add detr (wang-xinyu#606)

* add detr

* Update README.md

Author: freedenS

detr/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.6)
+
+project(detr)
+
+add_definitions(-std=c++11)
+
+option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE Debug)
+
+find_package(CUDA REQUIRED)
+
+include_directories(${PROJECT_SOURCE_DIR}/include)
+# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
+# cuda
+include_directories(/usr/local/cuda-10.2/include)
+link_directories(/usr/local/cuda-10.2/lib64)
+# tensorrt
+include_directories(/home/jushi/TensorRT-7.2.1.6/include)
+link_directories(/home/jushi/TensorRT-7.2.1.6/lib)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")
+
+find_package(OpenCV)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+add_executable(detr ${PROJECT_SOURCE_DIR}/detr.cpp)
+target_link_libraries(detr nvinfer)
+target_link_libraries(detr cudart)
+target_link_libraries(detr ${OpenCV_LIBS})
+
+add_definitions(-O2 -pthread)
+

detr/README.md

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+# DETR
+
+The Pytorch implementation is [facebookresearch/detr](https://github.com/facebookresearch/detr).
+
+For details see [End-to-End Object Detection with Transformers](https://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers).
+
+## Test Environment
+
+- GTX2080Ti / Ubuntu16.04 / cuda10.2 / cudnn8.0.4 / TensorRT7.2.1 / OpenCV4.2
+- GTX2080Ti / win10 / cuda10.2 / cudnn8.0.4 / TensorRT7.2.1 / OpenCV4.2 / VS2017
+
+## How to Run
+
+1. generate .wts from pytorch with .pth
+
+```
+// git clone https://github.com/facebookresearch/detr.git
+// go to facebookresearch/detr
+// download https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth
+// download https://raw.githubusercontent.com/freedenS/TestImage/main/demo.jpg
+// copy tensorrtx/detr/gen_wts.py and demo.jpg into facebookresearch/detr
+python gen_wts.py
+// a file 'detr.wts' will be generated.
+```
+
+2. build tensorrtx/detr and run
+
+```
+// put detr.wts into tensorrtx/detr
+// go to tensorrtx/detr
+// update parameters in detr.cpp if your model is trained on custom dataset.The parameters are corresponding to config in detr.
+mkdir build
+cd build
+cmake ..
+make
+sudo ./detr -s [.wts] // serialize model to plan file
+sudo ./detr -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed
+// For example
+sudo ./detr -s ../detr.wts detr.engine
+sudo ./detr -d detr.engine ../samples
+```
+
+3. check the images generated, as follows. _demo.jpg and so on.
+
+## NOTE
+
+- tensorrt use fixed input size, if the size of your data is different from the engine, you need to adjust your data and the result.
+- image preprocessing with c++ is a little different with python(opencv vs PIL)
+
+
+## Latency
+
+average cost of doInference(in detr.cpp) from second time with batch=1 under the ubuntu environment above
+
+|      | fp32    | fp16    | int8 |
+| ---- | ------- | ------- | ---- |
+| R50  | 19.57ms | 9.424ms | TODO |
+