,M2,r2);
+    cv::LUT(adjMap,M3,r3);
+
+    std::vector<cv::Mat> planes;
+    planes.push_back(r1);
+    planes.push_back(r2);
+    planes.push_back(r3);
+
+    cv::Mat dst;
+    cv::merge(planes,dst);
+    return dst;
+
+
+}
+
+cv::Mat berkeley_20_map(cv::Mat adjMap){
+
+    cv::Mat M1(1, 256, CV_8UC1), M2(1, 256, CV_8UC1), M3(1, 256, CV_8UC1);
+
+    //road
+    M3.at<uchar>(0)=128;
+    M2.at<uchar>(0)=64;
+    M1.at<uchar>(0)=128;
+
+    //sidewalk
+    M3.at<uchar>(1)=244;
+    M2.at<uchar>(1)=35;
+    M1.at<uchar>(1)=232;
+
+    //building
+    M3.at<uchar>(2)=70;
+    M2.at<uchar>(2)=70;
+    M1.at<uchar>(2)=70;
+
+    //wall
+    M3.at<uchar>(3)=102;
+    M2.at<uchar>(3)=102;
+    M1.at<uchar>(3)=156;
+
+    //fence
+    M3.at<uchar>(4)=90;
+    M2.at<uchar>(4)=120;
+    M1.at<uchar>(4)=150;
+
+    //pole
+    M3.at<uchar>(5)=153;
+    M2.at<uchar>(5)=153;
+    M1.at<uchar>(5)=153;
+
+    //traffic light
+    M3.at<uchar>(6)=250;
+    M2.at<uchar>(6)=170;
+    M1.at<uchar>(6)=30;
+
+    //traffic sign
+    M3.at<uchar>(7)=128;
+    M2.at<uchar>(7)=128;
+    M1.at<uchar>(7)=128;
+
+    //nature
+    M3.at<uchar>(8)=107;
+    M2.at<uchar>(8)=142;
+    M1.at<uchar>(8)=35;
+
+    //ground
+    M3.at<uchar>(9)=0;
+    M2.at<uchar>(9)=192;
+    M1.at<uchar>(9)=0;
+
+    //sky
+    M3.at<uchar>(10)=70;
+    M2.at<uchar>(10)=130;
+    M1.at<uchar>(10)=180;
+
+    //person
+    M3.at<uchar>(11)=220;
+    M2.at<uchar>(11)=20;
+    M1.at<uchar>(11)=60;
+
+    //rider
+    M3.at<uchar>(12)=255;
+    M2.at<uchar>(12)=0;
+    M1.at<uchar>(12)=100;
+
+    //car
+    M3.at<uchar>(13)=0;
+    M2.at<uchar>(13)=0;
+    M1.at<uchar>(13)=142;
+
+    //truck
+    M3.at<uchar>(14)=0;
+    M2.at<uchar>(14)=0;
+    M1.at<uchar>(14)=70;
+
+    //bus
+    M3.at<uchar>(15)=0;
+    M2.at<uchar>(15)=60;
+    M1.at<uchar>(15)=100;
+
+    //train
+    M3.at<uchar>(16)=0;
+    M2.at<uchar>(16)=0;
+    M1.at<uchar>(16)=192;
+
+    //motorbike
+    M3.at<uchar>(17)=0;
+    M2.at<uchar>(17)=0;
+    M1.at<uchar>(17)=230;
+
+    //bike
+    M3.at<uchar>(18)=119;
+    M2.at<uchar>(18)=11;
+    M1.at<uchar>(18)=32;
+
+    //void
+    for(int i=19;i<256;i++)
+    {
+        M1.at<uchar>(i)=0;
+        M2.at<uchar>(i)=0;
+        M3.at<uchar>(i)=0;
+    }
+
+    cv::Mat r1,r2,r3;
+
+    cv::LUT(adjMap,M1,r1);
+    cv::LUT(adjMap,M2,r2);
+    cv::LUT(adjMap,M3,r3);
+
+    std::vector<cv::Mat> planes;
+    planes.push_back(r1);
+    planes.push_back(r2);
+    planes.push_back(r3);
+
+    cv::Mat dst;
+    cv::merge(planes,dst);
+    return dst;
+
+}
+
+cv::Mat cityscapes_19_map(cv::Mat adjMap){
+
+    cv::Mat M1(1, 256, CV_8UC1), M2(1, 256, CV_8UC1), M3(1, 256, CV_8UC1);
+
+    //road
+    M3.at<uchar>(0)=128;
+    M2.at<uchar>(0)=64;
+    M1.at<uchar>(0)=128;
+
+    //sidewalk
+    M3.at<uchar>(1)=244;
+    M2.at<uchar>(1)=35;
+    M1.at<uchar>(1)=232;
+
+    //building
+    M3.at<uchar>(2)=70;
+    M2.at<uchar>(2)=70;
+    M1.at<uchar>(2)=70;
+
+    //wall
+    M3.at<uchar>(3)=102;
+    M2.at<uchar>(3)=102;
+    M1.at<uchar>(3)=156;
+
+    //fence
+    M3.at<uchar>(4)=190;
+    M2.at<uchar>(4)=153;
+    M1.at<uchar>(4)=153;
+
+    //pole
+    M3.at<uchar>(5)=153;
+    M2.at<uchar>(5)=153;
+    M1.at<uchar>(5)=153;
+
+    //traffic light
+    M3.at<uchar>(6)=250;
+    M2.at<uchar>(6)=170;
+    M1.at<uchar>(6)=30;
+
+    //traffic sign
+    M3.at<uchar>(7)=220;
+    M2.at<uchar>(7)=220;
+    M1.at<uchar>(7)=0;
+
+    //vegetation
+    M3.at<uchar>(8)=107;
+    M2.at<uchar>(8)=142;
+    M1.at<uchar>(8)=35;
+
+    //terrain
+    M3.at<uchar>(9)=152;
+    M2.at<uchar>(9)=251;
+    M1.at<uchar>(9)=152;
+
+    //sky
+    M3.at<uchar>(10)=70;
+    M2.at<uchar>(10)=130;
+    M1.at<uchar>(10)=180;
+
+    //person
+    M3.at<uchar>(11)=220;
+    M2.at<uchar>(11)=20;
+    M1.at<uchar>(11)=60;
+
+    //rider
+    M3.at<uchar>(12)=255;
+    M2.at<uchar>(12)=0;
+    M1.at<uchar>(12)=0;
+
+    //car
+    M3.at<uchar>(13)=0;
+    M2.at<uchar>(13)=0;
+    M1.at<uchar>(13)=142;
+
+    //truck
+    M3.at<uchar>(14)=0;
+    M2.at<uchar>(14)=0;
+    M1.at<uchar>(14)=70;
+
+    //bus
+    M3.at<uchar>(15)=0;
+    M2.at<uchar>(15)=60;
+    M1.at<uchar>(15)=100;
+
+    //train
+    M3.at<uchar>(16)=0;
+    M2.at<uchar>(16)=80;
+    M1.at<uchar>(16)=100;
+
+    //motorcycle
+    M3.at<uchar>(17)=0;
+    M2.at<uchar>(17)=0;
+    M1.at<uchar>(17)=230;
+
+    //bicycle
+    M3.at<uchar>(18)=119;
+    M2.at<uchar>(18)=11;
+    M1.at<uchar>(18)=32;
+
+    //void
+    for(int i=19;i<256;i++)
+    {
+        M1.at<uchar>(i)=0;
+        M2.at<uchar>(i)=0;
+        M3.at<uchar>(i)=0;
+    }
+
+    cv::Mat r1,r2,r3;
+
+    cv::LUT(adjMap,M1,r1);
+    cv::LUT(adjMap,M2,r2);
+    cv::LUT(adjMap,M3,r3);
+
+    std::vector<cv::Mat> planes;
+    planes.push_back(r1);
+    planes.push_back(r2);
+    planes.push_back(r3);
+
+    cv::Mat dst;
+    cv::merge(planes,dst);
+    return dst;
+
+}
+
+
+cv::Mat vizFloat2colorMap(cv::Mat map,double min, double max, int classes) {
+
+    if(min == 0 && max == 0)
+        cv::minMaxIdx(map, &min, &max);

-    double min;
-    double max;
-    cv::minMaxIdx(map, &min, &max);
    cv::Mat adjMap;
-    // expand your range to 0..255. Similar to histEq();
-    map.convertTo(adjMap,CV_8UC1, 255 / (max-min), -min); 
-    //return adjMap;
-
-    
    cv::Mat falseColorsMap;
-    applyColorMap(adjMap, falseColorsMap, cv::COLORMAP_HOT);
+    
+    switch (classes)
+    {
+    case 15:
+        map.convertTo(adjMap,CV_8UC1);
+        falseColorsMap = mapillary_15_map(adjMap);
+        break;
+    case 20:
+        map.convertTo(adjMap,CV_8UC1);
+        falseColorsMap = berkeley_20_map(adjMap);
+        break;
+    case 19:
+        map.convertTo(adjMap,CV_8UC1);
+        falseColorsMap = cityscapes_19_map(adjMap);
+        break;
+    
+    default:
+        // expand your range to 0..255. Similar to histEq();
+        map.convertTo(adjMap,CV_8UC1, 255 / (max-min), -min); 
+        applyColorMap(adjMap, falseColorsMap, cv::COLORMAP_JET);
+    }
    return falseColorsMap;
 }

-cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim) {
+cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int img_h, int img_w, double min, double max, int classes) {
    dnnType *data = nullptr;

    // copy to CPU
@@ -38,14 +404,13 @@ cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim) {
    cv::Mat grid = cv::Mat(gridSize, CV_8UC3, cv::Scalar(0));    

    for(int i=0; i<dim.c;i++) {
-        cv::Mat raw = vizFloat2colorMap(cv::Mat(cv::Size(dim.w, dim.h),CV_32FC1, data + dim.w*dim.h*i));
+        cv::Mat raw = vizFloat2colorMap(cv::Mat(cv::Size(dim.w, dim.h),CV_32FC1, data + dim.w*dim.h*i), min, max, classes);
        int r = i / gridDim;
        int c = i - r * gridDim;
        raw.copyTo(grid.rowRange(r*dim.h, r*dim.h + dim.h).colRange(c*dim.w, c*dim.w + dim.w));
    }

-    float ar = float(dim.w)/dim.h;
-    cv::Size vdim(ar*imgdim, imgdim);
+    cv::Size vdim(img_w, img_h);
    cv::Mat viz;
    cv::resize(grid, viz, vdim, 0, 0, 0);
    
@@ -59,7 +424,7 @@ cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim) {
 cv::Mat vizLayer2Mat(tk::dnn::Network *net, int layer, int imgdim) {
    if(layer >= net->num_layers)
        FatalError("Could not viz layer\n");
-    return vizData2Mat(net->layers[layer]->dstData, net->layers[layer]->output_dim, imgdim);
+    return vizData2Mat(net->layers[layer]->dstData, net->layers[layer]->output_dim, imgdim, imgdim);

    //cv::imwrite("viz/layer" + std::to_string(layer) + ".png", viz);
    //cv::imshow("layer", viz);
@@ -63,7 +63,7 @@ dnnType* Region::infer(dataDim_t &dim, dnnType* srcData) {
 }


-/* Intepret class */
+/* Interpret class */
 RegionInterpret::RegionInterpret(dataDim_t input_dim, dataDim_t output_dim, 
    int classes, int coords, int num, float thresh, std::string fname_weights) {

@@ -15,6 +15,11 @@ Reshape::Reshape(Network *net, dataDim_t new_dim) : Layer(net) {
    output_dim.w = new_dim.w;
    output_dim.l = new_dim.l;

+    output_dim = new_dim;
+
+    if(input_dim.tot() != output_dim.tot())
+        FatalError("Reshape dimension mismatch");
+
 }

 Reshape::~Reshape() {
@@ -0,0 +1,39 @@
+#include <iostream>
+
+#include "Layer.h"
+#include "kernels.h"
+
+namespace tk { namespace dnn {
+
+Resize::Resize(Network *net, int scale_c, int scale_h, int scale_w, bool fixed, ResizeMode_t mode) : Layer(net) {
+
+    this->mode = mode;
+    if(fixed){
+        output_dim.c = scale_c;
+        output_dim.h = scale_h;
+        output_dim.w = scale_w;
+    }
+    else{
+        output_dim.c *= scale_c;
+        output_dim.h *= scale_h;
+        output_dim.w *= scale_w;
+    }
+
+    checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
+}
+
+Resize::~Resize() {
+
+    checkCuda( cudaFree(dstData) );
+}
+
+dnnType* Resize::infer(dataDim_t &dim, dnnType* srcData) {
+
+    resizeForward(srcData, dstData, dim.n, dim.c, dim.h, dim.w,
+                   output_dim.c, output_dim.h, output_dim.w);
+    dim = output_dim;
+
+    return dstData;
+}
+
+}}
@@ -5,15 +5,16 @@

 namespace tk { namespace dnn {

-Shortcut::Shortcut(Network *net, Layer *backLayer) : Layer(net) {
+Shortcut::Shortcut(Network *net, Layer *backLayer, bool mul) : Layer(net) {

    this->backLayer = backLayer;
+    this->mul = mul;
    checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );

-    if( /*backLayer->output_dim.c != input_dim.c ||*/
-        backLayer->output_dim.w != input_dim.w ||
-        backLayer->output_dim.h != input_dim.h   )
+    if( ( backLayer->output_dim.c != input_dim.c && mul ) ||
+        (( backLayer->output_dim.w != input_dim.w ||  backLayer->output_dim.h != input_dim.h ) && !mul ) )  
        FatalError("Shortcut dim missmatch");
+    
 }

 Shortcut::~Shortcut() {
@@ -26,7 +27,7 @@ dnnType* Shortcut::infer(dataDim_t &dim, dnnType* srcData) {
    dataDim_t bdim = this->backLayer->output_dim;

    checkCuda(cudaMemcpy(dstData, srcData, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
-    shortcutForward(this->backLayer->dstData, dstData, dim.n, dim.c, dim.h, dim.w, 1, bdim.n, bdim.c, bdim.h, bdim.w, 1);
+    shortcutForward(this->backLayer->dstData, dstData, dim.n, dim.c, dim.h, dim.w, 1, bdim.n, bdim.c, bdim.h, bdim.w, 1, mul);

    //update data dimensions    
    dim = output_dim;
@@ -9,9 +9,10 @@
 #include "Layer.h"
 #include "kernels.h"

+
 namespace tk { namespace dnn {

-Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_masks, float scale_xy) : 
+Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_masks, float scale_xy, double nms_thresh, nmsKind_t nsm_kind, int new_coords) : 
    Layer(net) {
    this->final = true;

@@ -19,6 +20,9 @@ Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_
    this->num = num;
    this->n_masks = n_masks;
    this->scaleXY = scale_xy;
+    this->nms_thresh = nms_thresh;
+    this->nsm_kind = nsm_kind;
+    this->new_coords = new_coords;

    // load anchors
    if(fname_weights != "") {
@@ -59,12 +63,21 @@ int entry_index(int batch, int location, int entry,
           entry*input_dim.w*input_dim.h + loc;
 }

-Yolo::box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) {
+Yolo::box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, int new_coords) {
    Yolo::box b;
-    b.x = (i + x[index + 0*stride]) / lw;
-    b.y = (j + x[index + 1*stride]) / lh;
-    b.w = exp(x[index + 2*stride]) * biases[2*n]   / w;
-    b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
+
+    if(new_coords == 0){
+        b.x = (i + x[index + 0*stride]) / lw;
+        b.y = (j + x[index + 1*stride]) / lh;
+        b.w = exp(x[index + 2*stride]) * biases[2*n]   / w;
+        b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
+    }
+    else{
+        b.x = (i + x[index + 0 * stride] ) / lw;
+        b.y = (j + x[index + 1 * stride] ) / lh;
+        b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w;
+        b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h;
+    }
    return b;
 }

@@ -75,12 +88,16 @@ dnnType* Yolo::infer(dataDim_t &dim, dnnType* srcData) {
    for (int b = 0; b < dim.n; ++b){
        for(int n = 0; n < n_masks; ++n){
            int index = entry_index(b, n*dim.w*dim.h, 0, classes, input_dim, output_dim);
-            activationLOGISTICForward(srcData + index, dstData + index, 2*dim.w*dim.h);
+            if (new_coords == 1){
+                if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
+            }
+            else{
+                activationLOGISTICForward(srcData + index, dstData + index, 2*dim.w*dim.h);

-            if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
-            
-            index = entry_index(b, n*dim.w*dim.h, 4, classes, input_dim, output_dim);
-            activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*dim.w*dim.h);
+                if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
+                index = entry_index(b, n*dim.w*dim.h, 4, classes, input_dim, output_dim);
+                activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*dim.w*dim.h);
+            }
        }
    }

@@ -116,7 +133,7 @@ void correct_yolo_boxes(Yolo::detection *dets, int n, int w, int h, int netw, in
    }
 }

-int Yolo::computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh) {
+int Yolo::computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh, int new_coords) {

    if(predictions == nullptr)
        predictions = new dnnType[output_dim.tot()];
@@ -140,7 +157,7 @@ int Yolo::computeDetections(Yolo::detection *dets, int &ndets, int netw, int net
            if(objectness <= thresh) continue;
            int box_index  = entry_index(0, n*lw*lh + i, 0, classes, input_dim, output_dim);
            
-            dets[count].bbox = get_yolo_box(predictions, bias_h, mask_h[n], box_index, col, row, lw, lh, netw, neth, lw*lh);
+            dets[count].bbox = get_yolo_box(predictions, bias_h, mask_h[n], box_index, col, row, lw, lh, netw, neth, lw*lh, new_coords);
            dets[count].objectness = objectness;
            dets[count].classes = classes;
            for(j = 0; j < classes; ++j){
@@ -193,6 +210,32 @@ float yolo_box_iou(Yolo::box a, Yolo::box b)
    return yolo_box_intersection(a, b)/yolo_box_union(a, b);
 }

+void box_c(const Yolo::box a, const Yolo::box b, float& top, float& bot, float& left, float& right) {
+    top = (std::min)(a.y - a.h / 2, b.y - b.h / 2);
+    bot = (std::max)(a.y + a.h / 2, b.y + b.h / 2);
+    left = (std::min)(a.x - a.w / 2, b.x - b.w / 2);
+    right = (std::max)(a.x + a.w / 2, b.x + b.w / 2);
+}
+
+// https://github.com/Zzh-tju/DIoU-darknet
+// https://arxiv.org/abs/1911.08287
+float yolo_box_diou(const Yolo::box a, const Yolo::box b, const float nms_thresh=0.6)
+{
+    float top, bot, left, right;
+    box_c(a, b, top, bot, left, right);
+    float w = right - left;
+    float h = bot - top;
+    float c = w * w + h * h;
+    float iou = yolo_box_iou(a, b);
+    if (c == 0) 
+        return iou;
+    
+    float d = (a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y);
+    float u = pow(d / c, nms_thresh);
+    float diou_term = u;
+    return iou - diou_term;
+}
+
 int yolo_nms_comparator(const void *pa, const void *pb)
 {
    Yolo::detection a = *(Yolo::detection *)pa;
@@ -219,8 +262,7 @@ Yolo::detection *Yolo::allocateDetections(int nboxes, int classes) {
    return dets;
 }

-void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes) {
-    double nms_thresh = 0.45;
+void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes, double nms_thresh, nmsKind_t nsm_kind) {
    int total = ndets;

    int i, j, k;
@@ -236,6 +278,7 @@ void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes) {
    }
    total = k+1;

+    float thresh = 0.45f;
    for(k = 0; k < classes; ++k){
        for(i = 0; i < total; ++i){
            dets[i].sort_class = k;
@@ -246,13 +289,13 @@ void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes) {
            box a = dets[i].bbox;
            for(j = i+1; j < total; ++j){
                box b = dets[j].bbox;
-                if (yolo_box_iou(a, b) > nms_thresh){
+                if (nsm_kind == GREEDY_NMS && yolo_box_iou(a, b) > thresh)
+                    dets[j].prob[k] = 0;
+                else if (nsm_kind == DIOU_NMS && yolo_box_diou(a, b, nms_thresh) > thresh)
                    dets[j].prob[k] = 0;
-                }
            }
        }
    }
-
 }

 }}
@@ -3,13 +3,14 @@

 namespace tk { namespace dnn {

-bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes, const int n_batches) {
+bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes, const int n_batches, const float conf_thresh) {

    //convert network to tensorRT
    std::cout<<(tensor_path).c_str()<<"\n";
    netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );

    nBatches = n_batches;
+    confThreshold = conf_thresh;
    tk::dnn::dataDim_t idim = netRT->input_dim;    
    idim.n = nBatches;

@@ -31,6 +32,9 @@ bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes, c
        memcpy(yolo[i]->bias_h, yRT->bias, sizeof(dnnType)*num*nMasks*2);
        yolo[i]->input_dim = yolo[i]->output_dim = tk::dnn::dataDim_t(1, yRT->c, yRT->h, yRT->w);
        yolo[i]->classesNames = yRT->classesNames;
+        yolo[i]->nms_thresh = yRT->nms_thresh;
+        yolo[i]->nsm_kind = (tk::dnn::Yolo::nmsKind_t) yRT->nms_kind;
+        yolo[i]->new_coords = yRT->new_coords;
    }

    dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
@@ -90,9 +94,10 @@ void Yolo3Detection::preprocess(cv::Mat &frame, const int bi){
 void Yolo3Detection::postprocess(const int bi, const bool mAP){

    //get yolo outputs
-    dnnType *rt_out[netRT->pluginFactory->n_yolos]; 
-    for(int i=0; i<netRT->pluginFactory->n_yolos; i++) 
-        rt_out[i] = (dnnType*)netRT->buffersRT[i+1] + netRT->buffersDIM[i+1].tot()*bi;
+    std::vector<float *> rt_out;
+    //dnnType *rt_out[netRT->pluginFactory->n_yolos];
+    for(int i=0; i<netRT->pluginFactory->n_yolos; i++)
+        rt_out.push_back((dnnType*)netRT->buffersRT[i+1] + netRT->buffersDIM[i+1].tot()*bi);

    float x_ratio =  float(originalSize[bi].width) / float(netRT->input_dim.w);
    float y_ratio =  float(originalSize[bi].height) / float(netRT->input_dim.h);
@@ -101,18 +106,18 @@ void Yolo3Detection::postprocess(const int bi, const bool mAP){
    nDets = 0;
    for(int i=0; i<netRT->pluginFactory->n_yolos; i++) {
        yolo[i]->dstData = rt_out[i];
-        yolo[i]->computeDetections(dets, nDets, netRT->input_dim.w, netRT->input_dim.h, confThreshold);
+        yolo[i]->computeDetections(dets, nDets, netRT->input_dim.w, netRT->input_dim.h, confThreshold, yolo[i]->new_coords);
    }
-    tk::dnn::Yolo::mergeDetections(dets, nDets, classes);
+    tk::dnn::Yolo::mergeDetections(dets, nDets, classes, yolo[0]->nms_thresh, yolo[0]->nsm_kind);

    // fill detected
    detected.clear();
    for(int j=0; j<nDets; j++) {
        tk::dnn::Yolo::box b = dets[j].bbox;
-        int x0   = (b.x-b.w/2.);
-        int x1   = (b.x+b.w/2.);
-        int y0   = (b.y-b.h/2.);
-        int y1   = (b.y+b.h/2.);
+        float x0   = (b.x-b.w/2.);
+        float x1   = (b.x+b.w/2.);
+        float y0   = (b.y-b.h/2.);
+        float y1   = (b.y+b.h/2.);

        // convert to image coords
        x0 = x_ratio*x0;
@@ -0,0 +1,19 @@
+
+#include "demo_utils.h"
+
+void readCalibrationMatrix(const std::string& path, cv::Mat& calib_mat){
+    YAML::Node config   = YAML::LoadFile(path);
+
+    //read camera matrix
+    int rows = config["camera_matrix"]["rows"].as<int>();
+    int cols = config["camera_matrix"]["cols"].as<int>();
+
+    cv::Mat calib = cv::Mat(cv::Size(rows, cols), CV_32F);
+    float *vals = (float *)calib.data;
+    
+    for(int i=0; i < config["camera_matrix"]["data"].size(); ++i )
+        vals[i] = config["camera_matrix"]["data"][i].as<float>();
+
+    calib_mat = calib;
+
+}
@@ -63,7 +63,7 @@ double computeMap(  std::vector<Frame> &images,const int classes,
    
    int gt_checked = 0;

-    // for each detection comput IoU with groundtruth and match detetcion and 
+    // for each detection compute IoU with groundtruth and match detetcion and 
    // groundtruth with IoU greater than IoU_thresh
    for(auto &img:images){
        for(size_t i=0; i<img.det.size(); i++){
@@ -153,7 +153,7 @@ double computeMap(  std::vector<Frame> &images,const int classes,
        }
    }

-    //compute average precision for each class. Two methods are avaible, 
+    //compute average precision for each class. Two methods are available, 
    //based on map_points required
    double mean_average_precision = 0;
    double last_recall, last_precision, delta_recall;
@@ -287,7 +287,7 @@ void computeTPFPFN( std::vector<Frame> &images,const int classes,
        }
    }

-    //count all TP, FP, FN and compute precsion, recall and f1-score
+    //count all TP, FP, FN and compute precision, recall and f1-score
    double avg_precision = 0, avg_recall = 0, f1_score = 0;
    int TP = 0, FP = 0, FN = 0;
    for(size_t i=0; i<classes; i++){
@@ -1,7 +1,7 @@
 #include "kernels.h"

 __global__
-void activation_leaky(dnnType *input, dnnType *output, int size) {
+void activation_leaky(dnnType *input, dnnType *output, int size, float slope) {

    int i = blockDim.x*blockIdx.x + threadIdx.x;

@@ -9,7 +9,7 @@ void activation_leaky(dnnType *input, dnnType *output, int size) {
        if (input[i]>0)
            output[i] = input[i];
        else
-            output[i] = 0.1f*input[i];
+            output[i] = slope*input[i];
    }
 }

@@ -17,12 +17,12 @@ void activation_leaky(dnnType *input, dnnType *output, int size) {
 /**
    ELU activation function
 */
-void activationLEAKYForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream)
+void activationLEAKYForward(dnnType* srcData, dnnType* dstData, int size, float slope, cudaStream_t stream)
 {
    int blocks = (size+255)/256;
    int threads = 256;
    
-    activation_leaky<<<blocks, threads, 0, stream>>>(srcData, dstData, size);
+    activation_leaky<<<blocks, threads, 0, stream>>>(srcData, dstData, size, slope);
 }


@@ -18,7 +18,7 @@ inline int GET_BLOCKS(const int N)
 }


-__device__ float dmcn_im2col_bilinear(const float *bottom_data, const int data_width,
+__device__ __host__  float dmcn_im2col_bilinear(const float *bottom_data, const int data_width,
  const int height, const int width, float h, float w) {
 int h_low = floor(h);
 int w_low = floor(w);
@@ -1,5 +1,11 @@
 #include "kernelsThrust.h"

+void transformDep(float *src_begin, float *src_end, float *dst_begin, float *dst_end) {
+    int e = exp(-6);
+    thrust::transform(thrust::device, dst_begin, dst_end, thrust::make_constant_iterator(e), dst_begin, thrust::plus<float>());
+    thrust::transform(thrust::device, src_begin, src_end, dst_begin, dst_begin, thrust::divides<float>());
+    thrust::transform(thrust::device, dst_begin, dst_end, thrust::make_constant_iterator(-1.0), dst_begin, thrust::plus<float>());
+}

 void subtractWithThreshold(dnnType *src_begin, dnnType *src_end, dnnType *src2_begin, dnnType *src_out, struct threshold op){
    thrust::transform(thrust::device, src_begin, src_end, src2_begin, src_out, op);
@@ -34,6 +40,30 @@ void sortAndTopKonDevice(dnnType *src_begin, int *idsrc, float *topk_scores, int
    sortAndTopK_kernel<<<blocks, threads, 0>>>(src_begin, idsrc, topk_scores, topk_inds, topk_ys, topk_xs, size, K);   
 }

+__global__ 
+void maxElem_kernel(float *src_begin, float *dst_begin, const int n_classes, const int size){
+    int i = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i > size)
+        return;
+
+    float max = 0;
+    int max_idx = 0;
+    for( int j = i*n_classes; j < i*n_classes + n_classes; ++j ){
+        if( src_begin[j] > max ){
+            max = src_begin[j];
+            max_idx = j;
+        }
+    }
+
+    dst_begin[i] = max_idx - i*n_classes;
+}
+
+void maxElem(dnnType *src_begin, dnnType *dst_begin, const int c, const int h, const int w){
+    int blocks = (h*w)/32+1;
+    int threads = 32;
+    maxElem_kernel<<<blocks, threads, 0>>>(src_begin, dst_begin, c, h*w);   
+}
+
 void topKxyclasses(int *ids_begin, int *ids_end, const int K, const int size, const int wh, int *clses, int *xs, int *ys){    
    thrust::transform(thrust::device, ids_begin, ids_end, thrust::make_constant_iterator(wh), clses, thrust::divides<int>());
    thrust::transform(thrust::device, ids_begin, ids_end, thrust::make_constant_iterator(wh), ids_begin, thrust::modulus<int>());
@@ -51,6 +81,14 @@ void topKxyAddOffset(int * ids_begin, const int K, const int size,
    thrust::transform(thrust::device, intys_begin, intys_begin + K, src_out, ys_begin, thrust::plus<float>());
 }

+void getRecordsFromTopKId(int * ids_begin, const int K, const int ch, const int size, dnnType *src_begin, float *src_out, int *ids_out) {
+    for(int i=0; i<ch; i++) {
+        // thrust::gather(thrust::device, ids_begin, ids_begin + K, src_begin, src_out);
+        thrust::transform(thrust::device, ids_begin, ids_begin + K, thrust::make_constant_iterator(i*size), ids_out, thrust::plus<int>());
+        thrust::gather(thrust::device, ids_out, ids_out + K, src_begin, src_out+i*K);
+    }
+}
+
 void bboxes(int * ids_begin, const int K, const int size, float *xs_begin, float *ys_begin, 
            dnnType *src_begin, float *bbx0, float *bbx1, float *bby0, float *bby1,
            float *src_out, int *ids_out){ 
@@ -1,46 +1,33 @@
 #include "kernels.h"
 #include <stdio.h>
-#define MIN(a,b) (((a)<(b))?(a):(b))
-#define MAX(a,b) (((a)>(b))?(a):(b))

-__global__ void resize_kernel(  int i_N,float *x, int i_w, int i_h, int i_c,  
+__global__ void resize_kernel(  int size,float *x, int i_w, int i_h, int i_c,  
                                int o_w, int o_h, int o_c, int batch, float *out)
 {
-    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
-    if(i >= i_N) return;
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= size) return;

-    int out_index = i;
-    int out_w = i%o_w;
-    i = i/o_w;
-    int out_h = i%o_h;
-    i = i/o_h;
-    int out_c = i%o_c;
-    i = i/o_c;
+    int i = id % o_w;
+    id /= o_w;
+    int j = id % o_h;
+    id /= o_h;
+    int k = id % o_c;
+    id /= o_c;
+    int b = id % batch;

-    //copying last column/last row as padding
-    int in_index = ((i*i_c + MIN(out_c,i_c-1))*i_h +  MIN(out_h,i_h-1))*i_w + MIN(out_w, i_w-1);
-    out[out_index] = x[in_index];
+    int out_index = i + o_w*(j + o_h*(k + o_c*b));
+    int add_index = i/(o_w/i_w) + i_w*(j/(o_h/i_h) + i_h*(k + i_c*b));
+    out[out_index] = x[add_index];
 }


 void resizeForward(  dnnType* srcData, dnnType* dstData, int n, int i_c, int i_h, int i_w,
     int o_c, int o_h, int o_w, cudaStream_t stream )
 {
-    int i_size = n*i_c*i_h*i_w;
    int o_size = n*o_c*o_h*o_w;

    int blocks = (o_size+255)/256;
    int threads = 256;

-    if(i_c == o_c && i_h == o_h && i_w == o_w )
-    {
-        checkCuda(cudaMemcpy(dstData, srcData, i_size*sizeof(dnnType), cudaMemcpyDeviceToDevice));
-    }
-    else
-    {
-        checkCuda(cudaMemset(dstData, 0, o_size*sizeof(dnnType)));
-        resize_kernel<<<blocks, threads, 0, stream>>>(o_size, srcData, i_w, i_h, i_c, o_w, o_h, o_c, n, dstData);
-        // printDeviceVector(i_size, srcData);
-        // printDeviceVector(o_size, dstData);
-    }
+    resize_kernel<<<blocks, threads, 0, stream>>>(o_size, srcData, i_w, i_h, i_c, o_w, o_h, o_c, n, dstData);
 }
@@ -21,27 +21,60 @@ __global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stri
    //out[out_index] += add[add_index];
 }

+__global__ void shortcut_mul_kernel(int size, int minw, int minh, int minc, int sample, int batch, 
+                                int w1, int h1, int c1, dnnType *mul, 
+                                int w2, int h2, int c2, float s1, float s2, dnnType *out)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (id >= size) return;
+    int i = id % minw;
+    id /= minw;
+    int j = id % minh;
+    id /= minh;
+    int k = id % minc;
+    id /= minc;
+    int b = id % batch;
+
+    int out_index = i*sample + w1*(j*sample + h1*(k + c1*b));
+    out[out_index] = out[out_index] * mul[k + c2*b];
+}
+
 void shortcutForward(dnnType* srcData, dnnType* dstData, int n1, int c1, int h1, int w1, int s1,
                                                         int n2, int c2, int h2, int w2, int s2, 
-                     cudaStream_t stream)
+                                                         bool mul, cudaStream_t stream)
 {
    assert(n1 == n2);
    int batch = n1;

-    int minw = (w1 < w2) ? w1 : w2;
-    int minh = (h1 < h2) ? h1 : h2;
-    int minc = (c1 < c2) ? c1 : c2;
+    if(!mul){
+        int minw = (w1 < w2) ? w1 : w2;
+        int minh = (h1 < h2) ? h1 : h2;
+        int minc = (c1 < c2) ? c1 : c2;
+        int stride = w1/w2;
+        int sample = w2/w1;
+        assert(stride == h1/h2);
+        assert(sample == h2/h1);
+        if(stride < 1) stride = 1;
+        if(sample < 1) sample = 1;

-    int stride = w1/w2;
-    int sample = w2/w1;
-    assert(stride == h1/h2);
-    assert(sample == h2/h1);
-    if(stride < 1) stride = 1;
-    if(sample < 1) sample = 1;
+        int size = batch * minw * minh * minc;
+        int blocks = (size+255)/256;
+        int threads = 256;
+        
+        shortcut_kernel<<<blocks, threads, 0, stream>>>(size, minw, minh, minc, stride, sample, batch, 
+            w1, h1, c1, srcData, w2, h2, c2, s1, s2, dstData);  
+    } 
+    else{
+        int minw = w1;
+        int minh = h1;
+        int minc = c1;
+        int sample = 1;

-    int size = batch * minw * minh * minc;
-    int blocks = (size+255)/256;
-    int threads = 256;
-    shortcut_kernel<<<blocks, threads, 0, stream>>>(size, minw, minh, minc, stride, sample, batch, 
-        w1, h1, c1, srcData, w2, h2, c2, s1, s2, dstData);
+        int size = batch * minw * minh * minc;
+        int blocks = (size+255)/256;
+        int threads = 256;
+
+        shortcut_mul_kernel<<<blocks, threads, 0, stream>>>(size, minw, minh, minc, sample, batch, 
+            w1, h1, c1, srcData, w2, h2, c2, s1, s2, dstData);  
+    }
 }
@@ -23,14 +23,23 @@ bool fileExist(const char *fname) {
 void downloadWeightsifDoNotExist(const std::string& input_bin, const std::string& test_folder, const std::string& weights_url){
    if(!fileExist(input_bin.c_str())){
        std::string mkdir_cmd = "mkdir " + test_folder; 
-        std::string wget_cmd = "wget " + weights_url + " -O " + test_folder + "/weights.zip"; 
+        std::string wget_cmd = "curl " + weights_url + " --output " + test_folder + "/weights.zip";
+#ifdef __linux__
        std::string unzip_cmd = "unzip " + test_folder + "/weights.zip -d" + test_folder;
        std::string rm_cmd = "rm " + test_folder + "/weights.zip";
+
+#elif _WIN32
+
+        std::string unzip_cmd = "7z x " + test_folder + "/weights.zip -o" + test_folder;
+#endif 
        int err = 0;
        err = system(mkdir_cmd.c_str());
        err = system(wget_cmd.c_str());
        err = system(unzip_cmd.c_str());
+#ifdef __linux__
        err = system(rm_cmd.c_str());
+#endif
+
    }
 }

@@ -83,7 +92,7 @@ void printDeviceVector(int size, dnnType* vec_d, bool device){
        delete [] vec;
 }

-int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device, int limit) {
+int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device, int limit, bool verbose) {

    dnnType *data_h, *correct_h;
    const float eps = 0.02f;
@@ -102,6 +111,7 @@ int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device, int
    }
    int diffs = 0;
    for(int i=0; i<size; i++) {
+        // data_h[i] = data_h[i]*1e-2;
        if(data_h[i] != data_h[i] || correct_h[i] != correct_h[i] || //nan control
           fabs(data_h[i] - correct_h[i]) > eps) {
            diffs += 1;
@@ -117,13 +127,15 @@ int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device, int
        delete [] correct_h;
    }

-    std::cout<<" | ";
-    if(diffs == 0)
-        std::cout<<COL_GREENB<<"OK";
-    else
-        std::cout<<COL_REDB<<"Wrongs: "<<diffs;
+    if(verbose){
+        std::cout<<" | ";
+        if(diffs == 0)
+            std::cout<<COL_GREENB<<"OK";
+        else
+            std::cout<<COL_REDB<<"Wrongs: "<<diffs;

-    std::cout<<COL_END<<" ~"<<eps<<"\n";
+        std::cout<<COL_END<<" ~"<<eps<<"\n";
+    }
    return diffs;
 }

@@ -191,8 +203,12 @@ void getMemUsage(double& vm_usage_kb, double& resident_set_kb){
               >> O >> itrealvalue >> starttime >> vsize >> rss;

   stat_stream.close();
-
+#ifdef __linux__
   long page_size_kb = sysconf(_SC_PAGE_SIZE) / 1024; // in case x86-64 is configured to use 2MB pages
+#elif _WIN32
+ long page_size_kb = 4096/1024;
+#endif
+
   vm_usage_kb     = vsize / 1024.0;  
   resident_set_kb = rss * page_size_kb;
 }
@@ -227,4 +243,4 @@ void removePathAndExtension(const std::string &full_string, std::string &name){
        name = name.substr(previous, current);
    
    // std::cout<<"full string: "<<full_string<<" name: "<<name<<std::endl;
-}
+}
@@ -479,6 +479,18 @@ int main()
    //print network model
    net.print();

+//     for(int i=0; i<net.num_layers; i++) {
+//         if(net.layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net.layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net.layers[i]->dstData != nullptr) {
+//             cudaFree(net.layers[i]->dstData);
+//             net.layers[i]->dstData = nullptr;
+//         }
+//    }
+
    //convert network to tensorRT
    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_cnet"));

@@ -0,0 +1,562 @@
+#include <iostream>
+#include "tkdnn.h"
+
+const char *input_bin = "dla34_cnet3d/debug/input.bin";
+const char *conv1_bin = "dla34_cnet3d/layers/base-base_layer-0.bin";
+const char *conv2_bin = "dla34_cnet3d/layers/base-level0-0.bin";
+const char *conv3_bin = "dla34_cnet3d/layers/base-level1-0.bin";
+// s - stage, t - tree
+const char *s1_t1_conv1_bin = "dla34_cnet3d/layers/base-level2-tree1-conv1.bin";
+const char *s1_t1_conv2_bin = "dla34_cnet3d/layers/base-level2-tree1-conv2.bin";
+const char *s1_t1_project = "dla34_cnet3d/layers/base-level2-project-0.bin";
+const char *s1_t2_conv1_bin = "dla34_cnet3d/layers/base-level2-tree2-conv1.bin";
+const char *s1_t2_conv2_bin = "dla34_cnet3d/layers/base-level2-tree2-conv2.bin";
+const char *s1_root_conv1_bin = "dla34_cnet3d/layers/base-level2-root-conv.bin";
+const char *s2_t1_t1_conv1_bin = "dla34_cnet3d/layers/base-level3-tree1-tree1-conv1.bin";
+const char *s2_t1_t1_conv2_bin = "dla34_cnet3d/layers/base-level3-tree1-tree1-conv2.bin";
+const char *s2_t1_t1_project = "dla34_cnet3d/layers/base-level3-tree1-project-0.bin";
+const char *s2_t1_t2_conv1_bin = "dla34_cnet3d/layers/base-level3-tree1-tree2-conv1.bin";
+const char *s2_t1_t2_conv2_bin = "dla34_cnet3d/layers/base-level3-tree1-tree2-conv2.bin";
+const char *s2_t1_root_conv1_bin = "dla34_cnet3d/layers/base-level3-tree1-root-conv.bin";
+const char *s2_t2_t1_conv1_bin = "dla34_cnet3d/layers/base-level3-tree2-tree1-conv1.bin";
+const char *s2_t2_t1_conv2_bin = "dla34_cnet3d/layers/base-level3-tree2-tree1-conv2.bin";
+const char *s2_t2_t2_conv1_bin = "dla34_cnet3d/layers/base-level3-tree2-tree2-conv1.bin";
+const char *s2_t2_t2_conv2_bin = "dla34_cnet3d/layers/base-level3-tree2-tree2-conv2.bin";
+const char *s2_t2_root_conv1_bin = "dla34_cnet3d/layers/base-level3-tree2-root-conv.bin";
+const char *s3_t1_t1_conv1_bin = "dla34_cnet3d/layers/base-level4-tree1-tree1-conv1.bin";
+const char *s3_t1_t1_conv2_bin = "dla34_cnet3d/layers/base-level4-tree1-tree1-conv2.bin";
+const char *s3_t1_t1_project = "dla34_cnet3d/layers/base-level4-tree1-project-0.bin";
+const char *s3_t1_t2_conv1_bin = "dla34_cnet3d/layers/base-level4-tree1-tree2-conv1.bin";
+const char *s3_t1_t2_conv2_bin = "dla34_cnet3d/layers/base-level4-tree1-tree2-conv2.bin";
+const char *s3_t1_root_conv1_bin = "dla34_cnet3d/layers/base-level4-tree1-root-conv.bin";
+const char *s3_t2_t1_conv1_bin = "dla34_cnet3d/layers/base-level4-tree2-tree1-conv1.bin";
+const char *s3_t2_t1_conv2_bin = "dla34_cnet3d/layers/base-level4-tree2-tree1-conv2.bin";
+const char *s3_t2_t2_conv1_bin = "dla34_cnet3d/layers/base-level4-tree2-tree2-conv1.bin";
+const char *s3_t2_t2_conv2_bin = "dla34_cnet3d/layers/base-level4-tree2-tree2-conv2.bin";
+const char *s3_t2_root_conv1_bin = "dla34_cnet3d/layers/base-level4-tree2-root-conv.bin";
+const char *s4_t1_conv1_bin = "dla34_cnet3d/layers/base-level5-tree1-conv1.bin";
+const char *s4_t1_conv2_bin = "dla34_cnet3d/layers/base-level5-tree1-conv2.bin";
+const char *s4_t1_project = "dla34_cnet3d/layers/base-level5-project-0.bin";
+const char *s4_t2_conv1_bin = "dla34_cnet3d/layers/base-level5-tree2-conv1.bin";
+const char *s4_t2_conv2_bin = "dla34_cnet3d/layers/base-level5-tree2-conv2.bin";
+const char *s4_root_conv1_bin = "dla34_cnet3d/layers/base-level5-root-conv.bin";
+
+//final
+// const char *fc_bin = "dla34_cnet3d/layers/output.bin";
+
+const char *ida_0_p_1_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_0-proj_1-conv.bin";
+const char *ida_0_p_1_conv_bin = "dla34_cnet3d/layers/dla_up-ida_0-proj_1-conv-conv_offset_mask.bin";
+const char *ida_0_up_1_deconv_bin = "dla34_cnet3d/layers/dla_up-ida_0-up_1.bin";
+const char *ida_0_n_1_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_0-node_1-conv.bin";
+const char *ida_0_n_1_conv_bin = "dla34_cnet3d/layers/dla_up-ida_0-node_1-conv-conv_offset_mask.bin";
+
+const char *ida_1_p_1_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_1-proj_1-conv.bin";
+const char *ida_1_p_1_conv_bin = "dla34_cnet3d/layers/dla_up-ida_1-proj_1-conv-conv_offset_mask.bin";
+const char *ida_1_up_1_deconv_bin = "dla34_cnet3d/layers/dla_up-ida_1-up_1.bin";
+const char *ida_1_n_1_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_1-node_1-conv.bin";
+const char *ida_1_n_1_conv_bin = "dla34_cnet3d/layers/dla_up-ida_1-node_1-conv-conv_offset_mask.bin";
+const char *ida_1_p_2_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_1-proj_2-conv.bin";
+const char *ida_1_p_2_conv_bin = "dla34_cnet3d/layers/dla_up-ida_1-proj_2-conv-conv_offset_mask.bin";
+const char *ida_1_up_2_deconv_bin = "dla34_cnet3d/layers/dla_up-ida_1-up_2.bin";
+const char *ida_1_n_2_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_1-node_2-conv.bin";
+const char *ida_1_n_2_conv_bin = "dla34_cnet3d/layers/dla_up-ida_1-node_2-conv-conv_offset_mask.bin";
+
+const char *ida_2_p_1_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_2-proj_1-conv.bin";
+const char *ida_2_p_1_conv_bin = "dla34_cnet3d/layers/dla_up-ida_2-proj_1-conv-conv_offset_mask.bin";
+const char *ida_2_up_1_deconv_bin = "dla34_cnet3d/layers/dla_up-ida_2-up_1.bin";
+const char *ida_2_n_1_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_2-node_1-conv.bin";
+const char *ida_2_n_1_conv_bin = "dla34_cnet3d/layers/dla_up-ida_2-node_1-conv-conv_offset_mask.bin";
+const char *ida_2_p_2_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_2-proj_2-conv.bin";
+const char *ida_2_p_2_conv_bin = "dla34_cnet3d/layers/dla_up-ida_2-proj_2-conv-conv_offset_mask.bin";
+const char *ida_2_up_2_deconv_bin = "dla34_cnet3d/layers/dla_up-ida_2-up_2.bin";
+const char *ida_2_n_2_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_2-node_2-conv.bin";
+const char *ida_2_n_2_conv_bin = "dla34_cnet3d/layers/dla_up-ida_2-node_2-conv-conv_offset_mask.bin";
+const char *ida_2_p_3_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_2-proj_3-conv.bin";
+const char *ida_2_p_3_conv_bin = "dla34_cnet3d/layers/dla_up-ida_2-proj_3-conv-conv_offset_mask.bin";
+const char *ida_2_up_3_deconv_bin = "dla34_cnet3d/layers/dla_up-ida_2-up_3.bin";
+const char *ida_2_n_3_dcn_bin = "dla34_cnet3d/layers/dla_up-ida_2-node_3-conv.bin";
+const char *ida_2_n_3_conv_bin = "dla34_cnet3d/layers/dla_up-ida_2-node_3-conv-conv_offset_mask.bin";
+
+const char *ida_up_p_1_dcn_bin = "dla34_cnet3d/layers/ida_up-proj_1-conv.bin";
+const char *ida_up_p_1_conv_bin = "dla34_cnet3d/layers/ida_up-proj_1-conv-conv_offset_mask.bin";
+const char *ida_up_up_1_deconv_bin = "dla34_cnet3d/layers/ida_up-up_1.bin";
+const char *ida_up_n_1_dcn_bin = "dla34_cnet3d/layers/ida_up-node_1-conv.bin";
+const char *ida_up_n_1_conv_bin = "dla34_cnet3d/layers/ida_up-node_1-conv-conv_offset_mask.bin";
+const char *ida_up_p_2_dcn_bin = "dla34_cnet3d/layers/ida_up-proj_2-conv.bin";
+const char *ida_up_p_2_conv_bin = "dla34_cnet3d/layers/ida_up-proj_2-conv-conv_offset_mask.bin";
+const char *ida_up_up_2_deconv_bin = "dla34_cnet3d/layers/ida_up-up_2.bin";
+const char *ida_up_n_2_dcn_bin = "dla34_cnet3d/layers/ida_up-node_2-conv.bin";
+const char *ida_up_n_2_conv_bin = "dla34_cnet3d/layers/ida_up-node_2-conv-conv_offset_mask.bin";
+
+const char *hm_conv1_bin = "dla34_cnet3d/layers/hm-0.bin";
+const char *hm_conv2_bin = "dla34_cnet3d/layers/hm-2.bin";
+const char *wh_conv1_bin = "dla34_cnet3d/layers/wh-0.bin";
+const char *wh_conv2_bin = "dla34_cnet3d/layers/wh-2.bin";
+const char *reg_conv1_bin = "dla34_cnet3d/layers/reg-0.bin";
+const char *reg_conv2_bin = "dla34_cnet3d/layers/reg-2.bin";
+const char *dep_conv1_bin = "dla34_cnet3d/layers/dep-0.bin";
+const char *dep_conv2_bin = "dla34_cnet3d/layers/dep-2.bin";
+const char *rot_conv1_bin = "dla34_cnet3d/layers/rot-0.bin";
+const char *rot_conv2_bin = "dla34_cnet3d/layers/rot-2.bin";
+const char *dim_conv1_bin = "dla34_cnet3d/layers/dim-0.bin";
+const char *dim_conv2_bin = "dla34_cnet3d/layers/dim-2.bin";
+
+const char *output_bin[]={
+"dla34_cnet3d/debug/hm.bin",
+"dla34_cnet3d/debug/wh.bin",
+"dla34_cnet3d/debug/reg.bin",
+"dla34_cnet3d/debug/dep.bin",
+"dla34_cnet3d/debug/rot.bin",
+"dla34_cnet3d/debug/dim.bin"};
+
+int main()
+{
+
+    downloadWeightsifDoNotExist(input_bin, "dla34_cnet3d", "https://cloud.hipert.unimore.it/s/2MDyWGzQsTKMjmR/download");
+
+    // Network layout
+    tk::dnn::dataDim_t dim(1, 3, 512, 512, 1);
+    tk::dnn::Network net(dim);
+    tk::dnn::Layer *last1, *last2, *last3, *last4;
+    tk::dnn::Layer *base1, *base2, *base3, *base4, *base5, *base6, *ida1, *ida2_1, *ida2_2, *ida3_1, *ida3_2, *ida3_3, *idaup_1, *idaup_2;
+
+    tk::dnn::Conv2d conv1(&net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
+    tk::dnn::Activation relu1(&net, CUDNN_ACTIVATION_RELU);
+
+    tk::dnn::Conv2d conv2(&net, 16, 3, 3, 1, 1, 1, 1, conv2_bin, true);
+    tk::dnn::Activation relu2(&net, CUDNN_ACTIVATION_RELU);
+    base1 = &relu2;
+
+    tk::dnn::Conv2d conv3(&net, 32, 3, 3, 2, 2, 1, 1, conv3_bin, true);
+    tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
+    base2 = &relu3;
+    
+    // level 2
+    // tree 1
+        tk::dnn::Conv2d     s1_t1_conv1(&net, 64, 3, 3, 2, 2, 1, 1, s1_t1_conv1_bin, true);
+        tk::dnn::Activation s1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s1_t1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t1_conv2_bin, true);
+        last2 = &s1_t1_conv2;
+
+        // get the basicblock input and apply maxpool conv2d and relu
+        tk::dnn::Layer      *route_s1_t1_layers[1] = { base2 };
+        tk::dnn::Route      route_s1_t1(&net, route_s1_t1_layers, 1);
+        // downsample
+        tk::dnn::Pooling    s1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+        // project
+        tk::dnn::Conv2d     s1_t1_residual1_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_t1_project, true);
+        
+        tk::dnn::Shortcut   s1_t1_s1(&net, last2);
+        tk::dnn::Activation s1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    last1 = &s1_t1_relu;
+    // tree 2
+        tk::dnn::Conv2d     s1_t2_conv1(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv1_bin, true);
+        tk::dnn::Activation s1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s1_t2_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv2_bin, true);
+        
+        tk::dnn::Shortcut   s1_t2_s1(&net, last1);
+        tk::dnn::Activation s1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+        last2 = &s1_t2_relu;
+
+    // root
+        // join last1 and net in single input 128, 56, 56
+        tk::dnn::Layer      *route_s1_root_layers[2] = { last2, last1 };
+        tk::dnn::Route      route_s1_root(&net, route_s1_root_layers, 2);
+        tk::dnn::Conv2d     s1_root_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_root_conv1_bin, true);
+        tk::dnn::Activation s1_root_relu(&net, CUDNN_ACTIVATION_RELU);
+
+    base3 = &s1_root_relu;
+
+    // level 3
+    // tree 1
+        // tree 1
+            tk::dnn::Conv2d     s2_t1_t1_conv1(&net, 128, 3, 3, 2, 2, 1, 1, s2_t1_t1_conv1_bin, true);
+            tk::dnn::Activation s2_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);      
+        
+            tk::dnn::Conv2d     s2_t1_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t1_conv2_bin, true);
+            last2 = &s2_t1_t1_conv2;
+
+            // get the basicblock input and apply maxpool conv2d and relu
+            tk::dnn::Layer      *route_s2_t1_t1_layers[1] = { base3 };
+            tk::dnn::Route      route_s2_t1_t1(&net, route_s2_t1_t1_layers, 1);
+            // downsample
+            tk::dnn::Pooling    s2_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+            last4 = &s2_t1_t1_maxpool1;
+            // project
+            tk::dnn::Conv2d     s2_t1_t1_residual1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_t1_project, true);
+            
+            tk::dnn::Shortcut   s2_t1_t1_s1(&net, last2);
+            tk::dnn::Activation s2_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s2_t1_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s2_t1_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv1_bin, true);
+            tk::dnn::Activation s2_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s2_t1_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s2_t1_t2_s1(&net, last1);
+            tk::dnn::Activation s2_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s2_t1_t2_relu;
+
+        // root
+            // join last1 and net in single input 128, 56, 56
+            tk::dnn::Layer      *route_s2_t1_root_layers[2] = { last2, last1 };
+            tk::dnn::Route      route_s2_t1_root(&net, route_s2_t1_root_layers, 2);
+            tk::dnn::Conv2d     s2_t1_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_root_conv1_bin, true);
+            tk::dnn::Activation s2_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    last1 = &s2_t1_root_relu;
+    last3 = &s2_t1_root_relu;
+    // tree 2
+        // tree 1
+            tk::dnn::Conv2d     s2_t2_t1_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv1_bin, true);
+            tk::dnn::Activation s2_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s2_t2_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv2_bin, true);
+            tk::dnn::Shortcut   s2_t2_t1_s1(&net, last1);
+            tk::dnn::Activation s2_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s2_t2_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s2_t2_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv1_bin, true);
+            tk::dnn::Activation s2_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s2_t2_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s2_t2_t2_s1(&net, last1);
+            tk::dnn::Activation s2_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s2_t2_t2_relu;
+
+        // root
+            // join last1 and net in single input 128, 56, 56
+            tk::dnn::Layer      *route_s2_t2_root_layers[4] = { last2, last1, last4, last3};
+            tk::dnn::Route      route_s2_t2_root(&net, route_s2_t2_root_layers, 4);
+            tk::dnn::Conv2d     s2_t2_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t2_root_conv1_bin, true);
+            tk::dnn::Activation s2_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    base4 = &s2_t2_root_relu;
+
+    // level 4
+    // tree 1
+        // tree 1
+            tk::dnn::Conv2d     s3_t1_t1_conv1(&net, 256, 3, 3, 2, 2, 1, 1, s3_t1_t1_conv1_bin, true);
+            tk::dnn::Activation s3_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);      
+        
+            tk::dnn::Conv2d     s3_t1_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t1_conv2_bin, true);
+            last2 = &s3_t1_t1_conv2;
+
+            // get the basicblock input and apply maxpool conv2d and relu
+            tk::dnn::Layer      *route_s3_t1_t1_layers[1] = { base4 };
+            tk::dnn::Route      route_s3_t1_t1(&net, route_s3_t1_t1_layers, 1);
+            // downsample
+            tk::dnn::Pooling    s3_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+            last4 = &s3_t1_t1_maxpool1;
+            // project
+            tk::dnn::Conv2d     s3_t1_t1_residual1_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_t1_project, true);
+            
+            tk::dnn::Shortcut   s3_t1_t1_s1(&net, last2);
+            tk::dnn::Activation s3_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s3_t1_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s3_t1_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv1_bin, true);
+            tk::dnn::Activation s3_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s3_t1_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s3_t1_t2_s1(&net, last1);
+            tk::dnn::Activation s3_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s3_t1_t2_relu;
+
+        // root
+            // join last1 and net in single input 256, 56, 56
+            tk::dnn::Layer      *route_s3_t1_root_layers[2] = { last2, last1 };
+            tk::dnn::Route      route_s3_t1_root(&net, route_s3_t1_root_layers, 2);
+            tk::dnn::Conv2d     s3_t1_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_root_conv1_bin, true);
+            tk::dnn::Activation s3_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    last1 = &s3_t1_root_relu;
+    last3 = &s3_t1_root_relu;
+    // tree 2
+        // tree 1
+            tk::dnn::Conv2d     s3_t2_t1_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv1_bin, true);
+            tk::dnn::Activation s3_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s3_t2_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv2_bin, true);
+            tk::dnn::Shortcut   s3_t2_t1_s1(&net, last1);
+            tk::dnn::Activation s3_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s3_t2_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s3_t2_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv1_bin, true);
+            tk::dnn::Activation s3_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s3_t2_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s3_t2_t2_s1(&net, last1);
+            tk::dnn::Activation s3_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s3_t2_t2_relu;
+
+        // root
+            // join last1 and net in single input 256, 56, 56
+            tk::dnn::Layer      *route_s3_t2_root_layers[4] = { last2, last1, last4, last3};
+            tk::dnn::Route      route_s3_t2_root(&net, route_s3_t2_root_layers, 4);
+            tk::dnn::Conv2d     s3_t2_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t2_root_conv1_bin, true);
+            tk::dnn::Activation s3_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    base5 = &s3_t2_root_relu;
+
+    // level 5
+    // tree 1
+        tk::dnn::Conv2d     s4_t1_conv1(&net, 512, 3, 3, 2, 2, 1, 1, s4_t1_conv1_bin, true);
+        tk::dnn::Activation s4_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s4_t1_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t1_conv2_bin, true);
+        last2 = &s4_t1_conv2;
+
+        // get the basicblock input and apply maxpool conv2d and relu
+        tk::dnn::Layer      *route_s4_t1_layers[1] = { base5 };
+        tk::dnn::Route      route_s4_t1(&net, route_s4_t1_layers, 1);
+        // downsample
+        tk::dnn::Pooling    s4_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+        last4 = &s4_t1_maxpool1;
+        // project
+        tk::dnn::Conv2d     s4_t1_residual1_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_t1_project, true);
+        
+        tk::dnn::Shortcut   s4_t1_s1(&net, last2);
+        tk::dnn::Activation s4_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+    last1 = &s4_t1_relu;
+    
+    // tree 2
+        tk::dnn::Conv2d     s4_t2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv1_bin, true);
+        tk::dnn::Activation s4_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s4_t2_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv2_bin, true);
+        
+        tk::dnn::Shortcut   s4_t2_s1(&net, last1);
+        tk::dnn::Activation s4_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+        last2 = &s4_t2_relu;
+
+    // root
+        // join last1 and net in single input 128, 56, 56
+        tk::dnn::Layer      *route_s4_root_layers[3] = { last2, last1, last4 };
+        tk::dnn::Route      route_s4_root(&net, route_s4_root_layers, 3);
+        tk::dnn::Conv2d     s4_root_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_root_conv1_bin, true);
+        tk::dnn::Activation s4_root_relu(&net, CUDNN_ACTIVATION_RELU);
+
+    base6 = &s4_root_relu;
+    
+    //final
+    // tk::dnn::Pooling avgpool(&net, 7, 7, 7, 7, 0, 0, tk::dnn::POOLING_AVERAGE);
+    // tk::dnn::Dense   fc(&net, 1000, fc_bin);
+    
+    //ida 0 
+    tk::dnn::DeformConv2d   ida_0_p_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_p_1_dcn_bin, ida_0_p_1_conv_bin, true);
+    tk::dnn::Activation     ida_0_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_0_up_1_deconv(&net, 256, 4, 4, 2, 2, 1, 1, ida_0_up_1_deconv_bin, false, 256);
+    tk::dnn::Shortcut       ida_0_shortcut(&net, base5);    
+    tk::dnn::DeformConv2d   ida_0_n_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_n_1_dcn_bin, ida_0_n_1_conv_bin, true);
+    tk::dnn::Activation     ida_0_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida1 = &ida_0_n_1_relu;
+
+    //ida1-1
+    tk::dnn::Layer          *route_ida1_layers_1[1] = { base5 };
+    tk::dnn::Route          route_ida1_1(&net, route_ida1_layers_1, 1);
+    
+    tk::dnn::DeformConv2d   ida_1_p_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_1_dcn_bin, ida_1_p_1_conv_bin, true);
+    tk::dnn::Activation     ida_1_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_1_up_1_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_1_deconv_bin, false, 128);
+    tk::dnn::Shortcut       ida_1_shortcut1(&net, base4);    
+    tk::dnn::DeformConv2d   ida_1_n_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_1_dcn_bin, ida_1_n_1_conv_bin, true);
+    tk::dnn::Activation     ida_1_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida2_1 = &ida_1_n_1_relu;
+
+    //ida1-2
+    tk::dnn::Layer          *route_ida1_layers_2[1] = { ida1 };
+    tk::dnn::Route          route_ida1_2(&net, route_ida1_layers_2, 1);
+
+    tk::dnn::DeformConv2d   ida_1_p_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_2_dcn_bin, ida_1_p_2_conv_bin, true);
+    tk::dnn::Activation     ida_1_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_1_up_2_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_2_deconv_bin, false, 128);
+    tk::dnn::Shortcut       ida_1_shortcut2(&net, ida2_1);    
+    tk::dnn::DeformConv2d   ida_1_n_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_2_dcn_bin, ida_1_n_2_conv_bin, true);
+    tk::dnn::Activation     ida_1_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida2_2 = &ida_1_n_2_relu;
+
+    //ida2-1
+    tk::dnn::Layer          *route_ida2_layers_1[1] = { base4 };
+    tk::dnn::Route          route_ida2_1(&net, route_ida2_layers_1, 1);
+    
+    tk::dnn::DeformConv2d   ida_2_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_1_dcn_bin, ida_2_p_1_conv_bin, true);
+    tk::dnn::Activation     ida_2_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_2_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_1_deconv_bin, false, 64);
+    tk::dnn::Shortcut       ida_2_shortcut1(&net, base3);    
+    tk::dnn::DeformConv2d   ida_2_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_1_dcn_bin, ida_2_n_1_conv_bin, true);
+    tk::dnn::Activation     ida_2_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida3_1 = &ida_2_n_1_relu;
+
+    //ida2-2
+    tk::dnn::Layer          *route_ida2_layers_2[1] = { ida2_1 };
+    tk::dnn::Route          route_ida2_2(&net, route_ida2_layers_2, 1);
+    
+    tk::dnn::DeformConv2d   ida_2_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_2_dcn_bin, ida_2_p_2_conv_bin, true);
+    tk::dnn::Activation     ida_2_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_2_up_2_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_2_deconv_bin, false, 64);
+    tk::dnn::Shortcut       ida_2_shortcut2(&net, ida3_1);    
+    tk::dnn::DeformConv2d   ida_2_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_2_dcn_bin, ida_2_n_2_conv_bin, true);
+    tk::dnn::Activation     ida_2_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida3_2 = &ida_2_n_2_relu;
+
+    //ida2-3
+    tk::dnn::Layer          *route_ida2_layers_3[1] = { ida2_2 };
+    tk::dnn::Route          route_ida2_3(&net, route_ida2_layers_3, 1);
+    
+    tk::dnn::DeformConv2d   ida_2_p_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_3_dcn_bin, ida_2_p_3_conv_bin, true);
+    tk::dnn::Activation     ida_2_p_3_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_2_up_3_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_3_deconv_bin, false, 64);
+    tk::dnn::Shortcut       ida_2_shortcut3(&net, ida3_2);    
+    tk::dnn::DeformConv2d   ida_2_n_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_3_dcn_bin, ida_2_n_3_conv_bin, true);
+    tk::dnn::Activation     ida_2_n_3_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida3_3 = &ida_2_n_3_relu;
+
+    //idaup-1
+    tk::dnn::Layer          *route_idaup_layers_1[1] = { ida2_2 };
+    tk::dnn::Route          route_idaup_1(&net, route_idaup_layers_1, 1);
+    
+    tk::dnn::DeformConv2d   idaup_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_1_dcn_bin, ida_up_p_1_conv_bin, true);
+    tk::dnn::Activation     idaup_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       idaup_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_up_up_1_deconv_bin, false, 64);
+    tk::dnn::Shortcut       idaup_shortcut1(&net, ida3_3);    
+    tk::dnn::DeformConv2d   idaup_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_1_dcn_bin, ida_up_n_1_conv_bin, true);
+    tk::dnn::Activation     idaup_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    idaup_1 = &idaup_n_1_relu;
+
+    //idaup-2
+    tk::dnn::Layer          *route_idaup_layers_2[1] = { ida1 };
+    tk::dnn::Route          route_idaup_2(&net, route_idaup_layers_2, 1);
+
+    tk::dnn::DeformConv2d   idaup_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_2_dcn_bin, ida_up_p_2_conv_bin, true);
+    tk::dnn::Activation     idaup_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       idaup_up_2_deconv(&net, 64, 8, 8, 4, 4, 2, 2, ida_up_up_2_deconv_bin, false, 64);
+    tk::dnn::Shortcut       idaup_shortcut2(&net, idaup_1);    
+    tk::dnn::DeformConv2d   idaup_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_2_dcn_bin, ida_up_n_2_conv_bin, true);
+    tk::dnn::Activation     idaup_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    idaup_2 = &idaup_n_2_relu;
+
+    tk::dnn::Layer    *route_1_0_layers[1] = { idaup_2 };
+
+    // hm
+    tk::dnn::Conv2d     *hm_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
+    tk::dnn::Activation *hm_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *hm = new tk::dnn::Conv2d(&net, 3, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false);
+    hm->setFinal();
+    int kernel = 3; 
+    int pad = (kernel - 1)/2;
+    tk::dnn::Activation *hm_sig      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_SIGMOID);
+    tk::dnn::Pooling  *hmax                 = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX);
+    hmax->setFinal();
+
+    // wh
+    tk::dnn::Route    *route_1_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *wh_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
+    tk::dnn::Activation *wh_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false);        
+    wh->setFinal();
+    
+    // reg
+    tk::dnn::Route    *route_2_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *reg_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
+    tk::dnn::Activation *reg_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false);
+    reg->setFinal();
+
+    // dep
+    tk::dnn::Route    *route_3_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *dep_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, dep_conv1_bin, false);
+    tk::dnn::Activation *dep_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *dep = new tk::dnn::Conv2d(&net, 1, 1, 1, 1, 1, 0, 0, dep_conv2_bin, false);
+    dep->setFinal();
+
+    // rot
+    tk::dnn::Route    *route_4_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *rot_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, rot_conv1_bin, false);
+    tk::dnn::Activation *rot_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *rot = new tk::dnn::Conv2d(&net, 8, 1, 1, 1, 1, 0, 0, rot_conv2_bin, false);
+    rot->setFinal();
+
+    // dim
+    tk::dnn::Route    *route_5_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *dim_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, dim_conv1_bin, false);
+    tk::dnn::Activation *dim_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *dim_ = new tk::dnn::Conv2d(&net, 3, 1, 1, 1, 1, 0, 0, dim_conv2_bin, false);
+    dim_->setFinal();
+
+    // Load input
+    dnnType *data;
+    dnnType *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+    //printDeviceVector(64, data, true);
+
+    //print network model
+    net.print();
+
+    //convert network to tensorRT
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_cnet3d"));
+
+    tk::dnn::dataDim_t dim1 = dim; //input dim
+    printCenteredTitle(" CUDNN inference ", '=', 30);
+    {
+        dim1.print();
+        TKDNN_TSTART
+        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();
+    }
+
+    tk::dnn::Layer *outs[6] = { hm, wh, reg, dep, rot, dim_ }; 
+    int out_count = 1;
+    int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
+    for(int i=0; i<6; i++) {
+        printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
+        
+        outs[i]->output_dim.print();
+        
+        dnnType *out, *out_h;
+        int odim = outs[i]->output_dim.tot();
+        readBinaryFile(output_bin[i], odim, &out_h, &out);
+
+        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;
+}
@@ -353,6 +353,18 @@ int main()
    //print network model
    net.print();

+//     for(int i=0; i<net.num_layers; i++) {
+//         if(net.layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net.layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net.layers[i]->dstData != nullptr) {
+//             cudaFree(net.layers[i]->dstData);
+//             net.layers[i]->dstData = nullptr;
+//         }
+//    }
+
    //convert network to tensorRT
    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("resnet101_cnet"));

@@ -0,0 +1,632 @@
+#include <iostream>
+#include "tkdnn.h"
+const char *input_bin = "dla34_ctrack/debug/input_base-level0-0.bin";
+// const char *input_bin = "dla34_ctrack/debug/input.bin";
+// const char *pre_img_bin = "dla34_ctrack/debug/pre_imgages.bin";
+// const char *pre_hm_bin = "dla34_ctrack/debug/pre_hms.bin";
+// //pre
+// const char *pre_img_conv1_bin = "dla34_ctrack/layers/base-pre_img_layer-0.bin";
+// const char *pre_hm_conv1_bin = "dla34_ctrack/layers/base-pre_hm_layer-0.bin";
+// const char *conv1_bin = "dla34_ctrack/layers/base-base_layer-0.bin";
+
+const char *conv2_bin = "dla34_ctrack/layers/base-level0-0.bin";
+const char *conv3_bin = "dla34_ctrack/layers/base-level1-0.bin";
+// s - stage, t - tree
+const char *s1_t1_conv1_bin = "dla34_ctrack/layers/base-level2-tree1-conv1.bin";
+const char *s1_t1_conv2_bin = "dla34_ctrack/layers/base-level2-tree1-conv2.bin";
+const char *s1_t1_project = "dla34_ctrack/layers/base-level2-project-0.bin";
+const char *s1_t2_conv1_bin = "dla34_ctrack/layers/base-level2-tree2-conv1.bin";
+const char *s1_t2_conv2_bin = "dla34_ctrack/layers/base-level2-tree2-conv2.bin";
+const char *s1_root_conv1_bin = "dla34_ctrack/layers/base-level2-root-conv.bin";
+const char *s2_t1_t1_conv1_bin = "dla34_ctrack/layers/base-level3-tree1-tree1-conv1.bin";
+const char *s2_t1_t1_conv2_bin = "dla34_ctrack/layers/base-level3-tree1-tree1-conv2.bin";
+const char *s2_t1_t1_project = "dla34_ctrack/layers/base-level3-tree1-project-0.bin";
+const char *s2_t1_t2_conv1_bin = "dla34_ctrack/layers/base-level3-tree1-tree2-conv1.bin";
+const char *s2_t1_t2_conv2_bin = "dla34_ctrack/layers/base-level3-tree1-tree2-conv2.bin";
+const char *s2_t1_root_conv1_bin = "dla34_ctrack/layers/base-level3-tree1-root-conv.bin";
+const char *s2_t2_t1_conv1_bin = "dla34_ctrack/layers/base-level3-tree2-tree1-conv1.bin";
+const char *s2_t2_t1_conv2_bin = "dla34_ctrack/layers/base-level3-tree2-tree1-conv2.bin";
+const char *s2_t2_t2_conv1_bin = "dla34_ctrack/layers/base-level3-tree2-tree2-conv1.bin";
+const char *s2_t2_t2_conv2_bin = "dla34_ctrack/layers/base-level3-tree2-tree2-conv2.bin";
+const char *s2_t2_root_conv1_bin = "dla34_ctrack/layers/base-level3-tree2-root-conv.bin";
+const char *s3_t1_t1_conv1_bin = "dla34_ctrack/layers/base-level4-tree1-tree1-conv1.bin";
+const char *s3_t1_t1_conv2_bin = "dla34_ctrack/layers/base-level4-tree1-tree1-conv2.bin";
+const char *s3_t1_t1_project = "dla34_ctrack/layers/base-level4-tree1-project-0.bin";
+const char *s3_t1_t2_conv1_bin = "dla34_ctrack/layers/base-level4-tree1-tree2-conv1.bin";
+const char *s3_t1_t2_conv2_bin = "dla34_ctrack/layers/base-level4-tree1-tree2-conv2.bin";
+const char *s3_t1_root_conv1_bin = "dla34_ctrack/layers/base-level4-tree1-root-conv.bin";
+const char *s3_t2_t1_conv1_bin = "dla34_ctrack/layers/base-level4-tree2-tree1-conv1.bin";
+const char *s3_t2_t1_conv2_bin = "dla34_ctrack/layers/base-level4-tree2-tree1-conv2.bin";
+const char *s3_t2_t2_conv1_bin = "dla34_ctrack/layers/base-level4-tree2-tree2-conv1.bin";
+const char *s3_t2_t2_conv2_bin = "dla34_ctrack/layers/base-level4-tree2-tree2-conv2.bin";
+const char *s3_t2_root_conv1_bin = "dla34_ctrack/layers/base-level4-tree2-root-conv.bin";
+const char *s4_t1_conv1_bin = "dla34_ctrack/layers/base-level5-tree1-conv1.bin";
+const char *s4_t1_conv2_bin = "dla34_ctrack/layers/base-level5-tree1-conv2.bin";
+const char *s4_t1_project = "dla34_ctrack/layers/base-level5-project-0.bin";
+const char *s4_t2_conv1_bin = "dla34_ctrack/layers/base-level5-tree2-conv1.bin";
+const char *s4_t2_conv2_bin = "dla34_ctrack/layers/base-level5-tree2-conv2.bin";
+const char *s4_root_conv1_bin = "dla34_ctrack/layers/base-level5-root-conv.bin";
+
+//final
+// const char *fc_bin = "dla34_ctrack/layers/output.bin";
+
+const char *ida_0_p_1_dcn_bin = "dla34_ctrack/layers/dla_up-ida_0-proj_1-conv.bin";
+const char *ida_0_p_1_conv_bin = "dla34_ctrack/layers/dla_up-ida_0-proj_1-conv-conv_offset_mask.bin";
+const char *ida_0_up_1_deconv_bin = "dla34_ctrack/layers/dla_up-ida_0-up_1.bin";
+const char *ida_0_n_1_dcn_bin = "dla34_ctrack/layers/dla_up-ida_0-node_1-conv.bin";
+const char *ida_0_n_1_conv_bin = "dla34_ctrack/layers/dla_up-ida_0-node_1-conv-conv_offset_mask.bin";
+
+const char *ida_1_p_1_dcn_bin = "dla34_ctrack/layers/dla_up-ida_1-proj_1-conv.bin";
+const char *ida_1_p_1_conv_bin = "dla34_ctrack/layers/dla_up-ida_1-proj_1-conv-conv_offset_mask.bin";
+const char *ida_1_up_1_deconv_bin = "dla34_ctrack/layers/dla_up-ida_1-up_1.bin";
+const char *ida_1_n_1_dcn_bin = "dla34_ctrack/layers/dla_up-ida_1-node_1-conv.bin";
+const char *ida_1_n_1_conv_bin = "dla34_ctrack/layers/dla_up-ida_1-node_1-conv-conv_offset_mask.bin";
+const char *ida_1_p_2_dcn_bin = "dla34_ctrack/layers/dla_up-ida_1-proj_2-conv.bin";
+const char *ida_1_p_2_conv_bin = "dla34_ctrack/layers/dla_up-ida_1-proj_2-conv-conv_offset_mask.bin";
+const char *ida_1_up_2_deconv_bin = "dla34_ctrack/layers/dla_up-ida_1-up_2.bin";
+const char *ida_1_n_2_dcn_bin = "dla34_ctrack/layers/dla_up-ida_1-node_2-conv.bin";
+const char *ida_1_n_2_conv_bin = "dla34_ctrack/layers/dla_up-ida_1-node_2-conv-conv_offset_mask.bin";
+
+const char *ida_2_p_1_dcn_bin = "dla34_ctrack/layers/dla_up-ida_2-proj_1-conv.bin";
+const char *ida_2_p_1_conv_bin = "dla34_ctrack/layers/dla_up-ida_2-proj_1-conv-conv_offset_mask.bin";
+const char *ida_2_up_1_deconv_bin = "dla34_ctrack/layers/dla_up-ida_2-up_1.bin";
+const char *ida_2_n_1_dcn_bin = "dla34_ctrack/layers/dla_up-ida_2-node_1-conv.bin";
+const char *ida_2_n_1_conv_bin = "dla34_ctrack/layers/dla_up-ida_2-node_1-conv-conv_offset_mask.bin";
+const char *ida_2_p_2_dcn_bin = "dla34_ctrack/layers/dla_up-ida_2-proj_2-conv.bin";
+const char *ida_2_p_2_conv_bin = "dla34_ctrack/layers/dla_up-ida_2-proj_2-conv-conv_offset_mask.bin";
+const char *ida_2_up_2_deconv_bin = "dla34_ctrack/layers/dla_up-ida_2-up_2.bin";
+const char *ida_2_n_2_dcn_bin = "dla34_ctrack/layers/dla_up-ida_2-node_2-conv.bin";
+const char *ida_2_n_2_conv_bin = "dla34_ctrack/layers/dla_up-ida_2-node_2-conv-conv_offset_mask.bin";
+const char *ida_2_p_3_dcn_bin = "dla34_ctrack/layers/dla_up-ida_2-proj_3-conv.bin";
+const char *ida_2_p_3_conv_bin = "dla34_ctrack/layers/dla_up-ida_2-proj_3-conv-conv_offset_mask.bin";
+const char *ida_2_up_3_deconv_bin = "dla34_ctrack/layers/dla_up-ida_2-up_3.bin";
+const char *ida_2_n_3_dcn_bin = "dla34_ctrack/layers/dla_up-ida_2-node_3-conv.bin";
+const char *ida_2_n_3_conv_bin = "dla34_ctrack/layers/dla_up-ida_2-node_3-conv-conv_offset_mask.bin";
+
+const char *ida_up_p_1_dcn_bin = "dla34_ctrack/layers/ida_up-proj_1-conv.bin";
+const char *ida_up_p_1_conv_bin = "dla34_ctrack/layers/ida_up-proj_1-conv-conv_offset_mask.bin";
+const char *ida_up_up_1_deconv_bin = "dla34_ctrack/layers/ida_up-up_1.bin";
+const char *ida_up_n_1_dcn_bin = "dla34_ctrack/layers/ida_up-node_1-conv.bin";
+const char *ida_up_n_1_conv_bin = "dla34_ctrack/layers/ida_up-node_1-conv-conv_offset_mask.bin";
+const char *ida_up_p_2_dcn_bin = "dla34_ctrack/layers/ida_up-proj_2-conv.bin";
+const char *ida_up_p_2_conv_bin = "dla34_ctrack/layers/ida_up-proj_2-conv-conv_offset_mask.bin";
+const char *ida_up_up_2_deconv_bin = "dla34_ctrack/layers/ida_up-up_2.bin";
+const char *ida_up_n_2_dcn_bin = "dla34_ctrack/layers/ida_up-node_2-conv.bin";
+const char *ida_up_n_2_conv_bin = "dla34_ctrack/layers/ida_up-node_2-conv-conv_offset_mask.bin";
+
+const char *hm_conv1_bin = "dla34_ctrack/layers/hm-0.bin";
+const char *hm_conv2_bin = "dla34_ctrack/layers/hm-2.bin";
+const char *wh_conv1_bin = "dla34_ctrack/layers/wh-0.bin";
+const char *wh_conv2_bin = "dla34_ctrack/layers/wh-2.bin";
+const char *reg_conv1_bin = "dla34_ctrack/layers/reg-0.bin";
+const char *reg_conv2_bin = "dla34_ctrack/layers/reg-2.bin";
+const char *track_conv1_bin = "dla34_ctrack/layers/tracking-0.bin";
+const char *track_conv2_bin = "dla34_ctrack/layers/tracking-2.bin";
+const char *dep_conv1_bin = "dla34_ctrack/layers/dep-0.bin";
+const char *dep_conv2_bin = "dla34_ctrack/layers/dep-2.bin";
+const char *rot_conv1_bin = "dla34_ctrack/layers/rot-0.bin";
+const char *rot_conv2_bin = "dla34_ctrack/layers/rot-2.bin";
+const char *dim_conv1_bin = "dla34_ctrack/layers/dim-0.bin";
+const char *dim_conv2_bin = "dla34_ctrack/layers/dim-2.bin";
+const char *a_off_conv1_bin = "dla34_ctrack/layers/amodel_offset-0.bin";
+const char *a_off_conv2_bin = "dla34_ctrack/layers/amodel_offset-2.bin";
+
+const char *output_bin[]={
+"dla34_ctrack/debug/hm.bin",
+"dla34_ctrack/debug/wh.bin",
+"dla34_ctrack/debug/reg.bin",
+"dla34_ctrack/debug/tracking.bin",
+"dla34_ctrack/debug/dep.bin",
+"dla34_ctrack/debug/rot.bin",
+"dla34_ctrack/debug/dim.bin",
+"dla34_ctrack/debug/amodel_offset.bin"};
+// const char *output_bin = "dla34_ctrack/debug/base-level0-2.bin";
+int main()
+{
+
+    downloadWeightsifDoNotExist("dla34_ctrack/debug/input.bin", "dla34_ctrack", "https://cloud.hipert.unimore.it/s/rjNfgGL9FtAXLHp/download");
+
+    // Network layout
+    // tk::dnn::dataDim_t dim_in0(1, 3, 512, 512, 1);
+    // tk::dnn::dataDim_t dim_in1(1, 1, 512, 512, 1);
+    tk::dnn::dataDim_t dim_in0(1, 16, 512, 512, 1);
+    // dnnType *i0_d, *i1_d, *i2_d;
+    // dnnType *i0_h, *i1_h, *i2_h;
+    // checkCuda( cudaMalloc(&i0_d, dim_in0.tot()*sizeof(dnnType)) );
+    // checkCuda( cudaMalloc(&i1_d, dim_in1.tot()*sizeof(dnnType)) );
+    // checkCuda( cudaMalloc(&i2_d, dim_in0.tot()*sizeof(dnnType)) );
+
+    tk::dnn::Network net(dim_in0);
+    tk::dnn::Layer *last1, *last2, *last3, *last4;
+    tk::dnn::Layer *base1, *base2, *base3, *base4, *base5, *base6, *ida1, *ida2_1, *ida2_2, *ida3_1, *ida3_2, *ida3_3, *idaup_1, *idaup_2;
+    // tk::dnn::Layer *pre_img, *pre_hm;
+
+    // //pre-img
+    // readBinaryFile(pre_img_bin, dim_in0.tot(), &i0_h, &i0_d);
+    // tk::dnn::Input *in_pre_img = new tk::dnn::Input(&net, dim_in0, i0_d);
+    // tk::dnn::Conv2d     pre_img_conv1(&net, 16, 7, 7, 1, 1, 3, 3, pre_img_conv1_bin, true);
+    // tk::dnn::Activation pre_img_relu(&net, CUDNN_ACTIVATION_RELU);
+    // pre_img = &pre_img_relu;
+
+    // //pre-hm
+    // readBinaryFile(pre_hm_bin, dim_in1.tot(), &i1_h, &i1_d);
+    // tk::dnn::Input *in_pre_hm = new tk::dnn::Input(&net, dim_in1, i1_d);
+    // tk::dnn::Conv2d     pre_hm_conv1(&net, 16, 7, 7, 1, 1, 3, 3, pre_hm_conv1_bin, true);
+    // tk::dnn::Activation pre_hm_relu(&net, CUDNN_ACTIVATION_RELU);
+    // pre_hm = &pre_hm_relu;
+
+    // // image input
+    // readBinaryFile(input_bin, dim_in0.tot(), &i2_h, &i2_d);
+    // tk::dnn::Input *input_image = new tk::dnn::Input(&net, dim_in0, i2_d);
+    // tk::dnn::Conv2d *conv1 = new tk::dnn::Conv2d(&net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
+    // tk::dnn::Activation relu1(&net, CUDNN_ACTIVATION_RELU);
+
+    // tk::dnn::Shortcut s0_input(&net, pre_img);
+    // tk::dnn::Shortcut s1_input(&net, pre_hm);
+    
+    tk::dnn::Conv2d conv2(&net, 16, 3, 3, 1, 1, 1, 1, conv2_bin, true);
+    tk::dnn::Activation relu2(&net, CUDNN_ACTIVATION_RELU);
+    base1 = &relu2;
+  
+    tk::dnn::Conv2d conv3(&net, 32, 3, 3, 2, 2, 1, 1, conv3_bin, true);
+    tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
+    base2 = &relu3;
+    
+    // level 2
+    // tree 1
+        tk::dnn::Conv2d     s1_t1_conv1(&net, 64, 3, 3, 2, 2, 1, 1, s1_t1_conv1_bin, true);
+        tk::dnn::Activation s1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s1_t1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t1_conv2_bin, true);
+        last2 = &s1_t1_conv2;
+
+        // get the basicblock input and apply maxpool conv2d and relu
+        tk::dnn::Layer      *route_s1_t1_layers[1] = { base2 };
+        tk::dnn::Route      route_s1_t1(&net, route_s1_t1_layers, 1);
+        // downsample
+        tk::dnn::Pooling    s1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+        // project
+        tk::dnn::Conv2d     s1_t1_residual1_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_t1_project, true);
+        
+        tk::dnn::Shortcut   s1_t1_s1(&net, last2);
+        tk::dnn::Activation s1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    last1 = &s1_t1_relu;
+    // tree 2
+        tk::dnn::Conv2d     s1_t2_conv1(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv1_bin, true);
+        tk::dnn::Activation s1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s1_t2_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv2_bin, true);
+        
+        tk::dnn::Shortcut   s1_t2_s1(&net, last1);
+        tk::dnn::Activation s1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+        last2 = &s1_t2_relu;
+
+    // root
+        // join last1 and net in single input 128, 56, 56
+        tk::dnn::Layer      *route_s1_root_layers[2] = { last2, last1 };
+        tk::dnn::Route      route_s1_root(&net, route_s1_root_layers, 2);
+        tk::dnn::Conv2d     s1_root_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_root_conv1_bin, true);
+        tk::dnn::Activation s1_root_relu(&net, CUDNN_ACTIVATION_RELU);
+
+    base3 = &s1_root_relu;
+
+    // level 3
+    // tree 1
+        // tree 1
+            tk::dnn::Conv2d     s2_t1_t1_conv1(&net, 128, 3, 3, 2, 2, 1, 1, s2_t1_t1_conv1_bin, true);
+            tk::dnn::Activation s2_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);      
+        
+            tk::dnn::Conv2d     s2_t1_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t1_conv2_bin, true);
+            last2 = &s2_t1_t1_conv2;
+
+            // get the basicblock input and apply maxpool conv2d and relu
+            tk::dnn::Layer      *route_s2_t1_t1_layers[1] = { base3 };
+            tk::dnn::Route      route_s2_t1_t1(&net, route_s2_t1_t1_layers, 1);
+            // downsample
+            tk::dnn::Pooling    s2_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+            last4 = &s2_t1_t1_maxpool1;
+            // project
+            tk::dnn::Conv2d     s2_t1_t1_residual1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_t1_project, true);
+            
+            tk::dnn::Shortcut   s2_t1_t1_s1(&net, last2);
+            tk::dnn::Activation s2_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s2_t1_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s2_t1_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv1_bin, true);
+            tk::dnn::Activation s2_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s2_t1_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s2_t1_t2_s1(&net, last1);
+            tk::dnn::Activation s2_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s2_t1_t2_relu;
+
+        // root
+            // join last1 and net in single input 128, 56, 56
+            tk::dnn::Layer      *route_s2_t1_root_layers[2] = { last2, last1 };
+            tk::dnn::Route      route_s2_t1_root(&net, route_s2_t1_root_layers, 2);
+            tk::dnn::Conv2d     s2_t1_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_root_conv1_bin, true);
+            tk::dnn::Activation s2_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    last1 = &s2_t1_root_relu;
+    last3 = &s2_t1_root_relu;
+    // tree 2
+        // tree 1
+            tk::dnn::Conv2d     s2_t2_t1_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv1_bin, true);
+            tk::dnn::Activation s2_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s2_t2_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv2_bin, true);
+            tk::dnn::Shortcut   s2_t2_t1_s1(&net, last1);
+            tk::dnn::Activation s2_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s2_t2_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s2_t2_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv1_bin, true);
+            tk::dnn::Activation s2_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s2_t2_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s2_t2_t2_s1(&net, last1);
+            tk::dnn::Activation s2_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s2_t2_t2_relu;
+
+        // root
+            // join last1 and net in single input 128, 56, 56
+            tk::dnn::Layer      *route_s2_t2_root_layers[4] = { last2, last1, last4, last3};
+            tk::dnn::Route      route_s2_t2_root(&net, route_s2_t2_root_layers, 4);
+            tk::dnn::Conv2d     s2_t2_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t2_root_conv1_bin, true);
+            tk::dnn::Activation s2_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    base4 = &s2_t2_root_relu;
+
+    // level 4
+    // tree 1
+        // tree 1
+            tk::dnn::Conv2d     s3_t1_t1_conv1(&net, 256, 3, 3, 2, 2, 1, 1, s3_t1_t1_conv1_bin, true);
+            tk::dnn::Activation s3_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);      
+        
+            tk::dnn::Conv2d     s3_t1_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t1_conv2_bin, true);
+            last2 = &s3_t1_t1_conv2;
+
+            // get the basicblock input and apply maxpool conv2d and relu
+            tk::dnn::Layer      *route_s3_t1_t1_layers[1] = { base4 };
+            tk::dnn::Route      route_s3_t1_t1(&net, route_s3_t1_t1_layers, 1);
+            // downsample
+            tk::dnn::Pooling    s3_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+            last4 = &s3_t1_t1_maxpool1;
+            // project
+            tk::dnn::Conv2d     s3_t1_t1_residual1_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_t1_project, true);
+            
+            tk::dnn::Shortcut   s3_t1_t1_s1(&net, last2);
+            tk::dnn::Activation s3_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s3_t1_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s3_t1_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv1_bin, true);
+            tk::dnn::Activation s3_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s3_t1_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s3_t1_t2_s1(&net, last1);
+            tk::dnn::Activation s3_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s3_t1_t2_relu;
+
+        // root
+            // join last1 and net in single input 256, 56, 56
+            tk::dnn::Layer      *route_s3_t1_root_layers[2] = { last2, last1 };
+            tk::dnn::Route      route_s3_t1_root(&net, route_s3_t1_root_layers, 2);
+            tk::dnn::Conv2d     s3_t1_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_root_conv1_bin, true);
+            tk::dnn::Activation s3_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    last1 = &s3_t1_root_relu;
+    last3 = &s3_t1_root_relu;
+    // tree 2
+        // tree 1
+            tk::dnn::Conv2d     s3_t2_t1_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv1_bin, true);
+            tk::dnn::Activation s3_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s3_t2_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv2_bin, true);
+            tk::dnn::Shortcut   s3_t2_t1_s1(&net, last1);
+            tk::dnn::Activation s3_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+        last1 = &s3_t2_t1_relu;
+        
+        // tree 2
+            tk::dnn::Conv2d     s3_t2_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv1_bin, true);
+            tk::dnn::Activation s3_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+        
+            tk::dnn::Conv2d     s3_t2_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv2_bin, true);
+            
+            tk::dnn::Shortcut   s3_t2_t2_s1(&net, last1);
+            tk::dnn::Activation s3_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+            last2 = &s3_t2_t2_relu;
+
+        // root
+            // join last1 and net in single input 256, 56, 56
+            tk::dnn::Layer      *route_s3_t2_root_layers[4] = { last2, last1, last4, last3};
+            tk::dnn::Route      route_s3_t2_root(&net, route_s3_t2_root_layers, 4);
+            tk::dnn::Conv2d     s3_t2_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t2_root_conv1_bin, true);
+            tk::dnn::Activation s3_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
+    
+    base5 = &s3_t2_root_relu;
+
+    // level 5
+    // tree 1
+        tk::dnn::Conv2d     s4_t1_conv1(&net, 512, 3, 3, 2, 2, 1, 1, s4_t1_conv1_bin, true);
+        tk::dnn::Activation s4_t1_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s4_t1_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t1_conv2_bin, true);
+        last2 = &s4_t1_conv2;
+
+        // get the basicblock input and apply maxpool conv2d and relu
+        tk::dnn::Layer      *route_s4_t1_layers[1] = { base5 };
+        tk::dnn::Route      route_s4_t1(&net, route_s4_t1_layers, 1);
+        // downsample
+        tk::dnn::Pooling    s4_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
+        last4 = &s4_t1_maxpool1;
+        // project
+        tk::dnn::Conv2d     s4_t1_residual1_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_t1_project, true);
+        
+        tk::dnn::Shortcut   s4_t1_s1(&net, last2);
+        tk::dnn::Activation s4_t1_relu(&net, CUDNN_ACTIVATION_RELU);
+
+    last1 = &s4_t1_relu;
+    
+    // tree 2
+        tk::dnn::Conv2d     s4_t2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv1_bin, true);
+        tk::dnn::Activation s4_t2_relu1(&net, CUDNN_ACTIVATION_RELU);       
+       
+        tk::dnn::Conv2d     s4_t2_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv2_bin, true);
+        
+        tk::dnn::Shortcut   s4_t2_s1(&net, last1);
+        tk::dnn::Activation s4_t2_relu(&net, CUDNN_ACTIVATION_RELU);
+        last2 = &s4_t2_relu;
+
+    // root
+        // join last1 and net in single input 128, 56, 56
+        tk::dnn::Layer      *route_s4_root_layers[3] = { last2, last1, last4 };
+        tk::dnn::Route      route_s4_root(&net, route_s4_root_layers, 3);
+        tk::dnn::Conv2d     s4_root_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_root_conv1_bin, true);
+        tk::dnn::Activation s4_root_relu(&net, CUDNN_ACTIVATION_RELU);
+
+    base6 = &s4_root_relu;
+ 
+    //final
+    // tk::dnn::Pooling avgpool(&net, 7, 7, 7, 7, 0, 0, tk::dnn::POOLING_AVERAGE);
+    // tk::dnn::Dense   fc(&net, 1000, fc_bin);
+    
+    //ida 0 
+    tk::dnn::Layer          *route_ida0[1] = { base6 };
+    tk::dnn::Route          route_ida0_0(&net, route_ida0, 1);
+
+    tk::dnn::DeformConv2d   ida_0_p_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_p_1_dcn_bin, ida_0_p_1_conv_bin, true);
+    tk::dnn::Activation     ida_0_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_0_up_1_deconv(&net, 256, 4, 4, 2, 2, 1, 1, ida_0_up_1_deconv_bin, false, 256);
+    tk::dnn::Shortcut       ida_0_shortcut(&net, base5);    
+    tk::dnn::DeformConv2d   ida_0_n_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_n_1_dcn_bin, ida_0_n_1_conv_bin, true);
+    tk::dnn::Activation     ida_0_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida1 = &ida_0_n_1_relu;
+  
+    //ida1-1
+    tk::dnn::Layer          *route_ida1_layers_1[1] = { base5 };
+    tk::dnn::Route          route_ida1_1(&net, route_ida1_layers_1, 1);
+    
+    tk::dnn::DeformConv2d   ida_1_p_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_1_dcn_bin, ida_1_p_1_conv_bin, true);
+    tk::dnn::Activation     ida_1_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_1_up_1_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_1_deconv_bin, false, 128);
+    tk::dnn::Shortcut       ida_1_shortcut1(&net, base4);    
+    tk::dnn::DeformConv2d   ida_1_n_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_1_dcn_bin, ida_1_n_1_conv_bin, true);
+    tk::dnn::Activation     ida_1_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida2_1 = &ida_1_n_1_relu;
+
+    //ida1-2
+    tk::dnn::Layer          *route_ida1_layers_2[1] = { ida1 };
+    tk::dnn::Route          route_ida1_2(&net, route_ida1_layers_2, 1);
+
+    tk::dnn::DeformConv2d   ida_1_p_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_2_dcn_bin, ida_1_p_2_conv_bin, true);
+    tk::dnn::Activation     ida_1_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_1_up_2_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_2_deconv_bin, false, 128);
+    tk::dnn::Shortcut       ida_1_shortcut2(&net, ida2_1);    
+    tk::dnn::DeformConv2d   ida_1_n_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_2_dcn_bin, ida_1_n_2_conv_bin, true);
+    tk::dnn::Activation     ida_1_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida2_2 = &ida_1_n_2_relu;
+
+    //ida2-1
+    tk::dnn::Layer          *route_ida2_layers_1[1] = { base4 };
+    tk::dnn::Route          route_ida2_1(&net, route_ida2_layers_1, 1);
+    
+    tk::dnn::DeformConv2d   ida_2_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_1_dcn_bin, ida_2_p_1_conv_bin, true);
+    tk::dnn::Activation     ida_2_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_2_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_1_deconv_bin, false, 64);
+    tk::dnn::Shortcut       ida_2_shortcut1(&net, base3);    
+    tk::dnn::DeformConv2d   ida_2_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_1_dcn_bin, ida_2_n_1_conv_bin, true);
+    tk::dnn::Activation     ida_2_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida3_1 = &ida_2_n_1_relu;
+
+    //ida2-2
+    tk::dnn::Layer          *route_ida2_layers_2[1] = { ida2_1 };
+    tk::dnn::Route          route_ida2_2(&net, route_ida2_layers_2, 1);
+    
+    tk::dnn::DeformConv2d   ida_2_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_2_dcn_bin, ida_2_p_2_conv_bin, true);
+    tk::dnn::Activation     ida_2_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_2_up_2_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_2_deconv_bin, false, 64);
+    tk::dnn::Shortcut       ida_2_shortcut2(&net, ida3_1);    
+    tk::dnn::DeformConv2d   ida_2_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_2_dcn_bin, ida_2_n_2_conv_bin, true);
+    tk::dnn::Activation     ida_2_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida3_2 = &ida_2_n_2_relu;
+
+    //ida2-3
+    tk::dnn::Layer          *route_ida2_layers_3[1] = { ida2_2 };
+    tk::dnn::Route          route_ida2_3(&net, route_ida2_layers_3, 1);
+    
+    tk::dnn::DeformConv2d   ida_2_p_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_3_dcn_bin, ida_2_p_3_conv_bin, true);
+    tk::dnn::Activation     ida_2_p_3_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       ida_2_up_3_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_3_deconv_bin, false, 64);
+    tk::dnn::Shortcut       ida_2_shortcut3(&net, ida3_2);    
+    tk::dnn::DeformConv2d   ida_2_n_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_3_dcn_bin, ida_2_n_3_conv_bin, true);
+    tk::dnn::Activation     ida_2_n_3_relu(&net, CUDNN_ACTIVATION_RELU);
+    ida3_3 = &ida_2_n_3_relu;
+
+    //idaup-1
+    tk::dnn::Layer          *route_idaup_layers_1[1] = { ida2_2 };
+    tk::dnn::Route          route_idaup_1(&net, route_idaup_layers_1, 1);
+    
+    tk::dnn::DeformConv2d   idaup_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_1_dcn_bin, ida_up_p_1_conv_bin, true);
+    tk::dnn::Activation     idaup_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       idaup_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_up_up_1_deconv_bin, false, 64);
+    tk::dnn::Shortcut       idaup_shortcut1(&net, ida3_3);    
+    tk::dnn::DeformConv2d   idaup_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_1_dcn_bin, ida_up_n_1_conv_bin, true);
+    tk::dnn::Activation     idaup_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
+    idaup_1 = &idaup_n_1_relu;
+
+    //idaup-2
+    tk::dnn::Layer          *route_idaup_layers_2[1] = { ida1 };
+    tk::dnn::Route          route_idaup_2(&net, route_idaup_layers_2, 1);
+
+    tk::dnn::DeformConv2d   idaup_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_2_dcn_bin, ida_up_p_2_conv_bin, true);
+    tk::dnn::Activation     idaup_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::DeConv2d       idaup_up_2_deconv(&net, 64, 8, 8, 4, 4, 2, 2, ida_up_up_2_deconv_bin, false, 64);
+    tk::dnn::Shortcut       idaup_shortcut2(&net, idaup_1);    
+    tk::dnn::DeformConv2d   idaup_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_2_dcn_bin, ida_up_n_2_conv_bin, true);
+    tk::dnn::Activation     idaup_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
+    idaup_2 = &idaup_n_2_relu;
+ 
+    tk::dnn::Layer    *route_1_0_layers[1] = { idaup_2 };
+
+    // hm
+    tk::dnn::Conv2d     *hm_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
+    tk::dnn::Activation *hm_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *hm = new tk::dnn::Conv2d(&net, 10, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false);
+    hm->setFinal();
+
+    int kernel = 3; 
+    int pad = (kernel - 1)/2;
+    tk::dnn::Activation *hm_sig      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_SIGMOID);
+    tk::dnn::Pooling  *hmax                 = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX);
+    hmax->setFinal();
+
+    // wh
+    tk::dnn::Route    *route_1_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *wh_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
+    tk::dnn::Activation *wh_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false);        
+    wh->setFinal();
+   
+    // reg
+    tk::dnn::Route    *route_2_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *reg_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
+    tk::dnn::Activation *reg_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false);
+    reg->setFinal();
+
+    // tracking
+    tk::dnn::Route    *route_3_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *track_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, track_conv1_bin, false);
+    tk::dnn::Activation *track_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *track = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, track_conv2_bin, false);
+    track->setFinal();
+
+    // dep
+    tk::dnn::Route    *route_4_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *dep_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, dep_conv1_bin, false);
+    tk::dnn::Activation *dep_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *dep = new tk::dnn::Conv2d(&net, 1, 1, 1, 1, 1, 0, 0, dep_conv2_bin, false);
+    dep->setFinal();
+
+    // rot
+    tk::dnn::Route    *route_5_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *rot_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, rot_conv1_bin, false);
+    tk::dnn::Activation *rot_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *rot = new tk::dnn::Conv2d(&net, 8, 1, 1, 1, 1, 0, 0, rot_conv2_bin, false);
+    rot->setFinal();
+
+    // dim
+    tk::dnn::Route    *route_6_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *dim_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, dim_conv1_bin, false);
+    tk::dnn::Activation *dim_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *dim_ = new tk::dnn::Conv2d(&net, 3, 1, 1, 1, 1, 0, 0, dim_conv2_bin, false);
+    dim_->setFinal();
+
+    // amodel_offset
+    tk::dnn::Route    *route_7_0             = new tk::dnn::Route(&net, route_1_0_layers, 1);
+    tk::dnn::Conv2d     *a_off_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, a_off_conv1_bin, false);
+    tk::dnn::Activation *a_off_relu1      = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     *a_off = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, a_off_conv2_bin, false);
+    a_off->setFinal();
+
+
+    // Load input
+    dnnType *data;
+    dnnType *input_h;
+    readBinaryFile(input_bin, dim_in0.tot(), &input_h, &data);
+    //printDeviceVector(64, data, true);
+
+    //print network model
+    net.print();
+
+    //convert network to tensorRT
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_ctrack"));
+
+    tk::dnn::dataDim_t dim1 = dim_in0; //input dim
+    printCenteredTitle(" CUDNN inference ", '=', 30);
+    {
+        dim1.print();
+        TKDNN_TSTART
+        // tk::dnn::dataDim_t dim_aus;
+        // net.infer(dim_aus, nullptr);
+        net.infer(dim1, data);
+        TKDNN_TSTOP
+        dim1.print();
+    }
+        
+    tk::dnn::dataDim_t dim2 = dim_in0;
+    printCenteredTitle(" TENSORRT inference ", '=', 30);
+    {
+        dim2.print();
+        TKDNN_TSTART
+        netRT.infer(dim2, data);
+        TKDNN_TSTOP
+        dim2.print();
+    }
+    // dnnType *out, *out_h;
+    // int odim = net.layers[net.num_layers-1]->output_dim.tot();
+    // readBinaryFile(output_bin, odim, &out_h, &out);
+    // dnnType *cudnn_out;
+    // cudnn_out = net.layers[net.num_layers-1]->dstData;
+    // std::cout<<"CUDNN vs correct"; 
+    // checkResult(odim, cudnn_out, out);
+    
+    
+    tk::dnn::Layer *outs[8] = { hm, wh, reg, track, dep, rot, dim_, a_off}; 
+    int out_count = 1;
+    int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
+    for(int i=0; i<8; i++) {
+        printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
+        
+        outs[i]->output_dim.print();
+        
+        dnnType *out, *out_h;
+        int odim = outs[i]->output_dim.tot();
+        readBinaryFile(output_bin[i], odim, &out_h, &out);
+
+        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;
+}
@@ -0,0 +1,281 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=1
+width=512
+height=512
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Francesco Gatti	7c0620e391	test_all_test script save results in separate files	2022-03-30 15:47:41 +02:00
Micaela Verucchi	3bcc32ffdc	Fix nms thresh Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2022-02-15 19:57:57 +01:00
Micaela Verucchi	04de9908a6	Add yolo4-csp for crowdhuman dataset, add shelfnet for coco-stuff dataset, fix minor in demo.cpp Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-11-29 17:33:57 +01:00
Micaela Verucchi	9cbac460bc	Update README.md	2021-11-25 11:32:38 +01:00
Micaela Verucchi	24cdb4c4a7	Update Readme Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-11-24 18:20:26 +01:00
Micaela Verucchi	be5864748a	Use yaml config file for the demo instead of param list Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-11-23 16:29:40 +01:00
Micaela Verucchi	75c3cb0038	Add script to compare times_rtinference.csv files Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-11-23 13:01:58 +01:00
Micaela Verucchi	55df97afe1	Fix warnings, upgrade to C++14 Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-11-23 13:01:07 +01:00
Micaela Verucchi	d6fb6c6af4	Fix max elem (remove thrust) for segmentation Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-11-04 19:28:55 +01:00
Micaela Verucchi	eca10ac0a8	Update weights Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-09-19 00:16:31 +02:00
Micaela Verucchi	a992c9feb5	Update README.md	2021-07-23 16:50:15 +02:00
Micaela Verucchi	09080709a9	Remove Issues.md Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-23 15:23:47 +02:00
Micaela Verucchi	7521d10ba7	Update README with gifs Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-23 15:07:52 +02:00
Micaela Verucchi	ab349083bf	Merge with cnet works, all tests passed. Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-23 14:37:04 +02:00
Micaela Verucchi	4f9f27152a	Update READMEs Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-23 10:31:34 +02:00
Davide Sapienza	0292e21c33	Merge remote-tracking branch 'origin/master' into cnet	2021-07-22 17:03:23 +02:00
Davide Sapienza	2bb70859da	Add FPS results for CenterTrack 2D/3D Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-07-22 16:54:25 +02:00
Davide Sapienza	b9f82510d3	Add demo_utils. This commit splits the utils file into two different files: utils and demo_utils, to overcome some opencv problems caused by demo_utils includes. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-07-22 10:45:18 +02:00
Davide Sapienza	1216e8bb74	Add camera calibration file reading for demo3d and demoTracker. Some minor fixes. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-07-21 16:14:53 +02:00
Davide Sapienza	be2d361ac6	Update 3d detection This commit updates the CenternetDetection3d class and fixes some bugs. It also removes the resnet101 network for CenterNet 3D. This network doesn't exist. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-07-21 15:51:13 +02:00
Davide Sapienza	f78f7bfddc	Fix link in readme files Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-07-21 15:35:17 +02:00
Micaela Verucchi	f4e71a5a28	Add yolo4_berkeley_f1 Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-21 12:56:00 +02:00
Micaela Verucchi	89b1bb7bee	Add missing files Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 18:39:25 +02:00
Micaela Verucchi	318dfe4b55	merge with master Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 18:38:46 +02:00
Micaela Verucchi	946b1fe5f5	Update READMEs Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 17:30:32 +02:00
Micaela Verucchi	69256992a3	Add colormap for each segmentation dataset Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 15:29:26 +02:00
Micaela Verucchi	84ff978ccb	Merge with master, all tests passed Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 12:48:41 +02:00
Micaela Verucchi	4ca69836e9	Update README_seg Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 11:39:16 +02:00
Micaela Verucchi	b3c44a86a8	Fix shelfnet berkeley, update readme, minors Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-07-20 10:57:53 +02:00
Francesco Gatti	c306b36860	Update CMakeLists.txt	2021-06-17 15:31:25 +02:00
Francesco Gatti	9b78f143cb	Update CMakeLists.txt	2021-06-16 14:53:46 +02:00
Francesco Gatti	d847a4b852	Update CMakeLists.txt	2021-06-15 20:33:41 +02:00
Francesco Gatti	8df7d5fd1b	Update CMakeLists.txt	2021-06-15 20:31:58 +02:00
Francesco Gatti	6d7c456f72	cmake version fix	2021-06-15 19:39:34 +02:00
Francesco Gatti	b0fdeb4127	install targets	2021-06-15 19:38:13 +02:00
Francesco Gatti	ba8199a030	fix compile error on old tensorrt and remove useless prints	2021-05-16 22:33:45 +02:00
Francesco Gatti	6b8ae1e27c	remove wrong cuda arch that cause big performance gap fix #226	2021-05-16 22:16:21 +02:00
Davide Sapienza	25d02ef3ea	Update exporting weights README Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-05-11 17:30:24 +02:00
Davide Sapienza	fd56e64938	Update the README and split it into several files. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-05-11 17:02:43 +02:00
Davide Sapienza	34c1c3d577	Update cnet branch. This commit splits the demo3D in two demo: one for the 3D object detection and one for the tracking. It renames the files related to CenterTrack. It adds a new parameter to select the tracker mode (2D or 3D). Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-05-11 16:17:23 +02:00
Davide Sapienza	0dc96d2a9e	Improve CenterTrack. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-05-04 17:59:20 +02:00
Fabio Bagni	f8327e2dac	Merge with cnet	2021-05-04 11:50:08 +02:00
Fabio Bagni	10f39d1055	Fix tracker for batch size > 1	2021-05-04 11:21:05 +02:00
Davide Sapienza	d1ae1791d9	Fix a bug in the draw function of CenterTrack. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-05-03 18:56:07 +02:00
Davide Sapienza	ff6e0e010a	Fix a bug with batch > 1 Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-04-30 22:26:33 +02:00
Davide Sapienza	2367519799	Add the calibration matrix reading for CenterTrack Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-04-30 17:10:51 +02:00
Davide Sapienza	be6ad27c11	Batch size > 1 for the 3D demo. This commit lets to use differtent batch size for 3D CenterNet and CenterTrack. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2021-04-29 11:13:24 +02:00
Davide Sapienza	28fab9c3e1	Merge remote-tracking branch 'origin/master' into cnet	2021-04-27 14:43:39 +02:00
Francesco Gatti	a638592fc7	Merge pull request #199 from rickymedrano/rickymedrano-patch-1 Minor Readme Changes	2021-04-24 19:00:58 +02:00
Francesco Gatti	a473a02a44	Merge branch 'perseusdg-master'	2021-04-24 18:55:44 +02:00
Francesco Gatti	6611a91201	merge perseusdg windows10 support	2021-04-24 18:55:26 +02:00
Harshvardhan Chandirasekar	39323ca8d3	Update README.md	2021-04-14 17:22:26 +05:30
Harshvardhan Chandirasekar	f90ee8ab7d	Update CMakeLists.txt	2021-04-09 13:20:44 +05:30
perseusdg	1de804f98d	Code cleanup and readme fixes	2021-04-09 13:17:41 +05:30
perseusdg	37b2a5bd98	minor modifications	2021-04-08 00:57:58 +05:30
perseusdg	cc594f09ef	merge from gitlab	2021-04-08 00:28:00 +05:30
hchandirasekar	2e92944f1d	Opencv cuda fix	2021-03-31 03:44:51 -07:00
Harshvardhan Chandirasekar	5f3ab1472c	Update download_validation.py	2021-03-26 15:09:28 +01:00
hchandirasekar	7018d163ed	python download file	2021-03-26 19:37:44 +05:30
hchandirasekar	f12ec3c935	added yolo4x and yolo4-csp from the github repo and download file corrections	2021-03-26 12:18:46 +05:30
hchandirasekar	f3d1591430	ReadMe.md windows changes	2021-03-25 20:46:12 +05:30
hchandirasekar	44b71ae6f3	ReadMe.md windows changes	2021-03-25 20:44:43 +05:30
hchandirasekar	78859fe191	timer fix	2021-03-25 16:38:53 +05:30
hchandirasekar	e94e1f7622	tkdnn first patch for windows	2021-03-24 22:26:46 +05:30
Harshvardhan Chandirasekar	06787a931f	minor migrations	2021-03-17 23:06:50 +05:30
Harshvardhan Chandirasekar	304ab49897	shared_ptr migrations	2021-03-17 22:26:26 +05:30
hchandirasekar	6aa8666be5	Commits for msvc 16.9	2021-03-10 12:59:04 +05:30
Micaela Verucchi	4b3731928c	Add yolo4_320_coco2 (pedestrian and stop sign) Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-02-25 09:57:10 +01:00
Ricky Medrano	f055341af6	Minor Readme Changes Added Logistic as a viable activation you can use. Added conf-thresh as the 7th parameter in the ./demo call.	2021-02-09 07:57:57 -08:00
hchandirasekar	4a90314333	minor fixes in test.h	2021-01-26 23:11:58 +05:30
hchandirasekar	2d4dececb6	Builds on windows successfully,issues with deserialization and downloading weights	2021-01-26 20:17:43 +04:00
hchandirasekar	fb52444cdc	Replaced dynamic arrays with std::vector ,works on linux ..needs to be tested on windows after clearing up the lnk2019 error	2021-01-25 04:12:05 +05:30
Micaela Verucchi	adac8576b0	Add support to Scaled-YOLO4, update Yolov4x-mish (tested) Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2021-01-22 17:57:42 +01:00
perseusdg	512acd8cba	minor fixes	2021-01-21 09:29:27 +04:00
perseusdg	56feb54377	able to build kernels as shared object file(dll),and minor changes to lstm.cpp and utils.cpp to overcome minor msvc build errors	2021-01-21 00:22:15 +04:00
Davide Sapienza	59b0f434a7	Fix a bug in the 3D bounding boxes. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-14 15:32:35 +01:00
Davide Sapienza	1cfa199ee6	Add pre-processing and post-processing stats Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-09 19:43:51 +01:00
Davide Sapienza	9e1d7b3bb4	Update demo3d with show flag Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-09 18:05:54 +01:00
Davide Sapienza	dbc052865c	Fix a wrong path in CenternetDetection3DTrack.cpp Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-09 18:04:42 +01:00
Davide Sapienza	4543df8533	Update readme. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-07 18:56:07 +01:00
Davide Sapienza	48ecebe6dd	Add CenterTrack pre, post, visualization and demo. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-07 18:45:29 +01:00
Davide Sapienza	9f10c3f6e2	Add CenterTrack based on dla34. The implemented network refers to the nuScenes_3Dtracking.pth model. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-07 17:59:27 +01:00
Davide Sapienza	7f65ee0b2a	Merge remote-tracking branch 'origin/master' into cnet Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-12-07 17:45:44 +01:00
Micaela Verucchi	8fb5772ad9	Update README_seg.md	2020-11-24 13:03:02 +01:00
Micaela Verucchi	43d213ba36	Update README_seg.md	2020-11-24 12:44:36 +01:00
Micaela Verucchi	f137bcb694	Update README_seg Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-11-24 12:39:37 +01:00
Micaela Verucchi	a52e18b6e6	Add shelfnet_mapillary, README_seg, resize of input Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-11-24 12:37:33 +01:00
Micaela Verucchi	a17e7800b9	Add computation of #parameters, #MACC, and max feature map size in the tests Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-11-23 13:07:54 +01:00
Micaela Verucchi	b8855b9599	Update README Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-11-23 11:34:06 +01:00
Micaela Verucchi	702791e41a	Add support for yolov4x-mish. Changes: - add parameters nms_kind, nms_thresh, new_coords to yolo layer and darknet parser - added diou nms, new method to compute the BBs - created test for yolov4x-mish called yolo4x Tested, all tests work. Problem to solve: little loss in mAP of yolo4x Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-11-23 11:29:32 +01:00
Micaela Verucchi	86478f9384	Add yolo4_mmr test Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-10-23 11:40:55 +02:00
Micaela Verucchi	a0e7f05a50	Update README.md	2020-10-10 13:03:01 +02:00
Micaela Verucchi	d3372aad31	Update README with Xavier NX FPS results 15W4Core Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-09-15 14:09:02 +02:00
Micaela Verucchi	be818d5e3a	Update README with Xavier NX FPS results Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-09-15 10:50:24 +02:00
Micaela Verucchi	04b4a69107	Merge with master Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-09-14 17:30:02 +02:00
micaela	fa2b3d26cb	Fix typos (#107 ) Signed-off-by: micaela <micaelaverucchi@gmail.com>	2020-09-11 09:13:59 +02:00
Micaela Verucchi	38106a9495	Merge branch 'master' of https://github.com/ceccocats/tkDNN	2020-08-06 15:54:09 +02:00
Micaela Verucchi	df5443e017	Fix boxes also for Centernet Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-08-06 15:53:57 +02:00
Micaela Verucchi	8bef544bae	Update README.md	2020-08-06 11:05:26 +02:00
Micaela Verucchi	f778e1aa99	Fixed boxes to float, add conf thresh as param Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-08-05 19:55:10 +02:00
Micaela Verucchi	286e777300	Improved segmentation results, removed resize, code to reorder Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-22 19:21:46 +02:00
Micaela Verucchi	594947f301	Update file names map demo Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-08 11:27:09 +02:00
Micaela Verucchi	e7779ad773	Add conf_thresh parameter to detectors, add batches handling in map demo Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-08 09:52:36 +02:00
tk	65e2074dda	Merge branch 'eval' of https://github.com/ceccocats/tkDNN into eval	2020-07-06 17:58:08 +02:00
tk	a68f45cb4e	Merge with master, add yolov4tiny_512 Signed-off-by: tk <micaelaverucchi@gmail.com>	2020-07-06 17:57:50 +02:00
Micaela Verucchi	b4c8c2bbad	Add stats Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-03 18:45:20 +02:00
Micaela Verucchi	79cd96de6f	Add resize to original size, writing of segmentation Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-02 12:14:20 +02:00
Micaela Verucchi	a5cc4e3eda	Add berkeley test, add weights for shelfnets Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-01 10:53:24 +02:00
Micaela Verucchi	a4dca23111	Minor Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-07-01 10:22:31 +02:00
Micaela Verucchi	3bf9547502	Improve preprocessing Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-29 15:11:52 +02:00
Micaela Verucchi	d25803d438	Post-processing improved Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-24 18:46:25 +02:00
Micaela Verucchi	082920f3f5	Shelfnet works, also visualization. Postprocessing need to be parallelized Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-23 20:01:47 +02:00
Micaela Verucchi	94e558003d	Shelfnet works on tensorRT (shortcut need to be fixed) Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-23 12:50:24 +02:00
Micaela Verucchi	6fd261f628	Shelfnet works on cuDNN. To test everything else Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-22 18:11:19 +02:00
Micaela Verucchi	9f1e30eaa9	Add shelfnet. Resnet18backbone works Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-19 18:55:42 +02:00
Micaela Verucchi	8d08f5aade	New yolo4_512 download link Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-11 20:54:17 +02:00
Micaela Verucchi	e094a3e0fc	Add script for inference FPS Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-11 16:09:01 +02:00
Micaela Verucchi	c4e955eab5	Add different yolov4 size tests Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-06-11 12:43:34 +02:00
Davide Sapienza	3d2405323b	Add the downloading CenterNet weights and outputs for 3D Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-05-29 15:54:22 +02:00
Davide Sapienza	7a677d5c10	Add CenterNet based on Resnet101 for 3D, CUDNN and TensorRT work Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-05-29 14:54:36 +02:00
Davide Sapienza	f07f333ae5	Update README Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-05-27 18:06:26 +02:00
Davide Sapienza	6bdf47bae6	Add 3D demo program Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-05-27 18:05:38 +02:00
Davide Sapienza	ba8c282384	Add 3D CenterNet detection class Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-05-27 18:03:09 +02:00
Davide Sapienza	64098ad244	Add CenterNet based on DLA34 for 3D, CUDNN and TensorRT work Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2020-05-22 12:51:25 +02:00