SuperPoint TensorRT implementation (wang-xinyu#1034)

* adding tensorrt implementation of superpoint network.

* inference output result & todo list added to README

Author: xiang-wuu

superpoint/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.6)
+
+project(SuperPointNet)
+
+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(${PROJECT_SOURCE_DIR}/include)
+# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
+# cuda
+include_directories(/usr/local/cuda/include)
+link_directories(/usr/local/cuda/lib64)
+# tensorrt
+include_directories(/usr/include/x86_64-linux-gnu/)
+link_directories(/usr/lib/x86_64-linux-gnu/)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")
+
+find_package(OpenCV)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+add_executable(supernet ${PROJECT_SOURCE_DIR}/supernet.cpp ${PROJECT_SOURCE_DIR}/utils.cpp)
+target_link_libraries(supernet nvinfer)
+target_link_libraries(supernet cudart)
+target_link_libraries(supernet ${OpenCV_LIBS})
+
+add_definitions(-O2 -pthread)

superpoint/README.md

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+# SuperPoint
+
+The PyTorch implementation is from [magicleap/SuperPointPretrainedNetwork.](https://github.com/magicleap/SuperPointPretrainedNetwork)
+
+The pretrained models are from [magicleap/SuperPointPretrainedNetwork.](https://github.com/magicleap/SuperPointPretrainedNetwork)
+
+
+## Config
+
+- FP16/FP32 can be selected by the macro `USE_FP16` in supernet.cpp
+- GPU id and batch size can be selected by the macro `DEVICE` & `BATCH_SIZE` in supernet.cpp
+
+
+## How to Run
+1.Generate .wts file from the baseline pytorch implementation of pretrained model. The following example described how to generate superpoint_v1.wts from pytorch implementation of superpoint_v1. 
+```
+git clone https://github.com/xiang-wuu/SuperPointPretrainedNetwork
+cd SuperPointPretrainedNetwork
+git checkout deploy
+// copy tensorrtx/superpoint/gen_wts.py to here(SuperPointPretrainedNetwork)
+python gen_wts.py
+// a file 'superpoint_v1.wts' will be generated.
+// before running gen_wts.py python script make sure you cloned private fork and checkout to deploy branch.
+```
+
+2.Put .wts file into tensorrtx/superpoint, build and run
+```
+cd tensorrtx/superpoint
+mkdir build
+cd build
+cmake ..
+make
+./supernet -s SuperPointPretrainedNetwork/superpoint_v1.wts    // serialize model to plan file i.e. 'supernet.engine'
+```
+
+## Run Demo using SuperPointPretrainedNetwork Python Script
+The live demo can be run by inffering TensorRT generated engine file or by the pre-trained pytorch weight file , the `demo_superpoint.py` script is modified to infer automatically by either using TensorRT or PyTorch based on the provided input weight file.
+```
+cd SuperPointPretrainedNetwork
+python demo_superpoint.py assets/nyu_snippet.mp4 --cuda --weights_path tensorrtx/superpoint/build/supernet.engine
+// provide absolute path to supernet.engine as input weight file 
+python demo_superpoint.py assets/nyu_snippet.mp4 --cuda --weights_path superpoint_v1.pth
+// execute above command to infer using pytorch pre-trained weight files instead of tensorrt engine file.
+```
+
+## Output
+As from the below result there is no significant difference in the inferred output!
+<table>
+<th>
+PyTorch
+</th>
+<th>
+TensorRT
+</th>
+<tr>
+<td>
+<img src="https://user-images.githubusercontent.com/107029401/177322379-2782ca66-bcac-4cf6-b6d3-e1b4d4a8e171.gif"/>
+</td>
+<td>
+<img src="https://user-images.githubusercontent.com/107029401/177322387-c945b903-f233-4a43-bfd3-530c46f4f4db.gif"/>
+</td>
+</tr>
+</table>
+
+## TODO
+- [ ] Optimizing post-processing using custom TensorRT layer.
+- [ ] Benchmark validation for speed accuracy tradeoff with [hpatches](https://github.com/hpatches/hpatches-benchmark) dataset

superpoint/gen_wts.py

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+import torch
+import struct
+from model import SuperPointNet
+
+model_name = "superpoint_v1"
+
+net = SuperPointNet()
+net.load_state_dict(torch.load("superpoint_v1.pth"))
+net = net.cuda()
+net.eval()
+
+f = open(model_name + ".wts", "w")
+f.write("{}\n".format(len(net.state_dict().keys())))
+for k, v in net.state_dict().items():
+    vr = v.reshape(-1).cpu().numpy()
+    f.write("{} {}".format(k, len(vr)))
+    for vv in vr:
+        f.write(" ")
+        f.write(struct.pack(">f", float(vv)).hex())
+    f.write("\n")